Pharmacokinetics of low-dose nedaplatin and validation of AUC prediction in patients with non-small-cell lung carcinoma.

PubMed

Niioka, Takenori; Uno, Tsukasa; Yasui-Furukori, Norio; Takahata, Takenori; Shimizu, Mikiko; Sugawara, Kazunobu; Tateishi, Tomonori

2007-04-01

The aim of this study was to determine the pharmacokinetics of low-dose nedaplatin combined with paclitaxel and radiation therapy in patients having non-small-cell lung carcinoma and establish the optimal dosage regimen for low-dose nedaplatin. We also evaluated predictive accuracy of reported formulas to estimate the area under the plasma concentration-time curve (AUC) of low-dose nedaplatin. A total of 19 patients were administered a constant intravenous infusion of 20 mg/m(2) body surface area (BSA) nedaplatin for an hour, and blood samples were collected at 1, 2, 3, 4, 6, 8, and 19 h after the administration. Plasma concentrations of unbound platinum were measured, and the actual value of platinum AUC (actual AUC) was calculated based on these data. The predicted value of platinum AUC (predicted AUC) was determined by three predictive methods reported in previous studies, consisting of Bayesian method, limited sampling strategies with plasma concentration at a single time point, and simple formula method (SFM) without measured plasma concentration. Three error indices, mean prediction error (ME, measure of bias), mean absolute error (MAE, measure of accuracy), and root mean squared prediction error (RMSE, measure of precision), were obtained from the difference between the actual and the predicted AUC, to compare the accuracy between the three predictive methods. The AUC showed more than threefold inter-patient variation, and there was a favorable correlation between nedaplatin clearance and creatinine clearance (Ccr) (r = 0.832, P < 0.01). In three error indices, MAE and RMSE showed significant difference between the three AUC predictive methods, and the method of SFM had the most favorable results, in which %ME, %MAE, and %RMSE were 5.5, 10.7, and 15.4, respectively. The dosage regimen of low-dose nedaplatin should be established based on Ccr rather than on BSA. Since prediction accuracy of SFM, which did not require measured plasma concentration, was most favorable among the three methods evaluated in this study, SFM could be the most practical method to predict AUC of low-dose nedaplatin in a clinical situation judging from its high accuracy in predicting AUC without measured plasma concentration.

A new method to estimate average hourly global solar radiation on the horizontal surface

NASA Astrophysics Data System (ADS)

Pandey, Pramod K.; Soupir, Michelle L.

2012-10-01

A new model, Global Solar Radiation on Horizontal Surface (GSRHS), was developed to estimate the average hourly global solar radiation on the horizontal surfaces (Gh). The GSRHS model uses the transmission function (Tf,ij), which was developed to control hourly global solar radiation, for predicting solar radiation. The inputs of the model were: hour of day, day (Julian) of year, optimized parameter values, solar constant (H0), latitude, and longitude of the location of interest. The parameter values used in the model were optimized at a location (Albuquerque, NM), and these values were applied into the model for predicting average hourly global solar radiations at four different locations (Austin, TX; El Paso, TX; Desert Rock, NV; Seattle, WA) of the United States. The model performance was assessed using correlation coefficient (r), Mean Absolute Bias Error (MABE), Root Mean Square Error (RMSE), and coefficient of determinations (R2). The sensitivities of parameter to prediction were estimated. Results show that the model performed very well. The correlation coefficients (r) range from 0.96 to 0.99, while coefficients of determination (R2) range from 0.92 to 0.98. For daily and monthly prediction, error percentages (i.e. MABE and RMSE) were less than 20%. The approach we proposed here can be potentially useful for predicting average hourly global solar radiation on the horizontal surface for different locations, with the use of readily available data (i.e. latitude and longitude of the location) as inputs.

Bio-Optical Data Assimilation With Observational Error Covariance Derived From an Ensemble of Satellite Images

NASA Astrophysics Data System (ADS)

Shulman, Igor; Gould, Richard W.; Frolov, Sergey; McCarthy, Sean; Penta, Brad; Anderson, Stephanie; Sakalaukus, Peter

2018-03-01

An ensemble-based approach to specify observational error covariance in the data assimilation of satellite bio-optical properties is proposed. The observational error covariance is derived from statistical properties of the generated ensemble of satellite MODIS-Aqua chlorophyll (Chl) images. The proposed observational error covariance is used in the Optimal Interpolation scheme for the assimilation of MODIS-Aqua Chl observations. The forecast error covariance is specified in the subspace of the multivariate (bio-optical, physical) empirical orthogonal functions (EOFs) estimated from a month-long model run. The assimilation of surface MODIS-Aqua Chl improved surface and subsurface model Chl predictions. Comparisons with surface and subsurface water samples demonstrate that data assimilation run with the proposed observational error covariance has higher RMSE than the data assimilation run with "optimistic" assumption about observational errors (10% of the ensemble mean), but has smaller or comparable RMSE than data assimilation run with an assumption that observational errors equal to 35% of the ensemble mean (the target error for satellite data product for chlorophyll). Also, with the assimilation of the MODIS-Aqua Chl data, the RMSE between observed and model-predicted fractions of diatoms to the total phytoplankton is reduced by a factor of two in comparison to the nonassimilative run.

Uncertainties in the Shuttle Radar Topography Mission (SRTM) Heights: Insights from the Indian Himalaya and Peninsula

PubMed Central

Mukul, Manas; Srivastava, Vinee; Jade, Sridevi; Mukul, Malay

2017-01-01

The Shuttle Radar Topography Mission (SRTM) Digital Terrain Elevation Data (DTED) are used with the consensus view that it has a minimum vertical accuracy of 16 m absolute error at 90% confidence (Root Mean Square Error (RMSE) of 9.73 m) world-wide. However, vertical accuracy of the data decreases with increase in slope and elevation due to presence of large outliers and voids. Therefore, studies using SRTM data “as is”, especially in regions like the Himalaya, are not statistically meaningful. New data from ~200 high-precision static Global Position System (GPS) Independent Check Points (ICPs) in the Himalaya and Peninsular India indicate that only 1-arc X-Band data are usable “as is” in the Himalaya as it has height accuracy of 9.18 m (RMSE). In contrast, recently released (2014–2015) “as-is” 1-arc and widely used 3-arc C-Band data have a height accuracy of RMSE 23.53 m and 47.24 m and need to be corrected before use. Outlier and void filtering improves the height accuracy to RMSE 8 m, 10.14 m, 14.38 m for 1-arc X and C-Band and 3-arc C-Band data respectively. Our study indicates that the C-Band 90 m and 30 m DEMs are well-aligned and without any significant horizontal offset implying that area and length computations using both the datasets have identical values. PMID:28176825

The reliability and validity of a three-camera foot image system for obtaining foot anthropometrics.

PubMed

O'Meara, Damien; Vanwanseele, Benedicte; Hunt, Adrienne; Smith, Richard

2010-08-01

The purpose was to develop a foot image capture and measurement system with web cameras (the 3-FIS) to provide reliable and valid foot anthropometric measures with efficiency comparable to that of the conventional method of using a handheld anthropometer. Eleven foot measures were obtained from 10 subjects using both methods. Reliability of each method was determined over 3 consecutive days using the intraclass correlation coefficient and root mean square error (RMSE). Reliability was excellent for both the 3-FIS and the handheld anthropometer for the same 10 variables, and good for the fifth metatarsophalangeal joint height. The RMSE values over 3 days ranged from 0.9 to 2.2 mm for the handheld anthropometer, and from 0.8 to 3.6 mm for the 3-FIS. The RMSE values between the 3-FIS and the handheld anthropometer were between 2.3 and 7.4 mm. The 3-FIS required less time to collect and obtain the final variables than the handheld anthropometer. The 3-FIS provided accurate and reproducible results for each of the foot variables and in less time than the conventional approach of a handheld anthropometer.

Modeling rainfall-runoff process using soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Tombul, Mustafa

2013-02-01

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods.

Forecasting surface-layer atmospheric parameters at the Large Binocular Telescope site

NASA Astrophysics Data System (ADS)

Turchi, Alessio; Masciadri, Elena; Fini, Luca

2017-04-01

In this paper, we quantify the performance of an automated weather forecast system implemented on the Large Binocular Telescope (LBT) site at Mt Graham (Arizona) in forecasting the main atmospheric parameters close to the ground. The system employs a mesoscale non-hydrostatic numerical model (Meso-Nh). To validate the model, we compare the forecasts of wind speed, wind direction, temperature and relative humidity close to the ground with the respective values measured by instrumentation installed on the telescope dome. The study is performed over a large sample of nights uniformly distributed over 2 yr. The quantitative analysis is done using classical statistical operators [bias, root-mean-square error (RMSE) and σ] and contingency tables, which allows us to extract complementary key information, such as the percentage of correct detections (PC) and the probability of obtaining a correct detection within a defined interval of values (POD). The results of our study indicate that the model performance in forecasting the atmospheric parameters we have just cited are very good, in some cases excellent: RMSE for temperature is below 1°C, for relative humidity it is 14 per cent and for the wind speed it is around 2.5 m s-1. The relative error of the RMSE for wind direction varies from 9 to 17 per cent depending on the wind speed conditions. This work is performed in the context of the ALTA (Advanced LBT Turbulence and Atmosphere) Center project, whose final goal is to provide forecasts of all the atmospheric parameters and the optical turbulence to support LBT observations, adaptive optics facilities and interferometric facilities.

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

PubMed

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE < 10.3%. The external data evaluation showed that the models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

Batch growth kinetic studies of locally isolated cyanide-degrading Serratia marcescens strain AQ07.

PubMed

Karamba, Kabiru Ibrahim; Ahmad, Siti Aqlima; Zulkharnain, Azham; Yasid, Nur Adeela; Ibrahim, Salihu; Shukor, Mohd Yunus

2018-01-01

The evaluation of degradation and growth kinetics of Serratia marcescens strain AQ07 was carried out using three half-order models at all the initial concentrations of cyanide with the values of regression exceeding 0.97. The presence of varying cyanide concentrations reveals that the growth and degradation of bacteria were affected by the increase in cyanide concentration with a total halt at 700 ppm KCN after 72 h incubation. In this study, specific growth and degradation rates were found to trail the substrate inhibition kinetics. These two rates fitted well to the kinetic models of Teissier, Luong, Aiba and Heldane, while the performance of Monod model was found to be unsatisfactory. These models were used to clarify the substrate inhibition on the bacteria growth. The analyses of these models have shown that Luong model has fitted the experimental data with the highest coefficient of determination ( R 2 ) value of 0.9794 and 0.9582 with the lowest root mean square error (RMSE) value of 0.000204 and 0.001, respectively, for the specific rate of degradation and growth. It is the only model that illustrates the maximum substrate concentration ( S m ) of 713.4 and empirical constant ( n ) of 1.516. Tessier and Aiba fitted the experimental data with a R 2 value of 0.8002 and 0.7661 with low RMSE of 0.0006, respectively, for specific biodegradation rate, while having a R 2 value of 0.9 and RMSE of 0.001, respectively, for specific growth rate. Haldane has the lowest R 2 value of 0.67 and 0.78 for specific biodegradation and growth rate with RMSE of 0.0006 and 0.002, respectively. This indicates the level of the bacteria stability in varying concentrations of cyanide and the maximum cyanide concentration it can tolerate within a specific time period. The biokinetic constant predicted from this model demonstrates a good ability of the locally isolated bacteria in cyanide remediation in industrial effluents.

Autocalibration of a one-dimensional hydrodynamic-ecological model (DYRESM 4.0-CAEDYM 3.1) using a Monte Carlo approach: simulations of hypoxic events in a polymictic lake

NASA Astrophysics Data System (ADS)

Luo, Liancong; Hamilton, David; Lan, Jia; McBride, Chris; Trolle, Dennis

2018-03-01

Automated calibration of complex deterministic water quality models with a large number of biogeochemical parameters can reduce time-consuming iterative simulations involving empirical judgements of model fit. We undertook autocalibration of the one-dimensional hydrodynamic-ecological lake model DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test the applicability of this procedure for shallow, polymictic Lake Rotorua (New Zealand). The calibration procedure involved independently minimizing the root-mean-square error (RMSE), maximizing the Pearson correlation coefficient (r) and Nash-Sutcliffe efficient coefficient (Nr) for comparisons of model state variables against measured data. An assigned number of parameter permutations was used for 10 000 simulation iterations. The "optimal" temperature calibration produced a RMSE of 0.54 °C, Nr value of 0.99, and r value of 0.98 through the whole water column based on comparisons with 540 observed water temperatures collected between 13 July 2007 and 13 January 2009. The modeled bottom dissolved oxygen concentration (20.5 m below surface) was compared with 467 available observations. The calculated RMSE of the simulations compared with the measurements was 1.78 mg L-1, the Nr value was 0.75, and the r value was 0.87. The autocalibrated model was further tested for an independent data set by simulating bottom-water hypoxia events from 15 January 2009 to 8 June 2011 (875 days). This verification produced an accurate simulation of five hypoxic events corresponding to DO < 2 mg L-1 during summer of 2009-2011. The RMSE was 2.07 mg L-1, Nr value 0.62, and r value of 0.81, based on the available data set of 738 days. The autocalibration software of DYRESM-CAEDYM developed here is substantially less time-consuming and more efficient in parameter optimization than traditional manual calibration which has been the standard tool practiced for similar complex water quality models.

Auto-calibration of a one-dimensional hydrodynamic-ecological model using a Monte Carlo approach: simulation of hypoxic events in a polymictic lake

NASA Astrophysics Data System (ADS)

Luo, L.

2011-12-01

Automated calibration of complex deterministic water quality models with a large number of biogeochemical parameters can reduce time-consuming iterative simulations involving empirical judgements of model fit. We undertook auto-calibration of the one-dimensional hydrodynamic-ecological lake model DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test the applicability of this procedure for shallow, polymictic Lake Rotorua (New Zealand). The calibration procedure involved independently minimising the root-mean-square-error (RMSE), maximizing the Pearson correlation coefficient (r) and Nash-Sutcliffe efficient coefficient (Nr) for comparisons of model state variables against measured data. An assigned number of parameter permutations was used for 10,000 simulation iterations. The 'optimal' temperature calibration produced a RMSE of 0.54 °C, Nr-value of 0.99 and r-value of 0.98 through the whole water column based on comparisons with 540 observed water temperatures collected between 13 July 2007 - 13 January 2009. The modeled bottom dissolved oxygen concentration (20.5 m below surface) was compared with 467 available observations. The calculated RMSE of the simulations compared with the measurements was 1.78 mg L-1, the Nr-value was 0.75 and the r-value was 0.87. The autocalibrated model was further tested for an independent data set by simulating bottom-water hypoxia events for the period 15 January 2009 to 8 June 2011 (875 days). This verification produced an accurate simulation of five hypoxic events corresponding to DO < 2 mg L-1 during summer of 2009-2011. The RMSE was 2.07 mg L-1, Nr-value 0.62 and r-value of 0.81, based on the available data set of 738 days. The auto-calibration software of DYRESM-CAEDYM developed here is substantially less time-consuming and more efficient in parameter optimisation than traditional manual calibration which has been the standard tool practiced for similar complex water quality models.

Influence of sampling frequency and load calculation methods on quantification of annual river nutrient and suspended solids loads.

PubMed

Elwan, Ahmed; Singh, Ranvir; Patterson, Maree; Roygard, Jon; Horne, Dave; Clothier, Brent; Jones, Geoffrey

2018-01-11

Better management of water quality in streams, rivers and lakes requires precise and accurate estimates of different contaminant loads. We assessed four sampling frequencies (2 days, weekly, fortnightly and monthly) and five load calculation methods (global mean (GM), rating curve (RC), ratio estimator (RE), flow-stratified (FS) and flow-weighted (FW)) to quantify loads of nitrate-nitrogen (NO 3 - -N), soluble inorganic nitrogen (SIN), total nitrogen (TN), dissolved reactive phosphorus (DRP), total phosphorus (TP) and total suspended solids (TSS), in the Manawatu River, New Zealand. The estimated annual river loads were compared to the reference 'true' loads, calculated using daily measurements of flow and water quality from May 2010 to April 2011, to quantify bias (i.e. accuracy) and root mean square error 'RMSE' (i.e. accuracy and precision). The GM method resulted into relatively higher RMSE values and a consistent negative bias (i.e. underestimation) in estimates of annual river loads across all sampling frequencies. The RC method resulted in the lowest RMSE for TN, TP and TSS at monthly sampling frequency. Yet, RC highly overestimated the loads for parameters that showed dilution effect such as NO 3 - -N and SIN. The FW and RE methods gave similar results, and there was no essential improvement in using RE over FW. In general, FW and RE performed better than FS in terms of bias, but FS performed slightly better than FW and RE in terms of RMSE for most of the water quality parameters (DRP, TP, TN and TSS) using a monthly sampling frequency. We found no significant decrease in RMSE values for estimates of NO 3 - N, SIN, TN and DRP loads when the sampling frequency was increased from monthly to fortnightly. The bias and RMSE values in estimates of TP and TSS loads (estimated by FW, RE and FS), however, showed a significant decrease in the case of weekly or 2-day sampling. This suggests potential for a higher sampling frequency during flow peaks for more precise and accurate estimates of annual river loads for TP and TSS, in the study river and other similar conditions.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

NASA Astrophysics Data System (ADS)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg/day) in Jenderan catchment area.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

Harmonize input selection for sediment transport prediction

NASA Astrophysics Data System (ADS)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

Near-infrared spectrometry allows fast and extensive predictions of functional traits from dry leaves and branches.

PubMed

Costa, Flávia R C; Lang, Carla; Almeida, Danilo R A; Castilho, Carolina V; Poorter, Lourens

2018-05-16

The linking of individual functional traits to ecosystem processes is the basis for making generalizations in ecology, but the measurement of individual values is laborious and time consuming, preventing large-scale trait mapping. Also, in hyper-diverse systems, errors occur because identification is difficult, and species level values ignore intra-specific variation. To allow extensive trait mapping at the individual level, we evaluated the potential of Fourrier-Transformed Near Infra-Red Spectrometry (FT-NIR) to adequately describe 14 traits that are key for plant carbon, water, and nutrient balance. FT-NIR absorption spectra (1,000-2,500 nm) were obtained from dry leaves and branches of 1,324 trees of 432 species from a hyper-diverse Amazonian forest. FT-NIR spectra were related to measured traits for the same plants using partial least squares regressions. A further 80 plants were collected from a different site to evaluate model applicability across sites. Relative prediction error (RMSE rel ) was calculated as the percentage of the trait value range represented by the final model RMSE. The key traits used in most functional trait studies; specific leaf area, leaf dry matter content, wood density and wood dry matter content can be well predicted by the model (R 2  = 0.69-0.78, RMSE rel  = 9-11%), while leaf density, xylem proportion, bark density and bark dry matter content can be moderately well predicted (R 2  = 0.53-0.61, RMSE rel  = 14-17%). Community-weighted means of all traits were well estimated with NIR, as did the shape of the frequency distribution of the community values for the above key traits. The model developed at the core site provided good estimations of the key traits of a different site. An evaluation of the sampling effort indicated that 400 or less individuals may be sufficient for establishing a good local model. We conclude that FT-NIR is an easy, fast and cheap method for the large-scale estimation of individual plant traits that was previously impossible. The ability to use dry intact leaves and branches unlocks the potential for using herbarium material to estimate functional traits; thus advancing our knowledge of community and ecosystem functioning from local to global scales. © 2018 by the Ecological Society of America.

Domain-Level Assessment of the Weather Running Estimate-Nowcast (WREN) Model

DTIC Science & Technology

2016-11-01

Added by Decreased Grid Spacing 14 4.4 Performance Comparison of 2 WRE–N Configurations 18 4.5 Performance Comparison: Dumais WRE–N with FDDA vs. the...FDDA for 2 -m-AGL TMP (K) ..................................................... 15 Fig. 11 Bias and RMSE errors for the 3 grids for Dumais and Passner...WRE–N with FDDA for 2 -m-AGL DPT (K) ...................................................... 16 Fig. 12 Bias and RMSE errors for the 3 grids for Dumais

A Gompertzian model with random effects to cervical cancer growth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mazlan, Mazma Syahidatul Ayuni; Rosli, Norhayati

2015-05-15

In this paper, a Gompertzian model with random effects is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via maximum likehood estimation. We apply 4-stage Runge-Kutta (SRK4) for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of the cervical cancer growth. Low values of root mean-square error (RMSE) of Gompertzian model with random effect indicate good fits.

Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

NASA Astrophysics Data System (ADS)

Ebrahimi-Khusfi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-05-01

This study was carried out to evaluate possible improvements of the soil moisture (SM) retrievals from the SMAP observations, based on the synergy between SMAP and SMOS. We assessed the impacts of the vegetation and soil roughness parameters on SM retrievals from SMAP observations. To do so, the effects of three key input parameters including the vegetation optical depth (VOD), effective scattering albedo (ω) and soil roughness (HR) parameters were assessed with the emphasis on the synergy with the VOD product derived from SMOS-IC, a new and simpler version of the SMOS algorithm, over two years of data (April 2015 to April 2017). First, a comprehensive comparison of seven SM retrieval algorithms was made to find the best one for SM retrievals from the SMAP observations. All results were evaluated against in situ measurements over 548 stations from the International Soil Moisture Network (ISMN) in terms of four statistical metrics: correlation coefficient (R), root mean square error (RMSE), bias and unbiased RMSE (UbRMSE). The comparison of seven SM retrieval algorithms showed that the dual channel algorithm based on the additional use of the SMOS-IC VOD product (selected algorithm) led to the best results of SM retrievals over 378, 399, 330 and 271 stations (out of a total of 548 stations) in terms of R, RMSE, UbRMSE and both R & UbRMSE, respectively. Moreover, comparing the measured and retrieved SM values showed that this synergy approach led to an increase in median R value from 0.6 to 0.65 and a decrease in median UbRMSE from 0.09 m3/m3 to 0.06 m3/m3. Second, using the algorithm selected in a first step and defined above, the ω and HR parameters were calibrated over 218 rather homogenous ISMN stations. 72 combinations of various values of ω and HR were used for the calibration over different land cover classes. In this calibration process, the optimal values of ω and HR were found for the different land cover classes. The obtained results indicated that the impact of the VOD parameter on SM retrievals is more considerable than the effects of HR and ω. Overall, the inclusion of the VOD parameter in the SMAP SM retrieval algorithm was found to be a very interesting approach and showed the large potential benefit of the synergy between SMAP and SMOS.

Automatic, unstructured mesh optimization for simulation and assessment of tide- and surge-driven hydrodynamics in a longitudinal estuary: St. Johns River

NASA Astrophysics Data System (ADS)

Bacopoulos, Peter

2018-05-01

A localized truncation error analysis with complex derivatives (LTEA+CD) is applied recursively with advanced circulation (ADCIRC) simulations of tides and storm surge for finite element mesh optimization. Mesh optimization is demonstrated with two iterations of LTEA+CD for tidal simulation in the lower 200 km of the St. Johns River, located in northeast Florida, and achieves more than an over 50% decrease in the number of mesh nodes, relating to a twofold increase in efficiency, at a zero cost to model accuracy. The recursively generated meshes using LTEA+CD lead to successive reductions in the global cumulative truncation error associated with the model mesh. Tides are simulated with root mean square error (RMSE) of 0.09-0.21 m and index of agreement (IA) values generally in the 80s and 90s percentage ranges. Tidal currents are simulated with RMSE of 0.09-0.23 m s-1 and IA values of 97% and greater. Storm tide due to Hurricane Matthew 2016 is simulated with RMSE of 0.09-0.33 m and IA values of 75-96%. Analysis of the LTEA+CD results shows the M2 constituent to dominate the node spacing requirement in the St. Johns River, with the M4 and M6 overtides and the STEADY constituent contributing some. Friction is the predominant physical factor influencing the target element size distribution, especially along the main river stem, while frequency (inertia) and Coriolis (rotation) are supplementary contributing factors. The combination of interior- and boundary-type computational molecules, providing near-full coverage of the model domain, renders LTEA+CD an attractive mesh generation/optimization tool for complex coastal and estuarine domains. The mesh optimization procedure using LTEA+CD is automatic and extensible to other finite element-based numerical models. Discussion is provided on the scope of LTEA+CD, the starting point (mesh) of the procedure, the user-specified scaling of the LTEA+CD results, and the iteration (termination) of LTEA+CD for mesh optimization.

Soil moisture from ground-based networks and the North American Land Data Assimilation System Phase 2 Model: Are the right values somewhere in between?

NASA Astrophysics Data System (ADS)

Caldwell, T. G.; Scanlon, B. R.; Long, D.; Young, M.

2013-12-01

Soil moisture is the most enigmatic component of the water balance; nonetheless, it is inherently tied to every component of the hydrologic cycle, affecting the partitioning of both water and energy at the land surface. However, our ability to assess soil water storage capacity and status through measurement or modeling is challenged by error and scale. Soil moisture is as difficult to measure as it is to model, yet land surface models and remote sensing products require some means of validation. Here we compare the three major soil moisture monitoring networks across the US, including the USDA Soil Climate Assessment Network (SCAN), NOAA Climate Reference Network (USCRN), and Cosmic Ray Soil Moisture Observing System (COSMOS) to the soil moisture simulated using the North American Land Data Assimilation System (NLDAS) Phase 2. NLDAS runs in near real-time on a 0.125° (12 km) grid over the US, producing ensemble model outputs of surface fluxes and storage. We focus primarily on soil water storage (SWS) in the upper 0-0.1 m zone from the Noah Land Surface Model and secondarily on the effects of error propagation from atmospheric forcing and soil parameterization. No scaling of the observational data was attempted. We simply compared the extracted time series at the nearest grid center from NLDAS and assessed the results by standard model statistics including root mean square error (RMSE) and mean bias estimate (MBE) of the collocated ground station. Observed and modeled data were compared at both hourly and daily mean coordinated universal time steps. In all, ~300 stations were used for 2012. SCAN sites were found to be particularly troublesome at 5- and 10-cm depths. SWS at 163 SCAN sites departed significantly from Noah with a mean R2 of 0.38 × 0.0.23, a mean RMSE of 14.9 mm with a MBE of -13.5 mm. SWS at 111 USCRN sites has a mean R2 of 0.53 × 0.20, a mean RMSE of 8.2 mm with a MBE of -3.7 mm relative to Noah. Finally, 62 COSMOS sites, the instrument with the largest measurement footprint (0.03 km2), we calculated a mean R2 of 0.53 × 0.21, a mean RMSE of 9.7 mm with a MBE of -0.3 mm. Forcing errors and textural misclassifications correlate well with model biases, indicating that scale and structural errors are equally present in NLDAS. Scaling issues aside, these confounding errors make cal/val missions, such as NASA's upcoming Soil Moisture Active Passive (SMAP) mission, problematic without significant quality control and maintenance of for our monitoring networks. Land surface models, such as NLDAS-2, may provide valuable insight into our soil moisture data and somewhere in between the real values likely exist.

Dynamic Time Warping compared to established methods for validation of musculoskeletal models.

PubMed

Gaspar, Martin; Welke, Bastian; Seehaus, Frank; Hurschler, Christof; Schwarze, Michael

2017-04-11

By means of Multi-Body musculoskeletal simulation, important variables such as internal joint forces and moments can be estimated which cannot be measured directly. Validation can ensued by qualitative or by quantitative methods. Especially when comparing time-dependent signals, many methods do not perform well and validation is often limited to qualitative approaches. The aim of the present study was to investigate the capabilities of the Dynamic Time Warping (DTW) algorithm for comparing time series, which can quantify phase as well as amplitude errors. We contrast the sensitivity of DTW with other established metrics: the Pearson correlation coefficient, cross-correlation, the metric according to Geers, RMSE and normalized RMSE. This study is based on two data sets, where one data set represents direct validation and the other represents indirect validation. Direct validation was performed in the context of clinical gait-analysis on trans-femoral amputees fitted with a 6 component force-moment sensor. Measured forces and moments from amputees' socket-prosthesis are compared to simulated forces and moments. Indirect validation was performed in the context of surface EMG measurements on a cohort of healthy subjects with measurements taken of seven muscles of the leg, which were compared to simulated muscle activations. Regarding direct validation, a positive linear relation between results of RMSE and nRMSE to DTW can be seen. For indirect validation, a negative linear relation exists between Pearson correlation and cross-correlation. We propose the DTW algorithm for use in both direct and indirect quantitative validation as it correlates well with methods that are most suitable for one of the tasks. However, in DV it should be used together with methods resulting in a dimensional error value, in order to be able to interpret results more comprehensible. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effective Acceleration Model for the Arrival Time of Interplanetary Shocks driven by Coronal Mass Ejections

NASA Astrophysics Data System (ADS)

Paouris, Evangelos; Mavromichalaki, Helen

2017-12-01

In a previous work (Paouris and Mavromichalaki in Solar Phys. 292, 30, 2017), we presented a total of 266 interplanetary coronal mass ejections (ICMEs) with as much information as possible. We developed a new empirical model for estimating the acceleration of these events in the interplanetary medium from this analysis. In this work, we present a new approach on the effective acceleration model (EAM) for predicting the arrival time of the shock that preceds a CME, using data of a total of 214 ICMEs. For the first time, the projection effects of the linear speed of CMEs are taken into account in this empirical model, which significantly improves the prediction of the arrival time of the shock. In particular, the mean value of the time difference between the observed time of the shock and the predicted time was equal to +3.03 hours with a mean absolute error (MAE) of 18.58 hours and a root mean squared error (RMSE) of 22.47 hours. After the improvement of this model, the mean value of the time difference is decreased to -0.28 hours with an MAE of 17.65 hours and an RMSE of 21.55 hours. This improved version was applied to a set of three recent Earth-directed CMEs reported in May, June, and July of 2017, and we compare our results with the values predicted by other related models.

The prediction of speed and incline in outdoor running in humans using accelerometry.

PubMed

Herren, R; Sparti, A; Aminian, K; Schutz, Y

1999-07-01

To explore whether triaxial accelerometric measurements can be utilized to accurately assess speed and incline of running in free-living conditions. Body accelerations during running were recorded at the lower back and at the heel by a portable data logger in 20 human subjects, 10 men, and 10 women. After parameterizing body accelerations, two neural networks were designed to recognize each running pattern and calculate speed and incline. Each subject ran 18 times on outdoor roads at various speeds and inclines; 12 runs were used to calibrate the neural networks whereas the 6 other runs were used to validate the model. A small difference between the estimated and the actual values was observed: the square root of the mean square error (RMSE) was 0.12 m x s(-1) for speed and 0.014 radiant (rad) (or 1.4% in absolute value) for incline. Multiple regression analysis allowed accurate prediction of speed (RMSE = 0.14 m x s(-1)) but not of incline (RMSE = 0.026 rad or 2.6% slope). Triaxial accelerometric measurements allows an accurate estimation of speed of running and incline of terrain (the latter with more uncertainty). This will permit the validation of the energetic results generated on the treadmill as applied to more physiological unconstrained running conditions.

Simulating maize yield and bomass with spatial variability of soil field capacity

USGS Publications Warehouse

Ma, Liwang; Ahuja, Lajpat; Trout, Thomas; Nolan, Bernard T.; Malone, Robert W.

2015-01-01

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

PubMed Central

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. PMID:27213008

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography.

PubMed

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

Application of a soft computing technique in predicting the percentage of shear force carried by walls in a rectangular channel with non-homogeneous roughness.

PubMed

Khozani, Zohreh Sheikh; Bonakdari, Hossein; Zaji, Amir Hossein

2016-01-01

Two new soft computing models, namely genetic programming (GP) and genetic artificial algorithm (GAA) neural network (a combination of modified genetic algorithm and artificial neural network methods) were developed in order to predict the percentage of shear force in a rectangular channel with non-homogeneous roughness. The ability of these methods to estimate the percentage of shear force was investigated. Moreover, the independent parameters' effectiveness in predicting the percentage of shear force was determined using sensitivity analysis. According to the results, the GP model demonstrated superior performance to the GAA model. A comparison was also made between the GP program determined as the best model and five equations obtained in prior research. The GP model with the lowest error values (root mean square error ((RMSE) of 0.0515) had the best function compared with the other equations presented for rough and smooth channels as well as smooth ducts. The equation proposed for rectangular channels with rough boundaries (RMSE of 0.0642) outperformed the prior equations for smooth boundaries.

Support vector regression methodology for estimating global solar radiation in Algeria

NASA Astrophysics Data System (ADS)

Guermoui, Mawloud; Rabehi, Abdelaziz; Gairaa, Kacem; Benkaciali, Said

2018-01-01

Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaïa city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (r2), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

PubMed Central

2011-01-01

Background This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. Results Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. Conclusions Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR. PMID:22372859

High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals

NASA Astrophysics Data System (ADS)

Laloy, Eric; Huisman, Johan Alexander; Jacques, Diederik

2014-11-01

This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1 cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04 cm3 cm-3. This RMSE value reduces to less than 0.02 cm3 cm-3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors.

Evaluating the SSEBop approach for evapotranspiration mapping with landsat data using lysimetric observations in the semi-arid Texas High Plains

USGS Publications Warehouse

Senay, Gabriel; Gowda, Prasanna H.; Bohms, Stefanie; Howell, T.A.; Friedrichs, Mackenzie; Marek, T.H.; Verdin, James

2014-01-01

The operational Simplified Surface Energy Balance (SSEBop) approach was applied on 14 Landsat 5 thermal infrared images for mapping daily actual evapotranspiration (ETa) fluxes during the spring and summer seasons (March–October) in 2006 and 2007. Data from four large lysimeters, managed by the USDA-ARS Conservation and Production Research Laboratory were used for evaluating the SSEBop estimated ETa. Lysimeter fields are arranged in a 2 × 2 block pattern with two fields each managed under irrigated and dryland cropping systems. The modeled and observed daily ETa values were grouped as "irrigated" and "dryland" at four different aggregation periods (1-day, 2-day, 3 day and "seasonal") for evaluation. There was a strong linear relationship between observed and modeled ETa with R2 values ranging from 0.87 to 0.97. The root mean square error (RMSE), as percent of their respective mean values, were reduced progressively with 28, 24, 16 and 12% at 1-day, 2-day, 3-day, and seasonal aggregation periods, respectively. With a further correction of the underestimation bias (−11%), the seasonal RMSE reduced from 12 to 6%. The random error contribution to the total error was reduced from 86 to 20% while the bias' contribution increased from 14 to 80% when aggregated from daily to seasonal scale, respectively. This study shows the reliable performance of the SSEBop approach on the Landsat data stream with a transferable approach for use with the recently launched LDCM (Landsat Data Continuity Mission) Thermal InfraRed Sensor (TIRS) data. Thus, SSEBop can produce quick, reliable and useful ET estimations at various time scales with higher seasonal accuracy for use in regional water management decisions.

Prediction of plantar shear stress distribution by artificial intelligence methods.

PubMed

Yavuz, Metin; Ocak, Hasan; Hetherington, Vincent J; Davis, Brian L

2009-09-01

Shear forces under the human foot are thought to be responsible for various foot pathologies such as diabetic plantar ulcers and athletic blisters. Frictional shear forces might also play a role in the metatarsalgia observed among hallux valgus (HaV) and rheumatoid arthritis (RA) patients. Due to the absence of commercial devices capable of measuring shear stress distribution, a number of linear models were developed. All of these have met with limited success. This study used nonlinear methods, specifically neural network and fuzzy logic schemes, to predict the distribution of plantar shear forces based on vertical loading parameters. In total, 73 subjects were recruited; 17 had diabetic neuropathy, 14 had HaV, 9 had RA, 11 had frequent foot blisters, and 22 were healthy. A feed-forward neural network (NN) and adaptive neurofuzzy inference system (NFIS) were built. These systems were then applied to a custom-built platform, which collected plantar pressure and shear stress data as subjects walked over the device. The inputs to both models were peak pressure, peak pressure-time integral, and time to peak pressure, and the output was peak resultant shear. Root-mean-square error (RMSE) values were calculated to test the models' accuracy. RMSE/actual shear ratio varied between 0.27 and 0.40 for NN predictions. Similarly, NFIS estimations resulted in a 0.28-0.37 ratio for local peak values in all subject groups. On the other hand, error percentages for global peak shear values were found to be in the range 11.4-44.1. These results indicate that there is no direct relationship between pressure and shear magnitudes. Future research should aim to decrease error levels by introducing shear stress dependent variables into the models.

Magnetic resonance imaging-targeted, 3D transrectal ultrasound-guided fusion biopsy for prostate cancer: Quantifying the impact of needle delivery error on diagnosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Peter R., E-mail: pmarti46@uwo.ca; Cool, Derek W.; Romagnoli, Cesare

2014-07-15

Purpose: Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes. Methods: T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiologymore » resident contoured 81 suspicious regions, yielding 3D tumor surfaces that were registered to the 3D TRUS images using an iterative closest point prostate surface-based method to yield 3D binary images of the suspicious regions in the TRUS context. The probabilityP of obtaining a sample of tumor tissue in one biopsy core was calculated by integrating a 3D Gaussian distribution over each suspicious region domain. Next, the authors performed an exhaustive search to determine the maximum root mean squared error (RMSE, in mm) of a biopsy system that gives P ≥ 95% for each tumor sample, and then repeated this procedure for equal-volume spheres corresponding to each tumor sample. Finally, the authors investigated the effect of probe-axis-direction error on measured tumor burden by studying the relationship between the error and estimated percentage of core involvement. Results: Given a 3.5 mm RMSE for contemporary fusion biopsy systems,P ≥ 95% for 21 out of 81 tumors. The authors determined that for a biopsy system with 3.5 mm RMSE, one cannot expect to sample tumors of approximately 1 cm{sup 3} or smaller with 95% probability with only one biopsy core. The predicted maximum RMSE giving P ≥ 95% for each tumor was consistently greater when using spherical tumor shapes as opposed to no shape assumption. However, an assumption of spherical tumor shape for RMSE = 3.5 mm led to a mean overestimation of tumor sampling probabilities of 3%, implying that assuming spherical tumor shape may be reasonable for many prostate tumors. The authors also determined that a biopsy system would need to have a RMS needle delivery error of no more than 1.6 mm in order to sample 95% of tumors with one core. The authors’ experiments also indicated that the effect of axial-direction error on the measured tumor burden was mitigated by the 18 mm core length at 3.5 mm RMSE. Conclusions: For biopsy systems with RMSE ≥ 3.5 mm, more than one biopsy core must be taken from the majority of tumors to achieveP ≥ 95%. These observations support the authors’ perspective that some tumors of clinically significant sizes may require more than one biopsy attempt in order to be sampled during the first biopsy session. This motivates the authors’ ongoing development of an approach to optimize biopsy plans with the aim of achieving a desired probability of obtaining a sample from each tumor, while minimizing the number of biopsies. Optimized planning of within-tumor targets for MRI-3D TRUS fusion biopsy could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.« less

A comparison of methods to handle skew distributed cost variables in the analysis of the resource consumption in schizophrenia treatment.

PubMed

Kilian, Reinhold; Matschinger, Herbert; Löeffler, Walter; Roick, Christiane; Angermeyer, Matthias C

2002-03-01

Transformation of the dependent cost variable is often used to solve the problems of heteroscedasticity and skewness in linear ordinary least square regression of health service cost data. However, transformation may cause difficulties in the interpretation of regression coefficients and the retransformation of predicted values. The study compares the advantages and disadvantages of different methods to estimate regression based cost functions using data on the annual costs of schizophrenia treatment. Annual costs of psychiatric service use and clinical and socio-demographic characteristics of the patients were assessed for a sample of 254 patients with a diagnosis of schizophrenia (ICD-10 F 20.0) living in Leipzig. The clinical characteristics of the participants were assessed by means of the BPRS 4.0, the GAF, and the CAN for service needs. Quality of life was measured by WHOQOL-BREF. A linear OLS regression model with non-parametric standard errors, a log-transformed OLS model and a generalized linear model with a log-link and a gamma distribution were used to estimate service costs. For the estimation of robust non-parametric standard errors, the variance estimator by White and a bootstrap estimator based on 2000 replications were employed. Models were evaluated by the comparison of the R2 and the root mean squared error (RMSE). RMSE of the log-transformed OLS model was computed with three different methods of bias-correction. The 95% confidence intervals for the differences between the RMSE were computed by means of bootstrapping. A split-sample-cross-validation procedure was used to forecast the costs for the one half of the sample on the basis of a regression equation computed for the other half of the sample. All three methods showed significant positive influences of psychiatric symptoms and met psychiatric service needs on service costs. Only the log- transformed OLS model showed a significant negative impact of age, and only the GLM shows a significant negative influences of employment status and partnership on costs. All three models provided a R2 of about.31. The Residuals of the linear OLS model revealed significant deviances from normality and homoscedasticity. The residuals of the log-transformed model are normally distributed but still heteroscedastic. The linear OLS model provided the lowest prediction error and the best forecast of the dependent cost variable. The log-transformed model provided the lowest RMSE if the heteroscedastic bias correction was used. The RMSE of the GLM with a log link and a gamma distribution was higher than those of the linear OLS model and the log-transformed OLS model. The difference between the RMSE of the linear OLS model and that of the log-transformed OLS model without bias correction was significant at the 95% level. As result of the cross-validation procedure, the linear OLS model provided the lowest RMSE followed by the log-transformed OLS model with a heteroscedastic bias correction. The GLM showed the weakest model fit again. None of the differences between the RMSE resulting form the cross- validation procedure were found to be significant. The comparison of the fit indices of the different regression models revealed that the linear OLS model provided a better fit than the log-transformed model and the GLM, but the differences between the models RMSE were not significant. Due to the small number of cases in the study the lack of significance does not sufficiently proof that the differences between the RSME for the different models are zero and the superiority of the linear OLS model can not be generalized. The lack of significant differences among the alternative estimators may reflect a lack of sample size adequate to detect important differences among the estimators employed. Further studies with larger case number are necessary to confirm the results. Specification of an adequate regression models requires a careful examination of the characteristics of the data. Estimation of standard errors and confidence intervals by nonparametric methods which are robust against deviations from the normal distribution and the homoscedasticity of residuals are suitable alternatives to the transformation of the skew distributed dependent variable. Further studies with more adequate case numbers are needed to confirm the results.

The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

PubMed Central

Lee, Yung-Hsiang; Ho, Chung-Ru; Su, Feng-Chun; Kuo, Nan-Jung; Cheng, Yu-Hsin

2011-01-01

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%. PMID:22164030

An efficient optimization method to improve the measuring accuracy of oxygen saturation by using triangular wave optical signal

NASA Astrophysics Data System (ADS)

Li, Gang; Yu, Yue; Zhang, Cui; Lin, Ling

2017-09-01

The oxygen saturation is one of the important parameters to evaluate human health. This paper presents an efficient optimization method that can improve the accuracy of oxygen saturation measurement, which employs an optical frequency division triangular wave signal as the excitation signal to obtain dynamic spectrum and calculate oxygen saturation. In comparison to the traditional method measured RMSE (root mean square error) of SpO2 which is 0.1705, this proposed method significantly reduced the measured RMSE which is 0.0965. It is notable that the accuracy of oxygen saturation measurement has been improved significantly. The method can simplify the circuit and bring down the demand of elements. Furthermore, it has a great reference value on improving the signal to noise ratio of other physiological signals.

A comparison of neural network-based predictions of foF2 with the IRI-2012 model at conjugate points in Southeast Asia

NASA Astrophysics Data System (ADS)

Wichaipanich, Noraset; Hozumi, Kornyanat; Supnithi, Pornchai; Tsugawa, Takuya

2017-06-01

This paper presents the development of Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at three ionosonde stations near the magnetic equator of Southeast Asia. Two of these stations including Chiang Mai (18.76°N, 98.93°E, dip angle 12.7°N) and Kototabang (0.2°S, 100.3°E, dip angle 10.1°S) are at the conjugate points while Chumphon (10.72°N, 99.37°E, dip angle 3.0°N) station is near the equator. To produce the model, the feed forward network with backpropagation algorithm is applied. The NN is trained with the daily hourly values of foF2 during 2004-2012, except 2009, and the selected input parameters, which affect the foF2 variability, include day number (DN), hour number (HR), solar zenith angle (C), geographic latitude (θ), magnetic inclination (I), magnetic declination (D) and angle of meridian (M) relative to the sub-solar point, the 7-day mean of F10.7 (F10.7_7), the 81-day mean of SSN (SSN_81) and the 2-day mean of Ap (Ap_2). The foF2 data of 2009 and 2013 are then used for testing the NN model during the foF2 interpolation and extrapolation, respectively. To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI-2012 (CCIR and URSI options) model are compared. In general, the results show the same trends in foF2 variation between the models (NN and IRI-2012) and the observations in that they are higher during the day and lower at night. Besides, the results demonstrate that the proposed NN model can predict the foF2 values more closely during daytime than during nighttime as supported by the lower RMSE values during daytime (0.5 ≤ RMSE ≤ 1.0 for Chumphon and Kototabang, 0.7 ≤ RMSE ≤ 1.2 at Chiang Mai) and with the highest levels during nighttime (0.8 ≤ RMSE ≤ 1.5 for Chumphon and Kototabang, 1.2 ≤ RMSE ≤ 2.0 at Chiang Mai). Furthermore, the NN model predicts the foF2 values more accurately than the IRI model at the three sites on average, as clearly seen on the yearly RMSE averages. The RMSE values of NN model are lower than those of both CCIR and URSI options, and in terms of the yearly percentage improvements, the NN model gives improvement of around 10-15% in 2009 and 10% in 2013 for Chiang Mai, 20-25% in 2009 and 5-10% in 2013 for Chumphon, and around 18-25% in 2009 and 20-30% in 2013 at Kototabang. Although the NN model predicts the foF2 values closely to the observed data and produces more accurate prediction than the IRI models, in some cases, the IRI model performs better than the NN model. Hence, there is still room for further improvement of the proposed NN model.

Photoacoustic signals denoising of the glucose aqueous solutions using an improved wavelet threshold method

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Xiong, Zhihua

2016-10-01

The photoacoustic signals denoising of glucose is one of most important steps in the quality identification of the fruit because the real-time photoacoustic singals of glucose are easily interfered by all kinds of noises. To remove the noises and some useless information, an improved wavelet threshld function were proposed. Compared with the traditional wavelet hard and soft threshold functions, the improved wavelet threshold function can overcome the pseudo-oscillation effect of the denoised photoacoustic signals due to the continuity of the improved wavelet threshold function, and the error between the denoised signals and the original signals can be decreased. To validate the feasibility of the improved wavelet threshold function denoising, the denoising simulation experiments based on MATLAB programmimg were performed. In the simulation experiments, the standard test signal was used, and three different denoising methods were used and compared with the improved wavelet threshold function. The signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) values were used to evaluate the performance of the improved wavelet threshold function denoising. The experimental results demonstrate that the SNR value of the improved wavelet threshold function is largest and the RMSE value is lest, which fully verifies that the improved wavelet threshold function denoising is feasible. Finally, the improved wavelet threshold function denoising was used to remove the noises of the photoacoustic signals of the glucose solutions. The denoising effect is also very good. Therefore, the improved wavelet threshold function denoising proposed by this paper, has a potential value in the field of denoising for the photoacoustic singals.

[Study on the polarized reflectance-hyperspectral information fusion technology of tomato leaves nutrient diagnoses].

PubMed

Zhu, Wen-Jing; Mao, Han-Ping; Li, Qing-Lin; Liu, Hong-Yu; Sun, Jun; Zuo, Zhi-Yu; Chen, Yong

2014-09-01

With 25%, 50%, 75%, 100% and 150%, five levels of, nitrogen (N), phosphorus (P) and potassium (K) nutrition stress samples cultivated in Venlo type greenhouse soilless cultivation mode as the research object, polarized reflectance spectra and hyperspectral images of different nutrient deficiency greenhouse tomato leaves were acquired by using polarized reflectance spectroscopy system developed by our own research group and hyperspectral imaging system respectively. The relationship between a certain number of changes in the bump and texture of non-smooth surface of the nutrient stress leaf and the level of polarization reflected radiation was clarified by scanning electron microscopy (SEM). On the one hand, the polarization spectrum was converted into the degree of polarization through Stokes equation, and the four polarization characteristics between the polarization spectroscopy and reference measurement values of N, P and K respectively were extracted. On the other hand, the four characteristic wavelengths of N, P, K hyperspectral image data were determined respectively through the principal component analysis, followed by eight hyperspectral texture features extracted corresponding to the four characteristic wavelengths through correlation analysis. Polarization characteristics and hyperspectral texture features combined with each characteristics of N, P, K were extracted. These 12 characteristic variables were normalized by maximum-minimum value method. N, P, K nutrient levels quantitative diagnostic models were established by SVR. Results of models are as follows: the correlation coefficient of nitrogen r = 0.961 8, root mean square error RMSE= 0.451; correlation coefficient of phosphorus r = 0.916 3, root mean square error RMSE = 0.620; correlation coefficient of potassium r = 0.940 6, root mean square error RMSE = 0.494. The results show that high precision tomato leaves nutrition prediction model could be built by using polarized reflectance spectroscopy combined with high spectral information fusion technology and achieve good diagnoses effect. It has a great significance for the improvement of model accuracy and the development of special instruments. The research provides a new idea for the rapid detection of tomato nutrient content.

Simulating the Refractive Index Structure Constant ({C}_{n}^{2}) in the Surface Layer at Antarctica with a Mesoscale Model

NASA Astrophysics Data System (ADS)

Qing, Chun; Wu, Xiaoqing; Li, Xuebin; Tian, Qiguo; Liu, Dong; Rao, Ruizhong; Zhu, Wenyue

2018-01-01

In this paper, we introduce an approach wherein the Weather Research and Forecasting (WRF) model is coupled with the bulk aerodynamic method to estimate the surface layer refractive index structure constant (C n 2) above Taishan Station in Antarctica. First, we use the measured meteorological parameters to estimate C n 2 using the bulk aerodynamic method, and second, we use the WRF model output parameters to estimate C n 2 using the bulk aerodynamic method. Finally, the corresponding C n 2 values from the micro-thermometer are compared with the C n 2 values estimated using the WRF model coupled with the bulk aerodynamic method. We analyzed the statistical operators—the bias, root mean square error (RMSE), bias-corrected RMSE (σ), and correlation coefficient (R xy )—in a 20 day data set to assess how this approach performs. In addition, we employ contingency tables to investigate the estimation quality of this approach, which provides complementary key information with respect to the bias, RMSE, σ, and R xy . The quantitative results are encouraging and permit us to confirm the fine performance of this approach. The main conclusions of this study tell us that this approach provides a positive impact on optimizing the observing time in astronomical applications and provides complementary key information for potential astronomical sites.

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

NASA Technical Reports Server (NTRS)

Farhadi, Leila; Reichle, Rolf H.; DeLannoy, Gabrielle J. M.; Kimball, John S.

2014-01-01

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

Calibrating page sized Gafchromic EBT3 films

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crijns, W.; Maes, F.; Heide, U. A. van der

2013-01-15

Purpose: The purpose is the development of a novel calibration method for dosimetry with Gafchromic EBT3 films. The method should be applicable for pretreatment verification of volumetric modulated arc, and intensity modulated radiotherapy. Because the exposed area on film can be large for such treatments, lateral scan errors must be taken into account. The correction for the lateral scan effect is obtained from the calibration data itself. Methods: In this work, the film measurements were modeled using their relative scan values (Transmittance, T). Inside the transmittance domain a linear combination and a parabolic lateral scan correction described the observed transmittancemore » values. The linear combination model, combined a monomer transmittance state (T{sub 0}) and a polymer transmittance state (T{sub {infinity}}) of the film. The dose domain was associated with the observed effects in the transmittance domain through a rational calibration function. On the calibration film only simple static fields were applied and page sized films were used for calibration and measurements (treatment verification). Four different calibration setups were considered and compared with respect to dose estimation accuracy. The first (I) used a calibration table from 32 regions of interest (ROIs) spread on 4 calibration films, the second (II) used 16 ROIs spread on 2 calibration films, the third (III), and fourth (IV) used 8 ROIs spread on a single calibration film. The calibration tables of the setups I, II, and IV contained eight dose levels delivered to different positions on the films, while for setup III only four dose levels were applied. Validation was performed by irradiating film strips with known doses at two different time points over the course of a week. Accuracy of the dose response and the lateral effect correction was estimated using the dose difference and the root mean squared error (RMSE), respectively. Results: A calibration based on two films was the optimal balance between cost effectiveness and dosimetric accuracy. The validation resulted in dose errors of 1%-2% for the two different time points, with a maximal absolute dose error around 0.05 Gy. The lateral correction reduced the RMSE values on the sides of the film to the RMSE values at the center of the film. Conclusions: EBT3 Gafchromic films were calibrated for large field dosimetry with a limited number of page sized films and simple static calibration fields. The transmittance was modeled as a linear combination of two transmittance states, and associated with dose using a rational calibration function. Additionally, the lateral scan effect was resolved in the calibration function itself. This allows the use of page sized films. Only two calibration films were required to estimate both the dose and the lateral response. The calibration films were used over the course of a week, with residual dose errors Less-Than-Or-Slanted-Equal-To 2% or Less-Than-Or-Slanted-Equal-To 0.05 Gy.« less

Narrowing the surface temperature range in CMIP5 simulations over the Arctic

NASA Astrophysics Data System (ADS)

Hao, Mingju; Huang, Jianbin; Luo, Yong; Chen, Xin; Lin, Yanluan; Zhao, Zongci; Xu, Ying

2018-05-01

Much uncertainty exists in reproducing Arctic temperature using different general circulation models (GCMs). Therefore, evaluating the performance of GCMs in reproducing Arctic temperature is critically important. In our study, 32 GCMs in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) during the period 1900-2005 are used, and several metrics, i.e., bias, correlation coefficient ( R), and root mean square error (RMSE), are applied. The Cowtan data set is adopted as the reference data. The results suggest that the GCMs used can reasonably reproduce the Arctic warming trend during the period 1900-2005, as observed in the observational data, whereas a large variation of inter-model differences exists in modeling the Arctic warming magnitude. With respect to the reference data, most GCMs have large cold biases, whereas others have weak warm biases. Additionally, based on statistical thresholds, the models MIROC-ESM, CSIRO-Mk3-6-0, HadGEM2-AO, and MIROC-ESM-CHEM (bias ≤ ±0.10 °C, R ≥ 0.50, and RMSE ≤ 0.60 °C) are identified as well-performing GCMs. The ensemble of the four best-performing GCMs (ES4), with bias, R, and RMSE values of -0.03 °C, 0.72, and 0.39 °C, respectively, performs better than the ensemble with all 32 members, with bias, R, and RMSE values of -0.04 °C, 0.64, and 0.43 °C, respectively. Finally, ES4 is used to produce projections for the next century under the scenarios of RCP2.6, RCP4.5, and RCP8.0. The uncertainty in the projected temperature is greater in the higher emissions scenarios. Additionally, the projected temperature in the cold half year has larger variations than that in the warm half year.

Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

PubMed

Fraczkiewicz, Robert; Lobell, Mario; Göller, Andreas H; Krenz, Ursula; Schoenneis, Rolf; Clark, Robert D; Hillisch, Alexander

2015-02-23

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.

Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Aardt, Jan; Romanczyk, Paul; van Leeuwen, Martin

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud datamore » and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.« less

Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

DOE PAGES

Van Aardt, Jan; Romanczyk, Paul; van Leeuwen, Martin; ...

2016-04-04

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud datamore » and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.« less

Assessment of the global monthly mean surface insolation estimated from satellite measurements using global energy balance archive data

NASA Technical Reports Server (NTRS)

Li, Zhanqing; Whitlock, Charles H.; Charlock, Thomas P.

1995-01-01

Global sets of surface radiation budget (SRB) have been obtained from satellite programs. These satellite-based estimates need validation with ground-truth observations. This study validates the estimates of monthly mean surface insolation contained in two satellite-based SRB datasets with the surface measurements made at worldwide radiation stations from the Global Energy Balance Archive (GEBA). One dataset was developed from the Earth Radiation Budget Experiment (ERBE) using the algorithm of Li et al. (ERBE/SRB), and the other from the International Satellite Cloud Climatology Project (ISCCP) using the algorithm of Pinker and Laszlo and that of Staylor (GEWEX/SRB). Since the ERBE/SRB data contain the surface net solar radiation only, the values of surface insolation were derived by making use of the surface albedo data contained GEWEX/SRB product. The resulting surface insolation has a bias error near zero and a root-mean-square error (RMSE) between 8 and 28 W/sq m. The RMSE is mainly associated with poor representation of surface observations within a grid cell. When the number of surface observations are sufficient, the random error is estimated to be about 5 W/sq m with present satellite-based estimates. In addition to demonstrating the strength of the retrieving method, the small random error demonstrates how well the ERBE derives from the monthly mean fluxes at the top of the atmosphere (TOA). A larger scatter is found for the comparison of transmissivity than for that of insolation. Month to month comparison of insolation reveals a weak seasonal trend in bias error with an amplitude of about 3 W/sq m. As for the insolation data from the GEWEX/SRB, larger bias errors of 5-10 W/sq m are evident with stronger seasonal trends and almost identical RMSEs.

Estimation of core body temperature from skin temperature, heat flux, and heart rate using a Kalman filter.

PubMed

Welles, Alexander P; Xu, Xiaojiang; Santee, William R; Looney, David P; Buller, Mark J; Potter, Adam W; Hoyt, Reed W

2018-05-18

Core body temperature (T C ) is a key physiological metric of thermal heat-strain yet it remains difficult to measure non-invasively in the field. This work used combinations of observations of skin temperature (T S ), heat flux (HF), and heart rate (HR) to accurately estimate T C using a Kalman Filter (KF). Data were collected from eight volunteers (age 22 ± 4 yr, height 1.75 ± 0.10 m, body mass 76.4 ± 10.7 kg, and body fat 23.4 ± 5.8%, mean ± standard deviation) while walking at two different metabolic rates (∼350 and ∼550 W) under three conditions (warm: 25 °C, 50% relative humidity (RH); hot-humid: 35 °C, 70% RH; and hot-dry: 40 °C, 20% RH). Skin temperature and HF data were collected from six locations: pectoralis, inner thigh, scapula, sternum, rib cage, and forehead. Kalman filter variables were learned via linear regression and covariance calculations between T C and T S , HF, and HR. Root mean square error (RMSE) and bias were calculated to identify the best performing models. The pectoralis (RMSE 0.18 ± 0.04 °C; bias -0.01 ± 0.09 °C), rib (RMSE 0.18 ± 0.09 °C; bias -0.03 ± 0.09 °C), and sternum (RMSE 0.20 ± 0.10 °C; bias -0.04 ± 0.13 °C) were found to have the lowest error values when using T S , HF, and HR but, using only two of these measures provided similar accuracy. Copyright © 2018. Published by Elsevier Ltd.

Predicting the reference evapotranspiration based on tensor decomposition

NASA Astrophysics Data System (ADS)

Misaghian, Negin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Mohammadi, Kasra

2017-11-01

Most of the available models for reference evapotranspiration (ET0) estimation are based upon only an empirical equation for ET0. Thus, one of the main issues in ET0 estimation is the appropriate integration of time information and different empirical ET0 equations to determine ET0 and boost the precision. The FAO-56 Penman-Monteith, adjusted Hargreaves, Blaney-Criddle, Priestley-Taylor, and Jensen-Haise equations were utilized in this study for estimating ET0 for two stations of Belgrade and Nis in Serbia using collected data for the period of 1980 to 2010. Three-order tensor is used to capture three-way correlations among months, years, and ET0 information. Afterward, the latent correlations among ET0 parameters were found by the multiway analysis to enhance the quality of the prediction. The suggested method is valuable as it takes into account simultaneous relations between elements, boosts the prediction precision, and determines latent associations. Models are compared with respect to coefficient of determination ( R 2), mean absolute error (MAE), and root-mean-square error (RMSE). The proposed tensor approach has a R 2 value of greater than 0.9 for all selected ET0 methods at both selected stations, which is acceptable for the ET0 prediction. RMSE is ranged between 0.247 and 0.485 mm day-1 at Nis station and between 0.277 and 0.451 mm day-1 at Belgrade station, while MAE is between 0.140 and 0.337 mm day-1 at Nis and between 0.208 and 0.360 mm day-1 at Belgrade station. The best performances are achieved by Priestley-Taylor model at Nis station ( R 2 = 0.985, MAE = 0.140 mm day-1, RMSE = 0.247 mm day-1) and FAO-56 Penman-Monteith model at Belgrade station (MAE = 0.208 mm day-1, RMSE = 0.277 mm day-1, R 2 = 0.975).

Can Selforganizing Maps Accurately Predict Photometric Redshifts?

NASA Technical Reports Server (NTRS)

Way, Michael J.; Klose, Christian

2012-01-01

We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods

Comparison of infiltration models in NIT Kurukshetra campus

NASA Astrophysics Data System (ADS)

Singh, Balraj; Sihag, Parveen; Singh, Karan

2018-05-01

The aim of the present investigation is to evaluate the performance of infiltration models used to calculate the infiltration rate of the soils. Ten different locations were chosen to measure the infiltration rate in NIT Kurukshetra. The instrument used for the experimentation was double ring infiltrometer. Some of the popular infiltration models like Horton's, Philip's, Modified Philip's and Green-Ampt were fitted with infiltration test data and performance of the models was determined using Nash-Sutcliffe efficiency (NSE), coefficient of correlation (C.C) and Root mean square error (RMSE) criteria. The result suggests that Modified Philip's model is the most accurate model where values of C.C, NSE and RMSE vary from 0.9947-0.9999, 0.9877-0.9998 to 0.1402-0.6913 (mm/h), respectively. Thus, this model can be used to synthetically produce infiltration data in the absence of infiltration data under the same conditions.

Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Zhang, Zheng; Meyer, David J.; Danielson, Jeffrey J.

2012-01-01

The ASTER Global Digital Elevation Model Version 2 (GDEM v2) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009. The absolute vertical accuracy of GDEM v2 was calculated by comparison with more than 18,000 independent reference geodetic ground control points from the National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v2 is 8.68 meters. This compares with the RMSE of 9.34 meters for GDEM v1. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v2 mean error of -0.20 meters is a significant improvement over the GDEM v1 mean error of -3.69 meters. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover to examine the effects of cover types on measured errors. The GDEM v2 mean errors by land cover class verify that the presence of aboveground features (tree canopies and built structures) cause a positive elevation bias, as would be expected for an imaging system like ASTER. In open ground classes (little or no vegetation with significant aboveground height), GDEM v2 exhibits a negative bias on the order of 1 meter. GDEM v2 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v2 has elevations that are higher in the canopy than SRTM.

Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2016-08-01

In the present research, three artificial intelligence methods including Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as, 48 empirical equations (10, 12 and 26 equations were temperature-based, sunshine-based and meteorological parameters-based, respectively) were used to estimate daily solar radiation in Kerman, Iran in the period of 1992-2009. To develop the GEP, ANN and ANFIS models, depending on the used empirical equations, various combinations of minimum air temperature, maximum air temperature, mean air temperature, extraterrestrial radiation, actual sunshine duration, maximum possible sunshine duration, sunshine duration ratio, relative humidity and precipitation were considered as inputs in the mentioned intelligent methods. To compare the accuracy of empirical equations and intelligent models, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R2) indices were used. The results showed that in general, sunshine-based and meteorological parameters-based scenarios in ANN and ANFIS models presented high accuracy than mentioned empirical equations. Moreover, the most accurate method in the studied region was ANN11 scenario with five inputs. The values of RMSE, MAE, MARE and R2 indices for the mentioned model were 1.850 MJ m-2 day-1, 1.184 MJ m-2 day-1, 9.58% and 0.935, respectively.

The Performance of Five Bioelectrical Impedance Analysis Prediction Equations against Dual X-ray Absorptiometry in Estimating Appendicular Skeletal Muscle Mass in an Adult Australian Population

PubMed Central

Yu, Solomon C. Y.; Powell, Alice; Khow, Kareeann S. F.; Visvanathan, Renuka

2016-01-01

Appendicular skeletal muscle mass (ASM) is a diagnostic criterion for sarcopenia. Bioelectrical impedance analysis (BIA) offers a bedside approach to measure ASM but the performance of BIA prediction equations (PE) varies with ethnicities and body composition. We aim to validate the performance of five PEs in estimating ASM against estimation by dual-energy X-ray absorptiometry (DXA). We recruited 195 healthy adult Australians and ASM was measured using single-frequency BIA. Bland-Altman analysis was used to assess the predictive accuracy of ASM as determined by BIA against DXA. Precision (root mean square error (RMSE)) and bias (mean error (ME)) were calculated according to the method of Sheiner and Beal. Four PEs (except that by Kim) showed ASM values that correlated strongly with ASMDXA (r ranging from 0.96 to 0.97, p < 0.001). The Sergi equation performed the best with the lowest ME of −1.09 kg (CI: −0.84–−1.34, p < 0.001) and the RMSE was 2.09 kg (CI: 1.72–2.47). In men, the Kyle equation performed better with the lowest ME (−0.32 kg (CI: −0.66–0.02) and RMSE (1.54 kg (CI: 1.14–1.93)). The Sergi equation is applicable in adult Australians (Caucasian) whereas the Kyle equation can be considered in males. The need remains to validate PEs in other ethnicities and to develop equations suitable for multi-frequency BIA. PMID:27043617

Global Performance of a Fast Parameterization Scheme for Estimating Surface Solar Radiation from MODIS data

NASA Astrophysics Data System (ADS)

Tang, W.; Yang, K.; Sun, Z.; Qin, J.; Niu, X.

2016-12-01

A fast parameterization scheme named SUNFLUX is used in this study to estimate instantaneous surface solar radiation (SSR) based on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard both Terra and Aqua platforms. The scheme mainly takes into account the absorption and scattering processes due to clouds, aerosols and gas in the atmosphere. The estimated instantaneous SSR is evaluated against surface observations obtained from seven stations of the Surface Radiation Budget Network (SURFRAD), four stations in the North China Plain (NCP) and 40 stations of the Baseline Surface Radiation Network (BSRN). The statistical results for evaluation against these three datasets show that the relative root-mean-square error (RMSE) values of SUNFLUX are less than 15%, 16% and 17%, respectively. Daily SSR is derived through temporal upscaling from the MODIS-based instantaneous SSR estimates, and is validated against surface observations. The relative RMSE values for daily SSR estimates are about 16% at the seven SURFRAD stations, four NCP stations, 40 BSRN stations and 90 China Meteorological Administration (CMA) radiation stations.

Curve fitting methods for solar radiation data modeling

NASA Astrophysics Data System (ADS)

Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder

2014-10-01

This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R2. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.

Curve fitting methods for solar radiation data modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my; Singh, Balbir Singh Mahinder, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my

2014-10-24

This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R{sup 2}. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both withmore » two terms) gives better results as compare with the other fitting methods.« less

Mapping health assessment questionnaire disability index (HAQ-DI) score, pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) onto the EuroQol-5D (EQ-5D) utility score with the KORean Observational study Network for Arthritis (KORONA) registry data.

PubMed

Kim, Hye-Lin; Kim, Dam; Jang, Eun Jin; Lee, Min-Young; Song, Hyun Jin; Park, Sun-Young; Cho, Soo-Kyung; Sung, Yoon-Kyoung; Choi, Chan-Bum; Won, Soyoung; Bang, So-Young; Cha, Hoon-Suk; Choe, Jung-Yoon; Chung, Won Tae; Hong, Seung-Jae; Jun, Jae-Bum; Kim, Jinseok; Kim, Seong-Kyu; Kim, Tae-Hwan; Kim, Tae-Jong; Koh, Eunmi; Lee, Hwajeong; Lee, Hye-Soon; Lee, Jisoo; Lee, Shin-Seok; Lee, Sung Won; Park, Sung-Hoon; Shim, Seung-Cheol; Yoo, Dae-Hyun; Yoon, Bo Young; Bae, Sang-Cheol; Lee, Eui-Kyung

2016-04-01

The aim of this study was to estimate the mapping model for EuroQol-5D (EQ-5D) utility values using the health assessment questionnaire disability index (HAQ-DI), pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) in a large, nationwide cohort of rheumatoid arthritis (RA) patients in Korea. The KORean Observational study Network for Arthritis (KORONA) registry data on 3557 patients with RA were used. Data were randomly divided into a modeling set (80 % of the data) and a validation set (20 % of the data). The ordinary least squares (OLS), Tobit, and two-part model methods were employed to construct a model to map to the EQ-5D index. Using a combination of HAQ-DI, pain VAS, and DAS28, four model versions were examined. To evaluate the predictive accuracy of the models, the root-mean-square error (RMSE) and mean absolute error (MAE) were calculated using the validation dataset. A model that included HAQ-DI, pain VAS, and DAS28 produced the highest adjusted R (2) as well as the lowest Akaike information criterion, RMSE, and MAE, regardless of the statistical methods used in modeling set. The mapping equation of the OLS method is given as EQ-5D = 0.95-0.21 × HAQ-DI-0.24 × pain VAS/100-0.01 × DAS28 (adjusted R (2) = 57.6 %, RMSE = 0.1654 and MAE = 0.1222). Also in the validation set, the RMSE and MAE were shown to be the smallest. The model with HAQ-DI, pain VAS, and DAS28 showed the best performance, and this mapping model enabled the estimation of an EQ-5D value for RA patients in whom utility values have not been measured.

Use of a digital camera to monitor the growth and nitrogen status of cotton.

PubMed

Jia, Biao; He, Haibing; Ma, Fuyu; Diao, Ming; Jiang, Guiying; Zheng, Zhong; Cui, Jin; Fan, Hua

2014-01-01

The main objective of this study was to develop a nondestructive method for monitoring cotton growth and N status using a digital camera. Digital images were taken of the cotton canopies between emergence and full bloom. The green and red values were extracted from the digital images and then used to calculate canopy cover. The values of canopy cover were closely correlated with the normalized difference vegetation index and the ratio vegetation index and were measured using a GreenSeeker handheld sensor. Models were calibrated to describe the relationship between canopy cover and three growth properties of the cotton crop (i.e., aboveground total N content, LAI, and aboveground biomass). There were close, exponential relationships between canopy cover and three growth properties. And the relationships for estimating cotton aboveground total N content were most precise, the coefficients of determination (R(2)) value was 0.978, and the root mean square error (RMSE) value was 1.479 g m(-2). Moreover, the models were validated in three fields of high-yield cotton. The result indicated that the best relationship between canopy cover and aboveground total N content had an R(2) value of 0.926 and an RMSE value of 1.631 g m(-2). In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring cotton growth and N status.

Modeled and monitored variation in space and time of PCB-153 concentrations in air, sediment, soil and aquatic biota on a European scale.

PubMed

Hauck, Mara; Huijbregts, Mark A J; Hollander, Anne; Hendriks, A Jan; van de Meent, Dik

2010-08-15

We evaluated various modeling options for estimating concentrations of PCB-153 in the environment and in biota across Europe, using a nested multimedia fate model coupled with a bioaccumulation model. The most detailed model set up estimates concentrations in air, soil, fresh water sediment and fresh water biota with spatially explicit environmental characteristics and spatially explicit emissions to air and water in the period 1930-2005. Model performance was evaluated with the root mean square error (RMSE(log)), based on the difference between estimated and measured concentrations. The RMSE(log) was 5.4 for air, 5.6-6.3 for sediment and biota, and 5.5 for soil in the most detailed model scenario. Generally, model estimations tended to underestimate observed values for all compartments, except air. The decline in observed concentrations was also slightly underestimated by the model for the period where measurements were available (1989-2002). Applying a generic model setup with averaged emissions and averaged environmental characteristics, the RMSE(log) increased to 21 for air and 49 for sediment. For soil the RMSE(log) decreased to 3.5. We found that including spatial variation in emissions was most relevant for all compartments, except soil, while including spatial variation in environmental characteristics was less influential. For improving predictions of concentrations in sediment and aquatic biota, including emissions to water was found to be relevant as well. Copyright 2009 Elsevier B.V. All rights reserved.

Determining Sala mango qualities with the use of RGB images captured by a mobile phone camera

NASA Astrophysics Data System (ADS)

Yahaya, Ommi Kalsom Mardziah; Jafri, Mohd Zubir Mat; Aziz, Azlan Abdul; Omar, Ahmad Fairuz

2015-04-01

Sala mango (Mangifera indicia) is one of the Malaysia's most popular tropical fruits that are widely marketed within the country. The degrees of ripeness of mangoes have conventionally been evaluated manually on the basis of color parameters, but a simple non-destructive technique using the Samsung Galaxy Note 1 mobile phone camera is introduced to replace the destructive technique. In this research, color parameters in terms of RGB values acquired using the ENVI software system were linked to detect Sala mango quality parameters. The features of mango were extracted from the acquired images and then used to classify of fruit skin color, which relates to the stages of ripening. A multivariate analysis method, multiple linear regression, was employed with the purpose of using RGB color parameters to estimate the pH, soluble solids content (SSC), and firmness. The relationship between these qualities parameters of Sala mango and its mean pixel values in the RGB system is analyzed. Findings show that pH yields the highest accuracy with a correlation coefficient R = 0.913 and root mean square of error RMSE = 0.166 pH. Meanwhile, firmness has R = 0.875 and RMSE = 1.392 kgf, whereas soluble solid content has the lowest accuracy with R = 0.814 and RMSE = 1.218°Brix with the correlation between color parameters. Therefore, this non-invasive method can be used to determine the quality attributes of mangoes.

Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure

NASA Astrophysics Data System (ADS)

Checefsky, Walter A.; Abidin, Anas Z.; Nagarajan, Mahesh B.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2016-03-01

The current clinical standard for measuring Bone Mineral Density (BMD) is dual X-ray absorptiometry, however more recently BMD derived from volumetric quantitative computed tomography has been shown to demonstrate a high association with spinal fracture susceptibility. In this study, we propose a method of fracture risk assessment using structural properties of trabecular bone in spinal vertebrae. Experimental data was acquired via axial multi-detector CT (MDCT) from 12 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. Common image processing methods were used to annotate the trabecular compartment in the vertebral slices creating a circular region of interest (ROI) that excluded cortical bone for each slice. The pixels inside the ROI were converted to values indicative of BMD. High dimensional geometrical features were derived using the scaling index method (SIM) at different radii and scaling factors (SF). The mean BMD values within the ROI were then extracted and used in conjunction with a support vector machine to predict the failure load of the specimens. Prediction performance was measured using the root-mean-square error (RMSE) metric and determined that SIM combined with mean BMD features (RMSE = 0.82 +/- 0.37) outperformed MDCT-measured mean BMD (RMSE = 1.11 +/- 0.33) (p < 10-4). These results demonstrate that biomechanical strength prediction in vertebrae can be significantly improved through the use of SIM-derived texture features from trabecular bone.

Validation of the Conversion between the Mini-Mental State Examination and Montreal Cognitive assessment in Korean Patients with Parkinson’s Disease

PubMed Central

Kim, Ryul; Kim, Han-Joon; Kim, Aryun; Jang, Mi-Hee; Kim, Hyun Jeong; Jeon, Beomseok

2018-01-01

Objective Two conversion tables between the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) have recently been established for Parkinson’s disease (PD). This study aimed to validate them in Korean patients with PD and to evaluate whether they could be influenced by educational level. Methods A total of 391 patients with PD who undertook both the Korean MMSE and the Korean MoCA during the same session were retrospectively assessed. The mean, median, and root mean squared error (RMSE) of the difference between the true and converted MMSE scores and the intraclass correlation coefficient (ICC) were calculated according to educational level (6 or fewer years, 7–12 years, or 13 or more years). Results Both conversions had a median value of 0, with a small mean and RMSE of differences, and a high correlation between the true and converted MMSE scores. In the classification according to educational level, all groups had roughly similar values of the median, mean, RMSE, and ICC both within and between the conversions. Conclusion Our findings suggest that both MMSE-MoCA conversion tables are useful instruments for transforming MoCA scores into converted MMSE scores in Korean patients with PD, regardless of educational level. These will greatly enhance the utility of the existing cognitive data from the Korean PD population in clinical and research settings. PMID:29316782

Stochastic inversion of ocean color data using the cross-entropy method.

PubMed

Salama, Mhd Suhyb; Shen, Fang

2010-01-18

Improving the inversion of ocean color data is an ever continuing effort to increase the accuracy of derived inherent optical properties. In this paper we present a stochastic inversion algorithm to derive inherent optical properties from ocean color, ship and space borne data. The inversion algorithm is based on the cross-entropy method where sets of inherent optical properties are generated and converged to the optimal set using iterative process. The algorithm is validated against four data sets: simulated, noisy simulated in-situ measured and satellite match-up data sets. Statistical analysis of validation results is based on model-II regression using five goodness-of-fit indicators; only R2 and root mean square of error (RMSE) are mentioned hereafter. Accurate values of total absorption coefficient are derived with R2 > 0.91 and RMSE, of log transformed data, less than 0.55. Reliable values of the total backscattering coefficient are also obtained with R2 > 0.7 (after removing outliers) and RMSE < 0.37. The developed algorithm has the ability to derive reliable results from noisy data with R2 above 0.96 for the total absorption and above 0.84 for the backscattering coefficients. The algorithm is self contained and easy to implement and modify to derive the variability of chlorophyll-a absorption that may correspond to different phytoplankton species. It gives consistently accurate results and is therefore worth considering for ocean color global products.

The Drag-based Ensemble Model (DBEM) for Coronal Mass Ejection Propagation

NASA Astrophysics Data System (ADS)

Dumbović, Mateja; Čalogović, Jaša; Vršnak, Bojan; Temmer, Manuela; Mays, M. Leila; Veronig, Astrid; Piantschitsch, Isabell

2018-02-01

The drag-based model for heliospheric propagation of coronal mass ejections (CMEs) is a widely used analytical model that can predict CME arrival time and speed at a given heliospheric location. It is based on the assumption that the propagation of CMEs in interplanetary space is solely under the influence of magnetohydrodynamical drag, where CME propagation is determined based on CME initial properties as well as the properties of the ambient solar wind. We present an upgraded version, the drag-based ensemble model (DBEM), that covers ensemble modeling to produce a distribution of possible ICME arrival times and speeds. Multiple runs using uncertainty ranges for the input values can be performed in almost real-time, within a few minutes. This allows us to define the most likely ICME arrival times and speeds, quantify prediction uncertainties, and determine forecast confidence. The performance of the DBEM is evaluated and compared to that of ensemble WSA-ENLIL+Cone model (ENLIL) using the same sample of events. It is found that the mean error is ME = ‑9.7 hr, mean absolute error MAE = 14.3 hr, and root mean square error RMSE = 16.7 hr, which is somewhat higher than, but comparable to ENLIL errors (ME = ‑6.1 hr, MAE = 12.8 hr and RMSE = 14.4 hr). Overall, DBEM and ENLIL show a similar performance. Furthermore, we find that in both models fast CMEs are predicted to arrive earlier than observed, most likely owing to the physical limitations of models, but possibly also related to an overestimation of the CME initial speed for fast CMEs.

Forecasting Inflow and Outflow of Money Currency in East Java Using a Hybrid Exponential Smoothing and Calendar Variation Model

NASA Astrophysics Data System (ADS)

Susanti, Ana; Suhartono; Jati Setyadi, Hario; Taruk, Medi; Haviluddin; Pamilih Widagdo, Putut

2018-03-01

Money currency availability in Bank Indonesia can be examined by inflow and outflow of money currency. The objective of this research is to forecast the inflow and outflow of money currency in each Representative Office (RO) of BI in East Java by using a hybrid exponential smoothing based on state space approach and calendar variation model. Hybrid model is expected to generate more accurate forecast. There are two studies that will be discussed in this research. The first studies about hybrid model using simulation data that contain pattern of trends, seasonal and calendar variation. The second studies about the application of a hybrid model for forecasting the inflow and outflow of money currency in each RO of BI in East Java. The first of results indicate that exponential smoothing model can not capture the pattern calendar variation. It results RMSE values 10 times standard deviation of error. The second of results indicate that hybrid model can capture the pattern of trends, seasonal and calendar variation. It results RMSE values approaching the standard deviation of error. In the applied study, the hybrid model give more accurate forecast for five variables : the inflow of money currency in Surabaya, Malang, Jember and outflow of money currency in Surabaya and Kediri. Otherwise, the time series regression model yields better for three variables : outflow of money currency in Malang, Jember and inflow of money currency in Kediri.

Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

2017-12-01

This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that using EOF techniques can capture the groundwater flow tendency and detects the correction vector of the simulated error sources. Hence, the established EOF-based methodology can effectively and accurately identify the multiple recharges and hydrogeological parameters.

Estimation of metabolic energy expenditure from core temperature using a human thermoregulatory model.

PubMed

Welles, Alexander P; Buller, Mark J; Looney, David P; Rumpler, William V; Gribok, Andrei V; Hoyt, Reed W

2018-02-01

Human metabolic energy expenditure is critical to many scientific disciplines but can only be measured using expensive and/or restrictive equipment. The aim of this work is to determine whether the SCENARIO thermoregulatory model can be adapted to estimate metabolic rate (M) from core body temperature (T C ). To validate this method of M estimation, data were collected from fifteen test volunteers (age = 23 ± 3yr, height = 1.73 ± 0.07m, mass = 68.6 ± 8.7kg, body fat = 16.7 ± 7.3%; mean ± SD) who wore long sleeved nylon jackets and pants (I tot,clo = 1.22, I m = 0.41) during treadmill exercise tasks (32 trials; 7.8 ± 0.5km in 1h; air temp. = 22°C, 50% RH, wind speed = 0.35ms -1 ). Core body temperatures were recorded by ingested thermometer pill and M data were measured via whole room indirect calorimetry. Metabolic rate was estimated for 5min epochs in a two-step process. First, for a given epoch, a range of M values were input to the SCENARIO model and a corresponding range of T C values were output. Second, the output T C range value with the lowest absolute error relative to the observed T C for the given epoch was identified and its corresponding M range input was selected as the estimated M for that epoch. This process was then repeated for each subsequent remaining epoch. Root mean square error (RMSE), mean absolute error (MAE), and bias between observed and estimated M were 186W, 130 ± 174W, and 33 ± 183W, respectively. The RMSE for total energy expenditure by exercise period was 0.30 MJ. These results indicate that the SCENARIO model is useful for estimating M from T C when measurement is otherwise impractical. Published by Elsevier Ltd.

Stochastic growth logistic model with aftereffect for batch fermentation process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah

2014-06-19

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

Stochastic growth logistic model with aftereffect for batch fermentation process

NASA Astrophysics Data System (ADS)

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

2014-06-01

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression, Classification and Regression Tree, and Random Forest Models

PubMed Central

Qiu, Lefeng; Wang, Kai; Long, Wenli; Wang, Ke; Hu, Wei; Amable, Gabriel S.

2016-01-01

Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0–20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R2 value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R2 values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale. PMID:26964095

A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression, Classification and Regression Tree, and Random Forest Models.

PubMed

Qiu, Lefeng; Wang, Kai; Long, Wenli; Wang, Ke; Hu, Wei; Amable, Gabriel S

2016-01-01

Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0-20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R2 value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R2 values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale.

Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring

USGS Publications Warehouse

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

2012-01-01

agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.

CMSAF products Cloud Fraction Coverage and Cloud Type used for solar global irradiance estimation

NASA Astrophysics Data System (ADS)

Badescu, Viorel; Dumitrescu, Alexandru

2016-08-01

Two products provided by the climate monitoring satellite application facility (CMSAF) are the instantaneous Cloud Fractional Coverage (iCFC) and the instantaneous Cloud Type (iCTY) products. Previous studies based on the iCFC product show that the simple solar radiation models belonging to the cloudiness index class n CFC = 0.1-1.0 have rRMSE values ranging between 68 and 71 %. The products iCFC and iCTY are used here to develop simple models providing hourly estimates for solar global irradiance. Measurements performed at five weather stations of Romania (South-Eastern Europe) are used. Two three-class characterizations of the state-of-the-sky, based on the iCTY product, are defined. In case of the first new sky state classification, which is roughly related with cloud altitude, the solar radiation models proposed here perform worst for the iCTY class 4-15, with rRMSE values ranging between 46 and 57 %. The spreading error of the simple models is lower than that of the MAGIC model for the iCTY classes 1-4 and 15-19, but larger for iCTY classes 4-15. In case of the second new sky state classification, which takes into account in a weighted manner the chance for the sun to be covered by different types of clouds, the solar radiation models proposed here perform worst for the cloudiness index class n CTY = 0.7-0.1, with rRMSE values ranging between 51 and 66 %. Therefore, the two new sky state classifications based on the iCTY product are useful in increasing the accuracy of solar radiation models.

An evaluation of three growth and yield simulators for even-aged hardwood forests of the mid-Appalachian region

Treesearch

John R. Brooks; Gary W. Miller

2011-01-01

Data from even-aged hardwood stands in four ecoregions across the mid-Appalachian region were used to test projection accuracy for three available growth and yield software systems: SILVAH, the Forest Vegetation Simulator, and the Stand Damage Model. Average root mean squared error (RMSE) ranged from 20 to 140 percent of actual trees per acre while RMSE ranged from 2...

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar

PubMed Central

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-01

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R2 = 0.340, root-mean-square error (RMSE) = 81.89 g·m−2, and relative error of 14.1%). The improvement of multiple regressions to the R2 and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns. PMID:28106819

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

PubMed

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-19

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass ( R ² = 0.340, root-mean-square error (RMSE) = 81.89 g·m -2 , and relative error of 14.1%). The improvement of multiple regressions to the R ² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.

Spatial Interpolation of Aerosol Optical Depth Pollution: Comparison of Methods for the Development of Aerosol Distribution

NASA Astrophysics Data System (ADS)

Safarpour, S.; Abdullah, K.; Lim, H. S.; Dadras, M.

2017-09-01

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.

Evaluation of image quality and radiation dose by adaptive statistical iterative reconstruction technique level for chest CT examination.

PubMed

Hong, Sun Suk; Lee, Jong-Woong; Seo, Jeong Beom; Jung, Jae-Eun; Choi, Jiwon; Kweon, Dae Cheol

2013-12-01

The purpose of this research is to determine the adaptive statistical iterative reconstruction (ASIR) level that enables optimal image quality and dose reduction in the chest computed tomography (CT) protocol with ASIR. A chest phantom with 0-50 % ASIR levels was scanned and then noise power spectrum (NPS), signal and noise and the degree of distortion of peak signal-to-noise ratio (PSNR) and the root-mean-square error (RMSE) were measured. In addition, the objectivity of the experiment was measured using the American College of Radiology (ACR) phantom. Moreover, on a qualitative basis, five lesions' resolution, latitude and distortion degree of chest phantom and their compiled statistics were evaluated. The NPS value decreased as the frequency increased. The lowest noise and deviation were at the 20 % ASIR level, mean 126.15 ± 22.21. As a result of the degree of distortion, signal-to-noise ratio and PSNR at 20 % ASIR level were at the highest value as 31.0 and 41.52. However, maximum absolute error and RMSE showed the lowest deviation value as 11.2 and 16. In the ACR phantom study, all ASIR levels were within acceptable allowance of guidelines. The 20 % ASIR level performed best in qualitative evaluation at five lesions of chest phantom as resolution score 4.3, latitude 3.47 and the degree of distortion 4.25. The 20 % ASIR level was proved to be the best in all experiments, noise, distortion evaluation using ImageJ and qualitative evaluation of five lesions of a chest phantom. Therefore, optimal images as well as reduce radiation dose would be acquired when 20 % ASIR level in thoracic CT is applied.

Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis.

PubMed

Robert-Lachaine, Xavier; Mecheri, Hakim; Larue, Christian; Plamondon, André

2017-04-01

The potential of inertial measurement units (IMUs) for ergonomics applications appears promising. However, previous IMUs validation studies have been incomplete regarding aspects of joints analysed, complexity of movements and duration of trials. The objective was to determine the technological error and biomechanical model differences between IMUs and an optoelectronic system and evaluate the effect of task complexity and duration. Whole-body kinematics from 12 participants was recorded simultaneously with a full-body Xsens system where an Optotrak cluster was fixed on every IMU. Short functional movements and long manual material handling tasks were performed and joint angles were compared between the two systems. The differences attributed to the biomechanical model showed significantly greater (P ≤ .001) RMSE than the technological error. RMSE was systematically higher (P ≤ .001) for the long complex task with a mean on all joints of 2.8° compared to 1.2° during short functional movements. Definition of local coordinate systems based on anatomical landmarks or single posture was the most influent difference between the two systems. Additionally, IMUs accuracy was affected by the complexity and duration of the tasks. Nevertheless, technological error remained under 5° RMSE during handling tasks, which shows potential to track workers during their daily labour.

A Photometric Machine-Learning Method to Infer Stellar Metallicity

NASA Technical Reports Server (NTRS)

Miller, Adam A.

2015-01-01

Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version.

PubMed

Furuhama, A; Toida, T; Nishikawa, N; Aoki, Y; Yoshioka, Y; Shiraishi, H

2010-07-01

The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure-activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r(2 )> 0.7, RMSE 5, give acceptable q(2) values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.

Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics

NASA Astrophysics Data System (ADS)

Jahedi Rad, Shahpour; Kaveh, Mohammad; Sharabiani, Vali Rasooli; Taghinezhad, Ebrahim

2018-05-01

The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R 2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R 2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States.

PubMed

Zhang, Hongliang; Chen, Gang; Hu, Jianlin; Chen, Shu-Hua; Wiedinmyer, Christine; Kleeman, Michael; Ying, Qi

2014-03-01

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0

Spatial and temporal variability of precipitation in Serbia for the period 1961-2010

NASA Astrophysics Data System (ADS)

Milovanović, Boško; Schuster, Phillip; Radovanović, Milan; Vakanjac, Vesna Ristić; Schneider, Christoph

2017-10-01

Monthly, seasonal and annual sums of precipitation in Serbia were analysed in this paper for the period 1961-2010. Latitude, longitude and altitude of 421 precipitation stations and terrain features in their close environment (slope and aspect of terrain within a radius of 10 km around the station) were used to develop a regression model on which spatial distribution of precipitation was calculated. The spatial distribution of annual, June (maximum values for almost all of the stations) and February (minimum values for almost all of the stations) precipitation is presented. Annual precipitation amounts ranged from 500 to 600 mm to over 1100 mm. June precipitation ranged from 60 to 140 mm and February precipitation from 30 to 100 mm. The validation results expressed as root mean square error (RMSE) for monthly sums ranged from 3.9 mm in October (7.5% of the average precipitation for this month) to 6.2 mm in April (10.4%). For seasonal sums, RMSE ranged from 10.4 mm during autumn (6.1% of the average precipitation for this season) to 20.5 mm during winter (13.4%). On the annual scale, RMSE was 68 mm (9.5% of the average amount of precipitation). We further analysed precipitation trends using Sen's estimation, while the Mann-Kendall test was used for testing the statistical significance of the trends. For most parts of Serbia, the mean annual precipitation trends fell between -5 and +5 and +5 and +15 mm/decade. June precipitation trends were mainly between -8 and +8 mm/decade. February precipitation trends generally ranged from -3 to +3 mm/decade.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

PubMed

Bannan, Caitlin C; Burley, Kalistyn H; Chiu, Michael; Shirts, Michael R; Gilson, Michael K; Mobley, David L

2016-11-01

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge

PubMed Central

Bannan, Caitlin C.; Burley, Kalistyn H.; Chiu, Michael; Shirts, Michael R.; Gilson, Michael K.; Mobley, David L.

2016-01-01

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided. PMID:27677750

Accuracy of a Basketball Indoor Tracking System Based on Standard Bluetooth Low Energy Channels (NBN23®).

PubMed

Figueira, Bruno; Gonçalves, Bruno; Folgado, Hugo; Masiulis, Nerijus; Calleja-González, Julio; Sampaio, Jaime

2018-06-14

The present study aims to identify the accuracy of the NBN23 ® system, an indoor tracking system based on radio-frequency and standard Bluetooth Low Energy channels. Twelve capture tags were attached to a custom cart with fixed distances of 0.5, 1.0, 1.5, and 1.8 m. The cart was pushed along a predetermined course following the lines of a standard dimensions Basketball court. The course was performed at low speed (<10.0 km/h), medium speed (>10.0 km/h and <20.0 km/h) and high speed (>20.0 km/h). Root mean square error (RMSE) and percentage of variance accounted for (%VAF) were used as accuracy measures. The obtained data showed acceptable accuracy results for both RMSE and %VAF, despite the expected degree of error in position measurement at higher speeds. The RMSE for all the distances and velocities presented an average absolute error of 0.30 ± 0.13 cm with 90.61 ± 8.34 of %VAF, in line with most available systems, and considered acceptable for indoor sports. The processing of data with filter correction seemed to reduce the noise and promote a lower relative error, increasing the %VAF for each measured distance. Research using positional-derived variables in Basketball is still very scarce; thus, this independent test of the NBN23 ® tracking system provides accuracy details and opens up opportunities to develop new performance indicators that help to optimize training adaptations and performance.

Validation of the Aster Global Digital Elevation Model Version 3 Over the Conterminous United States

NASA Astrophysics Data System (ADS)

Gesch, D.; Oimoen, M.; Danielson, J.; Meyer, D.

2016-06-01

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of -1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from -2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

Validation of the ASTER Global Digital Elevation Model version 3 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Danielson, Jeffrey J.; Meyer, David; Halounova, L; Šafář, V.; Jiang, J.; Olešovská, H.; Dvořáček, P.; Holland, D.; Seredovich, V.A.; Muller, J.P.; Pattabhi Rama Rao, E.; Veenendaal, B.; Mu, L.; Zlatanova, S.; Oberst, J.; Yang, C.P.; Ban, Y.; Stylianidis, S.; Voženílek, V.; Vondráková, A.; Gartner, G.; Remondino, F.; Doytsher, Y.; Percivall, George; Schreier, G.; Dowman, I.; Streilein, A.; Ernst, J.

2016-01-01

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

Time series model for forecasting the number of new admission inpatients.

PubMed

Zhou, Lingling; Zhao, Ping; Wu, Dongdong; Cheng, Cheng; Huang, Hao

2018-06-15

Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. For the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. Hybrid model does not necessarily outperform its constituents' performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.

Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic.

PubMed

Kampel, Milton; Lorenzzetti, João A; Bentz, Cristina M; Nunes, Raul A; Paranhos, Rodolfo; Rudorff, Frederico M; Politano, Alexandre T

2009-01-01

Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3M(RAD)), Ocean Chlorophyll 4 bands (OC4v4(RAD)), and Ocean Chlorophyll 2 bands (OC2v4(RAD)). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3M(SAT), and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01(SAT)), and Carder(SAT). In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg/m(3). In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m(3) (OC2v4(RAD)). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m(3)) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m(3), respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of R(RS) by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.

Modulation of the relationship between external knee adduction moments and medial joint contact forces across subjects and activities.

PubMed

Trepczynski, Adam; Kutzner, Ines; Bergmann, Georg; Taylor, William R; Heller, Markus O

2014-05-01

The external knee adduction moment (EAM) is often considered a surrogate measure of the distribution of loads across the tibiofemoral joint during walking. This study was undertaken to quantify the relationship between the EAM and directly measured medial tibiofemoral contact forces (Fmed ) in a sample of subjects across a spectrum of activities. The EAM for 9 patients who underwent total knee replacement was calculated using inverse dynamics analysis, while telemetric implants provided Fmed for multiple repetitions of 10 activities, including walking, stair negotiation, sit-to-stand activities, and squatting. The effects of the factors "subject" and "activity" on the relationships between Fmed and EAM were quantified using mixed-effects regression analyses in terms of the root mean square error (RMSE) and the slope of the regression. Across subjects and activities a good correlation between peak EAM and Fmed values was observed, with an overall R(2) value of 0.88. However, the slope of the linear regressions varied between subjects by up to a factor of 2. At peak EAM and Fmed , the RMSE of the regression across all subjects was 35% body weight (%BW), while the maximum error was 127 %BW. The relationship between EAM and Fmed is generally good but varies considerably across subjects and activities. These findings emphasize the limitation of relying solely on the EAM to infer medial joint loading when excessive directed cocontraction of muscles exists and call for further investigations into the soft tissue-related mechanisms that modulate the internal forces at the knee. Copyright © 2014 by the American College of Rheumatology.

Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

NASA Astrophysics Data System (ADS)

Ausati, Shadi; Amanollahi, Jamil

2016-10-01

Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

Positioning performance analysis of the time sum of arrival algorithm with error features

NASA Astrophysics Data System (ADS)

Gong, Feng-xun; Ma, Yan-qiu

2018-03-01

The theoretical positioning accuracy of multilateration (MLAT) with the time difference of arrival (TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival (TSOA) algorithm from the root mean square error ( RMSE) and geometric dilution of precision (GDOP) in additive white Gaussian noise (AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.

An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors.

PubMed

McGrath, Timothy; Fineman, Richard; Stirling, Leia

2018-06-08

Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration motions. This paper presents a simple, physically-intuitive method for IMU-based measurement of the knee flexion/extension angle in gait without requiring alignment or discrete calibration, based on computationally-efficient and easy-to-implement Principle Component Analysis (PCA). The method is compared against an optical motion capture knee flexion/extension angle modeled through OpenSim. The method is evaluated using both measured and simulated IMU data in an observational study ( n = 15) with an absolute root-mean-square-error (RMSE) of 9.24∘ and a zero-mean RMSE of 3.49∘. Variation in error across subjects was found, made emergent by the larger subject population than previous literature considers. Finally, the paper presents an explanatory model of RMSE on IMU mounting location. The observational data suggest that RMSE of the method is a function of thigh IMU perturbation and axis estimation quality. However, the effect size for these parameters is small in comparison to potential gains from improved IMU orientation estimations. Results also highlight the need to set relevant datums from which to interpret joint angles for both truth references and estimated data.

Player Monitoring in Indoor Team Sports: Concurrent Validity of Inertial Measurement Units to Quantify Average and Peak Acceleration Values

PubMed Central

Roell, Mareike; Roecker, Kai; Gehring, Dominic; Mahler, Hubert; Gollhofer, Albert

2018-01-01

The increasing interest in assessing physical demands in team sports has led to the development of multiple sports related monitoring systems. Due to technical limitations, these systems primarily could be applied to outdoor sports, whereas an equivalent indoor locomotion analysis is not established yet. Technological development of inertial measurement units (IMU) broadens the possibilities for player monitoring and enables the quantification of locomotor movements in indoor environments. The aim of the current study was to validate an IMU measuring by determining average and peak human acceleration under indoor conditions in team sport specific movements. Data of a single wearable tracking device including an IMU (Optimeye S5, Catapult Sports, Melbourne, Australia) were compared to the results of a 3D motion analysis (MA) system (Vicon Motion Systems, Oxford, UK) during selected standardized movement simulations in an indoor laboratory (n = 56). A low-pass filtering method for gravity correction (LF) and two sensor fusion algorithms for orientation estimation [Complementary Filter (CF), Kalman-Filter (KF)] were implemented and compared with MA system data. Significant differences (p < 0.05) were found between LF and MA data but not between sensor fusion algorithms and MA. Higher precision and lower relative errors were found for CF (RMSE = 0.05; CV = 2.6%) and KF (RMSE = 0.15; CV = 3.8%) both compared to the LF method (RMSE = 1.14; CV = 47.6%) regarding the magnitude of the resulting vector and strongly emphasize the implementation of orientation estimation to accurately describe human acceleration. Comparing both sensor fusion algorithms, CF revealed slightly lower errors than KF and additionally provided valuable information about positive and negative acceleration values in all three movement planes with moderate to good validity (CV = 3.9 – 17.8%). Compared to x- and y-axis superior results were found for the z-axis. These findings demonstrate that IMU-based wearable tracking devices can successfully be applied for athlete monitoring in indoor team sports and provide potential to accurately quantify accelerations and decelerations in all three orthogonal axes with acceptable validity. An increase in accuracy taking magnetometers in account should be specifically pursued by future research. PMID:29535641

Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation

NASA Astrophysics Data System (ADS)

Bližňák, Vojtěch; Sokol, Zbyněk; Zacharov, Petr

2017-02-01

An evaluation of convective cloud forecasts performed with the numerical weather prediction (NWP) model COSMO and extrapolation of cloud fields is presented using observed data derived from the geostationary satellite Meteosat Second Generation (MSG). The present study focuses on the nowcasting range (1-5 h) for five severe convective storms in their developing stage that occurred during the warm season in the years 2012-2013. Radar reflectivity and extrapolated radar reflectivity data were assimilated for at least 6 h depending on the time of occurrence of convection. Synthetic satellite imageries were calculated using radiative transfer model RTTOV v10.2, which was implemented into the COSMO model. NWP model simulations of IR10.8 μm and WV06.2 μm brightness temperatures (BTs) with a horizontal resolution of 2.8 km were interpolated into the satellite projection and objectively verified against observations using Root Mean Square Error (RMSE), correlation coefficient (CORR) and Fractions Skill Score (FSS) values. Naturally, the extrapolation of cloud fields yielded an approximately 25% lower RMSE, 20% higher CORR and 15% higher FSS at the beginning of the second forecasted hour compared to the NWP model forecasts. On the other hand, comparable scores were observed for the third hour, whereas the NWP forecasts outperformed the extrapolation by 10% for RMSE, 15% for CORR and up to 15% for FSS during the fourth forecasted hour and 15% for RMSE, 27% for CORR and up to 15% for FSS during the fifth forecasted hour. The analysis was completed by a verification of the precipitation forecasts yielding approximately 8% higher RMSE, 15% higher CORR and up to 45% higher FSS when the NWP model simulation is used compared to the extrapolation for the first hour. Both the methods yielded unsatisfactory level of precipitation forecast accuracy from the fourth forecasted hour onward.

Blending of Radial HF Radar Surface Current and Model Using ETKF Scheme For The Sunda Strait

NASA Astrophysics Data System (ADS)

Mujiasih, Subekti; Riyadi, Mochammad; Wandono, Dr; Wayan Suardana, I.; Nyoman Gede Wiryajaya, I.; Nyoman Suarsa, I.; Hartanto, Dwi; Barth, Alexander; Beckers, Jean-Marie

2017-04-01

Preliminary study of data blending of surface current for Sunda Strait-Indonesia has been done using the analysis scheme of the Ensemble Transform Kalman Filter (ETKF). The method is utilized to combine radial velocity from HF Radar and u and v component of velocity from Global Copernicus - Marine environment monitoring service (CMEMS) model. The initial ensemble is based on the time variability of the CMEMS model result. Data tested are from 2 CODAR Seasonde radar sites in Sunda Strait and 2 dates such as 09 September 2013 and 08 February 2016 at 12.00 UTC. The radial HF Radar data has a hourly temporal resolution, 20-60 km of spatial range, 3 km of range resolution, 5 degree of angular resolution and spatial resolution and 11.5-14 MHz of frequency range. The u and v component of the model velocity represents a daily mean with 1/12 degree spatial resolution. The radial data from one HF radar site is analyzed and the result compared to the equivalent radial velocity from CMEMS for the second HF radar site. Error checking is calculated by root mean squared error (RMSE). Calculation of ensemble analysis and ensemble mean is using Sangoma software package. The tested R which represents observation error covariance matrix, is a diagonal matrix with diagonal elements equal 0.05, 0.5 or 1.0 m2/s2. The initial ensemble members comes from a model simulation spanning a month (September 2013 or February 2016), one year (2013) or 4 years (2013-2016). The spatial distribution of the radial current are analyzed and the RMSE values obtained from independent HF radar station are optimized. It was verified that the analysis reproduces well the structure included in the analyzed HF radar data. More importantly, the analysis was also improved relative to the second independent HF radar site. RMSE of the improved analysis is better than first HF Radar site Analysis. The best result of the blending exercise was obtained for observation error variance equal to 0.05 m2/s2. This study is still preliminary step, but it gives promising result for bigger size of data, combining other model and further development. Keyword: HF Radar, Sunda Strait, ETKF, CMEMS

Use of machine learning methods to reduce predictive error of groundwater models.

PubMed

Xu, Tianfang; Valocchi, Albert J; Choi, Jaesik; Amir, Eyal

2014-01-01

Quantitative analyses of groundwater flow and transport typically rely on a physically-based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data-driven models (DDMs) to reduce the predictive error of physically-based groundwater models. Two machine learning techniques, the instance-based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real-world case studies of the Republican River Compact Administration model and the Spokane Valley-Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root-mean-square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically-based model. © 2013, National GroundWater Association.

Leaf Level Chlorophyll Fluorescence Emission Spectra: Narrow Band versus Full 650-800 nm Retrievals

NASA Astrophysics Data System (ADS)

Middleton, E.; Zhang, Q.; Campbell, P. K.; Huemmrich, K. F.; Corp, L.; Cheng, Y.

2012-12-01

Recently, chlorophyll fluorescence (ChlF) retrievals in narrow spectral regions (< 1 nm, between 750-770 nm) of the near infrared (NIR) region of Earth's reflected radiation have been achieved from satellites, including the Japanese GOSAT and the European Space Agency's Sciamachy/Envisat. However, these retrievals sample the total full-spectrum ChlF and are made at non-optimal wavelengths since they are not located at the peak fluorescence emission features. We wish to estimate the total full-spectrum ChlF based on emissions obtained at selected wavelengths. For this, we drew upon leaf emission spectra measured on corn leaves obtained from a USDA experimental cornfield in MD (USA). These emission spectra were determined for the adaxial and abaxial (i.e., top and underside) surfaces of leaves measured throughout the 2008 and 2011 growing seasons (n>400) using a laboratory instrument (Fluorolog-3, Horiba Scientific, USA), recorded in either 1 nm or 5 nm increments with monochromatic excitation wavelengths of either 532 or 420 nm. The total ChlF signal was computed as the area under the continuous spectral emission curves, summing the emission intensities (counts per second) per waveband. The individual narrow (1 or 5 nm) waveband emission intensities were linearly related to full emission values, with variable success across the spectrum. Equations were developed to estimate total ChlF from these individual wavebands. Here, we report the results for the average adaxial/abaxial emissions. Very strong relationships were achieved for the relatively high fluorescence intensities at the red chlorophyll peak, centered at 685 nm (r2= 0.98, RMSE = 5.53 x 107 photons/s) and in the nearby O2-B atmospheric absorption feature centered at 688 nm (r2 = 0.94, RMSE = 4.04 x 107), as well as in the far-red peak centered at 740 nm (r2=0.94, RMSE = 5.98 x107). Very good retrieval success occurred for the O2-A atmospheric absorption feature on the declining NIR shoulder centered at 760 nm (r2 = 0.88, RMSE = 7.54 x 107). When perfect retrievals were assumed (0% noise), retrievals remained good in the low emission regions on either side of the peaks-- those associated with the H alpha line at 655 nm (r2 = 0.83, RMSE =8.87 x 107) and the far-NIR wavelengths recently utilized for satellite retrievals: a K line at 770 nm (r2 = 0.85, RMSE = 8.36 x 107) and the 750-770 nm interval (r2 = 0.88, RMSE = 6.92 x 107). However, the atmosphere and satellite observations are expected to add noise to retrievals. Adding 5% random error to these relationships did not seriously impair the retrieval successes in the red and far-red peaks (r2 ~ 0.85, RMSEs = 6.31 x 107). A greater impact occurred (reducing retrieval success by ~10%) when adding 5% noise for the far-NIR narrow band at 770 nm (r2 ~ 0.70, RMSE ~ 8.5 x 107). When a 10% random error was added, the retrieval successes fell to ~68 ± 7% for all retrieval wavebands, and RMSEs increased by a factor of 10. This laboratory approach will be critical to calibrate space borne retrievals, but additional information across plant species is needed. Furthermore, this experiment indicates that ChlF retrievals from space should include information from the red and far-red peak emission regions, since the true total fluorescence signal is the desired parameter for Earth carbon and energy budgets.

Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)

PubMed Central

Bardeen, Matthew

2017-01-01

Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively. PMID:29084169

Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV).

PubMed

Poblete, Tomas; Ortega-Farías, Samuel; Moreno, Miguel Angel; Bardeen, Matthew

2017-10-30

Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψ stem ). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψ stem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R²) obtained between ANN outputs and ground-truth measurements of Ψ stem were between 0.56-0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψ stem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively.

Fluid dynamics of coarctation of the aorta: analytical solution, in vitro validation and in vivo evaluation

NASA Astrophysics Data System (ADS)

Keshavarz-Motamed, Zahra

2015-11-01

Coarctation of the aorta (COA) is a congenital heart disease corresponding to a narrowing in the aorta. Cardiac catheterization is considered to be the reference standard for definitive evaluation of COA severity, based on the peak-to-peak trans-coarctation pressure gradient (PtoP TCPG) and instantaneous systolic value of trans-COA pressure gradient (TCPG). However, invasive cardiac catheterization may carry high risks given that undergoing multiple follow-up cardiac catheterizations in patients with COA is common. The objective of this study is to present an analytical description of the COA that estimates PtoP TCPG and TCPG without a need for high risk invasive data collection. Coupled Navier-Stokes and elastic deformation equations were solved analytically to estimate TCPG and PtoP TCPG. The results were validated against data measured in vitro (e.g., 90% COA: TCPG: root mean squared error (RMSE) = 3.93 mmHg; PtoP TCPG: RMSE = 7.9 mmHg). Moreover, the estimated PtoP TCPG resulted from the suggested analytical description was validated using clinical data in twenty patients with COA (maximum RMSE: 8.3 mmHg). Very good correlation and concordance were found between TCPG and PtoP TCPG obtained from the analytical formulation and in vitro and in vivo data. The suggested methodology can be considered as an alternative to cardiac catheterization and can help preventing its risks.

Applications of Hybrid Algorithm (Successive Over Relaxation and Inverse Distance Weighting) for Interpolating Rainfall Data Obtained from a Dense Network of Meteorological Stations in Metro Manila, Philippines

NASA Astrophysics Data System (ADS)

Yao, J. G.; Lagrosas, N.; Ampil, L. J. Y.; Lorenzo, G. R. H.; Simpas, J.

2016-12-01

A hybrid piecewise rainfall value interpolation algorithm was formulated using the commonly known Inverse Distance Weighting (IDW) and Gauss-Seidel variant Successive Over Relaxation (SOR) to interpolate rainfall values over Metro Manila, Philippines. Due to the fact that the SOR requires boundary values for its algorithm to work, the IDW method has been used to estimate rainfall values at the boundary. Iterations using SOR were then done on the defined boundaries to obtain the desired results corresponding to the lowest RMSE value. The hybrid method was applied to rainfall datasets obtained from a dense network of 30 stations in Metro Manila which has been collecting meteorological data every 5 minutes since 2012. Implementing the Davis Vantage Pro 2 Plus weather monitoring system, each station sends data to a central server which could be accessed through the website metroweather.com.ph. The stations are spread over approximately 625 sq km of area such that each station is approximately within 25 sq km from each other. The locations of the stations determined by the Metro Manila Development Authority (MMDA) are in critical sections of Metro Manila such as watersheds and flood-prone areas. Three cases have been investigated in this study, one for each type of rainfall present in Metro Manila: monsoon-induced (8/20/13), typhoon (6/29/13), and thunderstorm (7/3/15 & 7/4/15). The area where the rainfall stations are located is divided such that large measured rainfall values are used as part of the boundaries for the SOR. Measured station values found inside the area where SOR is implemented are compared with results from interpolated values. Root mean square error (RMSE) and correlation trends between measured and interpolated results are quantified. Results from typhoon, thunderstorm and monsoon cases show RMSE values ranged from 0.25 to 2.46 mm for typhoons, 1.55 to 10.69 mm for monsoon-induced rain and 0.01 to 6.27 mm for thunderstorms. R2 values, on the other hand, are 0.91, 0.89 and 0.76 for typhoons, monsoon-induced rain and thunderstorms, respectively. This study has shown that the method of approximating rainfall works and can be used in improved prediction, analysis and real time flood map generation.

Photo-Fenton degradation of a herbicide (2,4-D) in groundwater for conditions of natural pH and presence of inorganic anions.

PubMed

Conte, Leandro O; Schenone, Agustina V; Giménez, Bárbara N; Alfano, Orlando M

2018-04-05

The effects of four inorganic anions (Cl - , SO 4 2 -, HCO 3 - , NO 3 - ) usually present in groundwater were investigated on the photo-Fenton degradation of 2,4-dichlorophenoxyacetic acid (2,4-D). A kinetic model derived from a reaction sequence is proposed using the ferrioxalate complex as iron source at pH close to natural conditions (pH = 5). It was demonstrated that oxalate not only maintained iron in solution for the natural groundwater system, but also increased the photochemical activation of the process. Results showed that the minimum conversion of 2,4-D for the simulated groundwater after 180 min was 63.80%. This value was only 14.1% lower than the conversion achieved without anions. However, with all anions together, the consumption of hydrogen peroxide (HP) per mole of herbicide showed an increase with respect to the test without anions. Only one kinetic parameter was estimated for each anion applying a nonlinear regression method. Subsequently, these optimized kinetic constants were used to simulate the system behaviour, considering the influence of all the studied anions together. A good agreement between kinetic model predictions and experimental data was observed, with the following errors: RMSE 2,4-D  = 3.98 × 10 -3 mM, RMSE HP  = 1.83 × 10 -1  mM, RMSE OX  = 1.39 × 10 -2  mM, and RMSE 2,4-DCP  = 5.59 × 10 -3   mM. Copyright © 2018 Elsevier B.V. All rights reserved.

Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA

USGS Publications Warehouse

Huang, Shengli; Ramirez, Carlos; Conway, Scott; Kennedy, Kama; Kohler, Tanya; Liu, Jinxun

2016-01-01

High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by (i) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, (ii) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and (iii) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R2 of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an R2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m3·ha−1·year−1. Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.

A study comparison of two system model performance in estimated lifted index over Indonesia.

NASA Astrophysics Data System (ADS)

lestari, Juliana tri; Wandala, Agie

2018-05-01

Lifted index (LI) is one of atmospheric stability indices that used for thunderstorm forecasting. Numerical weather Prediction Models are essential for accurate weather forecast these day. This study has completed the attempt to compare the two NWP models these are Weather Research Forecasting (WRF) model and Global Forecasting System (GFS) model in estimates LI at 20 locations over Indonesia and verified the result with observation. Taylor diagram was used to comparing the models skill with shown the value of standard deviation, coefficient correlation and Root mean square error (RMSE). This study using the dataset on 00.00 UTC and 12.00 UTC during mid-March to Mid-April 2017. From the sample of LI distributions, both models have a tendency to overestimated LI value in almost all region in Indonesia while the WRF models has the better ability to catch the LI pattern distribution with observation than GFS model has. The verification result shows how both WRF and GFS model have such a weak relationship with observation except Eltari meteorologi station that its coefficient correlation reach almost 0.6 with the low RMSE value. Mean while WRF model have a better performance than GFS model. This study suggest that estimated LI of WRF model can provide the good performance for Thunderstorm forecasting over Indonesia in the future. However unsufficient relation between output models and observation in the certain location need a further investigation.

[Prediction of soil nutrients spatial distribution based on neural network model combined with goestatistics].

PubMed

Li, Qi-Quan; Wang, Chang-Quan; Zhang, Wen-Jiang; Yu, Yong; Li, Bing; Yang, Juan; Bai, Gen-Chuan; Cai, Yan

2013-02-01

In this study, a radial basis function neural network model combined with ordinary kriging (RBFNN_OK) was adopted to predict the spatial distribution of soil nutrients (organic matter and total N) in a typical hilly region of Sichuan Basin, Southwest China, and the performance of this method was compared with that of ordinary kriging (OK) and regression kriging (RK). All the three methods produced the similar soil nutrient maps. However, as compared with those obtained by multiple linear regression model, the correlation coefficients between the measured values and the predicted values of soil organic matter and total N obtained by neural network model increased by 12. 3% and 16. 5% , respectively, suggesting that neural network model could more accurately capture the complicated relationships between soil nutrients and quantitative environmental factors. The error analyses of the prediction values of 469 validation points indicated that the mean absolute error (MAE) , mean relative error (MRE), and root mean squared error (RMSE) of RBFNN_OK were 6.9%, 7.4%, and 5. 1% (for soil organic matter), and 4.9%, 6.1% , and 4.6% (for soil total N) smaller than those of OK (P<0.01), and 2.4%, 2.6% , and 1.8% (for soil organic matter), and 2.1%, 2.8%, and 2.2% (for soil total N) smaller than those of RK, respectively (P<0.05).

Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4.

PubMed

Bouillon-Pichault, Marion; Jullien, Vincent; Bazzoli, Caroline; Pons, Gérard; Tod, Michel

2011-02-01

The aim of this work was to determine whether optimizing the study design in terms of ages and sampling times for a drug eliminated solely via cytochrome P450 3A4 (CYP3A4) would allow us to accurately estimate the pharmacokinetic parameters throughout the entire childhood timespan, while taking into account age- and weight-related changes. A linear monocompartmental model with first-order absorption was used successively with three different residual error models and previously published pharmacokinetic parameters ("true values"). The optimal ages were established by D-optimization using the CYP3A4 maturation function to create "optimized demographic databases." The post-dose times for each previously selected age were determined by D-optimization using the pharmacokinetic model to create "optimized sparse sampling databases." We simulated concentrations by applying the population pharmacokinetic model to the optimized sparse sampling databases to create optimized concentration databases. The latter were modeled to estimate population pharmacokinetic parameters. We then compared true and estimated parameter values. The established optimal design comprised four age ranges: 0.008 years old (i.e., around 3 days), 0.192 years old (i.e., around 2 months), 1.325 years old, and adults, with the same number of subjects per group and three or four samples per subject, in accordance with the error model. The population pharmacokinetic parameters that we estimated with this design were precise and unbiased (root mean square error [RMSE] and mean prediction error [MPE] less than 11% for clearance and distribution volume and less than 18% for k(a)), whereas the maturation parameters were unbiased but less precise (MPE < 6% and RMSE < 37%). Based on our results, taking growth and maturation into account a priori in a pediatric pharmacokinetic study is theoretically feasible. However, it requires that very early ages be included in studies, which may present an obstacle to the use of this approach. First-pass effects, alternative elimination routes, and combined elimination pathways should also be investigated.

Error Estimation of Pathfinder Version 5.3 SST Level 3C Using Three-way Error Analysis

NASA Astrophysics Data System (ADS)

Saha, K.; Dash, P.; Zhao, X.; Zhang, H. M.

2017-12-01

One of the essential climate variables for monitoring as well as detecting and attributing climate change, is Sea Surface Temperature (SST). A long-term record of global SSTs are available with observations obtained from ships in the early days to the more modern observation based on in-situ as well as space-based sensors (satellite/aircraft). There are inaccuracies associated with satellite derived SSTs which can be attributed to the errors associated with spacecraft navigation, sensor calibrations, sensor noise, retrieval algorithms, and leakages due to residual clouds. Thus it is important to estimate accurate errors in satellite derived SST products to have desired results in its applications.Generally for validation purposes satellite derived SST products are compared against the in-situ SSTs which have inaccuracies due to spatio/temporal inhomogeneity between in-situ and satellite measurements. A standard deviation in their difference fields usually have contributions from both satellite as well as the in-situ measurements. A real validation of any geophysical variable must require the knowledge of the "true" value of the said variable. Therefore a one-to-one comparison of satellite based SST with in-situ data does not truly provide us the real error in the satellite SST and there will be ambiguity due to errors in the in-situ measurements and their collocation differences. A Triple collocation (TC) or three-way error analysis using 3 mutually independent error-prone measurements, can be used to estimate root-mean square error (RMSE) associated with each of the measurements with high level of accuracy without treating any one system a perfectly-observed "truth". In this study we are estimating the absolute random errors associated with Pathfinder Version 5.3 Level-3C SST product Climate Data record. Along with the in-situ SST data, the third source of dataset used for this analysis is the AATSR reprocessing of climate (ARC) dataset for the corresponding period. All three SST observations are collocated, and statistics of difference between each pair is estimated. Instead of using a traditional TC analysis we have implemented the Extended Triple Collocation (ETC) approach to estimate the correlation coefficient of each measurement system w.r.t. the unknown target variable along with their RMSE.

Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

NASA Astrophysics Data System (ADS)

Xu, Shiluo; Niu, Ruiqing

2018-02-01

Every year, landslides pose huge threats to thousands of people in China, especially those in the Three Gorges area. It is thus necessary to establish an early warning system to help prevent property damage and save peoples' lives. Most of the landslide displacement prediction models that have been proposed are static models. However, landslides are dynamic systems. In this paper, the total accumulative displacement of the Baijiabao landslide is divided into trend and periodic components using empirical mode decomposition. The trend component is predicted using an S-curve estimation, and the total periodic component is predicted using a long short-term memory neural network (LSTM). LSTM is a dynamic model that can remember historical information and apply it to the current output. Six triggering factors are chosen to predict the periodic term using the Pearson cross-correlation coefficient and mutual information. These factors include the cumulative precipitation during the previous month, the cumulative precipitation during a two-month period, the reservoir level during the current month, the change in the reservoir level during the previous month, the cumulative increment of the reservoir level during the current month, and the cumulative displacement during the previous month. When using one-step-ahead prediction, LSTM yields a root mean squared error (RMSE) value of 6.112 mm, while the support vector machine for regression (SVR) and the back-propagation neural network (BP) yield values of 10.686 mm and 8.237 mm, respectively. Meanwhile, the Elman network (Elman) yields an RMSE value of 6.579 mm. In addition, when using multi-step-ahead prediction, LSTM obtains an RMSE value of 8.648 mm, while SVR, BP and the Elman network obtains RSME values of 13.418 mm, 13.014 mm, and 13.370 mm. The predicted results indicate that, to some extent, the dynamic model (LSTM) achieves results that are more accurate than those of the static models (i.e., SVR and BP). LSTM even displays better performance than the Elman network, which is also a dynamic method.

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods. Copyright © 2017 Elsevier B.V. All rights reserved.

A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant

NASA Astrophysics Data System (ADS)

Hemmat Esfe, Mohammad; Tatar, Afshin; Ahangar, Mohammad Reza Hassani; Rostamian, Hossein

2018-02-01

Since the conventional thermal fluids such as water, oil, and ethylene glycol have poor thermal properties, the tiny solid particles are added to these fluids to increase their heat transfer improvement. As viscosity determines the rheological behavior of a fluid, studying the parameters affecting the viscosity is crucial. Since the experimental measurement of viscosity is expensive and time consuming, predicting this parameter is the apt method. In this work, three artificial intelligence methods containing Genetic Algorithm-Radial Basis Function Neural Networks (GA-RBF), Least Square Support Vector Machine (LS-SVM) and Gene Expression Programming (GEP) were applied to predict the viscosity of TiO2/SAE 50 nano-lubricant with Non-Newtonian power-law behavior using experimental data. The correlation factor (R2), Average Absolute Relative Deviation (AARD), Root Mean Square Error (RMSE), and Margin of Deviation were employed to investigate the accuracy of the proposed models. RMSE values of 0.58, 1.28, and 6.59 and R2 values of 0.99998, 0.99991, and 0.99777 reveal the accuracy of the proposed models for respective GA-RBF, CSA-LSSVM, and GEP methods. Among the developed models, the GA-RBF shows the best accuracy.

Predicting the digestible energy of corn determined with growing swine from nutrient composition and cross-species measurements.

PubMed

Smith, B; Hassen, A; Hinds, M; Rice, D; Jones, D; Sauber, T; Iiams, C; Sevenich, D; Allen, R; Owens, F; McNaughton, J; Parsons, C

2015-03-01

The DE values of corn grain for pigs will differ among corn sources. More accurate prediction of DE may improve diet formulation and reduce diet cost. Corn grain sources ( = 83) were assayed with growing swine (20 kg) in DE experiments with total collection of feces, with 3-wk-old broiler chick in nitrogen-corrected apparent ME (AME) trials and with cecectomized adult roosters in nitrogen-corrected true ME (TME) studies. Additional AME data for the corn grain source set was generated based on an existing near-infrared transmittance prediction model (near-infrared transmittance-predicted AME [NIT-AME]). Corn source nutrient composition was determined by wet chemistry methods. These data were then used to 1) test the accuracy of predicting swine DE of individual corn sources based on available literature equations and nutrient composition and 2) develop models for predicting DE of sources from nutrient composition and the cross-species information gathered above (AME, NIT-AME, and TME). The overall measured DE, AME, NIT-AME, and TME values were 4,105 ± 11, 4,006 ± 10, 4,004 ± 10, and 4,086 ± 12 kcal/kg DM, respectively. Prediction models were developed using 80% of the corn grain sources; the remaining 20% was reserved for validation of the developed prediction equation. Literature equations based on nutrient composition proved imprecise for predicting corn DE; the root mean square error of prediction ranged from 105 to 331 kcal/kg, an equivalent of 2.6 to 8.8% error. Yet among the corn composition traits, 4-variable models developed in the current study provided adequate prediction of DE (model ranging from 0.76 to 0.79 and root mean square error [RMSE] of 50 kcal/kg). When prediction equations were tested using the validation set, these models had a 1 to 1.2% error of prediction. Simple linear equations from AME, NIT-AME, or TME provided an accurate prediction of DE for individual sources ( ranged from 0.65 to 0.73 and RMSE ranged from 50 to 61 kcal/kg). Percentage error of prediction based on the validation data set was greater (1.4%) for the TME model than for the NIT-AME or AME models (1 and 1.2%, respectively), indicating that swine DE values could be accurately predicted by using AME or NIT-AME. In conclusion, regression equations developed from broiler measurements or from analyzed nutrient composition proved adequate to reliably predict the DE of commercially available corn hybrids for growing pigs.

Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography

NASA Astrophysics Data System (ADS)

Ghaffari Razin, Mir Reza; Voosoghi, Behzad

2016-08-01

Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17 × 1011 (electrons/m3) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.

Biomass Retrieval from L-Band Polarimetric UAVSAR Backscatter and PRISM Stereo Imagery

NASA Technical Reports Server (NTRS)

Zhang, Zhiyu; Ni, Wenjian; Sun, Guoqing; Huang, Wenli; Ranson, Kenneth J.; Cook, Bruce D.; Guo, Zhifeng

2017-01-01

The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4 ha (50 m x 50 m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t-and p-values were used to interpret the significance of the coefficients of each explanatory variables. The R(exp. 2), Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R(exp. 2) and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08 Mg/ha. With canopy height from PRISM as additional independent variable, R(exp. 2) increased to 0.76 and RMSE decreased to 39.74 Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42 Mg/ha with a R(exp. 2) of 0.77. With the canopy height from PRISM, R(exp. 2) increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above approximately 150 Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation.Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R(exp. 2) increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57 Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) wave-form data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant

Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

NASA Astrophysics Data System (ADS)

Tobin, Kenneth J.; Torres, Roberto; Crow, Wade T.; Bennett, Marvin E.

2017-09-01

This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997-2002; 2002-2005; 2005-2008; 2008-2011; 2011-2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash-Sutcliffe coefficients (NSs) for ARM ranged from -0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2-0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1-0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02-0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05-0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm

NASA Astrophysics Data System (ADS)

Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek

2017-11-01

Forecasting streamflow is vital for strategically planning, utilizing and redistributing water resources. In this paper, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection (IIS) algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model. To develop hybrid IIS-W-ANN model, a global predictor matrix is constructed for three local hydrological sites (Richmond, Gwydir, and Darling River) in Australia's agricultural (Murray-Darling) Basin. Model inputs comprised of statistically significant lagged combination of streamflow water level, are supplemented by meteorological data (i.e., precipitation, maximum and minimum temperature, mean solar radiation, vapor pressure and evaporation) as the potential model inputs. To establish robust forecasting models, iterative input selection (IIS) algorithm is applied to screen the best data from the predictor matrix and is integrated with the non-decimated maximum overlap discrete wavelet transform (MODWT) applied on the IIS-selected variables. This resolved the frequencies contained in predictor data while constructing a wavelet-hybrid (i.e., IIS-W-ANN and IIS-W-M5 Tree) model. Forecasting ability of IIS-W-ANN is evaluated via correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe Efficiency (ENS), root-mean-square-error (RMSE), and mean absolute error (MAE), including the percentage RMSE and MAE. While ANN models are seen to outperform M5 Tree executed for all hydrological sites, the IIS variable selector was efficient in determining the appropriate predictors, as stipulated by the better performance of the IIS coupled (ANN and M5 Tree) models relative to the models without IIS. When IIS-coupled models are integrated with MODWT, the wavelet-hybrid IIS-W-ANN and IIS-W-M5 Tree are seen to attain significantly accurate performance relative to their standalone counterparts. Importantly, IIS-W-ANN model accuracy outweighs IIS-ANN, as evidenced by a larger r and WI (by 7.5% and 3.8%, respectively) and a lower RMSE (by 21.3%). In comparison to the IIS-W-M5 Tree model, IIS-W-ANN model yielded larger values of WI = 0.936-0.979 and ENS = 0.770-0.920. Correspondingly, the errors (RMSE and MAE) ranged from 0.162-0.487 m and 0.139-0.390 m, respectively, with relative errors, RRMSE = (15.65-21.00) % and MAPE = (14.79-20.78) %. Distinct geographic signature is evident where the most and least accurately forecasted streamflow data is attained for the Gwydir and Darling River, respectively. Conclusively, this study advocates the efficacy of iterative input selection, allowing the proper screening of model predictors, and subsequently, its integration with MODWT resulting in enhanced performance of the models applied in streamflow forecasting.

Intranasal Pharmacokinetic Data for Triptans Such as Sumatriptan and Zolmitriptan Can Render Area Under the Curve (AUC) Predictions for the Oral Route: Strategy Development and Application.

PubMed

Srinivas, Nuggehally R; Syed, Muzeeb

2016-01-01

Limited pharmacokinetic sampling strategy may be useful for predicting the area under the curve (AUC) for triptans and may have clinical utility as a prospective tool for prediction. Using appropriate intranasal pharmacokinetic data, a Cmax vs. AUC relationship was established by linear regression models for sumatriptan and zolmitriptan. The predictions of the AUC values were performed using published mean/median Cmax data and appropriate regression lines. The quotient of observed and predicted values rendered fold-difference calculation. The mean absolute error (MAE), mean positive error (MPE), mean negative error (MNE), root mean square error (RMSE), correlation coefficient (r), and the goodness of the AUC fold prediction were used to evaluate the two triptans. Also, data from the mean concentration profiles at time points of 1 hour (sumatriptan) and 3 hours (zolmitriptan) were used for the AUC prediction. The Cmax vs. AUC models displayed excellent correlation for both sumatriptan (r = .9997; P < .001) and zolmitriptan (r = .9999; P < .001). Irrespective of the two triptans, the majority of the predicted AUCs (83%-85%) were within 0.76-1.25-fold difference using the regression model. The prediction of AUC values for sumatriptan or zolmitriptan using the concentration data that reflected the Tmax occurrence were in the proximity of the reported values. In summary, the Cmax vs. AUC models exhibited strong correlations for sumatriptan and zolmitriptan. The usefulness of the prediction of the AUC values was established by a rigorous statistical approach.

Modeling streamflow from coupled airborne laser scanning and acoustic Doppler current profiler data

USGS Publications Warehouse

Norris, Lam; Kean, Jason W.; Lyon, Steve

2016-01-01

The rating curve enables the translation of water depth into stream discharge through a reference cross-section. This study investigates coupling national scale airborne laser scanning (ALS) and acoustic Doppler current profiler (ADCP) bathymetric survey data for generating stream rating curves. A digital terrain model was defined from these data and applied in a physically based 1-D hydraulic model to generate rating curves for a regularly monitored location in northern Sweden. Analysis of the ALS data showed that overestimation of the streambank elevation could be adjusted with a root mean square error (RMSE) block adjustment using a higher accuracy manual topographic survey. The results of our study demonstrate that the rating curve generated from the vertically corrected ALS data combined with ADCP data had lower errors (RMSE = 0.79 m3/s) than the empirical rating curve (RMSE = 1.13 m3/s) when compared to streamflow measurements. We consider these findings encouraging as hydrometric agencies can potentially leverage national-scale ALS and ADCP instrumentation to reduce the cost and effort required for maintaining and establishing rating curves at gauging station sites similar to the Röån River.

Smoothness of In vivo Spectral Baseline Determined by Mean Squared Error

PubMed Central

Zhang, Yan; Shen, Jun

2013-01-01

Purpose A nonparametric smooth line is usually added to spectral model to account for background signals in vivo magnetic resonance spectroscopy (MRS). The assumed smoothness of the baseline significantly influences quantitative spectral fitting. In this paper, a method is proposed to minimize baseline influences on estimated spectral parameters. Methods In this paper, the non-parametric baseline function with a given smoothness was treated as a function of spectral parameters. Its uncertainty was measured by root-mean-squared error (RMSE). The proposed method was demonstrated with a simulated spectrum and in vivo spectra of both short echo time (TE) and averaged echo times. The estimated in vivo baselines were compared with the metabolite-nulled spectra, and the LCModel-estimated baselines. The accuracies of estimated baseline and metabolite concentrations were further verified by cross-validation. Results An optimal smoothness condition was found that led to the minimal baseline RMSE. In this condition, the best fit was balanced against minimal baseline influences on metabolite concentration estimates. Conclusion Baseline RMSE can be used to indicate estimated baseline uncertainties and serve as the criterion for determining the baseline smoothness of in vivo MRS. PMID:24259436

Noninvasive microwave ablation zone radii estimation using x-ray CT image analysis.

PubMed

Weiss, Noam; Goldberg, S Nahum; Nissenbaum, Yitzhak; Sosna, Jacob; Azhari, Haim

2016-08-01

The aims of this study were to noninvasively and automatically estimate both the radius of the ablated liver tissue and the radius encircling the treated zone, which also defines where the tissue is definitely untreated during a microwave (MW) thermal ablation procedure. Fourteen ex vivo bovine fresh liver specimens were ablated at 40 W using a 14 G microwave antenna, for durations of 3, 6, 8, and 10 min. The tissues were scanned every 5 s during the ablation using an x-ray CT scanner. In order to estimate the radius of the ablation zone, the acquired images were transformed into a polar presentation by displaying the Hounsfield units (HU) as a function of angle and radius. From this polar presentation, the average HU radial profile was analyzed at each time point and the ablation zone radius was estimated. In addition, textural analysis was applied to the original CT images. The proposed algorithm identified high entropy regions and estimated the treated zone radius per time. The estimated ablated zone radii as a function of treatment durations were compared, by means of correlation coefficient and root mean square error (RMSE) to gross pathology measurements taken immediately post-treatment from similarly ablated tissue. Both the estimated ablation radii and the treated zone radii demonstrated strong correlation with the measured gross pathology values (R(2) ≥ 0.89 and R(2) ≥ 0.86, respectively). The automated ablation radii estimation had an average discrepancy of less than 1 mm (RMSE = 0.65 mm) from the gross pathology measured values, while the treated zone radii showed a slight overestimation of approximately 1.5 mm (RMSE = 1.6 mm). Noninvasive monitoring of MW ablation using x-ray CT and image analysis is feasible. Automatic estimations of the ablation zone radius and the radius encompassing the treated zone that highly correlate with actual ablation measured values can be obtained. This technique can therefore potentially be used to obtain real time monitoring and improve the clinical outcome.

Application of aerosol speciation data as an in situ dust proxy for validation of the Dust Regional Atmospheric Model (DREAM)

NASA Astrophysics Data System (ADS)

Shaw, Patrick

The Dust REgional Atmospheric Model (DREAM) predicts concentrations of mineral dust aerosols in time and space, but validation is challenging with current in situ particulate matter (PM) concentration measurements. Measured levels of ambient PM often contain anthropogenic components as well as windblown mineral dust. In this study, two approaches to model validation were performed with data from preexisting air quality monitoring networks: using hourly concentrations of total PM with aerodynamic diameter less than 2.5 μm (PM 2.5); and using a daily averaged speciation-derived soil component. Validation analyses were performed for point locations within the cities of El Paso (TX), Austin (TX), Phoenix (AZ), Salt Lake City (UT) and Bakersfield (CA) for most of 2006. Hourly modeled PM 2.5 did not validate at all with hourly observations among the sites (combined R < 0.00, N = 24,302 hourly values). Aerosol chemical speciation data distinguished between mineral (soil) dust from anthropogenic ambient PM. As expected, statistically significant improvements in correlation among all stations (combined R = 0.16, N = 343 daily values) were found when the soil component alone was used to validate DREAM. The validation biases that result from anthropogenic aerosols were also reduced using the soil component. This is seen in the reduction of the root mean square error between hourly in situ versus hourly modeled (RMSE hourly = 18.6 μg m -3) and 24-h in situ speciation values versus daily averaged observed (RMSE soil = 12.0 μg m -3). However, the lack of a total reduction in RMSE indicates there is still room for improvement in the model. While the soil component is the theoretical proxy of choice for a dust transport model, the current sparse and infrequent sampling is not ideal for routine hourly air quality forecast validation.

Comparison of hourly surface downwelling solar radiation estimated from MSG-SEVIRI and forecast by the RAMS model with pyranometers over Italy

NASA Astrophysics Data System (ADS)

Federico, Stefano; Torcasio, Rosa Claudia; Sanò, Paolo; Casella, Daniele; Campanelli, Monica; Fokke Meirink, Jan; Wang, Ping; Vergari, Stefania; Diémoz, Henri; Dietrich, Stefano

2017-06-01

In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013-31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m-2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m-2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m-2, 14 %), while the maximum is for Aosta (181 W m-2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely model output statistics (MOS), is applied to improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation

USGS Publications Warehouse

Byrd, Kristin B.; O'Connell, Jessica L.; Di Tommaso, Stefania; Kelly, Maggi

2014-01-01

There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, Typha spp. and Schoenoplectus acutus, at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R2 = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R2 = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species (Typha spp.: %RMSE = 18.5%; S. acutus: %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R2 = 0.56, %RMSE = 20.9%, compared to R2 = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R2 = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

PubMed Central

Thap, Tharoeun; Chung, Heewon; Jeong, Changwon; Hwang, Ki-Eun; Kim, Hak-Ryul; Yoon, Kwon-Ha; Lee, Jinseok

2016-01-01

In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV1/FVC) based on the variable frequency complex demodulation method (VFCDM) is first proposed. We evaluated our proposed method on 26 subjects, including 13 healthy subjects and 13 chronic obstructive pulmonary disease (COPD) patients, by comparing with the parameters clinically obtained from pulmonary function tests (PFTs). For the healthy subjects, we found that an absolute error (AE) and a root mean squared error (RMSE) of the FEV1/FVC ratio were 4.49% ± 3.38% and 5.54%, respectively. For the COPD patients, we found that AE and RMSE from COPD patients were 10.30% ± 10.59% and 14.48%, respectively. For both groups, we compared the results using the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and found that VFCDM was superior to CWT and STFT. Further, to estimate other parameters, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and peak expiratory flow (PEF), regression analysis was conducted to establish a linear transformation. However, the parameters FVC, FEV1, and PEF had correlation factor r values of 0.323, 0.275, and −0.257, respectively, while FEV1/FVC had an r value of 0.814. The results obtained suggest that only the FEV1/FVC ratio can be accurately estimated from a smartphone built-in microphone. The other parameters, including FVC, FEV1, and PEF, were subjective and dependent on the subject’s familiarization with the test and performance of forced exhalation toward the microphone. PMID:27548164

QSPR for predicting chloroform formation in drinking water disinfection.

PubMed

Luilo, G B; Cabaniss, S E

2011-01-01

Chlorination is the most widely used technique for water disinfection, but may lead to the formation of chloroform (trichloromethane; TCM) and other by-products. This article reports the first quantitative structure-property relationship (QSPR) for predicting the formation of TCM in chlorinated drinking water. Model compounds (n = 117) drawn from 10 literature sources were divided into training data (n = 90, analysed by five-way leave-many-out internal cross-validation) and external validation data (n = 27). QSPR internal cross-validation had Q² = 0.94 and root mean square error (RMSE) of 0.09 moles TCM per mole compound, consistent with external validation Q2 of 0.94 and RMSE of 0.08 moles TCM per mole compound, and met criteria for high predictive power and robustness. In contrast, log TCM QSPR performed poorly and did not meet the criteria for predictive power. The QSPR predictions were consistent with experimental values for TCM formation from tannic acid and for model fulvic acid structures. The descriptors used are consistent with a relatively small number of important TCM precursor structures based upon 1,3-dicarbonyls or 1,3-diphenols.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

NASA Astrophysics Data System (ADS)

Bazzazi, Abbas Aghajani; Esmaeili, Mohammad

2012-12-01

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

Evaluation of freely available ancillary data used for detailed soil mapping in Brazil

NASA Astrophysics Data System (ADS)

Samuel-Rosa, Alessandro; Anjos, Lúcia; Vasques, Gustavo; Heuvelink, Gerard

2014-05-01

Brazil is one of the world's largest food producers, and is home of both largest rainforest and largest supply of renewable fresh water on Earth. However, it lacks detailed soil information in extensive areas of the country. The best soil map covering the entire country was published at a scale of 1:5,000,000. Termination of governmental support for systematic soil mapping in the 1980's made detailed soil mapping of the whole country a very difficult task to accomplish. Nowadays, due to new user-driven demands (e.g. precision agriculture), most detailed soil maps are produced for small size areas. Many of them rely on as is freely available ancillary data, although their accuracy is usually not reported or unknown. Results from a validation exercise that we performed using ground control points from a small hilly catchment (20 km²) in Southern Brazil (-53.7995ºE, -29.6355ºN) indicate that most freely available ancillary data needs some type of correction before use. Georeferenced and orthorectified RapidEye imagery (recently acquired by the Brazilian government) has a horizontal accuracy (root-mean-square error, RMSE) of 37 m, which is worse than the value published in the metadata (32 m). Like any remote sensing imagery, RapidEye imagery needs to be correctly registered before its use for soil mapping. Topographic maps produced by the Brazilian Army and derived geological maps (scale of 1:25,000) have a horizontal accuracy of 65 m, which is more than four times the maximum value allowed by Brazilian legislation (15 m). Worse results were found for geological maps derived from 1:50,000 topographic maps (RMSE = 147 m), for which the maximum allowed value is 30 m. In most cases positional errors are of systematic origin and can be easily corrected (e.g., affine transformation). ASTER GDEM has many holes and is very noisy, making it of little use in the studied area. TOPODATA, which is SRTM kriged from originally 3 to 1 arc-second by the Brazilian National Institute for Space Research, has a vertical accuracy of 19 m and is strongly affected by double-oblique stripes which were intensified by kriging. Many spurious sinks were created which are not easily corrected using either frequency filters or sink-filling algorithms. The exceptions are SRTM v4.1, which is the most vertically accurate DEM available (RMSE = 18.7 m), and Google Earth imagery compiled from various sources (positional accuracy of RMSE = 8 m). It is likely that most mapping efforts will continue to be employed in small size areas to fulfill local user-driven demands in the forthcoming years. Also, many new techniques and technologies will possibly be developed and employed for soil mapping. However, employing better quality ancillary data still is a challenge to be overcome to produce high-quality soil information to allow better decision making and land use policy in Brazil.

A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data

PubMed Central

Huynh, Tran; Quick, Harrison; Ramachandran, Gurumurthy; Banerjee, Sudipto; Stenzel, Mark; Sandler, Dale P.; Engel, Lawrence S.; Kwok, Richard K.; Blair, Aaron; Stewart, Patricia A.

2016-01-01

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method’s performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications. PMID:26209598

SU-F-J-175: Evaluation of Metal Artifact Reduction Algorithms in Computed Tomography and Their Application to Radiation Therapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Norris, H; Rangaraj, D; Kim, S

Purpose: High-Z (metal) implants in CT scans cause significant streak-like artifacts in the reconstructed dataset. This results in both inaccurate CT Hounsfield units for the tissue as well as obscuration of the target and organs at risk (OARs) for radiation therapy planning. Herein we analyze two metal artifact reduction algorithms: GE’s Smart MAR and a Metal Deletion Technique (MDT) for geometric and Hounsfield Unit (HU) accuracy. Methods: A CT-to-electron density phantom, with multiple inserts of various densities and a custom Cerrobend insert (Zeff=76.8), is utilized in this continuing study. The phantom is scanned without metal (baseline) and again with themore » metal insert. Using one set of projection data, reconstructed CT volumes are created with filtered-back-projection (FBP) and the MAR and the MDT algorithms. Regions-of-Interest (ROIs) are evaluated for each insert for HU accuracy; the metal insert’s Full-Width-Half-Maximum (FWHM) is used to evaluate the geometric accuracy. Streak severity is quantified with an HU error metric over the phantom volume. Results: The original FBP reconstruction has a Root-Mean-Square-Error (RMSE) of 57.55 HU (STD=29.19, range=−145.8 to +79.2) compared to baseline. The MAR reconstruction has a RMSE of 20.98 HU (STD=13.92, range=−18.3 to +61.7). The MDT reconstruction has a RMSE of 10.05 HU (STD=10.5, range=−14.8 to +18.6). FWHM for baseline=162.05; FBP=161.84 (−0.13%); MAR=162.36 (+0.19%); MDT=162.99 (+0.58%). Streak severity metric for FBP=19.73 (22.659% bad pixels); MAR=8.743 (9.538% bad); MDT=4.899 (5.303% bad). Conclusion: Image quality, in terms of HU accuracy, in the presence of high-Z metal objects in CT scans is improved by metal artifact reduction reconstruction algorithms. The MDT algorithm had the highest HU value accuracy (RMSE=10.05 HU) and best streak severity metric, but scored the worst in terms of geometric accuracy. Qualitatively, the MAR and MDT algorithms increased detectability of inserts, although there is a loss of in-plane resolution near the metallic insert.« less

Microwave measurement and modeling of the dielectric properties of vegetation

NASA Astrophysics Data System (ADS)

Shrestha, Bijay Lal

Some of the important applications of microwaves in the industrial, scientific and medical sectors include processing and treatment of various materials, and determining their physical properties. The dielectric properties of the materials of interest are paramount irrespective of the applications, hence, a wide range of materials covering food products, building materials, ores and fuels, and biological materials have been investigated for their dielectric properties. However, very few studies have been conducted towards the measurement of dielectric properties of green vegetations, including commercially important plant crops such as alfalfa. Because of its high nutritional value, there is a huge demand for this plant and its processed products in national and international markets, and an investigation into the possibility of applying microwaves to improve both the net yield and quality of the crop can be beneficial. Therefore, a dielectric measurement system based upon the probe reflection technique has been set up to measure dielectric properties of green plants over a frequency range from 300 MHz to 18 GHz, moisture contents from 12%, wet basis to 79%, wet basis, and temperatures from -15°C to 30°C. Dielectric properties of chopped alfalfa were measured with this system over frequency range of 300 MHz to 18 GHz, moisture content from 11.5%, wet basis, to 73%, wet basis, and density over the range from 139 kg m-3 to 716 kg m-3 at 23°C. The system accuracy was found to be +/-6% and +/-10% in measuring the dielectric constant and loss factor respectively. Empirical, semi empirical and theoretical models that require only moisture content and operating frequency were determined to represent the dielectric properties of both leaves and stems of alfalfa at 22°C. The empirical models fitted the measured dielectric data extremely well. The root mean square error (RMSE) and the coefficient of determination (r2) for dielectric constant and loss factor of leaves were 0.89 and 0.99, and 0.52 and 0.99 respectively. The RMSE and r2 values for dielectric constant and loss factor of stems were 0.89 and 0.99, and 0.77 and 0.99 respectively. Among semi empirical or theoretical models, Power law model showed better performance (RMSE = 1.78, r2 = 0.96) in modeling dielectric constant of leaves, and Debye-ColeCole model was more appropriate (RMSE = 1.23, r2 = 0.95) for the loss factor. For stems, the Debye-ColeCole models (developed on an assumption that they do not shrink as they dry) were found to be the best models to calculate the dielectric constant with RMSE 0.53 and r2 = 0.99, and dielectric loss factor with RMSE = 065 and r2 = 0.95. (Abstract shortened by UMI.)

A Comparison of the Forecast Skills among Three Numerical Models

NASA Astrophysics Data System (ADS)

Lu, D.; Reddy, S. R.; White, L. J.

2003-12-01

Three numerical weather forecast models, MM5, COAMPS and WRF, operating with a joint effort of NOAA HU-NCAS and Jackson State University (JSU) during summer 2003 have been chosen to study their forecast skills against observations. The models forecast over the same region with the same initialization, boundary condition, forecast length and spatial resolution. AVN global dataset have been ingested as initial conditions. Grib resolution of 27 km is chosen to represent the current mesoscale model. The forecasts with the length of 36h are performed to output the result with 12h interval. The key parameters used to evaluate the forecast skill include 12h accumulated precipitation, sea level pressure, wind, surface temperature and dew point. Precipitation is evaluated statistically using conventional skill scores, Threat Score (TS) and Bias Score (BS), for different threshold values based on 12h rainfall observations whereas other statistical methods such as Mean Error (ME), Mean Absolute Error(MAE) and Root Mean Square Error (RMSE) are applied to other forecast parameters.

Comparison Spatial Pattern of Land Surface Temperature with Mono Window Algorithm and Split Window Algorithm: A Case Study in South Tangerang, Indonesia

NASA Astrophysics Data System (ADS)

Bunai, Tasya; Rokhmatuloh; Wibowo, Adi

2018-05-01

In this paper, two methods to retrieve the Land Surface Temperature (LST) from thermal infrared data supplied by band 10 and 11 of the Thermal Infrared Sensor (TIRS) onboard the Landsat 8 is compared. The first is mono window algorithm developed by Qin et al. and the second is split window algorithm by Rozenstein et al. The purpose of this study is to perform the spatial distribution of land surface temperature, as well as to determine more accurate algorithm for retrieving land surface temperature by calculated root mean square error (RMSE). Finally, we present comparison the spatial distribution of land surface temperature by both of algorithm, and more accurate algorithm is split window algorithm refers to the root mean square error (RMSE) is 7.69° C.

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

NASA Astrophysics Data System (ADS)

Citakoglu, Hatice

2017-10-01

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient ( R 2 )) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

Developing a new solar radiation estimation model based on Buckingham theorem

NASA Astrophysics Data System (ADS)

Ekici, Can; Teke, Ismail

2018-06-01

While the value of solar radiation can be expressed physically in the days without clouds, this expression becomes difficult in cloudy and complicated weather conditions. In addition, solar radiation measurements are often not taken in developing countries. In such cases, solar radiation estimation models are used. Solar radiation prediction models estimate solar radiation using other measured meteorological parameters those are available in the stations. In this study, a solar radiation estimation model was obtained using Buckingham theorem. This theory has been shown to be useful in predicting solar radiation. In this study, Buckingham theorem is used to express the solar radiation by derivation of dimensionless pi parameters. This derived model is compared with temperature based models in the literature. MPE, RMSE, MBE and NSE error analysis methods are used in this comparison. Allen, Hargreaves, Chen and Bristow-Campbell models in the literature are used for comparison. North Dakota's meteorological data were used to compare the models. Error analysis were applied through the comparisons between the models in the literature and the model that is derived in the study. These comparisons were made using data obtained from North Dakota's agricultural climate network. In these applications, the model obtained within the scope of the study gives better results. Especially, in terms of short-term performance, it has been found that the obtained model gives satisfactory results. It has been seen that this model gives better accuracy in comparison with other models. It is possible in RMSE analysis results. Buckingham theorem was found useful in estimating solar radiation. In terms of long term performances and percentage errors, the model has given good results.

Predicting active-layer soil thickness using topographic variables at a small watershed scale

PubMed Central

Li, Aidi; Tan, Xing; Wu, Wei; Liu, Hongbin; Zhu, Jie

2017-01-01

Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters. PMID:28877196

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

PubMed

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG.

PubMed

Kamavuako, Ernest N; Scheme, Erik J; Englehart, Kevin B

2013-06-01

In this paper, the predictive capability of surface and untargeted intramuscular electromyography (EMG) was compared with respect to wrist-joint torque to quantify which type of measurement better represents joint torque during multiple degrees-of-freedom (DoF) movements for possible application in prosthetic control. Ten able-bodied subjects participated in the study. Surface and intramuscular EMG was recorded concurrently from the right forearm. The subjects were instructed to track continuous contraction profiles using single and combined DoF in two trials. The association between torque and EMG was assessed using an artificial neural network. Results showed a significant difference between the two types of EMG (P < 0.007) for all performance metrics: coefficient of determination (R(2)), Pearson correlation coefficient (PCC), and root mean square error (RMSE). The performance of surface EMG (R(2) = 0.93 ± 0.03; PCC = 0.98 ± 0.01; RMSE = 8.7 ± 2.1%) was found to be superior compared with intramuscular EMG (R(2) = 0.80 ± 0.07; PCC = 0.93 ± 0.03; RMSE = 14.5 ± 2.9%). The higher values of PCC compared with R(2) indicate that both methods are able to track the torque profile well but have some trouble (particularly intramuscular EMG) in estimating the exact amplitude. The possible cause for the difference, thus the low performance of intramuscular EMG, may be attributed to the very high selectivity of the recordings used in this study.

Estimating monthly temperature using point based interpolation techniques

NASA Astrophysics Data System (ADS)

Saaban, Azizan; Mah Hashim, Noridayu; Murat, Rusdi Indra Zuhdi

2013-04-01

This paper discusses the use of point based interpolation to estimate the value of temperature at an unallocated meteorology stations in Peninsular Malaysia using data of year 2010 collected from the Malaysian Meteorology Department. Two point based interpolation methods which are Inverse Distance Weighted (IDW) and Radial Basis Function (RBF) are considered. The accuracy of the methods is evaluated using Root Mean Square Error (RMSE). The results show that RBF with thin plate spline model is suitable to be used as temperature estimator for the months of January and December, while RBF with multiquadric model is suitable to estimate the temperature for the rest of the months.

Carbon dioxide emission prediction using support vector machine

NASA Astrophysics Data System (ADS)

Saleh, Chairul; Rachman Dzakiyullah, Nur; Bayu Nugroho, Jonathan

2016-02-01

In this paper, the SVM model was proposed for predict expenditure of carbon (CO2) emission. The energy consumption such as electrical energy and burning coal is input variable that affect directly increasing of CO2 emissions were conducted to built the model. Our objective is to monitor the CO2 emission based on the electrical energy and burning coal used from the production process. The data electrical energy and burning coal used were obtained from Alcohol Industry in order to training and testing the models. It divided by cross-validation technique into 90% of training data and 10% of testing data. To find the optimal parameters of SVM model was used the trial and error approach on the experiment by adjusting C parameters and Epsilon. The result shows that the SVM model has an optimal parameter on C parameters 0.1 and 0 Epsilon. To measure the error of the model by using Root Mean Square Error (RMSE) with error value as 0.004. The smallest error of the model represents more accurately prediction. As a practice, this paper was contributing for an executive manager in making the effective decision for the business operation were monitoring expenditure of CO2 emission.

DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children.

PubMed

Shi, Lei; Jiang, Fan; Ouyang, Fengxiu; Zhang, Jun; Wang, Zhimin; Shen, Xiaoming

2018-03-01

Age estimation is critical in forensic science, in competitive sports and games and in other age-related fields, but the current methods are suboptimal. The combination of age-associated DNA methylation markers with skeletal age (SA) and dental age (DA) may improve the accuracy and precision of age estimation, but no study has examined this topic. In the current study, we measured SA (GP, TW3-RUS, and TW3-Carpal methods) and DA (Demirjian and Willems methods) by X-ray examination in 124 Chinese children (78 boys and 46 girls) aged 6-15 years. To identify age-associated CpG sites, we analyzed methylome-wide DNA methylation profiling by using the Illumina HumanMethylation450 BeadChip system in 48 randomly selected children. Five CpG sites were identified as associated with chronologic age (CA), with an absolute value of Pearson's correlation coefficient (r)>0.5 (p<0.01) and a false discovery rate<0.01. The validation of age-associated CpG sites was performed using droplet digital PCR techniques in all 124 children. After validation, four CpG sites for boys and five CpG sites for girls were further adopted to build the age estimation model with SA and DA using multivariate linear stepwise regressions. These CpG sites were located at 4 known genes: DDO, PRPH2, DHX8, and ITGA2B and at one unknown gene with the Illumina ID number of 22398226. The accuracy of age estimation methods was compared according to the mean absolute error (MAE) and root mean square error (RMSE). The best single measure for SA was the TW3-RUS method (MAE=0.69years, RMSE=0.95years) in boys, and the GP method (MAE=0.74years, RMSE=0.94years) in girls. For DA, the Willems method was the best single measure for both boys (MAE=0.63years, RMSE=0.78years) and girls (MAE=0.54years, RMSE=0.68years). The models that incorporated SA and DA with the methylation levels of age-associated CpG sites provided the highest accuracy of age estimation in both boys (MAE=0.47years, R 2 =0.886) and girls (MAE=0.33years, R 2 =0.941). Cross validation of the results confirmed the reliability and validity of the models. In conclusion, age-associated DNA methylation markers in combination with SA and DA greatly improve the accuracy of age estimation in Chinese children. This method may be applied in forensic science, in competitive sports and games and in other age-related fields. Copyright © 2017. Published by Elsevier B.V.

Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach.

PubMed

Bartlett, Jonathan D; O'Connor, Fergus; Pitchford, Nathan; Torres-Ronda, Lorena; Robertson, Samuel J

2017-02-01

The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches. TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39-89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis. Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players. This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.

Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

2016-06-01

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China.

PubMed

Wen, Xiaohu; Fang, Jing; Diao, Meina; Zhang, Chuanqi

2013-05-01

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl(-)), calcium (Ca(2+)), total alkalinity, total hardness, nitrate nitrogen (NO3-N), and ammonical nitrogen (NH4-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO3-N, NH4-N, and Ca(2+). Cl(-) was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

Estimation of sensible and latent heat flux from natural sparse vegetation surfaces using surface renewal

NASA Astrophysics Data System (ADS)

Zapata, N.; Martínez-Cob, A.

2001-12-01

This paper reports a study undertaken to evaluate the feasibility of the surface renewal method to accurately estimate long-term evaporation from the playa and margins of an endorreic salty lagoon (Gallocanta lagoon, Spain) under semiarid conditions. High-frequency temperature readings were taken for two time lags ( r) and three measurement heights ( z) in order to get surface renewal sensible heat flux ( HSR) values. These values were compared against eddy covariance sensible heat flux ( HEC) values for a calibration period (25-30 July 2000). Error analysis statistics (index of agreement, IA; root mean square error, RMSE; and systematic mean square error, MSEs) showed that the agreement between HSR and HEC improved as measurement height decreased and time lag increased. Calibration factors α were obtained for all analyzed cases. The best results were obtained for the z=0.9 m ( r=0.75 s) case for which α=1.0 was observed. In this case, uncertainty was about 10% in terms of relative error ( RE). Latent heat flux values were obtained by solving the energy balance equation for both the surface renewal ( LESR) and the eddy covariance ( LEEC) methods, using HSR and HEC, respectively, and measurements of net radiation and soil heat flux. For the calibration period, error analysis statistics for LESR were quite similar to those for HSR, although errors were mostly at random. LESR uncertainty was less than 9%. Calibration factors were applied for a validation data subset (30 July-4 August 2000) for which meteorological conditions were somewhat different (higher temperatures and wind speed and lower solar and net radiation). Error analysis statistics for both HSR and LESR were quite good for all cases showing the goodness of the calibration factors. Nevertheless, the results obtained for the z=0.9 m ( r=0.75 s) case were still the best ones.

Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.

PubMed

Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha

2015-01-01

This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Gradient-based interpolation method for division-of-focal-plane polarimeters.

PubMed

Gao, Shengkui; Gruev, Viktor

2013-01-14

Recent advancements in nanotechnology and nanofabrication have allowed for the emergence of the division-of-focal-plane (DoFP) polarization imaging sensors. These sensors capture polarization properties of the optical field at every imaging frame. However, the DoFP polarization imaging sensors suffer from large registration error as well as reduced spatial-resolution output. These drawbacks can be improved by applying proper image interpolation methods for the reconstruction of the polarization results. In this paper, we present a new gradient-based interpolation method for DoFP polarimeters. The performance of the proposed interpolation method is evaluated against several previously published interpolation methods by using visual examples and root mean square error (RMSE) comparison. We found that the proposed gradient-based interpolation method can achieve better visual results while maintaining a lower RMSE than other interpolation methods under various dynamic ranges of a scene ranging from dim to bright conditions.

Development and Positioning Accuracy Assessment of Single-Frequency Precise Point Positioning Algorithms by Combining GPS Code-Pseudorange Measurements with Real-Time SSR Corrections

PubMed Central

Kim, Miso; Park, Kwan-Dong

2017-01-01

We have developed a suite of real-time precise point positioning programs to process GPS pseudorange observables, and validated their performance through static and kinematic positioning tests. To correct inaccurate broadcast orbits and clocks, and account for signal delays occurring from the ionosphere and troposphere, we applied State Space Representation (SSR) error corrections provided by the Seoul Broadcasting System (SBS) in South Korea. Site displacements due to solid earth tide loading are also considered for the purpose of improving the positioning accuracy, particularly in the height direction. When the developed algorithm was tested under static positioning, Kalman-filtered solutions produced a root-mean-square error (RMSE) of 0.32 and 0.40 m in the horizontal and vertical directions, respectively. For the moving platform, the RMSE was found to be 0.53 and 0.69 m in the horizontal and vertical directions. PMID:28598403

An optimized ensemble local mean decomposition method for fault detection of mechanical components

NASA Astrophysics Data System (ADS)

Zhang, Chao; Li, Zhixiong; Hu, Chao; Chen, Shuai; Wang, Jianguo; Zhang, Xiaogang

2017-03-01

Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE, corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions.

Application of RBFN network and GM (1, 1) for groundwater level simulation

NASA Astrophysics Data System (ADS)

Li, Zijun; Yang, Qingchun; Wang, Luchen; Martín, Jordi Delgado

2017-10-01

Groundwater is a prominent resource of drinking and domestic water in the world. In this context, a feasible water resources management plan necessitates acceptable predictions of groundwater table depth fluctuations, which can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. Due to the difficulties of identifying non-linear model structure and estimating the associated parameters, in this study radial basis function neural network (RBFNN) and GM (1, 1) models are used for the prediction of monthly groundwater level fluctuations in the city of Longyan, Fujian Province (South China). The monthly groundwater level data monitored from January 2003 to December 2011 are used in both models. The error criteria are estimated using the coefficient of determination ( R 2), mean absolute error (E) and root mean squared error (RMSE). The results show that both the models can forecast the groundwater level with fairly high accuracy, but the RBFN network model can be a promising tool to simulate and forecast groundwater level since it has a relatively smaller RMSE and MAE.

Assessment of climate change impact on yield of major crops in the Banas River Basin, India.

PubMed

Dubey, Swatantra Kumar; Sharma, Devesh

2018-09-01

Crop growth models like AquaCrop are useful in understanding the impact of climate change on crop production considering the various projections from global circulation models and regional climate models. The present study aims to assess the climate change impact on yield of major crops in the Banas River Basin i.e., wheat, barley and maize. Banas basin is part of the semi-arid region of Rajasthan state in India. AquaCrop model is used to calculate the yield of all the three crops for a historical period of 30years (1981-2010) and then compared with observed yield data. Root Mean Square Error (RMSE) values are calculated to assess the model accuracy in prediction of yield. Further, the calibrated model is used to predict the possible impacts of climate change and CO 2 concentration on crop yield using CORDEX-SA climate projections of three driving climate models (CNRM-CM5, CCSM4 and MPI-ESM-LR) for two different scenarios (RCP4.5 and RCP8.5) for the future period 2021-2050. RMSE values of simulated yield with respect to observed yield of wheat, barley and maize are 11.99, 16.15 and 19.13, respectively. It is predicted that crop yield of all three crops will increase under the climate change conditions for future period (2021-2050). Copyright © 2018 Elsevier B.V. All rights reserved.

Development of GP and GEP models to estimate an environmental issue induced by blasting operation.

PubMed

Faradonbeh, Roohollah Shirani; Hasanipanah, Mahdi; Amnieh, Hassan Bakhshandeh; Armaghani, Danial Jahed; Monjezi, Masoud

2018-05-21

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

Study on elevated-temperature flow behavior of Ni-Cr-Mo-B ultra-heavy-plate steel via experiment and modelling

NASA Astrophysics Data System (ADS)

Gao, Zhi-yu; Kang, Yu; Li, Yan-shuai; Meng, Chao; Pan, Tao

2018-04-01

Elevated-temperature flow behavior of a novel Ni-Cr-Mo-B ultra-heavy-plate steel was investigated by conducting hot compressive deformation tests on a Gleeble-3800 thermo-mechanical simulator at a temperature range of 1123 K–1423 K with a strain rate range from 0.01 s‑1 to10 s‑1 and a height reduction of 70%. Based on the experimental results, classic strain-compensated Arrhenius-type, a new revised strain-compensated Arrhenius-type and classic modified Johnson-Cook constitutive models were developed for predicting the high-temperature deformation behavior of the steel. The predictability of these models were comparatively evaluated in terms of statistical parameters including correlation coefficient (R), average absolute relative error (AARE), average root mean square error (RMSE), normalized mean bias error (NMBE) and relative error. The statistical results indicate that the new revised strain-compensated Arrhenius-type model could give prediction of elevated-temperature flow stress for the steel accurately under the entire process conditions. However, the predicted values by the classic modified Johnson-Cook model could not agree well with the experimental values, and the classic strain-compensated Arrhenius-type model could track the deformation behavior more accurately compared with the modified Johnson-Cook model, but less accurately with the new revised strain-compensated Arrhenius-type model. In addition, reasons of differences in predictability of these models were discussed in detail.

Accuracy evaluation of Fourier series analysis and singular spectrum analysis for predicting the volume of motorcycle sales in Indonesia

NASA Astrophysics Data System (ADS)

Sasmita, Yoga; Darmawan, Gumgum

2017-08-01

This research aims to evaluate the performance of forecasting by Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA) which are more explorative and not requiring parametric assumption. Those methods are applied to predicting the volume of motorcycle sales in Indonesia from January 2005 to December 2016 (monthly). Both models are suitable for seasonal and trend component data. Technically, FSA defines time domain as the result of trend and seasonal component in different frequencies which is difficult to identify in the time domain analysis. With the hidden period is 2,918 ≈ 3 and significant model order is 3, FSA model is used to predict testing data. Meanwhile, SSA has two main processes, decomposition and reconstruction. SSA decomposes the time series data into different components. The reconstruction process starts with grouping the decomposition result based on similarity period of each component in trajectory matrix. With the optimum of window length (L = 53) and grouping effect (r = 4), SSA predicting testing data. Forecasting accuracy evaluation is done based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The result shows that in the next 12 month, SSA has MAPE = 13.54 percent, MAE = 61,168.43 and RMSE = 75,244.92 and FSA has MAPE = 28.19 percent, MAE = 119,718.43 and RMSE = 142,511.17. Therefore, to predict volume of motorcycle sales in the next period should use SSA method which has better performance based on its accuracy.

Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy-LUR approaches.

PubMed

Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael; Smargiassi, Audrey

2014-09-01

Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data.

Surface Downward Longwave Radiation Retrieval Algorithm for GEO-KOMPSAT-2A/AMI

NASA Astrophysics Data System (ADS)

Ahn, Seo-Hee; Lee, Kyu-Tae; Rim, Se-Hun; Zo, Il-Sung; Kim, Bu-Yo

2018-05-01

This study contributes to the development of an algorithm to retrieve the Earth's surface downward longwave radiation (DLR) for 2nd Geostationary Earth Orbit KOrea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A)/Advanced Meteorological Imager (AMI). Regarding simulation data for algorithm development, we referred to Clouds and the Earth's Radiant Energy System (CERES), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis data. The clear sky DLR calculations were in good agreement with the Gangneung-Wonju National University (GWNU) Line-By-Line (LBL) model. Compared with CERES data, the Root Mean Square Error (RMSE) was 10.14Wm-2. In the case of cloudy sky DLR, we estimated the cloud base temperature empirically by utilizing cloud liquid water content (LWC) according to the cloud type. As a result, the correlation coefficients with CERES all sky DLRs were greater than 0.99. However, the RMSE between calculated DLR and CERES data was about 16.67Wm-2, due to ice clouds and problems of mismatched spatial and temporal resolutions for input data. This error may be reduced when GK-2A is launched and its products can be used as input data. Accordingly, further study is needed to improve the accuracy of DLR calculation by using high-resolution input data. In addition, when compared with BSRN surface-based observational data and retrieved DLR for all sky, the correlation coefficient was 0.86 and the RMSE was 31.55 Wm-2, which indicates relatively high accuracy. It is expected that increasing the number of experimental Cases will reduce the error.

[A site index model for Larix principis-rupprechtii plantation in Saihanba, north China].

PubMed

Wang, Dong-zhi; Zhang, Dong-yan; Jiang, Feng-ling; Bai, Ye; Zhang, Zhi-dong; Huang, Xuan-rui

2015-11-01

It is often difficult to estimate site indices for different types of plantation by using an ordinary site index model. The objective of this paper was to establish a site index model for plantations in varied site conditions, and assess the site qualities. In this study, a nonlinear mixed site index model was constructed based on data from the second class forest resources inventory and 173 temporary sample plots. The results showed that the main limiting factors for height growth of Larix principis-rupprechtii were elevation, slope, soil thickness and soil type. A linear regression model was constructed for the main constraining site factors and dominant tree height, with the coefficient of determination being 0.912, and the baseline age of Larix principis-rupprechtii determined as 20 years. The nonlinear mixed site index model parameters for the main site types were estimated (R2 > 0.85, the error between the predicted value and the actual value was in the range of -0.43 to 0.45, with an average root mean squared error (RMSE) in the range of 0.907 to 1.148). The estimation error between the predicted value and the actual value of dominant tree height for the main site types was in the confidence interval of [-0.95, 0.95]. The site quality of the high altitude-shady-sandy loam-medium soil layer was the highest and that of low altitude-sunny-sandy loam-medium soil layer was the lowest, while the other two sites were moderate.

Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy.

PubMed

Marwaha, Puneeta; Sunkaria, Ramesh Kumar

2017-02-01

Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales 't'. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entropy to the HRV time series of certain pathologies than that of healthy subjects, and to their corresponding randomized surrogate time series. This incorrect assessment of signal complexity may be due to the fact that these techniques suffer from the following limitations: (1) threshold value 'r' is updated as a function of long-term standard deviation and hence unable to explore the short-term variability as well as substantial variability inherited in beat-to-beat fluctuations of long-term HRV time series. (2) In RMSE, entropy values assigned to different filtered scaled time series are the result of changes in variance, but do not completely reflect the real structural organization inherited in original time series. In the present work, we propose an improved RMSE (I-RMSE) technique by introducing a new procedure to set the threshold value by taking into account the period-to-period variability inherited in a signal and evaluated it on simulated and real HRV database. The proposed I-RMSE assigns higher entropy to the age-matched healthy subjects than that of patients suffering from atrial fibrillation, congestive heart failure, sudden cardiac death and diabetes mellitus, for the entire time scales. The results strongly support the reduction in complexity of HRV time series in female group, old-aged, patients suffering from severe cardiovascular and non-cardiovascular diseases, and in their corresponding surrogate time series.

SU-E-I-08: Investigation of Deconvolution Methods for Blocker-Based CBCT Scatter Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, C; Jin, M; Ouyang, L

2015-06-15

Purpose: To investigate whether deconvolution methods can improve the scatter estimation under different blurring and noise conditions for blocker-based scatter correction methods for cone-beam X-ray computed tomography (CBCT). Methods: An “ideal” projection image with scatter was first simulated for blocker-based CBCT data acquisition by assuming no blurring effect and no noise. The ideal image was then convolved with long-tail point spread functions (PSF) with different widths to mimic the blurring effect from the finite focal spot and detector response. Different levels of noise were also added. Three deconvolution Methods: 1) inverse filtering; 2) Wiener; and 3) Richardson-Lucy, were used tomore » recover the scatter signal in the blocked region. The root mean square error (RMSE) of estimated scatter serves as a quantitative measure for the performance of different methods under different blurring and noise conditions. Results: Due to the blurring effect, the scatter signal in the blocked region is contaminated by the primary signal in the unblocked region. The direct use of the signal in the blocked region to estimate scatter (“direct method”) leads to large RMSE values, which increase with the increased width of PSF and increased noise. The inverse filtering is very sensitive to noise and practically useless. The Wiener and Richardson-Lucy deconvolution methods significantly improve scatter estimation compared to the direct method. For a typical medium PSF and medium noise condition, both methods (∼20 RMSE) can achieve 4-fold improvement over the direct method (∼80 RMSE). The Wiener method deals better with large noise and Richardson-Lucy works better on wide PSF. Conclusion: We investigated several deconvolution methods to recover the scatter signal in the blocked region for blocker-based scatter correction for CBCT. Our simulation results demonstrate that Wiener and Richardson-Lucy deconvolution can significantly improve the scatter estimation compared to the direct method.« less

Accounting for spatial variation of trabecular anisotropy with subject-specific finite element modeling moderately improves predictions of local subchondral bone stiffness at the proximal tibia.

PubMed

Nazemi, S Majid; Kalajahi, S Mehrdad Hosseini; Cooper, David M L; Kontulainen, Saija A; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D

2017-07-05

Previously, a finite element (FE) model of the proximal tibia was developed and validated against experimentally measured local subchondral stiffness. This model indicated modest predictions of stiffness (R 2 =0.77, normalized root mean squared error (RMSE%)=16.6%). Trabecular bone though was modeled with isotropic material properties despite its orthotropic anisotropy. The objective of this study was to identify the anisotropic FE modeling approach which best predicted (with largest explained variance and least amount of error) local subchondral bone stiffness at the proximal tibia. Local stiffness was measured at the subchondral surface of 13 medial/lateral tibial compartments using in situ macro indentation testing. An FE model of each specimen was generated assuming uniform anisotropy with 14 different combinations of cortical- and tibial-specific density-modulus relationships taken from the literature. Two FE models of each specimen were also generated which accounted for the spatial variation of trabecular bone anisotropy directly from clinical CT images using grey-level structure tensor and Cowin's fabric-elasticity equations. Stiffness was calculated using FE and compared to measured stiffness in terms of R 2 and RMSE%. The uniform anisotropic FE model explained 53-74% of the measured stiffness variance, with RMSE% ranging from 12.4 to 245.3%. The models which accounted for spatial variation of trabecular bone anisotropy predicted 76-79% of the variance in stiffness with RMSE% being 11.2-11.5%. Of the 16 evaluated finite element models in this study, the combination of Synder and Schneider (for cortical bone) and Cowin's fabric-elasticity equations (for trabecular bone) best predicted local subchondral bone stiffness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Anthropometric predictors of body fat as measured by hydrostatic weighing in Guatemalan adults.

PubMed

Ramirez-Zea, Manuel; Torun, Benjamin; Martorell, Reynaldo; Stein, Aryeh D

2006-04-01

Most predictive equations currently used to assess percentage body fat (%BF) were derived from persons in industrialized Western societies. We developed equations to predict %BF from anthropometric measurements in rural and urban Guatemalan adults. Body density was measured in 123 women and 114 men by using hydrostatic weighing and simultaneous measurement of residual lung volume. Anthropometric measures included weight (in kg), height (in cm), 4 skinfold thicknesses [(STs) in mm], and 6 circumferences (in cm). Sex-specific multiple linear regression models were developed with %BF as the dependent variable and age, residence (rural or urban), and all anthropometric measures as independent variables (the "full" model). A "simplified" model was developed by using age, residence, weight, height, and arm, abdominal, and calf circumferences as independent variables. The preferred full models were %BF = -80.261 - (weight x 0.623) + (height x 0.214) + (tricipital ST x 0.379) + (abdominal ST x 0.202) + (abdominal circumference x 0.940) + (thigh circumference x 0.316); root mean square error (RMSE) = 3.0; and pure error (PE) = 3.4 for men and %BF = -15.471 + (tricipital ST x 0.332) + (subscapular ST x 0.154) + (abdominal ST x 0.119) + (hip circumference x 0.356); RMSE = 2.4; and PE = 2.9 for women. The preferred simplified models were %BF = -48.472 - (weight x 0.257) + (abdominal circumference x 0.989); RMSE = 3.8; and PE = 3.7 for men and %BF = 19.420 + (weight x 0.385) - (height x 0.215) + (abdominal circumference x 0.265); RMSE = 3.5; and PE = 3.5 for women. These equations performed better in this developing-country population than did previously published equations.

Linear solvation energy relationship for the adsorption of synthetic organic compounds on single-walled carbon nanotubes in water.

PubMed

Ding, H; Chen, C; Zhang, X

2016-01-01

The linear solvation energy relationship (LSER) was applied to predict the adsorption coefficient (K) of synthetic organic compounds (SOCs) on single-walled carbon nanotubes (SWCNTs). A total of 40 log K values were used to develop and validate the LSER model. The adsorption data for 34 SOCs were collected from 13 published articles and the other six were obtained in our experiment. The optimal model composed of four descriptors was developed by a stepwise multiple linear regression (MLR) method. The adjusted r(2) (r(2)adj) and root mean square error (RMSE) were 0.84 and 0.49, respectively, indicating good fitness. The leave-one-out cross-validation Q(2) ([Formula: see text]) was 0.79, suggesting the robustness of the model was satisfactory. The external Q(2) ([Formula: see text]) and RMSE (RMSEext) were 0.72 and 0.50, respectively, showing the model's strong predictive ability. Hydrogen bond donating interaction (bB) and cavity formation and dispersion interactions (vV) stood out as the two most influential factors controlling the adsorption of SOCs onto SWCNTs. The equilibrium concentration would affect the fitness and predictive ability of the model, while the coefficients varied slightly.

Estimation of distributional parameters for censored trace level water quality data: 2. Verification and applications

USGS Publications Warehouse

Helsel, Dennis R.; Gilliom, Robert J.

1986-01-01

Estimates of distributional parameters (mean, standard deviation, median, interquartile range) are often desired for data sets containing censored observations. Eight methods for estimating these parameters have been evaluated by R. J. Gilliom and D. R. Helsel (this issue) using Monte Carlo simulations. To verify those findings, the same methods are now applied to actual water quality data. The best method (lowest root-mean-squared error (rmse)) over all parameters, sample sizes, and censoring levels is log probability regression (LR), the method found best in the Monte Carlo simulations. Best methods for estimating moment or percentile parameters separately are also identical to the simulations. Reliability of these estimates can be expressed as confidence intervals using rmse and bias values taken from the simulation results. Finally, a new simulation study shows that best methods for estimating uncensored sample statistics from censored data sets are identical to those for estimating population parameters. Thus this study and the companion study by Gilliom and Helsel form the basis for making the best possible estimates of either population parameters or sample statistics from censored water quality data, and for assessments of their reliability.

A novel validation and calibration method for motion capture systems based on micro-triangulation.

PubMed

Nagymáté, Gergely; Tuchband, Tamás; Kiss, Rita M

2018-06-06

Motion capture systems are widely used to measure human kinematics. Nevertheless, users must consider system errors when evaluating their results. Most validation techniques for these systems are based on relative distance and displacement measurements. In contrast, our study aimed to analyse the absolute volume accuracy of optical motion capture systems by means of engineering surveying reference measurement of the marker coordinates (uncertainty: 0.75 mm). The method is exemplified on an 18 camera OptiTrack Flex13 motion capture system. The absolute accuracy was defined by the root mean square error (RMSE) between the coordinates measured by the camera system and by engineering surveying (micro-triangulation). The original RMSE of 1.82 mm due to scaling error was managed to be reduced to 0.77 mm while the correlation of errors to their distance from the origin reduced from 0.855 to 0.209. A simply feasible but less accurate absolute accuracy compensation method using tape measure on large distances was also tested, which resulted in similar scaling compensation compared to the surveying method or direct wand size compensation by a high precision 3D scanner. The presented validation methods can be less precise in some respects as compared to previous techniques, but they address an error type, which has not been and cannot be studied with the previous validation methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

An improvement of the retrieval of temperature and relative humidity profiles from a combination of active and passive remote sensing

NASA Astrophysics Data System (ADS)

Che, Yunfei; Ma, Shuqing; Xing, Fenghua; Li, Siteng; Dai, Yaru

2018-03-01

This paper focuses on an improvement of the retrieval of atmospheric temperature and relative humidity profiles through combining active and passive remote sensing. Ground-based microwave radiometer and millimeter-wavelength cloud radar were used to acquire the observations. Cloud base height and cloud thickness determinations from cloud radar were added into the atmospheric profile retrieval process, and a back-propagation neural network method was used as the retrieval tool. Because a substantial amount of data are required to train a neural network, and as microwave radiometer data are insufficient for this purpose, 8 years of radiosonde data from Beijing were used as the database. The monochromatic radiative transfer model was used to calculate the brightness temperatures in the same channels as the microwave radiometer. Parts of the cloud base heights and cloud thicknesses in the training data set were also estimated using the radiosonde data. The accuracy of the results was analyzed through a comparison with L-band sounding radar data and quantified using the mean bias, root-mean-square error (RMSE), and correlation coefficient. The statistical results showed that an inversion with cloud information was the optimal method. Compared with the inversion profiles without cloud information, the RMSE values after adding cloud information reduced to varying degrees for the vast majority of height layers. These reductions were particularly clear in layers with clouds. The maximum reduction in the RMSE for the temperature profile was 2.2 K, while that for the humidity profile was 16%.

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Checefsky, Walter A.; Abidin, Anas Z.; Tsai, Halley; Wang, Xixi; Hobbs, Susan K.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2015-03-01

While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature `correlation' (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCTmeasured mean BMD (RMSE = 1.11 ± 0.17) (p< 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.

An algorithm of Saxena-Easo on fuzzy time series forecasting

NASA Astrophysics Data System (ADS)

Ramadhani, L. C.; Anggraeni, D.; Kamsyakawuni, A.; Hadi, A. F.

2018-04-01

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

Improved error estimates of a discharge algorithm for remotely sensed river measurements: Test cases on Sacramento and Garonne Rivers

NASA Astrophysics Data System (ADS)

Yoon, Yeosang; Garambois, Pierre-André; Paiva, Rodrigo C. D.; Durand, Michael; Roux, Hélène; Beighley, Edward

2016-01-01

We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the "true" hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Manning's equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River.

Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

2016-11-01

In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

NASA Astrophysics Data System (ADS)

Antuña-Marrero, Juan Carlos; Cachorro Revilla, Victoria; García Parrado, Frank; de Frutos Baraja, Ángel; Rodríguez Vega, Albeth; Mateos, David; Estevan Arredondo, René; Toledano, Carlos

2018-04-01

In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS-sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

Evaluation of exchange-correlation functionals for time-dependent density functional theory calculations on metal complexes.

PubMed

Holland, Jason P; Green, Jennifer C

2010-04-15

The electronic absorption spectra of a range of copper and zinc complexes have been simulated by using time-dependent density functional theory (TD-DFT) calculations implemented in Gaussian03. In total, 41 exchange-correlation (XC) functionals including first-, second-, and third-generation (meta-generalized gradient approximation) DFT methods were compared in their ability to predict the experimental electronic absorption spectra. Both pure and hybrid DFT methods were tested and differences between restricted and unrestricted calculations were also investigated by comparison of analogous neutral zinc(II) and copper(II) complexes. TD-DFT calculated spectra were optimized with respect to the experimental electronic absorption spectra by use of a Matlab script. Direct comparison of the performance of each XC functional was achieved both qualitatively and quantitatively by comparison of optimized half-band widths, root-mean-squared errors (RMSE), energy scaling factors (epsilon(SF)), and overall quality-of-fit (Q(F)) parameters. Hybrid DFT methods were found to outperform all pure DFT functionals with B1LYP, B97-2, B97-1, X3LYP, and B98 functionals providing the highest quantitative and qualitative accuracy in both restricted and unrestricted systems. Of the functionals tested, B1LYP gave the most accurate results with both average RMSE and overall Q(F) < 3.5% and epsilon(SF) values close to unity (>0.990) for the copper complexes. The XC functional performance in spin-restricted TD-DFT calculations on the zinc complexes was found to be slightly worse. PBE1PBE, mPW1PW91 and B1LYP gave the most accurate results with typical RMSE and Q(F) values between 5.3 and 7.3%, and epsilon(SF) around 0.930. These studies illustrate the power of modern TD-DFT calculations for exploring excited state transitions of metal complexes. 2009 Wiley Periodicals, Inc.

Estimating B1+ in the breast at 7 T using a generic template.

PubMed

van Rijssel, Michael J; Pluim, Josien P W; Luijten, Peter R; Gilhuijs, Kenneth G A; Raaijmakers, Alexander J E; Klomp, Dennis W J

2018-05-01

Dynamic contrast-enhanced MRI is the workhorse of breast MRI, where the diagnosis of lesions is largely based on the enhancement curve shape. However, this curve shape is biased by RF transmit (B 1 + ) field inhomogeneities. B 1 + field information is required in order to correct these. The use of a generic, coil-specific B 1 + template is proposed and tested. Finite-difference time-domain simulations for B 1 + were performed for healthy female volunteers with a wide range of breast anatomies. A generic B 1 + template was constructed by averaging simulations based on four volunteers. Three-dimensional B 1 + maps were acquired in 15 other volunteers. Root mean square error (RMSE) metrics were calculated between individual simulations and the template, and between individual measurements and the template. The agreement between the proposed template approach and a B 1 + mapping method was compared against the agreement between acquisition and reacquisition using the same mapping protocol. RMSE values (% of nominal flip angle) comparing individual simulations with the template were in the range 2.00-4.01%, with mean 2.68%. RMSE values comparing individual measurements with the template were in the range8.1-16%, with mean 11.7%. The agreement between the proposed template approach and a B 1 + mapping method was only slightly worse than the agreement between two consecutive acquisitions using the same mapping protocol in one volunteer: the range of agreement increased from ±16% of the nominal angle for repeated measurement to ±22% for the B 1 + template. With local RF transmit coils, intersubject differences in B 1 + fields of the breast are comparable to the accuracy of B 1 + mapping methods, even at 7 T. Consequently, a single generic B 1 + template suits subjects over a wide range of breast anatomies, eliminating the need for a time-consuming B 1 + mapping protocol. © 2018 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Estimating B 1 + in the breast at 7 T using a generic template

PubMed Central

Pluim, Josien P. W.; Luijten, Peter R.; Gilhuijs, Kenneth G. A.; Raaijmakers, Alexander J. E.; Klomp, Dennis W. J.

2018-01-01

Dynamic contrast‐enhanced MRI is the workhorse of breast MRI, where the diagnosis of lesions is largely based on the enhancement curve shape. However, this curve shape is biased by RF transmit (B 1 +) field inhomogeneities. B 1 + field information is required in order to correct these. The use of a generic, coil‐specific B 1 + template is proposed and tested. Finite‐difference time‐domain simulations for B 1 + were performed for healthy female volunteers with a wide range of breast anatomies. A generic B 1 + template was constructed by averaging simulations based on four volunteers. Three‐dimensional B 1 + maps were acquired in 15 other volunteers. Root mean square error (RMSE) metrics were calculated between individual simulations and the template, and between individual measurements and the template. The agreement between the proposed template approach and a B 1 + mapping method was compared against the agreement between acquisition and reacquisition using the same mapping protocol. RMSE values (% of nominal flip angle) comparing individual simulations with the template were in the range 2.00‐4.01%, with mean 2.68%. RMSE values comparing individual measurements with the template were in the range8.1‐16%, with mean 11.7%. The agreement between the proposed template approach and a B 1 + mapping method was only slightly worse than the agreement between two consecutive acquisitions using the same mapping protocol in one volunteer: the range of agreement increased from ±16% of the nominal angle for repeated measurement to ±22% for the B 1 + template. With local RF transmit coils, intersubject differences in B 1 + fields of the breast are comparable to the accuracy of B 1 + mapping methods, even at 7 T. Consequently, a single generic B 1 + template suits subjects over a wide range of breast anatomies, eliminating the need for a time‐consuming B 1 + mapping protocol. PMID:29570887

A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data.

PubMed

Huynh, Tran; Quick, Harrison; Ramachandran, Gurumurthy; Banerjee, Sudipto; Stenzel, Mark; Sandler, Dale P; Engel, Lawrence S; Kwok, Richard K; Blair, Aaron; Stewart, Patricia A

2016-01-01

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications. © The Author 2015. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

[Fractional vegetation cover of invasive Spartina alterniflora in coastal wetland using unmanned aerial vehicle (UAV)remote sensing].

PubMed

Zhou, Zai Ming; Yang, Yan Ming; Chen, Ben Qing

2016-12-01

The effective management and utilization of resources and ecological environment of coastal wetland require investigation and analysis in high precision of the fractional vegetation cover of invasive species Spartina alterniflora. In this study, Sansha Bay was selected as the experimental region, and visible and multi-spectral images obtained by low-altitude UAV in the region were used to monitor the fractional vegetation cover of S. alterniflora. Fractional vegetation cover parameters in the multi-spectral images were then estimated by NDVI index model, and the accuracy was tested against visible images as references. Results showed that vegetation covers of S. alterniflora in the image area were mainly at medium high level (40%-60%) and high level (60%-80%). Root mean square error (RMSE) between the NDVI model estimation values and true values was 0.06, while the determination coefficient R 2 was 0.92, indicating a good consistency between the estimation value and the true value.

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Parameter Optimisation and Uncertainty Analysis in Visual MODFLOW based Flow Model for predicting the groundwater head in an Eastern Indian Aquifer

NASA Astrophysics Data System (ADS)

Mohanty, B.; Jena, S.; Panda, R. K.

2016-12-01

The overexploitation of groundwater elicited in abandoning several shallow tube wells in the study Basin in Eastern India. For the sustainability of groundwater resources, basin-scale modelling of groundwater flow is indispensable for the effective planning and management of the water resources. The basic intent of this study is to develop a 3-D groundwater flow model of the study basin using the Visual MODFLOW Flex 2014.2 package and successfully calibrate and validate the model using 17 years of observed data. The sensitivity analysis was carried out to quantify the susceptibility of aquifer system to the river bank seepage, recharge from rainfall and agriculture practices, horizontal and vertical hydraulic conductivities, and specific yield. To quantify the impact of parameter uncertainties, Sequential Uncertainty Fitting Algorithm (SUFI-2) and Markov chain Monte Carlo (McMC) techniques were implemented. Results from the two techniques were compared and the advantages and disadvantages were analysed. Nash-Sutcliffe coefficient (NSE), Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Percent Deviation (Dv) and Root Mean Squared Error (RMSE) were adopted as criteria of model evaluation during calibration and validation of the developed model. NSE, R2, MAE, Dv and RMSE values for groundwater flow model during calibration and validation were in acceptable range. Also, the McMC technique was able to provide more reasonable results than SUFI-2. The calibrated and validated model will be useful to identify the aquifer properties, analyse the groundwater flow dynamics and the change in groundwater levels in future forecasts.

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

NASA Technical Reports Server (NTRS)

Huang, Jingfeng; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Zhang, Hai; Superczynski, Stephen; Ciren, Pubu; Holben, Brent N.; Petrenko, Maksym

2016-01-01

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-07-01

Soil temperature (T s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T s. For this aim, the monthly mean data of the T s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T min, T max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R s, R n, R a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u 2); and water vapor pressure (Vp) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R 2) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T max, T min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R 2 = 0.995) and for RH, V p, P, and u 2 inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R 2 = 0.996), respectively.

High Resolution Analysis of Dyke Tips and Segments, Using Drones

NASA Astrophysics Data System (ADS)

Dering, G.; Micklethwaite, S.; Cruden, A. R.

2016-12-01

We analyse outstanding exposures of dykes from both coastal (Western Australia) and high altitude glacier-polished (Sierra Nevada, California) outcrops, representing intrusion at shallow upper-crustal and mid-crustal conditions respectively. We covered 10,000 m^2 of outcrop area sampling the ground at a scale of 3-5 mm per pixel. Using Structure-from-Motion photogrammetry from ground-based and UAV photographs lacking GPS camera positions (>500 images per study), we generated and calibrated a 3D geometry of dense point clouds by selectively using 25-30 ground control points measured by high precision GPS (40-90 mm error). Ground control points used in the photogrammetric model building process typically yielded a root mean square error (RMSE) of 5 cm. Half the ground control points were withheld from the model building process and when they were compared against the model they yielded RMSE values only 6-10% higher than the points used for georeferencing, suggesting good internal consistency of the dataset and accuracy relative to the reference frame, at least for the purposes of this study. The structural orientations of the dykes and associated fractures were then extracted digitally using the iterative Random Sample Consensus method (RANSAC) and least-squares plane fitting. Furthermore, fracture intensity relative to dykes was measured along a series of scanlines and the running average and variance calculated. All results were compared against field measurements. Results show fracture intensity increases toward the dykes in the shallow crustal examples (West Australia) but no such fractures exist around the mid-crustal (Californian) dykes. Despite this there is a remarkable uniformity of geometry, and by implication process, between the two dyke sets. In order to extract full value from the big visual data now available to us, the near-future requires dedicated research into software solutions for expert-driven, semi-automatic mapping of geology and structure.

Sparse magnetic resonance imaging reconstruction using the bregman iteration

NASA Astrophysics Data System (ADS)

Lee, Dong-Hoon; Hong, Cheol-Pyo; Lee, Man-Woo

2013-01-01

Magnetic resonance imaging (MRI) reconstruction needs many samples that are sequentially sampled by using phase encoding gradients in a MRI system. It is directly connected to the scan time for the MRI system and takes a long time. Therefore, many researchers have studied ways to reduce the scan time, especially, compressed sensing (CS), which is used for sparse images and reconstruction for fewer sampling datasets when the k-space is not fully sampled. Recently, an iterative technique based on the bregman method was developed for denoising. The bregman iteration method improves on total variation (TV) regularization by gradually recovering the fine-scale structures that are usually lost in TV regularization. In this study, we studied sparse sampling image reconstruction using the bregman iteration for a low-field MRI system to improve its temporal resolution and to validate its usefulness. The image was obtained with a 0.32 T MRI scanner (Magfinder II, SCIMEDIX, Korea) with a phantom and an in-vivo human brain in a head coil. We applied random k-space sampling, and we determined the sampling ratios by using half the fully sampled k-space. The bregman iteration was used to generate the final images based on the reduced data. We also calculated the root-mean-square-error (RMSE) values from error images that were obtained using various numbers of bregman iterations. Our reconstructed images using the bregman iteration for sparse sampling images showed good results compared with the original images. Moreover, the RMSE values showed that the sparse reconstructed phantom and the human images converged to the original images. We confirmed the feasibility of sparse sampling image reconstruction methods using the bregman iteration with a low-field MRI system and obtained good results. Although our results used half the sampling ratio, this method will be helpful in increasing the temporal resolution at low-field MRI systems.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

2016-11-01

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.

Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules

NASA Astrophysics Data System (ADS)

Chung, Kee-Choo; Park, Hwangseo

2016-11-01

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (Δ G sol ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because Δ G sol values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient ( R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for Δ G sol were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental Δ G sol data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental Δ G sol data for organic solvents.

WE-G-BRD-01: A Data-Driven 4D-MRI Motion Model to Estimate Full Field-Of-View Abdominal Motion From 2D Image Navigators During MR-Linac Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stemkens, B; Tijssen, RHN; Denis de Senneville, B Denis

2015-06-15

Purpose: To estimate full field-of-view abdominal respiratory motion from fast 2D image navigators using a 4D-MRI based motion model. This will allow for radiation dose accumulation mapping during MR-Linac treatment. Methods: Experiments were conducted on a Philips Ingenia 1.5T MRI. First, a retrospectively ordered 4D-MRI was constructed using 3D transient-bSSFP with radial in-plane sampling. Motion fields were calculated through 3D non-rigid registration. From these motion fields a PCA-based abdominal motion model was constructed and used to warp a 3D reference volume to fast 2D cine-MR image navigators that can be used for real-time tracking. To test this procedure, a time-seriesmore » consisting of two interleaved orthogonal slices (sagittal and coronal), positioned on the pancreas or kidneys, were acquired for 1m38s (dynamic scan-time=0.196ms), during normal, shallow, or deep breathing. The coronal slices were used to update the optimal weights for the first two PCA components, in order to warp the 3D reference image and construct a dynamic 4D-MRI time-series. The interleaved sagittal slices served as an independent measure to test the model’s accuracy and fit. Spatial maps of the root-mean-squared error (RMSE) and histograms of the motion differences within the pancreas and kidneys were used to evaluate the method. Results: Cranio-caudal motion was accurately calculated within the pancreas using the model for normal and shallow breathing with an RMSE of 1.6mm and 1.5mm and a histogram median and standard deviation below 0.2 and 1.7mm, respectively. For deep-breathing an underestimation of the inhale amplitude was observed (RMSE=4.1mm). Respiratory-induced antero-posterior and lateral motion were correctly mapped (RMSE=0.6/0.5mm). Kidney motion demonstrated good motion estimation with RMSE-values of 0.95 and 2.4mm for the right and left kidney, respectively. Conclusion: We have demonstrated a method that can calculate dynamic 3D abdominal motion in a large volume, while acquiring real-time cine-MR images for MR-guided radiotherapy.« less

Contribution of Modis Satellite Image to Estimate the Daily Air Temperature in the Casablanca City, Morocco

NASA Astrophysics Data System (ADS)

Bahi, Hicham; Rhinane, Hassan; Bensalmia, Ahmed

2016-10-01

Air temperature is considered to be an essential variable for the study and analysis of meteorological regimes and chronics. However, the implementation of a daily monitoring of this variable is very difficult to achieve. It requires sufficient of measurements stations density, meteorological parks and favourable logistics. The present work aims to establish relationship between day and night land surface temperatures from MODIS data and the daily measurements of air temperature acquired between [2011-20112] and provided by the Department of National Meteorology [DMN] of Casablanca, Morocco. The results of the statistical analysis show significant interdependence during night observations with correlation coefficient of R2=0.921 and Root Mean Square Error RMSE=1.503 for Tmin while the physical magnitude estimated from daytime MODIS observation shows a relatively coarse error with R2=0.775 and RMSE=2.037 for Tmax. A method based on Gaussian process regression was applied to compute the spatial distribution of air temperature from MODIS throughout the city of Casablanca.

Predicting tropical plant physiology from leaf and canopy spectroscopy

NASA Astrophysics Data System (ADS)

Doughty, C.; Asner, G. P.; Martin, R.

2009-12-01

A broad understanding of tropical forest leaf photosynthesis has long been a goal for tropical forest ecologists, but elusive, due to difficult canopy access and great species diversity. In this paper, we develop an empirical model to predict light saturated sunlit tropical leaf photosynthesis based on leaf and canopy spectra with the goal of developing a high resolution remote sensing technique to measure canopy photosynthesis. To develop this model, we used the partial least squares (PLS) regression technique on three tropical forest datasets (~168 species), two in Hawaii and one in the tropical rainforest module of Biosphere 2 (B2L). For each species, we measured light saturated photosynthesis (A), light and CO2 saturated photosynthesis (Amax), day respiration (R), leaf spectra (400-2500 nm with 1 nm sampling), leaf nitrogen (N), chlorophyll A and B, carotenoids, and specific leaf area (SLA). On a subset of species we measured Jmax and Vcmax based on light and Aci curves. The model best predicted A (r2 = 0.74, root mean square error (RMSE) = 2.85 µmol m-2 s-1), R (r2 of 0.48, RMSE of -0.52 µmol m-2 s-1) followed by Amax (r2 of 0.47, RMSE of 5.1 µmol m-2 s-1), Jmax, (R2 = 0.52, RMSE = 39) and VCmax (R2 = 0.39, RMSE = 36). The PLS weightings, which indicate which wavelengths most contribute to the model, indicated that physiology weightings were most similar to nitrogen weightings, followed by chlorophyll and SLA. We combined leaf-level reflectance and transmittance with a canopy radiative transfer model to simulate top-of-canopy reflectance, and found that canopy spectra are a better predictor of light saturated photosynthesis more strongly (RMSE = 2.4 µmol m-2 s-1) than are leaf spectra (RMSE = 2.85 µmol m-2 s-1). The results suggest that there is potential for this technique to be used with high fidelity imaging spectrometers to remotely sense tropical forest canopy photosynthesis.

Estimating EQ-5D values from the Neck Disability Index and numeric rating scales for neck and arm pain.

PubMed

Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D

2014-09-01

The Neck Disability Index (NDI) and numeric rating scales (0 to 10) for neck pain and arm pain are widely used cervical spine disease-specific measures. Recent studies have shown that there is a strong relationship between the SF-6D and the NDI such that using a simple linear regression allows for the estimation of an SF-6D value from the NDI alone. Due to ease of administration and scoring, the EQ-5D is increasingly being used as a measure of utility in the clinical setting. The purpose of this study is to determine if the EQ-5D values can be estimated from commonly available cervical spine disease-specific health-related quality of life measures, much like the SF-6D. The EQ-5D, NDI, neck pain score, and arm pain score were prospectively collected in 3732 patients who presented to the authors' clinic with degenerative cervical spine disorders. Correlation coefficients for paired observations from multiple time points between the NDI, neck pain and arm pain scores, and EQ-5D were determined. Regression models were built to estimate the EQ-5D values from the NDI, neck pain, and arm pain scores. The mean age of the 3732 patients was 53.3 ± 12.2 years, and 43% were male. Correlations between the EQ-5D and the NDI, neck pain score, and arm pain score were statistically significant (p < 0.0001), with correlation coefficients of -0.77, -0.62, and -0.50, respectively. The regression equation 0.98947 + (-0.00705 × NDI) + (-0.00875 × arm pain score) + (-0.00877 × neck pain score) to predict EQ-5D had an R-square of 0.62 and a root mean square error (RMSE) of 0.146. The model using NDI alone had an R-square of 0.59 and a RMSE of 0.150. The model using the individual NDI items had an R-square of 0.46 and an RMSE of 0.172. The correlation coefficient between the observed and estimated EQ-5D scores was 0.79. There was no statistically significant difference between the actual EQ-5D score (0.603 ± 0.235) and the estimated EQ-5D score (0.603 ± 0.185) using the NDI, neck pain score, and arm pain score regression model. However, rounding off the coefficients to fewer than 5 decimal places produced less accurate results. The regression model estimating the EQ-5D from the NDI, neck pain score, and arm pain score accounted for 60% of the variability of the EQ-5D with a relatively large RMSE. This regression model may not be sufficient to accurately or reliably estimate actual EQ-5D values.

Monitoring Building Deformation with InSAR: Experiments and Validation.

PubMed

Yang, Kui; Yan, Li; Huang, Guoman; Chen, Chu; Wu, Zhengpeng

2016-12-20

Synthetic Aperture Radar Interferometry (InSAR) techniques are increasingly applied for monitoring land subsidence. The advantages of InSAR include high accuracy and the ability to cover large areas; nevertheless, research validating the use of InSAR on building deformation is limited. In this paper, we test the monitoring capability of the InSAR in experiments using two landmark buildings; the Bohai Building and the China Theater, located in Tianjin, China. They were selected as real examples to compare InSAR and leveling approaches for building deformation. Ten TerraSAR-X images spanning half a year were used in Permanent Scatterer InSAR processing. These extracted InSAR results were processed considering the diversity in both direction and spatial distribution, and were compared with true leveling values in both Ordinary Least Squares (OLS) regression and measurement of error analyses. The detailed experimental results for the Bohai Building and the China Theater showed a high correlation between InSAR results and the leveling values. At the same time, the two Root Mean Square Error (RMSE) indexes had values of approximately 1 mm. These analyses show that a millimeter level of accuracy can be achieved by means of InSAR technique when measuring building deformation. We discuss the differences in accuracy between OLS regression and measurement of error analyses, and compare the accuracy index of leveling in order to propose InSAR accuracy levels appropriate for monitoring buildings deformation. After assessing the advantages and limitations of InSAR techniques in monitoring buildings, further applications are evaluated.

The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: A test of emerging integrated approaches at Cwm Idwal, North Wales

NASA Astrophysics Data System (ADS)

Tonkin, T. N.; Midgley, N. G.; Graham, D. J.; Labadz, J. C.

2014-12-01

Novel topographic survey methods that integrate both structure-from-motion (SfM) photogrammetry and small unmanned aircraft systems (sUAS) are a rapidly evolving investigative technique. Due to the diverse range of survey configurations available and the infancy of these new methods, further research is required. Here, the accuracy, precision and potential applications of this approach are investigated. A total of 543 images of the Cwm Idwal moraine-mound complex were captured from a light (< 5 kg) semi-autonomous multi-rotor unmanned aircraft system using a consumer-grade 18 MP compact digital camera. The images were used to produce a DSM (digital surface model) of the moraines. The DSM is in good agreement with 7761 total station survey points providing a total vertical RMSE value of 0.517 m and vertical RMSE values as low as 0.200 m for less densely vegetated areas of the DSM. High-precision topographic data can be acquired rapidly using this technique with the resulting DSMs and orthorectified aerial imagery at sub-decimetre resolutions. Positional errors on the total station dataset, vegetation and steep terrain are identified as the causes of vertical disagreement. Whilst this aerial survey approach is advocated for use in a range of geomorphological settings, care must be taken to ensure that adequate ground control is applied to give a high degree of accuracy.

Measurement and modeling of particulate matter concentrations: Applying spatial analysis and regression techniques to assess air quality.

PubMed

Sajjadi, Seyed Ali; Zolfaghari, Ghasem; Adab, Hamed; Allahabadi, Ahmad; Delsouz, Mehri

2017-01-01

This paper presented the levels of PM 2.5 and PM 10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM 2.5 and PM 10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM 2.5 concentrations in the stations were between 10 and 500 μg/m 3 . Furthermore, the PM 10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m 3 . The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM 2.5 and 25.89 for PM 10 ). The best interpolation method for the particulate matter (PM 2.5 and PM 10 ) seemed to be IDW method. •The PM 10 and PM 2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016.•Concentrations of PM 2.5 and PM 10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000.•Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.

Performance Metrics for Soil Moisture Retrievals and Applications Requirements

USDA-ARS?s Scientific Manuscript database

Quadratic performance metrics such as root-mean-square error (RMSE) and time series correlation are often used to assess the accuracy of geophysical retrievals and true fields. These metrics are generally related; nevertheless each has advantages and disadvantages. In this study we explore the relat...

Developing a regional scale approach for modelling the impacts of fertiliser regime on N2O emissions in Ireland

NASA Astrophysics Data System (ADS)

Zimmermann, Jesko; Jones, Michael

2016-04-01

Agriculture can be significant contributor to greenhouse gas emissions, this is especially prevalent in Ireland where the agricultural sector accounts for a third of total emissions. The high emissions are linked to both the importance of agriculture in the Irish economy and the focus on dairy and beef production. In order to reduce emissions three main categories are explored: (1) reduction of methane emissions from cattle, (2) reduction of nitrous oxide emissions from fertilisation, and (3) fostering the carbon sequestration potential of soils. The presented research focuses on the latter two categories, especially changes in fertiliser amount and composition. Soil properties and climate conditions measured at the four experimental sites (two silage and two spring barley) were used to parameterise four biogeochemical models (DayCent, ECOSSE, DNDC 9.4, and DNDC 9.5). All sites had a range of different fertiliser regimes applied. This included changes in amount (0 to 500 kg N/ha on grassland and 0 to 200 kg N/ha on arable fields), fertiliser type (calcium ammonium nitrate and urea), and added inhibitors (the nitrification inhibitor DCD, and the urease inhibitor Agrotain). Overall, 20 different treatments were applied to the grassland sites, and 17 to the arable sites. Nitrous oxide emissions, measured in 2013 and 2014 at all sites using closed chambers, were made available to validate model results for these emissions. To assess model performance for the daily measurements, the Root Mean Square Error (RMSE) was compared to the measured 95% confidence interval of the measured data (RMSE95). Bias was tested comparing the relative error (RE) the 95 % confidence interval of the relative error (RE95). Preliminary results show mixed model performance, depending on the model, site, and the fertiliser regime. However, with the exception of urea fertilisation and added inhibitors, all scenarios were reproduced by at least one model with no statistically significant total error (RMSE < RMSE95) or bias (RE< RE95). A general trend observed was that model performance declined with increased fertilisation rates. Overall, DayCent showed the best performance, however it does not provide the possibility to model the addition urease inhibitors. The results suggest that modelling changes in fertiliser regime on a large scale may require a multi-model approach to assure best performance. Ultimately, the research aims to develop a GIS based platform to apply such an approach on a regional scale.

Distribution of Hydroxyl Groups in Kukersite Shale Oil: Quantitative Determination Using Fourier Transform Infrared (FT-IR) Spectroscopy.

PubMed

Baird, Zachariah Steven; Oja, Vahur; Järvik, Oliver

2015-05-01

This article describes the use of Fourier transform infrared (FT-IR) spectroscopy to quantitatively measure the hydroxyl concentrations among narrow boiling shale oil cuts. Shale oil samples were from an industrial solid heat carrier retort. Reference values were measured by titration and were used to create a partial least squares regression model from FT-IR data. The model had a root mean squared error (RMSE) of 0.44 wt% OH. This method was then used to study the distribution of hydroxyl groups among more than 100 shale oil cuts, which showed that hydroxyl content increased with the average boiling point of the cut up to about 350 °C and then leveled off and decreased.

Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java

NASA Astrophysics Data System (ADS)

Rahma Prillantika, Jessica; Apriliani, Erna; Wahyuningsih, Nuri

2018-03-01

Up to now, we often find data which have correlation between time and location. This data also known as spatial data. Inflation rate is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research, we do comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

PubMed

Basant, Nikita; Gupta, Shikha

2017-06-01

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

PubMed

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture

NASA Astrophysics Data System (ADS)

Santi, E.; Paloscia, S.; Pettinato, S.; Brocca, L.; Ciabatta, L.; Entekhabi, D.

2018-03-01

An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improved accuracy with respect to the original SMAP radiometric SMC product. A disaggregation technique based on the Smoothing filter based intensity modulation (SFIM) allows combining the radiometric and SAR data. Disaggregated microwave data are used as inputs of an Artificial Neural Networks (ANN) based algorithm, which is able to exploit the synergy between active and passive acquisitions. The algorithm is defined, trained and tested using the SMEX02 experimental dataset and data simulated by forward electromagnetic models based on the Radiative Transfer Theory. Then the algorithm is adapted to satellite data and tested using one year of SMAP, AMSR2 and Sentinel-1 co-located data on a flat agricultural area located in the Po Valley, in northern Italy. Spatially distributed SMC values at 0.1° × 0.1° resolution generated by the Soil Water Balance Model (SWBM) are considered as reference for this purpose. The synergy of SMAP, Sentinel-1 and AMSR2 allowed increasing the correlation between estimated and reference SMC from R ≅ 0.68 of the SMAP based retrieval up to R ≅ 0.86 of the combination SMAP + Sentinel-1 + AMSR2. The corresponding Root Mean Square Error (RMSE) decreased from RMSE ≅ 0.04 m3/m3 to RMSE ≅ 0.024 m3/m3.

Mathematical modeling based evaluation and simulation of boron removal in bioelectrochemical systems.

PubMed

Ping, Qingyun; Abu-Reesh, Ibrahim M; He, Zhen

2016-11-01

Boron removal is an arising issue in desalination plants due to boron's toxicity. As an emerging treatment concept, bioelectrochemical systems (BES) can achieve potentially cost-effective boron removal by taking advantage of cathodic-produced alkali. Prior studies have demonstrated successful removal of boron in microbial desalination cells (MDCs) and microbial fuel cells (MFCs), both of which are representative BES. Herein, mathematical models were developed to further evaluate boron removal by different BES and understand the key operating factors. The models delivered very good prediction of the boron concentration in the MDC integrated with Donnan Dialysis (DD) system with the lowest relative root-mean-square error (RMSE) of 0.00%; the predication of the MFC performance generated the highest RMSE of 18.55%. The model results of salt concentration, solution pH, and current generation were well fitted with experimental data for RMSE values mostly below 10%. The long term simulation of the MDC-DD system suggests that the accumulation of salt in the catholyte/stripping solution could have a positive impact on the removal of boron due to osmosis-driven convection. The current generation in the MDC may have little influence on the boron removal, while in the MFC the current-driven electromigration can contribute up to 40% of boron removal. Osmosis-induced convection transport of boron could be the major driving force for boron removal to a low level <2mgL(-1). The ratio between the anolyte and the catholyte flow rates should be kept >22.2 in order to avoid boron accumulation in the anolyte effluent. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

High-resolution precipitation data derived from dynamical downscaling using the WRF model for the Heihe River Basin, northwest China

NASA Astrophysics Data System (ADS)

Zhang, Xuezhen; Xiong, Zhe; Zheng, Jingyun; Ge, Quansheng

2018-02-01

The community of climate change impact assessments and adaptations research needs regional high-resolution (spatial) meteorological data. This study produced two downscaled precipitation datasets with spatial resolutions of as high as 3 km by 3 km for the Heihe River Basin (HRB) from 2011 to 2014 using the Weather Research and Forecast (WRF) model nested with Final Analysis (FNL) from the National Center for Environmental Prediction (NCEP) and ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF) (hereafter referred to as FNLexp and ERAexp, respectively). Both of the downscaling simulations generally reproduced the observed spatial patterns of precipitation. However, users should keep in mind that the two downscaled datasets are not exactly the same in terms of observations. In comparison to the remote sensing-based estimation, the FNLexp produced a bias of heavy precipitation centers. In comparison to the ground gauge-based measurements, for the warm season (May to September), the ERAexp produced more precipitation (root-mean-square error (RMSE) = 295.4 mm, across the 43 sites) and more heavy rainfall days, while the FNLexp produced less precipitation (RMSE = 115.6 mm) and less heavy rainfall days. Both the ERAexp and FNLexp produced considerably more precipitation for the cold season (October to April) with RMSE values of 119.5 and 32.2 mm, respectively, and more heavy precipitation days. Along with simulating a higher number of heavy precipitation days, both the FNLexp and ERAexp also simulated stronger extreme precipitation. Sensitivity experiments show that the bias of these simulations is much more sensitive to micro-physical parameterizations than to the spatial resolution of topography data. For the HRB, application of the WSM3 scheme may improve the performance of the WRF model.

Validation of drying models and rehydration characteristics of betel (Piper betel L.) leaves.

PubMed

Balasubramanian, S; Sharma, R; Gupta, R K; Patil, R T

2011-12-01

Effect of temperature on drying behaviour of betel leaves at drying air temperatures of 50, 60 and 70°C was investigated in tunnel as well as cabinet dryer. The L* and b* values increased whereas, a* values decreased, as the drying air temperature increased from 50 to 70°C in both the dryers, but the colour values remained higher for cabinet dryer than tunnel dryer in all cases. Eleven different drying models were compared according to their coefficients of determination (R(2)), root mean square error (RMSE) and chi square (χ (2)) to estimate drying curves. The results indicated that, logarithmic model and modified Page model could satisfactorily describe the drying curve of betel leaves for tunnel drying and cabinet dryer, respectively. In terms of colour quality, drying of betel leaves at 60°C in tunnel dryer and at 50°C in cabinet dryer was found optimum whereas, rehydration at 40°C produced the best acceptable product.

The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey.

PubMed

Ercanli, İlker; Kahriman, Aydın

2015-03-01

We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85 % of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529 Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5 % of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153 Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB.

Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

PubMed

Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

2014-08-15

A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.

Determination of the ultrasound power effects on flavonoid compounds from Psidium guajava L. using ANFIS

NASA Astrophysics Data System (ADS)

Ratu Ayu, Humairoh; Suryono, Suryono; Endro Suseno, Jatmiko; Kurniawati, Ratna

2018-05-01

The Adaptive Neural Fuzzy Inference System (ANFIS) model was used to predict and optimize the content of flavonoid compounds in guava leaves (Psidium Guajava L.). The extraction process was carried out by using ultrasound assisted extraction (UAE) with the variable parameters: temperature ranging from 25°C to 35°C, ultrasonic frequency (30 - 40 kHz) and extraction time (20 - 40 minutes). ANFIS learning procedure began by providing the input variable data set (temperature, frequency and time) and the output of the flavonoid compounds from the experiments that had been done. Subtractive clustering methods was used in the manufacture of FIS (fuzzy inference system) structures by varying the range of influence parameters to generate the ANFIS system. The ANFIS trainingsconducted wereaimed at minimum error value. The results showed that the best ANFIS models used a subtractive clustering method, in which the ranges of influence 0.1 were 0.70 x 10-4 for training RMSE, 8.11 for testing RMSE, 2.7 % MAPE, and 7.72 MAE. The optimum condition was obtained at a temperature of 35°C and frequency of 40 kHz, for 30 minutes. This result proves that the ANFIS model can be used to predict the content of flavonoid compounds in guava leaves.

Ambient Sound-Based Collaborative Localization of Indeterministic Devices

PubMed Central

Kamminga, Jacob; Le, Duc; Havinga, Paul

2016-01-01

Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5° and a standard deviation of 2.3°. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android. PMID:27649176

Calibration methods influence quantitative material decomposition in photon-counting spectral CT

NASA Astrophysics Data System (ADS)

Curtis, Tyler E.; Roeder, Ryan K.

2017-03-01

Photon-counting detectors and nanoparticle contrast agents can potentially enable molecular imaging and material decomposition in computed tomography (CT). Material decomposition has been investigated using both simulated and acquired data sets. However, the effect of calibration methods on material decomposition has not been systematically investigated. Therefore, the objective of this study was to investigate the influence of the range and number of contrast agent concentrations within a modular calibration phantom on quantitative material decomposition. A commerciallyavailable photon-counting spectral micro-CT (MARS Bioimaging) was used to acquire images with five energy bins selected to normalize photon counts and leverage the contrast agent k-edge. Material basis matrix values were determined using multiple linear regression models and material decomposition was performed using a maximum a posteriori estimator. The accuracy of quantitative material decomposition was evaluated by the root mean squared error (RMSE), specificity, sensitivity, and area under the curve (AUC). An increased maximum concentration (range) in the calibration significantly improved RMSE, specificity and AUC. The effects of an increased number of concentrations in the calibration were not statistically significant for the conditions in this study. The overall results demonstrated that the accuracy of quantitative material decomposition in spectral CT is significantly influenced by calibration methods, which must therefore be carefully considered for the intended diagnostic imaging application.

Simulating multi-scale oceanic processes around Taiwan on unstructured grids

NASA Astrophysics Data System (ADS)

Yu, Hao-Cheng; Zhang, Yinglong J.; Yu, Jason C. S.; Terng, C.; Sun, Weiling; Ye, Fei; Wang, Harry V.; Wang, Zhengui; Huang, Hai

2017-11-01

We validate a 3D unstructured-grid (UG) model for simulating multi-scale processes as occurred in Northwestern Pacific around Taiwan using recently developed new techniques (Zhang et al., Ocean Modeling, 102, 64-81, 2016) that require no bathymetry smoothing even for this region with prevalent steep bottom slopes and many islands. The focus is on short-term forecast for several months instead of long-term variability. Compared with satellite products, the errors for the simulated Sea-surface Height (SSH) and Sea-surface Temperature (SST) are similar to a reference data-assimilated global model. In the nearshore region, comparison with 34 tide gauges located around Taiwan indicates an average RMSE of 13 cm for the tidal elevation. The average RMSE for SST at 6 coastal buoys is 1.2 °C. The mean transport and eddy kinetic energy compare reasonably with previously published values and the reference model used to provide boundary and initial conditions. The model suggests ∼2-day interruption of Kuroshio east of Taiwan during a typhoon period. The effect of tidal mixing is shown to be significant nearshore. The multi-scale model is easily extendable to target regions of interest due to its UG framework and a flexible vertical gridding system, which is shown to be superior to terrain-following coordinates.

Photosynthetic Efficiency of Northern Forest Ecosystems Using a MODIS-Derived Photochemical Reflectance Index (PRI)

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Huemmrich, K. F.; Landis, D. R.; Black, T. A.; Barr, A. G.; McCaughey, J. H.

2016-01-01

This study evaluates a direct remote sensing approach from space for the determination of ecosystem photosynthetic light use efficiency (LUE), through measurement of vegetation reflectance changes expressed with the Photochemical Reflectance Index (PRI). The PRI is a normalized difference index based on spectral changes at a physiologically active wavelength (approximately 531 nanometers) as compared to a reference waveband, and is only available from a very few satellites. These include the two Moderate-Resolution Imaging Spectroradiometers (MODIS) on the Aqua and Terra satellites each of which have a narrow (10-nanometer) ocean band centered at 531 nanometers. We examined several PRI variations computed with candidate reference bands, since MODIS lacks the traditional 570-nanometer reference band. The PRI computed using MODIS land band 1 (620-670 nanometers) gave the best performance for daily LUE estimation. Through rigorous statistical analyses over a large image collection (n equals 420), the success of relating in situ daily tower-derived LUE to MODIS observations for northern forests was strongly influenced by satellite viewing geometry. LUE was calculated from CO2 fluxes (moles per moles of carbon absorbed quanta) measured at instrumented Canadian Carbon Program flux towers in four Canadian forests: a mature fir site in British Columbia, mature aspen and black spruce sites in Saskatchewan, and a mixed deciduous/coniferous forest site in Ontario. All aspects of the viewing geometry had significant effects on the MODIS-PRI, including the view zenith angle (VZA), the view azimuth angle, and the displacement of the view azimuth relative to the solar principal plane, in addition to illumination related variables.Nevertheless, we show that forward scatter sector views (VZA, 16 degrees-45 degrees) provided the strongest relationships to daily LUE, especially those collected in the early afternoon by Aqua (r squared = 0.83, RMSE (root mean square error) equals 0.003 moles per moles of carbon absorbed quanta). Nadir (VZA, 0 degrees plus or minus 15 degrees) and backscatter views (VZA, -16 degrees to -45 degrees) had lower performance in estimating LUE (nadir: r squared approximately equal to 0.62-0.67; backscatter: r squared approximately equal to 0.54-0.59) and similar estimation error (RMSE equals 0.004-0.005).When directional effects were not considered, only a moderately successful MODIS-PRI vs. LUE relationship (r squared equals 0.34, RMSE equals 0.007) was obtained in the full dataset (all views & sites, both satellites), but site-specific relationships were able to discriminate between coniferous and deciduous forests. Overall, MODIS-PRI values from Terra (late morning) were higher than those from Aqua (early afternoon), before/after the onset of diurnal stress responses expressed spectrally. Therefore, we identified ninety-two Terra-Aqua "same day" pairs, for which the sum of Terra morning and Aqua afternoon MODIS-PRI values (PRI (sub sum) using all available directional observations was linearly correlated with daily tower LUE (r squared equals 0.622, RMSE equals 0.013) and independent of site differences or meteorological information. Our study highlights the value of off-nadir directional reflectance observations, and the value of pairing morning and afternoon satellite observations to monitor stress responses that inhibit carbon uptake in Canadian forest ecosystems. In addition, we show that MODIS-PRI values, when derived from either: (i) forward views only, or (ii) Terra/Aqua same day (any view) combined observations, provided more accurate estimates of tower-measured daily LUE than those derived from either nadir or backscatter views or those calculated by the widely used semi-operational MODIS GPP model (MOD17) which is based on a theoretical maximum LUE and environmental data. Consequently, we demonstrate the importance of diurnal as well as off-nadir satellite observations for detecting vegetation physiological processes.

A comprehensive assessment of different evapotranspiration products using the site-level FLUXNET database

NASA Astrophysics Data System (ADS)

Liu, J.

2017-12-01

Accurately estimate of ET is crucial for studies of land-atmosphere interactions. A series of ET products have been developed recently relying on various simulation methods, however, uncertainties in accuracy of products limit their implications. In this study, accuracies of total 8 popular global ET products simulated based on satellite retrieves (ETMODIS and ETZhang), reanalysis (ETJRA55), machine learning method (ETJung) and land surface models (ETCLM, ETMOS, ETNoah and ETVIC) forcing by Global Land Data Assimilation System (GLDAS), respectively, were comprehensively evaluated against observations from eddy covariance FLUXNET sites by yearly, land cover and climate zones. The result shows that all simulated ET products tend to underestimate in the lower ET ranges or overestimate in higher ET ranges compared with ET observations. Through the examining of four statistic criterias, the root mean square error (RMSE), mean bias error (MBE), R2, and Taylor skill score (TSS), ETJung provided a high performance whether yearly or land cover or climatic zones. Satellite based ET products also have impressive performance. ETMODIS and ETZhang present comparable accuracy, while were skilled for different land cover and climate zones, respectively. Generally, the ET products from GLDAS show reasonable accuracy, despite ETCLM has relative higher RMSE and MBE for yearly, land cover and climate zones comparisons. Although the ETJRA55 shows comparable R2 with other products, its performance was constraint by the high RMSE and MBE. Knowledge from this study is crucial for ET products improvement and selection when they were used.

Arterial pressure transfer characteristics: effects of travel time.

PubMed

Westerhof, Berend E; Guelen, Ilja; Stok, Wim J; Wesseling, Karel H; Spaan, Jos A E; Westerhof, Nico; Bos, Willem Jan; Stergiopulos, Nikos

2007-02-01

We investigated the quantitative contribution of all local conduit arterial, blood, and distal load properties to the pressure transfer function from brachial artery to aorta. The model was based on anatomical data, Young's modulus, wall viscosity, blood viscosity, and blood density. A three-element windkessel represented the distal arterial tree. Sensitivity analysis was performed in terms of frequency and magnitude of the peak of the transfer function and in terms of systolic, diastolic, and pulse pressure in the aorta. The root mean square error (RMSE) described the accuracy in wave-shape prediction. The percent change of these variables for a 25% alteration of each of the model parameters was calculated. Vessel length and diameter are found to be the most important parameters determining pressure transfer. Systolic and diastolic pressure changed <3% and RMSE <1.8 mmHg for a 25% change in vessel length and diameter. To investigate how arterial tapering influences the pressure transfer, a single uniform lossless tube was modeled. This simplification introduced only small errors in systolic and diastolic pressures (1% and 0%, respectively), and wave shape was less well described (RMSE, approximately 2.1 mmHg). Local (arm) vasodilation affects the transfer function little, because it has limited effect on the reflection coefficient. Since vessel length and diameter translate into travel time, this parameter can describe the transfer accurately. We suggest that with a, preferably, noninvasively measured travel time, an accurate individualized description of pressure transfer can be obtained.

Local ensemble transform Kalman filter for ionospheric data assimilation: Observation influence analysis during a geomagnetic storm event

NASA Astrophysics Data System (ADS)

Durazo, Juan A.; Kostelich, Eric J.; Mahalov, Alex

2017-09-01

We propose a targeted observation strategy, based on the influence matrix diagnostic, that optimally selects where additional observations may be placed to improve ionospheric forecasts. This strategy is applied in data assimilation observing system experiments, where synthetic electron density vertical profiles, which represent those of Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa satellite 3, are assimilated into the Thermosphere-Ionosphere-Electrodynamics General Circulation Model using the local ensemble transform Kalman filter during the 26 September 2011 geomagnetic storm. During each analysis step, the observation vector is augmented with five synthetic vertical profiles optimally placed to target electron density errors, using our targeted observation strategy. Forecast improvement due to assimilation of augmented vertical profiles is measured with the root-mean-square error (RMSE) of analyzed electron density, averaged over 600 km regions centered around the augmented vertical profile locations. Assimilating vertical profiles with targeted locations yields about 60%-80% reduction in electron density RMSE, compared to a 15% average reduction when assimilating randomly placed vertical profiles. Assimilating vertical profiles whose locations target the zonal component of neutral winds (Un) yields on average a 25% RMSE reduction in Un estimates, compared to a 2% average improvement obtained with randomly placed vertical profiles. These results demonstrate that our targeted strategy can improve data assimilation efforts during extreme events by detecting regions where additional observations would provide the largest benefit to the forecast.

Evaluation of the kinetic energy of the torso by magneto-inertial measurement unit during the sit-to-stand movement.

PubMed

Lepetit, Kevin; Ben Mansour, Khalil; Boudaoud, Sofiane; Kinugawa-Bourron, Kiyoka; Marin, Frédéric

2018-01-23

Sit-to-stand tests are used in geriatrics as a qualitative issue in order to evaluate motor control and stability. In terms of measured indicators, it is traditionally the duration of the task that is reported, however it appears that the use of the kinetic energy as a new quantitative criterion allows getting a better understanding of musculoskeletal deficits of elderly subjects. The aim of this study was to determine the feasibility to obtain the measure of kinetic energy using magneto-inertial measurement units (MIMU) during sit-to-stand movements at various paces. 26 healthy subjects contributed to this investigation. Measured results were compared to a marker-based motion capture using the correlation coefficient and the normalized root mean square error (nRMSE). nRMSE were below 10% and correlation coefficients were over 0.97. In addition, errors on the mean kinetic energy were also investigated using Bland-Altman 95% limits of agreement (0.63 J-0.77 J), RMSE (0.29 J-0.38 J) and correlation coefficient (0.96-0.98). The results obtained highlighted that the method based on MIMU data could be an alternative to optoelectronic data acquisition to assess the kinetic energy of the torso during the sit-to-stand test, suggesting this method as being a promising alternative to determine kinetic energy during the sit-to-stand movement. Copyright © 2017 Elsevier Ltd. All rights reserved.

Kinetic Behavior of Escherichia coli on Various Cheeses under Constant and Dynamic Temperature.

PubMed

Kim, K; Lee, H; Gwak, E; Yoon, Y

2014-07-01

In this study, we developed kinetic models to predict the growth of pathogenic Escherichia coli on cheeses during storage at constant and changing temperatures. A five-strain mixture of pathogenic E. coli was inoculated onto natural cheeses (Brie and Camembert) and processed cheeses (sliced Mozzarella and sliced Cheddar) at 3 to 4 log CFU/g. The inoculated cheeses were stored at 4, 10, 15, 25, and 30°C for 1 to 320 h, with a different storage time being used for each temperature. Total bacteria and E. coli cells were enumerated on tryptic soy agar and MacConkey sorbitol agar, respectively. E. coli growth data were fitted to the Baranyi model to calculate the maximum specific growth rate (μ max; log CFU/g/h), lag phase duration (LPD; h), lower asymptote (log CFU/g), and upper asymptote (log CFU/g). The kinetic parameters were then analyzed as a function of storage temperature, using the square root model, polynomial equation, and linear equation. A dynamic model was also developed for varying temperature. The model performance was evaluated against observed data, and the root mean square error (RMSE) was calculated. At 4°C, E. coli cell growth was not observed on any cheese. However, E. coli growth was observed at 10°C to 30°C with a μ max of 0.01 to 1.03 log CFU/g/h, depending on the cheese. The μ max values increased as temperature increased, while LPD values decreased, and μ max and LPD values were different among the four types of cheese. The developed models showed adequate performance (RMSE = 0.176-0.337), indicating that these models should be useful for describing the growth kinetics of E. coli on various cheeses.

Assimilation of satellite altimeter data into an open ocean model

NASA Astrophysics Data System (ADS)

Vogeler, Armin; SchröTer, Jens

1995-08-01

Geosat sea surface height data are assimilated into an eddy-resolving quasi-geostrophic open ocean model using the adjoint technique. The method adjusts the initial conditions for all layers and is successful on the timescale of a few weeks. Time-varying values for the open boundaries are prescribed by a much larger quasi-geostrophic model of the Antarctic Circumpolar Current (ACC). Both models have the same resolution of approximately 20×20 km (1/3°×1/6°), have three layers, and include realistic bottom topography and coastlines. The open model box is embedded in the African sector of the ACC. For continuous assimilation of satellite data into the larger model the nudging technique is applied. These results are used for the adjoint optimization procedure as boundary conditions and as a first guess for the initial condition. For the open model box the difference between model and satellite sea surface height that remains after the nudging experiment amounts to a 19-cm root-mean-square error (rmse). By assimilation into the regional model this value can be reduced to a 6-cm rmse for an assimilation period of 20 days. Several experiments which attempt to improve the convergence of the iterative optimization method are reported. Scaling and regularization by smoothing have to be applied carefully. Especially during the first 10 iterations, the convergence can be improved considerably by low-pass filtering of the cost function gradient. The result of a perturbation experiment shows that for longer assimilation periods the influence of the boundary values becomes dominant and they should be determined inversely by data assimilation into the open ocean model.

[Prediction models of soil organic matter based on spectral curve in the upstream of Heihe basin].

PubMed

Liu, Jiao; Li, Yi; Liu, Shi-Bin

2013-12-01

Benefiting from the high spectral resolution, ground hyperspectral remote sensing technology can express the ground surface feature in detail, meanwhile, multispectral remote sensing has more advantages in studying the features in a large space time region, because of its long time-series images and wide coverage. Investigating the prediction models between the soil organic matter (SOM) content and the hyperspectral data and the sensitive bands based on different indices mathematically obtained from reflectance could combine the advantages of both kinds of spectral data, and provide a new method to search the spatio-temporal characteristics of SOM. Two hundred twenty three soil samples were chosen from the upper reaches of Heihe Basin to measure the SOM content and hyperspectral curve. Taking 181 of them, the stepwise linear regression methods were used to establish models between the SOM and five indices, including reflectance (lambda), reciprocal (REC), logarithm of the reciprocal (LR), continuum-removal (CR) and the first derivative reflectance (FDR). After then, the left 42 samples were used for model validation: firstly, the best model of the same index was chosen by the values of Pearson correlation coefficient (r) and Root mean squared error (RMSE) between the measured value and predicted value; secondly, the best models of different indices were compared. As a result, the model built by reflectance has a better estimation of SOM with the r: 0.863 and RMSE: 4.79. And the sensitive bands of the reflectance model contain 474 nm during TM1, 636 nm during TM3 and 1 632 nm during TM5. This result could be a reference for the retrieval of SOM content of the upper reaches by using the TM remote sensing data.

Explore the Impacts of River Flow and Water Quality on Fish Communities

NASA Astrophysics Data System (ADS)

Tsai, W. P.; Chang, F. J.; Lin, C. Y.; Hu, J. H.; Yu, C. J.; Chu, T. J.

2015-12-01

Owing to the limitation of geographical environment in Taiwan, the uneven temporal and spatial distribution of rainfall would cause significant impacts on river ecosystems. To pursue sustainable water resources development, integrity and rationality is important to water management planning. The water quality and the flow regimes of rivers are closely related to each other and affect river ecosystems simultaneously. Therefore, this study collects long-term observational heterogeneity data, which includes water quality parameters, stream flow and fish species in the Danshui River of norther Taiwan, and aims to explore the complex impacts of water quality and flow regime on fish communities in order to comprehend the situations of the eco-hydrological system in this river basin. First, this study improves the understanding of the relationship between water quality parameters, flow regime and fish species by using artificial neural networks (ANNs). The Self-organizing feature map (SOM) is an unsupervised learning process used to cluster, analyze and visualize a large number of data. The results of SOM show that nine clusters (3x3) forms the optimum map size based on the local minimum values of both quantization error (QE) and topographic error (TE). Second, the fish diversity indexes are estimated by using the Adapted network-based fuzzy inference system (ANFIS) based on key input factors determined by the Gamma Test (GT), which is a useful tool for reducing model dimension and the structure complexity of ANNs. The result reveals that the constructed models can effectively estimate fish diversity indexes and produce good estimation performance based on the 9 clusters identified by the SOM, in which RMSE is 0.18 and CE is 0.84 for the training data set while RMSE is 0.20 and CE is 0.80 for the testing data set.

Comparison of Two Methods for Calculating the Frictional Properties of Articular Cartilage Using a Simple Pendulum and Intact Mouse Knee Joints

PubMed Central

Drewniak, Elizabeth I.; Jay, Gregory D.; Fleming, Braden C.; Crisco, Joseph J.

2009-01-01

In attempts to better understand the etiology of osteoarthritis, a debilitating joint disease that results in the degeneration of articular cartilage in synovial joints, researchers have focused on joint tribology, the study of joint friction, lubrication, and wear. Several different approaches have been used to investigate the frictional properties of articular cartilage. In this study, we examined two analysis methods for calculating the coefficient of friction (μ) using a simple pendulum system and BL6 murine knee joints (n=10) as the fulcrum. A Stanton linear decay model (Lin μ) and an exponential model that accounts for viscous damping (Exp μ) were fit to the decaying pendulum oscillations. Root mean square error (RMSE), asymptotic standard error (ASE), and coefficient of variation (CV) were calculated to evaluate the fit and measurement precision of each model. This investigation demonstrated that while Lin μ was more repeatable, based on CV (5.0% for Lin μ; 18% for Exp μ), Exp μ provided a better fitting model, based on RMSE (0.165° for Exp μ; 0.391° for Lin μ) and ASE (0.033 for Exp μ; 0.185 for Lin μ), and had a significantly lower coefficient of friction value (0.022±0.007 for Exp μ; 0.042±0.016 for Lin μ) (p=0.001). This study details the use of a simple pendulum for examining cartilage properties in situ that will have applications investigating cartilage mechanics in a variety of species. The Exp μ model provided a more accurate fit to the experimental data for predicting the frictional properties of intact joints in pendulum systems. PMID:19632680

Validity of the microsoft kinect system in assessment of compensatory stepping behavior during standing and treadmill walking.

PubMed

Shani, Guy; Shapiro, Amir; Oded, Goldstein; Dima, Kagan; Melzer, Itshak

2017-01-01

Rapid compensatory stepping plays an important role in preventing falls when balance is lost; however, these responses cannot be accurately quantified in the clinic. The Microsoft Kinect™ system provides real-time anatomical landmark position data in three dimensions (3D), which may bridge this gap. Compensatory stepping reactions were evoked in 8 young adults by a sudden platform horizontal motion on which the subject stood or walked on a treadmill. The movements were recorded with both a 3D-APAS motion capture and Microsoft Kinect™ systems. The outcome measures consisted of compensatory step times (milliseconds) and length (centimeters). The average values of two standing and walking trials for Microsoft Kinect™ and the 3D-APAS systems were compared using t -test, Pearson's correlation, Altman-bland plots, and the average difference of root mean square error (RMSE) of joint position. The Microsoft Kinect™ had high correlations for the compensatory step times ( r  = 0.75-0.78, p  = 0.04) during standing and moderate correlations for walking ( r  = 0.53-0.63, p  = 0.05). The step length, however had a very high correlations for both standing and walking ( r  > 0.97, p  = 0.01). The RMSE showed acceptable differences during the perturbation trials with smallest relative error in anterior-posterior direction (2-3%) and the highest in the vertical direction (11-13%). No systematic bias were evident in the Bland and Altman graphs. The Microsoft Kinect™ system provides comparable data to a video-based 3D motion analysis system when assessing step length and less accurate but still clinically acceptable for step times during balance recovery when balance is lost and fall is initiated.

[Simulation of AquaCrop model and management practice optimization for dryland maize production under whole plastic-film mulching on double ridges].

PubMed

Zhang, Tao; Sun, Wei; Zhang, Feng Wei; Sun, Bu Gong; Wang, Ting; Wu, Jian Min

2017-03-18

In order to study the applicability of AquaCrop model for simulating dryland whole plastic-film mulching in double ridges cultivation mode and to find the best agronomic management measures, the data of nitrogen gradient test in 2014 and 2015 were selected to validate the variety and stress parameters in the model. The change trends of yield were simulated under different mana-gement measures. The results showed that the root mean square error (RMSE), normalized root mean square error (NRMSE) and the compliance index (d) of the measured and simulated production for all treatments were 717 kg·hm -2 , 10.0% and 0.96, respectively, the RMSE, NRMSE and d of the total biomass were 951 kg·hm -2 , 6.5% and 0.98, respectively, which indicated that the cultivation characteristics of the whole plastic-film mulching on double ridges maize in the dryland could be well reflected. The best fitting degree was 270 kg N·hm -2 from dynamic simulation analysis of canopy cover degrees and biomass, and with the increase of N stress, the simulation accuracy gradually declined. The best sowing time of the whole plastic-film mulching on double ridges maize in the middle part of Gansu Province was from late April to early May, the seeding density was 45000-65000 plants·hm -2 , the growth period was 130-145 days, and the nitrogen application rate was 240-280 kg·hm -2 . The results of this study had a certain reference value for the application of AcquaCrop model in arid region of Gansu, and would contribute to the transformation and popularization of agricultural cultivation techniques.

[Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

PubMed

Zhang, Ting-Long; Sun, Rui; Zhang, Rong-Hua; Zhang, Lei

2013-10-01

Model simulation and in situ observation are the two most important means in studying the water and carbon cycles of terrestrial ecosystems, but have their own advantages and shortcomings. To combine these two means would help to reflect the dynamic changes of ecosystem water and carbon fluxes more accurately. Data assimilation provides an effective way to integrate the model simulation and in situ observation. Based on the observation data from the Harvard Forest Environmental Monitoring Site (EMS), and by using ensemble Kalman Filter algorithm, this paper assimilated the field measured LAI and remote sensing LAI into the Biome-BGC model to simulate the water and carbon fluxes in Harvard forest area. As compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the field measured LAI in 1998, 1999, and 2006 increased the coefficient of determination R2 between model simulation and flux observation for the net ecosystem exchange (NEE) and evapotranspiration by 8.4% and 10.6%, decreased the sum of absolute error (SAE) and root mean square error (RMSE) of NEE by 17.7% and 21.2%, and decreased the SAE and RMSE of the evapotranspiration by 26. 8% and 28.3%, respectively. After assimilated the MODIS LAI products of 2000-2004 into the improved Biome-BGC model, the R2 between simulated and observed results of NEE and evapotranspiration was increased by 7.8% and 4.7%, the SAE and RMSE of NEE were decreased by 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration were decreased by 24.5% and 25.5%, respectively. It was suggested that the simulation accuracy of ecosystem water and carbon fluxes could be effectively improved if the field measured LAI or remote sensing LAI was integrated into the model.

Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

NASA Astrophysics Data System (ADS)

Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Boyles, Ryan

2016-12-01

Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m3/m3. Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m3/m3 was possible.

Geometric accuracy of LANDSAT-4 MSS image data

NASA Technical Reports Server (NTRS)

Welch, R.; Usery, E. L.

1983-01-01

Analyses of the LANDSAT-4 MSS image data of North Georgia provided by the EDC in CCT-p formats reveal that errors of approximately + or - 30 m in the raw data can be reduced to about + or - 55 m based on rectification procedures involving the use of 20 to 30 well-distributed GCPs and 2nd or 3rd degree polynomial equations. Higher order polynomials do not appear to improve the rectification accuracy. A subscene area of 256 x 256 pixels was rectified with a 1st degree polynomial to yield an RMSE sub xy value of + or - 40 m, indicating that USGS 1:24,000 scale quadrangle-sized areas of LANDSAT-4 data can be fitted to a map base with relatively few control points and simple equations. The errors in the rectification process are caused by the spatial resolution of the MSS data, by errors in the maps and GCP digitizing process, and by displacements caused by terrain relief. Overall, due to the improved pointing and attitude control of the spacecraft, the geometric quality of the LANDSAT-4 MSS data appears much improved over that of LANDSATS -1, -2 and -3.

Groundwater recharge estimation in semi-arid zone: a study case from the region of Djelfa (Algeria)

NASA Astrophysics Data System (ADS)

Ali Rahmani, S. E.; Chibane, Brahim; Boucefiène, Abdelkader

2017-09-01

Deficiency of surface water resources in semi-arid area makes the groundwater the most preferred resource to assure population increased needs. In this research we are going to quantify the rate of groundwater recharge using new hybrid model tack in interest the annual rainfall and the average annual temperature and the geological characteristics of the area. This hybrid model was tested and calibrated using a chemical tracer method called Chloride mass balance method (CMB). This hybrid model is a combination between general hydrogeological model and a hydrological model. We have tested this model in an aquifer complex in the region of Djelfa (Algeria). Performance of this model was verified by five criteria [Nash, mean absolute error (MAE), Root mean square error (RMSE), the coefficient of determination and the arithmetic mean error (AME)]. These new approximations facilitate the groundwater management in semi-arid areas; this model is a perfection and amelioration of the model developed by Chibane et al. This model gives a very interesting result, with low uncertainty. A new recharge class diagram was established by our model to get rapidly and quickly the groundwater recharge value for any area in semi-arid region, using temperature and rainfall.

A water quality index model using stepwise regression and neural networks models for the Piabanha River basin in Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Villas Boas, M. D.; Olivera, F.; Azevedo, J. S.

2013-12-01

The evaluation of water quality through 'indexes' is widely used in environmental sciences. There are a number of methods available for calculating water quality indexes (WQI), usually based on site-specific parameters. In Brazil, WQI were initially used in the 1970s and were adapted from the methodology developed in association with the National Science Foundation (Brown et al, 1970). Specifically, the WQI 'IQA/SCQA', developed by the Institute of Water Management of Minas Gerais (IGAM), is estimated based on nine parameters: Temperature Range, Biochemical Oxygen Demand, Fecal Coliforms, Nitrate, Phosphate, Turbidity, Dissolved Oxygen, pH and Electrical Conductivity. The goal of this study was to develop a model for calculating the IQA/SCQA, for the Piabanha River basin in the State of Rio de Janeiro (Brazil), using only the parameters measurable by a Multiparameter Water Quality Sonde (MWQS) available in the study area. These parameters are: Dissolved Oxygen, pH and Electrical Conductivity. The use of this model will allow to further the water quality monitoring network in the basin, without requiring significant increases of resources. The water quality measurement with MWQS is less expensive than the laboratory analysis required for the other parameters. The water quality data used in the study were obtained by the Geological Survey of Brazil in partnership with other public institutions (i.e. universities and environmental institutes) as part of the project "Integrated Studies in Experimental and Representative Watersheds". Two models were developed to correlate the values of the three measured parameters and the IQA/SCQA values calculated based on all nine parameters. The results were evaluated according to the following validation statistics: coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike information criterion (AIC) and Final Prediction Error (FPE). The first model was a linear stepwise regression between three independent variables (input) and one dependent variable (output) to establish an equation relating input to output. This model produced the following statistics: R2 = 0.85, RMSE = 6.19, AIC =0.65 and FPE = 1.93. The second model was a Feedforward Neural Network with one tan-sigmoid hidden layer (4 neurons) and one linear output layer. The neural network was trained based on a backpropagation algorithm using the input as predictors and the output as target. The following statistics were found: R2 = 0.95, RMSE = 4.86, AIC= 0.33 and FPE = 1.39. The second model produced a better fit than the first one, having a greater R2 and smaller RMSE, AIC and FPE. The best performance of the second method can be attributed to the fact that the water quality parameters often exhibit nonlinear behaviors and neural networks are capable of representing nonlinear relationship efficiently, while the regression is limited to linear relationships. References: Brown, R.M., McLelland, N.I., Deininger, R.A., Tozer, R.G.1970. A Water Quality Index-Do we dare? Water & Sewage Works, October: 339-343.

Wireless Monitoring of Liver Hemodynamics In Vivo

DOE PAGES

Akl, Tony J.; Wilson, Mark A.; Ericson, M. Nance; ...

2014-07-14

Liver transplants have their highest failure rate in the first two weeks following surgery. There are no devices for continuous, real-time monitoring of the graft, currently. Here, we present a continuous perfusion and oxygen consumption monitor based on photoplethysmography. The sensor is battery operated and communicates wirelessly with a data acquisition computer which provides the possibility of implantation provided sufficient miniaturization. In two in vivo porcine studies, the sensor tracked perfusion changes in hepatic tissue during vascular occlusions with a root mean square error (RMSE) of 0.125 mL/min/g of tissue. We show the possibility of using the pulsatile wave tomore » measure the arterial oxygen saturation similar to pulse oximetry. This signal is used as a feedback to extract the venous oxygen saturation from the DC levels. Arterial and venous oxygen saturation changes were measured with an RMSE of 2.19 and 1.39% respectively when no vascular occlusions were induced. The resulting error increased to 2.82 and 3.83% when vascular occlusions were induced during hypoxia. These errors are similar to the resolution of the oximetry catheter used as a reference. This work is the first realization of a wireless perfusion and oxygenation sensor for continuous monitoring of hepatic perfusion and oxygenation changes.« less

Indoor-to-outdoor particle concentration ratio model for human exposure analysis

NASA Astrophysics Data System (ADS)

Lee, Jae Young; Ryu, Sung Hee; Lee, Gwangjae; Bae, Gwi-Nam

2016-02-01

This study presents an indoor-to-outdoor particle concentration ratio (IOR) model for improved estimates of indoor exposure levels. This model is useful in epidemiological studies with large population, because sampling indoor pollutants in all participants' house is often necessary but impractical. As a part of a study examining the association between air pollutants and atopic dermatitis in children, 16 parents agreed to measure the indoor and outdoor PM10 and PM2.5 concentrations at their homes for 48 h. Correlation analysis and multi-step multivariate linear regression analysis was performed to develop the IOR model. Temperature and floor level were found to be powerful predictors of the IOR. Despite the simplicity of the model, it demonstrated high accuracy in terms of the root mean square error (RMSE). Especially for long-term IOR estimations, the RMSE was as low as 0.064 and 0.063 for PM10 and PM2.5, respectively. When using a prediction model in an epidemiological study, understanding the consequence of the modeling error and justifying the use of the model is very important. In the last section, this paper discussed the impact of the modeling error and developed a novel methodology to justify the use of the model.

High‐resolution trench photomosaics from image‐based modeling: Workflow and error analysis

USGS Publications Warehouse

Reitman, Nadine G.; Bennett, Scott E. K.; Gold, Ryan D.; Briggs, Richard; Duross, Christopher

2015-01-01

Photomosaics are commonly used to construct maps of paleoseismic trench exposures, but the conventional process of manually using image‐editing software is time consuming and produces undesirable artifacts and distortions. Herein, we document and evaluate the application of image‐based modeling (IBM) for creating photomosaics and 3D models of paleoseismic trench exposures, illustrated with a case‐study trench across the Wasatch fault in Alpine, Utah. Our results include a structure‐from‐motion workflow for the semiautomated creation of seamless, high‐resolution photomosaics designed for rapid implementation in a field setting. Compared with conventional manual methods, the IBM photomosaic method provides a more accurate, continuous, and detailed record of paleoseismic trench exposures in approximately half the processing time and 15%–20% of the user input time. Our error analysis quantifies the effect of the number and spatial distribution of control points on model accuracy. For this case study, an ∼87  m2 exposure of a benched trench photographed at viewing distances of 1.5–7 m yields a model with <2  cm root mean square error (rmse) with as few as six control points. Rmse decreases as more control points are implemented, but the gains in accuracy are minimal beyond 12 control points. Spreading control points throughout the target area helps to minimize error. We propose that 3D digital models and corresponding photomosaics should be standard practice in paleoseismic exposure archiving. The error analysis serves as a guide for future investigations that seek balance between speed and accuracy during photomosaic and 3D model construction.

High variability of the subjective visual vertical test of vertical perception, in some people with neck pain - Should this be a standard measure of cervical proprioception?

PubMed

Treleaven, Julia; Takasaki, Hiroshi

2015-02-01

Subjective visual vertical (SVV) assesses visual dependence for spacial orientation, via vertical perception testing. Using the computerized rod-and-frame test (CRFT), SVV is thought to be an important measure of cervical proprioception and might be greater in those with whiplash associated disorder (WAD), but to date research findings are inconsistent. The aim of this study was to investigate the most sensitive SVV error measurement to detect group differences between no neck pain control, idiopathic neck pain (INP) and WAD subjects. Cross sectional study. Neck Disability Index (NDI), Dizziness Handicap Inventory short form (DHIsf) and the average constant error (CE), absolute error (AE), root mean square error (RMSE), and variable error (VE) of the SVV were obtained from 142 subjects (48 asymptomatic, 36 INP, 42 WAD). The INP group had significantly (p < 0.03) greater VE and RMSE when compared to both the control and WAD groups. There were no differences seen between the WAD and controls. The results demonstrated that people with INP (not WAD), had an altered strategy for maintaining the perception of vertical by increasing variability of performance. This may be due to the complexity of the task. Further, the SVV performance was not related to reported pain or dizziness handicap. These findings are inconsistent with other measures of cervical proprioception in neck pain and more research is required before the SVV can be considered an important measure and utilized clinically. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Development of a 6DOF robotic motion phantom for radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Belcher, Andrew H.; Liu, Xinmin; Grelewicz, Zachary

Purpose: The use of medical technology capable of tracking patient motion or positioning patients along 6 degree-of-freedom (6DOF) has steadily increased in the field of radiation therapy. However, due to the complex nature of tracking and performing 6DOF motion, it is critical that such technology is properly verified to be operating within specifications in order to ensure patient safety. In this study, a robotic motion phantom is presented that can be programmed to perform highly accurate motion along any X (left–right), Y (superior–inferior), Z (anterior–posterior), pitch (around X), roll (around Y), and yaw (around Z) axes. In addition, highly synchronizedmore » motion along all axes can be performed in order to simulate the dynamic motion of a tumor in 6D. The accuracy and reproducibility of this 6D motion were characterized. Methods: An in-house designed and built 6D robotic motion phantom was constructed following the Stewart–Gough parallel kinematics platform archetype. The device was controlled using an inverse kinematics formulation, and precise movements in all 6 degrees-of-freedom (X, Y, Z, pitch, roll, and yaw) were performed, both simultaneously and separately for each degree-of-freedom. Additionally, previously recorded 6D cranial and prostate motions were effectively executed. The robotic phantom movements were verified using a 15 fps 6D infrared marker tracking system and the measured trajectories were compared quantitatively to the intended input trajectories. The workspace, maximum 6D velocity, backlash, and weight load capabilities of the system were also established. Results: Evaluation of the 6D platform demonstrated translational root mean square error (RMSE) values of 0.14, 0.22, and 0.08 mm over 20 mm in X and Y and 10 mm in Z, respectively, and rotational RMSE values of 0.16°, 0.06°, and 0.08° over 10° of pitch, roll, and yaw, respectively. The robotic stage also effectively performed controlled 6D motions, as well as reproduced cranial trajectories over 15 min, with a maximal RMSE of 0.04 mm translationally and 0.04° rotationally, and a prostate trajectory over 2 min, with a maximal RMSE of 0.06 mm translationally and 0.04° rotationally. Conclusions: This 6D robotic phantom has proven to be accurate under clinical standards and capable of reproducing tumor motion in 6D. Such functionality makes the robotic phantom usable for either quality assurance or research purposes.« less

Influence of stimulated Brillouin scattering on positioning accuracy of long-range dual Mach-Zehnder interferometric vibration sensors

NASA Astrophysics Data System (ADS)

He, Xiangge; Xie, Shangran; Cao, Shan; Liu, Fei; Zheng, Xiaoping; Zhang, Min; Yan, Han; Chen, Guocai

2016-11-01

The properties of noise induced by stimulated Brillouin scattering (SBS) in long-range interferometers and their influences on the positioning accuracy of dual Mach-Zehnder interferometric (DMZI) vibration sensing systems are studied. The SBS noise is found to be white and incoherent between the two arms of the interferometer in a 1-MHz bandwidth range. Experiments on 25-km long fibers show that the root mean square error (RMSE) of the positioning accuracy is consistent with the additive noise model for the time delay estimation theory. A low-pass filter can be properly designed to suppress the SBS noise and further achieve a maximum RMSE reduction of 6.7 dB.

Monitoring Building Deformation with InSAR: Experiments and Validation

PubMed Central

Yang, Kui; Yan, Li; Huang, Guoman; Chen, Chu; Wu, Zhengpeng

2016-01-01

Synthetic Aperture Radar Interferometry (InSAR) techniques are increasingly applied for monitoring land subsidence. The advantages of InSAR include high accuracy and the ability to cover large areas; nevertheless, research validating the use of InSAR on building deformation is limited. In this paper, we test the monitoring capability of the InSAR in experiments using two landmark buildings; the Bohai Building and the China Theater, located in Tianjin, China. They were selected as real examples to compare InSAR and leveling approaches for building deformation. Ten TerraSAR-X images spanning half a year were used in Permanent Scatterer InSAR processing. These extracted InSAR results were processed considering the diversity in both direction and spatial distribution, and were compared with true leveling values in both Ordinary Least Squares (OLS) regression and measurement of error analyses. The detailed experimental results for the Bohai Building and the China Theater showed a high correlation between InSAR results and the leveling values. At the same time, the two Root Mean Square Error (RMSE) indexes had values of approximately 1 mm. These analyses show that a millimeter level of accuracy can be achieved by means of InSAR technique when measuring building deformation. We discuss the differences in accuracy between OLS regression and measurement of error analyses, and compare the accuracy index of leveling in order to propose InSAR accuracy levels appropriate for monitoring buildings deformation. After assessing the advantages and limitations of InSAR techniques in monitoring buildings, further applications are evaluated. PMID:27999403

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

Development of a Direct Spectrophotometric and Chemometric Method for Determining Food Dye Concentrations.

PubMed

Arroz, Erin; Jordan, Michael; Dumancas, Gerard G

2017-07-01

An ultraviolet visible (UV-Vis) spectrophotometric and partial least squares (PLS) chemometric method was developed for the simultaneous determination of erythrosine B (red), Brilliant Blue, and tartrazine (yellow) dyes. A training set (n = 64) was generated using a full factorial design and its accuracy was tested in a test set (n = 13) using a Box-Behnken design. The test set garnered a root mean square error (RMSE) of 1.79 × 10 -7 for blue, 4.59 × 10 -7 for red, and 1.13 × 10 -6 for yellow dyes. The relatively small RMSE suggests only a small difference between predicted versus measured concentrations, demonstrating the accuracy of our model. The relative error of prediction (REP) for the test set were 11.73%, 19.52%, 19.38%, for blue, red, and yellow dyes, respectively. A comparable overlay between the actual candy samples and their replicated synthetic spectra were also obtained indicating the model as a potentially accurate method for determining concentrations of dyes in food samples.

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression.

PubMed

Hao, Z Q; Li, C M; Shen, M; Yang, X Y; Li, K H; Guo, L B; Li, X Y; Lu, Y F; Zeng, X Y

2015-03-23

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

NASA Astrophysics Data System (ADS)

Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

2018-04-01

Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

Accuracy Test of the OPLS-AA Force Field for Calculating Free Energies of Mixing and Comparison with PAC-MAC

PubMed Central

2017-01-01

We have calculated the excess free energy of mixing of 1053 binary mixtures with the OPLS-AA force field using two different methods: thermodynamic integration (TI) of molecular dynamics simulations and the Pair Configuration to Molecular Activity Coefficient (PAC-MAC) method. PAC-MAC is a force field based quasi-chemical method for predicting miscibility properties of various binary mixtures. The TI calculations yield a root mean squared error (RMSE) compared to experimental data of 0.132 kBT (0.37 kJ/mol). PAC-MAC shows a RMSE of 0.151 kBT with a calculation speed being potentially 1.0 × 104 times greater than TI. OPLS-AA force field parameters are optimized using PAC-MAC based on vapor–liquid equilibrium data, instead of enthalpies of vaporization or densities. The RMSE of PAC-MAC is reduced to 0.099 kBT by optimizing 50 force field parameters. The resulting OPLS-PM force field has a comparable accuracy as the OPLS-AA force field in the calculation of mixing free energies using TI. PMID:28418655

New software tools for enhanced precision in robot-assisted laser phonomicrosurgery.

PubMed

Dagnino, Giulio; Mattos, Leonardo S; Caldwell, Darwin G

2012-01-01

This paper describes a new software package created to enhance precision during robot-assisted laser phonomicrosurgery procedures. The new software is composed of three tools for camera calibration, automatic tumor segmentation, and laser tracking. These were designed and developed to improve the outcome of this demanding microsurgical technique, and were tested herein to produce quantitative performance data. The experimental setup was based on the motorized laser micromanipulator created by Istituto Italiano di Tecnologia and the experimental protocols followed are fully described in this paper. The results show the new tools are robust and effective: The camera calibration tool reduced residual errors (RMSE) to 0.009 ± 0.002 mm under 40× microscope magnification; the automatic tumor segmentation tool resulted in deep lesion segmentations comparable to manual segmentations (RMSE= 0.160 ± 0.028 mm under 40× magnification); and the laser tracker tool proved to be reliable even during cutting procedures (RMSE= 0.073 ± 0.023 mm under 40× magnification). These results demonstrate the new software package can provide excellent improvements to the previous microsurgical system, leading to important enhancements in surgical outcome.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

PubMed

Keshavarz, M; Mojra, A

2015-05-01

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest

NASA Astrophysics Data System (ADS)

Ali, Abebe Mohammed; Darvishzadeh, Roshanak; Skidmore, Andrew K.; Duren, Iris van; Heiden, Uta; Heurich, Marco

2016-03-01

Assessments of ecosystem functioning rely heavily on quantification of vegetation properties. The search is on for methods that produce reliable and accurate baseline information on plant functional traits. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate two functional leaf traits: leaf dry matter content (LDMC) and specific leaf area (SLA). Inversion of PROSPECT usually aims at quantifying its direct input parameters. This is the first time the technique has been used to indirectly model LDMC and SLA. Biophysical parameters of 137 leaf samples were measured in July 2013 in the Bavarian Forest National Park, Germany. Spectra of the leaf samples were measured using an ASD FieldSpec3 equipped with an integrating sphere. PROSPECT was inverted using a look-up table (LUT) approach. The LUTs were generated with and without using prior information. The effect of incorporating prior information on the retrieval accuracy was studied before and after stratifying the samples into broadleaf and conifer categories. The estimated values were evaluated using R2 and normalized root mean square error (nRMSE). Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered in the pooled samples. The use of prior information improved accuracy of the retrieved traits. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy level by using remotely sensed data.

Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

Assessment of the WRF-ARW model during fog conditions in a coastal arid region using different PBL schemes

NASA Astrophysics Data System (ADS)

Temimi, Marouane; Chaouch, Naira; Weston, Michael; Ghedira, Hosni

2017-04-01

This study covers five fog events reported in 2014 at Abu Dhabi International Airport in the United Arab Emirates (UAE). We assess the performance of WRF-ARW model during fog conditions and we intercompare seven different PBL schemes and assess their impact on the performance of the simulations. Seven PBL schemes, namely, Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Moller-Yamada Nakanishi and Niino (MYNN) level 2.5, Quasi-Normal Scale Elimination (QNSE-EDMF), Asymmetric Convective Model (ACM2), Grenier-Bretherton-McCaa (GBM) and MYNN level 3 were tested. Radiosonde data from the Abu Dhabi International Airport and surface measurements of relative humidity (RH), dew point temperature, wind speed, and temperature profiles were used to assess the performance of the model. All PBL schemes showed comparable skills with relatively higher performance with the QNSE scheme. The average RH Root Mean Square Error (RMSE) and BIAS for all PBLs were 15.75 % and -9.07 %, respectively, whereas the obtained RMSE and BIAS when QNSE was used were 14.65 % and -6.3 % respectively. Comparable skills were obtained for the rest of the variables. Local PBL schemes showed better performance than non-local schemes. Discrepancies between simulated and observed values were higher at the surface level compared to high altitude values. The sensitivity to lead time showed that best simulation performances were obtained when the lead time varies between 12 and 18 hours. In addition, the results of the simulations show that better performance is obtained when the starting condition is dry.

Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

NASA Astrophysics Data System (ADS)

Khaki, M.; Schumacher, M.; Forootan, E.; Kuhn, M.; Awange, J. L.; van Dijk, A. I. J. M.

2017-10-01

Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS estimates for improved data assimilation results.

An image registration-based technique for noninvasive vascular elastography

NASA Astrophysics Data System (ADS)

Valizadeh, Sina; Makkiabadi, Bahador; Mirbagheri, Alireza; Soozande, Mehdi; Manwar, Rayyan; Mozaffarzadeh, Moein; Nasiriavanaki, Mohammadreza

2018-02-01

Non-invasive vascular elastography is an emerging technique in vascular tissue imaging. During the past decades, several techniques have been suggested to estimate the tissue elasticity by measuring the displacement of the Carotid vessel wall. Cross correlation-based methods are the most prevalent approaches to measure the strain exerted in the wall vessel by the blood pressure. In the case of a low pressure, the displacement is too small to be apparent in ultrasound imaging, especially in the regions far from the center of the vessel, causing a high error of displacement measurement. On the other hand, increasing the compression leads to a relatively large displacement in the regions near the center, which reduces the performance of the cross correlation-based methods. In this study, a non-rigid image registration-based technique is proposed to measure the tissue displacement for a relatively large compression. The results show that the error of the displacement measurement obtained by the proposed method is reduced by increasing the amount of compression while the error of the cross correlationbased method rises for a relatively large compression. We also used the synthetic aperture imaging method, benefiting the directivity diagram, to improve the image quality, especially in the superficial regions. The best relative root-mean-square error (RMSE) of the proposed method and the adaptive cross correlation method were 4.5% and 6%, respectively. Consequently, the proposed algorithm outperforms the conventional method and reduces the relative RMSE by 25%.

Comparison of Low Cost Photogrammetric Survey with Tls and Leica Pegasus Backpack 3d Modelss

NASA Astrophysics Data System (ADS)

Masiero, A.; Fissore, F.; Guarnieri, A.; Piragnolo, M.; Vettore, A.

2017-11-01

This paper considers Leica backpack and photogrammetric surveys of a mediaeval bastion in Padua, Italy. Furhtermore, terrestrial laser scanning (TLS) survey is considered in order to provide a state of the art reconstruction of the bastion. Despite control points are typically used to avoid deformations in photogrammetric surveys and ensure correct scaling of the reconstruction, in this paper a different approach is considered: this work is part of a project aiming at the development of a system exploiting ultra-wide band (UWB) devices to provide correct scaling of the reconstruction. In particular, low cost Pozyx UWB devices are used to estimate camera positions during image acquisitions. Then, in order to obtain a metric reconstruction, scale factor in the photogrammetric survey is estimated by comparing camera positions obtained from UWB measurements with those obtained from photogrammetric reconstruction. Compared with the TLS survey, the considered photogrammetric model of the bastion results in a RMSE of 21.9cm, average error 13.4cm, and standard deviation 13.5cm. Excluding the final part of the bastion left wing, where the presence of several poles make reconstruction more difficult, (RMSE) fitting error is 17.3cm, average error 11.5cm, and standard deviation 9.5cm. Instead, comparison of Leica backpack and TLS surveys leads to an average error of 4.7cm and standard deviation 0.6cm (4.2cm and 0.3cm, respectively, by excluding the final part of the left wing).

Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion

NASA Astrophysics Data System (ADS)

Fu, Shichen; Li, Yiming; Zhang, Minjun; Zong, Kai; Cheng, Long; Wu, Miao

2018-01-01

To realize unmanned pose detection of a coalmine boom-type roadheader, an ultra-wideband (UWB) pose detection system (UPDS) for a roadheader is designed, which consists of four UWB positioning base stations and three roadheader positioning nodes. The positioning base stations are used in turn to locate the positioning nodes of the roadheader fuselage. Using 12 sets of distance measurement information, a time-of-arrival (TOA) positioning model is established to calculate the 3D coordinates of three positioning nodes of the roadheader fuselage, and the three attitude angles (heading, pitch, and roll angles) of the roadheader fuselage are solved. A range accuracy experiment of a UWB P440 module was carried out in a narrow and closed tunnel, and the experiment data show that the mean error and standard deviation of the module can reach below 2 cm. Based on the TOA positioning model of the UPDS, we propose a fusion-positioning algorithm based on a Caffery transform and Taylor series expansion (CTFPA). We derived the complete calculation process, designed a flowchart, and carried out a simulation of CTFPA in MATLAB, comparing 1000 simulated positioning nodes of CTFPA and the Caffery positioning algorithm (CPA) for a 95 m long tunnel. The positioning error field of the tunnel was established, and the influence of the spatial variation on the positioning accuracy of CPA and CTFPA was analysed. The simulation results show that, compared with CPA, the positioning accuracy of CTFPA is clearly improved, and the accuracy of each axis can reach more than 5 mm. The accuracy of the X-axis is higher than that of the Y- and Z-axes. In section X-Y of the tunnel, the root mean square error (RMSE) contours of CTFPA are clear and orderly, and with an increase in the measuring distance, RMSE increases linearly. In section X-Z, the RMSE contours are concentric circles, and the variation ratio is nonlinear.

New Model for Estimating Glomerular Filtration Rate in Patients With Cancer

PubMed Central

Janowitz, Tobias; Williams, Edward H.; Marshall, Andrea; Ainsworth, Nicola; Thomas, Peter B.; Sammut, Stephen J.; Shepherd, Scott; White, Jeff; Mark, Patrick B.; Lynch, Andy G.; Jodrell, Duncan I.; Tavaré, Simon; Earl, Helena

2017-01-01

Purpose The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing; however, the best method to estimate GFR in patients with cancer is unknown. We identify the most accurate and least biased method. Methods We obtained data on age, sex, height, weight, serum creatinine concentrations, and results for GFR from chromium-51 (51Cr) EDTA excretion measurements (51Cr-EDTA GFR) from white patients ≥ 18 years of age with histologically confirmed cancer diagnoses at the Cambridge University Hospital NHS Trust, United Kingdom. We developed a new multivariable linear model for GFR using statistical regression analysis. 51Cr-EDTA GFR was compared with the estimated GFR (eGFR) from seven published models and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an internal and external validation data set. We performed a comparison of carboplatin dosing accuracy on the basis of an absolute percentage error > 20%. Results Between August 2006 and January 2013, data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE, 15.00 mL/min; 95% CI, 14.12 to 16.00 mL/min) compared with all published models. Body surface area (BSA)–adjusted chronic kidney disease epidemiology (CKD-EPI) was the most accurate published model for eGFR (RMSE, 16.30 mL/min; 95% CI, 15.34 to 17.38 mL/min) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose absolute percentage error > 20% to 14.17% in contrast to 18.62% for the BSA-adjusted CKD-EPI and 25.51% for the Cockcroft-Gault formula. The results were externally validated. Conclusion In a large data set from patients with cancer, BSA-adjusted CKD-EPI is the most accurate published model to predict GFR. The new model improves this estimation and may present a new standard of care. PMID:28686534

New Model for Estimating Glomerular Filtration Rate in Patients With Cancer.

PubMed

Janowitz, Tobias; Williams, Edward H; Marshall, Andrea; Ainsworth, Nicola; Thomas, Peter B; Sammut, Stephen J; Shepherd, Scott; White, Jeff; Mark, Patrick B; Lynch, Andy G; Jodrell, Duncan I; Tavaré, Simon; Earl, Helena

2017-08-20

Purpose The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing; however, the best method to estimate GFR in patients with cancer is unknown. We identify the most accurate and least biased method. Methods We obtained data on age, sex, height, weight, serum creatinine concentrations, and results for GFR from chromium-51 ( 51 Cr) EDTA excretion measurements ( 51 Cr-EDTA GFR) from white patients ≥ 18 years of age with histologically confirmed cancer diagnoses at the Cambridge University Hospital NHS Trust, United Kingdom. We developed a new multivariable linear model for GFR using statistical regression analysis. 51 Cr-EDTA GFR was compared with the estimated GFR (eGFR) from seven published models and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an internal and external validation data set. We performed a comparison of carboplatin dosing accuracy on the basis of an absolute percentage error > 20%. Results Between August 2006 and January 2013, data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE, 15.00 mL/min; 95% CI, 14.12 to 16.00 mL/min) compared with all published models. Body surface area (BSA)-adjusted chronic kidney disease epidemiology (CKD-EPI) was the most accurate published model for eGFR (RMSE, 16.30 mL/min; 95% CI, 15.34 to 17.38 mL/min) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose absolute percentage error > 20% to 14.17% in contrast to 18.62% for the BSA-adjusted CKD-EPI and 25.51% for the Cockcroft-Gault formula. The results were externally validated. Conclusion In a large data set from patients with cancer, BSA-adjusted CKD-EPI is the most accurate published model to predict GFR. The new model improves this estimation and may present a new standard of care.

Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches

PubMed Central

Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael

2014-01-01

Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566 PMID:24879650

A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks.

PubMed

Yang, Eunjoo; Park, Hyun Woo; Choi, Yeon Hwa; Kim, Jusim; Munkhdalai, Lkhagvadorj; Musa, Ibrahim; Ryu, Keun Ho

2018-05-11

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt⁻Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

An application of ensemble/multi model approach for wind power production forecast.

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

2010-09-01

The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

Solid waste forecasting using modified ANFIS modeling.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; K N A, Maulud

2015-10-01

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98. To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

Automatic Sub-Pixel Co-Registration of LandSat-8 OLI and Sentinel-2A MSI Images Using Phase Correlation and Machine Learning Based Mapping

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Roger, Jean-Claude; Vermote, Eric F.; Masek, Jeffrey G.; Justice, Christopher O.

2017-01-01

This study investigates misregistration issues between Landsat-8/OLI and Sentinel-2A/MSI at 30 m resolution, and between multi-temporal Sentinel-2A images at 10 m resolution using a phase correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear Random Forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07+/-0.02 pixels at 30 m resolution, and 0.09+/-0.05 and 0.15+/-0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08+/-0.02 pixels at 30 m resolution and 0.12+/-0.06 (same Sentinel-2A orbits) and 0.20+/-0.09 (adjacent orbits) pixels at 10 m resolution.

Comparison and evaluation of satellite- and reanalysis-based precipitation products for water resources management in the Brahmaputra River basin

NASA Astrophysics Data System (ADS)

Saleh Khan, Abu; Sohel Masud, Md.; Abdulla Hel Kafi, Md.; Sultana, Tashrifa; Lopez Lopez, Patricia

2017-04-01

The Brahmaputra River, with a transboundary basin area of approx. 554,500 km2, has its origin on the northern slope of the Himalayas in China, from where it flows through India, Bhutan and finally Bangladesh. Brahmaputra basin's climatology is heavily conditioned by precipitation during the monsoon months, concentrating about the 85 % of the rainfall in this period and originating severe and frequent floods that impact specially the Bangladeshi population in the delta region. Recent campaigns to increase the quality and to share ground-based hydro-meteorological data, in particular precipitation, within the basin have provided limited results. Global rainfall data from satellite and reanalysis may improve the temporal and spatial availability of in-situ observations for advanced water resources management. This study aims to evaluate the applicability of several global precipitation products from satellite and reanalysis in comparison with in-situ data to quantify their added value for hydrological modeling at a basin and sub-basin scale for the Brahmaputra River. Precipitation products from CMORPH, TRMM-3B42, GsMAP, WFDEI, MSWEP and various combinations with ground-based data were evaluated at basin and sub-basin level at a daily and monthly temporal resolution. The Brahmaputra was delineated into 54 sub-basins for a more detailed evaluation of the precipitation products. The data were analysed and inter-compared for the time period from 2002 to 2010. Precipitation performance assessment was conducted including several indicators, such as probability of detection (POD), false alarm ratio (FAR), Pearson's correlation coefficient (r), bias and root mean square error (RMSE). Preliminary results indicate high correlation and low bias and RMSE values between WFDEI, TRMM-3B42 and CMORPH precipitation and in-situ observations at a monthly time scale. Lower correlations and higher bias and RMSE values were found between GsMAP and MSWEP and ground-observed precipitation. The best performance was achieved with TRMM-3B42 precipitation. Preliminary results also show that precipitation is better captured during monsoon season rather than in dry seasons with all the analysed precipitation products. Moreover, in the comparison at a sub-basin level, precipitation estimates are more accurate in those sub-basins located in the southern part of the Brahmaputra River basin. These results identify the added value of satellite-based and reanalysis derived precipitation products for improving available information and water resources management in the Brahmaputra River basin. Keywords: precipitation, earth observations, hydrological modeling, Brahmaputra River basin.

Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment

NASA Astrophysics Data System (ADS)

Lashkari, A.; Salehnia, N.; Asadi, S.; Paymard, P.; Zare, H.; Bannayan, M.

2018-05-01

The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000-2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAImax were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r 2 ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.

A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

NASA Astrophysics Data System (ADS)

Chen, Pengfei; Jing, Qi

2017-02-01

An assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLI images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R2, RMSE and RMSE% values of 0.61, 979 kg ha-1, and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R2, RMSE, and RMSE% values of 0.39, 1211 kg ha-1, and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction.

Characterizing Resident Space Object Earthshine Signature Variability

NASA Astrophysics Data System (ADS)

Van Cor, Jared D.

There are three major sources of illumination on objects in the near Earth space environment: Sunshine, Moonshine, and Earthshine. For objects in this environment (satellites, orbital debris, etc.) known as Resident Space Objects (RSOs), the sun and the moon have consistently small illuminating solid angles and can be treated as point sources; this makes their incident illumination easily modeled. The Earth on the other hand has a large illuminating solid angle, is heterogeneous, and is in a constant state of change. The objective of this thesis was to characterize the impact and variability of observed RSO Earthshine on apparent magnitude signatures in the visible optical spectral region. A key component of this research was creating Earth object models incorporating the reflectance properties of the Earth. Two Earth objects were created: a homogeneous diffuse Earth object and a time sensitive heterogeneous Earth object. The homogeneous diffuse Earth object has a reflectance equal to the average global albedo, a standard model used when modeling Earthshine. The time sensitive heterogeneous Earth object was created with two material maps representative of the dynamic reflectance of the surface of the earth, and a shell representative of the atmosphere. NASA's Moderate-resolution Imaging Spectroradiometer (MODIS) Earth observing satellite product libraries, MCD43C1 global surface BRDF map and MOD06 global fractional cloud map, were utilized to create the material maps, and a hybridized version of the Empirical Line Method (ELM) was used to create the atmosphere. This dynamic Earth object was validated by comparing simulated color imagery of the Earth to that taken by: NASAs Earth Polychromatic Imaging Camera (EPIC) located on the Deep Space Climate Observatory (DSCOVR), and by MODIS located on the Terra satellite. The time sensitive heterogeneous Earth object deviated from MODIS imagery by a spectral radiance root mean square error (RMSE) of +/-14.86 [watts/m. 2sr?m]over a sample of ROIs. Further analysis using EPIC imagery found a total albedo difference of +0.03% and a cross correlation of 0.656. Also compared to EPIC imagery it was found our heterogeneous Earth model produced a reflected Earthshine radiance RMSE of +/-28 [watts/m. 2sr?m] incident on diffuse sphericalRSOs, specular spherical RSOs, and diffuse flat plate RSOs with an altitude of 1000km; this resulted in an apparent magnitude error of +/-0.28. Furthermore, it was found our heterogeneous Earthmodel produced a reflected Earthshine radiance RMSE of +/-68 [watts/m. 2sr?m] for specular flat plate RSOs withan altitude of 1000km; this resulted in an apparent magnitude error of +/-0.68. The Earth objects were used in a workflow with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool to explore the impact of a range of characteristic RSO geometries, geographies, orientations, and materials on the signatures from an RSO due to Earthshine. An apparent magnitude was calculated and used to quantify RSO Earthshine signature variability; this is discussed in terms of the RMSE and maximum deviations of visible RSO Earthshine apparent magnitude signatures comparing the homogeneous Earth model to heterogeneous Earth model. The homogeneous diffuse Earth object was shown to approximate visible RSO Earthshine apparent magnitude signatures from spheres with a RMSE in reflected Earthshine apparent magnitude of +/-0.4 and a maximum apparent magnitude difference of 1.09 when compared to the heterogeneous Earth model. Similarly for diffuse flat plates, the visible RSO Earthshine apparent magnitude signature RMSE was shown to be +/-0.64, with a maximum apparent magnitude difference of 0.82. For specular flat plates, the visible RSO Earthshine apparent magnitude signature RMSE was shown to be +/-0.97 with maximum apparent magnitude difference of 2.26. This thesis explored only a portion of the parameter dependencies of Earth shine, but has enabled a preliminary understanding of visible RSO Earthshine signature variability and its geometric dependence. This research has demonstrated the impact of Earth heterogeneity on the observed apparent magnitude signatures of RSOs illuminated by Earthshine and the potential for error that comes with approximating the Earth as a diffuse homogeneous object.

Implications of drying temperature and humidity on the drying kinetics of seaweed

NASA Astrophysics Data System (ADS)

Ali, Majid Khan Majahar; Fudholi, Ahmad; Muthuvalu, M. S.; Sulaiman, Jumat; Yasir, Suhaimi Md

2017-11-01

A Low Temperature and Humidity Chamber Test tested in the Solar Energy Laboratory, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia. Experiments are attempted to study the effect of drying air temperature and humidity on the drying kinetics of seaweed Kappaphycus species Striatum besides to develop a model to estimate the drying curves. Simple method using a excel software is used in the analysis of raw data obtained from the drying experiment. The values of the parameters a, n and the constant k for the models are determined using a plot of curve drying models. Three different drying models are compared with experiment data seaweed drying at 30, 40, 50 and 60°C and relative humidity 20, 30 and 40% for seaweed. The higher drying temperatures and low relative humidity effects the moisture content that will be rapidly reduced. The most suitable model is selected to best describe the drying behavior of seaweed. The values of the coefficient of determination (R2), mean bias error (MBE) and root mean square error (RMSE) are used to determine the goodness or the quality of the fit. The Page model is showed a better fit to drying seaweed. The results from this study crucial for solar dryer development on pilot scale in Malaysia.

The Consequences of Ignoring Item Parameter Drift in Longitudinal Item Response Models

ERIC Educational Resources Information Center

Lee, Wooyeol; Cho, Sun-Joo

2017-01-01

Utilizing a longitudinal item response model, this study investigated the effect of item parameter drift (IPD) on item parameters and person scores via a Monte Carlo study. Item parameter recovery was investigated for various IPD patterns in terms of bias and root mean-square error (RMSE), and percentage of time the 95% confidence interval covered…

One way coupling of CMAQ and a road source dispersion model for fine scale air pollution predictions

PubMed Central

Beevers, Sean D.; Kitwiroon, Nutthida; Williams, Martin L.; Carslaw, David C.

2012-01-01

In this paper we have coupled the CMAQ and ADMS air quality models to predict hourly concentrations of NOX, NO2 and O3 for London at a spatial scale of 20 m × 20 m. Model evaluation has demonstrated reasonable agreement with measurements from 80 monitoring sites in London. For NO2 the model evaluation statistics gave 73% of the hourly concentrations within a factor of two of observations, a mean bias of −4.7 ppb and normalised mean bias of −0.17, a RMSE value of 17.7 and an r value of 0.58. The equivalent results for O3 were 61% (FAC2), 2.8 ppb (MB), 0.15 (NMB), 12.1 (RMSE) and 0.64 (r). Analysis of the errors in the model predictions by hour of the week showed the need for improvements in predicting the magnitude of road transport related NOX emissions as well as the hourly emissions scaling in the model. These findings are consistent with recent evidence of UK road transport NOX emissions, reported elsewhere. The predictions of wind speed using the WRF model also influenced the model results and contributed to the daytime over prediction of NOX concentrations at the central London background site at Kensington and Chelsea. An investigation of the use of a simple NO–NO2–O3 chemistry scheme showed good performance close to road sources, and this is also consistent with previous studies. The coupling of the two models raises an issue of emissions double counting. Here, we have put forward a pragmatic solution to this problem with the result that a median double counting error of 0.42% exists across 39 roadside sites in London. Finally, whilst the model can be improved, the current results show promise and demonstrate that the use of a combination of regional scale and local scale models can provide a practical modelling tool for policy development at intergovernmental, national and local authority level, as well as for use in epidemiological studies. PMID:23471172

Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran

NASA Astrophysics Data System (ADS)

Ghorbani, M. A.; Deo, Ravinesh C.; Yaseen, Zaher Mundher; H. Kashani, Mahsa; Mohammadi, Babak

2017-08-01

An accurate computational approach for the prediction of pan evaporation over daily time horizons is a useful decisive tool in sustainable agriculture and hydrological applications, particularly in designing the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this study, a hybrid predictive model (Multilayer Perceptron-Firefly Algorithm (MLP-FFA)) based on the FFA optimizer that is embedded within the MLP technique is developed and evaluated for its suitability for the prediction of daily pan evaporation. To develop the hybrid MLP-FFA model, the pan evaporation data measured between 2012 and 2014 for two major meteorological stations (Talesh and Manjil) located at Northern Iran are employed to train and test the predictive model. The ability of the hybrid MLP-FFA model is compared with the traditional MLP and support vector machine (SVM) models. The results are evaluated using five performance criteria metrics: root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NS), and the Willmott's Index (WI). Taylor diagrams are also used to examine the similarity between the observed and predicted pan evaporation data in the test period. Results show that an optimal MLP-FFA model outperforms the MLP and SVM model for both tested stations. For Talesh, a value of WI = 0.926, NS = 0.791, and RMSE = 1.007 mm day-1 is obtained using MLP-FFA model, compared with 0.912, 0.713, and 1.181 mm day-1 (MLP) and 0.916, 0.726, and 1.153 mm day-1 (SVM), whereas for Manjil, a value of WI = 0.976, NS = 0.922, and 1.406 mm day-1 is attained that contrasts 0.972, 0.901, and 1.583 mm day-1 (MLP) and 0.971, 0.893, and 1.646 mm day-1 (SVM). The results demonstrate the importance of the Firefly Algorithm applied to improve the performance of the MLP-FFA model, as verified through its better predictive performance compared to the MLP and SVM model.

Dynamic predictive model for growth of Salmonella spp. in scrambled egg mix.

PubMed

Li, Lin; Cepeda, Jihan; Subbiah, Jeyamkondan; Froning, Glenn; Juneja, Vijay K; Thippareddi, Harshavardhan

2017-06-01

Liquid egg products can be contaminated with Salmonella spp. during processing. A dynamic model for the growth of Salmonella spp. in scrambled egg mix - high solids (SEM) was developed and validated. SEM was prepared and inoculated with ca. 2 log CFU/mL of a five serovar Salmonella spp. cocktail. Salmonella spp. growth data at isothermal temperatures (10, 15, 20, 25, 30, 35, 37, 39, 41, 43, 45, and 47 °C) in SEM were collected. Baranyi model was used (primary model) to fit growth data and the maximum growth rate and lag phase duration for each temperature were determined. A secondary model was developed with maximum growth rate as a function of temperature. The model performance measures, root mean squared error (RMSE, 0.09) and pseudo-R 2 (1.00) indicated good fit for both primary and secondary models. A dynamic model was developed by integrating the primary and secondary models and validated using two sinusoidal temperature profiles, 5-15 °C (low temperature) for 480 h and 10-40 °C (high temperature) for 48 h. The RMSE values for the sinusoidal low and high temperature profiles were 0.47 and 0.42 log CFU/mL, respectively. The model can be used to predict Salmonella spp. growth in case of temperature abuse during liquid egg processing. Copyright © 2016. Published by Elsevier Ltd.

Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

NASA Astrophysics Data System (ADS)

Mansuy, N. R.; Paré, D.; Thiffault, E.

2015-12-01

Large-scale mapping of soil properties is increasingly important for environmental resource management. Whileforested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.The objective of this study was to generate continuous national maps of selected soil variables (C, N andsoil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using thekNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climaticvariables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. Therelative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave valuesranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered agood imputation in light of the low density of points used in this study. The study demonstrates strong capabilitiesfor mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of CanadaSystem, which is largely oriented towards the agricultural landbase. The methodology used here can potentiallycontribute to the national and international need for spatially explicit soil information in resource managementscience.

Velocity navigator for motion compensated thermometry.

PubMed

Maier, Florian; Krafft, Axel J; Yung, Joshua P; Stafford, R Jason; Elliott, Andrew; Dillmann, Rüdiger; Semmler, Wolfhard; Bock, Michael

2012-02-01

Proton resonance frequency shift thermometry is sensitive to breathing motion that leads to incorrect phase differences. In this work, a novel velocity-sensitive navigator technique for triggering MR thermometry image acquisition is presented. A segmented echo planar imaging pulse sequence was modified for velocity-triggered temperature mapping. Trigger events were generated when the estimated velocity value was less than 0.2 cm/s during the slowdown phase in parallel to the velocity-encoding direction. To remove remaining high-frequency spikes from pulsation in real time, a Kalman filter was applied to the velocity navigator data. A phantom experiment with heating and an initial volunteer experiment without heating were performed to show the applicability of this technique. Additionally, a breath-hold experiment was conducted for comparison. A temperature rise of ΔT = +37.3°C was seen in the phantom experiment, and a root mean square error (RMSE) outside the heated region of 2.3°C could be obtained for periodic motion. In the volunteer experiment, a RMSE of 2.7°C/2.9°C (triggered vs. breath hold) was measured. A novel velocity navigator with Kalman filter postprocessing in real time significantly improves the temperature accuracy over non-triggered acquisitions and suggests being comparable to a breath-held acquisition. The proposed technique might be clinically applied for monitoring of thermal ablations in abdominal organs.

Estimating groundwater levels using system identification models in Nzhelele and Luvuvhu areas, Limpopo Province, South Africa

NASA Astrophysics Data System (ADS)

Makungo, Rachel; Odiyo, John O.

2017-08-01

This study was focused on testing the ability of a coupled linear and non-linear system identification model in estimating groundwater levels. System identification provides an alternative approach for estimating groundwater levels in areas that lack data required by physically-based models. It also overcomes the limitations of physically-based models due to approximations, assumptions and simplifications. Daily groundwater levels for 4 boreholes, rainfall and evaporation data covering the period 2005-2014 were used in the study. Seventy and thirty percent of the data were used to calibrate and validate the model, respectively. Correlation coefficient (R), coefficient of determination (R2), root mean square error (RMSE), percent bias (PBIAS), Nash Sutcliffe coefficient of efficiency (NSE) and graphical fits were used to evaluate the model performance. Values for R, R2, RMSE, PBIAS and NSE ranged from 0.8 to 0.99, 0.63 to 0.99, 0.01-2.06 m, -7.18 to 1.16 and 0.68 to 0.99, respectively. Comparisons of observed and simulated groundwater levels for calibration and validation runs showed close agreements. The model performance mostly varied from satisfactory, good, very good and excellent. Thus, the model is able to estimate groundwater levels. The calibrated models can reasonably capture description between input and output variables and can, thus be used to estimate long term groundwater levels.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model.

PubMed

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-04-06

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R²) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R² of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods.

Model validation and error estimation of tsunami runup using high resolution data in Sadeng Port, Gunungkidul, Yogyakarta

NASA Astrophysics Data System (ADS)

Basith, Abdul; Prakoso, Yudhono; Kongko, Widjo

2017-07-01

A tsunami model using high resolution geometric data is indispensable in efforts to tsunami mitigation, especially in tsunami prone areas. It is one of the factors that affect the accuracy results of numerical modeling of tsunami. Sadeng Port is a new infrastructure in the Southern Coast of Java which could potentially hit by massive tsunami from seismic gap. This paper discusses validation and error estimation of tsunami model created using high resolution geometric data in Sadeng Port. Tsunami model validation uses the height wave of Tsunami Pangandaran 2006 recorded by Tide Gauge of Sadeng. Tsunami model will be used to accommodate the tsunami numerical modeling involves the parameters of earthquake-tsunami which is derived from the seismic gap. The validation results using t-test (student) shows that the height of the tsunami modeling results and observation in Tide Gauge of Sadeng are considered statistically equal at 95% confidence level and the value of the RMSE and NRMSE are 0.428 m and 22.12%, while the differences of tsunami wave travel time is 12 minutes.

Evaluating MTCLIM for incident daily solar radiation and humidity in diverse meteorological and topographical environments in the main Hawaiian Islands

NASA Astrophysics Data System (ADS)

Giambelluca, T. W.; Needham, H.; Longman, R. J.

2017-12-01

Continuous and high resolution climatologies are important inputs in determining future scenarios for land processes. In Hawaíi, a lack of continuous meteorological data has been a problem for both ecological and hydrological research of land-surface processes at daily time scales. For downward shortwave radiation (SWdown) and relative humidity (RH) climate variables, the number of surface stations which record daily values are limited and tend to be situated at city airports or in convenient locations leaving large sections of the islands underrepresented. The aim of this study is to evaluate the rationale behind using the mountain microclimate simulator MTCLIM to obtain a gridded observation based ensemble of SWdown and RH data at a daily increment for the period of 1990-2014 for the main Hawaiian Islands. Preliminary results, testing model output with observed data, show mean bias errors (%MBE) of 1.15 W/m2 for SWdown and -0.8% for RH. Mean absolute errors (%MAE) of 32.83 W/m2 SWdown and 14.96% RH, with root mean square errors (%RMSE) of 40.17 W/m2 SWdown and 11.75% RH. Further optimization of the model and additional methods to reduce errors are being investigated to improve the model's functionality with Hawaíi's extreme climate gradients.

Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP.

PubMed

Peak, Jasmine; Goranitis, Ilias; Day, Ed; Copello, Alex; Freemantle, Nick; Frew, Emma

2018-05-30

Economic evaluation normally requires information to be collected on outcome improvement using utility values. This is often not collected during the treatment of substance use disorders making cost-effectiveness evaluations of therapy difficult. One potential solution is the use of mapping to generate utility values from clinical measures. This study develops and evaluates mapping algorithms that could be used to predict the EuroQol-5D (EQ-5D-5 L) and the ICEpop CAPability measure for Adults (ICECAP-A) from the three commonly used clinical measures; the CORE-OM, the LDQ and the TOP measures. Models were estimated using pilot trial data of heroin users in opiate substitution treatment. In the trial the EQ-5D-5 L, ICECAP-A, CORE-OM, LDQ and TOP were administered at baseline, three and twelve month time intervals. Mapping was conducted using estimation and validation datasets. The normal estimation dataset, which comprised of baseline sample data, used ordinary least squares (OLS) and tobit regression methods. Data from the baseline and three month time periods were combined to create a pooled estimation dataset. Cluster and mixed regression methods were used to map from this dataset. Predictive accuracy of the models was assessed using the root mean square error (RMSE) and the mean absolute error (MAE). Algorithms were validated using sample data from the follow-up time periods. Mapping algorithms can be used to predict the ICECAP-A and the EQ-5D-5 L in the context of opiate dependence. Although both measures can be predicted, the ICECAP-A was better predicted by the clinical measures. There were no advantages of pooling the data. There were 6 chosen mapping algorithms, which had MAE scores ranging from 0.100 to 0.138 and RMSE scores ranging from 0.134 to 0.178. It is possible to predict the scores of the ICECAP-A and the EQ-5D-5 L with the use of mapping. In the context of opiate dependence, these algorithms provide the possibility of generating utility values from clinical measures and thus enabling economic evaluation of alternative therapy options. ISRCTN22608399 . Date of registration: 27/04/2012. Date of first randomisation: 14/08/2012.

The use of compressive sensing and peak detection in the reconstruction of microtubules length time series in the process of dynamic instability.

PubMed

Mahrooghy, Majid; Yarahmadian, Shantia; Menon, Vineetha; Rezania, Vahid; Tuszynski, Jack A

2015-10-01

Microtubules (MTs) are intra-cellular cylindrical protein filaments. They exhibit a unique phenomenon of stochastic growth and shrinkage, called dynamic instability. In this paper, we introduce a theoretical framework for applying Compressive Sensing (CS) to the sampled data of the microtubule length in the process of dynamic instability. To reduce data density and reconstruct the original signal with relatively low sampling rates, we have applied CS to experimental MT lament length time series modeled as a Dichotomous Markov Noise (DMN). The results show that using CS along with the wavelet transform significantly reduces the recovery errors comparing in the absence of wavelet transform, especially in the low and the medium sampling rates. In a sampling rate ranging from 0.2 to 0.5, the Root-Mean-Squared Error (RMSE) decreases by approximately 3 times and between 0.5 and 1, RMSE is small. We also apply a peak detection technique to the wavelet coefficients to detect and closely approximate the growth and shrinkage of MTs for computing the essential dynamic instability parameters, i.e., transition frequencies and specially growth and shrinkage rates. The results show that using compressed sensing along with the peak detection technique and wavelet transform in sampling rates reduces the recovery errors for the parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China

NASA Astrophysics Data System (ADS)

Wu, Wei; Xu, An-Ding; Liu, Hong-Bin

2015-01-01

Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.

Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

PubMed

Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

2017-09-26

Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

Development and application of GIS-based PRISM integration through a plugin approach

NASA Astrophysics Data System (ADS)

Lee, Woo-Seop; Chun, Jong Ahn; Kang, Kwangmin

2014-05-01

A PRISM (Parameter-elevation Regressions on Independent Slopes Model) QGIS-plugin was developed on Quantum GIS platform in this study. This Quantum GIS plugin system provides user-friendly graphic user interfaces (GUIs) so that users can obtain gridded meteorological data of high resolutions (1 km × 1 km). Also, this software is designed to run on a personal computer so that it does not require an internet access or a sophisticated computer system. This module is a user-friendly system that a user can generate PRISM data with ease. The proposed PRISM QGIS-plugin is a hybrid statistical-geographic model system that uses coarse resolution datasets (APHRODITE datasets in this study) with digital elevation data to generate the fine-resolution gridded precipitation. To validate the performance of the software, Prek Thnot River Basin in Kandal, Cambodia is selected for application. Overall statistical analysis shows promising outputs generated by the proposed plugin. Error measures such as RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) were used to evaluate the performance of the developed PRISM QGIS-plugin. Evaluation results using RMSE and MAPE were 2.76 mm and 4.2%, respectively. This study suggested that the plugin can be used to generate high resolution precipitation datasets for hydrological and climatological studies at a watershed where observed weather datasets are limited.

Downward Atmospheric Longwave Radiation in the City of Sao Paulo

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barbaro, Eduardo W.; Oliveira, Amauri P.; Soares, Jacyra

2009-03-11

This work evaluates objectively the consistency and quality of a 9 year dataset based on 5 minute average values of downward longwave atmospheric (LW) emission, shortwave radiation, temperature and relative humidity. All these parameters were observed simultaneously and continuously from 1997 to 2006 in the IAG micrometeorological platform, located at the top of the IAG-USP building. The pyrgeometer dome emission effect was removed using neural network technique reducing the downward long wave atmospheric emission error to 3.5%. The comparison, between the monthly average values of LW emission observed in Sao Paulo and satellite estimates from SRB-NASA project, indicated a verymore » good agreement. Furthermore, this work investigates the performance of 10 empirical expressions to estimate the LW emission at the surface. The comparison between the models indicates that Brunt's one presents the better results, with smallest ''MBE,''''RMSE'' and biggest ''d'' index of agreement, therefore Brunt is the most indicated model to estimate LW emission under clear sky conditions in the city of Sao Paulo.« less

Assessing the performance of the Tran-Blaha modified Becke-Johnson exchange potential for optical constants of semiconductors in the ultraviolet-visible light region

NASA Astrophysics Data System (ADS)

Nakano, Kousuke; Sakai, Tomohiro

2018-01-01

We report on the performance of density functional theory (DFT) with the Tran-Blaha modified Becke-Johnson exchange potential and the random phase approximation dielectric function for optical constants of semiconductors in the ultraviolet-visible (UV-Vis) light region. We calculate optical bandgaps Eg, refractive indices n, and extinction coefficients k of 70 semiconductors listed in the Handbook of Optical Constants of Solids [(Academic Press, 1985), Vol. 1; (Academic Press, 1991), Vol. 2; and (Academic Press, 1998), Vol. 3] and compare the results with experimental values. The results show that the calculated bandgaps and optical constants agree well with the experimental values to within 0.440 eV for Eg, 0.246-0.299 for n, and 0.207-0.598 for k in root mean squared error (RMSE). The small values of the RMSEs indicate that the optical constants of semiconductors in the UV-Vis region can be quantitatively predicted even by a low-cost DFT calculation of this type.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

PubMed

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

Comparison of Optimization and Two-point Methods in Estimation of Soil Water Retention Curve

NASA Astrophysics Data System (ADS)

Ghanbarian-Alavijeh, B.; Liaghat, A. M.; Huang, G.

2009-04-01

Soil water retention curve (SWRC) is one of the soil hydraulic properties in which its direct measurement is time consuming and expensive. Since, its measurement is unavoidable in study of environmental sciences i.e. investigation of unsaturated hydraulic conductivity and solute transport, in this study the attempt is to predict soil water retention curve from two measured points. By using Cresswell and Paydar (1996) method (two-point method) and an optimization method developed in this study on the basis of two points of SWRC, parameters of Tyler and Wheatcraft (1990) model (fractal dimension and air entry value) were estimated and then water content at different matric potentials were estimated and compared with their measured values (n=180). For each method, we used both 3 and 1500 kPa (case 1) and 33 and 1500 kPa (case 2) as two points of SWRC. The calculated RMSE values showed that in the Creswell and Paydar (1996) method, there exists no significant difference between case 1 and case 2. However, the calculated RMSE value in case 2 (2.35) was slightly less than case 1 (2.37). The results also showed that the developed optimization method in this study had significantly less RMSE values for cases 1 (1.63) and 2 (1.33) rather than Cresswell and Paydar (1996) method.

Application of VNIR diffuse reflectance spectroscopy to estimate soil organic carbon content, and content of different forms of iron and manganese

NASA Astrophysics Data System (ADS)

Klement, Ales; Jaksik, Ondrej; Kodesova, Radka; Drabek, Ondrej; Boruvka, Lubos

2013-04-01

Visible and near-infrared (VNIR) diffuse reflectance spectroscopy is a progressive method used for prediction of soil properties. Study was performed on the soils from the agricultural land from the south Moravia municipality of Brumovice. Studied area is characterized by a relatively flat upper part, a tributary valley in the middle and a colluvial fan at the bottom. Haplic Chernozem reminded at the flat upper part of the area. Regosols were formed at steep parts of the valley. Colluvial Chernozem and Colluvial soils were formed at the bottom parts of the valley and at the bottom part of the studied field. The goal of the study was to evaluate relationship between soil spectra curves and organic matter content, and different forms iron and manganese content (Mehlich III extract, ammonium oxalate extract and dithionite-citrate extract). Samples (87) were taken from the topsoil within regular grid covering studied area. The soil spectra curves (of air dry soil and sieved using 2 mm sieve) were measured in the laboratory using spectometer FieldSpec®3 (350 - 2 500 nm). The Fe and Mn contents in different extract were measured using ICP-OES (with an iCAP 6500 Radial ICP Emission spectrometer; Thermo Scientific, UK) under standard analytical conditions. Partial least squares regression (PLSR) was used for modeling of the relationship between spectra and measured soil properties. Prediction ability was evaluated using the R2, root mean square error (RMSE) and normalized root mean square deviation (NRMSD). The results showed the best prediction for Mn (R2 = 0.86, RMSE = 29, NRMSD = 0.11), Fe in ammonium oxalate extract (R2 = 0.82, RMSE = 171, NRMSD = 0.12) and organic matter content (R2 = 0.84, RMSE = 0.13, NRMSD = 0.09). The slightly worse prediction was obtained for Mn and Fe in citrate extract (R2 = 0.82, RMSE = 21, NRMSD = 0.10; R2 = 0.77, RMSE = 522, NRMSD = 0.23). Poor prediction was evaluated for Mn and Fe in Mehlich III extract (R2 = 0.43, RMSE = 13, NRMSD = 0.17; R2 = 0.39, RMSE = 13, NRMSD = 0.26). In general, the results confirmed that the measurement of soil spectral characteristics is a promising technology for a digital soil mapping and predicting studied soil properties. Acknowledgment: Authors acknowledge the financial support of the Ministry of Agriculture of the Czech Republic (grant No. QJ1230319) and the Czech Science Foundation (grant No. GA526/09/1762).

Investigating and Modelling Effects of Climatically and Hydrologically Indicators on the Urmia Lake Coastline Changes Using Time Series Analysis

NASA Astrophysics Data System (ADS)

Ahmadijamal, M.; Hasanlou, M.

2017-09-01

Study of hydrological parameters of lakes and examine the variation of water level to operate management on water resources are important. The purpose of this study is to investigate and model the Urmia Lake water level changes due to changes in climatically and hydrological indicators that affects in the process of level variation and area of this lake. For this purpose, Landsat satellite images, hydrological data, the daily precipitation, the daily surface evaporation and the daily discharge in total of the lake basin during the period of 2010-2016 have been used. Based on time-series analysis that is conducted on individual data independently with same procedure, to model variation of Urmia Lake level, we used polynomial regression technique and combined polynomial with periodic behavior. In the first scenario, we fit a multivariate linear polynomial to our datasets and determining RMSE, NRSME and R² value. We found that fourth degree polynomial can better fit to our datasets with lowest RMSE value about 9 cm. In the second scenario, we combine polynomial with periodic behavior for modeling. The second scenario has superiority comparing to the first one, by RMSE value about 3 cm.

Energy Performance Assessment of Radiant Cooling System through Modeling and Calibration at Component Level

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Yasin; Mathur, Jyotirmay; Bhandari, Mahabir S

2016-01-01

The paper describes a case study of an information technology office building with a radiant cooling system and a conventional variable air volume (VAV) system installed side by side so that performancecan be compared. First, a 3D model of the building involving architecture, occupancy, and HVAC operation was developed in EnergyPlus, a simulation tool. Second, a different calibration methodology was applied to develop the base case for assessing the energy saving potential. This paper details the calibration of the whole building energy model to the component level, including lighting, equipment, and HVAC components such as chillers, pumps, cooling towers, fans,more » etc. Also a new methodology for the systematic selection of influence parameter has been developed for the calibration of a simulated model which requires large time for the execution. The error at the whole building level [measured in mean bias error (MBE)] is 0.2%, and the coefficient of variation of root mean square error (CvRMSE) is 3.2%. The total errors in HVAC at the hourly are MBE = 8.7% and CvRMSE = 23.9%, which meet the criteria of ASHRAE 14 (2002) for hourly calibration. Different suggestions have been pointed out to generalize the energy saving of radiant cooling system through the existing building system. So a base case model was developed by using the calibrated model for quantifying the energy saving potential of the radiant cooling system. It was found that a base case radiant cooling system integrated with DOAS can save 28% energy compared with the conventional VAV system.« less

The Argos-CLS Kalman Filter: Error Structures and State-Space Modelling Relative to Fastloc GPS Data.

PubMed

Lowther, Andrew D; Lydersen, Christian; Fedak, Mike A; Lovell, Phil; Kovacs, Kit M

2015-01-01

Understanding how an animal utilises its surroundings requires its movements through space to be described accurately. Satellite telemetry is the only means of acquiring movement data for many species however data are prone to varying amounts of spatial error; the recent application of state-space models (SSMs) to the location estimation problem have provided a means to incorporate spatial errors when characterising animal movements. The predominant platform for collecting satellite telemetry data on free-ranging animals, Service Argos, recently provided an alternative Doppler location estimation algorithm that is purported to be more accurate and generate a greater number of locations that its predecessor. We provide a comprehensive assessment of this new estimation process performance on data from free-ranging animals relative to concurrently collected Fastloc GPS data. Additionally, we test the efficacy of three readily-available SSM in predicting the movement of two focal animals. Raw Argos location estimates generated by the new algorithm were greatly improved compared to the old system. Approximately twice as many Argos locations were derived compared to GPS on the devices used. Root Mean Square Errors (RMSE) for each optimal SSM were less than 4.25 km with some producing RMSE of less than 2.50 km. Differences in the biological plausibility of the tracks between the two focal animals used to investigate the utility of SSM highlights the importance of considering animal behaviour in movement studies. The ability to reprocess Argos data collected since 2008 with the new algorithm should permit questions of animal movement to be revisited at a finer resolution.

Waveform LiDAR processing: comparison of classic approaches and optimized Gold deconvolution to characterize vegetation structure and terrain elevation

NASA Astrophysics Data System (ADS)

Zhou, T.; Popescu, S. C.; Krause, K.

2016-12-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: 1) direct decomposition, 2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from discrete LiDAR data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, < 0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, < 1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (< 1.01m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE.

Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

NASA Astrophysics Data System (ADS)

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

PubMed

Salem, Salem Ibrahim; Higa, Hiroto; Kim, Hyungjun; Kobayashi, Hiroshi; Oki, Kazuo; Oki, Taikan

2017-07-31

Numerous algorithms have been proposed to retrieve chlorophyll- a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m -3 , 16.25 mg·m -3 , and 19.05 mg·m -3 , respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll- a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m -3 ), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m -3 ).

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

PubMed Central

Higa, Hiroto; Kobayashi, Hiroshi; Oki, Kazuo

2017-01-01

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3). PMID:28758984

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

PubMed Central

Almeida, Fernando R.; Brayner, Angelo; Rodrigues, Joel J. P. C.; Maia, Jose E. Bessa

2017-01-01

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE). PMID:28590450

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering.

PubMed

Almeida, Fernando R; Brayner, Angelo; Rodrigues, Joel J P C; Maia, Jose E Bessa

2017-06-07

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering . To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees

Treesearch

K.P. Poudel; H. Temesgen

2016-01-01

Estimating aboveground biomass and its components requires sound statistical formulation and evaluation. Using data collected from 55 destructively sampled trees in different parts of Oregon, we evaluated the performance of three groups of methods to estimate total aboveground biomass and (or) its components based on the bias and root mean squared error (RMSE) that...

Measurement and Modeling of Ecosystem Risk and Recovery for In Situ Treatment of Contaminated Sediments

DTIC Science & Technology

2011-02-01

µECD Gas chromatography - micro electron capture detector HPAH high molecular weight polyaromatic hydrocarbon HOC Hydrophobic organic compound IR...hydrocarbon PCB Polychlorinated biphenyl PE Polyethylene PED Polyethylene devices PFC Perfluorinated chemical POM Polyoxymethylene PRC...Performance reference compound RMSE Root Mean Squared Error SPME Solid Phase Micro Extraction SERDP Strategic Environmental Research and Development

Validation of Core Temperature Estimation Algorithm

DTIC Science & Technology

2016-01-29

plot of observed versus estimated core temperature with the line of identity (dashed) and the least squares regression line (solid) and line equation...estimated PSI with the line of identity (dashed) and the least squares regression line (solid) and line equation in the top left corner. (b) Bland...for comparison. The root mean squared error (RMSE) was also computed, as given by Equation 2.

A drifting GPS buoy for retrieving effective riverbed bathymetry

NASA Astrophysics Data System (ADS)

Hostache, R.; Matgen, P.; Giustarini, L.; Teferle, F. N.; Tailliez, C.; Iffly, J.-F.; Corato, G.

2015-01-01

Spatially distributed riverbed bathymetry information are rarely available but mandatory for accurate hydrodynamic modeling. This study aims at evaluating the potential of the Global Navigation Satellite System (GNSS), like for instance Global Positioning System (GPS), for retrieving such data. Drifting buoys equipped with navigation systems such as GPS enable the quasi-continuous measurement of water surface elevation, from virtually any point in the world. The present study investigates the potential of assimilating GNSS-derived water surface elevation measurements into hydraulic models in order to retrieve effective riverbed bathymetry. First tests with a GPS dual-frequency receiver show that the root mean squared error (RMSE) on the elevation measurement equals 30 cm provided that a differential post processing is performed. Next, synthetic observations of a drifting buoy were generated assuming a 30 cm average error of Water Surface Elevation (WSE) measurements. By assimilating the synthetic observation into a 1D-Hydrodynamic model, we show that the riverbed bathymetry can be retrieved with an accuracy of 36 cm. Moreover, the WSEs simulated by the hydrodynamic model using the retrieved bathymetry are in good agreement with the synthetic "truth", exhibiting an RMSE of 27 cm.

Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information

NASA Astrophysics Data System (ADS)

Zhu, Xiaohua; Li, Chuanrong; Tang, Lingli

2018-03-01

Leaf area index (LAI) is a key structural characteristic of vegetation and plays a significant role in global change research. Several methods and remotely sensed data have been evaluated for LAI estimation. This study aimed to evaluate the suitability of the look-up-table (LUT) approach for crop LAI retrieval from Satellite Pour l'Observation de la Terre (SPOT)-5 data and establish an LUT approach for LAI inversion based on scale information. The LAI inversion result was validated by in situ LAI measurements, indicating that the LUT generated based on the PROSAIL (PROSPECT+SAIL: properties spectra + scattering by arbitrarily inclined leaves) model was suitable for crop LAI estimation, with a root mean square error (RMSE) of ˜0.31m2 / m2 and determination coefficient (R2) of 0.65. The scale effect of crop LAI was analyzed based on Taylor expansion theory, indicating that when the SPOT data aggregated by 200 × 200 pixel, the relative error is significant with 13.7%. Finally, an LUT method integrated with scale information was proposed in this article, improving the inversion accuracy with RMSE of 0.20 m2 / m2 and R2 of 0.83.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

Predicting online ratings based on the opinion spreading process

NASA Astrophysics Data System (ADS)

He, Xing-Sheng; Zhou, Ming-Yang; Zhuo, Zhao; Fu, Zhong-Qian; Liu, Jian-Guo

2015-10-01

Predicting users' online ratings is always a challenge issue and has drawn lots of attention. In this paper, we present a rating prediction method by combining the user opinion spreading process with the collaborative filtering algorithm, where user similarity is defined by measuring the amount of opinion a user transfers to another based on the primitive user-item rating matrix. The proposed method could produce a more precise rating prediction for each unrated user-item pair. In addition, we introduce a tunable parameter λ to regulate the preferential diffusion relevant to the degree of both opinion sender and receiver. The numerical results for Movielens and Netflix data sets show that this algorithm has a better accuracy than the standard user-based collaborative filtering algorithm using Cosine and Pearson correlation without increasing computational complexity. By tuning λ, our method could further boost the prediction accuracy when using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as measurements. In the optimal cases, on Movielens and Netflix data sets, the corresponding algorithmic accuracy (MAE and RMSE) are improved 11.26% and 8.84%, 13.49% and 10.52% compared to the item average method, respectively.

Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Younes, Mohammed Y

2016-09-01

Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%. Copyright © 2015 Elsevier Ltd. All rights reserved.

Artificial neural networks to predict activity type and energy expenditure in youth.

PubMed

Trost, Stewart G; Wong, Weng-Keen; Pfeiffer, Karen A; Zheng, Yonglei

2012-09-01

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous-intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip, and VO2 was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE). As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

PubMed

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

Sensitivity of the meteorological model WRF-ARW to planetary boundary layer schemes during fog conditions in a coastal arid region

NASA Astrophysics Data System (ADS)

Chaouch, Naira; Temimi, Marouane; Weston, Michael; Ghedira, Hosni

2017-05-01

In this study, we intercompare seven different PBL schemes in WRF in the United Arab Emirates (UAE) and we assess their impact on the performance of the simulations. The study covered five fog events reported in 2014 at Abu Dhabi International Airport. The analysis of Synoptic conditions indicated that during all examined events, the UAE was under a high geopotential pressure and light wind that does not exceed 7 m/s at 850 hPa ( 1.5 km). Seven PBL schemes, namely, Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Moller-Yamada Nakanishi and Niino (MYNN) level 2.5, Quasi-Normal Scale Elimination (QNSE-EDMF), Asymmetric Convective Model (ACM2), Grenier-Bretherton-McCaa (GBM) and MYNN level 3 were tested. In situ observations used in the model's assessment included radiosonde data from the Abu Dhabi International Airport and surface measurements of relative humidity (RH), dew point temperature, wind speed, and temperature profiles. Overall, all the tested PBL schemes showed comparable skills with relatively higher performance with the QNSE scheme. The average RH Root Mean Square Error (RMSE) and BIAS for all PBLs were 15.75% and - 9.07%, respectively, whereas the obtained RMSE and BIAS when QNSE was used were 14.65% and - 6.3% respectively. Comparable skills were obtained for the rest of the variables. Local PBL schemes showed better performance than non-local schemes. Discrepancies between simulated and observed values were higher at the surface level compared to high altitude values. The sensitivity to lead time showed that best simulation performances were obtained when the lead time varies between 12 and 18 h. In addition, the results of the simulations show that better performance is obtained when the starting condition is dry.

A new wavefront sensor with polar symmetry: quantitative comparisons with a Shack-Hartmann wavefront sensor.

PubMed

Carvalho, Luis Alberto; Castro, Jarbas; Chamon, Wallace; Schor, Paulo

2006-11-01

A novel wavefront sensor has been developed. It follows the same principle of the Shack-Hartmann wavefront sensor in that it is based on slope information. However, it has a different symmetry, which may offer benefits in terms of application. The new wavefront sensor consists of a set of donut-shaped acrylic lenses with a charge coupled device located at the focal plane. From detection of shift in the radial direction, radial slopes are computed for 2880 points. Theoretical computations for higher order aberrations and lower order aberrations were implemented for the Shack-Hartmann wavefront sensor and the new wavefront sensor, and practical measurements were conducted on several sphere-cylinder trial lenses. The overall mean value of root mean square error (RMSE) (in microns) for theoretical computations was 0.03 for the Shack-Hartmann wavefront sensor and 0.02 for the new wavefront sensor. The mean value of RMSE for lower order aberrations (1-5) was 0.01 and 0.00003, and for higher order aberrations was 0.02 and 0.02, for the Shack-Hartmann and new wavefront sensors, respectively. For practical measurements (sphere, cylinder, axis), the standard deviation was 0.04 diopters (D), 0.04 D, and 4 degrees for the new wavefront sensor and 0.02 D, 0.02 D, and 5 degrees for the Shack-Hartmann wavefront sensor. Precision of the new wavefront sensor when measuring astigmatic and spherical surfaces is compatible with the Shack-Hartmann wavefront sensor. Centration with this new sensor is an absolute process using the center of the entrance pupil, which is where the line of site passes. This wavefront sensor, similar to the Shack-Hartmann sensor, does not eliminate the possibility of tilt. For more conclusive and statistically valid data, in vivo measurements are needed.

Improving the Accuracy of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Kumar, Sujay; Chien, Janety Y. L.; Riggs, George A.

2012-01-01

The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System standard snow products from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE with SWE values calculated from snow depths reported at 1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables

NASA Astrophysics Data System (ADS)

Nurminen, Kimmo; Karjalainen, Mika; Yu, Xiaowei; Hyyppä, Juha; Honkavaara, Eija

2013-09-01

Recent research results have shown that the performance of digital surface model extraction using novel high-quality photogrammetric images and image matching is a highly competitive alternative to laser scanning. In this article, we proceed to compare the performance of these two methods in the estimation of plot-level forest variables. Dense point clouds extracted from aerial frame images were used to estimate the plot-level forest variables needed in a forest inventory covering 89 plots. We analyzed images with 60% and 80% forward overlaps and used test plots with off-nadir angles of between 0° and 20°. When compared to reference ground measurements, the airborne laser scanning (ALS) data proved to be the most accurate: it yielded root mean square error (RMSE) values of 6.55% for mean height, 11.42% for mean diameter, and 20.72% for volume. When we applied a forward overlap of 80%, the corresponding results from aerial images were 6.77% for mean height, 12.00% for mean diameter, and 22.62% for volume. A forward overlap of 60% resulted in slightly deteriorated RMSE values of 7.55% for mean height, 12.20% for mean diameter, and 22.77% for volume. According to our results, the use of higher forward overlap produced only slightly better results in the estimation of these forest variables. Additionally, we found that the estimation accuracy was not significantly impacted by the increase in the off-nadir angle. Our results confirmed that digital aerial photographs were about as accurate as ALS in forest resources estimation as long as a terrain model was available.

Coupling HYDRUS-1D Code with PA-DDS Algorithms for Inverse Calibration

NASA Astrophysics Data System (ADS)

Wang, Xiang; Asadzadeh, Masoud; Holländer, Hartmut

2017-04-01

Numerical modelling requires calibration to predict future stages. A standard method for calibration is inverse calibration where generally multi-objective optimization algorithms are used to find a solution, e.g. to find an optimal solution of the van Genuchten Mualem (VGM) parameters to predict water fluxes in the vadose zone. We coupled HYDRUS-1D with PA-DDS to add a new, robust function for inverse calibration to the model. The PA-DDS method is a recently developed multi-objective optimization algorithm, which combines Dynamically Dimensioned Search (DDS) and Pareto Archived Evolution Strategy (PAES). The results were compared to a standard method (Marquardt-Levenberg method) implemented in HYDRUS-1D. Calibration performance is evaluated using observed and simulated soil moisture at two soil layers in the Southern Abbotsford, British Columbia, Canada in the terms of the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE). Results showed low RMSE values of 0.014 and 0.017 and strong NSE values of 0.961 and 0.939. Compared to the results by the Marquardt-Levenberg method, we received better calibration results for deeper located soil sensors. However, VGM parameters were similar comparing with previous studies. Both methods are equally computational efficient. We claim that a direct implementation of PA-DDS into HYDRUS-1D should reduce the computation effort further. This, the PA-DDS method is efficient for calibrating recharge for complex vadose zone modelling with multiple soil layer and can be a potential tool for calibration of heat and solute transport. Future work should focus on the effectiveness of PA-DDS for calibrating more complex versions of the model with complex vadose zone settings, with more soil layers, and against measured heat and solute transport. Keywords: Recharge, Calibration, HYDRUS-1D, Multi-objective Optimization

Multiphysics Modeling of Microwave Heating of a Frozen Heterogeneous Meal Rotating on a Turntable.

PubMed

Pitchai, Krishnamoorthy; Chen, Jiajia; Birla, Sohan; Jones, David; Gonzalez, Ric; Subbiah, Jeyamkondan

2015-12-01

A 3-dimensional (3-D) multiphysics model was developed to understand the microwave heating process of a real heterogeneous food, multilayered frozen lasagna. Near-perfect 3-D geometries of food package and microwave oven were used. A multiphase porous media model combining the electromagnetic heat source with heat and mass transfer, and incorporating phase change of melting and evaporation was included in finite element model. Discrete rotation of food on the turntable was incorporated. The model simulated for 6 min of microwave cooking of a 450 g frozen lasagna kept at the center of the rotating turntable in a 1200 W domestic oven. Temperature-dependent dielectric and thermal properties of lasagna ingredients were measured and provided as inputs to the model. Simulated temperature profiles were compared with experimental temperature profiles obtained using a thermal imaging camera and fiber-optic sensors. The total moisture loss in lasagna was predicted and compared with the experimental moisture loss during cooking. The simulated spatial temperature patterns predicted at the top layer was in good agreement with the corresponding patterns observed in thermal images. Predicted point temperature profiles at 6 different locations within the meal were compared with experimental temperature profiles and root mean square error (RMSE) values ranged from 6.6 to 20.0 °C. The predicted total moisture loss matched well with an RMSE value of 0.54 g. Different layers of food components showed considerably different heating performance. Food product developers can use this model for designing food products by understanding the effect of thickness and order of each layer, and material properties of each layer, and packaging shape on cooking performance. © 2015 Institute of Food Technologists®

Recent bathymetric variability of sandbars at Duck, NC

NASA Astrophysics Data System (ADS)

Ladner, H.; Palmsten, M. L.

2016-02-01

Sediment transport and sandbar migration are unresolved research topics due to the complex interaction between waves, currents, and sediments in the nearshore region. Previous studies have led to better fundamental understanding of sediment transport, but the capability to make precise short term estimates is still limited. One challenge in predicting sediment transport is the sparse bathymetric data available to ground-truth predictions. A recently developed algorithm, cBathy, uses video images to estimate the nearshore bathymetry from wave celerity. This new method can provide an extensive time series of bathymetric change in order to further study the physics of short term sediment transport. The cBathy code is still under development and needs further testing for accuracy. The objective of this work is to validate cBathy estimates of bathymetry and quantify sandbar behavior over a two month period by analyzing the position of the sandbar crest. The bias between the cBathy estimate and survey on 04/02/15 was 0.24 m and root mean square error (RMSE) was 0.50 m. The bias for the cBathy estimate and survey on 05/19/15 was -0.02 m and RMSE was 0.39 m. The bias and RMSE we observed were comparable previous estimates. As expected, errors were largest in shallower water depths where assumptions made by the cBathy algorithm were not valid. Over the two month period, the mean cross-shore location of the primary sandbar at the alongshore location of 200 m was approximately 216 m, with a standard deviation of 16 m. The mean cross-shore location of the primary sandbar at the alongshore location of 850 m was approximately 205 m, with a standard deviation of 17 m.

Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

2016-09-01

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

Using the Microsoft Kinect™ to assess 3-D shoulder kinematics during computer use.

PubMed

Xu, Xu; Robertson, Michelle; Chen, Karen B; Lin, Jia-Hua; McGorry, Raymond W

2017-11-01

Shoulder joint kinematics has been used as a representative indicator to investigate musculoskeletal symptoms among computer users for office ergonomics studies. The traditional measurement of shoulder kinematics normally requires a laboratory-based motion tracking system which limits the field studies. In the current study, a portable, low cost, and marker-less Microsoft Kinect™ sensor was examined for its feasibility on shoulder kinematics measurement during computer tasks. Eleven healthy participants performed a standardized computer task, and their shoulder kinematics data were measured by a Kinect sensor and a motion tracking system concurrently. The results indicated that placing the Kinect sensor in front of the participants would yielded a more accurate shoulder kinematics measurements then placing the Kinect sensor 15° or 30° to one side. The results also showed that the Kinect sensor had a better estimate on shoulder flexion/extension, compared with shoulder adduction/abduction and shoulder axial rotation. The RMSE of front-placed Kinect sensor on shoulder flexion/extension was less than 10° for both the right and the left shoulder. The measurement error of the front-placed Kinect sensor on the shoulder adduction/abduction was approximately 10° to 15°, and the magnitude of error is proportional to the magnitude of that joint angle. After the calibration, the RMSE on shoulder adduction/abduction were less than 10° based on an independent dataset of 5 additional participants. For shoulder axial rotation, the RMSE of front-placed Kinect sensor ranged between approximately 15° to 30°. The results of the study suggest that the Kinect sensor can provide some insight on shoulder kinematics for improving office ergonomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Accurate prediction of energy expenditure using a shoe-based activity monitor.

PubMed

Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

2011-07-01

The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

PubMed

Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

2018-02-01

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10  mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10  mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10  mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10  mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for over 30,000 chemicals.

Fat-free mass prediction equations for bioelectric impedance analysis compared to dual energy X-ray absorptiometry in obese adolescents: a validation study.

PubMed

Hofsteenge, Geesje H; Chinapaw, Mai J M; Weijs, Peter J M

2015-10-15

In clinical practice, patient friendly methods to assess body composition in obese adolescents are needed. Therefore, the bioelectrical impedance analysis (BIA) related fat-free mass (FFM) prediction equations (FFM-BIA) were evaluated in obese adolescents (age 11-18 years) compared to FFM measured by dual-energy x-ray absorptiometry (FFM-DXA) and a new population specific FFM-BIA equation is developed. After an overnight fast, the subjects attended the outpatient clinic. After measuring height and weight, a full body scan by dual-energy x-ray absorptiometry (DXA) and a BIA measurement was performed. Thirteen predictive FFM-BIA equations based on weight, height, age, resistance, reactance and/or impedance were systematically selected and compared to FFM-DXA. Accuracy of FFM-BIA equations was evaluated by the percentage adolescents predicted within 5% of FFM-DXA measured, the mean percentage difference between predicted and measured values (bias) and the Root Mean Squared prediction Error (RMSE). Multiple linear regression was conducted to develop a new BIA equation. Validation was based on 103 adolescents (60% girls), age 14.5 (sd1.7) years, weight 94.1 (sd15.6) kg and FFM-DXA of 56.1 (sd9.8) kg. The percentage accurate estimations varied between equations from 0 to 68%; bias ranged from -29.3 to +36.3% and RMSE ranged from 2.8 to 12.4 kg. An alternative prediction equation was developed: FFM = 0.527 * H(cm)(2)/Imp + 0.306 * weight - 1.862 (R(2) = 0.92, SEE = 2.85 kg). Percentage accurate prediction was 76%. Compared to DXA, the Gray equation underestimated the FFM with 0.4 kg (55.7 ± 8.3), had an RMSE of 3.2 kg, 63% accurate prediction and the smallest bias of (-0.1%). When split by sex, the Gray equation had the narrowest range in accurate predictions, bias, and RMSE. For the assessment of FFM with BIA, the Gray-FFM equation appears to be the most accurate, but 63% is still not at an acceptable accuracy level for obese adolescents. The new equation appears to be appropriate but await further validation. DXA measurement remains the method of choice for FFM in obese adolescents. Netherlands Trial Register ( ISRCTN27626398).

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

PubMed Central

Xu, Shengxiang; Shi, Xuezheng; Wang, Meiyan; Zhao, Yongcun

2016-01-01

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg−1; R2 (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg−1; R2 = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy. PMID:26974821

Modeling Hydrodynamics and Heat Transport in Upper Klamath Lake, Oregon, and Implications for Water Quality

USGS Publications Warehouse

Wood, Tamara M.; Cheng, Ralph T.; Gartner, Jeffrey W.; Hoilman, Gene R.; Lindenberg, Mary K.; Wellman, Roy E.

2008-01-01

The three-dimensional numerical model UnTRIM was used to model hydrodynamics and heat transport in Upper Klamath Lake, Oregon, between mid-June and mid-September in 2005 and between mid-May and mid-October in 2006. Data from as many as six meteorological stations were used to generate a spatially interpolated wind field to use as a forcing function. Solar radiation, air temperature, and relative humidity data all were available at one or more sites. In general, because the available data for all inflows and outflows did not adequately close the water budget as calculated from lake elevation and stage-capacity information, a residual inflow or outflow was used to assure closure of the water budget. Data used for calibration in 2005 included lake elevation at 3 water-level gages around the lake, water currents at 5 Acoustic Doppler Current Profiler (ADCP) sites, and temperature at 16 water-quality monitoring locations. The calibrated model accurately simulated the fluctuations of the surface of the lake caused by daily wind patterns. The use of a spatially variable surface wind interpolated from two sites on the lake and four sites on the shoreline generally resulted in more accurate simulation of the currents than the use of a spatially invariant surface wind as observed at only one site on the lake. The simulation of currents was most accurate at the deepest site (ADCP1, where the velocities were highest) using a spatially variable surface wind; the mean error (ME) and root mean square error (RMSE) for the depth-averaged speed over a 37-day simulation from July 26 to August 31, 2005, were 0.50 centimeter per second (cm/s) and 3.08 cm/s, respectively. Simulated currents at the remaining sites were less accurate and, in general, underestimated the measured currents. The maximum errors in simulated currents were at a site near the southern end of the trench at the mouth of Howard Bay (ADCP7), where the ME and RMSE in the depth-averaged speed were 3.02 and 4.38 cm/s, respectively. The range in ME of the temperature simulations over the same period was ?0.94 to 0.73 degrees Celsius (?C), and the RMSE ranged from 0.43 to 1.12?C. The model adequately simulated periods of stratification in the deep trench when complete mixing did not occur for several days at a time. The model was validated using boundary conditions and forcing functions from 2006 without changing any calibration parameters. A spatially variable wind was used. Data for the model validation periods in 2006 included lake elevation at 4 gages around the lake, currents collected at 2 ADCP sites, and temperature collected at 21 water-quality monitoring locations. Errors generally were larger than in 2005. ME and RMSE in the simulated velocity at ADCP1 were 2.30 cm/s and 3.88 cm/s, respectively, for the same 37-day simulation over which errors were computed for 2005. The ME in temperature over the same period ranged from ?0.56 to 1.5?C and the RMSE ranged from 0.41 to 1.86?C. Numerical experiments with conservative tracers were used to demonstrate the prevailing clockwise circulation patterns in the lake, and to show the influence of water from the deep trench located along the western shoreline of the lake on fish habitat in the northern part of the lake. Because water exiting the trench is split into two pathways, the numerical experiments indicate that bottom water from the trench has a stronger influence on water quality in the northern part of the lake, and surface water from the trench has a stronger influence on the southern part of the lake. This may be part of the explanation for why episodes of low dissolved oxygen tend to be more severe in the northern than in the southern part of the lake.

A Proposed Extension to the Soil Moisture and Ocean Salinity Level 2 Algorithm for Mixed Forest and Moderate Vegetation Pixels

NASA Technical Reports Server (NTRS)

Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse; Kim, Edward

2011-01-01

The Soil Moisture and Ocean Salinity (SMOS)mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km.Uncertainty in the retrieval of soilmoisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soilmoisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops),with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE,-3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, -0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

The evaluation of unmanned aerial system-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds

NASA Astrophysics Data System (ADS)

Ouédraogo, Mohamar Moussa; Degré, Aurore; Debouche, Charles; Lisein, Jonathan

2014-06-01

Agricultural watersheds tend to be places of intensive farming activities that permanently modify their microtopography. The surface characteristics of the soil vary depending on the crops that are cultivated in these areas. Agricultural soil microtopography plays an important role in the quantification of runoff and sediment transport because the presence of crops, crop residues, furrows and ridges may impact the direction of water flow. To better assess such phenomena, 3-D reconstructions of high-resolution agricultural watershed topography are essential. Fine-resolution topographic data collection technologies can be used to discern highly detailed elevation variability in these areas. Knowledge of the strengths and weaknesses of existing technologies used for data collection on agricultural watersheds may be helpful in choosing an appropriate technology. This study assesses the suitability of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for collecting the fine-resolution topographic data required to generate accurate, high-resolution digital elevation models (DEMs) in a small watershed area (12 ha). Because of farming activity, 14 TLS scans (≈ 25 points m- 2) were collected without using high-definition surveying (HDS) targets, which are generally used to mesh adjacent scans. To evaluate the accuracy of the DEMs created from the TLS scan data, 1098 ground control points (GCPs) were surveyed using a real time kinematic global positioning system (RTK-GPS). Linear regressions were then applied to each DEM to remove vertical errors from the TLS point elevations, errors caused by the non-perpendicularity of the scanner's vertical axis to the local horizontal plane, and errors correlated with the distance to the scanner's position. The scans were then meshed to generate a DEMTLS with a 1 × 1 m spatial resolution. The Agisoft PhotoScan and MicMac software packages were used to process the aerial photographs and generate a DEMPSC (Agisoft PhotoScan) and DEMMCM (MicMac), respectively, with spatial resolutions of 1 × 1 m. Comparing the DEMs with the 1098 GCPs showed that the DEMTLS was the most accurate data product, with a root mean square error (RMSE) of 4.5 cm, followed by the DEMMCM and the DEMPSC, which had RMSE values of 9.0 and 13.9 cm, respectively. The DEMPSC had absolute errors along the border of the study area that ranged from 15.0 to 52.0 cm, indicating the presence of systematic errors. Although the derived DEMMCM was accurate, an error analysis along a transect showed that the errors in the DEMMCM data tended to increase in areas of lower elevation. Compared with TLS, UAS is a promising tool for data collection because of its flexibility and low operational cost. However, improvements are needed in the photogrammetric processing of the aerial photographs to remove non-linear distortions.

Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle.

PubMed

Deo, Ravinesh C; Downs, Nathan; Parisi, Alfio V; Adamowski, Jan F; Quilty, John M

2017-05-01

Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ s ) data, was developed using an extreme learning machine (ELM) algorithm. An ELM algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (0500-1700h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELM architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of θ s as the predictor data was developed. ELM's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E NS ), root mean square error (RMSE), and mean absolute error (MAE) between observed and forecasted UVI. Using these metrics, the ELM model's performance was compared to that of existing methods: multivariate adaptive regression spline (MARS), M5 Model Tree, and a semi-empirical (Pro6UV) clear sky model. Based on RMSE and MAE values, the ELM model (0.255, 0.346, respectively) outperformed the MARS (0.310, 0.438) and M5 Model Tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELM, MARS and M5 Model Tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELM model's absolute errors were small in magnitude (±0.25), whereas the MARS and M5 Model Tree models generated 53% and 48% of such errors, respectively, indicating the latter models' errors to be distributed in larger magnitude error range. In terms of peak global UVI forecasting, with half the level of error, the ELM model outperformed MARS and M5 Model Tree. A comparison of the magnitude of hourly-cumulated errors of 10-min lead time forecasts for diffuse and global UVI highlighted ELM model's greater accuracy compared to MARS, M5 Model Tree or Pro6UV models. This confirmed the versatility of an ELM model drawing on θ s data for VSTR forecasting of UVI at near real-time horizon. When applied to the goal of enhancing expert systems, ELM-based accurate forecasts capable of reacting quickly to measured conditions can enhance real-time exposure advice for the public, mitigating the potential for solar UV-exposure-related disease. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound

PubMed Central

Odéen, Henrik; Todd, Nick; Diakite, Mahamadou; Minalga, Emilee; Payne, Allison; Parker, Dennis L.

2014-01-01

Purpose: To investigate k-space subsampling strategies to achieve fast, large field-of-view (FOV) temperature monitoring using segmented echo planar imaging (EPI) proton resonance frequency shift thermometry for MR guided high intensity focused ultrasound (MRgHIFU) applications. Methods: Five different k-space sampling approaches were investigated, varying sample spacing (equally vs nonequally spaced within the echo train), sampling density (variable sampling density in zero, one, and two dimensions), and utilizing sequential or centric sampling. Three of the schemes utilized sequential sampling with the sampling density varied in zero, one, and two dimensions, to investigate sampling the k-space center more frequently. Two of the schemes utilized centric sampling to acquire the k-space center with a longer echo time for improved phase measurements, and vary the sampling density in zero and two dimensions, respectively. Phantom experiments and a theoretical point spread function analysis were performed to investigate their performance. Variable density sampling in zero and two dimensions was also implemented in a non-EPI GRE pulse sequence for comparison. All subsampled data were reconstructed with a previously described temporally constrained reconstruction (TCR) algorithm. Results: The accuracy of each sampling strategy in measuring the temperature rise in the HIFU focal spot was measured in terms of the root-mean-square-error (RMSE) compared to fully sampled “truth.” For the schemes utilizing sequential sampling, the accuracy was found to improve with the dimensionality of the variable density sampling, giving values of 0.65 °C, 0.49 °C, and 0.35 °C for density variation in zero, one, and two dimensions, respectively. The schemes utilizing centric sampling were found to underestimate the temperature rise, with RMSE values of 1.05 °C and 1.31 °C, for variable density sampling in zero and two dimensions, respectively. Similar subsampling schemes with variable density sampling implemented in zero and two dimensions in a non-EPI GRE pulse sequence both resulted in accurate temperature measurements (RMSE of 0.70 °C and 0.63 °C, respectively). With sequential sampling in the described EPI implementation, temperature monitoring over a 192 × 144 × 135 mm3 FOV with a temporal resolution of 3.6 s was achieved, while keeping the RMSE compared to fully sampled “truth” below 0.35 °C. Conclusions: When segmented EPI readouts are used in conjunction with k-space subsampling for MR thermometry applications, sampling schemes with sequential sampling, with or without variable density sampling, obtain accurate phase and temperature measurements when using a TCR reconstruction algorithm. Improved temperature measurement accuracy can be achieved with variable density sampling. Centric sampling leads to phase bias, resulting in temperature underestimations. PMID:25186406

Refined shape model fitting methods for detecting various types of phenological information on major U.S. crops

NASA Astrophysics Data System (ADS)

Sakamoto, Toshihiro

2018-04-01

Crop phenological information is a critical variable in evaluating the influence of environmental stress on the final crop yield in spatio-temporal dimensions. Although the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Dynamics product (MCD12Q2) is widely used in place of crop phenological information, the definitions of MCD12Q2-derived phenological events (e.g. green-up date, dormancy date) were not completely consistent with those of crop development stages used in statistical surveys (e.g. emerged date, harvested date). It has been necessary to devise an alternative method focused on detecting continental-scale crop developmental stages using a different approach. Therefore, this study aimed to refine the Shape Model Fitting (SMF) method to improve its applicability to multiple major U.S. crops. The newly-refined SMF methods could estimate the timing of 36 crop-development stages of major U.S. crops, including corn, soybeans, winter wheat, spring wheat, barley, sorghum, rice, and cotton. The newly-developed calibration process did not require any long-term field observation data, and could calibrate crop-specific phenological parameters, which were used as coefficients in estimated equation, by using only freely accessible public data. The calibration of phenological parameters was conducted in two steps. In the first step, the national common phenological parameters, referred to as X0[base], were calibrated by using the statistical data of 2008. The SMF method coupled using X0[base] was named the rSMF[base] method. The second step was a further calibration to gain regionally-adjusted phenological parameters for each state, referred to as X0[local], by using additional statistical data of 2015 and 2016. The rSMF method using the X0[local] was named the rSMF[local] method. This second calibration process improved the estimation accuracy for all tested crops. When applying the rSMF[base] method to the validation data set (2009-2014), the root mean square error (RMSE) of the rSMF[base]-derived estimates ranged from 7.1 days (corn) to 15.7 days (winter wheat). When using the rSMF[local] method, the RMSE of the rSMF[local]-derived estimates improved and ranged from 5.6 days (corn) to 12.3 days (winter wheat). The results showed that the second calibration step for the rSMF[local] method could correct the region-dependent bias error between the rSMF[base]-derived estimates and the statistical data. A comparison between the performances of the refined SMF methods and the MCD12Q2 products, indicated that both of the rSMF methods were superior to the MCD12Q2 products in estimating all phenological stages, except for the case of the rSMF[base]-derived barley emerged stages. The phenological stages for which the rSMF[local] showed the best estimation accuracy were the corn silking stage (RMSE = 4.3 days); the soybeans dropping leaves stage (RMSE = 4.9 days); the headed stages of winter wheat (RMSE = 11.1 days), barley (RMSE = 6.1 days), and sorghum (RMSE = 9.5 days); the spring-wheat harvested stage (RMSE = 5.5 days); the rice emerged stage (RMSE = 5.5 days), and the cotton squaring stage (RMSE = 6.6 days). These were more accurate than the results achieved by the MCD12Q2 products. In addition, the rSMF[local]-derived estimates were superior in terms of the reproducibility of the annual variation range, particularly of the late reproductive stages, such as the mature and harvested stages. The crop phenology maps derived from the SMF [local] method were also in good agreement with the relevant maps derived from statistics, and could reveal the characteristic spatial pattern of the key phenological stages at the continental scale with fine spatial resolution. For example, the winter-wheat headed stage clearly became later from south to north. The cotton squaring stage became earlier from the central region towards both coastal regions.

Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes.

PubMed

Bell, Charreau S; Obstein, Keith L; Valdastri, Pietro

2013-11-01

Colorectal cancer is one of the leading causes of cancer-related deaths in the world, although it can be effectively treated if detected early. Teleoperated flexible endoscopes are an emerging technology to ease patient apprehension about the procedure, and subsequently increase compliance. Essential to teleoperation is robust feedback reflecting the change in pose (i.e., position and orientation) of the tip of the endoscope. The goal of this study is to first describe a novel image-based tracking system for teleoperated flexible endoscopes, and subsequently determine its viability in a clinical setting. The proposed approach leverages artificial neural networks (ANNs) to learn the mapping that links the optical flow between two sequential images to the change in the pose of the camera. Secondly, the study investigates for the first time how narrow band illumination (NBI) - today available in commercial gastrointestinal endoscopes - can be applied to enhance feature extraction, and quantify the effect of NBI and white light illumination (WLI), as well as their color information, on the strength of features extracted from the endoscopic camera stream. In order to provide the best features for the neural networks to learn the change in pose based on the image stream, we investigated two different imaging modalities - WLI and NBI - and we applied two different spatial partitions - lumen-centered and grid-based - to create descriptors used as input to the ANNs. An experiment was performed to compare the error of these four variations, measured in root mean square error (RMSE) from ground truth given by a robotic arm, to that of a commercial state-of-the-art magnetic tracker. The viability of this technique for a clinical setting was then tested using the four ANN variations, a magnetic tracker, and a commercial colonoscope. The trial was performed by an expert endoscopist (>2000 lifetime procedures) on a colonoscopy training model with porcine blood, and the RMSE of the ANN output was calculated with respect to the magnetic tracker readings. Using the image stream obtained from the commercial endoscope, the strength of features extracted was evaluated. In the first experiment, the best ANNs resulted from grid-based partitioning under WLI (2.42mm RMSE) for position, and from lumen-centered partitioning under NBI (1.69° RMSE) for rotation. By comparison, the performance of the tracker was 2.49mm RMSE in position and 0.89° RMSE in rotation. The trial with the commercial endoscope indicated that lumen-centered partitioning was the best overall, while NBI outperformed WLI in terms of illumination modality. The performance of lumen-centered partitioning with NBI was 1.03±0.8mm RMSE in positional degrees of freedom (DOF), and 1.26±0.98° RMSE in rotational DOF, while with WLI, the performance was 1.56±1.15mm RMSE in positional DOF and 2.45±1.90° RMSE in rotational DOF. Finally, the features extracted under NBI were found to be twice as strong as those extracted under WLI, but no significance in feature strengths was observed between a grayscale version of the image, and the red, blue, and green color channels. This work demonstrates that both WLI and NBI, combined with feature partitioning based on the anatomy of the colon, provide valid mechanisms for endoscopic camera pose estimation via image stream. Illumination provided by WLI and NBI produce ANNs with similar performance which are comparable to that of a state-of-the-art magnetic tracker. However, NBI produces features that are stronger than WLI, which enables more robust feature tracking, and better performance of the ANN in terms of accuracy. Thus, NBI with lumen-centered partitioning resulted the best approach among the different variations tested for vision-based pose estimation. The proposed approach takes advantage of components already available in commercial gastrointestinal endoscopes to provide accurate feedback about the motion of the tip of the endoscope. This solution may serve as an enabling technology for closed-loop control of teleoperated flexible endoscopes. Copyright © 2013 Elsevier B.V. All rights reserved.

Updated MISR Dark Water Research Aerosol Retrieval Algorithm - Part 1: Coupled 1.1 km Ocean Surface Chlorophyll a Retrievals with Empirical Calibration Corrections

NASA Technical Reports Server (NTRS)

Limbacher, James A.; Kahn, Ralph A.

2017-01-01

As aerosol amount and type are key factors in the 'atmospheric correction' required for remote-sensing chlorophyll alpha concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chl(sub in situ) less than 1.5 mg m(exp -3), the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov- Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p greater than 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl less than 1.5 mg m(exp -3), MISR and MODIS show very good agreement: r = 0.96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

Updated MISR dark water research aerosol retrieval algorithm - Part 1: Coupled 1.1 km ocean surface chlorophyll a retrievals with empirical calibration corrections

NASA Astrophysics Data System (ADS)

Limbacher, James A.; Kahn, Ralph A.

2017-04-01

As aerosol amount and type are key factors in the atmospheric correction required for remote-sensing chlorophyll a concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chlin situ < 1.5 mg m-3, the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov-Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p > 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl < 1.5 mg m-3, MISR and MODIS show very good agreement: r = 0. 96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

Modeling number of claims and prediction of total claim amount

NASA Astrophysics Data System (ADS)

Acar, Aslıhan Şentürk; Karabey, Uǧur

2017-07-01

In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.

An empirical understanding of triple collocation evaluation measure

NASA Astrophysics Data System (ADS)

Scipal, Klaus; Doubkova, Marcela; Hegyova, Alena; Dorigo, Wouter; Wagner, Wolfgang

2013-04-01

Triple collocation method is an advanced evaluation method that has been used in the soil moisture field for only about half a decade. The method requires three datasets with an independent error structure that represent an identical phenomenon. The main advantages of the method are that it a) doesn't require a reference dataset that has to be considered to represent the truth, b) limits the effect of random and systematic errors of other two datasets, and c) simultaneously assesses the error of three datasets. The objective of this presentation is to assess the triple collocation error (Tc) of the ASAR Global Mode Surface Soil Moisture (GM SSM 1) km dataset and highlight problems of the method related to its ability to cancel the effect of error of ancillary datasets. In particular, the goal is to a) investigate trends in Tc related to the change in spatial resolution from 5 to 25 km, b) to investigate trends in Tc related to the choice of a hydrological model, and c) to study the relationship between Tc and other absolute evaluation methods (namely RMSE and Error Propagation EP). The triple collocation method is implemented using ASAR GM, AMSR-E, and a model (either AWRA-L, GLDAS-NOAH, or ERA-Interim). First, the significance of the relationship between the three soil moisture datasets was tested that is a prerequisite for the triple collocation method. Second, the trends in Tc related to the choice of the third reference dataset and scale were assessed. For this purpose the triple collocation is repeated replacing AWRA-L with two different globally available model reanalysis dataset operating at different spatial resolution (ERA-Interim and GLDAS-NOAH). Finally, the retrieved results were compared to the results of the RMSE and EP evaluation measures. Our results demonstrate that the Tc method does not eliminate the random and time-variant systematic errors of the second and the third dataset used in the Tc. The possible reasons include the fact a) that the TC method could not fully function with datasets acting at very different spatial resolutions, or b) that the errors were not fully independent as initially assumed.

Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea

NASA Astrophysics Data System (ADS)

Jeong, Sang-Hun; Kim, Jinah; Heo, Ki-Young; Park, Kwang-Soon

2017-04-01

In winter season, large-height swell-like waves have occurred on the East coast of Korea, causing property damages and loss of human life. It is known that those waves are generated by a local strong wind made by temperate cyclone moving to eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages. In Korea, an operational oceanographic system (KOOS) has been developed by the Korea institute of ocean science and technology (KIOST) and KOOS provides daily basis 72-hours' ocean forecasts such as wind, water elevation, sea currents, water temperature, salinity, and waves which are computed from not only meteorological and hydrodynamic model (WRF, ROMS, MOM, and MOHID) but also wave models (WW-III and SWAN). In order to evaluate the model performance and guarantee a certain level of accuracy of ocean forecasts, a Skill Assessment (SA) system was established as a one of module in KOOS. It has been performed through comparison of model results with in-situ observation data and model errors have been quantified with skill scores. Statistics which are used in skill assessment are including a measure of both errors and correlations such as root-mean-square-error (RMSE), root-mean-square-error percentage (RMSE%), mean bias (MB), correlation coefficient (R), scatter index (SI), circular correlation (CC) and central frequency (CF) that is a frequency with which errors lie within acceptable error criteria. It should be utilized simultaneously not only to quantify an error but also to improve an accuracy of forecasts by providing a feedback interactively. However, in an abnormal phenomena such as high-height swell-like waves in the East coast of Korea, it requires more advanced and optimized error quantification method that allows to predict the abnormal waves well and to improve the accuracy of forecasts by supporting modification of physics and numeric on numerical models through sensitivity test. In this study, we proposed an appropriate method of error quantification especially on abnormal high waves which are occurred by local weather condition. Furthermore, we introduced that how the quantification errors are contributed to improve wind-wave modeling by applying data assimilation and utilizing reanalysis data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Seán, E-mail: walshsharp@gmail.com; Department of Oncology, Gray Institute for Radiation Oncology and Biology, University of Oxford, Oxford OX3 7DQ; Roelofs, Erik

Purpose: A fully heterogeneous population averaged mechanistic tumor control probability (TCP) model is appropriate for the analysis of external beam radiotherapy (EBRT). This has been accomplished for EBRT photon treatment of intermediate-risk prostate cancer. Extending the TCP model for low and high-risk patients would be beneficial in terms of overall decision making. Furthermore, different radiation treatment modalities such as protons and carbon-ions are becoming increasingly available. Consequently, there is a need for a complete TCP model. Methods: A TCP model was fitted and validated to a primary endpoint of 5-year biological no evidence of disease clinical outcome data obtained frommore » a review of the literature for low, intermediate, and high-risk prostate cancer patients (5218 patients fitted, 1088 patients validated), treated by photons, protons, or carbon-ions. The review followed the preferred reporting item for systematic reviews and meta-analyses statement. Treatment regimens include standard fractionation and hypofractionation treatments. Residual analysis and goodness of fit statistics were applied. Results: The TCP model achieves a good level of fit overall, linear regression results in a p-value of <0.000 01 with an adjusted-weighted-R{sup 2} value of 0.77 and a weighted root mean squared error (wRMSE) of 1.2%, to the fitted clinical outcome data. Validation of the model utilizing three independent datasets obtained from the literature resulted in an adjusted-weighted-R{sup 2} value of 0.78 and a wRMSE of less than 1.8%, to the validation clinical outcome data. The weighted mean absolute residual across the entire dataset is found to be 5.4%. Conclusions: This TCP model fitted and validated to clinical outcome data, appears to be an appropriate model for the inclusion of all clinical prostate cancer risk categories, and allows evaluation of current EBRT modalities with regard to tumor control prediction.« less

CEDAR-GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

NASA Astrophysics Data System (ADS)

Shim, J. S.; Rastätter, L.; Kuznetsova, M.; Bilitza, D.; Codrescu, M.; Coster, A. J.; Emery, B. A.; Fedrizzi, M.; Förster, M.; Fuller-Rowell, T. J.; Gardner, L. C.; Goncharenko, L.; Huba, J.; McDonald, S. E.; Mannucci, A. J.; Namgaladze, A. A.; Pi, X.; Prokhorov, B. E.; Ridley, A. J.; Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Zhu, L.

2017-10-01

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground-based GPS TEC measurements during the 2006 AGU storm event (14-15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics-based, coupled ionosphere-thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 1016 el m-2) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.

Comparative Time Series Analysis of Aerosol Optical Depth over Sites in United States and China Using ARIMA Modeling

NASA Astrophysics Data System (ADS)

Li, X.; Zhang, C.; Li, W.

2017-12-01

Long-term spatiotemporal analysis and modeling of aerosol optical depth (AOD) distribution is of paramount importance to study radiative forcing, climate change, and human health. This study is focused on the trends and variations of AOD over six stations located in United States and China during 2003 to 2015, using satellite-retrieved Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 retrievals and ground measurements derived from Aerosol Robotic NETwork (AERONET). An autoregressive integrated moving average (ARIMA) model is applied to simulate and predict AOD values. The R2, adjusted R2, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC) are used as indices to select the best fitted model. Results show that there is a persistent decreasing trend in AOD for both MODIS data and AERONET data over three stations. Monthly and seasonal AOD variations reveal consistent aerosol patterns over stations along mid-latitudes. Regional differences impacted by climatology and land cover types are observed for the selected stations. Statistical validation of time series models indicates that the non-seasonal ARIMA model performs better for AERONET AOD data than for MODIS AOD data over most stations, suggesting the method works better for data with higher quality. By contrast, the seasonal ARIMA model reproduces the seasonal variations of MODIS AOD data much more precisely. Overall, the reasonably predicted results indicate the applicability and feasibility of the stochastic ARIMA modeling technique to forecast future and missing AOD values.

Modeling nitrate-nitrogen removal process in first-flush reactor for stormwater treatment.

PubMed

Deng, Zhiqiang; Sun, Shaowei; Gang, Daniel Dianchen

2012-08-01

Stormwater runoff is one of the most common non-point sources of water pollution to rivers, lakes, estuaries, and coastal beaches. While most pollutants and nutrients, including nitrate-nitrogen, in stormwater are discharged into receiving waters during the first-flush period, no existing best management practices (BMPs) are specifically designed to capture and treat the first-flush portion of urban stormwater runoff. This paper presents a novel BMP device for highway and urban stormwater treatment with emphasis on numerical modeling of the new BMP, called first-flush reactor (FFR). A new model, called VART-DN model, for simulation of denitrification process in the designed first-flush reactor was developed using the variable residence time (VART) model. The VART-DN model is capable of simulating various processes and mechanisms responsible for denitrification in the FFR. Based on sensitivity analysis results of model parameters, the denitrification process is sensitive to the temperature correction factor (b), maximum nitrate-nitrogen decay rate (K (max)), actual varying residence time (T (v)), the constant decay rate of denitrifiying bacteria (v (dec)), temperature (T), biomass inhibition constant (K (b)), maximum growth rate of denitrifiying bacteria (v (max)), denitrifying bacteria concentration (X), longitudinal dispersion coefficient (K (s)), and half-saturation constant of dissolved carbon for biomass (K (Car-X)); a 10% increase in the model parameter values causes a change in model root mean square error (RMSE) of -28.02, -16.16, -12.35, 11.44, -9.68, 10.61, -16.30, -9.27, 6.58 and 3.89%, respectively. The VART-DN model was tested using the data from laboratory experiments conducted using highway stormwater and secondary wastewater. Model results for the denitrification process of highway stormwater showed a good agreement with observed data and the simulation error was less than 9.0%. The RMSE and the coefficient of determination for simulating denitrification process of wastewater were 0.5167 and 0.6912, respectively, demonstrating the efficacy of the VART-DN model.

[Fire behavior of Quercus mongolica leaf litter fuelbed under zero-slope and no-wind conditions. II. Analysis and modelling of fireline intensity, fuel consumption, and combustion efficiency].

PubMed

Zhang, Ji-Li; Liu, Bo-Fei; Di, Xue-Ying; Chu, Teng-Fei; Jin, Sen

2013-12-01

Mongolian oak (Quercus mongolica) is an important constructive and accompanying species in mixed broadleaf-conifer forest in Northeast China, In this paper, a laboratory burning experiment was conducted under zero-slope and no-wind conditions to study the effects of fuel moisture content, loading, and thickness on the fireline intensity, fuel consumption, and combustion efficiency of the Mongolian oak leaf litter fuelbed. The fuel moisture content, loading, and thickness all had significant effects on the three fire behavior indices, and there existed interactions between these three affecting factors. Among the known models, the Byram model could be suitable for the prediction of local leaf litter fire intensity only after re-parameterization. The re-estimated alpha and beta parameters of the re-parameterized Byram model were 98.009 and 1.099, with an adjusted determination coefficient of 0.745, the rooted mean square error (RMSE) of 8.676 kW x m(-1), and the mean relative error (MRE) of 21%, respectively (R2 = 0.745). The re-estimated a and b by the burning efficiency method proposed by Albini were 0.069 and 0.169, and the re-estimated values were all higher than 93%, being mostly overestimated. The Consume model had a stronger suitability for the fuel. The R2 of the general linear models established for fireline intensity, fuel consumption, and burning efficiency was 0.82, 0.73 and 0.53, and the RMSE was 8.266 kW x m(-1) 0.081 kg x m(-2), and 0.203, respectively. In low intensity surface fires, the fine fuels could not be completely consumed, and thus, to consider the leaf litter and fine fuel in some forest ecosystems being completely consumed would overestimate the carbon release from forest fires.

Self-Paced Reaching after Stroke: A Quantitative Assessment of Longitudinal and Directional Sensitivity Using the H-Man Planar Robot for Upper Limb Neurorehabilitation.

PubMed

Hussain, Asif; Budhota, Aamani; Hughes, Charmayne Mary Lee; Dailey, Wayne D; Vishwanath, Deshmukh A; Kuah, Christopher W K; Yam, Lester H L; Loh, Yong J; Xiang, Liming; Chua, Karen S G; Burdet, Etienne; Campolo, Domenico

2016-01-01

Technology aided measures offer a sensitive, accurate and time-efficient approach for the assessment of sensorimotor function after neurological insult compared to standard clinical assessments. This study investigated the sensitivity of robotic measures to capture differences in planar reaching movements as a function of neurological status (stroke, healthy), direction (front, ipsilateral, contralateral), movement segment (outbound, inbound), and time (baseline, post-training, 2-week follow-up) using a planar, two-degrees of freedom, robotic-manipulator (H-Man). Twelve chronic stroke (age: 55 ± 10.0 years, 5 female, 7 male, time since stroke: 11.2 ± 6.0 months) and nine aged-matched healthy participants (age: 53 ± 4.3 years, 5 female, 4 male) participated in this study. Both healthy and stroke participants performed planar reaching movements in contralateral, ipsilateral and front directions with the H-Man, and the robotic measures, spectral arc length (SAL), normalized time to peak velocities ( T peakN ), and root-mean square error (RMSE) were evaluated. Healthy participants went through a one-off session of assessment to investigate the baseline. Stroke participants completed a 2-week intensive robotic training plus standard arm therapy (8 × 90 min sessions). Motor function for stroke participants was evaluated prior to training (baseline, week-0), immediately following training (post-training, week-2), and 2-weeks after training (follow-up, week-4) using robotic assessment and the clinical measures Fugl-Meyer Assessment (FMA), Activity-Research-Arm Test (ARAT), and grip-strength. Robotic assessments were able to capture differences due to neurological status, movement direction, and movement segment. Movements performed by stroke participants were less-smooth, featured longer T peakN , and larger RMSE values, compared to healthy controls. Significant movement direction differences were observed, with improved reaching performance for the front, compared to ipsilateral and contralateral movement directions. There were group differences depending on movement segment. Outbound reaching movements were smoother and featured longer T peakN values than inbound movements for control participants, whereas SAL, T peakN , and RMSE values were similar regardless of movement segment for stroke patients. Significant change in performance was observed between initial and post-assessments using H-Man in stroke participants, compared to conventional scales which showed no significant difference. Results of the study indicate the potential of H-Man as a sensitive tool for tracking changes in performance compared to ordinal scales (i.e., FM, ARAT).

Using High Spatial Resolution Digital Imagery

DTIC Science & Technology

2005-02-01

digital base maps were high resolution U.S. Geological Survey (USGS) Digital Orthophoto Quarter Quadrangles (DOQQ). The Root Mean Square Errors (RMSE...next step was to assign real world coordinates to the linear im- age. The mosaics were geometrically registered to the panchromatic orthophotos ...useable thematic map from high-resolution imagery. A more practical approach may be to divide the Refuge into a set of smaller areas, or tiles

Evaluating the Performance of a New Model for Predicting the Growth of Clostridium perfringens in Cooked, Uncured Meat and Poultry Products under Isothermal, Heating, and Dynamically Cooling Conditions.

PubMed

Huang, Lihan

2016-07-01

Clostridium perfringens type A is a significant public health threat and its spores may germinate, outgrow, and multiply during cooling of cooked meats. This study applies a new C. perfringens growth model in the USDA Integrated Pathogen Modeling Program-Dynamic Prediction (IPMP Dynamic Prediction) Dynamic Prediction to predict the growth from spores of C. perfringens in cooked uncured meat and poultry products using isothermal, dynamic heating, and cooling data reported in the literature. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square error (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors (MRE) of predictions range from -0.40 to 0.02 Log colony forming units (CFU)/g, with a RMSE of approximately 0.6 Log CFU/g. For cooling, the end point predictions are conservative in nature, with an MRE of -1.16 Log CFU/g for single-rate cooling and -0.66 Log CFU/g for dual-rate cooling. The RMSE is between 0.6 and 0.7 Log CFU/g. Compared with other models reported in the literature, this model makes more accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single-rate cooling and 66.7% for dual-rate cooling, while the fail-dangerous predictions are between 0% and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to predict the growth of C. perfringens in uncured cooked meats and evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Validation of geometric accuracy of Global Land Survey (GLS) 2000 data

USGS Publications Warehouse

Rengarajan, Rajagopalan; Sampath, Aparajithan; Storey, James C.; Choate, Michael J.

2015-01-01

The Global Land Survey (GLS) 2000 data were generated from Geocover™ 2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™ 2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, orthorectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™ 2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.

Numerical Analysis of Storm Surge and Seiche at Tokyo Bay caused by the 2 Similar Typhoons, Typhoon Phanphon and Vongfong in 2014

NASA Astrophysics Data System (ADS)

Iwamoto, T.; Takagawa, T.

2017-12-01

A long period damped oscillation, or seiche, sometimes happens inside a harbor after passing typhoon. For some cases, a maximum sea level is observed due to the superposition of astronomical tide and seiche rather than a peak of storm surge. Hence to clarify seiche factors for reducing disaster potential is important, a long-period seiche with a fundamental period of 5.46 hours in Tokyo Bay (Konishi, 2008) was investigated through numerical simulations and analyses. We examined the case of Typhoon Phanphon and Vongfong in 2014 (Hereafter Case P and V). The intensity and moving velocity were similar and the best-tracks were an arc-shaped, typical one approaching to Tokyo Bay. The track of Case V was about 1.5 degree higher latitude than that of Case P, only Typhoon Phanphon caused significant seiche.Firstly, numerical simulations for the 2 storm surges at Tokyo Bay were conducted by Regional Ocean Modeling System (ROMS) and Meso-Scale Model Grid Point Values (MSM-GPV). MSM-GPV gave the 10m wind speed and Sea Level Pressure (SLP), especially the Mean Error (ME) and Root Mean Squire Error (RMSE) of SLP were low compared to the 12 JMA observation points data (Case P: ME -0.303hPa, RMSE 1.87hPa, Case V: ME -0.285hPa, RMSE 0.74hPa). The computational results showed that the maximum of storm surge was underestimated but the difference was less than 20cm at 5 observation points in Tokyo Bay(Fig.1, 2).Then, power spectrals, coherences and phase differences of storm surges at the 5 observation points were obtained by spectral analysis of observed and simulated waveforms. For Case P, the phase-difference between the bay mouth and innermost part of Tokyo Bay was little, and coherence was almost 1(Fig.3, 4). However, for Case V, coherence was small around the fundamental period of 5.46 hours. Furthermore, Empirical Orthogonal Function (EOF) analysis of storm surge, SLP and sea surface stress were conducted. The contributions of EOF1 were above 90% for the all variables, the gradient of storm surge EOF1 was parallel to the bay axis for Case P, but about 50-degree oblique from the axis for Case V(Fig.5, 6). In addition, the EOF1 of SLP for Case P showed a concentric circle structure above Tokyo Bay, besides the structure was not appeared for Case V.

Leaf Surface Effects on Retrieving Chlorophyll Content from Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Qiu, Feng; Chen, JingMing; Ju, Weimin; Wang, Jun; Zhang, Qian

2017-04-01

Light reflected directly from the leaf surface without entering the surface layer is not influenced by leaf internal biochemical content. Leaf surface reflectance varies from leaf to leaf due to differences in the surface roughness features and is relatively more important in strong absorption spectral regions. Therefore it introduces dispersion of data points in the relationship between biochemical concentration and reflectance (especially in the visible region). Separation of surface from total leaf reflection is important to improve the link between leaf pigments content and remote sensing data. This study aims to estimate leaf surface reflectance from hyperspectral remote sensing data and retrieve chlorophyll content by inverting a modified PROSPECT model. Considering leaf surface reflectance is almost the same in the visible and near infrared spectral regions, a surface layer with a reflectance independent of wavelength but varying from leaf to leaf was added to the PROSPECT model. The specific absorption coefficients of pigments were recalibrated. Then the modified model was inverted on independent datasets to check the performance of the model in predicting the chlorophyll content. Results show that differences in estimated surface layer reflectance of various species are noticeable. Surface reflectance of leaves with epicuticular waxes and trichomes is usually higher than other samples. Reconstruction of leaf reflectance and transmittance in the 400-1000 nm wavelength region using the modified PROSPECT model is excellent with low root mean square error (RMSE) and bias. Improvements for samples with high surface reflectance (e.g. maize) are significant, especially for high pigment leaves. Moreover, chlorophyll retrieved from inversion of the modified model is consequently improved (RMSE from 5.9-13.3 ug/cm2 with mean value 8.1 ug/cm2, while mean correlation coefficient is 0.90) compared to results of PROSPECT-5 (RMSE from 9.6-20.2 ug/cm2 with mean value 13.1 ug/cm2, while mean correlation coefficient is 0.81). Underestimation of high chlorophyll content, which is due to underestimation of reflectance in the visible region of PROSPECT, is partially corrected or alleviated. Improvements are particularly noticeable for leaves with high surface reflectance or high chlorophyll content, which both lead to large proportions of surface reflectance to the total leaf reflectance.

Regional characteristics of the relationship between columnar AOD and surface PM2.5: Application of lidar aerosol extinction profiles over Baltimore-Washington Corridor during DISCOVER-AQ

NASA Astrophysics Data System (ADS)

Chu, D. Allen; Ferrare, Richard; Szykman, James; Lewis, Jasper; Scarino, Amy; Hains, Jennifer; Burton, Sharon; Chen, Gao; Tsai, Tzuchin; Hostetler, Chris; Hair, Johnathan; Holben, Brent; Crawford, James

2015-01-01

The first field campaign of DISCOVER-AQ (Deriving Information on Surface conditions from COlumn and VERtically resolved observations relevant to Air Quality) took place in July 2011 over Baltimore-Washington Corridor (BWC). A suite of airborne remote sensing and in-situ sensors was deployed along with ground networks for mapping vertical and horizontal distribution of aerosols. Previous researches were based on a single lidar station because of the lack of regional coverage. This study uses the unique airborne HSRL (High Spectral Resolution Lidar) data to baseline PM2.5 (particulate matter of aerodynamic diameter less than 2.5 μm) estimates and applies to regional air quality with satellite AOD (Aerosol Optical Depth) retrievals over BWC (∼6500 km2). The linear approximation takes into account aerosols aloft above AML (Aerosol Mixing Layer) by normalizing AOD with haze layer height (i.e., AOD/HLH). The estimated PM2.5 mass concentrations by HSRL AOD/HLH are shown within 2 RMSE (Root Mean Square Error ∼9.6 μg/m3) with correlation ∼0.88 with the observed over BWC. Similar statistics are shown when applying HLH data from a single location over the distance of 100 km. In other words, a single lidar is feasible to cover the range of 100 km with expected uncertainties. The employment of MPLNET-AERONET (MicroPulse Lidar NETwork - AErosol RObotic NETwork) measurements at NASA GSFC produces similar statistics of PM2.5 estimates as those derived by HSRL. The synergy of active and passive remote sensing aerosol measurements provides the foundation for satellite application of air quality on a daily basis. For the optimal range of 10 km, the MODIS-estimated PM2.5 values are found satisfactory at 27 (out of 36) sunphotometer locations with mean RMSE of 1.6-3.3 μg/m3 relative to PM2.5 estimated by sunphotometers. The remaining 6 of 8 marginal sites are found in the coastal zone, for which associated large RMSE values ∼4.5-7.8 μg/m3 are most likely due to overestimated AOD because of water-contaminated pixels.

Water requirements of short rotation poplar coppice: Experimental and modelling analyses across Europe

DOE PAGES

Fischer, Milan; Zenone, Terenzio; Trnka, Miroslav; ...

2017-12-26

Poplars are among the most widely used short rotation woody coppice (SRWC) species but due to their assumed high water use, concerns have been raised with respect to large-scale exploitation and potentially detrimental effects on water resources. Here we present a quantitative analysis of the water requirements of poplar SRWC using experimental data and a soil water balance modelling approach at three different sites across Europe. We used (i) eddy covariance (EC) measurements (2004–2006) at an irrigated SRWC grown on a previous rice paddy in northern Italy, (ii) Bowen ratio and energy balance (BREB) measurements (2008–2015) and EC (2011–2015) atmore » a SRWC in rain-fed uplands in the Czech Republic, and (iii) EC measurements (2010–2013) at a SRWC on a previous agricultural land with a shallow water table in Belgium. Without any calibration against water balance component measurements, simulations by the newly developed soil water balance model R-4ET were compared with evapotranspiration (ET) measurements by EC and BREB with a resulting mean root mean square error (RMSE) of 0.75 mm day -1. In general, there was better agreement between EC and the model (RMSE = 0.66 mm day -1) when EC data were adjusted for lack of energy balance closure. A comparison of the simulated and measured soil water content yielded a mean RMSE of 0.03 m 3 m -3. The mean annual crop coefficient, i.e. the ratio between actual and reference ET, was 0.82 (ranging from 0.65 to 0.95) while the monthly maxima reached 1.16. These values indicated that ET of poplar SRWC was significantly lower than ET of a well-watered grass cover at the annual time scale, but exceeded ET of the reference cover at shorter time scales during the growing season. We show that the model R-4ET is a simple, yet reliable tool for the assessment of water requirements of existing or planned SRWC. For very simple assessments on an annual basis, using a crop coefficient of 0.86 (adjusted to a sub-humid climate), representing an average value across the three sites in years with no evident drought stress, is supported by this analysis.« less

Water requirements of short rotation poplar coppice: Experimental and modelling analyses across Europe

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fischer, Milan; Zenone, Terenzio; Trnka, Miroslav

Poplars are among the most widely used short rotation woody coppice (SRWC) species but due to their assumed high water use, concerns have been raised with respect to large-scale exploitation and potentially detrimental effects on water resources. Here we present a quantitative analysis of the water requirements of poplar SRWC using experimental data and a soil water balance modelling approach at three different sites across Europe. We used (i) eddy covariance (EC) measurements (2004–2006) at an irrigated SRWC grown on a previous rice paddy in northern Italy, (ii) Bowen ratio and energy balance (BREB) measurements (2008–2015) and EC (2011–2015) atmore » a SRWC in rain-fed uplands in the Czech Republic, and (iii) EC measurements (2010–2013) at a SRWC on a previous agricultural land with a shallow water table in Belgium. Without any calibration against water balance component measurements, simulations by the newly developed soil water balance model R-4ET were compared with evapotranspiration (ET) measurements by EC and BREB with a resulting mean root mean square error (RMSE) of 0.75 mm day -1. In general, there was better agreement between EC and the model (RMSE = 0.66 mm day -1) when EC data were adjusted for lack of energy balance closure. A comparison of the simulated and measured soil water content yielded a mean RMSE of 0.03 m 3 m -3. The mean annual crop coefficient, i.e. the ratio between actual and reference ET, was 0.82 (ranging from 0.65 to 0.95) while the monthly maxima reached 1.16. These values indicated that ET of poplar SRWC was significantly lower than ET of a well-watered grass cover at the annual time scale, but exceeded ET of the reference cover at shorter time scales during the growing season. We show that the model R-4ET is a simple, yet reliable tool for the assessment of water requirements of existing or planned SRWC. For very simple assessments on an annual basis, using a crop coefficient of 0.86 (adjusted to a sub-humid climate), representing an average value across the three sites in years with no evident drought stress, is supported by this analysis.« less

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

DOE PAGES

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.; ...

2017-10-30

We simulated the entire month of January, 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model, and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, Sym-H, AL, and cross polar cap potential (CPCP). We find that the model does an excellent job of predicting the Sym-H index, with an RMSE of 17-18 nT. Kp is predicted well during storm-time conditions, but over-predicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonablymore » well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to over-predict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resoution, with the exception of the rate of occurrence for strongly negative AL values. As a result, the use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.« less

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.

We simulated the entire month of January, 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model, and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, Sym-H, AL, and cross polar cap potential (CPCP). We find that the model does an excellent job of predicting the Sym-H index, with an RMSE of 17-18 nT. Kp is predicted well during storm-time conditions, but over-predicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonablymore » well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to over-predict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resoution, with the exception of the rate of occurrence for strongly negative AL values. As a result, the use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.« less

Retrieving Land Surface Temperature from Hyperspectral Thermal Infrared Data Using a Multi-Channel Method

PubMed Central

Zhong, Xinke; Huo, Xing; Ren, Chao; Labed, Jelila; Li, Zhao-Liang

2016-01-01

Land Surface Temperature (LST) is a key parameter in climate systems. The methods for retrieving LST from hyperspectral thermal infrared data either require accurate atmospheric profile data or require thousands of continuous channels. We aim to retrieve LST for natural land surfaces from hyperspectral thermal infrared data using an adapted multi-channel method taking Land Surface Emissivity (LSE) properly into consideration. In the adapted method, LST can be retrieved by a linear function of 36 brightness temperatures at Top of Atmosphere (TOA) using channels where LSE has high values. We evaluated the adapted method using simulation data at nadir and satellite data near nadir. The Root Mean Square Error (RMSE) of the LST retrieved from the simulation data is 0.90 K. Compared with an LST product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat, the error in the LST retrieved from the Infared Atmospheric Sounding Interferometer (IASI) is approximately 1.6 K. The adapted method can be used for the near-real-time production of an LST product and to provide the physical method to simultaneously retrieve atmospheric profiles, LST, and LSE with a first-guess LST value. The limitations of the adapted method are that it requires the minimum LSE in the spectral interval of 800–950 cm−1 larger than 0.95 and it has not been extended for off-nadir measurements. PMID:27187408

A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion.

PubMed

Fan, Feiyi; Yan, Yuepeng; Tang, Yongzhong; Zhang, Hao

2017-12-01

Monitoring pulse oxygen saturation (SpO 2 ) and heart rate (HR) using photoplethysmography (PPG) signal contaminated by a motion artifact (MA) remains a difficult problem, especially when the oximeter is not equipped with a 3-axis accelerometer for adaptive noise cancellation. In this paper, we report a pioneering investigation on the impact of altering the frame length of Molgedey and Schuster independent component analysis (ICAMS) on performance, design a multi-classifier fusion strategy for selecting the PPG correlated signal component, and propose a novel approach to extract SpO 2 and HR readings from PPG signal contaminated by strong MA interference. The algorithm comprises multiple stages, including dual frame length ICAMS, a multi-classifier-based PPG correlated component selector, line spectral analysis, tree-based HR monitoring, and post-processing. Our approach is evaluated by multi-subject tests. The root mean square error (RMSE) is calculated for each trial. Three statistical metrics are selected as performance evaluation criteria: mean RMSE, median RMSE and the standard deviation (SD) of RMSE. The experimental results demonstrate that a shorter ICAMS analysis window probably results in better performance in SpO 2 estimation. Notably, the designed multi-classifier signal component selector achieved satisfactory performance. The subject tests indicate that our algorithm outperforms other baseline methods regarding accuracy under most criteria. The proposed work can contribute to improving the performance of current pulse oximetry and personal wearable monitoring devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3

NASA Astrophysics Data System (ADS)

Domonkos, Peter; Coll, John

2018-04-01

The impact of missing data on the efficiency of homogenisation with ACMANTv3 is examined with simulated monthly surface air temperature test datasets. The homogeneous database is derived from an earlier benchmarking of daily temperature data in the USA, and then outliers and inhomogeneities (IHs) are randomly inserted into the time series. Three inhomogeneous datasets are generated and used, one with relatively few and small IHs, another one with IHs of medium frequency and size, and a third one with large and frequent IHs. All of the inserted IHs are changes to the means. Most of the IHs are single sudden shifts or pair of shifts resulting in platform-shaped biases. Each test dataset consists of 158 time series of 100 years length, and their mean spatial correlation is 0.68-0.88. For examining the impacts of missing data, seven experiments are performed, in which 18 series are left complete, while variable quantities (10-70%) of the data of the other 140 series are removed. The results show that data gaps have a greater impact on the monthly root mean squared error (RMSE) than the annual RMSE and trend bias. When data with a large ratio of gaps is homogenised, the reduction of the upper 5% of the monthly RMSE is the least successful, but even there, the efficiency remains positive. In terms of reducing the annual RMSE and trend bias, the efficiency is 54-91%. The inclusion of short and incomplete series with sufficient spatial correlation in all cases improves the efficiency of homogenisation with ACMANTv3.

Arm-eye coordination test to objectively quantify motor performance and muscles activation in persons after stroke undergoing robot-aided rehabilitation training: a pilot study.

PubMed

Song, Rong; Tong, Kai-Yu; Hu, Xiaoling; Li, Le; Sun, Rui

2013-09-01

This study designed an arm-eye coordination test to investigate the effectiveness of the robot-aided rehabilitation for persons after stroke. Six chronic poststroke subjects were recruited to attend a 20-session robot-aided rehabilitation training of elbow joint. Before and after the training program, subjects were asked to perform voluntary movements of elbow flection and extension by following sinusoidal trajectories at different velocities with visual feedback on their joint positions. The elbow angle and the electromyographic signal of biceps and triceps as well as clinical scores were evaluated together with the parameters. Performance was objectively quantified by root mean square error (RMSE), root mean square jerk (RMSJ), range of motion (ROM), and co-contraction index (CI). After 20 sessions, RMSE and ROM improved significantly in both the affected and the unaffected side based on two-way ANOVA (P < 0.05). There was significant lower RMSJ in the affected side at higher velocities (P < 0.05). There was significant negative correlation between average RMSE with different tracking velocities and Fugl-Meyer shoulder-elbow score (P < 0.05). There was also significant negative correlation between average RMSE and average ROM (P < 0.05), and moderate nonsignificant negative correlation with RMSJ, and CI. The characterization of velocity-dependent deficiencies, monitoring of training-induced improvement, and the correlation between quantitative parameters and clinical scales could enable the exploration of effects of different types of treatment and design progress-based training method to accelerate the processes of recovery.

Reliability of Semi-Automated Segmentations in Glioblastoma.

PubMed

Huber, T; Alber, G; Bette, S; Boeckh-Behrens, T; Gempt, J; Ringel, F; Alberts, E; Zimmer, C; Bauer, J S

2017-06-01

In glioblastoma, quantitative volumetric measurements of contrast-enhancing or fluid-attenuated inversion recovery (FLAIR) hyperintense tumor compartments are needed for an objective assessment of therapy response. The aim of this study was to evaluate the reliability of a semi-automated, region-growing segmentation tool for determining tumor volume in patients with glioblastoma among different users of the software. A total of 320 segmentations of tumor-associated FLAIR changes and contrast-enhancing tumor tissue were performed by different raters (neuroradiologists, medical students, and volunteers). All patients underwent high-resolution magnetic resonance imaging including a 3D-FLAIR and a 3D-MPRage sequence. Segmentations were done using a semi-automated, region-growing segmentation tool. Intra- and inter-rater-reliability were addressed by intra-class-correlation (ICC). Root-mean-square error (RMSE) was used to determine the precision error. Dice score was calculated to measure the overlap between segmentations. Semi-automated segmentation showed a high ICC (> 0.985) for all groups indicating an excellent intra- and inter-rater-reliability. Significant smaller precision errors and higher Dice scores were observed for FLAIR segmentations compared with segmentations of contrast-enhancement. Single rater segmentations showed the lowest RMSE for FLAIR of 3.3 % (MPRage: 8.2 %). Both, single raters and neuroradiologists had the lowest precision error for longitudinal evaluation of FLAIR changes. Semi-automated volumetry of glioblastoma was reliably performed by all groups of raters, even without neuroradiologic expertise. Interestingly, segmentations of tumor-associated FLAIR changes were more reliable than segmentations of contrast enhancement. In longitudinal evaluations, an experienced rater can detect progressive FLAIR changes of less than 15 % reliably in a quantitative way which could help to detect progressive disease earlier.

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

NASA Astrophysics Data System (ADS)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

Model for toroidal velocity in H-mode plasmas in the presence of internal transport barriers

NASA Astrophysics Data System (ADS)

Chatthong, B.; Onjun, T.; Singhsomroje, W.

2010-06-01

A model for predicting toroidal velocity in H-mode plasmas in the presence of internal transport barriers (ITBs) is developed using an empirical approach. In this model, it is assumed that the toroidal velocity is directly proportional to the local ion temperature. This model is implemented in the BALDUR integrated predictive modelling code so that simulations of ITB plasmas can be carried out self-consistently. In these simulations, a combination of a semi-empirical mixed Bohm/gyro-Bohm (mixed B/gB) core transport model that includes ITB effects and NCLASS neoclassical transport is used to compute a core transport. The boundary is taken to be at the top of the pedestal, where the pedestal values are described using a theory-based pedestal model based on a combination of magnetic and flow shear stabilization pedestal width scaling and an infinite-n ballooning pressure gradient model. The combination of the mixed B/gB core transport model with ITB effects, together with the pedestal and the toroidal velocity models, is used to simulate the time evolution of plasma current, temperature and density profiles of 10 JET optimized shear discharges. It is found that the simulations can reproduce an ITB formation in these discharges. Statistical analyses including root mean square error (RMSE) and offset are used to quantify the agreement. It is found that the averaged RMSE and offset among these discharges are about 24.59% and -0.14%, respectively.

Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem

NASA Astrophysics Data System (ADS)

Li, Zhenhai; Nie, Chenwei; Yang, Guijun; Xu, Xingang; Jin, Xiuliang; Gu, Xiaohe

2014-10-01

Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R2 and RMSE values were 0.377 and 14.495 μg cm-2 for NAMK, and 0.503 and 10.661 μg cm-2 for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model

PubMed Central

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-01-01

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R2) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R2 of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods. PMID:29642395

Assessing deep and shallow learning methods for quantitative prediction of acute chemical toxicity.

PubMed

Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders

2018-05-02

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30,000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Estimation of winter wheat canopy nitrogen density at different growth stages based on Multi-LUT approach

NASA Astrophysics Data System (ADS)

Li, Zhenhai; Li, Na; Li, Zhenhong; Wang, Jianwen; Liu, Chang

2017-10-01

Rapid real-time monitoring of wheat nitrogen (N) status is crucial for precision N management during wheat growth. In this study, Multi Lookup Table (Multi-LUT) approach based on the N-PROSAIL model parameters setting at different growth stages was constructed to estimating canopy N density (CND) in winter wheat. The results showed that the estimated CND was in line with with measured CND, with the determination coefficient (R2) and the corresponding root mean square error (RMSE) values of 0.80 and 1.16 g m-2, respectively. Time-consuming of one sample estimation was only 6 ms under the test machine with CPU configuration of Intel(R) Core(TM) i5-2430 @2.40GHz quad-core. These results confirmed the potential of using Multi-LUT approach for CND retrieval in winter wheat at different growth stages and under variables climatic conditions.

Numerical Study on Sensitivity of Pollutant Dispersion on Turbulent Schmidt Number in a Street Canyon

NASA Astrophysics Data System (ADS)

WANG, J.; Kim, J.

2014-12-01

In this study, sensitivity of pollutant dispersion on turbulent Schmidt number (Sct) was investigated in a street canyon using a computational fluid dynamics (CFD) model. For this, numerical simulations with systematically varied Sct were performed and the CFD model results were validated against a wind‒tunnel measurement data. The results showed that root mean square error (RMSE) was quite dependent on Sct and dispersion patterns of non‒reactive scalar pollutant with different Sct were quite different among the simulation results. The RMSE was lowest in the case of Sct = 0.35 and the apparent dispersion pattern was most similar to the wind‒tunnel data in the case of Sct = 0.35. Also, numerical simulations using spatially weighted Sct were additionally performed in order for the best reproduction of the wind‒tunnel data. Detailed method and procedure to find the best reproduction will be presented.

Retrieving air humidity, global solar radiation, and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part I: humidity

NASA Astrophysics Data System (ADS)

Lobit, P.; López Pérez, L.; Lhomme, J. P.; Gómez Tagle, A.

2017-07-01

This study evaluates the dew point method (Allen et al. 1998) to estimate atmospheric vapor pressure from minimum temperature, and proposes an improved model to estimate it from maximum and minimum temperature. Both methods were evaluated on 786 weather stations in Mexico. The dew point method induced positive bias in dry areas but also negative bias in coastal areas, and its average root mean square error for all evaluated stations was 0.38 kPa. The improved model assumed a bi-linear relation between estimated vapor pressure deficit (difference between saturated vapor pressure at minimum and average temperature) and measured vapor pressure deficit. The parameters of these relations were estimated from historical annual median values of relative humidity. This model removed bias and allowed for a root mean square error of 0.31 kPa. When no historical measurements of relative humidity were available, empirical relations were proposed to estimate it from latitude and altitude, with only a slight degradation on the model accuracy (RMSE = 0.33 kPa, bias = -0.07 kPa). The applicability of the method to other environments is discussed.

Estimation of Thermal Sensation Based on Wrist Skin Temperatures.

PubMed

Sim, Soo Young; Koh, Myung Jun; Joo, Kwang Min; Noh, Seungwoo; Park, Sangyun; Kim, Youn Ho; Park, Kwang Suk

2016-03-23

Thermal comfort is an essential environmental factor related to quality of life and work effectiveness. We assessed the feasibility of wrist skin temperature monitoring for estimating subjective thermal sensation. We invented a wrist band that simultaneously monitors skin temperatures from the wrist (i.e., the radial artery and ulnar artery regions, and upper wrist) and the fingertip. Skin temperatures from eight healthy subjects were acquired while thermal sensation varied. To develop a thermal sensation estimation model, the mean skin temperature, temperature gradient, time differential of the temperatures, and average power of frequency band were calculated. A thermal sensation estimation model using temperatures of the fingertip and wrist showed the highest accuracy (mean root mean square error [RMSE]: 1.26 ± 0.31). An estimation model based on the three wrist skin temperatures showed a slightly better result to the model that used a single fingertip skin temperature (mean RMSE: 1.39 ± 0.18). When a personalized thermal sensation estimation model based on three wrist skin temperatures was used, the mean RMSE was 1.06 ± 0.29, and the correlation coefficient was 0.89. Thermal sensation estimation technology based on wrist skin temperatures, and combined with wearable devices may facilitate intelligent control of one's thermal environment.

Estimation of Thermal Sensation Based on Wrist Skin Temperatures

PubMed Central

Sim, Soo Young; Koh, Myung Jun; Joo, Kwang Min; Noh, Seungwoo; Park, Sangyun; Kim, Youn Ho; Park, Kwang Suk

2016-01-01

Thermal comfort is an essential environmental factor related to quality of life and work effectiveness. We assessed the feasibility of wrist skin temperature monitoring for estimating subjective thermal sensation. We invented a wrist band that simultaneously monitors skin temperatures from the wrist (i.e., the radial artery and ulnar artery regions, and upper wrist) and the fingertip. Skin temperatures from eight healthy subjects were acquired while thermal sensation varied. To develop a thermal sensation estimation model, the mean skin temperature, temperature gradient, time differential of the temperatures, and average power of frequency band were calculated. A thermal sensation estimation model using temperatures of the fingertip and wrist showed the highest accuracy (mean root mean square error [RMSE]: 1.26 ± 0.31). An estimation model based on the three wrist skin temperatures showed a slightly better result to the model that used a single fingertip skin temperature (mean RMSE: 1.39 ± 0.18). When a personalized thermal sensation estimation model based on three wrist skin temperatures was used, the mean RMSE was 1.06 ± 0.29, and the correlation coefficient was 0.89. Thermal sensation estimation technology based on wrist skin temperatures, and combined with wearable devices may facilitate intelligent control of one’s thermal environment. PMID:27023538

Evaluating the Height of Biomass Burning Smoke Aerosols Retrieved from Synergistic Use of Multiple Satellite Sensors Over Southeast Asia

NASA Technical Reports Server (NTRS)

Lee, Jaehwa; Hsu, N. Christina; Bettenhausen, Corey; Sayer, Andrew M.; Seftor, Colin J.; Jeong, Myeong-Jae; Tsay, Si-Chee; Welton, Ellsworth J.; Wang, Sheng-Hsiang; Chen, Wei-Nai

2016-01-01

This study evaluates the height of biomass burning smoke aerosols retrieved from a combined use of Visible Infrared Imaging Radiometer Suite (VIIRS), Ozone Mapping and Profiler Suite (OMPS), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. The retrieved heights are compared against space borne and ground-based lidar measurements during the peak biomass burning season (March and April) over Southeast Asia from 2013 to 2015. Based on the comparison against CALIOP, a quality assurance (QA) procedure is developed. It is found that 74 (8184) of the retrieved heights fall within 1 km of CALIOP observations for unfiltered (QA-filtered) data, with root-mean-square error (RMSE) of 1.1 km (0.81.0 km). Eliminating the requirement of CALIOP observations from the retrieval process significantly increases the temporal coverage with only a slight decrease in the retrieval accuracy; for best QA data, 64 of data fall within 1 km of CALIOP observations with RMSE of 1.1 km. When compared with Micro-Pulse Lidar Network (MPLNET) measurements deployed at Doi Ang Khang, Thailand, the retrieved heights show RMSE of 1.7 km (1.1 km) for unfiltered (QA-filtered) data for the complete algorithm, and 0.9 km (0.8 km) for the simplified algorithm.

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

PubMed

Smyczynska, Joanna; Hilczer, Maciej; Smyczynska, Urszula; Stawerska, Renata; Tadeusiewicz, Ryszard; Lewinski, Andrzej

2015-01-01

The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness. Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH. For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability. ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.

Studies on Radar Sensor Networks

DTIC Science & Technology

2007-08-08

scheme in which 2-D image was created via adding voltages with the appropriate time offset. Simulation results show that our DCT-based scheme works...using RSNs in terms of the probability of miss detection PMD and the root mean square error (RMSE). Simulation results showed that multi-target detection... Simulation results are presented to evaluate the feasibility and effectiveness of the proposed JMIC algorithm in a query surveillance region. 5 SVD-QR and

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

Calculation of distribution coefficients in the SAMPL5 challenge from atomic solvation parameters and surface areas.

PubMed

Santos-Martins, Diogo; Fernandes, Pedro Alexandrino; Ramos, Maria João

2016-11-01

In the context of SAMPL5, we submitted blind predictions of the cyclohexane/water distribution coefficient (D) for a series of 53 drug-like molecules. Our method is purely empirical and based on the additive contribution of each solute atom to the free energy of solvation in water and in cyclohexane. The contribution of each atom depends on the atom type and on the exposed surface area. Comparatively to similar methods in the literature, we used a very small set of atomic parameters: only 10 for solvation in water and 1 for solvation in cyclohexane. As a result, the method is protected from overfitting and the error in the blind predictions could be reasonably estimated. Moreover, this approach is fast: it takes only 0.5 s to predict the distribution coefficient for all 53 SAMPL5 compounds, allowing its application in virtual screening campaigns. The performance of our approach (submission 49) is modest but satisfactory in view of its efficiency: the root mean square error (RMSE) was 3.3 log D units for the 53 compounds, while the RMSE of the best performing method (using COSMO-RS) was 2.1 (submission 16). Our method is implemented as a Python script available at https://github.com/diogomart/SAMPL5-DC-surface-empirical .

Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements

NASA Astrophysics Data System (ADS)

Chen, Jiang; Zhu, Weining; Tian, Yong Q.; Yu, Qian; Zheng, Yuhan; Huang, Litong

2017-07-01

Colored dissolved organic matter (CDOM) and chlorophyll-a (Chla) are important water quality parameters and play crucial roles in aquatic environment. Remote sensing of CDOM and Chla concentrations for inland lakes is often limited by low spatial resolution. The newly launched Sentinel-2 satellite is equipped with high spatial resolution (10, 20, and 60 m). Empirical band ratio models were developed to derive CDOM and Chla concentrations in Lake Huron. The leave-one-out cross-validation method was used for model calibration and validation. The best CDOM retrieval algorithm is a B3/B5 model with accuracy coefficient of determination (R2)=0.884, root-mean-squared error (RMSE)=0.731 m-1, relative root-mean-squared error (RRMSE)=28.02%, and bias=-0.1 m-1. The best Chla retrieval algorithm is a B5/B4 model with accuracy R2=0.49, RMSE=9.972 mg/m3, RRMSE=48.47%, and bias=-0.116 mg/m3. Neural network models were further implemented to improve inversion accuracy. The applications of the two best band ratio models to Sentinel-2 imagery with 10 m×10 m pixel size presented the high potential of the sensor for monitoring water quality of inland lakes.

Approximation of the exponential integral (well function) using sampling methods

NASA Astrophysics Data System (ADS)

Baalousha, Husam Musa

2015-04-01

Exponential integral (also known as well function) is often used in hydrogeology to solve Theis and Hantush equations. Many methods have been developed to approximate the exponential integral. Most of these methods are based on numerical approximations and are valid for a certain range of the argument value. This paper presents a new approach to approximate the exponential integral. The new approach is based on sampling methods. Three different sampling methods; Latin Hypercube Sampling (LHS), Orthogonal Array (OA), and Orthogonal Array-based Latin Hypercube (OA-LH) have been used to approximate the function. Different argument values, covering a wide range, have been used. The results of sampling methods were compared with results obtained by Mathematica software, which was used as a benchmark. All three sampling methods converge to the result obtained by Mathematica, at different rates. It was found that the orthogonal array (OA) method has the fastest convergence rate compared with LHS and OA-LH. The root mean square error RMSE of OA was in the order of 1E-08. This method can be used with any argument value, and can be used to solve other integrals in hydrogeology such as the leaky aquifer integral.

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.

PubMed

Heddam, Salim

2014-01-01

In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling.

Long-term surface EMG monitoring using K-means clustering and compressive sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

Motion management within two respiratory-gating windows: feasibility study of dual quasi-breath-hold technique in gated medical procedures

NASA Astrophysics Data System (ADS)

Kim, Taeho; Kim, Siyong; Park, Yang-Kyun; Youn, Kaylin K.; Keall, Paul; Lee, Rena

2014-11-01

A dual quasi-breath-hold (DQBH) technique is proposed for respiratory motion management (a hybrid technique combining breathing-guidance with breath-hold task in the middle). The aim of this study is to test a hypothesis that the DQBH biofeedback system improves both the capability of motion management and delivery efficiency. Fifteen healthy human subjects were recruited for two respiratory motion measurements (free breathing and DQBH biofeedback breathing for 15 min). In this study, the DQBH biofeedback system utilized the abdominal position obtained using an real-time position management (RPM) system (Varian Medical Systems, Palo Alto, USA) to audio-visually guide a human subject for 4 s breath-hold at EOI and 90% EOE (EOE90%) to improve delivery efficiency. We investigated the residual respiratory motion and the delivery efficiency (duty-cycle) of abdominal displacement within the gating window. The improvement of the abdominal motion reproducibility was evaluated in terms of cycle-to-cycle displacement variability, respiratory period and baseline drift. The DQBH biofeedback system improved the abdominal motion management capability compared to that with free breathing. With a phase based gating (mean ± std: 55  ±  5%), the averaged root mean square error (RMSE) of the abdominal displacement in the dual-gating windows decreased from 2.26 mm of free breathing to 1.16 mm of DQBH biofeedback (p-value = 0.007). The averaged RMSE of abdominal displacement over the entire respiratory cycles reduced from 2.23 mm of free breathing to 1.39 mm of DQBH biofeedback breathing in the dual-gating windows (p-value = 0.028). The averaged baseline drift dropped from 0.9 mm min-1 with free breathing to 0.09 mm min-1 with DQBH biofeedback (p-value = 0.048). The averaged duty-cycle with an 1 mm width of displacement bound increased from 15% of free breathing to 26% of DQBH biofeedback (p-value = 0.003). The study demonstrated that the DQBH biofeedback system has the potential to significantly reduce the residual respiratory motion with the improved duty cycle during the respiratory gating procedure.

Neural-Network Approach to Hyperspectral Data Analysis for Volcanic Ash Clouds Monitoring

NASA Astrophysics Data System (ADS)

Piscini, Alessandro; Ventress, Lucy; Carboni, Elisa; Grainger, Roy Gordon; Del Frate, Fabio

2015-11-01

In this study three artificial neural networks (ANN) were implemented in order to emulate a retrieval model and to estimate the ash Aerosol optical Depth (AOD), particle effective radius (reff) and cloud height from volcanic eruption using hyperspectral remotely sensed data. ANNs were trained using a selection of Infrared Atmospheric Sounding Interferometer (IASI) channels in Thermal Infrared (TIR) as inputs, and the corresponding ash parameters retrieved obtained using the Oxford retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajo ̈kull volcano (Iceland) occurred in 2010. The results of validation provided root mean square error (RMSE) values between neural network outputs and targets lower than standard deviation (STD) of corresponding target outputs, therefore demonstrating the feasibility to estimate volcanic ash parameters using an ANN approach, and its importance in near real time monitoring activities, owing to its fast application. A high accuracy has been achieved for reff and cloud height estimation, while a decreasing in accuracy was obtained when applying the NN approach for AOD estimation, in particular for those values not well characterized during NN training phase.

[Retrieval of crown closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model].

PubMed

Wang, Cong; Du, Hua-qiang; Zhou, Guo-mo; Xu, Xiao-jun; Sun, Shao-bo; Gao, Guo-long

2015-05-01

This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of crown closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of crown closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in crown closure estimation, which was closer to the actual condition in moso bamboo forest.

Uncertainty in Pedotransfer Functions from Soil Survey Data

NASA Astrophysics Data System (ADS)

Pachepsky, Y. A.; Rawls, W. J.

2002-05-01

Pedotransfer functions (PTFs) are empirical relationships between hard-to-get soil parameters, i.e. hydraulic properties, and more easily obtainable basic soil properties, such as texture. Use of PTFs in large-scale projects and pilot studies relies on data of soil survey that provides soil basic data as a categorical information. Unlike numerical variables, categorical data cannot be directly used in statistical regressions or neural networks to develop PTFs. Objectives of this work were (a) to find and test techniques to develop PTFs for soil water retention and saturated hydraulic conductivity with soil categorical data as inputs, (b) to evaluate sources of uncertainty in results of such PTFs and to research opportunities of mitigating the uncertainty. We used a subset of about 12,000 samples from the US National Soil characterization database to estimate water retention, and the data set for circa 1000 hydraulic conductivity measurements done in the US. Regression trees and polynomial neural networks based on dummy coding were the techniques tried for the PTF development. The jackknife validation was used to prevent the over-parameterization. Both techniques were equally efficient in developing PTFs, but regression trees gave much more transparent results. Textural class was the leading predictor with RMSE values of about 6.5 and 4.1 vol.% for water retention at -33 and -1500 kPa, respectively. The RMSE values decreased 10% when the laboratory textural analysis was used to establish the textural class. Textural class in the field was determined correctly only in 41% of all cases. To mitigate this source of error, we added slopes, position on the slope classes, and land surface shape classes to the list of PTF inputs. Regression trees generated topotextural groups that encompassed several textural classes. Using topographic variables and soil horizon appeared to be the way to make up for errors made in field determination of texture. Adding field descriptors of soil structure to the field-determined textural class gave similar results. No large improvement was achieved probably because textural class, topographic descriptors and structure descriptors were correlated predictors in many cases. Both median values and uncertainty of the saturated hydraulic conductivity had a power-law decrease as clay content increased. Defining two classes of bulk density helped to estimate hydraulic conductivity within textural classes. We conclude that categorical field soil survey data can be used in PTF-based estimating soil water retention and saturated hydraulic conductivity with quantified uncertainty

Gold - A novel deconvolution algorithm with optimization for waveform LiDAR processing

NASA Astrophysics Data System (ADS)

Zhou, Tan; Popescu, Sorin C.; Krause, Keith; Sheridan, Ryan D.; Putman, Eric

2017-07-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, <0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, <1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01 m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE. Additionally, the high level of uncertainty occurs more on areas with high slope and high vegetation. This study provides an alternative and innovative approach for waveform processing that will benefit high fidelity processing of waveform LiDAR data to characterize vegetation structures.

Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects.

PubMed

Pradhan, Sudeep; Song, Byungjeong; Lee, Jaeyeon; Chae, Jung-Woo; Kim, Kyung Im; Back, Hyun-Moon; Han, Nayoung; Kwon, Kwang-Il; Yun, Hwi-Yeol

2017-12-01

Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects. In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2 ), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results. The rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset. The classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling.

Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

NASA Astrophysics Data System (ADS)

Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

2013-04-01

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

[Application of wavelet neural networks model to forecast incidence of syphilis].

PubMed

Zhou, Xian-Feng; Feng, Zi-Jian; Yang, Wei-Zhong; Li, Xiao-Song

2011-07-01

To apply Wavelet Neural Networks (WNN) model to forecast incidence of Syphilis. Back Propagation Neural Network (BPNN) and WNN were developed based on the monthly incidence of Syphilis in Sichuan province from 2004 to 2008. The accuracy of forecast was compared between the two models. In the training approximation, the mean absolute error (MAE), rooted mean square error (RMSE) and mean absolute percentage error (MAPE) were 0.0719, 0.0862 and 11.52% respectively for WNN, and 0.0892, 0.1183 and 14.87% respectively for BPNN. The three indexes for generalization of models were 0.0497, 0.0513 and 4.60% for WNN, and 0.0816, 0.1119 and 7.25% for BPNN. WNN is a better model for short-term forecasting of Syphilis.

Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

NASA Astrophysics Data System (ADS)

Zhao, Hong; Li, Changjun; Li, Hongping; Lv, Kebo; Zhao, Qinghui

2016-06-01

The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°-25°N, 105°-125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Constitutive Modeling of High-Temperature Flow Behavior of an Nb Micro-alloyed Hot Stamping Steel

NASA Astrophysics Data System (ADS)

Zhang, Shiqi; Feng, Ding; Huang, Yunhua; Wei, Shizhong; Mohrbacher, Hardy; Zhang, Yue

2016-03-01

The thermal deformation behavior and constitutive models of an Nb micro-alloyed 22MnB5 steel were investigated by conducting isothermal uniaxial tensile tests at the temperature range of 873-1223 K with strain rates of 0.1-10 s-1. The results indicated that the investigated steel showed typical work hardening and dynamic recovery behavior during hot deformation, and the flow stress decreased with a decrease in strain rate and/or an increase in temperature. On the basis of the experimental data, the modified Johnson-Cook (modified JC), modified Norton-Hoff (modified NH), and Arrhenius-type (AT) constitutive models were established for the subject steel. However, the flow stress values predicted by these three models revealed some remarkable deviations from the experimental values for certain experimental conditions. Therefore, a new combined modified Norton-Hoff and Arrhenius-type constitutive model (combined modified NH-AT model), which accurately reflected both the work hardening and dynamic recovery behavior of the subject steel, was developed by introducing the modified parameter k ɛ. Furthermore, the accuracy of these constitutive models was assessed by the correlation coefficient, the average absolute relative error, and the root mean square error, which indicated that the flow stress values computed by the combined modified NH-AT model were highly consistent with the experimental values (R = 0.998, AARE = 1.63%, RMSE = 3.85 MPa). The result confirmed that the combined modified NH-AT model was suitable for the studied Nb micro-alloyed hot stamping steel. Additionally, the practicability of the new model was also verified using finite element simulations in ANSYS/LS-DYNA, and the results confirmed that the new model was practical and highly accurate.

Evaluation of high resolution global satellite precipitation products using daily raingauge data over the Upper Blue Nile Basin

NASA Astrophysics Data System (ADS)

Sahlu, Dejene; Moges, Semu; Anagnostou, Emmanouil; Nikolopoulos, Efthymios; Hailu, Dereje; Mei, Yiwen

2017-04-01

Water resources assessment, planning and management in Africa is often constrained by the lack of reliable spatio-temporal rainfall data. Satellite products are steadily growing and offering useful alternative datasets of rainfall globally. The aim of this paper is to examine the error characteristics of the main available global satellite precipitation products with the view of improving the reliability of wet season (June to September) and small rainy season rainfall datasets over the Upper Blue Nile Basin. The study utilized six satellite derived precipitation datasets at 0.25-deg spatial grid size and daily temporal resolution:1) the near real-time (3B42_RT) and gauge adjusted (3B42_V7) products of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), 2) gauge adjusted and unadjusted Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products and 3) the gauge adjusted and un-adjusted product of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Morphing technique (CMORPH) over the period of 2000 to 2013.The error analysis utilized statistical techniques using bias ratio (Bias), correlation coefficient (CC) and root-mean-square-error (RMSE). Mean relative error (MRE), CC and RMSE metrics are further examined for six categories of 10th, 25th, 50th, 75th, 90thand 95th percentile rainfall thresholds. The skill of the satellite estimates is evaluated using categorical error metrics of missed rainfall volume fraction (MRV), falsely detected rainfall volume fraction (FRV), probability of detection (POD) and False Alarm Ratio (FAR). Results showed that six satellite based rainfall products underestimated wet season (June to September) gauge precipitation, with the exception of non-adjusted PERSIANN that overestimated the initial part of the rainy season (March to May). During the wet season, adjusted CMORPH has relatively better bias ratio (89 %) followed by 3B42_V7 (88%), adjusted-PERSIANN (81%), and non-adjusted products have relatively lower bias ratio. The results from CC statistic range from 0.34 to 0.43 for the wet season with adjusted products having slightly higher values. The initial rainy season has relatively higher CC than the wet season. Results from the categorical error metrics showed that CMORPH products have higher POD (91%), which are better in avoiding detecting false rainfall events in the wet season. For the initial rainy season PERSIANN (<50%), TMPA and CMORPH products are nearly equivalent (63-67%). On the other hand, FAR is below 0.1% for all products while in the wet season is higher (10-25%). In terms of rainfall volume of missed and false detected rainfall, CMORPH exhibited lower MRV ( 4.5%) than the TMPA and PERSIANN products (11-19%.) in the wet season. MRV for the initial rainy season was 20% for TMPA and CMORPH products and above 30% for PERSIANN products. All products are nearly equivalent in the wet season in terms of FRV (< 0.2%). The magnitude of MRE increases with gauge rainfall threshold categories with 3B42-V7 and adjusted CMORPH having lower magnitude, showing that underestimation of rainfall increases with increasing rainfall magnitude. CC also decreases with gauge rainfall threshold categories with CMORPH products having slightly higher values. Overall, all satellite products underestimated (overestimated) lower (higher) quantiles quantiles. We have observed that among the six satellite rainfall products the adjusted CMORPH has relatively better potential to improve wet season rainfall estimate and 3B42-V7 that initial rainy season in the Upper Blue Nile Basin.

Empirical prediction of net portal appearance of volatile fatty acids, glucose, and their secondary metabolites (beta-hydroxybutyrate, lactate) from dietary characteristics in ruminants: A meta-analysis approach.

PubMed

Loncke, C; Ortigues-Marty, I; Vernet, J; Lapierre, H; Sauvant, D; Nozière, P

2009-01-01

The current trend in energy feeding systems for ruminants toward a nutrient-based system requires dietary energy supply to be determined in terms of amount and nature of absorbed energy-yielding nutrients. The objective of this study was to establish response equations on the net portal appearance (NPA) of VFA and glucose, and their secondary metabolites beta-hydroxybutyrate (BHBA) and lactate, to changes in intake level and chemical dietary characteristics based on the Institut National de la Recherche Agronomique Feed Evaluation System for Ruminants. Meta-analyses were applied on published data compiled from the FLORA database, which pools the results on net splanchnic nutrient fluxes in multi-catheterized ruminants from international publications. For each nutrient, several prediction variables were tested. We obtained robust models for intakes up to 30 g of DM x d(-1) x kg of BW(-1) and diets containing less than 70 g of concentrate per 100 g of DM. These models were designed to predict the NPA (mmol x h(-1) x kg of BW(-1)) of total VFA based on the amount of ruminally fermented OM (RfOM) intake [adjusted R(2) (R(2)(adj)) = 0.95; residual means square errors (RMSE) = 0.24], to predict VFA profile (mol/100 mol of total VFA) based on type of RfOM intake (acetate: R(2)(adj) = 0.85, RMSE = 2.2; propionate: R(2)(adj) = 0.76, RMSE = 2.2; butyrate: R(2)(adj) = 0.76, RMSE = 1.09), and to predict the NPA (mmol x h(-1) x kg of BW(-1)) of glucose based on the starch digested in the small intestine independent of ruminant species, and while presenting no interfering factors on the residuals and individual slopes. The model predicting the NPA (mmol x h(-1) x kg of BW(-1)) of BHBA based on the amount of RfOM intake (R(2)(adj) = 0.91; RMSE = 0.036) was species-dependent, and the model predicting NPA (mmol x h(-1) x kg of BW(-1)) of lactate based on starch digested in the rumen (R(2)(adj) = 0.77; RMSE = 0.042) presented a wide dispersion. However, the NPA (mmol x h(-1) x kg of BW(-1)) of BHBA was related to the NPA of both butyrate (R(2)(adj) = 0.85; RMSE = 0.054) and acetate (R(2)(adj) = 0.85; RMSE = 0.052), and the NPA (mmol x h(-1) x kg of BW (-1)) of lactate was related to the NPA of propionate (R(2)(adj) = 0.51; RMSE = 0.096). This research showed that it is possible to accurately predict the amount and nature of absorbed nutrient fluxes based on dietary characteristics in both sheep and cattle. This work aims to quantify the consequences of digestion and portal-drained viscera metabolism on nutrient availability. These results can provide deeper insight into biological processes and help develop improved tools for dietary formulation.

Estimating system parameters for solvent-water and plant cuticle-water using quantum chemically estimated Abraham solute parameters.

PubMed

Liang, Yuzhen; Torralba-Sanchez, Tifany L; Di Toro, Dominic M

2018-04-18

Polyparameter Linear Free Energy Relationships (pp-LFERs) using Abraham system parameters have many useful applications. However, developing the Abraham system parameters depends on the availability and quality of the Abraham solute parameters. Using Quantum Chemically estimated Abraham solute Parameters (QCAP) is shown to produce pp-LFERs that have lower root mean square errors (RMSEs) of predictions for solvent-water partition coefficients than parameters that are estimated using other presently available methods. pp-LFERs system parameters are estimated for solvent-water, plant cuticle-water systems, and for novel compounds using QCAP solute parameters and experimental partition coefficients. Refitting the system parameter improves the calculation accuracy and eliminates the bias. Refitted models for solvent-water partition coefficients using QCAP solute parameters give better results (RMSE = 0.278 to 0.506 log units for 24 systems) than those based on ABSOLV (0.326 to 0.618) and QSPR (0.294 to 0.700) solute parameters. For munition constituents and munition-like compounds not included in the calibration of the refitted model, QCAP solute parameters produce pp-LFER models with much lower RMSEs for solvent-water partition coefficients (RMSE = 0.734 and 0.664 for original and refitted model, respectively) than ABSOLV (4.46 and 5.98) and QSPR (2.838 and 2.723). Refitting plant cuticle-water pp-LFER including munition constituents using QCAP solute parameters also results in lower RMSE (RMSE = 0.386) than that using ABSOLV (0.778) and QSPR (0.512) solute parameters. Therefore, for fitting a model in situations for which experimental data exist and system parameters can be re-estimated, or for which system parameters do not exist and need to be developed, QCAP is the quantum chemical method of choice.

Clinical anthropometrics and body composition from 3D whole-body surface scans.

PubMed

Ng, B K; Hinton, B J; Fan, B; Kanaya, A M; Shepherd, J A

2016-11-01

Obesity is a significant worldwide epidemic that necessitates accessible tools for robust body composition analysis. We investigated whether widely available 3D body surface scanners can provide clinically relevant direct anthropometrics (circumferences, areas and volumes) and body composition estimates (regional fat/lean masses). Thirty-nine healthy adults stratified by age, sex and body mass index (BMI) underwent whole-body 3D scans, dual energy X-ray absorptiometry (DXA), air displacement plethysmography and tape measurements. Linear regressions were performed to assess agreement between 3D measurements and criterion methods. Linear models were derived to predict DXA body composition from 3D scan measurements. Thirty-seven external fitness center users underwent 3D scans and bioelectrical impedance analysis for model validation. 3D body scan measurements correlated strongly to criterion methods: waist circumference R 2 =0.95, hip circumference R 2 =0.92, surface area R 2 =0.97 and volume R 2 =0.99. However, systematic differences were observed for each measure due to discrepancies in landmark positioning. Predictive body composition equations showed strong agreement for whole body (fat mass R 2 =0.95, root mean square error (RMSE)=2.4 kg; fat-free mass R 2 =0.96, RMSE=2.2 kg) and arms, legs and trunk (R 2 =0.79-0.94, RMSE=0.5-1.7 kg). Visceral fat prediction showed moderate agreement (R 2 =0.75, RMSE=0.11 kg). 3D surface scanners offer precise and stable automated measurements of body shape and composition. Software updates may be needed to resolve measurement biases resulting from landmark positioning discrepancies. Further studies are justified to elucidate relationships between body shape, composition and metabolic health across sex, age, BMI and ethnicity groups, as well as in those with metabolic disorders.

Measuring joint kinematics of treadmill walking and running: Comparison between an inertial sensor based system and a camera-based system.

PubMed

Nüesch, Corina; Roos, Elena; Pagenstert, Geert; Mündermann, Annegret

2017-05-24

Inertial sensor systems are becoming increasingly popular for gait analysis because their use is simple and time efficient. This study aimed to compare joint kinematics measured by the inertial sensor system RehaGait® with those of an optoelectronic system (Vicon®) for treadmill walking and running. Additionally, the test re-test repeatability of kinematic waveforms and discrete parameters for the RehaGait® was investigated. Twenty healthy runners participated in this study. Inertial sensors and reflective markers (PlugIn Gait) were attached according to respective guidelines. The two systems were started manually at the same time. Twenty consecutive strides for walking and running were recorded and each software calculated sagittal plane ankle, knee and hip kinematics. Measurements were repeated after 20min. Ensemble means were analyzed calculating coefficients of multiple correlation for waveforms and root mean square errors (RMSE) for waveforms and discrete parameters. After correcting the offset between waveforms, the two systems/models showed good agreement with coefficients of multiple correlation above 0.950 for walking and running. RMSE of the waveforms were below 5° for walking and below 8° for running. RMSE for ranges of motion were between 4° and 9° for walking and running. Repeatability analysis of waveforms showed very good to excellent coefficients of multiple correlation (>0.937) and RMSE of 3° for walking and 3-7° for running. These results indicate that in healthy subjects sagittal plane joint kinematics measured with the RehaGait® are comparable to those using a Vicon® system/model and that the measured kinematics have a good repeatability, especially for walking. Copyright © 2017 Elsevier Ltd. All rights reserved.

Validation of the Leap Motion Controller using markered motion capture technology.

PubMed

Smeragliuolo, Anna H; Hill, N Jeremy; Disla, Luis; Putrino, David

2016-06-14

The Leap Motion Controller (LMC) is a low-cost, markerless motion capture device that tracks hand, wrist and forearm position. Integration of this technology into healthcare applications has begun to occur rapidly, making validation of the LMC׳s data output an important research goal. Here, we perform a detailed evaluation of the kinematic data output from the LMC, and validate this output against gold-standard, markered motion capture technology. We instructed subjects to perform three clinically-relevant wrist (flexion/extension, radial/ulnar deviation) and forearm (pronation/supination) movements. The movements were simultaneously tracked using both the LMC and a marker-based motion capture system from Motion Analysis Corporation (MAC). Adjusting for known inconsistencies in the LMC sampling frequency, we compared simultaneously acquired LMC and MAC data by performing Pearson׳s correlation (r) and root mean square error (RMSE). Wrist flexion/extension and radial/ulnar deviation showed good overall agreement (r=0.95; RMSE=11.6°, and r=0.92; RMSE=12.4°, respectively) with the MAC system. However, when tracking forearm pronation/supination, there were serious inconsistencies in reported joint angles (r=0.79; RMSE=38.4°). Hand posture significantly influenced the quality of wrist deviation (P<0.005) and forearm supination/pronation (P<0.001), but not wrist flexion/extension (P=0.29). We conclude that the LMC is capable of providing data that are clinically meaningful for wrist flexion/extension, and perhaps wrist deviation. It cannot yet return clinically meaningful data for measuring forearm pronation/supination. Future studies should continue to validate the LMC as updated versions of their software are developed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Google Earth elevation data extraction and accuracy assessment for transportation applications

PubMed Central

Wang, Yinsong; Zou, Yajie; Henrickson, Kristian; Wang, Yinhai; Tang, Jinjun; Park, Byung-Jung

2017-01-01

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications. PMID:28445480

Google Earth elevation data extraction and accuracy assessment for transportation applications.

PubMed

Wang, Yinsong; Zou, Yajie; Henrickson, Kristian; Wang, Yinhai; Tang, Jinjun; Park, Byung-Jung

2017-01-01

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications.

In-Flight Study of Helmet-Mounted Symbology System Concepts in Degraded Visual Environments.

PubMed

Cheung, Bob; Craig, Gregory; Steels, Brad; Sceviour, Robert; Cosman, Vaughn; Jennings, Sion; Holst, Peter

2015-08-01

During approach and departure in rotary wing aircraft, a sudden loss of external visual reference precipitates spatial disorientation. There were 10 Royal Canadian Air Force (RCAF) Griffon pilots who participated in an in-flight investigation of a 3-dimensional conformal Helmet Display Tracking System (HDTS) and the BrownOut Symbology System (BOSS) aboard an Advanced System Research Aircraft. For each symbology system, pilots performed a two-stage departure followed by a single-stage approach. The presentation order of the two symbology systems was randomized across the pilots. Subjective measurements included situation awareness, mental effort, perceived performance, perceptual cue rating, NASA Task Load Index, and physiological response. Objective performance included aircraft speed, altitude, attitude, and distance from the landing point, control position, and control activity. Repeated measures analysis of variance and planned comparison tests for the subjective and objective responses were performed. For both maneuvers, the HDTS system afforded better situation awareness, lower workload, better perceptual cueing in attitude, horizontal and vertical translation, and lower overall workload index. During the two-stage departure, HDTS achieved less lateral drift from initial takeoff and hover, lower root mean square error (RMSE) in altitude during hover, and lower track error during the acceleration to forward flight. During the single-stage approach, HDTS achieved less error in lateral and longitudinal position offset from the landing point and lower RMSE in heading. In both maneuvers, pilots exhibited higher control activity when using HDTS, which suggested that more pertinent information was available to the pilots. Pilots preferred the HDTS system.

Mapping boreal forest biomass from a SRTM and TanDEM-X based on canopy height model and Landsat spectral indices

NASA Astrophysics Data System (ADS)

Sadeghi, Yaser; St-Onge, Benoît; Leblon, Brigitte; Prieur, Jean-François; Simard, Marc

2018-06-01

We propose a method for mapping above-ground biomass (AGB) (Mg ha-1) in boreal forests based predominantly on Landsat 8 images and on canopy height models (CHM) generated using interferometric synthetic aperture radar (InSAR) from the Shuttle Radar Topographic Mission (SRTM) and the TanDEM-X mission. The original SRTM digital elevation model (DEM) was corrected by modelling the respective effects of landform and land cover on its errors and then subtracted from a TanDEM-X DSM to produce a SAR CHM. Among all the landform factors, the terrain curvature had the largest effect on SRTM elevation errors, with a r2 of 0.29. The NDSI was the best predictor of the residual SRTM land cover error, with a r2 of 0.30. The final SAR CHM had a RMSE of 2.45 m, with a bias of 0.07 m, compared to a lidar-based CHM. An AGB prediction model was developed based on a combination of the SAR CHM, TanDEM-X coherence, Landsat 8 NDVI, and other vegetation indices of RVI, DVI, GRVI, EVI, LAI, GNDVI, SAVI, GVI, Brightness, Greenness, and Wetness. The best results were obtained using a Random forest regression algorithm, at the stand level, yielding a RMSE of 26 Mg ha-1 (34% of average biomass), with a r2 of 0.62. This method has the potential of creating spatially continuous biomass maps over entire biomes using only spaceborne sensors and requiring only low-intensity calibration.

Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound

DOE Office of Scientific and Technical Information (OSTI.GOV)

Odéen, Henrik, E-mail: h.odeen@gmail.com; Diakite, Mahamadou; Todd, Nick

2014-09-15

Purpose: To investigate k-space subsampling strategies to achieve fast, large field-of-view (FOV) temperature monitoring using segmented echo planar imaging (EPI) proton resonance frequency shift thermometry for MR guided high intensity focused ultrasound (MRgHIFU) applications. Methods: Five different k-space sampling approaches were investigated, varying sample spacing (equally vs nonequally spaced within the echo train), sampling density (variable sampling density in zero, one, and two dimensions), and utilizing sequential or centric sampling. Three of the schemes utilized sequential sampling with the sampling density varied in zero, one, and two dimensions, to investigate sampling the k-space center more frequently. Two of the schemesmore » utilized centric sampling to acquire the k-space center with a longer echo time for improved phase measurements, and vary the sampling density in zero and two dimensions, respectively. Phantom experiments and a theoretical point spread function analysis were performed to investigate their performance. Variable density sampling in zero and two dimensions was also implemented in a non-EPI GRE pulse sequence for comparison. All subsampled data were reconstructed with a previously described temporally constrained reconstruction (TCR) algorithm. Results: The accuracy of each sampling strategy in measuring the temperature rise in the HIFU focal spot was measured in terms of the root-mean-square-error (RMSE) compared to fully sampled “truth.” For the schemes utilizing sequential sampling, the accuracy was found to improve with the dimensionality of the variable density sampling, giving values of 0.65 °C, 0.49 °C, and 0.35 °C for density variation in zero, one, and two dimensions, respectively. The schemes utilizing centric sampling were found to underestimate the temperature rise, with RMSE values of 1.05 °C and 1.31 °C, for variable density sampling in zero and two dimensions, respectively. Similar subsampling schemes with variable density sampling implemented in zero and two dimensions in a non-EPI GRE pulse sequence both resulted in accurate temperature measurements (RMSE of 0.70 °C and 0.63 °C, respectively). With sequential sampling in the described EPI implementation, temperature monitoring over a 192 × 144 × 135 mm{sup 3} FOV with a temporal resolution of 3.6 s was achieved, while keeping the RMSE compared to fully sampled “truth” below 0.35 °C. Conclusions: When segmented EPI readouts are used in conjunction with k-space subsampling for MR thermometry applications, sampling schemes with sequential sampling, with or without variable density sampling, obtain accurate phase and temperature measurements when using a TCR reconstruction algorithm. Improved temperature measurement accuracy can be achieved with variable density sampling. Centric sampling leads to phase bias, resulting in temperature underestimations.« less

Validation of Globsnow-2 Snow Water Equivalent Over Eastern Canada

NASA Technical Reports Server (NTRS)

Larue, Fanny; Royer, Alain; De Seve, Danielle; Langlois, Alexandre; Roy, Alexandre R.; Brucker, Ludovic

2017-01-01

In Qubec, Eastern Canada, snowmelt runoff contributes more than 30% of the annual energy reserve for hydroelectricity production, and uncertainties in annual maximum snow water equivalent (SWE) over the region are one of the main constraints for improved hydrological forecasting. Current satellite-based methods for mapping SWE over Qubec's main hydropower basins do not meet Hydro-Qubec operational requirements for SWE accuracies with less than 15% error. This paper assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines microwave satellite data and in situ measurements, for hydrological applications in Qubec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with space- and time-matched values from a comprehensive dataset of in situ SWE measurements (a total of 38,990 observations in Eastern Canada). The root mean square error (RMSE) of the GS-2 SWE product is 94.1+/- 20.3 mm, corresponding to an overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover type. However, compared to a typical stand-alone brightness temperature channel difference algorithm, the assimilation of surface information in the GS-2 algorithm clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of trends in annual mean and maximum SWE between surface observations and GS-2 over 1980-2009 showed agreement for increasing trends over southern Qubec, but less agreement on the sign and magnitude of trends over northern Qubec. Extended at a continental scale, the GS-2 SWE trends highlight a strong regional variability.

An empirical model for estimating solar radiation in the Algerian Sahara

NASA Astrophysics Data System (ADS)

Benatiallah, Djelloul; Benatiallah, Ali; Bouchouicha, Kada; Hamouda, Messaoud; Nasri, Bahous

2018-05-01

The present work aims to determine the empirical model R.sun that will allow us to evaluate the solar radiation flues on a horizontal plane and in clear-sky on the located Adrar city (27°18 N and 0°11 W) of Algeria and compare with the results measured at the localized site. The expected results of this comparison are of importance for the investment study of solar systems (solar power plants for electricity production, CSP) and also for the design and performance analysis of any system using the solar energy. Statistical indicators used to evaluate the accuracy of the model where the mean bias error (MBE), root mean square error (RMSE) and coefficient of determination. The results show that for global radiation, the daily correlation coefficient is 0.9984. The mean absolute percentage error is 9.44 %. The daily mean bias error is -7.94 %. The daily root mean square error is 12.31 %.

Validation of YCAR algorithm over East Asia TCCON sites

NASA Astrophysics Data System (ADS)

Kim, W.; Kim, J.; Jung, Y.; Lee, H.; Goo, T. Y.; Cho, C. H.; Lee, S.

2016-12-01

In order to reduce the retrieval error of TANSO-FTS column averaged CO2 concentration (XCO2) induced by aerosol, we develop the Yonsei university CArbon Retrieval (YCAR) algorithm using aerosol information from TANSO-Cloud and Aerosol Imager (TANSO-CAI), providing simultaneous aerosol optical depth properties for the same geometry and optical path along with the FTS. Also we validate the retrieved results using ground-based TCCON measurement. Particularly this study first utilized the measurements at Anmyeondo, the only TCCON site located in South Korea, which can improve the quality of validation in East Asia. After the post screening process, YCAR algorithms have higher data availability by 33 - 85 % than other operational algorithms (NIES, ACOS, UoL). Although the YCAR algorithm has higher data availability, regression analysis with TCCON measurements are better or similar to other algorithms; Regression line of YCAR algorithm is close to linear identity function with RMSE of 2.05, bias of - 0.86 ppm. According to error analysis, retrieval error of YCAR algorithm is 1.394 - 1.478 ppm at East Asia. In addition, spatio-temporal sampling error of 0.324 - 0.358 ppm for each single sounding retrieval is also analyzed with Carbon Tracker - Asia data. These results of error analysis reveal the reliability and accuracy of latest version of our YCAR algorithm. Both XCO2 values retrieved using YCAR algorithm on TANSO-FTS and TCCON measurements show the consistent increasing trend about 2.3 - 2.6 ppm per year. Comparing to the increasing rate of global background CO2 amount measured in Mauna Loa, Hawaii (2 ppm per year), the increasing trend in East Asia shows about 30% higher trend due to the rapid increase of CO2 emission from the source region.

High Accuracy 3D Processing of Satellite Imagery

NASA Technical Reports Server (NTRS)

Gruen, A.; Zhang, L.; Kocaman, S.

2007-01-01

Automatic DSM/DTM generation reproduces not only general features, but also detailed features of the terrain relief. Height accuracy of around 1 pixel in cooperative terrain. RMSE values of 1.3-1.5 m (1.0-2.0 pixels) for IKONOS and RMSE values of 2.9-4.6 m (0.5-1.0 pixels) for SPOT5 HRS. For 3D city modeling, the manual and semi-automatic feature extraction capability of SAT-PP provides a good basis. The tools of SAT-PP allowed the stereo-measurements of points on the roofs in order to generate a 3D city model with CCM The results show that building models with main roof structures can be successfully extracted by HRSI. As expected, with Quickbird more details are visible.

Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit.

PubMed

Hibbing, Paul R; Lamunion, Samuel R; Kaplan, Andrew S; Crouter, Scott E

2018-05-01

The purpose of this study was to explore whether gyroscope and magnetometer data from the ActiGraph GT9X improved accelerometer-based predictions of energy expenditure (EE). Thirty participants (mean ± SD: age, 23.0 ± 2.3 yr; body mass index, 25.2 ± 3.9 kg·m) volunteered to complete the study. Participants wore five GT9X monitors (right hip, both wrists, and both ankles) while performing 10 activities ranging from rest to running. A Cosmed K4b was worn during the trial, as a criterion measure of EE (30-s averages) expressed in METs. Triaxial accelerometer data (80 Hz) were converted to milli-G using Euclidean norm minus one (ENMO; 1-s epochs). Gyroscope data (100 Hz) were expressed as a vector magnitude (GVM) in degrees per second (1-s epochs) and magnetometer data (100 Hz) were expressed as direction changes per 5 s. Minutes 4-6 of each activity were used for analysis. Three two-regression algorithms were developed for each wear location: 1) ENMO, 2) ENMO and GVM, and 3) ENMO, GVM, and direction changes. Leave-one-participant-out cross-validation was used to evaluate the root mean square error (RMSE) and mean absolute percent error (MAPE) of each algorithm. Adding gyroscope to accelerometer-only algorithms resulted in RMSE reductions between 0.0 METs (right wrist) and 0.17 METs (right ankle), and MAPE reductions between 0.1% (right wrist) and 6.0% (hip). When direction changes were added, RMSE changed by ≤0.03 METs and MAPE by ≤0.21%. The combined use of gyroscope and accelerometer at the hip and ankles improved individual-level prediction of EE compared with accelerometer only. For the wrists, adding gyroscope produced negligible changes. The magnetometer did not meaningfully improve estimates for any algorithms.

Simultaneous Retrieval of Effective Refractive Index and Density from Size Distribution and Light Scattering Data: Weakly-Absorbing Aerosol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kassianov, Evgueni I.; Barnard, James C.; Pekour, Mikhail S.

2014-10-01

We propose here a novel approach for retrieving in parallel the effective density and real refractive index of weakly absorbing aerosol from optical and size distribution measurements. Here we define “weakly absorbing” as aerosol single-scattering albedos that exceed 0.95 at 0.5 um.The required optical measurements are the scattering coefficient and the hemispheric backscatter fraction, obtained in this work from an integrating nephelometer. The required size spectra come from a Scanning Mobility Particle Sizer and an Aerodynamic Particle Sizer. The performance of this approach is first evaluated using a sensitivity study with synthetically generated but measurement-related inputs. The sensitivity study revealsmore » that the proposed approach is robust to random noise; additionally the uncertainties of the retrieval are almost linearly proportional to the measurement errors, and these uncertainties are smaller for the real refractive index than for the effective density. Next, actual measurements are used to evaluate our approach. These measurements include the optical, microphysical, and chemical properties of weakly absorbing aerosol which are representative of a variety of coastal summertime conditions observed during the Two-Column Aerosol Project (TCAP; http://campaign.arm.gov/tcap/). The evaluation includes calculating the root mean square error (RMSE) between the aerosol characteristics retrieved by our approach, and the same quantities calculated using the conventional volume mixing rule for chemical constituents. For dry conditions (defined in this work as relative humidity less than 55%) and sub-micron particles, a very good (RMSE~3%) and reasonable (RMSE~28%) agreement is obtained for the retrieved real refractive index (1.49±0.02) and effective density (1.68±0.21), respectively. Our approach permits discrimination between the retrieved aerosol characteristics of sub-micron and sub-10micron particles. The evaluation results also reveal that the retrieved density and refractive index tend to decrease with an increase of the relative humidity.« less

MODIS Tree Cover Validation for the Circumpolar Taiga-Tundra Transition Zone

NASA Technical Reports Server (NTRS)

Montesano, P. M.; Nelson, R.; Sun, G.; Margolis, H.; Kerber, A.; Ranson, K. J.

2009-01-01

A validation of the 2005 500m MODIS vegetation continuous fields (VCF) tree cover product in the circumpolar taiga-tundra ecotone was performed using high resolution Quickbird imagery. Assessing the VCF's performance near the northern limits of the boreal forest can help quantify the accuracy of the product within this vegetation transition area. The circumpolar region was divided into longitudinal zones and validation sites were selected in areas of varying tree cover where Quickbird imagery is available in Google Earth. Each site was linked to the corresponding VCF pixel and overlaid with a regular dot grid within the VCF pixel's boundary to estimate percent tree crown cover in the area. Percent tree crown cover was estimated using Quickbird imagery for 396 sites throughout the circumpolar region and related to the VCF's estimates of canopy cover for 2000-2005. Regression results of VCF inter-annual comparisons (2000-2005) and VCF-Quickbird image-interpreted estimates indicate that: (1) Pixel-level, inter-annual comparisons of VCF estimates of percent canopy cover were linearly related (mean R(sup 2) = 0.77) and exhibited an average root mean square error (RMSE) of 10.1 % and an average root mean square difference (RMSD) of 7.3%. (2) A comparison of image-interpreted percent tree crown cover estimates based on dot counts on Quickbird color images by two different interpreters were more variable (R(sup 2) = 0.73, RMSE = 14.8%, RMSD = 18.7%) than VCF inter-annual comparisons. (3) Across the circumpolar boreal region, 2005 VCF-Quickbird comparisons were linearly related, with an R(sup 2) = 0.57, a RMSE = 13.4% and a RMSD = 21.3%, with a tendency to over-estimate areas of low percent tree cover and anomalous VCF results in Scandinavia. The relationship of the VCF estimates and ground reference indicate to potential users that the VCF's tree cover values for individual pixels, particularly those below 20% tree cover, may not be precise enough to monitor 500m pixel-level tree cover in the taiga-tundra transition zone.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

PubMed Central

2013-01-01

Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side. PMID:24059743

Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-based Estimates of Surface Albedo

NASA Technical Reports Server (NTRS)

Roman, Miguel O.; Gatebe, Charles K.; Shuai, Yanmin; Wang, Zhuosen; Gao, Feng; Masek, Jeff; Schaaf, Crystal B.

2012-01-01

The quantification of uncertainty of global surface albedo data and products is a critical part of producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the ability to overcome the spatial scaling errors that can contribute on the order of 20% disagreement between satellite and field-measured values. Here, we present the results from an uncertain ty analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat albedo retrievals, based on collocated comparisons with tower and airborne multi-angular measurements collected at the Atmospheric Radiation Measurement Program s (ARM) Cloud and Radiation Testbed (CART) site during the 2007 Cloud and Land Surface Interaction Campaign (CLAS33 IC 07). Using standard error propagation techniques, airborne measurements obtained by NASA s Cloud Absorption Radiometer (CAR) were used to quantify the uncertainties associated with MODIS and Landsat albedos across a broad range of mixed vegetation and structural types. Initial focus was on evaluating inter-sensor consistency through assessments of temporal stability, as well as examining the overall performance of satellite-derived albedos obtained at all diurnal solar zenith angles. In general, the accuracy of the MODIS and Landsat albedos remained under a 10% margin of error in the SW(0.3 - 5.0 m) domain. However, results reveal a high degree of variability in the RMSE (root mean square error) and bias of albedos in both the visible (0.3 - 0.7 m) and near-infrared (0.3 - 5.0 m) broadband channels; where, in some cases, retrieval uncertainties were found to be in excess of 20%. For the period of CLASIC 07, the primary factors that contributed to uncertainties in the satellite-derived albedo values include: (1) the assumption of temporal stability in the retrieval of 500 m MODIS BRDF values over extended periods of cloud-contaminated observations; and (2) the assumption of spatial 45 and structural uniformity at the Landsat (30 m) pixel scale.

2D segmentation of intervertebral discs and its degree of degeneration from T2-weighted magnetic resonance images

NASA Astrophysics Data System (ADS)

Castro-Mateos, Isaac; Pozo, José Maria; Lazary, Aron; Frangi, Alejandro F.

2014-03-01

Low back pain (LBP) is a disorder suffered by a large population around the world. A key factor causing this illness is Intervertebral Disc (IVD) degeneration, whose early diagnosis could help in preventing this widespread condition. Clinicians base their diagnosis on visual inspection of 2D slices of Magnetic Resonance (MR) images, which is subject to large interobserver variability. In this work, an automatic classification method is presented, which provides the Pfirrmann degree of degeneration from a mid-sagittal MR slice. The proposed method utilizes Active Contour Models, with a new geometrical energy, to achieve an initial segmentation, which is further improved using fuzzy C-means. Then, IVDs are classified according to their degree of degeneration. This classification is attained by employing Adaboost on five specific features: the mean and the variance of the probability map of the nucleus using two different approaches and the eccentricity of the fitting ellipse to the contour of the IVD. The classification method was evaluated using a cohort of 150 intervertebral discs assessed by three experts, resulting in a mean specificity (93%) and sensitivity (83%) similar to the one provided by every expert with respect to the most voted value. The segmentation accuracy was evaluated using the Dice Similarity Index (DSI) and Root Mean Square Error (RMSE) of the point-to-contour distance. The mean DSI ± 2 standard deviation was 91:7% ±5:6%, the mean RMSE was 0:82mm and the 95 percentile was 1:36mm. These results were found accurate when compared to the state-of-the-art.

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.

PubMed

Tohidinik, Hamid Reza; Mohebali, Mehdi; Mansournia, Mohammad Ali; Niakan Kalhori, Sharareh R; Ali-Akbarpour, Mohsen; Yazdani, Kamran

2018-05-22

To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision. © 2018 John Wiley & Sons Ltd.

Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

NASA Astrophysics Data System (ADS)

Kamath, Aditya; Vargas-Hernández, Rodrigo A.; Krems, Roman V.; Carrington, Tucker; Manzhos, Sergei

2018-06-01

For molecules with more than three atoms, it is difficult to fit or interpolate a potential energy surface (PES) from a small number of (usually ab initio) energies at points. Many methods have been proposed in recent decades, each claiming a set of advantages. Unfortunately, there are few comparative studies. In this paper, we compare neural networks (NNs) with Gaussian process (GP) regression. We re-fit an accurate PES of formaldehyde and compare PES errors on the entire point set used to solve the vibrational Schrödinger equation, i.e., the only error that matters in quantum dynamics calculations. We also compare the vibrational spectra computed on the underlying reference PES and the NN and GP potential surfaces. The NN and GP surfaces are constructed with exactly the same points, and the corresponding spectra are computed with the same points and the same basis. The GP fitting error is lower, and the GP spectrum is more accurate. The best NN fits to 625/1250/2500 symmetry unique potential energy points have global PES root mean square errors (RMSEs) of 6.53/2.54/0.86 cm-1, whereas the best GP surfaces have RMSE values of 3.87/1.13/0.62 cm-1, respectively. When fitting 625 symmetry unique points, the error in the first 100 vibrational levels is only 0.06 cm-1 with the best GP fit, whereas the spectrum on the best NN PES has an error of 0.22 cm-1, with respect to the spectrum computed on the reference PES. This error is reduced to about 0.01 cm-1 when fitting 2500 points with either the NN or GP. We also find that the GP surface produces a relatively accurate spectrum when obtained based on as few as 313 points.

Hybrid empirical mode decomposition- ARIMA for forecasting exchange rates

NASA Astrophysics Data System (ADS)

Abadan, Siti Sarah; Shabri, Ani; Ismail, Shuhaida

2015-02-01

This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian financial crisis causing the exchange rates are fixed to 3.800 from 2nd of September 1998 until 21st of July 2005. Thus, the chosen data in this paper is the post-July 2005 data, starting from August 2005 to July 2010. The comparative study using root mean square error (RMSE) and mean absolute error (MAE) showed that the EMD-ARIMA outperformed the single-ARIMA and the random walk benchmark model.

Remote Sensing of Clouds for Solar Forecasting Applications

NASA Astrophysics Data System (ADS)

Mejia, Felipe

A method for retrieving cloud optical depth (tauc) using a UCSD developed ground- based Sky Imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various tauc produced by a Radiative Transfer Model (RTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (SZA), tau c , solar pixel an- gle/scattering angle (SPA), and pixel zenith angle/view angle (PZA). The effects of these parameters are described and the functions for radiance, Ilambda (tau c ,SZA,SPA,PZA) , and the red-blue ratio, RBR(tauc ,SZA,SPA,PZA) , are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for tau c , where RBR increases with tauc up to about tauc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Imeaslambda (SPA,PZA) , in addition to RBRmeas (SPA,PZA ) to obtain a unique solution for tauc . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min [ MH96a ] method for overcast skies. tau c values ranged from 0-80 with values over 80 being capped and registered as 80. A tauc RMSE of 2.5 between the Min method [ MH96b ] and the USI are observed. The MWR and USI have an RMSE of 2.2 which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms. Using the RRBR tauc estimate as an input we then explore the potential of using tomographic techniques for 3-D cloud reconstruction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients. Reconstruction accuracy is explored for different products, including surface irradiance, extinction coefficients and Liquid Water Path, as a function of the number of available sky imagers (SIs) and setup distance. Increasing the number of cameras improves the accuracy of the 3-D reconstruction: For surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal while k error continues to decrease with more cameras. The ideal distance between imagers was also explored: For a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction for surface irradiance, while k error continued to decrease with increasing decrease. An iterative reconstruction technique was also used to improve the results of the ART by minimizing the error between input images and reconstructed simulations. For the best case of a nine imager deployment, the ART and iterative method resulted in 53.4% and 33.6% mean average error (MAE) for the extinction coefficients, respectively. The tomographic methods were then tested on real world test cases in the Uni- versity of California San Diego's (UCSD) solar testbed. Five UCSD sky imagers (USI) were installed across the testbed based on the best performing distances in simulations. Topographic obstruction is explored as a source of error by analyzing the increased error with obstruction in the field of view of the horizon. As more of the horizon is obstructed the error increases. If at least a field of view of 70° is available for the camera the accuracy is within 2% of the full field of view. Errors caused by stray light are also explored by removing the circumsolar region from images and comparing the cloud reconstruction to a full image. Removing less than 30% of the circumsolar region image and GHI errors were within 0.2% of the full image while errors in k increased 1%. Removing more than 30° around the sun resulted in inaccurate cloud reconstruction. Using four of the five USI a 3D cloud is reconstructed and compared to the fifth camera. The image of the fifth camera (excluded from the reconstruction) was then simulated and found to have a 22.9% error compared to the ground truth.

A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

2016-07-01

Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea. To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases. Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated. In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively. The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method. © 2014 John Wiley & Sons Ltd.

Evaluation of SEBS, SEBAL, and METRIC models in estimation of the evaporation from the freshwater lakes (Case study: Amirkabir dam, Iran)

NASA Astrophysics Data System (ADS)

Zamani Losgedaragh, Saeideh; Rahimzadegan, Majid

2018-06-01

Evapotranspiration (ET) estimation is of great importance due to its key role in water resource management. Surface energy modeling tools such as Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration with Internalized Calibration (METRIC), and the Surface Energy Balance System (SEBS) can estimate the amount of evapotranspiration for every pixel of the satellite images. The main objective of this research is evaporation investigation from the freshwater bodies using SEBAL, METRIC, and SEBS. For this purpose, the Amirkabir dam reservoir and its nearby agricultural lands in a semi-arid climate were selected and studied from 2011 to 2017 as the study area. The implementations of this study were accomplished on 16 satellite images of Landsat TM5 and OLI. Then, SEBAL, METRIC, and SEBS were implemented on the selected images. Moreover, the corresponding pan evaporate measurements on the reservoir bank were considered as the ground truth data. Regarding to the results, SEBAL is not a reliable method to evaluate freshwater evaporation with the coefficient of determination (R2) of 0.36 and the Root Mean Square Error (RMSE) of 5.1 mm. On the other hand, METRIC with RMSE and R2 of 0.57 and 2.02 mm and SEBS with RMSE and R2 of 0.93 and 0.62 demonstrated a relatively good performance.

Long-term body weight fluctuation is associated with metabolic syndrome independent of current body mass index among Japanese men.

PubMed

Zhang, Huiming; Tamakoshi, Koji; Yatsuya, Hiroshi; Murata, Chiyoe; Wada, Keiko; Otsuka, Rei; Nagasawa, Nobue; Ishikawa, Miyuki; Sugiura, Kaichiro; Matsushita, Kunihiro; Hori, Yoko; Kondo, Takaaki; Toyoshima, Hideaki

2005-01-01

The relation between weight fluctuation and the risk of cardiovascular disease (CVD) is fairly consistent, although the physiologic basis for the relationship is uncertain. In the present study the association between long-term weight fluctuation and the development of metabolic syndrome (MS), a potent CVD risk factor, was investigated. A cross-sectional study of 664 Japanese men aged 40-49 years was conducted. The root mean square error around the slope of weight on age (weight - RMSE) was calculated by a simple linear regression model, in which the subject's actual weights at ages 20, 25, 30 years and 5 years prior to the study, as well as current weight, were dependent variables against the subject's age as the independent variable. Weight-RMSE was significantly and positively associated with the prevalence of each MS components (high blood pressure, hypertriglyceridemia, low-high density lipoprotein-cholesterol, high fasting glucose, and obesity). Such associations, as well as clustering of the MS component together with RMSE increase, were apparent among subjects with body mass index (BMI) <25 kg/m2, although the prevalence of MS or its components was much higher among overweight subjects (BMI >or=25 kg/m2). Development of MS possibly explains the risk of CVD not only in overweight or obese persons, but also in normal-weight persons with large weight fluctuation.

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

PubMed Central

Liu, Tao; Zhu, Guanghu; Lin, Hualiang; Zhang, Yonghui; He, Jianfeng; Deng, Aiping; Peng, Zhiqiang; Xiao, Jianpeng; Rutherford, Shannon; Xie, Runsheng; Zeng, Weilin; Li, Xing; Ma, Wenjun

2017-01-01

Background Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. Methodology and principal findings A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Conclusions Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. PMID:28263988

Head repositioning accuracy to neutral: a comparative study of error calculation.

PubMed

Hill, Robert; Jensen, Pål; Baardsen, Tor; Kulvik, Kristian; Jull, Gwendolen; Treleaven, Julia

2009-02-01

Deficits in cervical proprioception have been identified in subjects with neck pain through the measure of head repositioning accuracy (HRA). Nevertheless there appears to be no general consensus regarding the construct of measurement of error used for calculating HRA. This study investigated four different mathematical methods of measurement of error to determine if there were any differences in their ability to discriminate between a control group and subjects with a whiplash associated disorder. The four methods for measuring cervical joint position error were calculated using a previous data set consisting of 50 subjects with whiplash complaining of dizziness (WAD D), 50 subjects with whiplash not complaining of dizziness (WAD ND) and 50 control subjects. The results indicated that no one measure of HRA uniquely detected or defined the differences between the whiplash and control groups. Constant error (CE) was significantly different between the whiplash and control groups from extension (p<0.05). Absolute errors (AEs) and root mean square errors (RMSEs) demonstrated differences between the two WAD groups in rotation trials (p<0.05). No differences were seen with variable error (VE). The results suggest that a combination of AE (or RMSE) and CE are probably the most suitable measures for analysis of HRA.

Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study

PubMed Central

Chan, Christy KY; Li, Christien KH; To, Olivia TL; Lai, William HS; Tse, Gary; Poh, Yukkee C; Poh, Ming-Zher

2017-01-01

Background Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color. Objective The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app. Methods A total of 40 healthy participants (20 men; mean age 24.7, SD 5.2 years; von Luschan skin color range 14-27) underwent treadmill exercise using the Bruce protocol. We recorded simultaneous PPG signals for each participant by having them (1) facing the front camera and (2) placing their index fingertip over an iPhone’s back camera. We analyzed the PPG signals from the Cardiio-Heart Rate Monitor + 7 Minute Workout (Cardiio) smartphone app for HR measurements compared with a continuous 12-lead electrocardiogram (ECG) as the reference. Recordings of 20 seconds’ duration each were acquired at rest, and immediately after moderate- (50%-70% maximum HR) and vigorous- (70%-85% maximum HR) intensity exercise, and repeated successively until return to resting HR. We used Bland-Altman plots to examine agreement between ECG and PPG-estimated HR. The accuracy criterion was root mean square error (RMSE) ≤5 beats/min or ≤10%, whichever was greater, according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation EC-13 standard. Results We analyzed a total of 631 fingertip and 626 facial PPG measurements. Fingertip PPG-estimated HRs were strongly correlated with resting ECG HR (r=.997, RMSE=1.03 beats/min or 1.40%), postmoderate-intensity exercise (r=.994, RMSE=2.15 beats/min or 2.53%), and postvigorous-intensity exercise HR (r=.995, RMSE=2.01 beats/min or 1.93%). The correlation of facial PPG-estimated HR was stronger with resting ECG HR (r=.997, RMSE=1.02 beats/min or 1.44%) than with postmoderate-intensity exercise (r=.982, RMSE=3.68 beats/min or 4.11%) or with postvigorous-intensity exercise (r=.980, RMSE=3.84 beats/min or 3.73%). Bland-Altman plots showed better agreement between ECG and fingertip PPG-estimated HR than between ECG and facial PPG-estimated HR. Conclusions We found that HR detection by the Cardiio smartphone app was accurate at rest and after moderate- and vigorous-intensity exercise in a healthy young adult sample. Contact-free facial PPG detection is more convenient but is less accurate than finger PPG due to body motion after exercise. PMID:28288955

Optimizing Uas Image Acquisition and Geo-Registration for Precision Agriculture

NASA Astrophysics Data System (ADS)

Hearst, A. A.; Cherkauer, K. A.; Rainey, K. M.

2014-12-01

Unmanned Aircraft Systems (UASs) can acquire imagery of crop fields in various spectral bands, including the visible, near-infrared, and thermal portions of the spectrum. By combining techniques of computer vision, photogrammetry, and remote sensing, these images can be stitched into precise, geo-registered maps, which may have applications in precision agriculture and other industries. However, the utility of these maps will depend on their positional accuracy. Therefore, it is important to quantify positional accuracy and consider the tradeoffs between accuracy, field site setup, and the computational requirements for data processing and analysis. This will enable planning of data acquisition and processing to obtain the required accuracy for a given project. This study focuses on developing and evaluating methods for geo-registration of raw aerial frame photos acquired by a small fixed-wing UAS. This includes visual, multispectral, and thermal imagery at 3, 6, and 14 cm/pix resolutions, respectively. The study area is 10 hectares of soybean fields at the Agronomy Center for Research and Education (ACRE) at Purdue University. The dataset consists of imagery from 6 separate days of flights (surveys) and supporting ground measurements. The Direct Sensor Orientation (DiSO) and Integrated Sensor Orientation (InSO) methods for geo-registration are tested using 16 Ground Control Points (GCPs). Subsets of these GCPs are used to test for the effects of different numbers and spatial configurations of GCPs on positional accuracy. The horizontal and vertical Root Mean Squared Error (RMSE) is used as the primary metric of positional accuracy. Preliminary results from 1 of the 6 surveys show that the DiSO method (0 GCPs used) achieved an RMSE in the X, Y, and Z direction of 2.46 m, 1.04 m, and 1.91 m, respectively. InSO using 5 GCPs achieved an RMSE of 0.17 m, 0.13 m, and 0.44 m. InSO using 10 GCPs achieved an RMSE of 0.10 m, 0.09 m, and 0.12 m. Further analysis will identify the optimal spatial configuration and number of GCPs needed to achieve sub-meter RMSE, which is considered a benchmark for precision agriculture purposes. Additional benefits of superior positional accuracy will also be explored.

Genetic parameters of body weight and ascites in broilers: effect of different incidence rates of ascites syndrome.

PubMed

Ahmadpanah, J; Ghavi Hossein-Zadeh, N; Shadparvar, A A; Pakdel, A

2017-02-01

1. The objectives of the current study were to investigate the effect of incidence rate (5%, 10%, 20%, 30% and 50%) of ascites syndrome on the expression of genetic characteristics for body weight at 5 weeks of age (BW5) and AS and to compare different methods of genetic parameter estimation for these traits. 2. Based on stochastic simulation, a population with discrete generations was created in which random mating was used for 10 generations. Two methods of restricted maximum likelihood and Bayesian approach via Gibbs sampling were used for the estimation of genetic parameters. A bivariate model including maternal effects was used. The root mean square error for direct heritabilities was also calculated. 3. The results showed that when incidence rates of ascites increased from 5% to 30%, the heritability of AS increased from 0.013 and 0.005 to 0.110 and 0.162 for linear and threshold models, respectively. 4. Maternal effects were significant for both BW5 and AS. Genetic correlations were decreased by increasing incidence rates of ascites in the population from 0.678 and 0.587 at 5% level of ascites to 0.393 and -0.260 at 50% occurrence for linear and threshold models, respectively. 5. The RMSE of direct heritability from true values for BW5 was greater based on a linear-threshold model compared with the linear model of analysis (0.0092 vs. 0.0015). The RMSE of direct heritability from true values for AS was greater based on a linear-linear model (1.21 vs. 1.14). 6. In order to rank birds for ascites incidence, it is recommended to use a threshold model because it resulted in higher heritability estimates compared with the linear model and that BW5 could be one of the main components of selection goals.

Application of 3D triangulations of airborne laser scanning data to estimate boreal forest leaf area index

NASA Astrophysics Data System (ADS)

Majasalmi, Titta; Korhonen, Lauri; Korpela, Ilkka; Vauhkonen, Jari

2017-07-01

We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAIe). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAIe. The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAIe and canopy volume for large areas. The LAIe from triangulations was validated with field measured LAIe and compared with a reference LAIe calculated from ALS data using logarithmic model based on Beer's law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAIe. We investigated the influence species and season on the triangulated LAIe and demonstrated the relationship between triangulated LAIe and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAIe from triangulations and field measured values agreed the most using the highest pulse density data (RMSE = 0.63, the coefficient of determination (R2) = 0.53). Yet, the LAIe calculated using ACI-index agreed better with the field measured LAIe (RMSE = 0.53 and R2 = 0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAIe only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.

A device for characterising the mechanical properties of the plantar soft tissue of the foot.

PubMed

Parker, D; Cooper, G; Pearson, S; Crofts, G; Howard, D; Busby, P; Nester, C

2015-11-01

The plantar soft tissue is a highly functional viscoelastic structure involved in transferring load to the human body during walking. A Soft Tissue Response Imaging Device was developed to apply a vertical compression to the plantar soft tissue whilst measuring the mechanical response via a combined load cell and ultrasound imaging arrangement. Accuracy of motion compared to input profiles; validation of the response measured for standard materials in compression; variability of force and displacement measures for consecutive compressive cycles; and implementation in vivo with five healthy participants. Static displacement displayed average error of 0.04 mm (range of 15 mm), and static load displayed average error of 0.15 N (range of 250 N). Validation tests showed acceptable agreement compared to a Houndsfield tensometer for both displacement (CMC > 0.99 RMSE > 0.18 mm) and load (CMC > 0.95 RMSE < 4.86 N). Device motion was highly repeatable for bench-top tests (ICC = 0.99) and participant trials (CMC = 1.00). Soft tissue response was found repeatable for intra (CMC > 0.98) and inter trials (CMC > 0.70). The device has been shown to be capable of implementing complex loading patterns similar to gait, and of capturing the compressive response of the plantar soft tissue for a range of loading conditions in vivo. Copyright © 2015. Published by Elsevier Ltd.

Forecasting the Incidence and Prevalence of Patients with End-Stage Renal Disease in Malaysia up to the Year 2040

PubMed Central

Adnan, Tassha Hilda; Hashim, Nadiah Hanis; Mohan, Kirubashni; Kim Liong, Ang; Ahmad, Ghazali; Bak Leong, Goh; Bavanandan, Sunita; Haniff, Jamaiyah

2017-01-01

Background. The incidence of patients with end-stage renal disease (ESRD) requiring dialysis has been growing rapidly in Malaysia from 18 per million population (pmp) in 1993 to 231 pmp in 2013. Objective. To forecast the incidence and prevalence of ESRD patients who will require dialysis treatment in Malaysia until 2040. Methodology. Univariate forecasting models using the number of new and current dialysis patients, by the Malaysian Dialysis and Transplant Registry from 1993 to 2013 were used. Four forecasting models were evaluated, and the model with the smallest error was selected for the prediction. Result. ARIMA (0, 2, 1) modeling with the lowest error was selected to predict both the incidence (RMSE = 135.50, MAPE = 2.85, and MAE = 87.71) and the prevalence (RMSE = 158.79, MAPE = 1.29, and MAE = 117.21) of dialysis patients. The estimated incidences of new dialysis patients in 2020 and 2040 are 10,208 and 19,418 cases, respectively, while the estimated prevalence is 51,269 and 106,249 cases. Conclusion. The growth of ESRD patients on dialysis in Malaysia can be expected to continue at an alarming rate. Effective steps to address and curb further increase in new patients requiring dialysis are urgently needed, in order to mitigate the expected financial and health catastrophes associated with the projected increase of such patients. PMID:28348890

Estimation of traveltime and longitudinal dispersion in streams in West Virginia

USGS Publications Warehouse

Wiley, Jeffrey B.; Messinger, Terence

2013-01-01

Traveltime and dispersion data are important for understanding and responding to spills of contaminants in waterways. The U.S. Geological Survey (USGS), in cooperation with West Virginia Bureau for Public Health, Office of Environmental Health Services, compiled and evaluated traveltime and longitudinal dispersion data representative of many West Virginia waterways. Traveltime and dispersion data were not available for streams in the northwestern part of the State. Compiled data were compared with estimates determined from national equations previously published by the USGS. The evaluation summarized procedures and examples for estimating traveltime and dispersion on streams in West Virginia. National equations developed by the USGS can be used to predict traveltime and dispersion for streams located in West Virginia, but the predictions will be less accurate than those made with graphical interpolation between measurements. National equations for peak concentration, velocity of the peak concentration, and traveltime of the leading edge had root mean square errors (RMSE) of 0.426 log units (127 percent), 0.505 feet per second (ft/s), and 3.78 hours (h). West Virginia data fit the national equations for peak concentration, velocity of the peak concentration, and traveltime of the leading edge with RMSE of 0.139 log units (38 percent), 0.630 ft/s, and 3.38 h, respectively. The national equation for maximum possible velocity of the peak concentration exceeded 99 percent and 100 percent of observed values from the national data set and West Virginia-only data set, respectively. No RMSE was reported for time of passage of a dye cloud, as estimated using the national equation; however, the estimates made using the national equations had a root mean square error of 3.82 h when compared to data gathered for this study. Traveltime and dispersion estimates can be made from the plots of traveltime as a function of streamflow and location for streams with plots available, but estimates can be made using the national equations for streams without plots. The estimating procedures are not valid for regulated stream reaches that were not individually studied or streamflows outside the limits studied. Rapidly changing streamflow and inadequate mixing across the stream channel affect traveltime and dispersion, and reduce the accuracy of estimates. Increases in streamflow typically result in decreases in the peak concentration and traveltime of the peak concentration. Decreases in streamflow typically result in increases in the peak concentration and traveltime of the peak concentration. Traveltimes will likely be less than those determined using the estimating equations and procedures if the spill is in the center of the stream, and traveltimes will likely be greater than those determined using the estimating equations and procedures if the spill is near the streambank.

Fish measurement using Android smart phone: the example of swamp eel

NASA Astrophysics Data System (ADS)

Chen, Baisong; Fu, Zhuo; Ouyang, Haiying; Sun, Yingze; Ge, Changshui; Hu, Jing

The body length and weight are critical physiological parameters for fishes, especially eel-like fishes like swamp eel(Monopterusalbus).Fast and accurate measuring of body length is significant for swamp eel culturing as well as its resource investigation and protection. This paper presents an Android smart phone-based photogrammetry technology for measuring and estimating the length and weight of swamp eel. This method utilizes the feature that the ratio of lengths of two objects within an image is equal to that of in reality to measure the length of swamp eels. And then, it estimates the weight via a pre-built length-weight regression model. Analysis and experimental results have indicated that this method is a fast and accurate method for length and weight measurements of swamp eel. The cross-validation results shows that the RMSE (root-mean-square error) of total length measurement of swamp eel is0.4 cm, and the RMSE of weight estimation is 11 grams.

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

NASA Astrophysics Data System (ADS)

Komeilian, H.; Mohyeddin Bateni, S.; Xu, T.; Nielson, J.

2015-05-01

Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009-2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson's correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

A comparison of spatial interpolation methods for soil temperature over a complex topographical region

NASA Astrophysics Data System (ADS)

Wu, Wei; Tang, Xiao-Ping; Ma, Xue-Qing; Liu, Hong-Bin

2016-08-01

Soil temperature variability data provide valuable information on understanding land-surface ecosystem processes and climate change. This study developed and analyzed a spatial dataset of monthly mean soil temperature at a depth of 10 cm over a complex topographical region in southwestern China. The records were measured at 83 stations during the period of 1961-2000. Nine approaches were compared for interpolating soil temperature. The accuracy indicators were root mean square error (RMSE), modelling efficiency (ME), and coefficient of residual mass (CRM). The results indicated that thin plate spline with latitude, longitude, and elevation gave the best performance with RMSE varying between 0.425 and 0.592 °C, ME between 0.895 and 0.947, and CRM between -0.007 and 0.001. A spatial database was developed based on the best model. The dataset showed that larger seasonal changes of soil temperature were from autumn to winter over the region. The northern and eastern areas with hilly and low-middle mountains experienced larger seasonal changes.

Estimating accuracy of land-cover composition from two-stage cluster sampling

USGS Publications Warehouse

Stehman, S.V.; Wickham, J.D.; Fattorini, L.; Wade, T.D.; Baffetta, F.; Smith, J.H.

2009-01-01

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias. ?? 2009 Elsevier Inc.

Image quality improvement in cone-beam CT using the super-resolution technique.

PubMed

Oyama, Asuka; Kumagai, Shinobu; Arai, Norikazu; Takata, Takeshi; Saikawa, Yusuke; Shiraishi, Kenshiro; Kobayashi, Takenori; Kotoku, Jun'ichi

2018-04-05

This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.

Solubility of organic compounds in octanol: Improved predictions based on the geometrical fragment approach.

PubMed

Mathieu, Didier

2017-09-01

Two new models are introduced to predict the solubility of chemicals in octanol (S oct ), taking advantage of the extensive character of log(S oct ) through a decomposition of molecules into so-called geometrical fragments (GF). They are extensively validated and their compliance with regulatory requirements is demonstrated. The first model requires just a molecular formula as input. Despite an extreme simplicity, it performs as well as an advanced random forest model involving 86 descriptors, with a root mean square error (RMSE) of 0.64 log units for an external test set of 100 molecules. For the second one, which requires the melting point T m as input, introducing GF descriptors reduces the RMSE from about 0.7 to <0.5 log units, a performance that could previously be obtained only through the use of Abraham descriptors. A script is provided for easy application of the models, taking into account the limits of their applicability domains. Copyright © 2017 Elsevier Ltd. All rights reserved.

Forecasting hotspots in East Kutai, Kutai Kartanegara, and West Kutai as early warning information

NASA Astrophysics Data System (ADS)

Wahyuningsih, S.; Goejantoro, R.; Rizki, N. A.

2018-04-01

The aims of this research are to model hotspots and forecast hotspot 2017 in East Kutai, Kutai Kartanegara and West Kutai. The methods which used in this research were Holt exponential smoothing, Holt’s additive dump trend method, Holt-Winters’ additive method, additive decomposition method, multiplicative decomposition method, Loess decomposition method and Box-Jenkins method. For smoothing techniques, additive decomposition is better than Holt’s exponential smoothing. The hotspots model using Box-Jenkins method were Autoregressive Moving Average ARIMA(1,1,0), ARIMA(0,2,1), and ARIMA(0,1,0). Comparing the results from all methods which were used in this research, and based on Root of Mean Squared Error (RMSE), show that Loess decomposition method is the best times series model, because it has the least RMSE. Thus the Loess decomposition model used to forecast the number of hotspot. The forecasting result indicatethat hotspots pattern tend to increase at the end of 2017 in Kutai Kartanegara and West Kutai, but stationary in East Kutai.

High-resolution global grids of revised Priestley-Taylor and Hargreaves-Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation

NASA Astrophysics Data System (ADS)

Aschonitis, Vassilis G.; Papamichail, Dimitris; Demertzi, Kleoniki; Colombani, Nicolo; Mastrocicco, Micol; Ghirardini, Andrea; Castaldelli, Giuseppe; Fano, Elisa-Anna

2017-08-01

The objective of the study is to provide global grids (0.5°) of revised annual coefficients for the Priestley-Taylor (P-T) and Hargreaves-Samani (H-S) evapotranspiration methods after calibration based on the ASCE (American Society of Civil Engineers)-standardized Penman-Monteith method (the ASCE method includes two reference crops: short-clipped grass and tall alfalfa). The analysis also includes the development of a global grid of revised annual coefficients for solar radiation (Rs) estimations using the respective Rs formula of H-S. The analysis was based on global gridded climatic data of the period 1950-2000. The method for deriving annual coefficients of the P-T and H-S methods was based on partial weighted averages (PWAs) of their mean monthly values. This method estimates the annual values considering the amplitude of the parameter under investigation (ETo and Rs) giving more weight to the monthly coefficients of the months with higher ETo values (or Rs values for the case of the H-S radiation formula). The method also eliminates the effect of unreasonably high or low monthly coefficients that may occur during periods where ETo and Rs fall below a specific threshold. The new coefficients were validated based on data from 140 stations located in various climatic zones of the USA and Australia with expanded observations up to 2016. The validation procedure for ETo estimations of the short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed the standard methods reducing the estimated root mean square error (RMSE) in ETo values by 40 and 25 %, respectively. The estimations of Rs using the H-S formula with revised coefficients reduced the RMSE by 28 % in comparison to the standard H-S formula. Finally, a raster database was built consisting of (a) global maps for the mean monthly ETo values estimated by ASCE-standardized method for both reference crops, (b) global maps for the revised annual coefficients of the P-T and H-S evapotranspiration methods for both reference crops and a global map for the revised annual coefficient of the H-S radiation formula and (c) global maps that indicate the optimum locations for using the standard P-T and H-S methods and their possible annual errors based on reference values. The database can support estimations of ETo and solar radiation for locations where climatic data are limited and it can support studies which require such estimations on larger scales (e.g. country, continent, world). The datasets produced in this study are archived in the PANGAEA database (https://doi.org/10.1594/PANGAEA.868808) and in the ESRN database (http://www.esrn-database.org or http://esrn-database.weebly.com).

SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zawisza, I; Ren, L; Yin, F

Purpose: Respiratory-gated radiotherapy and dynamic tracking employ real-time imaging and surrogate motion-monitoring methods with tumor motion prediction in advance of real-time. This study investigated respiratory motion data length on prediction accuracy of tumor motion. Methods: Predictions generated from the algorithm are validated against a one-dimensional surrogate signal of amplitude versus time. Prediction consists of three major components: extracting top-ranked subcomponents from training data matching the last respiratory cycle; calculating weighting factors from best-matched subcomponents; fusing data proceeding best-matched subcomponents with respective weighting factors to form predictions. Predictions for one respiratory cycle (∼3-6seconds) were assessed using 351 patient data from themore » respiratory management device. Performance was evaluated for correlation coefficient and root mean square error (RMSE) between prediction and final respiratory cycle. Results: Respiratory prediction results fell into two classes, where best predictions for 70 cycles or less performed using relative prediction and greater than 70 cycles are predicted similarly using relative and derivative relative. For 70 respiratory cycles or less, the average correlation between prediction and final respiratory cycle was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0003, 0.9985±0.0023, and 0.9981±0.0023 with RMSE values of 0.0091±0.0030, 0.0091±0.0030, 0.0305±0.0051, 0.0299±0.0259, and 0.0299±0.0259 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 37, 65, 20, 22, and 22. For data with greater than 70 cycles average correlation was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0004, 0.9988±0.0020, and 0.9988±0.0020 with RMSE values of 0.0081±0.0031, 0.0082±0.0033, 0.0306±0.0056, 0.0218±0.0222, and 0.0218±0.0222 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 24, 44, 42, 30, and 45. Conclusion: The prediction algorithms are effective in estimating surrogate motion in advance. These results indicate an advantage in using relative prediction for shorter data and either relative or derivative relative prediction for longer data.« less

Usability of NASA Satellite Imagery-Based Daily Solar Radiation for Crop Yield Simulation and Management Decisions

NASA Astrophysics Data System (ADS)

Yang, H.; Cassman, K. G.; Stackhouse, P. W.; Hoell, J. M.

2007-12-01

We tested the usability of NASA satellite imagery-based daily solar radiation for farm-specific crop yield simulation and management decisions using the Hybrid-Maize model (www.hybridmaize.unl.edu). Solar radiation is one of the key inputs for crop yield simulation. Farm-specific crop management decisions using simulation models require long-term (i.e., 20 years or longer) daily local weather data including solar radiation for assessing crop yield potential and its variation, optimizing crop planting date, and predicting crop yield in a real time mode. Weather stations that record daily solar radiation have sparse coverage and many of them have record shorter than 15 years. Based on satellite imagery and other remote sensed information, NASA has provided estimates of daily climatic data including solar radiation at a resolution of 1 degree grid over the earth surface from 1983 to 2005. NASA is currently continuing to update the database and has plans to provide near real-time data in the future. This database, which is free to the public at http://power.larc.nasa.gov, is a potential surrogate for ground- measured climatic data for farm-specific crop yield simulation and management decisions. In this report, we quantified (1) the similarities between NASA daily solar radiation and ground-measured data atr 20 US sites and four international sites, and (2) the accuracy and precision of simulated corn yield potential and its variability using NASA solar radiation coupled with other weather data from ground measurements. The 20 US sites are in the western Corn Belt, including Iowa, South Dakota, Nebraska, and Kansas. The four international sites are Los Banos in the Philippines, Beijing in China, Cali in Columbia, and Ibatan in Nigeria. Those sites were selected because of their high quality weather record and long duration (more than 20 years on average). We found that NASA solar radiation was highly significantly correlated (mean r2 =0.88**) with the ground measurements at the 20 US sites, while the correlation was poor (mean r2=0.55**, though significant) at the four international sites. At the 20 US sites, the mean root mean square error (RMSE) between NASA solar radiation and the ground data was 2.7 MJ/m2/d, or 19% of the mean daily ground data. At the four international sites, the mean RMSE was 4.0 MJ/m2/d, or 25% of the mean daily ground value. Large differences between NASA solar radiation and the ground data were likely associated with tropical environment or significant variation in elevation within a short distance. When using NASA solar radiation coupled with other weather data from ground measurements, the simulated corn yields were highly significantly correlated (mean r2=0.85**) with those using complete ground weather data at the 20 US sites, while the correlation (mean r2=0.48**) was poor at the four international sites. The mean RMSE between the simulated corn yields of the two batches was 0.50 Mg/ha, or 3% of the mean absolute value using the ground data. At the four international sites, the RMSE of the simulated yields was 1.5 Mg/ha, or 13% of the mean absolute value using the ground data. We conclude that the NASA satellite imagery-based daily solar radiation is a reasonably reliable surrogate for the ground observations for farm-specific crop yield simulation and management decisions, especially at locations where ground-measured solar radiation is unavailable.

Spatial hydrological drought characteristics in Karkheh River basin, southwest Iran using copulas

NASA Astrophysics Data System (ADS)

Dodangeh, Esmaeel; Shahedi, Kaka; Shiau, Jenq-Tzong; MirAkbari, Maryam

2017-08-01

Investigation on drought characteristics such as severity, duration, and frequency is crucial for water resources planning and management in a river basin. While the methodology for multivariate drought frequency analysis is well established by applying the copulas, the estimation on the associated parameters by various parameter estimation methods and the effects on the obtained results have not yet been investigated. This research aims at conducting a comparative analysis between the maximum likelihood parametric and non-parametric method of the Kendall τ estimation method for copulas parameter estimation. The methods were employed to study joint severity-duration probability and recurrence intervals in Karkheh River basin (southwest Iran) which is facing severe water-deficit problems. Daily streamflow data at three hydrological gauging stations (Tang Sazbon, Huleilan and Polchehr) near the Karkheh dam were used to draw flow duration curves (FDC) of these three stations. The Q_{75} index extracted from the FDC were set as threshold level to abstract drought characteristics such as drought duration and severity on the basis of the run theory. Drought duration and severity were separately modeled using the univariate probabilistic distributions and gamma-GEV, LN2-exponential, and LN2-gamma were selected as the best paired drought severity-duration inputs for copulas according to the Akaike Information Criteria (AIC), Kolmogorov-Smirnov and chi-square tests. Archimedean Clayton, Frank, and extreme value Gumbel copulas were employed to construct joint cumulative distribution functions (JCDF) of droughts for each station. Frank copula at Tang Sazbon and Gumbel at Huleilan and Polchehr stations were identified as the best copulas based on the performance evaluation criteria including AIC, BIC, log-likelihood and root mean square error (RMSE) values. Based on the RMSE values, nonparametric Kendall-τ is preferred to the parametric maximum likelihood estimation method. The results showed greater drought return periods by the parametric ML method in comparison to the nonparametric Kendall τ estimation method. The results also showed that stations located in tributaries (Huleilan and Polchehr) have close return periods, while the station along the main river (Tang Sazbon) has the smaller return periods for the drought events with identical drought duration and severity.

Applying GIPL2.0 Model to assess the permafrost dynamics on the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Wu, T.

2017-12-01

The modeling of active layer and permafrost distribution is of great importance to understand the permafrost dynamics of cold regions, especially in those regions where are difficult to approach such as the Qinghai-Tibet Plateau (QTP). In this study we have applied the Geophysical Institute Permafrost Lab model (GIPL2.0) to estimate the active layer thickness and assess the permafrost thermal regime on the QTP. The GIPL 2.0 have been widely applied in the Arctic regions of Alaska, however less on the QTP. The model has been calibrated according to the four active layer in-situ measurement sites which have different underlying surface and soil characteristics. We extended the original GIPL2 model depth to the depth of 18 m. After the calibration of the GIPL2.0 at those four sites, the first-hand single point model is expanded to a regional model. The key permafrost parameters were simulated, including active layer thickness (ALT), mean annual ground temperature (MAGT) at multiple soil layers, and the permafrost classification was also carried out in order to study the permafrost the thermal stability across the QTP. To validate the performance of expanded regional-GIPL2 model, we compare simulated ALT and MAGT at the depth of zero annual amplitude (DZAA) with observed data. It is demonstrated that the modifications regional-GIPL2 model are able to improve the accuracy of permafrost thermal regime simulations greatly on the QTP. The simulated ALT are generally underestimate the observed ones with the MBE value of -0.14 m and the RMSE value of 0.22 m. For the MAGT at the DZAA of all 51 sites, the simulation errors range from - 0.9 ° to 0.9 ° with the RMSE value of 0.41 °. For the whole permafrost area of the QTP, the simulated ALT ranges from 0 to 8 m, with an average of 2.30 m. The simulated results indicate that most of regions were underlain by the sub-stable permafrost and less regions were underlain by the extremely stable permafrost.

Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

NASA Astrophysics Data System (ADS)

Zareie, Sajad; Khosravi, Hassan; Nasiri, Abouzar; Dastorani, Mostafa

2016-11-01

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation to existing land use types are very important to the investigation of the urban microclimate. In arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary for urban planning to control or reduce surface temperature. The internal factors and environmental conditions of Yazd city have important roles in the formation of special thermal conditions in Iran. In this paper, we used the temperature-emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (R2) were used for validation of retrieved LST values. The RMSE of 0.9 and 0.87 °C and R2 of 0.98 and 0.99 were obtained for the 1998 and 2009 images, respectively. Land use types for the city of Yazd were identified and relationships between land use types, land surface temperature and normalized difference vegetation index (NDVI) were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. The results showed an increase of 1.45 °C in the average surface temperature. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and fallow lands in Yazd caused a rise in surface temperature during the 11-year period.

Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model.

PubMed

Oghli, Mostafa Ghelich; Dehlaghi, Vahab; Zadeh, Ali Mohammad; Fallahi, Alireza; Pooyan, Mohammad

2014-07-01

Assessment of cardiac right-ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right-ventricle functional parameters from cardiac MRI images contains segmentation of right-ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right-ventricle in short axis MRI images. After segmentation of right-ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right-ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root-mean-square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤ 10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right-ventricle led us to use a shape prior based method and this work can develop by four-dimensional processing for determining the first ventricular slices.

Climatological Modeling of Monthly Air Temperature and Precipitation in Egypt through GIS Techniques

NASA Astrophysics Data System (ADS)

El Kenawy, A.

2009-09-01

This paper describes a method for modeling and mapping four climatic variables (maximum temperature, minimum temperature, mean temperature and total precipitation) in Egypt using a multiple regression approach implemented in a GIS environment. In this model, a set of variables including latitude, longitude, elevation within a distance of 5, 10 and 15 km, slope, aspect, distance to the Mediterranean Sea, distance to the Red Sea, distance to the Nile, ratio between land and water masses within a radius of 5, 10, 15 km, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Temperature Index (NDTI) and reflectance are included as independent variables. These variables were integrated as raster layers in MiraMon software at a spatial resolution of 1 km. Climatic variables were considered as dependent variables and averaged from quality controlled and homogenized 39 series distributing across the entire country during the period of (1957-2006). For each climatic variable, digital and objective maps were finally obtained using the multiple regression coefficients at monthly, seasonal and annual timescale. The accuracy of these maps were assessed through cross-validation between predicted and observed values using a set of statistics including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean bias Error (MBE) and D Willmott statistic. These maps are valuable in the sense of spatial resolution as well as the number of observatories involved in the current analysis.

Utilization of advanced calibration techniques in stochastic rock fall analysis of quarry slopes

NASA Astrophysics Data System (ADS)

Preh, Alexander; Ahmadabadi, Morteza; Kolenprat, Bernd

2016-04-01

In order to study rock fall dynamics, a research project was conducted by the Vienna University of Technology and the Austrian Central Labour Inspectorate (Federal Ministry of Labour, Social Affairs and Consumer Protection). A part of this project included 277 full-scale drop tests at three different quarries in Austria and recording key parameters of the rock fall trajectories. The tests involved a total of 277 boulders ranging from 0.18 to 1.8 m in diameter and from 0.009 to 8.1 Mg in mass. The geology of these sites included strong rock belonging to igneous, metamorphic and volcanic types. In this paper the results of the tests are used for calibration and validation a new stochastic computer model. It is demonstrated that the error of the model (i.e. the difference between observed and simulated results) has a lognormal distribution. Selecting two parameters, advanced calibration techniques including Markov Chain Monte Carlo Technique, Maximum Likelihood and Root Mean Square Error (RMSE) are utilized to minimize the error. Validation of the model based on the cross validation technique reveals that in general, reasonable stochastic approximations of the rock fall trajectories are obtained in all dimensions, including runout, bounce heights and velocities. The approximations are compared to the measured data in terms of median, 95% and maximum values. The results of the comparisons indicate that approximate first-order predictions, using a single set of input parameters, are possible and can be used to aid practical hazard and risk assessment.

Assessment of Satellite Precipitation Products in the Philippine Archipelago

NASA Astrophysics Data System (ADS)

Ramos, M. D.; Tendencia, E.; Espana, K.; Sabido, J.; Bagtasa, G.

2016-06-01

Precipitation is the most important weather parameter in the Philippines. Made up of more than 7100 islands, the Philippine archipelago is an agricultural country that depends on rain-fed crops. Located in the western rim of the North West Pacific Ocean, this tropical island country is very vulnerable to tropical cyclones that lead to severe flooding events. Recently, satellite-based precipitation estimates have improved significantly and can serve as alternatives to ground-based observations. These data can be used to fill data gaps not only for climatic studies, but can also be utilized for disaster risk reduction and management activities. This study characterized the statistical errors of daily precipitation from four satellite-based rainfall products from (1) the Tropical Rainfall Measuring Mission (TRMM), (2) the CPC Morphing technique (CMORPH) of NOAA and (3) the Global Satellite Mapping of Precipitation (GSMAP) and (4) Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks (PERSIANN). Precipitation data were compared to 52 synoptic weather stations located all over the Philippines. Results show GSMAP to have over all lower bias and CMORPH with lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In addition, a dichotomous rainfall test reveals GSMAP and CMORPH have low Proportion Correct (PC) for convective and stratiform rainclouds, respectively. TRMM consistently showed high PC for almost all raincloud types. Moreover, all four satellite precipitation showed high Correct Negatives (CN) values for the north-western part of the country during the North-East monsoon and spring monsoonal transition periods.

Application of Molecular Dynamics Simulations in Molecular Property Prediction I: Density and Heat of Vaporization

PubMed Central

Wang, Junmei; Tingjun, Hou

2011-01-01

Molecular mechanical force field (FF) methods are useful in studying condensed phase properties. They are complementary to experiment and can often go beyond experiment in atomic details. Even a FF is specific for studying structures, dynamics and functions of biomolecules, it is still important for the FF to accurately reproduce the experimental liquid properties of small molecules that represent the chemical moieties of biomolecules. Otherwise, the force field may not describe the structures and energies of macromolecules in aqueous solutions properly. In this work, we have carried out a systematic study to evaluate the General AMBER Force Field (GAFF) in studying densities and heats of vaporization for a large set of organic molecules that covers the most common chemical functional groups. The latest techniques, such as the particle mesh Ewald (PME) for calculating electrostatic energies, and Langevin dynamics for scaling temperatures, have been applied in the molecular dynamics (MD) simulations. For density, the average percent error (APE) of 71 organic compounds is 4.43% when compared to the experimental values. More encouragingly, the APE drops to 3.43% after the exclusion of two outliers and four other compounds for which the experimental densities have been measured with pressures higher than 1.0 atm. For heat of vaporization, several protocols have been investigated and the best one, P4/ntt0, achieves an average unsigned error (AUE) and a root-mean-square error (RMSE) of 0.93 and 1.20 kcal/mol, respectively. How to reduce the prediction errors through proper van der Waals (vdW) parameterization has been discussed. An encouraging finding in vdW parameterization is that both densities and heats of vaporization approach their “ideal” values in a synchronous fashion when vdW parameters are tuned. The following hydration free energy calculation using thermodynamic integration further justifies the vdW refinement. We conclude that simple vdW parameterization can significantly reduce the prediction errors. We believe that GAFF can greatly improve its performance in predicting liquid properties of organic molecules after a systematic vdW parameterization, which will be reported in a separate paper. PMID:21857814

Estimating net surface shortwave radiation from Chinese geostationary meteorological satellite FengYun-2D (FY-2D) data under clear sky.

PubMed

Zhang, Xiaoyu; Li, Lingling

2016-03-21

Net surface shortwave radiation (NSSR) significantly affects regional and global climate change, and is an important aspect of research on surface radiation budget balance. Many previous studies have proposed methods for estimating NSSR. This study proposes a method to calculate NSSR using FY-2D short-wave channel data. Firstly, a linear regression model is established between the top-of-atmosphere (TOA) broadband albedo (r) and the narrowband reflectivity (ρ₁), based on data simulated with MODTRAN 4.2. Secondly, the relationship between surface absorption coefficient (as) and broadband albedo (r) is determined by dividing the surface type into land, sea, or snow&ice, and NSSR can then be calculated. Thirdly, sensitivity analysis is performed for errors associated with sensor noise, vertically integrated atmospheric water content, view zenith angle and solar zenith angle. Finally, validation using ground measurements is performed. Results show that the root mean square error (RMSE) between the estimated and actual r is less than 0.011 for all conditions, and the RMSEs between estimated and real NSSR are 26.60 W/m², 9.99 W/m², and 23.40 W/m², using simulated data for land, sea, and snow&ice surfaces, respectively. This indicates that the proposed method can be used to adequately estimate NSSR. Additionally, we compare field measurements from TaiYuan and ChangWu ecological stations with estimates using corresponding FY-2D data acquired from January to April 2012, on cloud-free days. Results show that the RMSE between the estimated and actual NSSR is 48.56W/m², with a mean error of -2.23W/m². Causes of errors also include measurement accuracy and estimations of atmospheric water vertical contents. This method is only suitable for cloudless conditions.

A flexible wearable sensor for knee flexion assessment during gait.

PubMed

Papi, Enrica; Bo, Yen Nee; McGregor, Alison H

2018-05-01

Gait analysis plays an important role in the diagnosis and management of patients with movement disorders but it is usually performed within a laboratory. Recently interest has shifted towards the possibility of conducting gait assessments in everyday environments thus facilitating long-term monitoring. This is possible by using wearable technologies rather than laboratory based equipment. This study aims to validate a novel wearable sensor system's ability to measure peak knee sagittal angles during gait. The proposed system comprises a flexible conductive polymer unit interfaced with a wireless acquisition node attached over the knee on a pair of leggings. Sixteen healthy volunteers participated to two gait assessments on separate occasions. Data was simultaneously collected from the novel sensor and a gold standard 10 camera motion capture system. The relationship between sensor signal and reference knee flexion angles was defined for each subject to allow the transformation of sensor voltage outputs to angular measures (degrees). The knee peak flexion angle from the sensor and reference system were compared by means of root mean square error (RMSE), absolute error, Bland-Altman plots and intra-class correlation coefficients (ICCs) to assess test-retest reliability. Comparisons of knee peak flexion angles calculated from the sensor and gold standard yielded an absolute error of 0.35(±2.9°) and RMSE of 1.2(±0.4)°. Good agreement was found between the two systems with the majority of data lying within the limits of agreement. The sensor demonstrated high test-retest reliability (ICCs>0.8). These results show the ability of the sensor to monitor knee peak sagittal angles with small margins of error and in agreement with the gold standard system. The sensor has potential to be used in clinical settings as a discreet, unobtrusive wearable device allowing for long-term gait analysis. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Comparison of different interpolation methods for spatial distribution of soil organic carbon and some soil properties in the Black Sea backward region of Turkey

NASA Astrophysics Data System (ADS)

Göl, Ceyhun; Bulut, Sinan; Bolat, Ferhat

2017-10-01

The purpose of this research is to compare the spatial variability of soil organic carbon (SOC) in four adjacent land uses including the cultivated area, the grassland area, the plantation area and the natural forest area in the semi - arid region of Black Sea backward region of Turkey. Some of the soil properties, including total nitrogen, SOC, soil organic matter, and bulk density were measured on a grid with a 50 m sampling distance on the top soil (0-15 cm depth). Accordingly, a total of 120 samples were taken from the four adjacent land uses. Data was analyzed using geostatistical methods. The methods used were: Block kriging (BK), co - kriging (CK) with organic matter, total nitrogen and bulk density as auxiliary variables and inverse distance weighting (IDW) methods with the power of 1, 2 and 4. The methods were compared using a performance criteria that included root mean square error (RMSE), mean absolute error (MAE) and the coefficient of correlation (r). The one - way ANOVA test showed that differences between the natural (0.6653 ± 0.2901) - plantation forest (0.7109 ± 0.2729) areas and the grassland (1.3964 ± 0.6828) - cultivated areas (1.5851 ± 0.5541) were statistically significant at 0.05 level (F = 28.462). The best model for describing spatially variation of SOC was CK with the lowest error criteria (RMSE = 0.3342, MAE = 0.2292) and the highest coefficient of correlation (r = 0.84). The spatial structure of SOC could be well described by the spherical model. The nugget effect indicated that SOC was moderately dependent on the study area. The error distributions of the model showed that the improved model was unbiased in predicting the spatial distribution of SOC. This study's results revealed that an explanatory variable linked SOC increased success of spatial interpolation methods. In subsequent studies, this case should be taken into account for reaching more accurate outputs.

Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG

NASA Astrophysics Data System (ADS)

Chakraborty, Amitav; Roy, Sumit; Banerjee, Rahul

2018-03-01

This experimental work highlights the inherent capability of an adaptive-neuro fuzzy inference system (ANFIS) based model to act as a robust system identification tool (SIT) in prognosticating the performance and emission parameters of an existing diesel engine running of diesel-LPG dual fuel mode. The developed model proved its adeptness by successfully harnessing the effects of the input parameters of load, injection duration and LPG energy share on output parameters of BSFCEQ, BTE, NOX, SOOT, CO and HC. Successive evaluation of the ANFIS model, revealed high levels of resemblance with the already forecasted ANN results for the same input parameters and it was evident that similar to ANN, ANFIS also has the innate ability to act as a robust SIT. The ANFIS predicted data harmonized the experimental data with high overall accuracy. The correlation coefficient (R) values are stretched in between 0.99207 to 0.999988. The mean absolute percentage error (MAPE) tallies were recorded in the range of 0.02-0.173% with the root mean square errors (RMSE) in acceptable margins. Hence the developed model is capable of emulating the actual engine parameters with commendable ranges of accuracy, which in turn would act as a robust prediction platform in the future domains of optimization.

Estimating 3D Leaf and Stem Shape of Nursery Paprika Plants by a Novel Multi-Camera Photography System

PubMed Central

Zhang, Yu; Teng, Poching; Shimizu, Yo; Hosoi, Fumiki; Omasa, Kenji

2016-01-01

For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six variables of nursery paprika plants and investigated the accuracy of 3D models reconstructed from photos taken by four lens types at four different positions. The results demonstrated that error between the estimated and measured values was small, and the root-mean-square errors (RMSE) for leaf width/length and stem height/diameter were 1.65 mm (R2 = 0.98) and 0.57 mm (R2 = 0.99), respectively. The accuracies of the 3D model reconstruction of leaf and stem by a 28-mm lens at the first and third camera positions were the highest, and the number of reconstructed fine-scale 3D model shape surfaces of leaf and stem is the most. The results confirmed the practicability of our new method for the reconstruction of fine-scale plant model and accurate estimation of the plant parameters. They also displayed that our system is a good system for capturing high-resolution 3D images of nursery plants with high efficiency. PMID:27314348

Comparison of Precipitation from Gauge and Tropical Rainfall Measurement Mission (TRMM) for River Basins of India

NASA Astrophysics Data System (ADS)

Mondal, A.; Chandniha, S. K.; Lakshmi, V.; Kundu, S.; Hashemi, H.

2017-12-01

This study compares the monthly precipitation from the gridded rain gauge data collected by India Meteorological Department (IMD) and the retrievals from the Tropical Rainfall Measurement Mission (TRMM) for the river basins of India using the TRMM Multisatellite Precipitation Analysis (TMPA) version 7 (V7). The IMD and TMPA datasets have the same spatial resolution (0.25°×0.25°) and extend from 1998 to 2013. The TRMM data accuracy for the river basins is assessed by comparison with IMD using root mean square error (RMSE), normalized mean square error (NMSE), Nash-Sutcliffe coefficient (NASH) and correlation coefficient (CC) methods. The Mann-Kendall (MK) and modified Mann-Kendall (MMK) tests have been applied for analyzing the data trend, and the change has been detected by Sen's Slope using both data sets for annual and seasonal time periods. The change in intensity of precipitation is estimated by percentage for comparing actual differences in various river basins. Variation in precipitation is high (>100 mm represents >15% of average annual precipitation) in Brahmaputra, rivers draining into Myanmar (RDM), rivers draining into Bangladesh (RDB), east flowing rivers between Mahanadi and Godavari (EMG), east flowing rivers between Pennar and Cauvery (EPC), Cauvery and Tapi. The NASH and CC values vary between 0.80 to 0.98 and 0.87 to 0.99 in all river basins except area of north Ladakh not draining into Indus (NLI) and east flowing rivers south of Cauvery (ESC), while RMSE and NMSE vary from 15.95 to 101.68 mm and 2.66 to 58.38 mm, respectively. The trends for TMPA and IMD datasets from 1998 to 2013 are quite similar in MK (except 4 river basins) and MMK (except 3 river basins). The estimated results imply that the TMPA precipitation show good agreement and can be used in climate studies and hydrological simulations in locations/river basins where the number of rain gauge stations is not adequate to quantify the spatial variability of precipitation. Keywords: Precipitation data comparison, IMD, TRMM, river basins, Mann-Kendall test

Modeling Seasonality in Carbon Dioxide Emissions From Fossil Fuel Consumption

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Andres, R. J.

2004-05-01

Using United States data, a method is developed to estimate the monthly consumption of solid, liquid and gaseous fossil fuels using monthly sales data to estimate the relative monthly proportions of the total annual national fossil fuel use. These proportions are then used to estimate the total monthly carbon dioxide emissions for each state. From these data, the goal is to develop mathematical models that describe the seasonal flux in consumption for each type of fuel, as well as the total emissions for the nation. The time series models have two components. First, the general long-term yearly trend is determined with regression models for the annual totals. After removing the general trend, two alternatives are considered for modeling the seasonality. The first alternative uses the mean of the monthly proportions to predict the seasonal distribution. Because the seasonal patterns are fairly consistent in the United States, this is an effective modeling technique. Such regularity, however, may not be present with data from other nations. Therefore, as a second alternative, an ordinary least squares autoregressive model is used. This model is chosen for its ability to accurately describe dependent data and for its predictive capacity. It also has a meaningful interpretation, as each coefficient in the model quantifies the dependency for each corresponding time lag. Most importantly, it is dynamic, and able to adapt to anomalies and changing patterns. The order of the autoregressive model is chosen by the Akaike Information Criterion (AIC), which minimizes the predicted variance for all models of increasing complexity. To model the monthly fuel consumption, the annual trend is combined with the seasonal model. The models for each fuel type are then summed together to predict the total carbon dioxide emissions. The prediction error is estimated with the root mean square error (RMSE) from the actual estimated emission values. Overall, the models perform very well, with relative RMSE less than 10% for all fuel types, and under 5% for the national total emissions. Development of successful models is important to better understand and predict global environmental impacts from fossil fuel consumption.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

PubMed Central

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-01-01

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. PMID:27314363

Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution

USGS Publications Warehouse

Huang, C.; Townshend, J.R.G.; Liang, S.; Kalluri, S.N.V.; DeFries, R.S.

2002-01-01

Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size. ?? 2002 Elsevier Science Inc. All rights reserved.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

PubMed

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-06-15

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data

NASA Astrophysics Data System (ADS)

Chemura, Abel; Mutanga, Onisimo; Odindi, John; Kutywayo, Dumisani

2018-04-01

Nitrogen (N) is the most limiting factor to coffee development and productivity. Therefore, development of rapid, spatially explicit and temporal remote sensing-based approaches to determine spatial variability of coffee foliar N are imperative for increasing yields, reducing production costs and mitigating environmental impacts associated with excessive N applications. This study sought to assess the value of Sentinel-2 MSI spectral bands and vegetation indices in empirical estimation of coffee foliar N content at landscape level. Results showed that coffee foliar N is related to Sentinel-2 MSI B4 (R2 = 0.32), B6 (R2 = 0.49), B7 (R2 = 0.42), B8 (R2 = 0.57) and B12 (R2 = 0.24) bands. Vegetation indices were more related to coffee foliar N as shown by the Inverted Red-Edge Chlorophyll Index - IRECI (R2 = 0.66), Relative Normalized Difference Index - RNDVI (R2 = 0.48), CIRE1 (R2 = 0.28), and Normalized Difference Infrared Index - NDII (R2 = 0.37). These variables were also identified by the random forest variable optimisation as the most valuable in coffee foliar N prediction. Modelling coffee foliar N using vegetation indices produced better accuracy (R2 = 0.71 with RMSE = 0.27 for all and R2 = 0.73 with RMSE = 0.25 for optimized variables), compared to using spectral bands (R2 = 0.57 with RMSE = 0.32 for all and R2 = 0.58 with RMSE = 0.32 for optimized variables). Combining optimized bands and vegetation indices produced the best results in coffee foliar N modelling (R2 = 0.78, RMSE = 0.23). All the three best performing models (all vegetation indices, optimized vegetation indices and combining optimal bands and optimal vegetation indices) established that 15.2 ha (4.7%) of the total area under investigation had low foliar N levels (<2.5%). This study demonstrates the value of Sentinel-2 MSI data, particularly vegetation indices in modelling coffee foliar N at landscape scale.

Bioconcentration model for non-ionic, polar, and ionizable organic compounds in amphipod.

PubMed

Chen, Ciara Chun; Kuo, Dave Ta Fu

2018-05-01

The present study presents a bioconcentration model for non-ionic, polar, and ionizable organic compounds in amphipod based on first-order kinetics. Uptake rate constant k 1 is modeled as logk1=10.81logKOW + 0.15 (root mean square error [RMSE] = 0.52). Biotransformation rate constant k M is estimated using an existing polyparameter linear free energy relationship model. Respiratory elimination k 2 is calculated as modeled k 1 over theoretical biota-water partition coefficient K biow considering the contributions of lipid, protein, carbohydrate, and water. With negligible contributions of growth and egestion over a typical amphipod bioconcentration experiment, the bioconcentration factor (BCF) is modeled as k 1 /(k M  + k 2 ) (RMSE = 0.68). The proposed model performs well for non-ionic organic compounds (log K OW range = 3.3-7.62) within 1 log-unit error margin. Approximately 12% of the BCFs are underpredicted for polar and ionizable compounds. However, >50% of the estimated k 2 values are found to exceed the total depuration rate constants. Analyses suggest that these excessive k 2 values and underpredicted BCFs reflect underestimation in K biow , which may be improved by incorporating exoskeleton as a relevant partitioning component and refining the membrane-water partitioning model. The immediate needs to build up high-quality experimental k M values, explore the sorptive role of exoskeleton, and investigate the prevalence of k 2 overestimation in other bioconcentration models are also identified. The resulting BCF model can support, within its limitations, the ecotoxicological and risk assessment of emerging polar and ionizable organic contaminants in aquatic environments and advance the science of invertebrate bioaccumulation. Environ Toxicol Chem 2018;37:1378-1386. © 2018 SETAC. © 2018 SETAC.

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

PubMed Central

Safiuddin, Md.; Raman, Sudharshan N.; Abdus Salam, Md.; Jumaat, Mohd. Zamin

2016-01-01

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN. PMID:28773520

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.

PubMed

Safiuddin, Md; Raman, Sudharshan N; Abdus Salam, Md; Jumaat, Mohd Zamin

2016-05-20

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination ( R ²) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.

Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

NASA Astrophysics Data System (ADS)

Kearney, Michael R.; Maino, James L.

2018-06-01

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell's infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3-75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0-50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0-2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m-3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m-3). Comparisons at the continental scale to 0-2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 &rmse 15% vs. r 0.48 &rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

Biodrying of sewage sludge: kinetics of volatile solids degradation under different initial moisture contents and air-flow rates.

PubMed

Villegas, Manuel; Huiliñir, Cesar

2014-12-01

This study focuses on the kinetics of the biodegradation of volatile solids (VS) of sewage sludge for biodrying under different initial moisture contents (Mc) and air-flow rates (AFR). For the study, a 3(2) factorial design, whose factors were AFR (1, 2 or 3L/minkgTS) and initial Mc (59%, 68% and 78% w.b.), was used. Using seven kinetic models and a nonlinear regression method, kinetic parameters were estimated and the models were analyzed with two statistical indicators. Initial Mc of around 68% increases the temperature matrix and VS consumption, with higher moisture removal at lower initial Mc values. Lower AFRs gave higher matrix temperatures and VS consumption, while higher AFRs increased water removal. The kinetic models proposed successfully simulate VS biodegradation, with root mean square error (RMSE) between 0.007929 and 0.02744, and they can be used as a tool for satisfactory prediction of VS in biodrying. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stochastic differential equation (SDE) model of opening gold share price of bursa saham malaysia

NASA Astrophysics Data System (ADS)

Hussin, F. N.; Rahman, H. A.; Bahar, A.

2017-09-01

Black and Scholes option pricing model is one of the most recognized stochastic differential equation model in mathematical finance. Two parameter estimation methods have been utilized for the Geometric Brownian model (GBM); historical and discrete method. The historical method is a statistical method which uses the property of independence and normality logarithmic return, giving out the simplest parameter estimation. Meanwhile, discrete method considers the function of density of transition from the process of diffusion normal log which has been derived from maximum likelihood method. These two methods are used to find the parameter estimates samples of Malaysians Gold Share Price data such as: Financial Times and Stock Exchange (FTSE) Bursa Malaysia Emas, and Financial Times and Stock Exchange (FTSE) Bursa Malaysia Emas Shariah. Modelling of gold share price is essential since fluctuation of gold affects worldwide economy nowadays, including Malaysia. It is found that discrete method gives the best parameter estimates than historical method due to the smallest Root Mean Square Error (RMSE) value.

A system of equations to approximate the pharmacokinetic parameters of lacosamide at steady state from one plasma sample.

PubMed

Cawello, Willi; Schäfer, Carina

2014-08-01

Frequent plasma sampling to monitor pharmacokinetic (PK) profile of antiepileptic drugs (AEDs), is invasive, costly and time consuming. For drugs with a well-defined PK profile, such as AED lacosamide, equations can accurately approximate PK parameters from one steady-state plasma sample. Equations were derived to approximate steady-state peak and trough lacosamide plasma concentrations (Cpeak,ss and Ctrough,ss, respectively) and area under concentration-time curve during dosing interval (AUCτ,ss) from one plasma sample. Lacosamide (ka: ∼2 h(-1); ke: ∼0.05 h(-1), corresponding to half-life of 13 h) was calculated to reach Cpeak,ss after ∼1 h (tmax,ss). Equations were validated by comparing approximations to reference PK parameters obtained from single plasma samples drawn 3-12h following lacosamide administration, using data from double-blind, placebo-controlled, parallel-group PK study. Values of relative bias (accuracy) between -15% and +15%, and root mean square error (RMSE) values≤15% (precision) were considered acceptable for validation. Thirty-five healthy subjects (12 young males; 11 elderly males, 12 elderly females) received lacosamide 100mg/day for 4.5 days. Equation-derived PK values were compared to reference mean Cpeak,ss, Ctrough,ss and AUCτ,ss values. Equation-derived PK data had a precision of 6.2% and accuracy of -8.0%, 2.9%, and -0.11%, respectively. Equation-derived versus reference PK values for individual samples obtained 3-12h after lacosamide administration showed correlation (R2) range of 0.88-0.97 for AUCτ,ss. Correlation range for Cpeak,ss and Ctrough,ss was 0.65-0.87. Error analyses for individual sample comparisons were independent of time. Derived equations approximated lacosamide Cpeak,ss, Ctrough,ss and AUCτ,ss using one steady-state plasma sample within validation range. Approximated PK parameters were within accepted validation criteria when compared to reference PK values. Copyright © 2014 Elsevier B.V. All rights reserved.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

Solar radiation and precipitable water modeling for Turkey using artificial neural networks

NASA Astrophysics Data System (ADS)

Şenkal, Ozan

2015-08-01

Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water and solar radiation in a given location and given date (month), given altitude, temperature, pressure and humidity in Turkey (26-45ºE and 36-42ºN) during the period of 2000-2002. Resilient Propagation (RP) learning algorithms and logistic sigmoid transfer function were used in the network. To train the network, meteorological measurements taken by the Turkish State Meteorological Service (TSMS) and Wyoming University for the period from 2000 to 2002 from five stations distributed in Turkey were used as training data. Data from years (2000 and 2001) were used for training, while the year 2002 was used for testing and validating the model. The RP algorithm were first used for determination of the precipitable water and subsequently, computation of the solar radiation, in these stations Root Mean Square Error (RMSE) between the estimated and measured values for monthly mean daily sum for precipitable water and solar radiation values have been found as 0.0062 gr/cm2 and 0.0603 MJ/m2 (training cities), 0.5652 gr/cm2 and 3.2810 MJ/m2 (testing cities), respectively.

Tourism forecasting using modified empirical mode decomposition and group method of data handling

NASA Astrophysics Data System (ADS)

Yahya, N. A.; Samsudin, R.; Shabri, A.

2017-09-01

In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

PubMed

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Reconstruction of regional mean temperature for East Asia since 1900s and its uncertainties

NASA Astrophysics Data System (ADS)

Hua, W.

2017-12-01

Regional average surface air temperature (SAT) is one of the key variables often used to investigate climate change. Unfortunately, because of the limited observations over East Asia, there were also some gaps in the observation data sampling for regional mean SAT analysis, which was important to estimate past climate change. In this study, the regional average temperature of East Asia since 1900s is calculated by the Empirical Orthogonal Function (EOF)-based optimal interpolation (OA) method with considering the data errors. The results show that our estimate is more precise and robust than the results from simple average, which provides a better way for past climate reconstruction. In addition to the reconstructed regional average SAT anomaly time series, we also estimated uncertainties of reconstruction. The root mean square error (RMSE) results show that the the error decreases with respect to time, and are not sufficiently large to alter the conclusions on the persist warming in East Asia during twenty-first century. Moreover, the test of influence of data error on reconstruction clearly shows the sensitivity of reconstruction to the size of the data error.

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

PubMed

Heddam, Salim

2014-11-01

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

PubMed Central

Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui

2016-01-01

Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555

Limited predictive ability of surrogate indices of insulin sensitivity/resistance in Asian-Indian men.

PubMed

Muniyappa, Ranganath; Irving, Brian A; Unni, Uma S; Briggs, William M; Nair, K Sreekumaran; Quon, Michael J; Kurpad, Anura V

2010-12-01

Insulin resistance is highly prevalent in Asian Indians and contributes to worldwide public health problems, including diabetes and related disorders. Surrogate measurements of insulin sensitivity/resistance are used frequently to study Asian Indians, but these are not formally validated in this population. In this study, we compared the ability of simple surrogate indices to accurately predict insulin sensitivity as determined by the reference glucose clamp method. In this cross-sectional study of Asian-Indian men (n = 70), we used a calibration model to assess the ability of simple surrogate indices for insulin sensitivity [quantitative insulin sensitivity check index (QUICKI), homeostasis model assessment (HOMA2-IR), fasting insulin-to-glucose ratio (FIGR), and fasting insulin (FI)] to predict an insulin sensitivity index derived from the reference glucose clamp method (SI(Clamp)). Predictive accuracy was assessed by both root mean squared error (RMSE) of prediction as well as leave-one-out cross-validation-type RMSE of prediction (CVPE). QUICKI, FIGR, and FI, but not HOMA2-IR, had modest linear correlations with SI(Clamp) (QUICKI: r = 0.36; FIGR: r = -0.36; FI: r = -0.27; P < 0.05). No significant differences were noted among CVPE or RMSE from any of the surrogate indices when compared with QUICKI. Surrogate measurements of insulin sensitivity/resistance such as QUICKI, FIGR, and FI are easily obtainable in large clinical studies, but these may only be useful as secondary outcome measurements in assessing insulin sensitivity/resistance in clinical studies of Asian Indians.

An evaluation of light intensity functions for determination of shaded reference stream metabolism.

PubMed

Zell, Chris; Hubbart, Jason A

2012-04-30

The performance of three single-station whole stream metabolism models were evaluated within three shaded, seasonally hypoxic, Missouri reference streams using high resolution (15-minute) dissolved oxygen (DO), temperature, and light intensity data collected during the summers (July-September) of 2006-2008. The model incorporating light intensity data consistently achieved a lower root mean square error (median RMSE = 0.20 mg L(-1)) relative to models assuming sinusoidal light intensity functions (median RMSE = 0.28 mg L(-1)) and constant diel temperature (median RMSE = 0.53 mg L(-1)). Incorporation of site-specific light intensity into metabolism models better predicted morning DO concentrations and exposure to hypoxic conditions in shaded study streams. Model choice significantly affected (p < 0.05) rate estimates for daily average photosynthesis. Low reaeration (pooled site mean 1.1 day(-1) at 20 °C) coupled with summer temperatures (pooled site mean = 25.8 °C) and low to moderate community respiration (site median 1.0-3.0 g O(2) m(-2) day(-1)) yielded diel dissolved oxygen concentrations near or below critical aquatic life thresholds in studied reference streams. Quantifying these process combinations in best-available or least-disturbed (i.e., reference) systems advances our understanding of regional dissolved oxygen expectations and informs environmental management policy. Additional research is warranted to better link landscape processes with distributed sources that contribute to community respiration. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Method for Deriving All-Sky Evapotranspiration From the Synergistic Use of Remotely Sensed Images and Meteorological Data

NASA Astrophysics Data System (ADS)

Leng, Pei; Li, Zhao-Liang; Duan, Si-Bo; Tang, Ronglin; Gao, Mao-Fang

2017-12-01

Evapotranspiration (ET) is an important component of the water and energy cycle. The present study develops a practical approach for generating all-sky ET with the synergistic use of satellite images and meteorological data. In this approach, the ET over clear-sky pixels is estimated from a two-stage land surface temperature (LST)/fractional vegetation cover feature space method where the dry/wet edges are determined from theoretical calculations. For cloudy pixels, the Penman-Monteith equation is used to calculate the ET where no valid remotely sensed LST is available. An evaluation of the method with ET collected at ground-based large aperture scintillometer measurements at the Yucheng Comprehensive Experimental Station (YCES) in China is performed over a growth period from April to October 2010. The results show that the root-mean-square error (RMSE) and bias over clear-sky pixels are 57.3 W/m2 and 18.2 W/m2, respectively, whereas an RMSE of 69.3 W/m2 with a bias of 12.3 W/m2 can be found over cloudy pixels. Moreover, a reasonable overall RMSE of 65.3 W/m2 with a bias of 14.4 W/m2 at the YCES can be obtained under all-sky conditions, indicating a promising prospect for the derivation of all-sky ET using currently available satellite and meteorological data at a regional or global scale in future developments.

Using Anisotropic 3D Minkowski Functionals for Trabecular Bone Characterization and Biomechanical Strength Prediction in Proximal Femur Specimens

PubMed Central

Nagarajan, Mahesh B.; De, Titas; Lochmüller, Eva-Maria; Eckstein, Felix; Wismüller, Axel

2017-01-01

The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk. PMID:29170581

Monitoring of beer fermentation based on hybrid electronic tongue.

PubMed

Kutyła-Olesiuk, Anna; Zaborowski, Michał; Prokaryn, Piotr; Ciosek, Patrycja

2012-10-01

Monitoring of biotechnological processes, including fermentation is extremely important because of the rapidly occurring changes in the composition of the samples during the production. In the case of beer, the analysis of physicochemical parameters allows for the determination of the stage of fermentation process and the control of its possible perturbations. As a tool to control the beer production process a sensor array can be used, composed of potentiometric and voltammetric sensors (so-called hybrid Electronic Tongue, h-ET). The aim of this study is to apply electronic tongue system to distinguish samples obtained during alcoholic fermentation. The samples originate from batch of homemade beer fermentation and from two stages of the process: fermentation reaction and maturation of beer. The applied sensor array consists of 10 miniaturized ion-selective electrodes (potentiometric ET) and silicon based 3-electrode voltammetric transducers (voltammetric ET). The obtained results were processed using Partial Least Squares (PLS) and Partial Least Squares-Discriminant Analysis (PLS-DA). For potentiometric data, voltammetric data, and combined potentiometric and voltammetric data, comparison of the classification ability was conducted based on Root Mean Squared Error (RMSE), sensitivity, specificity, and coefficient F calculation. It is shown, that in the contrast to the separately used techniques, the developed hybrid system allowed for a better characterization of the beer samples. Data fusion in hybrid ET enables to obtain better results both in qualitative analysis (RMSE, specificity, sensitivity) and in quantitative analysis (RMSE, R(2), a, b). Copyright © 2012 Elsevier B.V. All rights reserved.

A simple linear model for estimating ozone AOT40 at forest sites from raw passive sampling data.

PubMed

Ferretti, Marco; Cristofolini, Fabiana; Cristofori, Antonella; Gerosa, Giacomo; Gottardini, Elena

2012-08-01

A rapid, empirical method is described for estimating weekly AOT40 from ozone concentrations measured with passive samplers at forest sites. The method is based on linear regression and was developed after three years of measurements in Trentino (northern Italy). It was tested against an independent set of data from passive sampler sites across Italy. It provides good weekly estimates compared with those measured by conventional monitors (0.85 ≤R(2)≤ 0.970; 97 ≤ RMSE ≤ 302). Estimates obtained using passive sampling at forest sites are comparable to those obtained by another estimation method based on modelling hourly concentrations (R(2) = 0.94; 131 ≤ RMSE ≤ 351). Regression coefficients of passive sampling are similar to those obtained with conventional monitors at forest sites. Testing against an independent dataset generated by passive sampling provided similar results (0.86 ≤R(2)≤ 0.99; 65 ≤ RMSE ≤ 478). Errors tend to accumulate when weekly AOT40 estimates are summed to obtain the total AOT40 over the May-July period, and the median deviation between the two estimation methods based on passive sampling is 11%. The method proposed does not require any assumptions, complex calculation or modelling technique, and can be useful when other estimation methods are not feasible, either in principle or in practice. However, the method is not useful when estimates of hourly concentrations are of interest.

Comparison of Predicted and Measured Soil Retention Curve in Lombardy Region Northern of Italy

NASA Astrophysics Data System (ADS)

Wassar, Fatma; Rienzner, Michele; Chiaradia, Enrico Antonio; Gandolfi, Claudio

2013-04-01

Water retention characteristics are crucial input parameters in any modeling study on water flow and solute transport. These properties are difficult to measure and therefore the use of both direct and indirect methods is required in order to adequately describe them with sufficient accuracy. Several field methods, laboratory methods and theoretical models for such determinations exist, each having their own limitations and advantages (Stephens, 1994). Therefore, extensive comparisons between estimated, field and laboratory results to determine it still requires their validity for a range of different soils and specific cases. This study attempts to make a contribution specifically in this connection. The soil water retention characteristics were determined in two representative sites (PMI-1 and PMI-5) located in Landriano field, in Lombardy region, northern Italy. In the laboratory, values of both volumetric water content (θ) and soil water matric potential (h) are measured in the same sample using the tensiometric box and pressure plate apparatus. Field determination of soil water retention involved measurements of soil water content with SENTEK probes, and matric potential with tensiometers. The retention curve characteristics were also determined using some of the most commonly cited and some recently developed PTFs that use soil properties such as particle-size distribution (sand, silt, and clay content), organic matter or organic Carbon content, and dry bulk density. Field methods are considered to be more representative than laboratory and estimation methods for determining water retention characteristics (Marion et al., 1996). Therefore, field retention curves were compared against retention curves obtained from laboratory measurements and PTFs estimations. The performances of laboratory and PTFs in predicting field measured data were evaluated using root mean square error (RMSE) and bias. The comparison showed that laboratory measurements were the most accurate. They had the highest ranking for the validation indices (RMSE ranging between 2.4 and 7.7% and bias between 0.1 and 6.4%). The second best technique was the PTF Rosetta (Schaap et al. 2001). They perform only slightly poorer than the laboratory measurements (RMSE ranging between 2.7 and 10% and bias between 0.3 and 7.7%). The lowest prediction accuracy is observed for the Rawls and Brakensiek (1985) PTF (RMSE ranging between 6.3 and 17% and bias between 5 and 10%) which is in contradiction with previous finding (Calzolari et al., 2001), showing that this function is well representing the retention characteristics of the area. We conclude that the Rosetta PTF developed by Schaap et al (2001) appears to be well suited to predict the soil moisture retention curve from easily available soil properties in the Lombardy area and further field investigations would be useful to reinforce this finding. Keywords: water retention curve; laboratory measurements; field measurements; pedotransfert functions; comparison.

The CM SAF ATOVS data record: overview of methodology and evaluation of total column water and profiles of tropospheric humidity

NASA Astrophysics Data System (ADS)

Courcoux, N.; Schröder, M.

2015-12-01

Recently, the reprocessed Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) tropospheric water vapour and temperature data record was released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM~SAF). ATOVS observations from infrared and microwave sounders onboard the National Oceanic and Atmospheric Agency (NOAA)-15-19 satellites and EUMETSAT's Meteorological Operational (Metop-A) satellite have been consistently reprocessed to generate 13 years (1999-2011) of global water vapour and temperature daily and monthly means with a spatial resolution of 90 km × 90 km. The data set is referenced under the following digital object identifier (DOI): doi:10.5676/EUM_SAF_CM/WVT_ATOVS/V001. After preprocessing, a maximum likelihood solution scheme was applied to the observations to simultaneously infer temperature and water vapour profiles. In a post-processing step, an objective interpolation method (Kriging) was applied to allow for gap filling. The product suite includes total precipitable water vapour (TPW), layer-integrated precipitable water vapour (LPW) and layer mean temperature for five tropospheric layers between the surface and 200 hPa, as well as specific humidity and temperature at six tropospheric levels between 1000 and 200 hPa. To our knowledge, this is the first time that the ATOVS record (1998-now) has been consistently reprocessed (1999-2011) to retrieve water vapour. TPW and LPW products were compared to corresponding products from the Global Climate Observing System (GCOS) Upper-Air Network (GUAN) radiosonde observations and from the Atmospheric Infrared Sounder (AIRS) version 5 satellite data record. TPW shows a good agreement with the GUAN radiosonde data: average bias and root mean square error (RMSE) are -0.2 and 3.3 kg m-2, respectively. For LPW, the maximum absolute (relative) bias and RMSE values decrease (increase) strongly with height. The maximum bias and RMSE are found at the lowest layer and are -0.7 and 2.5 kg m-2, respectively. While the RMSE relative to AIRS is generally smaller, the TPW bias relative to AIRS is larger, with dominant contributions from precipitating areas. The consistently reprocessed ATOVS data record exhibits improved quality and stability relative to the operational CM SAF products when compared to the TPW from GUAN radiosonde data over the period 2004-2011. Finally, it became evident that the change in the number of satellites used for the retrieval combined with the use of the Kriging leads to breakpoints in the ATOVS data record; therefore, a variability analysis of the data record is not recommended for the time period from January 1999 to January 2001.

Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study.

PubMed

Yan, Bryan P; Chan, Christy Ky; Li, Christien Kh; To, Olivia Tl; Lai, William Hs; Tse, Gary; Poh, Yukkee C; Poh, Ming-Zher

2017-03-13

Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color. The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app. A total of 40 healthy participants (20 men; mean age 24.7, SD 5.2 years; von Luschan skin color range 14-27) underwent treadmill exercise using the Bruce protocol. We recorded simultaneous PPG signals for each participant by having them (1) facing the front camera and (2) placing their index fingertip over an iPhone's back camera. We analyzed the PPG signals from the Cardiio-Heart Rate Monitor + 7 Minute Workout (Cardiio) smartphone app for HR measurements compared with a continuous 12-lead electrocardiogram (ECG) as the reference. Recordings of 20 seconds' duration each were acquired at rest, and immediately after moderate- (50%-70% maximum HR) and vigorous- (70%-85% maximum HR) intensity exercise, and repeated successively until return to resting HR. We used Bland-Altman plots to examine agreement between ECG and PPG-estimated HR. The accuracy criterion was root mean square error (RMSE) ≤5 beats/min or ≤10%, whichever was greater, according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation EC-13 standard. We analyzed a total of 631 fingertip and 626 facial PPG measurements. Fingertip PPG-estimated HRs were strongly correlated with resting ECG HR (r=.997, RMSE=1.03 beats/min or 1.40%), postmoderate-intensity exercise (r=.994, RMSE=2.15 beats/min or 2.53%), and postvigorous-intensity exercise HR (r=.995, RMSE=2.01 beats/min or 1.93%). The correlation of facial PPG-estimated HR was stronger with resting ECG HR (r=.997, RMSE=1.02 beats/min or 1.44%) than with postmoderate-intensity exercise (r=.982, RMSE=3.68 beats/min or 4.11%) or with postvigorous-intensity exercise (r=.980, RMSE=3.84 beats/min or 3.73%). Bland-Altman plots showed better agreement between ECG and fingertip PPG-estimated HR than between ECG and facial PPG-estimated HR. We found that HR detection by the Cardiio smartphone app was accurate at rest and after moderate- and vigorous-intensity exercise in a healthy young adult sample. Contact-free facial PPG detection is more convenient but is less accurate than finger PPG due to body motion after exercise. ©Bryan P Yan, Christy KY Chan, Christien KH Li, Olivia TL To, William HS Lai, Gary Tse, Yukkee C Poh, Ming-Zher Poh. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 13.03.2017.

Clinical anthropometrics and body composition from 3D whole-body surface scans

PubMed Central

Ng, BK; Hinton, BJ; Fan, B; Kanaya, AM; Shepherd, JA

2017-01-01

BACKGROUND/OBJECTIVES Obesity is a significant worldwide epidemic that necessitates accessible tools for robust body composition analysis. We investigated whether widely available 3D body surface scanners can provide clinically relevant direct anthropometrics (circumferences, areas and volumes) and body composition estimates (regional fat/lean masses). SUBJECTS/METHODS Thirty-nine healthy adults stratified by age, sex and body mass index (BMI) underwent whole-body 3D scans, dual energy X-ray absorptiometry (DXA), air displacement plethysmography and tape measurements. Linear regressions were performed to assess agreement between 3D measurements and criterion methods. Linear models were derived to predict DXA body composition from 3D scan measurements. Thirty-seven external fitness center users underwent 3D scans and bioelectrical impedance analysis for model validation. RESULTS 3D body scan measurements correlated strongly to criterion methods: waist circumference R2 = 0.95, hip circumference R2 = 0.92, surface area R2 = 0.97 and volume R2 = 0.99. However, systematic differences were observed for each measure due to discrepancies in landmark positioning. Predictive body composition equations showed strong agreement for whole body (fat mass R2 = 0.95, root mean square error (RMSE) = 2.4 kg; fat-free mass R2 = 0.96, RMSE = 2.2 kg) and arms, legs and trunk (R2 = 0.79–0.94, RMSE = 0.5–1.7 kg). Visceral fat prediction showed moderate agreement (R2 = 0.75, RMSE = 0.11 kg). CONCLUSIONS 3D surface scanners offer precise and stable automated measurements of body shape and composition. Software updates may be needed to resolve measurement biases resulting from landmark positioning discrepancies. Further studies are justified to elucidate relationships between body shape, composition and metabolic health across sex, age, BMI and ethnicity groups, as well as in those with metabolic disorders. PMID:27329614

Irregularity of the posterior corneal surface during applanation using a curved femtosecond laser interface and microkeratome cutting head.

PubMed

Vetter, Jan M; Holtz, Carsten; Vossmerbaeumer, Urs; Pfeiffer, Norbert

2012-03-01

To evaluate the irregularity of the posterior corneal surface and intrastromal dissection during the preparation of donor tissue for Descemet stripping automated endothelial keratoplasty (DSAEK) using a curved interface femtosecond laser and microkeratome. Sixteen human donor corneas unsuitable for transplantation were divided into two groups: a femtosecond (FS) laser group (n=7) using the VisuMax femtosecond laser (Carl Zeiss Meditec) and a microkeratome group (n=9) using the Amadeus II microkeratome (Ziemer Ophthalmic Group). The corneas were fixed on artificial anterior chambers. Horizontal cross-sections were obtained using spectral-domain optical coherence tomography prior to applanation, during applanation, as well as during and after intrastromal dissection at 450-μm corneal depth. The posterior surface and the dissection line were evaluated for irregularity by fitting a second-order polynomial curve using regression analysis and obtaining the root-mean-square error (RMSE). Groups were compared using analysis of variance. The RMSE of the posterior surface prior to applanation was 9.7 ± 3.1 μm in the FS laser group and 10.2 ± 2.3 μm in the microkeratome group. The RMSE increased to 50.7 ± 9.4 μm and 20.9 ± 6.1 μm during applanation and decreased again to 10.6 ± 1.4 μm and 8.1 ± 1.8 μm after applanation in the FS laser and microkeratome groups, respectively. The RMSE of the intrastromal cut was 19.5 ± 5.7 μm in the FS laser group and 7.7 ± 3.0 μm in the microkeratome group (P<.001). Our results show significantly greater irregularity with the curved interface femtosecond laser-assisted cleavage compared to microkeratome-assisted corneal dissection, possibly due to applanation-derived deformation of the posterior cornea. Copyright 2012, SLACK Incorporated.

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

PubMed Central

Chang, Howard H.; Hu, Xuefei; Liu, Yang

2014-01-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial–temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003–2005. Via cross-validation experiments, our model had an out-of-sample prediction R2 of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m3 between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial–temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined. PMID:24368510

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling.

PubMed

Chang, Howard H; Hu, Xuefei; Liu, Yang

2014-07-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.

Regression modeling of gas-particle partitioning of atmospheric oxidized mercury from temperature data

NASA Astrophysics Data System (ADS)

Cheng, Irene; Zhang, Leiming; Blanchard, Pierrette

2014-10-01

Models describing the partitioning of atmospheric oxidized mercury (Hg(II)) between the gas and fine particulate phases were developed as a function of temperature. The models were derived from regression analysis of the gas-particle partitioning parameters, defined by a partition coefficient (Kp) and Hg(II) fraction in fine particles (fPBM) and temperature data from 10 North American sites. The generalized model, log(1/Kp) = 12.69-3485.30(1/T) (R2 = 0.55; root-mean-square error (RMSE) of 1.06 m3/µg for Kp), predicted the observed average Kp at 7 of the 10 sites. Discrepancies between the predicted and observed average Kp were found at the sites impacted by large Hg sources because the model had not accounted for the different mercury speciation profile and aerosol compositions of different sources. Site-specific equations were also generated from average Kp and fPBM corresponding to temperature interval data. The site-specific models were more accurate than the generalized Kp model at predicting the observations at 9 of the 10 sites as indicated by RMSE of 0.22-0.5 m3/µg for Kp and 0.03-0.08 for fPBM. Both models reproduced the observed monthly average values, except for a peak in Hg(II) partitioning observed during summer at two locations. Weak correlations between the site-specific model Kp or fPBM and observations suggest the role of aerosol composition, aerosol water content, and relative humidity factors on Hg(II) partitioning. The use of local temperature data to parameterize Hg(II) partitioning in the proposed models potentially improves the estimation of mercury cycling in chemical transport models and elsewhere.

Improved inland water levels from SAR altimetry using novel empirical and physical retrackers

NASA Astrophysics Data System (ADS)

Villadsen, Heidi; Deng, Xiaoli; Andersen, Ole B.; Stenseng, Lars; Nielsen, Karina; Knudsen, Per

2016-06-01

Satellite altimetry has proven a valuable resource of information on river and lake levels where in situ data are sparse or non-existent. In this study several new methods for obtaining stable inland water levels from CryoSat-2 Synthetic Aperture Radar (SAR) altimetry are presented and evaluated. In addition, the possible benefits from combining physical and empirical retrackers are investigated. The retracking methods evaluated in this paper include the physical SAR Altimetry MOde Studies and Applications (SAMOSA3) model, a traditional subwaveform threshold retracker, the proposed Multiple Waveform Persistent Peak (MWaPP) retracker, and a method combining the physical and empirical retrackers. Using a physical SAR waveform retracker over inland water has not been attempted before but shows great promise in this study. The evaluation is performed for two medium-sized lakes (Lake Vänern in Sweden and Lake Okeechobee in Florida), and in the Amazon River in Brazil. Comparing with in situ data shows that using the SAMOSA3 retracker generally provides the lowest root-mean-squared-errors (RMSE), closely followed by the MWaPP retracker. For the empirical retrackers, the RMSE values obtained when comparing with in situ data in Lake Vänern and Lake Okeechobee are in the order of 2-5 cm for well-behaved waveforms. Combining the physical and empirical retrackers did not offer significantly improved mean track standard deviations or RMSEs. Based on these studies, it is suggested that future SAR derived water levels are obtained using the SAMOSA3 retracker whenever information about other physical properties apart from range is desired. Otherwise we suggest using the empirical MWaPP retracker described in this paper, which is both easy to implement, computationally efficient, and gives a height estimate for even the most contaminated waveforms.

Brain Volume Estimation Enhancement by Morphological Image Processing Tools.

PubMed

Zeinali, R; Keshtkar, A; Zamani, A; Gharehaghaji, N

2017-12-01

Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution. In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth. The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method. Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.

Validation of bioelectrical impedance analysis for total body water assessment against the deuterium dilution technique in Asian children.

PubMed

Liu, A; Byrne, N M; Ma, G; Nasreddine, L; Trinidad, T P; Kijboonchoo, K; Ismail, M N; Kagawa, M; Poh, B K; Hills, A P

2011-12-01

To develop and cross-validate bioelectrical impedance analysis (BIA) prediction equations of total body water (TBW) and fat-free mass (FFM) for Asian pre-pubertal children from China, Lebanon, Malaysia, Philippines and Thailand. Height, weight, age, gender, resistance and reactance measured by BIA were collected from 948 Asian children (492 boys and 456 girls) aged 8-10 years from the five countries. The deuterium dilution technique was used as the criterion method for the estimation of TBW and FFM. The BIA equations were developed using stepwise multiple regression analysis and cross-validated using the Bland-Altman approach. The BIA prediction equation for the estimation of TBW was as follows: TBW=0.231 × height(2)/resistance+0.066 × height+0.188 × weight+0.128 × age+0.500 × sex-0.316 × Thais-4.574 (R (2)=88.0%, root mean square error (RMSE)=1.3 kg), and for the estimation of FFM was as follows: FFM=0.299 × height(2)/resistance+0.086 × height+0.245 × weight+0.260 × age+0.901 × sex-0.415 × ethnicity (Thai ethnicity =1, others = 0)-6.952 (R (2)=88.3%, RMSE=1.7 kg). No significant difference between measured and predicted values for the whole cross-validation sample was found. However, the prediction equation for estimation of TBW/FFM tended to overestimate TBW/FFM at lower levels whereas underestimate at higher levels of TBW/FFM. Accuracy of the general equation for TBW and FFM was also valid at each body mass index category. Ethnicity influences the relationship between BIA and body composition in Asian pre-pubertal children. The newly developed BIA prediction equations are valid for use in Asian pre-pubertal children.