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.

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

PubMed

Ke-Wei, Wang; Yu, Wu; Jin-Ping, Li; Yu-Yu, Jiang

2016-07-12

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

PubMed Central

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T.; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P.; Rötter, Reimund P.; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. PMID:27055028

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

PubMed

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P; Rötter, Reimund P; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Increasing the applicability of density functional theory. V. X-ray absorption spectra with ionization potential corrected exchange and correlation potentials.

PubMed

Verma, Prakash; Bartlett, Rodney J

2016-07-21

Core excitation energies are computed with time-dependent density functional theory (TD-DFT) using the ionization energy corrected exchange and correlation potential QTP(0,0). QTP(0,0) provides C, N, and O K-edge spectra to about an electron volt. A mean absolute error (MAE) of 0.77 and a maximum error of 2.6 eV is observed for QTP(0,0) for many small molecules. TD-DFT based on QTP (0,0) is then used to describe the core-excitation spectra of the 22 amino acids. TD-DFT with conventional functionals greatly underestimates core excitation energies, largely due to the significant error in the Kohn-Sham occupied eigenvalues. To the contrary, the ionization energy corrected potential, QTP(0,0), provides excellent approximations (MAE of 0.53 eV) for core ionization energies as eigenvalues of the Kohn-Sham equations. As a consequence, core excitation energies are accurately described with QTP(0,0), as are the core ionization energies important in X-ray photoionization spectra or electron spectroscopy for chemical analysis.

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.

Methodological variations and their effects on reported medication administration error rates.

PubMed

McLeod, Monsey Chan; Barber, Nick; Franklin, Bryony Dean

2013-04-01

Medication administration errors (MAEs) are a problem, yet methodological variation between studies presents a potential barrier to understanding how best to increase safety. Using the UK as a case-study, we systematically summarised methodological variations in MAE studies, and their effects on reported MAE rates. Nine healthcare databases were searched for quantitative observational MAE studies in UK hospitals. Methodological variations were analysed and meta-analysis of MAE rates performed using studies that used the same definitions. Odds ratios (OR) were calculated to compare MAE rates between intravenous (IV) and non-IV doses, and between paediatric and adult doses. We identified 16 unique studies reporting three MAE definitions, 44 MAE subcategories and four different denominators. Overall adult MAE rates were 5.6% of a total of 21 533 non-IV opportunities for error (OE) (95% CI 4.6% to 6.7%) and 35% of a total of 154 IV OEs (95% CI 2% to 68%). MAEs were five times more likely in IV than non-IV doses (pooled OR 5.1; 95% CI 3.5 to 7.5). Including timing errors of ±30 min increased the MAE rate from 27% to 69% of 320 IV doses in one study. Five studies were unclear as to whether the denominator included dose omissions; omissions accounted for 0%-13% of IV doses and 1.8%-5.1% of non-IV doses. Wide methodological variations exist even within one country, some with significant effects on reported MAE rates. We have made recommendations for future MAE studies; these may be applied both within and outside the UK.

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.

Relationship between postoperative refractive outcomes and cataract density: multiple regression analysis.

PubMed

Ueda, Tetsuo; Ikeda, Hitoe; Ota, Takeo; Matsuura, Toyoaki; Hara, Yoshiaki

2010-05-01

To evaluate the relationship between cataract density and the deviation from the predicted refraction. Department of Ophthalmology, Nara Medical University, Kashihara, Japan. Axial length (AL) was measured in eyes with mainly nuclear cataract using partial coherence interferometry (IOLMaster). The postoperative AL was measured in pseudophakic mode. The AL difference was calculated by subtracting the postoperative AL from the preoperative AL. Cataract density was measured with the pupil dilated using anterior segment Scheimpflug imaging (EAS-1000). The predicted postoperative refraction was calculated using the SRK/T formula. The subjective refraction 3 months postoperatively was also measured. The mean absolute prediction error (MAE) (mean of absolute difference between predicted postoperative refraction and spherical equivalent of postoperative subjective refraction) was calculated. The relationship between the MAE and cataract density, age, preoperative visual acuity, anterior chamber depth, corneal radius of curvature, and AL difference was evaluated using multiple regression analysis. In the 96 eyes evaluated, the MAE was correlated with cataract density (r = 0.37, P = .001) and the AL difference (r = 0.34, P = .003) but not with the other parameters. The AL difference was correlated with cataract density (r = 0.53, P<.0001). The postoperative refractive outcome was affected by cataract density. This should be taken into consideration in eyes with a higher density cataract. (c) 2010 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

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.

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

PubMed

Gupta, Rajarshi

2016-05-01

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

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.

Comparison of INAR(1)-Poisson model and Markov prediction model in forecasting the number of DHF patients in west java Indonesia

NASA Astrophysics Data System (ADS)

Ahdika, Atina; Lusiyana, Novyan

2017-02-01

World Health Organization (WHO) noted Indonesia as the country with the highest dengue (DHF) cases in Southeast Asia. There are no vaccine and specific treatment for DHF. One of the efforts which can be done by both government and resident is doing a prevention action. In statistics, there are some methods to predict the number of DHF cases to be used as the reference to prevent the DHF cases. In this paper, a discrete time series model, INAR(1)-Poisson model in specific, and Markov prediction model are used to predict the number of DHF patients in West Java Indonesia. The result shows that MPM is the best model since it has the smallest value of MAE (mean absolute error) and MAPE (mean absolute percentage error).

An Evaluation of Portable Wet Bulb Globe Temperature Monitor Accuracy.

PubMed

Cooper, Earl; Grundstein, Andrew; Rosen, Adam; Miles, Jessica; Ko, Jupil; Curry, Patrick

2017-12-01

  Wet bulb globe temperature (WBGT) is the gold standard for assessing environmental heat stress during physical activity. Many manufacturers of commercially available instruments fail to report WBGT accuracy.   To determine the accuracy of several commercially available WBGT monitors compared with a standardized reference device.   Observational study.   Field test.   Six commercially available WBGT devices.   Data were recorded for 3 sessions (1 in the morning and 2 in the afternoon) at 2-minute intervals for at least 2 hours. Mean absolute error (MAE), root mean square error (RMSE), mean bias error (MBE), and the Pearson correlation coefficient ( r) were calculated to determine instrument performance compared with the reference unit.   The QUESTemp° 34 (MAE = 0.24°C, RMSE = 0.44°C, MBE = -0.64%) and Extech HT30 Heat Stress Wet Bulb Globe Temperature Meter (Extech; MAE = 0.61°C, RMSE = 0.79°C, MBE = 0.44%) demonstrated the least error in relation to the reference standard, whereas the General WBGT8778 Heat Index Checker (General; MAE = 1.18°C, RMSE = 1.34°C, MBE = 4.25%) performed the poorest. The QUESTemp° 34 and Kestrel 4400 Heat Stress Tracker units provided conservative measurements that slightly overestimated the WBGT provided by the reference unit. Finally, instruments using the psychrometric wet bulb temperature (General, REED Heat Index WBGT Meter, and WBGT-103 Heat Stroke Checker) tended to underestimate the WBGT, and the resulting values more frequently fell into WBGT-based activity categories with fewer restrictions as defined by the American College of Sports Medicine.   The QUESTemp° 34, followed by the Extech, had the smallest error compared with the reference unit. Moreover, the QUESTemp° 34, Extech, and Kestrel units appeared to offer conservative yet accurate assessments of the WBGT, potentially minimizing the risk of allowing physical activity to continue in stressful heat environments. Instruments using the psychrometric wet bulb temperature tended to underestimate WBGT under low wind-speed conditions. Accurate WBGT interpretations are important to enable clinicians to guide activities in hot and humid weather conditions.

Willingness of nurses to report medication administration errors in southern Taiwan: a cross-sectional survey.

PubMed

Lin, Yu-Hua; Ma, Su-mei

2009-01-01

Underreporting of medication administering errors (MAEs) is a threat to the quality of nursing care. The reasons for MAEs are complex and vary by health professional and institution. The purpose of this study was to explore the prevalence of MAEs and the willingness of nurses to report them. A cross-sectional study was conducted involving a survey of 14 medical surgical hospitals in southern Taiwan. Nurses voluntarily participated in this study. A structured questionnaire was completed by 605 participants. Data were collected from February 1, 2005 to March 15, 2005 using the following instruments: MAEs Unwillingness to Report Scale, Medication Errors Etiology Questionnaire, and Personal Features Questionnaire. One additional question was used to identify the willingness of nurses to report medication errors: "When medication errors occur, should they be reported to the department?" This question helped to identify the willingness or lack thereof, to report incident errors. The results indicated that 66.9% of the nurses reported experiencing MAEs and 87.7% of the nurses had a willingness to report the MAEs if there were no consequences for reporting. The nurses' willingness to report MAEs differed by job position, nursing grade, type of hospital, and hospital funding. The final logistic regression model demonstrated hospital funding to be the only statistically significant factor. The odds of a willingness to report MAEs increased 2.66-fold in private hospitals (p = 0.032, CI = 1.09 to 6.49), and 3.28 in nonprofit hospitals (p = 0.00, CI = 1.73 to 6.21) when compared to public hospitals. This study demonstrates that reporting of MAEs should be anonymous and without negative consequences in order to monitor and guide improvements in hospital medication systems.

Comparison of the WSA-ENLIL model with three CME cone types

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.; Na, H.

2013-07-01

We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.Abstract (2,250 Maximum Characters): We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.

Comparative Efficacy of the New Optical Biometer on Intraocular Lens Power Calculation (AL-Scan versus IOLMaster).

PubMed

Ha, Ahnul; Wee, Won Ryang; Kim, Mee Kum

2018-05-15

To evaluate the agreement in axial length (AL), keratometry, and anterior chamber depth measurements between AL-Scan and IOLMaster biometers and to compare the efficacy of the AL-Scan on intraocular lens (IOL) power calculations and refractive outcomes with those obtained by the IOLMaster. Medical records of 48 eyes from 48 patients who underwent uneventful phacoemulsification and IOL insertion were retrospectively reviewed. One of the two types of monofocal aspheric IOLs were implanted (Tecnis ZCB00 [Tecnis, n = 34] or CT Asphina 509M [Asphina, n = 14]). Two different partial coherence interferometers measured and compared AL, keratometry (2.4 mm), anterior chamber depth, and IOL power calculations with SRK/T, Hoffer Q, Holladay2, and Haigis formulas. The difference between expected and actual final refractive error was compared as refractive mean error (ME), refractive mean absolute error (MAE), and median absolute error (MedAE). AL measured by the AL-Scan was shorter than that measured by the IOLMaster (p = 0.029). The IOL power of Tecnis did not differ between the four formulas; however, the Asphina measurement calculated using Hoffer Q for the AL-Scan was lower (0.28 diopters, p = 0.015) than that calculated by the IOLMaster. There were no statistically significant differences between the calculations by MAE and MedAE for the four formulas in either IOL. In SRK/T, ME in Tecnis-inserted eyes measured by AL-Scan showed a tendency toward myopia (p = 0.032). Measurement by AL-Scan provides reliable biometry data and power calculations compared to the IOLMaster; however, refractive outcomes of Tecnis-inserted eyes by AL-Scan calculated using SRK/T can show a slight myopic tendency. © 2018 The Korean Ophthalmological Society.

Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin.

PubMed

Longato, Enrico; Garrido, Maria; Saccardo, Desy; Montesinos Guevara, Camila; Mani, Ali R; Bolognesi, Massimo; Amodio, Piero; Facchinetti, Andrea; Sparacino, Giovanni; Montagnese, Sara

2017-01-01

A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.

Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin

PubMed Central

Longato, Enrico; Garrido, Maria; Saccardo, Desy; Montesinos Guevara, Camila; Mani, Ali R.; Bolognesi, Massimo; Amodio, Piero; Facchinetti, Andrea; Sparacino, Giovanni

2017-01-01

A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints. PMID:28666029

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

PubMed Central

Young, Alistair A.; Li, Xiaosong

2014-01-01

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

PubMed

Ni, Yizhao; Lingren, Todd; Hall, Eric S; Leonard, Matthew; Melton, Kristin; Kirkendall, Eric S

2018-05-01

Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001). The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

Assessment of Demirjian's 8-teeth technique of age estimation and Indian-specific formulas in an East Indian population: A cross-sectional study.

PubMed

Rath, Hemamalini; Rath, Rachna; Mahapatra, Sandeep; Debta, Tribikram

2017-01-01

The age of an individual can be assessed by a plethora of widely available tooth-based techniques, among which radiological methods prevail. The Demirjian's technique of age assessment based on tooth development stages has been extensively investigated in different populations of the world. The present study is to assess the applicability of Demirjian's modified 8-teeth technique in age estimation of population of East India (Odisha), utilizing Acharya's Indian-specific cubic functions. One hundred and six pretreatment orthodontic radiographs of patients in an age group of 7-23 years with representation from both genders were assessed for eight left mandibular teeth and scored as per the Demirjian's 9-stage criteria for teeth development stages. Age was calculated on the basis of Acharya's Indian formula. Statistical analysis was performed to compare the estimated and actual age. All data were analyzed using SPSS 20.0 (SPSS Inc., Chicago, Illinois, USA) and MS Excel Package. The results revealed that the mean absolute error (MAE) in age estimation of the entire sample was 1.3 years with 50% of the cases having an error rate within ± 1 year. The MAE in males and females (7-16 years) was 1.8 and 1.5, respectively. Likewise, the MAE in males and females (16.1-23 years) was 1.1 and 1.3, respectively. The low error rate in estimating age justifies the application of this modified technique and Acharya's Indian formulas in the present East Indian population.

Causes of medication administration errors in hospitals: a systematic review of quantitative and qualitative evidence.

PubMed

Keers, Richard N; Williams, Steven D; Cooke, Jonathan; Ashcroft, Darren M

2013-11-01

Underlying systems factors have been seen to be crucial contributors to the occurrence of medication errors. By understanding the causes of these errors, the most appropriate interventions can be designed and implemented to minimise their occurrence. This study aimed to systematically review and appraise empirical evidence relating to the causes of medication administration errors (MAEs) in hospital settings. Nine electronic databases (MEDLINE, EMBASE, International Pharmaceutical Abstracts, ASSIA, PsycINFO, British Nursing Index, CINAHL, Health Management Information Consortium and Social Science Citations Index) were searched between 1985 and May 2013. Inclusion and exclusion criteria were applied to identify eligible publications through title analysis followed by abstract and then full text examination. English language publications reporting empirical data on causes of MAEs were included. Reference lists of included articles and relevant review papers were hand searched for additional studies. Studies were excluded if they did not report data on specific MAEs, used accounts from individuals not directly involved in the MAE concerned or were presented as conference abstracts with insufficient detail. A total of 54 unique studies were included. Causes of MAEs were categorised according to Reason's model of accident causation. Studies were assessed to determine relevance to the research question and how likely the results were to reflect the potential underlying causes of MAEs based on the method(s) used. Slips and lapses were the most commonly reported unsafe acts, followed by knowledge-based mistakes and deliberate violations. Error-provoking conditions influencing administration errors included inadequate written communication (prescriptions, documentation, transcription), problems with medicines supply and storage (pharmacy dispensing errors and ward stock management), high perceived workload, problems with ward-based equipment (access, functionality), patient factors (availability, acuity), staff health status (fatigue, stress) and interruptions/distractions during drug administration. Few studies sought to determine the causes of intravenous MAEs. A number of latent pathway conditions were less well explored, including local working culture and high-level managerial decisions. Causes were often described superficially; this may be related to the use of quantitative surveys and observation methods in many studies, limited use of established error causation frameworks to analyse data and a predominant focus on issues other than the causes of MAEs among studies. As only English language publications were included, some relevant studies may have been missed. Limited evidence from studies included in this systematic review suggests that MAEs are influenced by multiple systems factors, but if and how these arise and interconnect to lead to errors remains to be fully determined. Further research with a theoretical focus is needed to investigate the MAE causation pathway, with an emphasis on ensuring interventions designed to minimise MAEs target recognised underlying causes of errors to maximise their impact.

Quality improvements in decreasing medication administration errors made by nursing staff in an academic medical center hospital: a trend analysis during the journey to Joint Commission International accreditation and in the post-accreditation era

PubMed Central

Wang, Hua-fen; Jin, Jing-fen; Feng, Xiu-qin; Huang, Xin; Zhu, Ling-ling; Zhao, Xiao-ying; Zhou, Quan

2015-01-01

Background Medication errors may occur during prescribing, transcribing, prescription auditing, preparing, dispensing, administration, and monitoring. Medication administration errors (MAEs) are those that actually reach patients and remain a threat to patient safety. The Joint Commission International (JCI) advocates medication error prevention, but experience in reducing MAEs during the period of before and after JCI accreditation has not been reported. Methods An intervention study, aimed at reducing MAEs in hospitalized patients, was performed in the Second Affiliated Hospital of Zhejiang University, Hangzhou, People’s Republic of China, during the journey to JCI accreditation and in the post-JCI accreditation era (first half-year of 2011 to first half-year of 2014). Comprehensive interventions included organizational, information technology, educational, and process optimization-based measures. Data mining was performed on MAEs derived from a compulsory electronic reporting system. Results The number of MAEs continuously decreased from 143 (first half-year of 2012) to 64 (first half-year of 2014), with a decrease in occurrence rate by 60.9% (0.338% versus 0.132%, P<0.05). The number of MAEs related to high-alert medications decreased from 32 (the second half-year of 2011) to 16 (the first half-year of 2014), with a decrease in occurrence rate by 57.9% (0.0787% versus 0.0331%, P<0.05). Omission was the top type of MAE during the first half-year of 2011 to the first half-year of 2014, with a decrease by 50% (40 cases versus 20 cases). Intravenous administration error was the top type of error regarding administration route, but it continuously decreased from 64 (first half-year of 2012) to 27 (first half-year of 2014). More experienced registered nurses made fewer medication errors. The number of MAEs in surgical wards was twice that in medicinal wards. Compared with non-intensive care units, the intensive care units exhibited higher occurrence rates of MAEs (1.81% versus 0.24%, P<0.001). Conclusion A 3-and-a-half-year intervention program on MAEs was confirmed to be effective. MAEs made by nursing staff can be reduced, but cannot be eliminated. The depth, breadth, and efficiency of multidiscipline collaboration among physicians, pharmacists, nurses, information engineers, and hospital administrators are pivotal to safety in medication administration. JCI accreditation may help health systems enhance the awareness and ability to prevent MAEs and achieve successful quality improvements. PMID:25767393

Quality improvements in decreasing medication administration errors made by nursing staff in an academic medical center hospital: a trend analysis during the journey to Joint Commission International accreditation and in the post-accreditation era.

PubMed

Wang, Hua-Fen; Jin, Jing-Fen; Feng, Xiu-Qin; Huang, Xin; Zhu, Ling-Ling; Zhao, Xiao-Ying; Zhou, Quan

2015-01-01

Medication errors may occur during prescribing, transcribing, prescription auditing, preparing, dispensing, administration, and monitoring. Medication administration errors (MAEs) are those that actually reach patients and remain a threat to patient safety. The Joint Commission International (JCI) advocates medication error prevention, but experience in reducing MAEs during the period of before and after JCI accreditation has not been reported. An intervention study, aimed at reducing MAEs in hospitalized patients, was performed in the Second Affiliated Hospital of Zhejiang University, Hangzhou, People's Republic of China, during the journey to JCI accreditation and in the post-JCI accreditation era (first half-year of 2011 to first half-year of 2014). Comprehensive interventions included organizational, information technology, educational, and process optimization-based measures. Data mining was performed on MAEs derived from a compulsory electronic reporting system. The number of MAEs continuously decreased from 143 (first half-year of 2012) to 64 (first half-year of 2014), with a decrease in occurrence rate by 60.9% (0.338% versus 0.132%, P<0.05). The number of MAEs related to high-alert medications decreased from 32 (the second half-year of 2011) to 16 (the first half-year of 2014), with a decrease in occurrence rate by 57.9% (0.0787% versus 0.0331%, P<0.05). Omission was the top type of MAE during the first half-year of 2011 to the first half-year of 2014, with a decrease by 50% (40 cases versus 20 cases). Intravenous administration error was the top type of error regarding administration route, but it continuously decreased from 64 (first half-year of 2012) to 27 (first half-year of 2014). More experienced registered nurses made fewer medication errors. The number of MAEs in surgical wards was twice that in medicinal wards. Compared with non-intensive care units, the intensive care units exhibited higher occurrence rates of MAEs (1.81% versus 0.24%, P<0.001). A 3-and-a-half-year intervention program on MAEs was confirmed to be effective. MAEs made by nursing staff can be reduced, but cannot be eliminated. The depth, breadth, and efficiency of multidiscipline collaboration among physicians, pharmacists, nurses, information engineers, and hospital administrators are pivotal to safety in medication administration. JCI accreditation may help health systems enhance the awareness and ability to prevent MAEs and achieve successful quality improvements.

Improved modification for the density-functional theory calculation of thermodynamic properties for C-H-O composite compounds.

PubMed

Liu, Min Hsien; Chen, Cheng; Hong, Yaw Shun

2005-02-08

A three-parametric modification equation and the least-squares approach are adopted to calibrating hybrid density-functional theory energies of C(1)-C(10) straight-chain aldehydes, alcohols, and alkoxides to accurate enthalpies of formation DeltaH(f) and Gibbs free energies of formation DeltaG(f), respectively. All calculated energies of the C-H-O composite compounds were obtained based on B3LYP6-311++G(3df,2pd) single-point energies and the related thermal corrections of B3LYP6-31G(d,p) optimized geometries. This investigation revealed that all compounds had 0.05% average absolute relative error (ARE) for the atomization energies, with mean value of absolute error (MAE) of just 2.1 kJ/mol (0.5 kcal/mol) for the DeltaH(f) and 2.4 kJ/mol (0.6 kcal/mol) for the DeltaG(f) of formation.

Stellar Atmospheric Parameterization Based on Deep Learning

NASA Astrophysics Data System (ADS)

Pan, R. Y.; Li, X. R.

2016-07-01

Deep learning is a typical learning method widely studied in machine learning, pattern recognition, and artificial intelligence. This work investigates the stellar atmospheric parameterization problem by constructing a deep neural network with five layers. The proposed scheme is evaluated on both real spectra from Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with Kurucz's New Opacity Distribution Function (NEWODF) model. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for the effective temperature (T_{eff}/K), 0.0058 for lg (T_{eff}/K), 0.1706 for surface gravity (lg (g/(cm\\cdot s^{-2}))), and 0.1294 dex for metallicity ([Fe/H]), respectively; On the theoretic spectra, the MAEs are 15.34 for T_{eff}/K, 0.0011 for lg (T_{eff}/K), 0.0214 for lg (g/(cm\\cdot s^{-2})), and 0.0121 dex for [Fe/H], respectively.

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.

Deep learning methods for protein torsion angle prediction.

PubMed

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Smartphone application for mechanical quality assurance of medical linear accelerators

NASA Astrophysics Data System (ADS)

Kim, Hwiyoung; Lee, Hyunseok; In Park, Jong; Choi, Chang Heon; Park, So-Yeon; Kim, Hee Jung; Kim, Young Suk; Ye, Sung-Joon

2017-06-01

Mechanical quality assurance (QA) of medical linear accelerators consists of time-consuming and human-error-prone procedures. We developed a smartphone application system for mechanical QA. The system consists of two smartphones: one attached to a gantry for obtaining real-time information on the mechanical parameters of the medical linear accelerator, and another displaying real-time information via a Bluetooth connection with the former. Motion sensors embedded in the smartphone were used to measure gantry and collimator rotations. Images taken by the smartphone’s high-resolution camera were processed to evaluate accuracies of jaw-positioning, crosshair centering and source-to-surface distance (SSD). The application was developed using Android software development kit and OpenCV library. The accuracy and precision of the system was validated against an optical rotation stage and digital calipers, prior to routine QA measurements of five medical linear accelerators. The system accuracy and precision in measuring angles and lengths were determined to be 0.05  ±  0.05° and 0.25  ±  0.14 mm, respectively. The mean absolute errors (MAEs) in QA measurements of gantry and collimator rotation were 0.05  ±  0.04° and 0.05  ±  0.04°, respectively. The MAE in QA measurements of light field was 0.39  ±  0.36 mm. The MAEs in QA measurements of crosshair centering and SSD were 0.40  ±  0.35 mm and 0.41  ±  0.32 mm, respectively. In conclusion, most routine mechanical QA procedures could be performed using the smartphone application system with improved precision and within a shorter time-frame, while eliminating potential human errors.

Smartphone application for mechanical quality assurance of medical linear accelerators.

PubMed

Kim, Hwiyoung; Lee, Hyunseok; Park, Jong In; Choi, Chang Heon; Park, So-Yeon; Kim, Hee Jung; Kim, Young Suk; Ye, Sung-Joon

2017-06-07

Mechanical quality assurance (QA) of medical linear accelerators consists of time-consuming and human-error-prone procedures. We developed a smartphone application system for mechanical QA. The system consists of two smartphones: one attached to a gantry for obtaining real-time information on the mechanical parameters of the medical linear accelerator, and another displaying real-time information via a Bluetooth connection with the former. Motion sensors embedded in the smartphone were used to measure gantry and collimator rotations. Images taken by the smartphone's high-resolution camera were processed to evaluate accuracies of jaw-positioning, crosshair centering and source-to-surface distance (SSD). The application was developed using Android software development kit and OpenCV library. The accuracy and precision of the system was validated against an optical rotation stage and digital calipers, prior to routine QA measurements of five medical linear accelerators. The system accuracy and precision in measuring angles and lengths were determined to be 0.05  ±  0.05° and 0.25  ±  0.14 mm, respectively. The mean absolute errors (MAEs) in QA measurements of gantry and collimator rotation were 0.05  ±  0.04° and 0.05  ±  0.04°, respectively. The MAE in QA measurements of light field was 0.39  ±  0.36 mm. The MAEs in QA measurements of crosshair centering and SSD were 0.40  ±  0.35 mm and 0.41  ±  0.32 mm, respectively. In conclusion, most routine mechanical QA procedures could be performed using the smartphone application system with improved precision and within a shorter time-frame, while eliminating potential human errors.

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.

Generation, Validation, and Application of Abundance Map Reference Data for Spectral Unmixing

NASA Astrophysics Data System (ADS)

Williams, McKay D.

Reference data ("ground truth") maps traditionally have been used to assess the accuracy of imaging spectrometer classification algorithms. However, these reference data can be prohibitively expensive to produce, often do not include sub-pixel abundance estimates necessary to assess spectral unmixing algorithms, and lack published validation reports. Our research proposes methodologies to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. We generated scene-wide AMRD for three different remote sensing scenes using our remotely sensed reference data (RSRD) technique, which spatially aggregates unmixing results from fine scale imagery (e.g., 1-m Ground Sample Distance (GSD)) to co-located coarse scale imagery (e.g., 10-m GSD or larger). We validated the accuracy of this methodology by estimating AMRD in 51 randomly-selected 10 m x 10 m plots, using seven independent methods and observers, including field surveys by two observers, imagery analysis by two observers, and RSRD using three algorithms. Results indicated statistically-significant differences between all versions of AMRD, suggesting that all forms of reference data need to be validated. Given these significant differences between the independent versions of AMRD, we proposed that the mean of all (MOA) versions of reference data for each plot and class were most likely to represent true abundances. We then compared each version of AMRD to MOA. Best case accuracy was achieved by a version of imagery analysis, which had a mean coverage area error of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving a mean error of 3.0%, with a standard deviation of 6.3%, showing the potential of RSRD-based AMRD generation. Application of validated AMRD to specific coarse scale imagery involved three main parts: 1) spatial alignment of coarse and fine scale imagery, 2) aggregation of fine scale abundances to produce coarse scale imagery-specific AMRD, and 3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point spread function (PSF) aggregation, and found that the PSF approach returned lower error, but that rectangular aggregation more accurately estimated true abundances at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including mean absolute error (MAE) and linear regression (LR). We additionally introduced reference data mean adjusted MAE (MA-MAE), and reference data confidence interval adjusted MAE (CIA-MAE), which account for known error in the reference data itself. MA-MAE analysis indicated that fully constrained linear unmixing of coarse scale imagery across all three scenes returned an error of 10.83% per class and pixel, with regression analysis yielding a slope = 0.85, intercept = 0.04, and R2 = 0.81. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance.

Systematic literature review of hospital medication administration errors in children

PubMed Central

Ameer, Ahmed; Dhillon, Soraya; Peters, Mark J; Ghaleb, Maisoon

2015-01-01

Objective Medication administration is the last step in the medication process. It can act as a safety net to prevent unintended harm to patients if detected. However, medication administration errors (MAEs) during this process have been documented and thought to be preventable. In pediatric medicine, doses are usually administered based on the child’s weight or body surface area. This in turn increases the risk of drug miscalculations and therefore MAEs. The aim of this review is to report MAEs occurring in pediatric inpatients. Methods Twelve bibliographic databases were searched for studies published between January 2000 and February 2015 using “medication administration errors”, “hospital”, and “children” related terminologies. Handsearching of relevant publications was also carried out. A second reviewer screened articles for eligibility and quality in accordance with the inclusion/exclusion criteria. Key findings A total of 44 studies were systematically reviewed. MAEs were generally defined as a deviation of dose given from that prescribed; this included omitted doses and administration at the wrong time. Hospital MAEs in children accounted for a mean of 50% of all reported medication error reports (n=12,588). It was also identified in a mean of 29% of doses observed (n=8,894). The most prevalent type of MAEs related to preparation, infusion rate, dose, and time. This review has identified five types of interventions to reduce hospital MAEs in children: barcode medicine administration, electronic prescribing, education, use of smart pumps, and standard concentration. Conclusion This review has identified a wide variation in the prevalence of hospital MAEs in children. This is attributed to the definition and method used to investigate MAEs. The review also illustrated the complexity and multifaceted nature of MAEs. Therefore, there is a need to develop a set of safety measures to tackle these errors in pediatric practice. PMID:29354530

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

NASA Astrophysics Data System (ADS)

Cannon, A. J.; Hsieh, W. W.

2008-02-01

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska

USGS Publications Warehouse

Ji, Lei; Wylie, Bruce K.; Brown, Dana R. N.; Peterson, Birgit E.; Alexander, Heather D.; Mack, Michelle C.; Rover, Jennifer R.; Waldrop, Mark P.; McFarland, Jack W.; Chen, Xuexia; Pastick, Neal J.

2015-01-01

Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha−1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha−1(relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

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.

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.

PubMed

Zhou, Lingling; Xia, Jing; Yu, Lijing; Wang, Ying; Shi, Yun; Cai, Shunxiang; Nie, Shaofa

2016-03-23

We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis.

TU-AB-BRA-03: Atlas-Based Algorithms with Local Registration-Goodness Weighting for MRI-Driven Electron Density Mapping

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

Farjam, R; Tyagi, N; Veeraraghavan, H

Purpose: To develop image-analysis algorithms to synthesize CT with accurate electron densities for MR-only radiotherapy of head & neck (H&N) and pelvis anatomies. Methods: CT and 3T-MRI (Philips, mDixon sequence) scans were randomly selected from a pool of H&N (n=11) and pelvis (n=12) anatomies to form an atlas. All MRIs were pre-processed to eliminate scanner and patient-induced intensity inhomogeneities and standardize their intensity histograms. CT and MRI for each patient were then co-registered to construct CT-MRI atlases. For more accurate CT-MR fusion, bone intensities in CT were suppressed to improve the similarity between CT and MRI. For a new patient,more » all CT-MRI atlases are deformed onto the new patients’ MRI initially. A newly-developed generalized registration error (GRE) metric was then calculated as a measure of local registration accuracy. The synthetic CT value at each point is a 1/GRE-weighted average of CTs from all CT-MR atlases. For evaluation, the mean absolute error (MAE) between the original and synthetic CT (generated in a leave-one-out scheme) was computed. The planning dose from the original and synthetic CT was also compared. Results: For H&N patients, MAE was 67±9, 114±22, and 116±9 HU over the entire-CT, air and bone regions, respectively. For pelvis anatomy, MAE was 47±5 and 146±14 for the entire and bone regions. In comparison with MIRADA medical, an FDA-approved registration tool, we found that our proposed registration strategy reduces MAE by ∼30% and ∼50% over the entire and bone regions, respectively. GRE-weighted strategy further lowers MAE by ∼15% to ∼40%. Our primary dose calculation also showed highly consistent results between the original and synthetic CT. Conclusion: We’ve developed a novel image-analysis technique to synthesize CT for H&N and pelvis anatomies. Our proposed image fusion strategy and GRE metric help generate more accurate synthetic CT using locally more similar atlases (Support: Philips Healthcare). The research is supported by Philips HealthCare.« less

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).

[Comparison of three daily global solar radiation models].

PubMed

Yang, Jin-Ming; Fan, Wen-Yi; Zhao, Ying-Hui

2014-08-01

Three daily global solar radiation estimation models ( Å-P model, Thornton-Running model and model provided by Liu Ke-qun et al.) were analyzed and compared using data of 13 weather stations from 1982 to 2012 from three northeastern provinces and eastern Inner Mongolia. After cross-validation analysis, the result showed that mean absolute error (MAE) for each model was 1.71, 2.83 and 1.68 MJ x m(-2) x d(-1) respectively, showing that Å-P model and model provided by Liu Ke-qun et al. which used percentage of sunshine had an advantage over Thornton-Running model which didn't use percentage of sunshine. Model provided by Liu Ke-qun et al. played a good effect on the situation of non-sunshine, and its MAE and bias percentage were 18.5% and 33.8% smaller than those of Å-P model, respectively. High precision results could be obtained by using the simple linear model of Å-P. Å-P model, Thornton-Running model and model provided by Liu Ke-qun et al. overvalued daily global solar radiation by 12.2%, 19.2% and 9.9% respectively. MAE for each station varied little with the spatial change of location, and annual MAE decreased with the advance of years. The reason for this might be that the change of observation accuracy caused by the replacement of radiation instrument in 1993. MAEs for rainy days, non-sunshine days and warm seasons of the three models were greater than those for days without rain, sunshine days and cold seasons respectively, showing that different methods should be used for different weather conditions on estimating solar radiation with meteorological elements.

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.

Comparison of the biometric formulas used for applanation A-scan ultrasound biometry.

PubMed

Özcura, Fatih; Aktaş, Serdar; Sağdık, Hacı Murat; Tetikoğlu, Mehmet

2016-10-01

The purpose of the study was to compare the accuracy of various biometric formulas for predicting postoperative refraction determined using applanation A-scan ultrasound. This retrospective comparative study included 485 eyes that underwent uneventful phacoemulsification with intraocular lens (IOL) implantation. Applanation A-scan ultrasound biometry and postoperative manifest refraction were obtained in all eyes. Biometric data were entered into each of the five IOL power calculation formulas: SRK-II, SRK/T, Holladay I, Hoffer Q, and Binkhorst II. All eyes were divided into three groups according to axial length: short (≤22.0 mm), average (22.0-25.0 mm), and long (≥25.0 mm) eyes. The postoperative spherical equivalent was calculated and compared with the predicted refractive error using each biometric formula. The results showed that all formulas had significantly lower mean absolute error (MAE) in comparison with Binkhorst II formula (P < 0.01). The lowest MAE was obtained with the SRK-II for average (0.49 ± 0.40 D) and short (0.67 ± 0.54 D) eyes and the SRK/T for long (0.61 ± 0.50 D) eyes. The highest postoperative hyperopic shift was seen with the SRK-II for average (46.8 %), short (28.1 %), and long (48.4 %) eyes. The highest postoperative myopic shift was seen with the Holladay I for average (66.4 %) and long (71.0 %) eyes and the SRK/T for short eyes (80.6 %). In conclusion, the SRK-II formula produced the lowest MAE in average and short eyes and the SRK/T formula produced the lowest MAE in long eyes. The SRK-II has the highest postoperative hyperopic shift in all eyes. The highest postoperative myopic shift is with the Holladay I for average and long eyes and SRK/T for short eyes.

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).

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

PubMed

Ingle, Brandall L; Veber, Brandon C; Nichols, John W; Tornero-Velez, Rogelio

2016-11-28

The free fraction of a xenobiotic in plasma (F ub ) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict F ub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18F ub . The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for F ub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

A Novel Displacement and Tilt Detection Method Using Passive UHF RFID Technology.

PubMed

Lai, Xiaozheng; Cai, Zhirong; Xie, Zeming; Zhu, Hailong

2018-05-21

The displacement and tilt angle of an object are useful information for wireless monitoring applications. In this paper, a low-cost detection method based on passive radio frequency identification (RFID) technology is proposed. This method uses a standard ultrahigh-frequency (UHF) RFID reader to measure the phase variation of the tag response and detect the displacement and tilt angle of RFID tags attached to the targeted object. An accurate displacement result can be detected by the RFID system with a linearly polarized (LP) reader antenna. Based on the displacement results, an accurate tilt angle can also be detected by the RFID system with a circularly polarized (CP) reader antenna, which has been proved to have a linear relationship with the phase parameter of the tag’s backscattered wave. As far as accuracy is concerned, the mean absolute error (MAE) of displacement is less than 2 mm and the MAE of the tilt angle is less than 2.5° for an RFID system with 500 mm working range.

Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method

NASA Astrophysics Data System (ADS)

Khandelwal, Manoj; Monjezi, M.

2013-03-01

Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.

Examination of Spectral Transformations on Spectral Mixture Analysis

NASA Astrophysics Data System (ADS)

Deng, Y.; Wu, C.

2018-04-01

While many spectral transformation techniques have been applied on spectral mixture analysis (SMA), few study examined their necessity and applicability. This paper focused on exploring the difference between spectrally transformed schemes and untransformed scheme to find out which transformed scheme performed better in SMA. In particular, nine spectrally transformed schemes as well as untransformed scheme were examined in two study areas. Each transformed scheme was tested 100 times using different endmember classes' spectra under the endmember model of vegetation- high albedo impervious surface area-low albedo impervious surface area-soil (V-ISAh-ISAl-S). Performance of each scheme was assessed based on mean absolute error (MAE). Statistical analysis technique, Paired-Samples T test, was applied to test the significance of mean MAEs' difference between transformed and untransformed schemes. Results demonstrated that only NSMA could exceed the untransformed scheme in all study areas. Some transformed schemes showed unstable performance since they outperformed the untransformed scheme in one area but weakened the SMA result in another region.

Understanding the many-body expansion for large systems. II. Accuracy considerations

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

Lao, Ka Un; Liu, Kuan-Yu; Richard, Ryan M.

2016-04-28

To complement our study of the role of finite precision in electronic structure calculations based on a truncated many-body expansion (MBE, or “n-body expansion”), we examine the accuracy of such methods in the present work. Accuracy may be defined either with respect to a supersystem calculation computed at the same level of theory as the n-body calculations, or alternatively with respect to high-quality benchmarks. Both metrics are considered here. In applications to a sequence of water clusters, (H{sub 2}O){sub N=6−55} described at the B3LYP/cc-pVDZ level, we obtain mean absolute errors (MAEs) per H{sub 2}O monomer of ∼1.0 kcal/mol for two-bodymore » expansions, where the benchmark is a B3LYP/cc-pVDZ calculation on the entire cluster. Three- and four-body expansions exhibit MAEs of 0.5 and 0.1 kcal/mol/monomer, respectively, without resort to charge embedding. A generalized many-body expansion truncated at two-body terms [GMBE(2)], using 3–4 H{sub 2}O molecules per fragment, outperforms all of these methods and affords a MAE of ∼0.02 kcal/mol/monomer, also without charge embedding. GMBE(2) requires significantly fewer (although somewhat larger) subsystem calculations as compared to MBE(4), reducing problems associated with floating-point roundoff errors. When compared to high-quality benchmarks, we find that error cancellation often plays a critical role in the success of MBE(n) calculations, even at the four-body level, as basis-set superposition error can compensate for higher-order polarization interactions. A many-body counterpoise correction is introduced for the GMBE, and its two-body truncation [GMBCP(2)] is found to afford good results without error cancellation. Together with a method such as ωB97X-V/aug-cc-pVTZ that can describe both covalent and non-covalent interactions, the GMBE(2)+GMBCP(2) approach provides an accurate, stable, and tractable approach for large systems.« less

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.

Informing the Human Plasma Protein Binding of ...

EPA Pesticide Factsheets

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18 Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0

A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

PubMed

Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

2018-01-01

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users. © 2017 John Wiley & Sons Ltd.

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans

PubMed Central

Zhou, Lingling; Xia, Jing; Yu, Lijing; Wang, Ying; Shi, Yun; Cai, Shunxiang; Nie, Shaofa

2016-01-01

Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. Methods: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. Results: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. Conclusions: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis. PMID:27023573

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.

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.

Sea surface temperature predictions using a multi-ocean analysis ensemble scheme

NASA Astrophysics Data System (ADS)

Zhang, Ying; Zhu, Jieshun; Li, Zhongxian; Chen, Haishan; Zeng, Gang

2017-08-01

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982-2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

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.

[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.

Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

NASA Astrophysics Data System (ADS)

Lahmiri, Salim; Boukadoum, Mounir

2015-08-01

We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

Correction of a Technical Error in the Golf Swing: Error Amplification Versus Direct Instruction.

PubMed

Milanese, Chiara; Corte, Stefano; Salvetti, Luca; Cavedon, Valentina; Agostini, Tiziano

2016-01-01

Performance errors drive motor learning for many tasks. The authors' aim was to determine which of two strategies, method of amplification of error (MAE) or direct instruction (DI), would be more beneficial for error correction during a full golfing swing with a driver. Thirty-four golfers were randomly assigned to one of three training conditions (MAE, DI, and control). Participants were tested in a practice session in which each golfer performed 7 pretraining trials, 6 training-intervention trials, and 7 posttraining trials; and a retention test after 1 week. An optoeletronic motion capture system was used to measure the kinematic parameters of each golfer's performance. Results showed that MAE is an effective strategy for correcting the technical errors leading to a rapid improvement in performance. These findings could have practical implications for sport psychology and physical education because, while practice is obviously necessary for improving learning, the efficacy of the learning process is essential in enhancing learners' motivation and sport enjoyment.

Wind power application research on the fusion of the determination and ensemble prediction

NASA Astrophysics Data System (ADS)

Lan, Shi; Lina, Xu; Yuzhu, Hao

2017-07-01

The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0-24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

PubMed

Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang

2017-07-01

It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

Utilization of a novel digital measurement tool for quantitative assessment of upper extremity motor dexterity: a controlled pilot study.

PubMed

Getachew, Ruth; Lee, Sunghoon I; Kimball, Jon A; Yew, Andrew Y; Lu, Derek S; Li, Charles H; Garst, Jordan H; Ghalehsari, Nima; Paak, Brian H; Razaghy, Mehrdad; Espinal, Marie; Ostowari, Arsha; Ghavamrezaii, Amir A; Pourtaheri, Sahar; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C

2014-08-13

The current methods of assessing motor function rely primarily on the clinician's judgment of the patient's physical examination and the patient's self-administered surveys. Recently, computerized handgrip tools have been designed as an objective method to quantify upper-extremity motor function. This pilot study explores the use of the MediSens handgrip as a potential clinical tool for objectively assessing the motor function of the hand. Eleven patients with cervical spondylotic myelopathy (CSM) were followed for three months. Eighteen age-matched healthy participants were followed for two months. The neuromotor function and the patient-perceived motor function of these patients were assessed with the MediSens device and the Oswestry Disability Index respectively. The MediSens device utilized a target tracking test to investigate the neuromotor capacity of the participants. The mean absolute error (MAE) between the target curve and the curve tracing achieved by the participants was used as the assessment metric. The patients' adjusted MediSens MAE scores were then compared to the controls. The CSM patients were further classified as either "functional" or "nonfunctional" in order to validate the system's responsiveness. Finally, the correlation between the MediSens MAE score and the ODI score was investigated. The control participants had lower MediSens MAE scores of 8.09%±1.60%, while the cervical spinal disorder patients had greater MediSens MAE scores of 11.24%±6.29%. Following surgery, the functional CSM patients had an average MediSens MAE score of 7.13%±1.60%, while the nonfunctional CSM patients had an average score of 12.41%±6.32%. The MediSens MAE and the ODI scores showed a statistically significant correlation (r=-0.341, p<1.14×10⁻⁵). A Bland-Altman plot was then used to validate the agreement between the two scores. Furthermore, the percentage improvement of the the two scores after receiving the surgical intervention showed a significant correlation (r=-0.723, p<0.04). The MediSens handgrip device is capable of identifying patients with impaired motor function of the hand. The MediSens handgrip scores correlate with the ODI scores and may serve as an objective alternative for assessing motor function of the hand.

Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset

PubMed

Jeyasingh, Suganthi; Veluchamy, Malathi

2017-05-01

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Creative Commons Attribution License

Provider risk factors for medication administration error alerts: analyses of a large-scale closed-loop medication administration system using RFID and barcode.

PubMed

Hwang, Yeonsoo; Yoon, Dukyong; Ahn, Eun Kyoung; Hwang, Hee; Park, Rae Woong

2016-12-01

To determine the risk factors and rate of medication administration error (MAE) alerts by analyzing large-scale medication administration data and related error logs automatically recorded in a closed-loop medication administration system using radio-frequency identification and barcodes. The subject hospital adopted a closed-loop medication administration system. All medication administrations in the general wards were automatically recorded in real-time using radio-frequency identification, barcodes, and hand-held point-of-care devices. MAE alert logs recorded during a full 1 year of 2012. We evaluated risk factors for MAE alerts including administration time, order type, medication route, the number of medication doses administered, and factors associated with nurse practices by logistic regression analysis. A total of 2 874 539 medication dose records from 30 232 patients (882.6 patient-years) were included in 2012. We identified 35 082 MAE alerts (1.22% of total medication doses). The MAE alerts were significantly related to administration at non-standard time [odds ratio (OR) 1.559, 95% confidence interval (CI) 1.515-1.604], emergency order (OR 1.527, 95%CI 1.464-1.594), and the number of medication doses administered (OR 0.993, 95%CI 0.992-0.993). Medication route, nurse's employment duration, and working schedule were also significantly related. The MAE alert rate was 1.22% over the 1-year observation period in the hospital examined in this study. The MAE alerts were significantly related to administration time, order type, medication route, the number of medication doses administered, nurse's employment duration, and working schedule. The real-time closed-loop medication administration system contributed to improving patient safety by preventing potential MAEs. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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.

Comparison of intraocular lens power prediction using immersion ultrasound and optical biometry with and without formula optimization.

PubMed

Nemeth, Gabor; Nagy, Attila; Berta, Andras; Modis, Laszlo

2012-09-01

Comparison of postoperative refraction results using ultrasound biometry with closed immersion shell and optical biometry. Three hundred and sixty-four eyes of 306 patients (age: 70.6 ± 12.8 years) underwent cataract surgery where intraocular lenses calculated by SRK/T formula were implanted. In 159 cases immersion ultrasonic biometry, in 205 eyes optical biometry was used. Differences between predicted and actual postoperative refractions were calculated both prior to and after optimization with the SRK/T formula, after which we analysed the similar data in the case of Holladay, Haigis, and Hoffer-Q formulas. Mean absolute error (MAE) and the percentage rate of patients within ±0.5 and ±1.0 D difference in the predicted error were calculated with these four formulas. MAE was 0.5-0.7 D in cases of both methods with SRK/T, Holladay, and Hoffer-Q formula, but higher with Haigis formula. With no optimization, 60-65 % of the patients were under 0.5 D error in the immersion group (except for Haigis formula). Using the optical method, this value was slightly higher (62-67 %), however, in this case, Haigis formula also did not perform so well (45 %). Refraction results significantly improved with Holladay, Hoffer-Q, and Haigis formulas in both groups. The rate of patients under 0.5 D error increased to 65 % by the immersion technique, and up to 80 % by the optical one. According to our results, optical biometry offers only slightly better outcomes compared to those of immersion shell with no optimized formulas. However, in case of new generation formulas with both methods, the optimization of IOL-constants give significantly better results.

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.

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.

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.

RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.

PubMed

Gao, Yujuan; Wang, Sheng; Deng, Minghua; Xu, Jinbo

2018-05-08

Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging. In this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds. Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.

A novel alkaloid isolated from Crotalaria paulina and identified by NMR and DFT calculations

NASA Astrophysics Data System (ADS)

Oliveira, Ramon Prata; Demuner, Antonio Jacinto; Alvarenga, Elson Santiago; Barbosa, Luiz Claudio Almeida; de Melo Silva, Thiago

2018-01-01

Pyrrolizidine alkaloids (PAs) are secondary metabolites found in Crotalaria genus and are known to have several biological activities. A novel macrocycle bislactone alkaloid, coined ethylcrotaline, was isolated and purified from the aerial parts of Crotalaria paulina. The novel macrocycle was identified with the aid of high resolution mass spectrometry and advanced nuclear magnetic resonance techniques. The relative stereochemistry of the alkaloid was defined by comparing the calculated quantum mechanical hydrogen and carbon chemical shifts of eight candidate structures with the experimental NMR data. The best fit between the eight candidate structures and the experimental NMR chemical shifts was defined by the DP4 statistical analyses and the Mean Absolute Error (MAE) calculations.

Understanding the causes of intravenous medication administration errors in hospitals: a qualitative critical incident study

PubMed Central

Keers, Richard N; Williams, Steven D; Cooke, Jonathan; Ashcroft, Darren M

2015-01-01

Objectives To investigate the underlying causes of intravenous medication administration errors (MAEs) in National Health Service (NHS) hospitals. Setting Two NHS teaching hospitals in the North West of England. Participants Twenty nurses working in a range of inpatient clinical environments were identified and recruited using purposive sampling at each study site. Primary outcome measures Semistructured interviews were conducted with nurse participants using the critical incident technique, where they were asked to discuss perceived causes of intravenous MAEs that they had been directly involved with. Transcribed interviews were analysed using the Framework approach and emerging themes were categorised according to Reason's model of accident causation. Results In total, 21 intravenous MAEs were discussed containing 23 individual active failures which included slips and lapses (n=11), mistakes (n=8) and deliberate violations of policy (n=4). Each active failure was associated with a range of error and violation provoking conditions. The working environment was implicated when nurses lacked healthcare team support and/or were exposed to a perceived increased workload during ward rounds, shift changes or emergencies. Nurses frequently reported that the quality of intravenous dose-checking activities was compromised due to high perceived workload and working relationships. Nurses described using approaches such as subconscious functioning and prioritising to manage their duties, which at times contributed to errors. Conclusions Complex interactions between active and latent failures can lead to intravenous MAEs in hospitals. Future interventions may need to be multimodal in design in order to mitigate these risks and reduce the burden of intravenous MAEs. PMID:25770226

Application of Holt exponential smoothing and ARIMA method for data population in West Java

NASA Astrophysics Data System (ADS)

Supriatna, A.; Susanti, D.; Hertini, E.

2017-01-01

One method of time series that is often used to predict data that contains trend is Holt. Holt method using different parameters used in the original data which aims to smooth the trend value. In addition to Holt, ARIMA method can be used on a wide variety of data including data pattern containing a pattern trend. Data actual of population from 1998-2015 contains the trends so can be solved by Holt and ARIMA method to obtain the prediction value of some periods. The best method is measured by looking at the smallest MAPE and MAE error. The result using Holt method is 47.205.749 populations in 2016, 47.535.324 populations in 2017, and 48.041.672 populations in 2018, with MAPE error is 0,469744 and MAE error is 189.731. While the result using ARIMA method is 46.964.682 populations in 2016, 47.342.189 in 2017, and 47.899.696 in 2018, with MAPE error is 0,4380 and MAE is 176.626.

Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model

NASA Astrophysics Data System (ADS)

Wang, Weijie; Lu, Yanmin

2018-03-01

Most existing Collaborative Filtering (CF) algorithms predict a rating as the preference of an active user toward a given item, which is always a decimal fraction. Meanwhile, the actual ratings in most data sets are integers. In this paper, we discuss and demonstrate why rounding can bring different influences to these two metrics; prove that rounding is necessary in post-processing of the predicted ratings, eliminate of model prediction bias, improving the accuracy of the prediction. In addition, we also propose two new rounding approaches based on the predicted rating probability distribution, which can be used to round the predicted rating to an optimal integer rating, and get better prediction accuracy compared to the Basic Rounding approach. Extensive experiments on different data sets validate the correctness of our analysis and the effectiveness of our proposed rounding approaches.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Evaluation of 16 genotype-guided Warfarin Dosing Algorithms in 310 Korean Patients Receiving Warfarin Treatment: Poor Prediction Performance in VKORC1 1173C Carriers.

PubMed

Yang, Mina; Choi, Rihwa; Kim, June Soo; On, Young Keun; Bang, Oh Young; Cho, Hyun-Jung; Lee, Soo-Youn

2016-12-01

The purpose of this study was to evaluate the performance of 16 previously published warfarin dosing algorithms in Korean patients. The 16 algorithms were selected through a literature search and evaluated using a cohort of 310 Korean patients with atrial fibrillation or cerebral infarction who were receiving warfarin therapy. A large interindividual variation (up to 11-fold) in warfarin dose was observed (median, 25 mg/wk; range, 7-77 mg/wk). Estimated dose and actual maintenance dose correlated well overall (r range, 0.52-0.73). Mean absolute error (MAE) of the 16 algorithms ranged from -1.2 to -20.1 mg/wk. The percentage of patients whose estimated dose fell within 20% of the actual dose ranged from 1.0% to 49%. All algorithms showed poor accuracy with increased MAE in a higher dose range. Performance of the dosing algorithms was worse in patients with VKORC1 1173TC or CC than in total (r range, 0.38-0.61 vs 0.52-0.73; MAE range, -2.6 to -28.0 mg/wk vs -1.2 to -20.1 mg/wk). The algorithms had comparable prediction abilities but showed limited accuracy depending on ethnicity, warfarin dose, and VKORC1 genotype. Further studies are needed to develop genotype-guided warfarin dosing algorithms with greater accuracy in the Korean population. Copyright © 2016 Elsevier HS Journals, Inc. 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.

Medication Administration Errors in an Adult Emergency Department of a Tertiary Health Care Facility in Ghana.

PubMed

Acheampong, Franklin; Tetteh, Ashalley Raymond; Anto, Berko Panyin

2016-12-01

This study determined the incidence, types, clinical significance, and potential causes of medication administration errors (MAEs) at the emergency department (ED) of a tertiary health care facility in Ghana. This study used a cross-sectional nonparticipant observational technique. Study participants (nurses) were observed preparing and administering medication at the ED of a 2000-bed tertiary care hospital in Accra, Ghana. The observations were then compared with patients' medication charts, and identified errors were clarified with staff for possible causes. Of the 1332 observations made, involving 338 patients and 49 nurses, 362 had errors, representing 27.2%. However, the error rate excluding "lack of drug availability" fell to 12.8%. Without wrong time error, the error rate was 22.8%. The 2 most frequent error types were omission (n = 281, 77.6%) and wrong time (n = 58, 16%) errors. Omission error was mainly due to unavailability of medicine, 48.9% (n = 177). Although only one of the errors was potentially fatal, 26.7% were definitely clinically severe. The common themes that dominated the probable causes of MAEs were unavailability, staff factors, patient factors, prescription, and communication problems. This study gives credence to similar studies in different settings that MAEs occur frequently in the ED of hospitals. Most of the errors identified were not potentially fatal; however, preventive strategies need to be used to make life-saving processes such as drug administration in such specialized units error-free.

Unifying distance-based goodness-of-fit indicators for hydrologic model assessment

NASA Astrophysics Data System (ADS)

Cheng, Qinbo; Reinhardt-Imjela, Christian; Chen, Xi; Schulte, Achim

2014-05-01

The goodness-of-fit indicator, i.e. efficiency criterion, is very important for model calibration. However, recently the knowledge about the goodness-of-fit indicators is all empirical and lacks a theoretical support. Based on the likelihood theory, a unified distance-based goodness-of-fit indicator termed BC-GED model is proposed, which uses the Box-Cox (BC) transformation to remove the heteroscedasticity of model errors and the generalized error distribution (GED) with zero-mean to fit the distribution of model errors after BC. The BC-GED model can unify all recent distance-based goodness-of-fit indicators, and reveals the mean square error (MSE) and the mean absolute error (MAE) that are widely used goodness-of-fit indicators imply statistic assumptions that the model errors follow the Gaussian distribution and the Laplace distribution with zero-mean, respectively. The empirical knowledge about goodness-of-fit indicators can be also easily interpreted by BC-GED model, e.g. the sensitivity to high flow of the goodness-of-fit indicators with large power of model errors results from the low probability of large model error in the assumed distribution of these indicators. In order to assess the effect of the parameters (i.e. the BC transformation parameter λ and the GED kurtosis coefficient β also termed the power of model errors) of BC-GED model on hydrologic model calibration, six cases of BC-GED model were applied in Baocun watershed (East China) with SWAT-WB-VSA model. Comparison of the inferred model parameters and model simulation results among the six indicators demonstrates these indicators can be clearly separated two classes by the GED kurtosis β: β >1 and β ≤ 1. SWAT-WB-VSA calibrated by the class β >1 of distance-based goodness-of-fit indicators captures high flow very well and mimics the baseflow very badly, but it calibrated by the class β ≤ 1 mimics the baseflow very well, because first the larger value of β, the greater emphasis is put on high flow and second the derivative of GED probability density function at zero is zero as β >1, but discontinuous as β ≤ 1, and even infinity as β < 1 with which the maximum likelihood estimation can guarantee the model errors approach zero as well as possible. The BC-GED that estimates the parameters (i.e. λ and β) of BC-GED model as well as hydrologic model parameters is the best distance-based goodness-of-fit indicator because not only the model validation using groundwater levels is very good, but also the model errors fulfill the statistic assumption best. However, in some cases of model calibration with a few observations e.g. calibration of single-event model, for avoiding estimation of the parameters of BC-GED model the MAE i.e. the boundary indicator (β = 1) of the two classes, can replace the BC-GED, because the model validation of MAE is best.

Use of 3H/3He Ages to evaluate and improve groundwater flow models in a complex buried-valley aquifer

USGS Publications Warehouse

Sheets, Rodney A.; Bair, E. Scott; Rowe, Gary L.

1998-01-01

Combined use of the tritium/helium 3 (3H/3He) dating technique and particle-tracking analysis can improve flow-model calibration. As shown at two sites in the Great Miami buried-valley aquifer in southwestern Ohio, the combined use of 3H/3He age dating and particle tracking led to a lower mean absolute error between measured heads and simulated heads than in the original calibrated models and/or between simulated travel times and 3H/3He ages. Apparent groundwater ages were obtained for water samples collected from 44 wells at two locations where previously constructed finite difference models of groundwater flow were available (Mound Plant and Wright-Patterson Air Force Base (WPAFB)). The two-layer Mound Plant model covers 11 km2 within the buried-valley aquifer. The WPAFB model has three layers and covers 262 km2 within the buried-valley aquifer and adjacent bedrock uplands. Sampled wells were chosen along flow paths determined from potentiometric maps or particle-tracking analyses. Water samples were collected at various depths within the aquifer. In the Mound Plant area, samples used for comparison of 3H/3He ages with simulated travel times were from wells completed in the uppermost model layer. Simulated travel times agreed well with 3H/3He ages. The mean absolute error (MAE) was 3.5 years. Agreement in ages at WPAFB decreased with increasing depth in the system. The MAEs were 1.63, 17.2, and 255 years for model layers 1, 2, and 3, respectively. Discrepancies between the simulated travel times and 3H/3He ages were assumed to be due to improper conceptualization or incorrect parameterization of the flow models. Selected conceptual and parameter modifications to the models resulted in improved agreement between 3H/3He ages and simulated travel times and between measured and simulated heads and flows.

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.

Intraocular lens power calculations for cataract surgery after phototherapeutic keratectomy in granular corneal dystrophy type 2.

PubMed

Jung, Se Hwan; Han, Kyung Eun; Sgrignoli, Bradford; Kim, Tae-Im; Lee, Hyung Keun; Kim, Eung Kweon

2012-10-01

To investigate the predictability of various intraocular lens (IOL) power calculation methods in granular corneal dystrophy type 2 (GCD2) with prior phototherapeutic keratectomy (PTK) and to suggest the more predictable IOL power calculation method. Medical records of 20 eyes from 16 patients with GCD2, all having undergone cataract surgery after PTK, were retrospectively evaluated. Postoperative cataract refractive errors were compared with target diopters (D) using IOL power calculation methods as follows: 1) myopic and 2) hyperopic Haigis-L formula in IOLMaster (Carl Zeiss Meditec); 3) SRK/T formula using 4.5-mm zone Holladay equivalent keratometry readings (EKRs) (single-K Holladay EKRs method); 4) central keratometry power of true net power map in the Pentacam system (Oculus Optikgeräte GmbH); and 5) clinical history, Aramberri double-K, and double-K Holladay EKRs methods. Topographic status of corneal curvature after PTK was evaluated. Fourteen (70%) of 20 eyes showed central island formation after PTK. When central island was present, the mean absolute error (MAE) using the hyperopic Haigis-L formula was 0.25±0.15 D. When central island was not present, the myopic Haigis-L formula showed MAE of 0.33±0.16 D. When central island formation and IOLMaster keratometry underestimation were present, the hyperopic Haigis-L formula showed the least MAE of 0.26±0.08 D when switching the IOL-Master keratometry values equal to 4.5-mm zone Holladay EKRs. In planning for cataract surgery after PTK in GCD2, topographic analysis for central island formation is necessary. With or without central island formation, the hyperopic or myopic Haigis-L formula can be applied. When IOLMaster keratometry shows underestimation, the Haigis-L formula using 4.5-mm zone Holladay EKRs can be considered. Copyright 2012, SLACK Incorporated.

WE-AB-207A-02: John’s Equation Based Consistency Condition and Incomplete Projection Restoration Upon Circular Orbit CBCT

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

Ma, J; Qi, H; Wu, S

Purpose: In transmitted X-ray tomography imaging, projections are sometimes incomplete due to a variety of reasons, such as geometry inaccuracy, defective detector cells, etc. To address this issue, we have derived a direct consistency condition based on John’s Equation, and proposed a method to effectively restore incomplete projections based on this consistency condition. Methods: Through parameter substitutions, we have derived a direct consistency condition equation from John’s equation, in which the left side is only projection derivative of view and the right side is projection derivative of other geometrical parameters. Based on this consistency condition, a projection restoration method ismore » proposed, which includes five steps: 1) Forward projecting reconstructed image and using linear interpolation to estimate the incomplete projections as the initial result; 2) Performing Fourier transform on the projections; 3) Restoring the incomplete frequency data using the consistency condition equation; 4) Performing inverse Fourier transform; 5) Repeat step 2)∼4) until our criteria is met to terminate the iteration. Results: A beam-blocking-based scatter correction case and a bad-pixel correction case were used to demonstrate the efficacy and robustness of our restoration method. The mean absolute error (MAE), signal noise ratio (SNR) and mean square error (MSE) were employed as our evaluation metrics of the reconstructed images. For the scatter correction case, the MAE is reduced from 63.3% to 71.7% with 4 iterations. Compared with the existing Patch’s method, the MAE of our method is further reduced by 8.72%. For the bad-pixel case, the SNR of the reconstructed image by our method is increased from 13.49% to 21.48%, with the MSE being decreased by 45.95%, compared with linear interpolation method. Conclusion: Our studies have demonstrated that our restoration method based on the new consistency condition could effectively restore the incomplete projections, especially for their high frequency component.« less

Stellar Atmospheric Parameterization Based on Deep Learning

NASA Astrophysics Data System (ADS)

Pan, Ru-yang; Li, Xiang-ru

2017-07-01

Deep learning is a typical learning method widely studied in the fields of machine learning, pattern recognition, and artificial intelligence. This work investigates the problem of stellar atmospheric parameterization by constructing a deep neural network with five layers, and the node number in each layer of the network is respectively 3821-500-100-50-1. The proposed scheme is verified on both the real spectra measured by the Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with the Kurucz's New Opacity Distribution Function (NEWODF) model, to make an automatic estimation for three physical parameters: the effective temperature (Teff), surface gravitational acceleration (lg g), and metallic abundance (Fe/H). The results show that the stacked autoencoder deep neural network has a better accuracy for the estimation. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for Teff/K, 0.0058 for (lg Teff/K), 0.1706 for lg (g/(cm·s-2)), and 0.1294 dex for the [Fe/H], respectively; On the theoretic spectra, the MAEs are 15.34 for Teff/K, 0.0011 for lg (Teff/K), 0.0214 for lg(g/(cm · s-2)), and 0.0121 dex for [Fe/H], respectively.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

PubMed Central

Kim, Yong-Hyuk; Ha, Ji-Hun; Kim, Na-Young; Im, Hyo-Hyuc; Sim, Sangjin; Choi, Reno K. Y.

2016-01-01

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network. PMID:27524999

Phosphorus allotropes: Stability of black versus red phosphorus re-examined by means of the van der Waals inclusive density functional method

NASA Astrophysics Data System (ADS)

Aykol, Muratahan; Doak, Jeff W.; Wolverton, C.

2017-06-01

We evaluate the energetic stabilities of white, red, and black allotropes of phosphorus using density functional theory (DFT) and hybrid functional methods, van der Waals (vdW) corrections (DFT+vdW and hybrid+vdW), vdW density functionals, and random phase approximation (RPA). We find that stability of black phosphorus over red-V (i.e., the violet form) is not ubiquitous among these methods, and the calculated enthalpies for the reaction phosphorus (red-V)→phosphorus (black) are scattered between -20 and 40 meV/atom. With local density and generalized gradient approximations, and hybrid functionals, mean absolute errors (MAEs) in densities of P allotropes relative to experiments are found to be around 10%-25%, whereas with vdW-inclusive methods, MAEs in densities drop below ˜5 %. While the inconsistency among the density functional methods could not shed light on the stability puzzle of black versus red phosphorus, comparison of their accuracy in predicting densities and the supplementary RPA results on relative stabilities indicate that opposite to the common belief, black and red phosphorus are almost degenerate, or the red-V (violet) form of phosphorus might even be the ground state.

Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city-China

NASA Astrophysics Data System (ADS)

Tang, Jinjun; Zhang, Shen; Chen, Xinqiang; Liu, Fang; Zou, Yajie

2018-03-01

Understanding Origin-Destination distribution of taxi trips is very important for improving effects of transportation planning and enhancing quality of taxi services. This study proposes a new method based on Entropy-Maximizing theory to model OD distribution in Harbin city using large-scale taxi GPS trajectories. Firstly, a K-means clustering method is utilized to partition raw pick-up and drop-off location into different zones, and trips are assumed to start from and end at zone centers. A generalized cost function is further defined by considering travel distance, time and fee between each OD pair. GPS data collected from more than 1000 taxis at an interval of 30 s during one month are divided into two parts: data from first twenty days is treated as training dataset and last ten days is taken as testing dataset. The training dataset is used to calibrate model while testing dataset is used to validate model. Furthermore, three indicators, mean absolute error (MAE), root mean square error (RMSE) and mean percentage absolute error (MPAE), are applied to evaluate training and testing performance of Entropy-Maximizing model versus Gravity model. The results demonstrate Entropy-Maximizing model is superior to Gravity model. Findings of the study are used to validate the feasibility of OD distribution from taxi GPS data in urban system.

Mapping health outcome measures from a stroke registry to EQ-5D weights.

PubMed

Ghatnekar, Ola; Eriksson, Marie; Glader, Eva-Lotta

2013-03-07

To map health outcome related variables from a national register, not part of any validated instrument, with EQ-5D weights among stroke patients. We used two cross-sectional data sets including patient characteristics, outcome variables and EQ-5D weights from the national Swedish stroke register. Three regression techniques were used on the estimation set (n=272): ordinary least squares (OLS), Tobit, and censored least absolute deviation (CLAD). The regression coefficients for "dressing", "toileting", "mobility", "mood", "general health" and "proxy-responders" were applied to the validation set (n=272), and the performance was analysed with mean absolute error (MAE) and mean square error (MSE). The number of statistically significant coefficients varied by model, but all models generated consistent coefficients in terms of sign. Mean utility was underestimated in all models (least in OLS) and with lower variation (least in OLS) compared to the observed. The maximum attainable EQ-5D weight ranged from 0.90 (OLS) to 1.00 (Tobit and CLAD). Health states with utility weights <0.5 had greater errors than those with weights ≥ 0.5 (P<0.01). This study indicates that it is possible to map non-validated health outcome measures from a stroke register into preference-based utilities to study the development of stroke care over time, and to compare with other conditions in terms of utility.

3D Tendon Strain Estimation Using High-frequency Volumetric Ultrasound Images: A Feasibility Study.

PubMed

Carvalho, Catarina; Slagmolen, Pieter; Bogaerts, Stijn; Scheys, Lennart; D'hooge, Jan; Peers, Koen; Maes, Frederik; Suetens, Paul

2018-03-01

Estimation of strain in tendons for tendinopathy assessment is a hot topic within the sports medicine community. It is believed that, if accurately estimated, existing treatment and rehabilitation protocols can be improved and presymptomatic abnormalities can be detected earlier. State-of-the-art studies present inaccurate and highly variable strain estimates, leaving this problem without solution. Out-of-plane motion, present when acquiring two-dimensional (2D) ultrasound (US) images, is a known problem and may be responsible for such errors. This work investigates the benefit of high-frequency, three-dimensional (3D) US imaging to reduce errors in tendon strain estimation. Volumetric US images were acquired in silico, in vitro, and ex vivo using an innovative acquisition approach that combines the acquisition of 2D high-frequency US images with a mechanical guided system. An affine image registration method was used to estimate global strain. 3D strain estimates were then compared with ground-truth values and with 2D strain estimates. The obtained results for in silico data showed a mean absolute error (MAE) of 0.07%, 0.05%, and 0.27% for 3D estimates along axial, lateral direction, and elevation direction and a respective MAE of 0.21% and 0.29% for 2D strain estimates. Although 3D could outperform 2D, this does not occur in in vitro and ex vivo settings, likely due to 3D acquisition artifacts. Comparison against the state-of-the-art methods showed competitive results. The proposed work shows that 3D strain estimates are more accurate than 2D estimates but acquisition of appropriate 3D US images remains a challenge.

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

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.

Effect of trabeculectomy on the accuracy of intraocular lens calculations in patients with open-angle glaucoma.

PubMed

Bae, Hyoung Won; Lee, Yun Ha; Kim, Do Wook; Lee, Taekjune; Hong, Samin; Seong, Gong Je; Kim, Chan Yun

2016-08-01

The objective of the study is to examine the effect of trabeculectomy on intraocular lens power calculations in patients with open-angle glaucoma (OAG) undergoing cataract surgery. The design is retrospective data analysis. There are a total of 55 eyes of 55 patients with OAG who had a cataract surgery alone or in combination with trabeculectomy. We classified OAG subjects into the following groups based on surgical history: only cataract surgery (OC group), cataract surgery after prior trabeculectomy (CAT group), and cataract surgery performed in combination with trabeculectomy (CCT group). Differences between actual and predicted postoperative refractive error. Mean error (ME, difference between postoperative and predicted SE) in the CCT group was significantly lower (towards myopia) than that of the OC group (P = 0.008). Additionally, mean absolute error (MAE, absolute value of ME) in the CAT group was significantly greater than in the OC group (P = 0.006). Using linear mixed models, the ME calculated with the SRK II formula was more accurate than the ME predicted by the SRK T formula in the CAT (P = 0.032) and CCT (P = 0.035) groups. The intraocular lens power prediction accuracy was lower in the CAT and CCT groups than in the OC group. The prediction error was greater in the CAT group than in the OC group, and the direction of the prediction error tended to be towards myopia in the CCT group. The SRK II formula may be more accurate in predicting residual refractive error in the CAT and CCT groups. © 2016 Royal Australian and New Zealand College of Ophthalmologists.

The juvenile face as a suitable age indicator in child pornography cases: a pilot study on the reliability of automated and visual estimation approaches.

PubMed

Ratnayake, M; Obertová, Z; Dose, M; Gabriel, P; Bröker, H M; Brauckmann, M; Barkus, A; Rizgeliene, R; Tutkuviene, J; Ritz-Timme, S; Marasciuolo, L; Gibelli, D; Cattaneo, C

2014-09-01

In cases of suspected child pornography, the age of the victim represents a crucial factor for legal prosecution. The conventional methods for age estimation provide unreliable age estimates, particularly if teenage victims are concerned. In this pilot study, the potential of age estimation for screening purposes is explored for juvenile faces. In addition to a visual approach, an automated procedure is introduced, which has the ability to rapidly scan through large numbers of suspicious image data in order to trace juvenile faces. Age estimations were performed by experts, non-experts and the Demonstrator of a developed software on frontal facial images of 50 females aged 10-19 years from Germany, Italy, and Lithuania. To test the accuracy, the mean absolute error (MAE) between the estimates and the real ages was calculated for each examiner and the Demonstrator. The Demonstrator achieved the lowest MAE (1.47 years) for the 50 test images. Decreased image quality had no significant impact on the performance and classification results. The experts delivered slightly less accurate MAE (1.63 years). Throughout the tested age range, both the manual and the automated approach led to reliable age estimates within the limits of natural biological variability. The visual analysis of the face produces reasonably accurate age estimates up to the age of 18 years, which is the legally relevant age threshold for victims in cases of pedo-pornography. This approach can be applied in conjunction with the conventional methods for a preliminary age estimation of juveniles depicted on images.

A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest

USGS Publications Warehouse

Liang, Liang; Schwartz, Mark D.; Zhuosen Wang,; Gao, Feng; Schaaf, Crystal B.; Bin Tan,; Morisette, Jeffrey T.; Zhang, Xiaoyang

2014-01-01

Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m × 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring.

Forecasting Emergency Department Crowding: An External, Multi-Center Evaluation

PubMed Central

Hoot, Nathan R.; Epstein, Stephen K.; Allen, Todd L.; Jones, Spencer S.; Baumlin, Kevin M.; Chawla, Neal; Lee, Anna T.; Pines, Jesse M.; Klair, Amandeep K.; Gordon, Bradley D.; Flottemesch, Thomas J.; LeBlanc, Larry J.; Jones, Ian; Levin, Scott R.; Zhou, Chuan; Gadd, Cynthia S.; Aronsky, Dominik

2009-01-01

Objective To apply a previously described tool to forecast ED crowding at multiple institutions, and to assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count. Methods The ForecastED tool was validated using historical data from five institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n = 52,560) at four sites and 10 months (n = 44,064) at the fifth. Three outcome measures – the waiting count, occupancy level, and boarding count – were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared to observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured using the median absolute error (MAE). Results The tool was successfully used for five different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at four out of five sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the MAE of the waiting count ranged between 0.6 and 3.1 patients, the MAE of the occupancy level ranged between 9.0 and 14.5% of beds, and the MAE of the boarding count ranged between 0.9 and 2.7 patients. Conclusion The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at five external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems. PMID:19716629

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.

Fast adaptive diamond search algorithm for block-matching motion estimation using spatial correlation

NASA Astrophysics Data System (ADS)

Park, Sang-Gon; Jeong, Dong-Seok

2000-12-01

In this paper, we propose a fast adaptive diamond search algorithm (FADS) for block matching motion estimation. Many fast motion estimation algorithms reduce the computational complexity by the UESA (Unimodal Error Surface Assumption) where the matching error monotonically increases as the search moves away from the global minimum point. Recently, many fast BMAs (Block Matching Algorithms) make use of the fact that global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the neighboring blocks. We move the search origin according to the motion vectors of the spatially neighboring blocks and their MAEs (Mean Absolute Errors). The computer simulation shows that the proposed algorithm has almost the same computational complexity with DS (Diamond Search), but enhances PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS (Full Search), even for the large motion with half the computational load.

Modeling of surface dust concentrations using neural networks and kriging

NASA Astrophysics Data System (ADS)

Buevich, Alexander G.; Medvedev, Alexander N.; Sergeev, Alexander P.; Tarasov, Dmitry A.; Shichkin, Andrey V.; Sergeeva, Marina V.; Atanasova, T. B.

2016-12-01

Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP)) and generalized regression neural network (GRNN)), and two geostatistical approaches: (kriging and cokriging), are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE), root mean square error (RMSE) and relative root mean square error (RRMSE). Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the probable emissions source. The results of work confirm that trained ANN may be more suitable tool for modeling of dust concentrations in snow cover.

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.

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.

Neuromuscular function of the quadriceps muscle during isometric maximal, submaximal and submaximal fatiguing voluntary contractions in knee osteoarthrosis patients

PubMed Central

Jacksteit, Robert; Jackszis, Mario; Feldhege, Frank; Weippert, Matthias; Mittelmeier, Wolfram; Bader, Rainer; Skripitz, Ralf; Behrens, Martin

2017-01-01

Introduction Knee osteoarthrosis (KOA) is commonly associated with a dysfunction of the quadriceps muscle which contributes to alterations in motor performance. The underlying neuromuscular mechanisms of muscle dysfunction are not fully understood. The main objective of this study was to analyze how KOA affects neuromuscular function of the quadriceps muscle during different contraction intensities. Materials and methods The following parameters were assessed in 20 patients and 20 healthy controls: (i) joint position sense, i.e. position control (mean absolute error, MAE) at 30° and 50° of knee flexion, (ii) simple reaction time task performance, (iii) isometric maximal voluntary torque (IMVT) and root mean square of the EMG signal (RMS-EMG), (iv) torque control, i.e. accuracy (MAE), absolute fluctuation (standard deviation, SD), relative fluctuation (coefficient of variation, CV) and periodicity (mean frequency, MNF) of the torque signal at 20%, 40% and 60% IMVT, (v) EMG-torque relationship at 20%, 40% and 60% IMVT and (vi) performance fatigability, i.e. time to task failure (TTF) at 40% IMVT. Results Compared to the control group, the KOA group displayed: (i) significantly higher MAE of the angle signal at 30° (99.3%; P = 0.027) and 50° (147.9%; P < 0.001), (ii) no significant differences in reaction time, (iii) significantly lower IMVT (-41.6%; P = 0.001) and tendentially lower RMS-EMG of the rectus femoris (-33.7%; P = 0.054), (iv) tendentially higher MAE of the torque signal at 20% IMVT (65.9%; P = 0.068), significantly lower SD of the torque signal at all three torque levels and greater MNF at 60% IMVT (44.8%; P = 0.018), (v) significantly increased RMS-EMG of the vastus lateralis at 20% (70.8%; P = 0.003) and 40% IMVT (33.3%; P = 0.034), significantly lower RMS-EMG of the biceps femoris at 20% (-63.6%; P = 0.044) and 40% IMVT (-41.3%; P = 0.028) and tendentially lower at 60% IMVT (-24.3%; P = 0.075) and (vi) significantly shorter TTF (-51.1%; P = 0.049). Conclusion KOA is not only associated with a deterioration of IMVT and neuromuscular activation, but also with an impaired position and torque control at submaximal torque levels, an altered EMG-torque relationship and a higher performance fatigability of the quadriceps muscle. It is recommended that the rehabilitation includes strengthening and fatiguing exercises at maximal and submaximal force levels. PMID:28505208

Validation of satellite based precipitation over diverse topography of Pakistan

NASA Astrophysics Data System (ADS)

Iqbal, Muhammad Farooq; Athar, H.

2018-03-01

This study evaluates the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product data with 0.25° × 0.25° spatial and post-real-time 3 h temporal resolution using point-based Surface Precipitation Gauge (SPG) data from 40 stations, for the period 1998-2013, and using gridded Asian Precipitation ˗ Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) data abbreviated as APH data with 0.25° × 0.25° spatial and daily temporal resolution for the period 1998-2007, over vulnerable and data sparse regions of Pakistan (24-37° N and 62-75° E). To evaluate the performance of TMPA relative to SPG and APH, four commonly used statistical indicator metrics including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC) are employed on daily, monthly, seasonal as well as on annual timescales. The TMPA slightly overestimated both SPG and APH at daily, monthly, and annual timescales, however close results were obtained between TMPA and SPG as compared to those between TMPA and APH, on the same timescale. The TMPA overestimated both SPG and APH during the Pre-Monsoon and Monsoon seasons, whereas it underestimated during the Post-Monsoon and Winter seasons, with different magnitudes. Agreement between TMPA and SPG was good in plain and medium elevation regions, whereas TMPA overestimated APH in 31 stations. The magnitudes of MAE and RMSE were high at daily timescale as compared to monthly and annual timescales. Relatively large MAE was observed in stations located over high elevation regions, whereas minor MAE was recorded in plain area stations at daily, monthly, and annual timescales. A strong positive linear relationship between TMPA and SPG was established at monthly (0.98), seasonal (0.93 to 0.98) and annual (0.97) timescales. Precipitation increased with the increase of elevation, and not only elevation but latitude also affected the intensity and amount of precipitation in Pakistan. It is evident that TMPA overestimates SPG in some regions and seasons and underestimates in other regions and seasons. It is thus determined from the current study that TMPA gives better results on annual, seasonal, and monthly timescales as compared to daily timescale. The TMPA might be used in all the four seasons including Winter, Pre-Monsoon, Monsoon, and Post-Monsoon. The TMPA mostly underestimates both SPG and APH in high elevation regions, whereas in plain and medium elevation regions it gives better results. This study concludes that TMPA can be a good substitute of SPG for water resource management in plain and medium elevation regions in central and northern parts of Pakistan, during all four seasons.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

PubMed Central

Wang, Ying; Lu, Zhouqin; Tian, Lihong; Tan, Li; Shi, Yun; Nie, Shaofa; Liu, Li

2014-01-01

Backgrounds/Objective Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas. Methods A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model. Results The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10−4, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10−4, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend. Conclusion The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases. PMID:25119882

Mapping health outcome measures from a stroke registry to EQ-5D weights

PubMed Central

2013-01-01

Purpose To map health outcome related variables from a national register, not part of any validated instrument, with EQ-5D weights among stroke patients. Methods We used two cross-sectional data sets including patient characteristics, outcome variables and EQ-5D weights from the national Swedish stroke register. Three regression techniques were used on the estimation set (n = 272): ordinary least squares (OLS), Tobit, and censored least absolute deviation (CLAD). The regression coefficients for “dressing“, “toileting“, “mobility”, “mood”, “general health” and “proxy-responders” were applied to the validation set (n = 272), and the performance was analysed with mean absolute error (MAE) and mean square error (MSE). Results The number of statistically significant coefficients varied by model, but all models generated consistent coefficients in terms of sign. Mean utility was underestimated in all models (least in OLS) and with lower variation (least in OLS) compared to the observed. The maximum attainable EQ-5D weight ranged from 0.90 (OLS) to 1.00 (Tobit and CLAD). Health states with utility weights <0.5 had greater errors than those with weights ≥0.5 (P < 0.01). Conclusion This study indicates that it is possible to map non-validated health outcome measures from a stroke register into preference-based utilities to study the development of stroke care over time, and to compare with other conditions in terms of utility. PMID:23496957

Magnetic Resonance–Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region

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

Zheng, Weili; Kim, Joshua P.; Kadbi, Mo

2015-11-01

Purpose: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Methods and Materials: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessedmore » by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. Results: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone–air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. Conclusions: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain.« less

Magnetic Resonance-Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region.

PubMed

Zheng, Weili; Kim, Joshua P; Kadbi, Mo; Movsas, Benjamin; Chetty, Indrin J; Glide-Hurst, Carri K

2015-11-01

To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone-air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

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.

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.

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.

Use of Selected Goodness-of-Fit Statistics to Assess the Accuracy of a Model of Henry Hagg Lake, Oregon

NASA Astrophysics Data System (ADS)

Rounds, S. A.; Sullivan, A. B.

2004-12-01

Assessing a model's ability to reproduce field data is a critical step in the modeling process. For any model, some method of determining goodness-of-fit to measured data is needed to aid in calibration and to evaluate model performance. Visualizations and graphical comparisons of model output are an excellent way to begin that assessment. At some point, however, model performance must be quantified. Goodness-of-fit statistics, including the mean error (ME), mean absolute error (MAE), root mean square error, and coefficient of determination, typically are used to measure model accuracy. Statistical tools such as the sign test or Wilcoxon test can be used to test for model bias. The runs test can detect phase errors in simulated time series. Each statistic is useful, but each has its limitations. None provides a complete quantification of model accuracy. In this study, a suite of goodness-of-fit statistics was applied to a model of Henry Hagg Lake in northwest Oregon. Hagg Lake is a man-made reservoir on Scoggins Creek, a tributary to the Tualatin River. Located on the west side of the Portland metropolitan area, the Tualatin Basin is home to more than 450,000 people. Stored water in Hagg Lake helps to meet the agricultural and municipal water needs of that population. Future water demands have caused water managers to plan for a potential expansion of Hagg Lake, doubling its storage to roughly 115,000 acre-feet. A model of the lake was constructed to evaluate the lake's water quality and estimate how that quality might change after raising the dam. The laterally averaged, two-dimensional, U.S. Army Corps of Engineers model CE-QUAL-W2 was used to construct the Hagg Lake model. Calibrated for the years 2000 and 2001 and confirmed with data from 2002 and 2003, modeled parameters included water temperature, ammonia, nitrate, phosphorus, algae, zooplankton, and dissolved oxygen. Several goodness-of-fit statistics were used to quantify model accuracy and bias. Model performance was judged to be excellent for water temperature (annual ME: -0.22 to 0.05 ° C; annual MAE: 0.62 to 0.68 ° C) and dissolved oxygen (annual ME: -0.28 to 0.18 mg/L; annual MAE: 0.43 to 0.92 mg/L), showing that the model is sufficiently accurate for future water resources planning and management.

A portable respiratory rate estimation system with a passive single-lead electrocardiogram acquisition module.

PubMed

Nayan, Nazrul Anuar; Risman, Nur Sabrina; Jaafar, Rosmina

2016-07-27

Among vital signs of acutely ill hospital patients, respiratory rate (RR) is a highly accurate predictor of health deterioration. This study proposes a system that consists of a passive and non-invasive single-lead electrocardiogram (ECG) acquisition module and an ECG-derived respiratory (EDR) algorithm in the working prototype of a mobile application. Before estimating RR that produces the EDR rate, ECG signals were evaluated based on the signal quality index (SQI). The SQI algorithm was validated quantitatively using the PhysioNet/Computing in Cardiology Challenge 2011 training data set. The RR extraction algorithm was validated by adopting 40 MIT PhysioNet Multiparameter Intelligent Monitoring in Intensive Care II data set. The estimated RR showed a mean absolute error (MAE) of 1.4 compared with the ``gold standard'' RR. The proposed system was used to record 20 ECGs of healthy subjects and obtained the estimated RR with MAE of 0.7 bpm. Results indicate that the proposed hardware and algorithm could replace the manual counting method, uncomfortable nasal airflow sensor, chest band, and impedance pneumotachography often used in hospitals. The system also takes advantage of the prevalence of smartphone usage and increase the monitoring frequency of the current ECG of patients with critical illnesses.

Evaluation of genotype-guided acenocoumarol dosing algorithms in Russian patients.

PubMed

Sychev, Dmitriy Alexeyevich; Rozhkov, Aleksandr Vladimirovich; Ananichuk, Anna Viktorovna; Kazakov, Ruslan Evgenyevich

2017-05-24

Acenocoumarol dose is normally determined via step-by-step adjustment process based on International Normalized Ratio (INR) measurements. During this time, the risk of adverse reactions is especially high. Several genotype-based acenocoumarol dosing algorithms have been created to predict ideal doses at the start of anticoagulant therapy. Nine dosing algorithms were selected through a literature search. These were evaluated using a cohort of 63 patients with atrial fibrillation receiving acenocoumarol therapy. None of the existing algorithms could predict the ideal acenocoumarol dose in 50% of Russian patients. The Wolkanin-Bartnik algorithtm based on European population was the best-performing one with the highest correlation values (r=0.397), mean absolute error (MAE) 0.82 (±0.61). EU-PACT also managed to give an estimate within the ideal range in 43% of the cases. The two least accurate results were yielded by the Indian population-based algorithms. Among patients receiving amiodarone, algorithms by Schie and Tong proved to be the most effective with the MAE of 0.48±0.42 mg/day and 0.56±0.31 mg/day, respectively. Patient ethnicity and amiodarone intake are factors that must be considered when building future algorithms. Further research is required to find the perfect dosing formula of acenocoumarol maintenance doses in Russian patients.

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.

PubMed

Sadrawi, Muammar; Fan, Shou-Zen; Abbod, Maysam F; Jen, Kuo-Kuang; Shieh, Jiann-Shing

2015-01-01

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

PubMed Central

Sadrawi, Muammar; Fan, Shou-Zen; Abbod, Maysam F.; Jen, Kuo-Kuang; Shieh, Jiann-Shing

2015-01-01

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly. PMID:26568957

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

NASA Astrophysics Data System (ADS)

Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

2018-03-01

Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.

Uncertainty Analysis of Downscaled CMIP5 Precipitation Data for Louisiana, USA

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.

2014-12-01

The downscaled CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two downscaling techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 downscaled general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 downscaled GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.

Cost Prediction Using a Survival Grouping Algorithm: An Application to Incident Prostate Cancer Cases.

PubMed

Onukwugha, Eberechukwu; Qi, Ran; Jayasekera, Jinani; Zhou, Shujia

2016-02-01

Prognostic classification approaches are commonly used in clinical practice to predict health outcomes. However, there has been limited focus on use of the general approach for predicting costs. We applied a grouping algorithm designed for large-scale data sets and multiple prognostic factors to investigate whether it improves cost prediction among older Medicare beneficiaries diagnosed with prostate cancer. We analysed the linked Surveillance, Epidemiology and End Results (SEER)-Medicare data, which included data from 2000 through 2009 for men diagnosed with incident prostate cancer between 2000 and 2007. We split the survival data into two data sets (D0 and D1) of equal size. We trained the classifier of the Grouping Algorithm for Cancer Data (GACD) on D0 and tested it on D1. The prognostic factors included cancer stage, age, race and performance status proxies. We calculated the average difference between observed D1 costs and predicted D1 costs at 5 years post-diagnosis with and without the GACD. The sample included 110,843 men with prostate cancer. The median age of the sample was 74 years, and 10% were African American. The average difference (mean absolute error [MAE]) per person between the real and predicted total 5-year cost was US$41,525 (MAE US$41,790; 95% confidence interval [CI] US$41,421-42,158) with the GACD and US$43,113 (MAE US$43,639; 95% CI US$43,062-44,217) without the GACD. The 5-year cost prediction without grouping resulted in a sample overestimate of US$79,544,508. The grouping algorithm developed for complex, large-scale data improves the prediction of 5-year costs. The prediction accuracy could be improved by utilization of a richer set of prognostic factors and refinement of categorical specifications.

A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa

PubMed Central

Kerry, Ruth; Goovaerts, Pierre; Smit, Izak P.J.; Ingram, Ben R.

2015-01-01

Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many years, the whole park was surveyed, but in 1998 a transect survey approach was adopted. This is cheaper and less time consuming but leaves gaps in the data spatially. Also the distance method currently employed by the park only gives estimates of total species populations but not their spatial distribution. We compare the ability of multiple indicator kriging and area-to-point Poisson kriging to accurately map species distribution in the park. A leave-one-out cross-validation approach indicates that multiple indicator kriging makes poor estimates of the number of animals, particularly the few large counts, as the indicator variograms for such high thresholds are pure nugget. Poisson kriging was applied to the prediction of two types of abundance data: spatial density and proportion of a given species. Both Poisson approaches had standardized mean absolute errors (St. MAEs) of animal counts at least an order of magnitude lower than multiple indicator kriging. The spatial density, Poisson approach (1), gave the lowest St. MAEs for the most abundant species and the proportion, Poisson approach (2), did for the least abundant species. Incorporating environmental data into Poisson approach (2) further reduced St. MAEs. PMID:25729318

A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa.

PubMed

Kerry, Ruth; Goovaerts, Pierre; Smit, Izak P J; Ingram, Ben R

Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many years, the whole park was surveyed, but in 1998 a transect survey approach was adopted. This is cheaper and less time consuming but leaves gaps in the data spatially. Also the distance method currently employed by the park only gives estimates of total species populations but not their spatial distribution. We compare the ability of multiple indicator kriging and area-to-point Poisson kriging to accurately map species distribution in the park. A leave-one-out cross-validation approach indicates that multiple indicator kriging makes poor estimates of the number of animals, particularly the few large counts, as the indicator variograms for such high thresholds are pure nugget. Poisson kriging was applied to the prediction of two types of abundance data: spatial density and proportion of a given species. Both Poisson approaches had standardized mean absolute errors (St. MAEs) of animal counts at least an order of magnitude lower than multiple indicator kriging. The spatial density, Poisson approach (1), gave the lowest St. MAEs for the most abundant species and the proportion, Poisson approach (2), did for the least abundant species. Incorporating environmental data into Poisson approach (2) further reduced St. MAEs.

Evaluation of Voice Acoustics as Predictors of Clinical Depression Scores.

PubMed

Hashim, Nik Wahidah; Wilkes, Mitch; Salomon, Ronald; Meggs, Jared; France, Daniel J

2017-03-01

The aim of the present study was to determine if acoustic measures of voice, characterizing specific spectral and timing properties, predict clinical ratings of depression severity measured in a sample of patients using the Hamilton Depression Rating Scale (HAMD) and Beck Depression Inventory (BDI-II). This is a prospective study. Voice samples and clinical depression scores were collected prospectively from consenting adult patients who were referred to psychiatry from the adult emergency department or primary care clinics. The patients were audio-recorded as they read a standardized passage in a nearly closed-room environment. Mean Absolute Error (MAE) between actual and predicted depression scores was used as the primary outcome measure. The average MAE between predicted and actual HAMD scores was approximately two scores for both men and women, and the MAE for the BDI-II scores was approximately one score for men and eight scores for women. Timing features were predictive of HAMD scores in female patients while a combination of timing features and spectral features was predictive of scores in male patients. Timing features were predictive of BDI-II scores in male patients. Voice acoustic features extracted from read speech demonstrated variable effectiveness in predicting clinical depression scores in men and women. Voice features were highly predictive of HAMD scores in men and women, and BDI-II scores in men, respectively. The methodology is feasible for diagnostic applications in diverse clinical settings as it can be implemented during a standard clinical interview in a normal closed room and without strict control on the recording environment. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Applying airline safety practices to medication administration.

PubMed

Pape, Theresa M

2003-04-01

Medication administration errors (MAE) continue as major problems for health care institutions, nurses, and patients. However, MAEs are often the result of system failures leading to patient injury, increased hospital costs, and blaming. Costs include those related to increased hospital length of stay and legal expenses. Contributing factors include distractions, lack of focus, poor communication, and failure to follow standard protocols during medication administration.

Surface modeling of soil antibiotics.

PubMed

Shi, Wen-jiao; Yue, Tian-xiang; Du, Zheng-ping; Wang, Zong; Li, Xue-wen

2016-02-01

Large numbers of livestock and poultry feces are continuously applied into soils in intensive vegetable cultivation areas, and then some veterinary antibiotics are persistent existed in soils and cause health risk. For the spatial heterogeneity of antibiotic residues, developing a suitable technique to interpolate soil antibiotic residues is still a challenge. In this study, we developed an effective interpolator, high accuracy surface modeling (HASM) combined vegetable types, to predict the spatial patterns of soil antibiotics, using 100 surface soil samples collected from an intensive vegetable cultivation area located in east of China, and the fluoroquinolones (FQs), including ciprofloxacin (CFX), enrofloxacin (EFX) and norfloxacin (NFX), were analyzed as the target antibiotics. The results show that vegetable type is an effective factor to be combined to improve the interpolator performance. HASM achieves less mean absolute errors (MAEs) and root mean square errors (RMSEs) for total FQs (NFX+CFX+EFX), NFX, CFX and EFX than kriging with external drift (KED), stratified kriging (StK), ordinary kriging (OK) and inverse distance weighting (IDW). The MAE of HASM for FQs is 55.1 μg/kg, and the MAEs of KED, StK, OK and IDW are 99.0 μg/kg, 102.8 μg/kg, 106.3 μg/kg and 108.7 μg/kg, respectively. Further, RMSE simulated by HASM for FQs (CFX, EFX and NFX) are 106.2 μg/kg (88.6 μg/kg, 20.4 μg/kg and 39.2 μg/kg), and less 30% (27%, 22% and 36%), 33% (27%, 27% and 43%), 38% (34%, 23% and 41%) and 42% (32%, 35% and 51%) than the ones by KED, StK, OK and IDW, respectively. HASM also provides better maps with more details and more consistent maximum and minimum values of soil antibiotics compared with the measured data. The better performance can be concluded that HASM takes the vegetable type information as global approximate information, and takes local sampling data as its optimum control constraints. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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

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 hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus

NASA Astrophysics Data System (ADS)

Aquino, Ronald L.; Alcantara, Nialle Loui Mar T.; Addawe, Rizavel C.

2017-11-01

The Selar crumenophthalmus with the English name big-eyed scad fish, locally known as matang-baka, is one of the fishes commonly caught along the waters of La Union, Philippines. The study deals with the forecasting of catch volumes of big-eyed scad fish for commercial consumption. The data used are quarterly caught volumes of big-eyed scad fish from 2002 to first quarter of 2017. This actual data is available from the open stat database published by the Philippine Statistics Authority (PSA)whose task is to collect, compiles, analyzes and publish information concerning different aspects of the Philippine setting. Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Network (ANN) model and the Hybrid model consisting of ARIMA and ANN were developed to forecast catch volumes of big-eyed scad fish. Statistical errors such as Mean Absolute Errors (MAE) and Root Mean Square Errors (RMSE) were computed and compared to choose the most suitable model for forecasting the catch volume for the next few quarters. A comparison of the results of each model and corresponding statistical errors reveals that the hybrid model, ARIMA-ANN (2,1,2)(6:3:1), is the most suitable model to forecast the catch volumes of the big-eyed scad fish for the next few quarters.

Population pharmacokinetics modeling of oxcarbazepine to characterize drug interactions in Chinese children with epilepsy

PubMed Central

Wang, Yang; Zhang, Hua-nian; Niu, Chang-he; Gao, Ping; Chen, Yu-jun; Peng, Jing; Liu, Mao-chang; Xu, Hua

2014-01-01

Aim: To develop a population pharmacokinetics model of oxcarbazepine in Chinese pediatric patients with epilepsy, and to study the interactions between oxcarbazepine and other antiepileptic drugs (AEDs). Methods: A total of 688 patients with epilepsy aged 2 months to 18 years were divided into model (n=573) and valid (n=115) groups. Serum concentrations of the main active metabolite of oxcarbazepine, 10-hydroxycarbazepine (MHD), were determined 0.5–48 h after the last dosage. A population pharmacokinetics (PPK) model was constructed using NLME software. This model was internally evaluated using Bootstrapping and goodness-of-fit plots inspection. The data of the valid group were used to calculate the mean prediction error (MPE), mean absolute prediction error (MAE), mean squared prediction error (MSE) and the 95% confidence intervals (95% CI) to externally evaluate the model. Results: The population values of pharmacokinetic parameters estimated in the final model were as follows: Ka=0.83 h-1, Vd=0.67 L/kg, and CL=0.035 L·kg−1·h−1. The enzyme-inducing AEDs (carbamazepine, phenytoin, phenobarbital) and newer generation AEDs (levetiracetam, lamotrigine, topiramate) increased the weight-normalized CL value of MHD by 17.4% and 10.5%, respectively, whereas the enzyme-inhibiting AED valproic acid decreased it by 3%. No significant association was found between the CL value of MHD and the other covariates. For the final model, the evaluation results (95% CI) were MPE=0.01 (−0.07–0.10) mg/L, MAE=0.46 (0.40–0.51) mg/L, MSE=0.39 (0.27–0.51) (mg/L)2. Conclusion: A PPK model of OXC in Chinese pediatric patients with epilepsy is established. The enzyme-inducing AEDs and some newer generation AEDs (lamotrigine, topiramate) could slightly increase the metabolism of MHD. PMID:25220641

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

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.

Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15.

PubMed

Mpundu-Kaambwa, Christine; Chen, Gang; Russo, Remo; Stevens, Katherine; Petersen, Karin Dam; Ratcliffe, Julie

2017-04-01

The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based. This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis. The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models. The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE. Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research.

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys.

PubMed

Song, Hao; Ruan, Dan; Liu, Wenyang; Stenger, V Andrew; Pohmann, Rolf; Fernández-Seara, Maria A; Nair, Tejas; Jung, Sungkyu; Luo, Jingqin; Motai, Yuichi; Ma, Jingfei; Hazle, John D; Gach, H Michael

2017-03-01

Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition. A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers. The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm. Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient. © 2017 American Association of Physicists in Medicine.

Estimating nonrigid motion from inconsistent intensity with robust shape features

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

Liu, Wenyang; Ruan, Dan, E-mail: druan@mednet.ucla.edu; Department of Radiation Oncology, University of California, Los Angeles, California 90095

2013-12-15

Purpose: To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Methods: Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, andmore » regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. Results: To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. Conclusions: The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.« less

Estimating nonrigid motion from inconsistent intensity with robust shape features.

PubMed

Liu, Wenyang; Ruan, Dan

2013-12-01

To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

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.

Multiple regression based imputation for individualizing template human model from a small number of measured dimensions.

PubMed

Nohara, Ryuki; Endo, Yui; Murai, Akihiko; Takemura, Hiroshi; Kouchi, Makiko; Tada, Mitsunori

2016-08-01

Individual human models are usually created by direct 3D scanning or deforming a template model according to the measured dimensions. In this paper, we propose a method to estimate all the necessary dimensions (full set) for the human model individualization from a small number of measured dimensions (subset) and human dimension database. For this purpose, we solved multiple regression equation from the dimension database given full set dimensions as the objective variable and subset dimensions as the explanatory variables. Thus, the full set dimensions are obtained by simply multiplying the subset dimensions to the coefficient matrix of the regression equation. We verified the accuracy of our method by imputing hand, foot, and whole body dimensions from their dimension database. The leave-one-out cross validation is employed in this evaluation. The mean absolute errors (MAE) between the measured and the estimated dimensions computed from 4 dimensions (hand length, breadth, middle finger breadth at proximal, and middle finger depth at proximal) in the hand, 2 dimensions (foot length, breadth, and lateral malleolus height) in the foot, and 1 dimension (height) and weight in the whole body are computed. The average MAE of non-measured dimensions were 4.58% in the hand, 4.42% in the foot, and 3.54% in the whole body, while that of measured dimensions were 0.00%.

New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring.

PubMed

van Gastel, Mark; Stuijk, Sander; de Haan, Gerard

2016-12-07

Finger-oximeters are ubiquitously used for patient monitoring in hospitals worldwide. Recently, remote measurement of arterial blood oxygenation (SpO 2 ) with a camera has been demonstrated. Both contact and remote measurements, however, require the subject to remain static for accurate SpO 2 values. This is due to the use of the common ratio-of-ratios measurement principle that measures the relative pulsatility at different wavelengths. Since the amplitudes are small, they are easily corrupted by motion-induced variations. We introduce a new principle that allows accurate remote measurements even during significant subject motion. We demonstrate the main advantage of the principle, i.e. that the optimal signature remains the same even when the SNR of the PPG signal drops significantly due to motion or limited measurement area. The evaluation uses recordings with breath-holding events, which induce hypoxemia in healthy moving subjects. The events lead to clinically relevant SpO 2 levels in the range 80-100%. The new principle is shown to greatly outperform current remote ratio-of-ratios based methods. The mean-absolute SpO 2 -error (MAE) is about 2 percentage-points during head movements, where the benchmark method shows a MAE of 24 percentage-points. Consequently, we claim ours to be the first method to reliably measure SpO 2 remotely during significant subject motion.

Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Xiang-ru

2017-07-01

The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature (Teff), surface gravitational acceleration (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (Teff /K), 0.1622 for lg (g/(cm · s-2)), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg (Teff /K), 0.0098 for lg (g/(cm · s-2)), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters.

Estimation of size of tropical cyclones in the North Indian Ocean using Oceansat-2 scatterometer high-resolution wind products

NASA Astrophysics Data System (ADS)

Jaiswal, Neeru; Ha, Doan Thi Thu; Kishtawal, C. M.

2018-03-01

Tropical cyclone (TC) is one of the most intense weather hazards, especially for the coastal regions, as it causes huge devastation through gale winds and torrential floods during landfall. Thus, accurate prediction of TC is of great importance to reduce the loss of life and damage to property. Most of the cyclone track prediction model requires size of TC as an important parameter in order to simulate the vortex. TC size is also required in the impact assessment of TC affected regions. In the present work, the size of TCs formed in the North Indian Ocean (NIO) has been estimated using the high resolution surface wind observations from oceansat-2 scatterometer. The estimated sizes of cyclones were compared to the radius of outermost closed isobar (ROCI) values provided by Joint Typhoon warning Center (JTWC) by plotting their histograms and computing the correlation and mean absolute error (MAE). The correlation and MAE between the OSCAT wind based TC size estimation and JTWC-ROCI values was found 0.69 and 33 km, respectively. The results show that the sizes of cyclones estimated by OSCAT winds are in close agreement to the JTWC-ROCI. The ROCI values of JTWC were analyzed to study the variations in the size of tropical cyclones in NIO during different time of the diurnal cycle and intensity stages.

Comparison of the CME-associated shock arrival times at the earth using the WSA-ENLIL model with three cone models

NASA Astrophysics Data System (ADS)

Jang, S.; Moon, Y.; Na, H.

2012-12-01

We have made a comparison of CME-associated shock arrival times at the earth based on the WSA-ENLIL model with three cone models using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters from Michalek et al. (2007) as well as their associated interplanetary (IP) shocks. For this study we consider three different cone models (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine CME cone parameters (radial velocity, angular width and source location), which are used for input parameters of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the elliptical cone model is 10 hours, which is about 2 hours smaller than those of the other models. However, this value is still larger than that (8.7 hours) of an empirical model by Kim et al. (2007). We are investigating several possibilities on relatively large errors of the WSA-ENLIL cone model, which may be caused by CME-CME interaction, background solar wind speed, and/or CME density enhancement.

On the application of the Principal Component Analysis for an efficient climate downscaling of surface wind fields

NASA Astrophysics Data System (ADS)

Chavez, Roberto; Lozano, Sergio; Correia, Pedro; Sanz-Rodrigo, Javier; Probst, Oliver

2013-04-01

With the purpose of efficiently and reliably generating long-term wind resource maps for the wind energy industry, the application and verification of a statistical methodology for the climate downscaling of wind fields at surface level is presented in this work. This procedure is based on the combination of the Monte Carlo and the Principal Component Analysis (PCA) statistical methods. Firstly the Monte Carlo method is used to create a huge number of daily-based annual time series, so called climate representative years, by the stratified sampling of a 33-year-long time series corresponding to the available period of the NCAR/NCEP global reanalysis data set (R-2). Secondly the representative years are evaluated such that the best set is chosen according to its capability to recreate the Sea Level Pressure (SLP) temporal and spatial fields from the R-2 data set. The measure of this correspondence is based on the Euclidean distance between the Empirical Orthogonal Functions (EOF) spaces generated by the PCA (Principal Component Analysis) decomposition of the SLP fields from both the long-term and the representative year data sets. The methodology was verified by comparing the selected 365-days period against a 9-year period of wind fields generated by dynamical downscaling the Global Forecast System data with the mesoscale model SKIRON for the Iberian Peninsula. These results showed that, compared to the traditional method of dynamical downscaling any random 365-days period, the error in the average wind velocity by the PCA's representative year was reduced by almost 30%. Moreover the Mean Absolute Errors (MAE) in the monthly and daily wind profiles were also reduced by almost 25% along all SKIRON grid points. These results showed also that the methodology presented maximum error values in the wind speed mean of 0.8 m/s and maximum MAE in the monthly curves of 0.7 m/s. Besides the bulk numbers, this work shows the spatial distribution of the errors across the Iberian domain and additional wind statistics such as the velocity and directional frequency. Additional repetitions were performed to prove the reliability and robustness of this kind-of statistical-dynamical downscaling method.

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.

PubMed

Vidaki, Athina; Ballard, David; Aliferi, Anastasia; Miller, Thomas H; Barron, Leon P; Syndercombe Court, Denise

2017-05-01

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R 2 =0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R 2 =0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R 2 =0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq ® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data

NASA Astrophysics Data System (ADS)

Vuolo, Francesco; Ng, Wai-Tim; Atzberger, Clement

2017-05-01

This paper introduces a novel methodology for generating 15-day, smoothed and gap-filled time series of high spatial resolution data. The approach is based on templates from high quality observations to fill data gaps that are subsequently filtered. We tested our method for one large contiguous area (Bavaria, Germany) and for nine smaller test sites in different ecoregions of Europe using Landsat data. Overall, our results match the validation dataset to a high degree of accuracy with a mean absolute error (MAE) of 0.01 for visible bands, 0.03 for near-infrared and 0.02 for short-wave-infrared. Occasionally, the reconstructed time series are affected by artefacts due to undetected clouds. Less frequently, larger uncertainties occur as a result of extended periods of missing data. Reliable cloud masks are highly warranted for making full use of time series.

Spot measurement of heart rate based on morphology of PhotoPlethysmoGraphic (PPG) signals.

PubMed

Madhan Mohan, P; Nagarajan, V; Vignesh, J C

2017-02-01

Due to increasing health consciousness among people, it is imperative to have low-cost health care devices to measure the vital parameters like heart rate and arterial oxygen saturation (SpO 2 ). In this paper, an efficient heart rate monitoring algorithm based on the morphology of photoplethysmography (PPG) signals to measure the spot heart rate (HR) and its real-time implementation is proposed. The algorithm does pre-processing and detects the onsets and systolic peaks of the PPG signal to estimate the heart rate of the subject. Since the algorithm is based on the morphology of the signal, it works well when the subject is not moving, which is a typical test case. So, this algorithm is developed mainly to measure the heart rate at on-demand applications. Real-time experimental results indicate the heart rate accuracy of 99.5%, mean absolute percentage error (MAPE) of 1.65%, mean absolute error (MAE) of 1.18 BPM and reference closeness factor (RCF) of 0.988. The results further show that the average response time of the algorithm to give the spot HR is 6.85 s, so that the users need not wait longer to see their HR. The hardware implementation results show that the algorithm only requires 18 KBytes of total memory and runs at high speed with 0.85 MIPS. So, this algorithm can be targeted to low-cost embedded platforms.

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Ebtehaj, Isa; Bonakdari, Hossein; Deo, Ravinesh C.; Danandeh Mehr, Ali; Mohtar, Wan Hanna Melini Wan; Diop, Lamine; El-shafie, Ahmed; Singh, Vijay P.

2017-11-01

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the firefly algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. The results of the ANFIS-FFA model is compared with the classical ANFIS model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy Inference Systems (FIS) generation. The historical monthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study. Sixteen different input combinations with one to five time-lagged input variables are incorporated into the ANFIS-FFA and ANFIS models to consider the antecedent seasonal variations in historical streamflow data. The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (r) are used to evaluate the forecasting performance of ANFIS-FFA model. In conjunction with these metrics, the refined Willmott's Index (Drefined), Nash-Sutcliffe coefficient (ENS) and Legates and McCabes Index (ELM) are also utilized as the normalized goodness-of-fit metrics. Comparison of the results reveals that the FFA is able to improve the forecasting accuracy of the hybrid ANFIS-FFA model (r = 1; RMSE = 0.984; MAE = 0.364; ENS = 1; ELM = 0.988; Drefined = 0.994) applied for the monthly streamflow forecasting in comparison with the traditional ANFIS model (r = 0.998; RMSE = 3.276; MAE = 1.553; ENS = 0.995; ELM = 0.950; Drefined = 0.975). The results also show that the ANFIS-FFA is not only superior to the ANFIS model but also exhibits a parsimonious modelling framework for streamflow forecasting by incorporating a smaller number of input variables required to yield the comparatively better performance. It is construed that the FFA optimizer can thus surpass the accuracy of the traditional ANFIS model in general, and is able to remove the false (inaccurately) forecasted data in the ANFIS model for extremely low flows. The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model.

Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: test of a new scheme and implications for inversion algorithms.

PubMed

Sun, Deyong; Hu, Chuanmin; Qiu, Zhongfeng; Wang, Shengqiang

2015-06-01

A new scheme has been proposed by Lee et al. (2014) to reconstruct hyperspectral (400 - 700 nm, 5 nm resolution) remote sensing reflectance (R_rs(λ), sr^-1) of representative global waters using measurements at 15 spectral bands. This study tested its applicability to optically complex turbid inland waters in China, where R_rs(λ) are typically much higher than those used in Lee et al. (2014). Strong interdependence of R_rs(λ) between neighboring bands (≤ 10 nm interval) was confirmed, with Pearson correlation coefficient (PCC) mostly above 0.98. The scheme of Lee et al. (2014) for R_rs(λ) re-construction with its original global parameterization worked well with this data set, while new parameterization showed improvement in reducing uncertainties in the reconstructed R_rs(λ). Mean absolute error (MAE_Rrs(λ_i)) in the reconstructed R_rs(λ) was mostly < 0.0002 sr^-1 between 400 and 700nm, and mean relative error (MRE_Rrs(λ_i)) was < 1% when the comparison was made between reconstructed and measured R_rs(λ) spectra. When R_rs(λ) at the MODIS bands were used to reconstruct the hyperspectral R_rs(λ), MAE_Rrs(λ_i) was < 0.001 sr^-1 and MRE_Rrs(λ_i) was < 3%. When R_rs(λ) at the MERIS bands were used, MAE_Rrs(λ_i) in the reconstructed hyperspectral R_rs(λ) was < 0.0004 sr^-1 and MRE_Rrs(λ_i) was < 1%. These results have significant implications for inversion algorithms to retrieve concentrations of phytoplankton pigments (e.g., chlorophyll-a or Chla, and phycocyanin or PC) and total suspended materials (TSM) as well as absorption coefficient of colored dissolved organic matter (CDOM), as some of the algorithms were developed from in situ R_rs(λ) data using spectral bands that may not exist on satellite sensors.

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Kisi, Ozgur; Singh, Vijay P.

2017-02-01

Drought forecasting using standardized metrics of rainfall is a core task in hydrology and water resources management. Standardized Precipitation Index (SPI) is a rainfall-based metric that caters for different time-scales at which the drought occurs, and due to its standardization, is well-suited for forecasting drought at different periods in climatically diverse regions. This study advances drought modelling using multivariate adaptive regression splines (MARS), least square support vector machine (LSSVM), and M5Tree models by forecasting SPI in eastern Australia. MARS model incorporated rainfall as mandatory predictor with month (periodicity), Southern Oscillation Index, Pacific Decadal Oscillation Index and Indian Ocean Dipole, ENSO Modoki and Nino 3.0, 3.4 and 4.0 data added gradually. The performance was evaluated with root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (r2). Best MARS model required different input combinations, where rainfall, sea surface temperature and periodicity were used for all stations, but ENSO Modoki and Pacific Decadal Oscillation indices were not required for Bathurst, Collarenebri and Yamba, and the Southern Oscillation Index was not required for Collarenebri. Inclusion of periodicity increased the r2 value by 0.5-8.1% and reduced RMSE by 3.0-178.5%. Comparisons showed that MARS superseded the performance of the other counterparts for three out of five stations with lower MAE by 15.0-73.9% and 7.3-42.2%, respectively. For the other stations, M5Tree was better than MARS/LSSVM with lower MAE by 13.8-13.4% and 25.7-52.2%, respectively, and for Bathurst, LSSVM yielded more accurate result. For droughts identified by SPI ≤ - 0.5, accurate forecasts were attained by MARS/M5Tree for Bathurst, Yamba and Peak Hill, whereas for Collarenebri and Barraba, M5Tree was better than LSSVM/MARS. Seasonal analysis revealed disparate results where MARS/M5Tree was better than LSSVM. The results highlight the importance of periodicity in drought forecasting and also ascertains that model accuracy scales with geographic/seasonal factors due to complexity of drought and its relationship with inputs and data attributes that can affect the evolution of drought events.

[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).

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.

Linearly Supporting Feature Extraction for Automated Estimation of Stellar Atmospheric Parameters

NASA Astrophysics Data System (ADS)

Li, Xiangru; Lu, Yu; Comte, Georges; Luo, Ali; Zhao, Yongheng; Wang, Yongjun

2015-05-01

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H]. “Linearly supporting” means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)bs; third, estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both the wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate {{T}{\\tt{eff} }}, 62 features for log g, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Parameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log {{T}{\\tt{eff} }} (83 K for {{T}{\\tt{eff} }}), 0.2345 dex for log g, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log {{T}{\\tt{eff} }} (32 K for {{T}{\\tt{eff} }}), 0.0337 dex for log g, and 0.0268 dex for [Fe/H].

Performance of a SiPM based semi-monolithic scintillator PET detector

NASA Astrophysics Data System (ADS)

Zhang, Xianming; Wang, Xiaohui; Ren, Ning; Kuang, Zhonghua; Deng, Xinhan; Fu, Xin; Wu, San; Sang, Ziru; Hu, Zhanli; Liang, Dong; Liu, Xin; Zheng, Hairong; Yang, Yongfeng

2017-10-01

A depth encoding PET detector module using semi-monolithic scintillation crystal single-ended readout by a SiPM array was built and its performance was measured. The semi-monolithic scintillator detector consists of 11 polished LYSO slices measuring 1  ×  11.6  ×  10 mm3. The slices are glued together with enhanced specular reflector (ESR) in between and outside of the slices. The bottom surface of the slices is coupled to a 4  ×  4 SiPM array with a 1 mm light guide and silicon grease between them. No reflector is used on the top surface and two sides of the slices to reduce the scintillation photon reflection. The signals of the 4  ×  4 SiPM array are grouped along rows and columns separately into eight signals. Four SiPM column signals are used to identify the slices according to the center of the gravity of the scintillation photon distribution in the pixelated direction. Four SiPM row signals are used to estimate the y (monolithic direction) and z (depth of interaction) positions according to the center of the gravity and the width of the scintillation photon distribution in the monolithic direction, respectively. The detector was measured with 1 mm sampling interval in both the y and z directions with electronic collimation by using a 0.25 mm diameter 22Na point source and a 1  ×  1  ×  20 mm3 LYSO crystal detector. An average slice based energy resolution of 14.9% was obtained. All slices of 1 mm thick were clearly resolved and a detector with even thinner slices could be used. The y positions calculated with the center of gravity method are different for interactions happening at the same y, but different z positions due to depth dependent edge effects. The least-square minimization and the maximum likelihood positioning algorithms were developed and both methods improved the spatial resolution at the edges of the detector as compared with the center of gravity method. A mean absolute error (MAE) which is defined as the probability-weighted mean of the absolute value of the positioning error is used to evaluate the spatial resolution. An average MAE spatial resolution of ~1.15 mm was obtained in both y and z directions without rejection of the multiple scattering events. The average MAE spatial resolution was ~0.7 mm in both y and z directions after the multiple scattering events were rejected. The timing resolution of the detector is 575 ps. In the next step, long rectangle detector will be built to reduce edge effects and improve the spatial resolution of the semi-monolithic detector. Thick detector up to 20 mm will be explored and the positioning algorithms will be further optimized.

Performance of a SiPM based semi-monolithic scintillator PET detector.

PubMed

Zhang, Xianming; Wang, Xiaohui; Ren, Ning; Kuang, Zhonghua; Deng, Xinhan; Fu, Xin; Wu, San; Sang, Ziru; Hu, Zhanli; Liang, Dong; Liu, Xin; Zheng, Hairong; Yang, Yongfeng

2017-09-21

A depth encoding PET detector module using semi-monolithic scintillation crystal single-ended readout by a SiPM array was built and its performance was measured. The semi-monolithic scintillator detector consists of 11 polished LYSO slices measuring 1  ×  11.6  ×  10 mm 3 . The slices are glued together with enhanced specular reflector (ESR) in between and outside of the slices. The bottom surface of the slices is coupled to a 4  ×  4 SiPM array with a 1 mm light guide and silicon grease between them. No reflector is used on the top surface and two sides of the slices to reduce the scintillation photon reflection. The signals of the 4  ×  4 SiPM array are grouped along rows and columns separately into eight signals. Four SiPM column signals are used to identify the slices according to the center of the gravity of the scintillation photon distribution in the pixelated direction. Four SiPM row signals are used to estimate the y (monolithic direction) and z (depth of interaction) positions according to the center of the gravity and the width of the scintillation photon distribution in the monolithic direction, respectively. The detector was measured with 1 mm sampling interval in both the y and z directions with electronic collimation by using a 0.25 mm diameter 22 Na point source and a 1  ×  1  ×  20 mm 3 LYSO crystal detector. An average slice based energy resolution of 14.9% was obtained. All slices of 1 mm thick were clearly resolved and a detector with even thinner slices could be used. The y positions calculated with the center of gravity method are different for interactions happening at the same y, but different z positions due to depth dependent edge effects. The least-square minimization and the maximum likelihood positioning algorithms were developed and both methods improved the spatial resolution at the edges of the detector as compared with the center of gravity method. A mean absolute error (MAE) which is defined as the probability-weighted mean of the absolute value of the positioning error is used to evaluate the spatial resolution. An average MAE spatial resolution of ~1.15 mm was obtained in both y and z directions without rejection of the multiple scattering events. The average MAE spatial resolution was ~0.7 mm in both y and z directions after the multiple scattering events were rejected. The timing resolution of the detector is 575 ps. In the next step, long rectangle detector will be built to reduce edge effects and improve the spatial resolution of the semi-monolithic detector. Thick detector up to 20 mm will be explored and the positioning algorithms will be further optimized.

Data Fusion of Gridded Snow Products Enhanced with Terrain Covariates and a Simple Snow Model

NASA Astrophysics Data System (ADS)

Snauffer, A. M.; Hsieh, W. W.; Cannon, A. J.

2017-12-01

Hydrologic planning requires accurate estimates of regional snow water equivalent (SWE), particularly areas with hydrologic regimes dominated by spring melt. While numerous gridded data products provide such estimates, accurate representations are particularly challenging under conditions of mountainous terrain, heavy forest cover and large snow accumulations, contexts which in many ways define the province of British Columbia (BC), Canada. One promising avenue of improving SWE estimates is a data fusion approach which combines field observations with gridded SWE products and relevant covariates. A base artificial neural network (ANN) was constructed using three of the best performing gridded SWE products over BC (ERA-Interim/Land, MERRA and GLDAS-2) and simple location and time covariates. This base ANN was then enhanced to include terrain covariates (slope, aspect and Terrain Roughness Index, TRI) as well as a simple 1-layer energy balance snow model driven by gridded bias-corrected ANUSPLIN temperature and precipitation values. The ANN enhanced with all aforementioned covariates performed better than the base ANN, but most of the skill improvement was attributable to the snow model with very little contribution from the terrain covariates. The enhanced ANN improved station mean absolute error (MAE) by an average of 53% relative to the composing gridded products over the province. Interannual peak SWE correlation coefficient was found to be 0.78, an improvement of 0.05 to 0.18 over the composing products. This nonlinear approach outperformed a comparable multiple linear regression (MLR) model by 22% in MAE and 0.04 in interannual correlation. The enhanced ANN has also been shown to estimate better than the Variable Infiltration Capacity (VIC) hydrologic model calibrated and run for four BC watersheds, improving MAE by 22% and correlation by 0.05. The performance improvements of the enhanced ANN are statistically significant at the 5% level across the province and in four out of five physiographic regions.

Verification of Pharmacogenetics-Based Warfarin Dosing Algorithms in Han-Chinese Patients Undertaking Mechanic Heart Valve Replacement

PubMed Central

Zhao, Li; Chen, Chunxia; Li, Bei; Dong, Li; Guo, Yingqiang; Xiao, Xijun; Zhang, Eryong; Qin, Li

2014-01-01

Objective To study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement. Methods We searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms. Results A total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88–4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges. Conclusions All of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement. PMID:24728385

Verification of pharmacogenetics-based warfarin dosing algorithms in Han-Chinese patients undertaking mechanic heart valve replacement.

PubMed

Zhao, Li; Chen, Chunxia; Li, Bei; Dong, Li; Guo, Yingqiang; Xiao, Xijun; Zhang, Eryong; Qin, Li

2014-01-01

To study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement. We searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms. A total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88-4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges. All of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement.

Scheimpflug camera combined with placido-disk corneal topography and optical biometry for intraocular lens power calculation.

PubMed

Kirgiz, Ahmet; Atalay, Kurşat; Kaldirim, Havva; Cabuk, Kubra Serefoglu; Akdemir, Mehmet Orcun; Taskapili, Muhittin

2017-08-01

The purpose of this study was to compare the keratometry (K) values obtained by the Scheimpflug camera combined with placido-disk corneal topography (Sirius) and optical biometry (Lenstar) for intraocular lens (IOL) power calculation before the cataract surgery, and to evaluate the accuracy of postoperative refraction. 50 eyes of 40 patients were scheduled to have phacoemulsification with the implantation of a posterior chamber intraocular lens. The IOL power was calculated using the SRK/T formula with Lenstar K and K readings from Sirius. Simulated K (SimK), K at 3-, 5-, and 7-mm zones from Sirius were compared with Lenstar K readings. The accuracy of these parameters was determined by calculating the mean absolute error (MAE). The mean Lenstar K value was 44.05 diopters (D) ±1.93 (SD) and SimK, K at 3-, 5-, and 7-mm zones were 43.85 ± 1.91, 43.88 ± 1.9, 43.84 ± 1.9, 43.66 ± 1.85 D, respectively. There was no statistically significant difference between the K readings (P = 0.901). When Lenstar was used for the corneal power measurements, MAE was 0.42 ± 0.33 D, but when simK of Sirius was used, it was 0.37 ± 0.32 D (the lowest MAE (0.36 ± 0.32 D) was achieved as a result of 5 mm K measurement), but it was not statistically significant (P = 0.892). Of all the K readings of Sirius and Lenstar, Sirius 5-mm zone K readings were the best in predicting a more precise IOL power. The corneal power measurements with the Scheimpflug camera combined with placido-disk corneal topography can be safely used for IOL power calculation.

Prediction of Beck Depression Inventory (BDI-II) Score Using Acoustic Measurements in a Sample of Iium Engineering Students

NASA Astrophysics Data System (ADS)

Fikri Zanil, Muhamad; Nur Wahidah Nik Hashim, Nik; Azam, Huda

2017-11-01

Psychiatrist currently relies on questionnaires and interviews for psychological assessment. These conservative methods often miss true positives and might lead to death, especially in cases where a patient might be experiencing suicidal predisposition but was only diagnosed as major depressive disorder (MDD). With modern technology, an assessment tool might aid psychiatrist with a more accurate diagnosis and thus hope to reduce casualty. This project will explore on the relationship between speech features of spoken audio signal (reading) in Bahasa Malaysia with the Beck Depression Inventory scores. The speech features used in this project were Power Spectral Density (PSD), Mel-frequency Ceptral Coefficients (MFCC), Transition Parameter, formant and pitch. According to analysis, the optimum combination of speech features to predict BDI-II scores include PSD, MFCC and Transition Parameters. The linear regression approach with sequential forward/backward method was used to predict the BDI-II scores using reading speech. The result showed 0.4096 mean absolute error (MAE) for female reading speech. For male, the BDI-II scores successfully predicted 100% less than 1 scores difference with MAE of 0.098437. A prediction system called Depression Severity Evaluator (DSE) was developed. The DSE managed to predict one out of five subjects. Although the prediction rate was low, the system precisely predict the score within the maximum difference of 4.93 for each person. This demonstrates that the scores are not random numbers.

Estimation of Surface Air Temperature Over Central and Eastern Eurasia from MODIS Land Surface Temperature

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.

2011-01-01

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed land surface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.

PubMed

Franklin, Bryony Dean; O'Grady, Kara; Donyai, Parastou; Jacklin, Ann; Barber, Nick

2007-08-01

To assess the impact of a closed-loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. Before-and-after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. Closed-loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. Prescribing errors were identified in 3.8% of 2450 medication orders pre-intervention and 2.0% of 2353 orders afterwards (p<0.001; chi(2) test). MAEs occurred in 7.0% of 1473 non-intravenous doses pre-intervention and 4.3% of 1139 afterwards (p = 0.005; chi(2) test). Patient identity was not checked for 82.6% of 1344 doses pre-intervention and 18.9% of 1291 afterwards (p<0.001; chi(2) test). Medical staff required 15 s to prescribe a regular inpatient drug pre-intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre-intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; chi(2) test). A closed-loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication-related tasks increased.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

NASA Astrophysics Data System (ADS)

Cawiding, Olive R.; Natividad, Gina May R.; Bato, Crisostomo V.; Addawe, Rizavel C.

2017-11-01

The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.

A hybrid approach to estimate the complex motions of clouds in sky images

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

Peng, Zhenzhou; Yu, Dantong; Huang, Dong

Tracking the motion of clouds is essential to forecasting the weather and to predicting the short-term solar energy generation. Existing techniques mainly fall into two categories: variational optical flow, and block matching. In this article, we summarize recent advances in estimating cloud motion using ground-based sky imagers and quantitatively evaluate state-of-the-art approaches. Then we propose a hybrid tracking framework to incorporate the strength of both block matching and optical flow models. To validate the accuracy of the proposed approach, we introduce a series of synthetic images to simulate the cloud movement and deformation, and thereafter comprehensively compare our hybrid approachmore » with several representative tracking algorithms over both simulated and real images collected from various sites/imagers. The results show that our hybrid approach outperforms state-of-the-art models by reducing at least 30% motion estimation errors compared with the ground-truth motions in most of simulated image sequences. Furthermore, our hybrid model demonstrates its superior efficiency in several real cloud image datasets by lowering at least 15% Mean Absolute Error (MAE) between predicted images and ground-truth images.« less

A hybrid approach to estimate the complex motions of clouds in sky images

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2016-09-14

Tracking the motion of clouds is essential to forecasting the weather and to predicting the short-term solar energy generation. Existing techniques mainly fall into two categories: variational optical flow, and block matching. In this article, we summarize recent advances in estimating cloud motion using ground-based sky imagers and quantitatively evaluate state-of-the-art approaches. Then we propose a hybrid tracking framework to incorporate the strength of both block matching and optical flow models. To validate the accuracy of the proposed approach, we introduce a series of synthetic images to simulate the cloud movement and deformation, and thereafter comprehensively compare our hybrid approachmore » with several representative tracking algorithms over both simulated and real images collected from various sites/imagers. The results show that our hybrid approach outperforms state-of-the-art models by reducing at least 30% motion estimation errors compared with the ground-truth motions in most of simulated image sequences. Furthermore, our hybrid model demonstrates its superior efficiency in several real cloud image datasets by lowering at least 15% Mean Absolute Error (MAE) between predicted images and ground-truth images.« less

Comparison of three artificial intelligence techniques for discharge routing

NASA Astrophysics Data System (ADS)

Khatibi, Rahman; Ghorbani, Mohammad Ali; Kashani, Mahsa Hasanpour; Kisi, Ozgur

2011-06-01

SummaryThe inter-comparison of three artificial intelligence (AI) techniques are presented using the results of river flow/stage timeseries, that are otherwise handled by traditional discharge routing techniques. These models comprise Artificial Neural Network (ANN), Adaptive Nero-Fuzzy Inference System (ANFIS) and Genetic Programming (GP), which are for discharge routing of Kizilirmak River, Turkey. The daily mean river discharge data with a period between 1999 and 2003 were used for training and testing the models. The comparison includes both visual and parametric approaches using such statistic as Coefficient of Correlation (CC), Mean Absolute Error (MAE) and Mean Square Relative Error (MSRE), as well as a basic scoring system. Overall, the results indicate that ANN and ANFIS have mixed fortunes in discharge routing, and both have different abilities in capturing and reproducing some of the observed information. However, the performance of GP displays a better edge over the other two modelling approaches in most of the respects. Attention is given to the information contents of recorded timeseries in terms of their peak values and timings, where one performance measure may capture some of the information contents but be ineffective in others. Thus, this makes a case for compiling knowledge base for various modelling techniques.

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.

Prediction of the reference evapotranspiration using a chaotic approach.

PubMed

Wang, Wei-guang; Zou, Shan; Luo, Zhao-hui; Zhang, Wei; Chen, Dan; Kong, Jun

2014-01-01

Evapotranspiration is one of the most important hydrological variables in the context of water resources management. An attempt was made to understand and predict the dynamics of reference evapotranspiration from a nonlinear dynamical perspective in this study. The reference evapotranspiration data was calculated using the FAO Penman-Monteith equation with the observed daily meteorological data for the period 1966-2005 at four meteorological stations (i.e., Baotou, Zhangbei, Kaifeng, and Shaoguan) representing a wide range of climatic conditions of China. The correlation dimension method was employed to investigate the chaotic behavior of the reference evapotranspiration series. The existence of chaos in the reference evapotranspiration series at the four different locations was proved by the finite and low correlation dimension. A local approximation approach was employed to forecast the daily reference evapotranspiration series. Low root mean square error (RSME) and mean absolute error (MAE) (for all locations lower than 0.31 and 0.24, resp.), high correlation coefficient (CC), and modified coefficient of efficiency (for all locations larger than 0.97 and 0.8, resp.) indicate that the predicted reference evapotranspiration agrees well with the observed one. The encouraging results indicate the suitableness of chaotic approach for understanding and predicting the dynamics of the reference evapotranspiration.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2013-11-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2014-06-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

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.

Integrating Satellite and Surface Sensor Networks for Irrigation Management Applications in California

NASA Astrophysics Data System (ADS)

Melton, F. S.; Johnson, L.; Post, K. M.; Guzman, A.; Zaragoza, I.; Spellenberg, R.; Rosevelt, C.; Michaelis, A.; Nemani, R. R.; Cahn, M.; Frame, K.; Temesgen, B.; Eching, S.

2016-12-01

Satellite mapping of evapotranspiration (ET) from irrigated agricultural lands can provide agricultural producers and water managers with information that can be used to optimize agricultural water use, especially in regions with limited water supplies. The timely delivery of information on agricultural crop water requirements has the potential to make irrigation scheduling more practical, convenient, and accurate. We present a system for irrigation scheduling and management support in California and describe lessons learned from the development and implementation of the system. The Satellite Irrigation Management Support (SIMS) framework integrates satellite data with information from agricultural weather networks to map crop canopy development, basal crop coefficients (Kcb), and basal crop evapotranspiration (ETcb) at the scale of individual fields. Information is distributed to agricultural producers and water managers via a web-based irrigation management decision support system and web data services. SIMS also provides an application programming interface (API) that facilitates integration with other irrigation decision support tools, estimation of total crop evapotranspiration (ETc) and calculation of on-farm water use efficiency metrics. Accuracy assessments conducted in commercial fields for more than a dozen crop types to date have shown that SIMS seasonal ETcb estimates are within 10% mean absolute error (MAE) for well-watered crops and within 15% across all crop types studied, and closely track daily ETc and running totals of ETc measured in each field. Use of a soil water balance model to correct for soil evaporation and crop water stress reduces this error to less than 8% MAE across all crop types studied to date relative to field measurements of ETc. Results from irrigation trials conducted by the project for four vegetable crops have also demonstrated the potential for use of ET-based irrigation management strategies to reduce total applied water by 20-40% relative to grower standard practices while maintaining crop yields and quality.

Converting Parkinson-Specific Scores into Health State Utilities to Assess Cost-Utility Analysis.

PubMed

Chen, Gang; Garcia-Gordillo, Miguel A; Collado-Mateo, Daniel; Del Pozo-Cruz, Borja; Adsuar, José C; Cordero-Ferrera, José Manuel; Abellán-Perpiñán, José María; Sánchez-Martínez, Fernando Ignacio

2018-06-07

The aim of this study was to compare the Parkinson's Disease Questionnaire-8 (PDQ-8) with three multi-attribute utility (MAU) instruments (EQ-5D-3L, EQ-5D-5L, and 15D) and to develop mapping algorithms that could be used to transform PDQ-8 scores into MAU scores. A cross-sectional study was conducted. A final sample of 228 evaluable patients was included in the analyses. Sociodemographic and clinical data were also collected. Two EQ-5D questionnaires were scored using Spanish tariffs. Two models and three statistical techniques were used to estimate each model in the direct mapping framework for all three MAU instruments, including the most widely used ordinary least squares (OLS), the robust MM-estimator, and the generalized linear model (GLM). For both EQ-5D-3L and EQ-5D-5L, indirect response mapping based on an ordered logit model was also conducted. Three goodness-of-fit tests were employed to compare the models: the mean absolute error (MAE), the root-mean-square error (RMSE), and the intra-class correlation coefficient (ICC) between the predicted and observed utilities. Health state utility scores ranged from 0.61 (EQ-5D-3L) to 0.74 (15D). The mean PDQ-8 score was 27.51. The correlation between overall PDQ-8 score and each MAU instrument ranged from - 0.729 (EQ-5D-5L) to - 0.752 (EQ-5D-3L). A mapping algorithm based on PDQ-8 items had better performance than using the overall score. For the two EQ-5D questionnaires, in general, the indirect mapping approach had comparable or even better performance than direct mapping based on MAE. Mapping algorithms developed in this study enable the estimation of utility values from the PDQ-8. The indirect mapping equations reported for two EQ-5D questionnaires will further facilitate the calculation of EQ-5D utility scores using other country-specific tariffs.

Assignment of the relative and absolute stereochemistry of two novel epoxides using NMR and DFT-GIAO calculations

NASA Astrophysics Data System (ADS)

Moraes, F. C.; Alvarenga, E. S.; Demuner, A. J.; Viana, V. M.

2018-07-01

Considering the potential biological application of isobenzofuranones, especially as agrochemical defensives, two novel epoxides, (1aR,2R,2aR,5S,5aS,6S,6aS)-5-(hydroxymethyl)hexahydro-2,6-methanooxireno[2,3-f]isobenzofuran-3(1aH)-one (9), and (1aS,2S,2aR,5S,5aS,6R,6aR)-5-(hydroxymethyl)hexahydro-2,6-methanooxireno[2,3-f]isobenzofuran-3(1aH)-one (10), were synthesized from the readily available D-mannitol in six steps. The multiplicities of the hydrogens located at the bridge of the bicycle are distinct for epoxides 9 and 10 due to W coupling, and this feature was employed to confirm the assignment of these nuclei. Besides analyses of the 2D NMR spectra, the assignments of the nuclei at the epoxide ring were also inferred from information obtained by theoretical calculations. The calculated 1H and 13C NMR chemical shifts for eight candidate structures were compared with the experimental chemical shifts of 9 and 10 by measuring the mean absolute errors (MAE) and by the DP4 statistical analysis. The structures and relative configurations of 9, and 10 were determined via NMR spectroscopy assisted with theoretical calculations. As consequence of the enantioselective syntheses starting from a natural polyol, the absolute configurations of the epoxides 9 and 10 were also defined.

Applicability of AgMERRA Forcing Dataset to Fill Gaps in Historical in-situ Meteorological Data

NASA Astrophysics Data System (ADS)

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

2015-12-01

Integrated assessment studies of food production systems use crop models to simulate the effects of climate and socio-economic changes on food security. Climate forcing data is one of those key inputs of crop models. This study evaluated the performance of AgMERRA climate forcing dataset to fill gaps in historical in-situ meteorological data for different climatic regions of Iran. AgMERRA dataset intercompared with in- situ observational dataset for daily maximum and minimum temperature and precipitation during 1980-2010 periods via Root Mean Square error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) for 17 stations in four climatic regions included humid and moderate, cold, dry and arid, hot and humid. Moreover, probability distribution function and cumulative distribution function compared between model and observed data. The results of measures of agreement between AgMERRA data and observed data demonstrated that there are small errors in model data for all stations. Except for stations which are located in cold regions, model data in the other stations illustrated under-prediction for daily maximum temperature and precipitation. However, it was not significant. In addition, probability distribution function and cumulative distribution function showed the same trend for all stations between model and observed data. Therefore, the reliability of AgMERRA dataset is high to fill gaps in historical observations in different climatic regions of Iran as well as it could be applied as a basis for future climate scenarios.

Design and construction of miniature artificial ecosystem based on dynamic response optimization

NASA Astrophysics Data System (ADS)

Hu, Dawei; Liu, Hong; Tong, Ling; Li, Ming; Hu, Enzhu

The miniature artificial ecosystem (MAES) is a combination of man, silkworm, salad and mi-croalgae to partially regenerate O2 , sanitary water and food, simultaneously dispose CO2 and wastes, therefore it have a fundamental life support function. In order to enhance the safety and reliability of MAES and eliminate the influences of internal variations and external dis-turbances, it was necessary to configure MAES as a closed-loop control system, and it could be considered as a prototype for future bioregenerative life support system. However, MAES is a complex system possessing large numbers of parameters, intricate nonlinearities, time-varying factors as well as uncertainties, hence it is difficult to perfectly design and construct a prototype through merely conducting experiments by trial and error method. Our research presented an effective way to resolve preceding problem by use of dynamic response optimiza-tion. Firstly the mathematical model of MAES with first-order nonlinear ordinary differential equations including parameters was developed based on relevant mechanisms and experimental data, secondly simulation model of MAES was derived on the platform of MatLab/Simulink to perform model validation and further digital simulations, thirdly reference trajectories of de-sired dynamic response of system outputs were specified according to prescribed requirements, and finally optimization for initial values, tuned parameter and independent parameters was carried out using the genetic algorithm, the advanced direct search method along with parallel computing methods through computer simulations. The result showed that all parameters and configurations of MAES were determined after a series of computer experiments, and its tran-sient response performances and steady characteristics closely matched the reference curves. Since the prototype is a physical system that represents the mathematical model with reason-able accuracy, so the process of designing and constructing a prototype of MAES is the reverse of mathematical modeling, and must have prerequisite assists from these results of computer simulation.

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.

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

NASA Astrophysics Data System (ADS)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.

2017-06-01

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.

Zeolitic Imidazolate Framework-8 Membrane for H2/CO2 Separation: Experimental and Modeling

NASA Astrophysics Data System (ADS)

Lai, L. S.; Yeong, Y. F.; Lau, K. K.; Azmi, M. S.; Chew, T. L.

2018-03-01

In this work, ZIF-8 membrane synthesized through solvent evaporation secondary seeded growth was tested for single gas permeation and binary gases separation of H2 and CO2. Subsequently, a modified mathematical modeling combining the effects of membrane and support layers was applied to represent the gas transport properties of ZIF-8 membrane. Results showed that, the membrane has exhibited H2/CO2 ideal selectivity of 5.83 and separation factor of 3.28 at 100 kPa and 303 K. Besides, the experimental results were fitted well with the simulated results by demonstrating means absolute error (MAE) values ranged from 1.13 % to 3.88 % for single gas permeation and 10.81 % to 21.22 % for binary gases separation. Based on the simulated data, most of the H2 and CO2 gas molecules have transported through the molecular pores of membrane layer, which was up to 70 %. Thus, the gas transport of the gases is mainly dominated by adsorption and diffusion across the membrane.

Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis.

PubMed

Hamada, Yuki; Stow, Douglas A; Roberts, Dar A; Franklin, Janet; Kyriakidis, Phaedon C

2013-04-01

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches--object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)--for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25 × 25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50 × 50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.

A semi-mechanistic model of dead fine fuel moisture for Temperate and Mediterranean ecosystems

NASA Astrophysics Data System (ADS)

Resco de Dios, Víctor; Fellows, Aaron; Boer, Matthias; Bradstock, Ross; Nolan, Rachel; Goulden, Michel

2014-05-01

Fire is a major disturbance in terrestrial ecosystems globally. It has an enormous economic and social cost, and leads to fatalities in the worst cases. The moisture content of the vegetation (fuel moisture) is one of the main determinants of fire risk. Predicting the moisture content of dead and fine fuel (< 2.5 cm in diameter) is particularly important, as this is often the most important component of the fuel complex for fire propagation. A variety of drought indices, empirical and mechanistic models have been proposed to model fuel moisture. A commonality across these different approaches is that they have been neither validated across large temporal datasets nor validated across broadly different vegetation types. Here, we present the results of a study performed at 6 locations in California, USA (5 sites) and New South Wales, Australia (1 site), where 10-hours fuel moisture content was continuously measured every 30 minutes during one full year at each site. We observed that drought indices did not accurately predict fuel moisture, and that empirical and mechanistic models both needed site-specific calibrations, which hinders their global application as indices of fuel moisture. We developed a novel, single equation and semi-mechanistic model, based on atmospheric vapor-pressure deficit. Across sites and years, mean absolute error (MAE) of predicted fuel moisture was 4.7%. MAE dropped <1% in the critical range of fuel moisture <10%. The high simplicity, accuracy and precision of our model makes it suitable for a wide range of applications: from operational purposes, to global vegetation models.

Improvement of forecast skill for severe weather by merging radar-based extrapolation and storm-scale NWP corrected forecast

NASA Astrophysics Data System (ADS)

Wang, Gaili; Wong, Wai-Kin; Hong, Yang; Liu, Liping; Dong, Jili; Xue, Ming

2015-03-01

The primary objective of this study is to improve the performance of deterministic high resolution rainfall forecasts caused by severe storms by merging an extrapolation radar-based scheme with a storm-scale Numerical Weather Prediction (NWP) model. Effectiveness of Multi-scale Tracking and Forecasting Radar Echoes (MTaRE) model was compared with that of a storm-scale NWP model named Advanced Regional Prediction System (ARPS) for forecasting a violent tornado event that developed over parts of western and much of central Oklahoma on May 24, 2011. Then the bias corrections were performed to improve the forecast accuracy of ARPS forecasts. Finally, the corrected ARPS forecast and radar-based extrapolation were optimally merged by using a hyperbolic tangent weight scheme. The comparison of forecast skill between MTaRE and ARPS in high spatial resolution of 0.01° × 0.01° and high temporal resolution of 5 min showed that MTaRE outperformed ARPS in terms of index of agreement and mean absolute error (MAE). MTaRE had a better Critical Success Index (CSI) for less than 20-min lead times and was comparable to ARPS for 20- to 50-min lead times, while ARPS had a better CSI for more than 50-min lead times. Bias correction significantly improved ARPS forecasts in terms of MAE and index of agreement, although the CSI of corrected ARPS forecasts was similar to that of the uncorrected ARPS forecasts. Moreover, optimally merging results using hyperbolic tangent weight scheme further improved the forecast accuracy and became more stable.

CMIP5 downscaling and its uncertainty in China

NASA Astrophysics Data System (ADS)

Yue, TianXiang; Zhao, Na; Fan, ZeMeng; Li, Jing; Chen, ChuanFa; Lu, YiMin; Wang, ChenLiang; Xu, Bing; Wilson, John

2016-11-01

A comparison between the Coupled Model Intercomparison Project Phase 5 (CMIP5) data and observations at 735 meteorological stations indicated that mean annual temperature (MAT) was underestimated about 1.8 °C while mean annual precipitation (MAP) was overestimated about 263 mm in general across the whole of China. A statistical analysis of China-CMIP5 data demonstrated that MAT exhibits spatial stationarity, while MAP exhibits spatial non-stationarity. MAT and MAP data from the China-CMIP5 dataset were downscaled by combining statistical approaches with a method for high accuracy surface modeling (HASM). A statistical transfer function (STF) of MAT was formulated using minimized residuals output by HASM with an ordinary least squares (OLS) linear equation that used latitude and elevation as independent variables, abbreviated as HASM-OLS. The STF of MAP under a BOX-COX transformation was derived as a combination of minimized residuals output by HASM with a geographically weight regression (GWR) using latitude, longitude, elevation and impact coefficient of aspect as independent variables, abbreviated as HASM-GB. Cross validation, using observational data from the 735 meteorological stations across China for the period 1976 to 2005, indicates that the largest uncertainty occurred on the Tibet plateau with mean absolute errors (MAEs) of MAT and MAP as high as 4.64 °C and 770.51 mm, respectively. The downscaling processes of HASM-OLS and HASM-GB generated MAEs of MAT and MAP that were 67.16% and 77.43% lower, respectively across the whole of China on average, and 88.48% and 97.09% lower for the Tibet plateau.

QSAR modeling of β-lactam binding to human serum proteins

NASA Astrophysics Data System (ADS)

Hall, L. Mark; Hall, Lowell H.; Kier, Lemont B.

2003-02-01

The binding of beta-lactams to human serum proteins was modeled with topological descriptors of molecular structure. Experimental data was the concentration of protein-bound drug expressed as a percent of the total plasma concentration (percent fraction bound, PFB) for 87 penicillins and for 115 β-lactams. The electrotopological state indices (E-State) and the molecular connectivity chi indices were found to be the basis of two satisfactory models. A data set of 74 penicillins from a drug design series was successfully modeled with statistics: r2=0.80, s = 12.1, q2=0.76, spress=13.4. This model was then used to predict protein binding (PFB) for 13 commercial penicillins, resulting in a very good mean absolute error, MAE = 12.7 and correlation coefficient, q2=0.84. A group of 28 cephalosporins were combined with the penicillin data to create a dataset of 115 beta-lactams that was successfully modeled: r2=0.82, s = 12.7, q2=0.78, spress=13.7. A ten-fold 10% leave-group-out (LGO) cross-validation procedure was implemented, leading to very good statistics: MAE = 10.9, spress=14.0, q2 (or r2 press)=0.78. The models indicate a combination of general and specific structure features that are important for estimating protein binding in this class of antibiotics. For the β-lactams, significant factors that increase binding are presence and electron accessibility of aromatic rings, halogens, methylene groups, and =N- atoms. Significant negative influence on binding comes from amine groups and carbonyl oxygen atoms.

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

NASA Astrophysics Data System (ADS)

Rawat, Kishan Singh; Sehgal, Vinay Kumar; Pradhan, Sanatan; Ray, Shibendu S.

2018-03-01

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ o_{RH}), differences of circular vertical and horizontal σ o (σ o_{RV} {-} σ o_{RH}) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height ({RMS}_{height}). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ o. Near surface SM measurements were related to σ o_{RH}, σ o_{RV} {-} σ o_{RH} derived using 5.35 GHz (C-band) image of RISAT-1 and {RMS}_{height}. The roughness component derived in terms of {RMS}_{height} showed a good positive correlation with σ o_{RV} {-} σ o_{RH} (R2 = 0.65). By considering all the major influencing factors (σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}), an SEM was developed where SM (volumetric) predicted values depend on σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}. This SEM showed R2 of 0.87 and adjusted R2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement ({SM}_{Observed}) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash-Sutcliffe efficiency (NSE) = 0.91 ({≈ } 1), index of agreement (d) = 1, coefficient of determination (R2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences ({S}d2) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

Legal, ethical and practical considerations in research involving nurses with dyslexia.

PubMed

Gillin, Nicola

2015-09-01

To discuss the legal, ethical and practical considerations in UK studies involving nurses with dyslexia and medication administration errors (MAEs). Nurses with dyslexia are a vulnerable population as they are susceptible to misrepresentation in research, especially that which involves a sensitive topic such as MAEs. Nurses with dyslexia may be particularly vulnerable to research that could exploit, implicate or attribute unsafe practice to them and their disability. Special consideration should be exercised when researching this population. Despite the potential for legal, ethical and practical issues, MAEs and nurses with dyslexia are under-researched areas and warrant further research. Benefits can be gained, not only by participants but also those with a vested interest in how best to support dyslexic nurses in clinical practice. Through effective design, risks can be identified and minimised, and the research made viable, ethically sound and ultimately beneficial to all those involved.

The impact of a closed‐loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before‐and‐after study

PubMed Central

Franklin, Bryony Dean; O'Grady, Kara; Donyai, Parastou; Jacklin, Ann; Barber, Nick

2007-01-01

Objectives To assess the impact of a closed‐loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. Design, setting and participants Before‐and‐after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. Intervention Closed‐loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. Main outcome measures Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. Results Prescribing errors were identified in 3.8% of 2450 medication orders pre‐intervention and 2.0% of 2353 orders afterwards (p<0.001; χ2 test). MAEs occurred in 7.0% of 1473 non‐intravenous doses pre‐intervention and 4.3% of 1139 afterwards (p = 0.005; χ2 test). Patient identity was not checked for 82.6% of 1344 doses pre‐intervention and 18.9% of 1291 afterwards (p<0.001; χ2 test). Medical staff required 15 s to prescribe a regular inpatient drug pre‐intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre‐intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; χ2 test). Conclusions A closed‐loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication‐related tasks increased. PMID:17693676

Critical Test of Some Computational Chemistry Methods for Prediction of Gas-Phase Acidities and Basicities.

PubMed

Toomsalu, Eve; Koppel, Ilmar A; Burk, Peeter

2013-09-10

Gas-phase acidities and basicities were calculated for 64 neutral bases (covering the scale from 139.9 kcal/mol to 251.9 kcal/mol) and 53 neutral acids (covering the scale from 299.5 kcal/mol to 411.7 kcal/mol). The following methods were used: AM1, PM3, PM6, PDDG, G2, G2MP2, G3, G3MP2, G4, G4MP2, CBS-QB3, B1B95, B2PLYP, B2PLYPD, B3LYP, B3PW91, B97D, B98, BLYP, BMK, BP86, CAM-B3LYP, HSEh1PBE, M06, M062X, M06HF, M06L, mPW2PLYP, mPW2PLYPD, O3LYP, OLYP, PBE1PBE, PBEPBE, tHCTHhyb, TPSSh, VSXC, X3LYP. The addition of the Grimmes empirical dispersion correction (D) to B2PLYP and mPW2PLYP was evaluated, and it was found that adding this correction gave more-accurate results when considering acidities. Calculations with B3LYP, B97D, BLYP, B2PLYPD, and PBE1PBE methods were carried out with five basis sets (6-311G**, 6-311+G**, TZVP, cc-pVTZ, and aug-cc-pVTZ) to evaluate the effect of basis sets on the accuracy of calculations. It was found that the best basis sets when considering accuracy of results and needed time were 6-311+G** and TZVP. Among semiempirical methods AM1 had the best ability to reproduce experimental acidities and basicities (the mean absolute error (mae) was 7.3 kcal/mol). Among DFT methods the best method considering accuracy, robustness, and computation time was PBE1PBE/6-311+G** (mae = 2.7 kcal/mol). Four Gaussian-type methods (G2, G2MP2, G4, and G4MP2) gave similar results to each other (mae = 2.3 kcal/mol). Gaussian-type methods are quite accurate, but their downside is the relatively long computational time.

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.

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

NASA Astrophysics Data System (ADS)

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2015-01-01

The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

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.

An application of seasonal ARIMA models on group commodities to forecast Philippine merchandise exports performance

NASA Astrophysics Data System (ADS)

Natividad, Gina May R.; Cawiding, Olive R.; Addawe, Rizavel C.

2017-11-01

The increase in the merchandise exports of the country offers information about the Philippines' trading role within the global economy. Merchandise exports statistics are used to monitor the country's overall production that is consumed overseas. This paper investigates the comparison between two models obtained by a) clustering the commodity groups into two based on its proportional contribution to the total exports, and b) treating only the total exports. Different seasonal autoregressive integrated moving average (SARIMA) models were then developed for the clustered commodities and for the total exports based on the monthly merchandise exports of the Philippines from 2011 to 2016. The data set used in this study was retrieved from the Philippine Statistics Authority (PSA) which is the central statistical authority in the country responsible for primary data collection. A test for significance of the difference between means at 0.05 level of significance was then performed on the forecasts produced. The result indicates that there is a significant difference between the mean of the forecasts of the two models. Moreover, upon a comparison of the root mean square error (RMSE) and mean absolute error (MAE) of the models, it was found that the models used for the clustered groups outperform the model for the total exports.

Prediction of the Reference Evapotranspiration Using a Chaotic Approach

PubMed Central

Wang, Wei-guang; Zou, Shan; Luo, Zhao-hui; Zhang, Wei; Kong, Jun

2014-01-01

Evapotranspiration is one of the most important hydrological variables in the context of water resources management. An attempt was made to understand and predict the dynamics of reference evapotranspiration from a nonlinear dynamical perspective in this study. The reference evapotranspiration data was calculated using the FAO Penman-Monteith equation with the observed daily meteorological data for the period 1966–2005 at four meteorological stations (i.e., Baotou, Zhangbei, Kaifeng, and Shaoguan) representing a wide range of climatic conditions of China. The correlation dimension method was employed to investigate the chaotic behavior of the reference evapotranspiration series. The existence of chaos in the reference evapotranspiration series at the four different locations was proved by the finite and low correlation dimension. A local approximation approach was employed to forecast the daily reference evapotranspiration series. Low root mean square error (RSME) and mean absolute error (MAE) (for all locations lower than 0.31 and 0.24, resp.), high correlation coefficient (CC), and modified coefficient of efficiency (for all locations larger than 0.97 and 0.8, resp.) indicate that the predicted reference evapotranspiration agrees well with the observed one. The encouraging results indicate the suitableness of chaotic approach for understanding and predicting the dynamics of the reference evapotranspiration. PMID:25133221

Validity of an Integrative Method for Processing Physical Activity Data.

PubMed

Ellingson, Laura D; Schwabacher, Isaac J; Kim, Youngwon; Welk, Gregory J; Cook, Dane B

2016-08-01

Accurate assessments of both physical activity and sedentary behaviors are crucial to understand the health consequences of movement patterns and to track changes over time and in response to interventions. The study evaluates the validity of an integrative, machine learning method for processing activity monitor data in relation to a portable metabolic analyzer (Oxycon mobile [OM]) and direct observation (DO). Forty-nine adults (age 18-40 yr) each completed 5-min bouts of 15 activities ranging from sedentary to vigorous intensity in a laboratory setting while wearing ActiGraph (AG) on the hip, activPAL on the thigh, and OM. Estimates of energy expenditure (EE) and categorization of activity intensity were obtained from the AG processed with Lyden's sojourn (SOJ) method and from our new sojourns including posture (SIP) method, which integrates output from the AG and activPAL. Classification accuracy and estimates of EE were then compared with criterion measures (OM and DO) using confusion matrices and comparisons of the mean absolute error of log-transformed data (MAE ln Q). The SIP method had a higher overall classification agreement (79%, 95% CI = 75%-82%) than the SOJ (56%, 95% CI = 52%-59%) based on DO. Compared with OM, estimates of EE from SIP had lower mean absolute error of log-transformed data than SOJ for light-intensity (0.21 vs 0.27), moderate-intensity (0.33 vs 0.42), and vigorous-intensity (0.16 vs 0.35) activities. The SIP method was superior to SOJ for distinguishing between sedentary and light activities as well as estimating EE at higher intensities. Thus, SIP is recommended for research in which accuracy of measurement across the full range of activity intensities is of interest.

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.

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.

An improved model for soil surface temperature from air temperature in permafrost regions of Qinghai-Xizang (Tibet) Plateau of China

NASA Astrophysics Data System (ADS)

Hu, Guojie; Wu, Xiaodong; Zhao, Lin; Li, Ren; Wu, Tonghua; Xie, Changwei; Pang, Qiangqiang; Cheng, Guodong

2017-08-01

Soil temperature plays a key role in hydro-thermal processes in environments and is a critical variable linking surface structure to soil processes. There is a need for more accurate temperature simulation models, particularly in Qinghai-Xizang (Tibet) Plateau (QXP). In this study, a model was developed for the simulation of hourly soil surface temperatures with air temperatures. The model incorporated the thermal properties of the soil, vegetation cover, solar radiation, and water flux density and utilized field data collected from Qinghai-Xizang (Tibet) Plateau (QXP). The model was used to simulate the thermal regime at soil depths of 5 cm, 10 cm and 20 cm and results were compared with those from previous models and with experimental measurements of ground temperature at two different locations. The analysis showed that the newly developed model provided better estimates of observed field temperatures, with an average mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) of 1.17 °C, 1.30 °C and 13.84 %, 0.41 °C, 0.49 °C and 5.45 %, 0.13 °C, 0.18 °C and 2.23 % at 5 cm, 10 cm and 20 cm depths, respectively. These findings provide a useful reference for simulating soil temperature and may be incorporated into other ecosystem models requiring soil temperature as an input variable for modeling permafrost changes under global warming.

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

NASA Astrophysics Data System (ADS)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II).

PubMed

Zhao, Guo; Wang, Hui; Liu, Gang; Wang, Zhiqiang

2016-09-21

An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results.

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.

The radial speed-expansion speed relation for Earth-directed CMEs

NASA Astrophysics Data System (ADS)

Mäkelä, P.; Gopalswamy, N.; Yashiro, S.

2016-05-01

Earth-directed coronal mass ejections (CMEs) are the main drivers of major geomagnetic storms. Therefore, a good estimate of the disturbance arrival time at Earth is required for space weather predictions. The STEREO and SOHO spacecraft were viewing the Sun in near quadrature during January 2010 to September 2012, providing a unique opportunity to study the radial speed (Vrad)-expansion speed (Vexp) relationship of Earth-directed CMEs. This relationship is useful in estimating the Vrad of Earth-directed CMEs, when they are observed from Earth view only. We selected 19 Earth-directed CMEs observed by the Large Angle and Spectrometric Coronagraph (LASCO)/C3 coronagraph on SOHO and the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI)/COR2 coronagraph on STEREO during January 2010 to September 2012. We found that of the three tested geometric CME models the full ice-cream cone model of the CME describes best the Vrad-Vexp relationship, as suggested by earlier investigations. We also tested the prediction accuracy of the empirical shock arrival (ESA) model proposed by Gopalswamy et al. (2005a), while estimating the CME propagation speeds from the CME expansion speeds. If we use STEREO observations to estimate the CME width required to calculate the Vrad from the Vexp measurements, the mean absolute error (MAE) of the shock arrival times of the ESA model is 8.4 h. If the LASCO measurements are used to estimate the CME width, the MAE still remains below 17 h. Therefore, by using the simple Vrad-Vexp relationship to estimate the Vrad of the Earth-directed CMEs, the ESA model is able to predict the shock arrival times with accuracy comparable to most other more complex models.

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Dispersion-correcting potentials can significantly improve the bond dissociation enthalpies and noncovalent binding energies predicted by density-functional theory

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

DiLabio, Gino A., E-mail: Gino.DiLabio@nrc.ca; Department of Chemistry, University of British Columbia, Okanagan, 3333 University Way, Kelowna, British Columbia V1V 1V7; Koleini, Mohammad

2014-05-14

Dispersion-correcting potentials (DCPs) are atom-centered Gaussian functions that are applied in a manner that is similar to effective core potentials. Previous work on DCPs has focussed on their use as a simple means of improving the ability of conventional density-functional theory methods to predict the binding energies of noncovalently bonded molecular dimers. We show in this work that DCPs developed for use with the LC-ωPBE functional along with 6-31+G(2d,2p) basis sets are capable of simultaneously improving predicted noncovalent binding energies of van der Waals dimer complexes and covalent bond dissociation enthalpies in molecules. Specifically, the DCPs developed herein for themore » C, H, N, and O atoms provide binding energies for a set of 66 noncovalently bonded molecular dimers (the “S66” set) with a mean absolute error (MAE) of 0.21 kcal/mol, which represents an improvement of more than a factor of 10 over unadorned LC-ωPBE/6-31+G(2d,2p) and almost a factor of two improvement over LC-ωPBE/6-31+G(2d,2p) used in conjunction with the “D3” pairwise dispersion energy corrections. In addition, the DCPs reduce the MAE of calculated X-H and X-Y (X,Y = C, H, N, O) bond dissociation enthalpies for a set of 40 species from 3.2 kcal/mol obtained with unadorned LC-ωPBE/6-31+G(2d,2p) to 1.6 kcal/mol. Our findings demonstrate that broad improvements to the performance of DFT methods may be achievable through the use of DCPs.« less

Comparison of the Performance of the Warfarin Pharmacogenetics Algorithms in Patients with Surgery of Heart Valve Replacement and Heart Valvuloplasty.

PubMed

Xu, Hang; Su, Shi; Tang, Wuji; Wei, Meng; Wang, Tao; Wang, Dongjin; Ge, Weihong

2015-09-01

A large number of warfarin pharmacogenetics algorithms have been published. Our research was aimed to evaluate the performance of the selected pharmacogenetic algorithms in patients with surgery of heart valve replacement and heart valvuloplasty during the phase of initial and stable anticoagulation treatment. 10 pharmacogenetic algorithms were selected by searching PubMed. We compared the performance of the selected algorithms in a cohort of 193 patients during the phase of initial and stable anticoagulation therapy. Predicted dose was compared to therapeutic dose by using a predicted dose percentage that falls within 20% threshold of the actual dose (percentage within 20%) and mean absolute error (MAE). The average warfarin dose for patients was 3.05±1.23mg/day for initial treatment and 3.45±1.18mg/day for stable treatment. The percentages of the predicted dose within 20% of the therapeutic dose were 44.0±8.8% and 44.6±9.7% for the initial and stable phases, respectively. The MAEs of the selected algorithms were 0.85±0.18mg/day and 0.93±0.19mg/day, respectively. All algorithms had better performance in the ideal group than in the low dose and high dose groups. The only exception is the Wadelius et al. algorithm, which had better performance in the high dose group. The algorithms had similar performance except for the Wadelius et al. and Miao et al. algorithms, which had poor accuracy in our study cohort. The Gage et al. algorithm had better performance in both phases of initial and stable treatment. Algorithms had relatively higher accuracy in the >50years group of patients on the stable phase. Copyright © 2015 Elsevier Ltd. All rights reserved.

Biometry and intraocular lens power calculation results with a new optical biometry device: comparison with the gold standard.

PubMed

Kaswin, Godefroy; Rousseau, Antoine; Mgarrech, Mohamed; Barreau, Emmanuel; Labetoulle, Marc

2014-04-01

To evaluate the agreement in axial length (AL), keratometry (K), anterior chamber depth (ACD) measurements; intraocular lens (IOL) power calculations; and predictability using a new partial coherence interferometry (PCI) optical biometer (AL-Scan) and a reference (gold standard) PCI optical biometer (IOLMaster 500). Service d'Ophtalmologie, Hopital Bicêtre, APHP Université, Paris, France. Evaluation of a diagnostic device. One eye of consecutive patients scheduled for cataract surgery was measured. Biometry was performed with the new biometer and the reference biometer. Comparisons were performed for AL, average K at 2.4 mm, ACD, IOL power calculations with the Haigis and SRK/T formulas, and postoperative predictability of the devices. A P value less than 0.05 was statistically significant. The study enrolled 50 patients (mean age 72.6 years±4.2 SEM). There was a good correlation between biometers for AL, K, and ACD measurements (r=0.999, r=0.933, and r=0.701, respectively) and between IOL power calculation with the Haigis formula (r=0.972) and the SRK/T formula (r=0.981). The mean absolute error (MAE) in IOL power prediction was 0.42±0.08 diopter (D) with the new biometer and 0.44±0.08 D with the reference biometer. The MAE was 0.20 D with the Haigis formula and 0.19 with the SRK/T formula (P=.36). The new PCI biometer provided valid measurements compared with the current gold standard, indicating that the new device can be used for IOL power calculations for routine cataract surgery. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2014 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Assessing the accuracy of microwave radiometers and radio acoustic sounding systems for wind energy applications

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

Bianco, Laura; Friedrich, Katja; Wilczak, James M.

To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models.The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO's 300 m tower equipped at six levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and six temperature ( T) andmore » relative humidity (RH) sensors; and approximately 60 radiosonde launches.In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers (MWRs) as well as virtual temperature ( T v) measured by co-located wind profiling radars equipped with radio acoustic sounding systems (RASSs). Results indicate a mean absolute error (MAE) in the temperature retrieved by the microwave radiometers below 1.5 K in the lowest 5?km of the atmosphere and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 K in the layer of the atmosphere covered by these measurements (up to approximately 1.6-2 km). We also investigated the benefit of the vertical velocity correction applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data.Water vapor density (WVD) profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding and finding a mean absolute error below 0.5 g m -3 in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. Furthermore, an encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.« less

Assessing the accuracy of microwave radiometers and radio acoustic sounding systems for wind energy applications

DOE PAGES

Bianco, Laura; Friedrich, Katja; Wilczak, James M.; ...

2017-05-09

To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models.The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO's 300 m tower equipped at six levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and six temperature ( T) andmore » relative humidity (RH) sensors; and approximately 60 radiosonde launches.In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers (MWRs) as well as virtual temperature ( T v) measured by co-located wind profiling radars equipped with radio acoustic sounding systems (RASSs). Results indicate a mean absolute error (MAE) in the temperature retrieved by the microwave radiometers below 1.5 K in the lowest 5?km of the atmosphere and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 K in the layer of the atmosphere covered by these measurements (up to approximately 1.6-2 km). We also investigated the benefit of the vertical velocity correction applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data.Water vapor density (WVD) profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding and finding a mean absolute error below 0.5 g m -3 in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. Furthermore, an encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.« less

Assessing the accuracy of microwave radiometers and radio acoustic sounding systems for wind energy applications

NASA Astrophysics Data System (ADS)

Bianco, Laura; Friedrich, Katja; Wilczak, James M.; Hazen, Duane; Wolfe, Daniel; Delgado, Ruben; Oncley, Steven P.; Lundquist, Julie K.

2017-05-01

To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models.The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO's 300 m tower equipped at six levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and six temperature (T) and relative humidity (RH) sensors; and approximately 60 radiosonde launches.In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers (MWRs) as well as virtual temperature (Tv) measured by co-located wind profiling radars equipped with radio acoustic sounding systems (RASSs). Results indicate a mean absolute error (MAE) in the temperature retrieved by the microwave radiometers below 1.5 K in the lowest 5 km of the atmosphere and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 K in the layer of the atmosphere covered by these measurements (up to approximately 1.6-2 km). We also investigated the benefit of the vertical velocity correction applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data.Water vapor density (WVD) profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding and finding a mean absolute error below 0.5 g m-3 in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. An encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.

Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning

NASA Astrophysics Data System (ADS)

Burgos, Ninon; Guerreiro, Filipa; McClelland, Jamie; Presles, Benoît; Modat, Marc; Nill, Simeon; Dearnaley, David; deSouza, Nandita; Oelfke, Uwe; Knopf, Antje-Christin; Ourselin, Sébastien; Cardoso, M. Jorge

2017-06-01

To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average 45.7+/- 4.6 HU and the ME -1.6+/- 7.7 HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of -0.14 % in the PTV for {{D}98 % } , and between -0.14 % and 0.05% in the PTV, bladder, rectum and femur heads for D mean and {{D}2 % } . Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP.

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.

Improved estimation of sediment source contributions by concentration-dependent Bayesian isotopic mixing model

NASA Astrophysics Data System (ADS)

Ram Upadhayay, Hari; Bodé, Samuel; Griepentrog, Marco; Bajracharya, Roshan Man; Blake, Will; Cornelis, Wim; Boeckx, Pascal

2017-04-01

The implementation of compound-specific stable isotope (CSSI) analyses of biotracers (e.g. fatty acids, FAs) as constraints on sediment-source contributions has become increasingly relevant to understand the origin of sediments in catchments. The CSSI fingerprinting of sediment utilizes CSSI signature of biotracer as input in an isotopic mixing model (IMM) to apportion source soil contributions. So far source studies relied on the linear mixing assumptions of CSSI signature of sources to the sediment without accounting for potential effects of source biotracer concentration. Here we evaluated the effect of FAs concentration in sources on the accuracy of source contribution estimations in artificial soil mixture of three well-separated land use sources. Soil samples from land use sources were mixed to create three groups of artificial mixture with known source contributions. Sources and artificial mixture were analysed for δ13C of FAs using gas chromatography-combustion-isotope ratio mass spectrometry. The source contributions to the mixture were estimated using with and without concentration-dependent MixSIAR, a Bayesian isotopic mixing model. The concentration-dependent MixSIAR provided the closest estimates to the known artificial mixture source contributions (mean absolute error, MAE = 10.9%, and standard error, SE = 1.4%). In contrast, the concentration-independent MixSIAR with post mixing correction of tracer proportions based on aggregated concentration of FAs of sources biased the source contributions (MAE = 22.0%, SE = 3.4%). This study highlights the importance of accounting the potential effect of a source FA concentration for isotopic mixing in sediments that adds realisms to mixing model and allows more accurate estimates of contributions of sources to the mixture. The potential influence of FA concentration on CSSI signature of sediments is an important underlying factor that determines whether the isotopic signature of a given source is observable even after equilibrium. Therefore inclusion of FA concentrations of the sources in the IMM formulation is standard procedure for accurate estimation of source contributions. The post model correction approach that dominates the CSSI fingerprinting causes bias, especially if the FAs concentration of sources differs substantially.

Assessment of Orbital-Optimized MP2.5 for Thermochemistry and Kinetics: Dramatic Failures of Standard Perturbation Theory Approaches for Aromatic Bond Dissociation Energies and Barrier Heights of Radical Reactions.

PubMed

Soydaş, Emine; Bozkaya, Uğur

2015-04-14

An assessment of orbital-optimized MP2.5 (OMP2.5) [ Bozkaya, U.; Sherrill, C. D. J. Chem. Phys. 2014, 141, 204105 ] for thermochemistry and kinetics is presented. The OMP2.5 method is applied to closed- and open-shell reaction energies, barrier heights, and aromatic bond dissociation energies. The performance of OMP2.5 is compared with that of the MP2, OMP2, MP2.5, MP3, OMP3, CCSD, and CCSD(T) methods. For most of the test sets, the OMP2.5 method performs better than MP2.5 and CCSD, and provides accurate results. For barrier heights of radical reactions and aromatic bond dissociation energies OMP2.5-MP2.5, OMP2-MP2, and OMP3-MP3 differences become obvious. Especially, for aromatic bond dissociation energies, standard perturbation theory (MP) approaches dramatically fail, providing mean absolute errors (MAEs) of 22.5 (MP2), 17.7 (MP2.5), and 12.8 (MP3) kcal mol(-1), while the MAE values of the orbital-optimized counterparts are 2.7, 2.4, and 2.4 kcal mol(-1), respectively. Hence, there are 5-8-folds reductions in errors when optimized orbitals are employed. Our results demonstrate that standard MP approaches dramatically fail when the reference wave function suffers from the spin-contamination problem. On the other hand, the OMP2.5 method can reduce spin-contamination in the unrestricted Hartree-Fock (UHF) initial guess orbitals. For overall evaluation, we conclude that the OMP2.5 method is very helpful not only for challenging open-shell systems and transition-states but also for closed-shell molecules. Hence, one may prefer OMP2.5 over MP2.5 and CCSD as an O(N(6)) method, where N is the number of basis functions, for thermochemistry and kinetics. The cost of the OMP2.5 method is comparable with that of CCSD for energy computations. However, for analytic gradient computations, the OMP2.5 method is only half as expensive as CCSD.

[Modeling evapotranspiration of greenhouse tomato under different water conditions based on the dual crop coefficient method].

PubMed

Gong, Xue Wen; Liu, Hao; Sun, Jing Sheng; Ma, Xiao Jian; Wang, Wan Ning; Cui, Yong Sheng

2017-04-18

An experiment was conducted to investigate soil evaporation (E), crop transpiration (T), evapotranspiration (ET) and the ratio of evaporation to evapotranspiration (E/ET) of drip-irrigated tomato, which was planted in a typical solar greenhouse in the North China, under different water conditions [irrigation amount was determined based on accumulated pan evaporation (E p ) of 20 cm pan evaporation, and two treatments were designed with full irrigation (0.9E p ) and deficit irrigation (0.5E p )] at different growth stages in 2015 and 2016 at Xinxiang Comprehensive Experimental Station, Chinese Academy of Agricultural Sciences. Effects of deficit irrigation on crop coefficient (K c ) and variation of water stress coefficient (K s ) throughout the growing season were also discussed. E, T and ET of tomato were calculated with a dual crop coefficient approach, and compared with the measured data. Results indicated that E in the full irrigation was 21.5% and 20.4% higher than that in the deficit irrigation in 2015 and 2016, respectively, accounting for 24.0% and 25.0% of ET in the whole growing season. The maximum E/ET was measured in the initial stage of tomato, while the minimum obtained in the middle stage. The K c the full irrigation was 0.45, 0.89, 1.06 and 0.93 in the initial, development, middle, and late stage of tomato, and 0.45, 0.89, 0.87 and 0.41 the deficit irrigation. The K s the deficit irrigation was 0.98, 0.93, 0.78 and 0.39 in the initial, development, middle, and late stage, respectively. The dual crop coefficient method could accurately estimate ET of greenhouse tomato under different water conditions in 2015 and 2016 seasons with the mean absolute error (MAE) of 0.36-0.48 mm·d -1 , root mean square error (RMSE) of 0.44-0.65 mm·d -1 . The method also estimated E and T accurately with MAE of 0.15-0.19 and 0.26-0.56 mm·d -1 , and with RMSE of 0.20-0.24 and 0.33-0.72 mm·d -1 , respectively.

Spatiotemporal exposure modeling of ambient erythemal ultraviolet radiation.

PubMed

VoPham, Trang; Hart, Jaime E; Bertrand, Kimberly A; Sun, Zhibin; Tamimi, Rulla M; Laden, Francine

2016-11-24

Ultraviolet B (UV-B) radiation plays a multifaceted role in human health, inducing DNA damage and representing the primary source of vitamin D for most humans; however, current U.S. UV exposure models are limited in spatial, temporal, and/or spectral resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model by downscaling from an area- to point-level spatial resolution using fine-scale ancillary data. A stratified ATP residual kriging approach was used to predict average July noon-time erythemal UV (UV Ery ) (mW/m 2 ) biennially from 1998 to 2012 by downscaling National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) gridded remote sensing images to a 1 km spatial resolution. Ancillary data were incorporated in random intercept linear mixed-effects regression models. Modeling was performed separately within nine U.S. regions to satisfy stationarity and account for locally varying associations between UV Ery and predictors. Cross-validation was used to compare ATP residual kriging models and NASA grids to UV-B Monitoring and Research Program (UVMRP) measurements (gold standard). Predictors included in the final regional models included surface albedo, aerosol optical depth (AOD), cloud cover, dew point, elevation, latitude, ozone, surface incoming shortwave flux, sulfur dioxide (SO 2 ), year, and interactions between year and surface albedo, AOD, cloud cover, dew point, elevation, latitude, and SO 2 . ATP residual kriging models more accurately estimated UV Ery at UVMRP monitoring stations on average compared to NASA grids across the contiguous U.S. (average mean absolute error [MAE] for ATP, NASA: 15.8, 20.3; average root mean square error [RMSE]: 21.3, 25.5). ATP residual kriging was associated with positive percent relative improvements in MAE (0.6-31.5%) and RMSE (3.6-29.4%) across all regions compared to NASA grids. ATP residual kriging incorporating fine-scale spatial predictors can provide more accurate, high-resolution UV Ery estimates compared to using NASA grids and can be used in epidemiologic studies examining the health effects of ambient UV.

2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

PubMed Central

Zhao, Manman; Zheng, Linfeng; Qiu, Chun

2017-01-01

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2 = 0.565 (cross-validated correlation coefficient) and r2 = 0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR. PMID:28630865

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

NASA Astrophysics Data System (ADS)

Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

2016-12-01

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

Modelling of PM10 concentration for industrialized area in Malaysia: A case study in Shah Alam

NASA Astrophysics Data System (ADS)

N, Norazian Mohamed; Abdullah, M. M. A.; Tan, Cheng-yau; Ramli, N. A.; Yahaya, A. S.; Fitri, N. F. M. Y.

In Malaysia, the predominant air pollutants are suspended particulate matter (SPM) and nitrogen dioxide (NO2). This research is on PM10 as they may trigger harm to human health as well as environment. Six distributions, namely Weibull, log-normal, gamma, Rayleigh, Gumbel and Frechet were chosen to model the PM10 observations at the chosen industrial area i.e. Shah Alam. One-year period hourly average data for 2006 and 2007 were used for this research. For parameters estimation, method of maximum likelihood estimation (MLE) was selected. Four performance indicators that are mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2) and prediction accuracy (PA), were applied to determine the goodness-of-fit criteria of the distributions. The best distribution that fits with the PM10 observations in Shah Alamwas found to be log-normal distribution. The probabilities of the exceedences concentration were calculated and the return period for the coming year was predicted from the cumulative density function (cdf) obtained from the best-fit distributions. For the 2006 data, Shah Alam was predicted to exceed 150 μg/m3 for 5.9 days in 2007 with a return period of one occurrence per 62 days. For 2007, the studied area does not exceed the MAAQG of 150 μg/m3

Monitoring of chlorophyll-a and sea surface silicate concentrations in the south part of Cheju island in the East China sea using MODIS data

NASA Astrophysics Data System (ADS)

Zhang, Yuanzhi; Huang, Zhaojun; Fu, Dongyang; Tsou, Jin Yeu; Jiang, Tingchen; Liang, X. San; Lu, Xia

2018-05-01

Continually supplied with nutrients, phytoplankton maintains high productivity under ideal illumination and temperature conditions. Data in the south part of Cheju Island in the East China Sea (ECS), which has experienced a spring bloom since the 2000s, were acquired during a research cruise in the spring of 2007. Compared with in-situ measurements, MODIS chlorophyll-a measurements showed high stability in this area. Excluding some invalid stations data, the relationships between nutrients and chlorophyll-a concentrations in the study area were examined and compared with the results in 2015. A high positive correlation between silicate and chlorophyll-a concentration was identified, and a regression relationship was proposed. MODIS chlorophyll-a measurements and sea surface temperature were utilized to determine surface silicate distribution. The silicate concentration retrieved from MODIS exhibited good agreement with in-situ measurements with R2 of 0.803, root mean square error (RMSE) of 0.326 μmol/L (8.23%), and mean absolute error (MAE) of 0.925 μmol/L (23.38%). The study provides a new solution to identify nutrient distributions using satellite data such as MODIS for water bodies, but the method still needs to be refined to determine the relationship of chlorophyll-a and nutrients during other seasons to monitor water quality in this and other areas.

On the interpolation of volumetric water content in research catchments

NASA Astrophysics Data System (ADS)

Dlamini, Phesheya; Chaplot, Vincent

Digital Soil Mapping (DSM) is widely used in the environmental sciences because of its accuracy and efficiency in producing soil maps compared to the traditional soil mapping. Numerous studies have investigated how the sampling density and the interpolation process of data points affect the prediction quality. While, the interpolation process is straight forward for primary attributes such as soil gravimetric water content (θg) and soil bulk density (ρb), the DSM of volumetric water content (θv), the product of θg by ρb, may either involve direct interpolations of θv (approach 1) or independent interpolation of ρb and θg data points and subsequent multiplication of ρb and θg maps (approach 2). The main objective of this study was to compare the accuracy of these two mapping approaches for θv. A 23 ha grassland catchment in KwaZulu-Natal, South Africa was selected for this study. A total of 317 data points were randomly selected and sampled during the dry season in the topsoil (0-0.05 m) for θg by ρb estimation. Data points were interpolated following approaches 1 and 2, and using inverse distance weighting with 3 or 12 neighboring points (IDW3; IDW12), regular spline with tension (RST) and ordinary kriging (OK). Based on an independent validation set of 70 data points, OK was the best interpolator for ρb (mean absolute error, MAE of 0.081 g cm-3), while θg was best estimated using IDW12 (MAE = 1.697%) and θv by IDW3 (MAE = 1.814%). It was found that approach 1 underestimated θv. Approach 2 tended to overestimate θv, but reduced the prediction bias by an average of 37% and only improved the prediction accuracy by 1.3% compared to approach 1. Such a great benefit of approach 2 (i.e., the subsequent multiplication of interpolated maps of primary variables) was unexpected considering that a higher sampling density (∼14 data point ha-1 in the present study) tends to minimize the differences between interpolations techniques and approaches. In the context of much lower sampling densities, as generally encountered in environmental studies, one can thus expect approach 2 to yield significantly greater accuracy than approach 1. This approach 2 seems promising and can be further tested for DSM of other secondary variables.

Temperature Variability Associated with the Middle Atmosphere Electrodynamics (MAE-1) Campaign

NASA Technical Reports Server (NTRS)

Schmidlin, F. J.

1999-01-01

Meteorological rockets launched during the Middle Atmosphere Electrodynamics (MAE-1) Campaign in October 1980 from Andoya Rocket Range (ARR), Norway, exhibited large and unexpected temperature variability. Temperatures were found to vary as much as 20 C within a few hours and demonstrated a similar type of variability from one day to the next. Following examination of the reduced rocketsonde profiles the question was raised whether the observed variability was due to natural atmospheric variability or instrument malfunction. Small-scale variability, as observed, may result from one or multiple sources, e.g., intense storms upstream from the observing site, orography such as mountain waves off of the Greenland Plateau, convective activity, gravity waves, etc. Arranging the observations spaced over time showed that the perturbations moved downward. Prior to MAE-1 very few small rocketsonde measurements had been launched from ARR, thus the quality of the initial measurements in early October caused concern when the large variability was noted. We discuss the errors of the rocketsonde measurements, graphically review the nature of the variability observed, compare the data with other measurements, and postulate a possible cause for the variability.

Effect of single-particle magnetostriction on the shear modulus of compliant magnetoactive elastomers.

PubMed

Kalita, Viktor M; Snarskii, Andrei A; Shamonin, Mikhail; Zorinets, Denis

2017-03-01

The influence of an external magnetic field on the static shear strain and the effective shear modulus of a magnetoactive elastomer (MAE) is studied theoretically in the framework of a recently introduced approach to the single-particle magnetostriction mechanism [V. M. Kalita et al., Phys. Rev. E 93, 062503 (2016)10.1103/PhysRevE.93.062503]. The planar problem of magnetostriction in an MAE with magnetically soft inclusions in the form of a thin disk (platelet) having the magnetic anisotropy in the plane of this disk is solved analytically. An external magnetic field acts with torques on magnetic filler particles, creates mechanical stresses in the vicinity of inclusions, induces shear strain, and increases the effective shear modulus of these composite materials. It is shown that the largest effect of the magnetic field on the effective shear modulus should be expected in MAEs with soft elastomer matrices, where the shear modulus of the matrix is less than the magnetic anisotropy constant of inclusions. It is derived that the effective shear modulus is nonlinearly dependent on the external magnetic field and approaches the saturation value in magnetic fields exceeding the field of particle anisotropy. It is shown that model calculations of the effective shear modulus correspond to a phenomenological definition of effective elastic moduli and magnetoelastic coupling constants. The obtained theoretical results compare well with known experimental data. Determination of effective elastic coefficients in MAEs and their dependence on magnetic field is discussed. The concentration dependence of the effective shear modulus at higher filler concentrations has been estimated using the method of Padé approximants, which predicts that both the absolute and relative changes of the magnetic-field-dependent effective shear modulus will significantly increase with the growing concentration of filler particles.

Effect of single-particle magnetostriction on the shear modulus of compliant magnetoactive elastomers

NASA Astrophysics Data System (ADS)

Kalita, Viktor M.; Snarskii, Andrei A.; Shamonin, Mikhail; Zorinets, Denis

2017-03-01

The influence of an external magnetic field on the static shear strain and the effective shear modulus of a magnetoactive elastomer (MAE) is studied theoretically in the framework of a recently introduced approach to the single-particle magnetostriction mechanism [V. M. Kalita et al., Phys. Rev. E 93, 062503 (2016), 10.1103/PhysRevE.93.062503]. The planar problem of magnetostriction in an MAE with magnetically soft inclusions in the form of a thin disk (platelet) having the magnetic anisotropy in the plane of this disk is solved analytically. An external magnetic field acts with torques on magnetic filler particles, creates mechanical stresses in the vicinity of inclusions, induces shear strain, and increases the effective shear modulus of these composite materials. It is shown that the largest effect of the magnetic field on the effective shear modulus should be expected in MAEs with soft elastomer matrices, where the shear modulus of the matrix is less than the magnetic anisotropy constant of inclusions. It is derived that the effective shear modulus is nonlinearly dependent on the external magnetic field and approaches the saturation value in magnetic fields exceeding the field of particle anisotropy. It is shown that model calculations of the effective shear modulus correspond to a phenomenological definition of effective elastic moduli and magnetoelastic coupling constants. The obtained theoretical results compare well with known experimental data. Determination of effective elastic coefficients in MAEs and their dependence on magnetic field is discussed. The concentration dependence of the effective shear modulus at higher filler concentrations has been estimated using the method of Padé approximants, which predicts that both the absolute and relative changes of the magnetic-field-dependent effective shear modulus will significantly increase with the growing concentration of filler particles.

Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid

2018-04-01

Evapotranspiration (ET) is considered as a key factor in hydrological and climatological studies, agricultural water management, irrigation scheduling, etc. It can be directly measured using lysimeters. Moreover, other methods such as empirical equations and artificial intelligence methods can be used to model ET. In the recent years, artificial intelligence methods have been widely utilized to estimate reference evapotranspiration (ETo). In the present study, local and external performances of multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were assessed for estimating daily ETo. For this aim, daily weather data of six stations with different climates in Iran, namely Urmia and Tabriz (semi-arid), Isfahan and Shiraz (arid), Yazd and Zahedan (hyper-arid) were employed during 2000-2014. Two types of input patterns consisting of weather data-based and lagged ETo data-based scenarios were considered to develop the models. Four statistical indicators including root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE) were used to check the accuracy of models. The local performance of models revealed that the MARS and GEP approaches have the capability to estimate daily ETo using the meteorological parameters and the lagged ETo data as inputs. Nevertheless, the MARS had the best performance in the weather data-based scenarios. On the other hand, considerable differences were not observed in the models' accuracy for the lagged ETo data-based scenarios. In the innovation of this study, novel hybrid models were proposed in the lagged ETo data-based scenarios through combination of MARS and GEP models with autoregressive conditional heteroscedasticity (ARCH) time series model. It was concluded that the proposed novel models named MARS-ARCH and GEP-ARCH improved the performance of ETo modeling compared to the single MARS and GEP. In addition, the external analysis of the performance of models at stations with similar climatic conditions denoted the applicability of nearby station' data for estimation of the daily ETo at target station.

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.

Evaluation of the TMPA-3B42 precipitation product using a high-density rain gauge network over complex terrain in northeastern Iberia

NASA Astrophysics Data System (ADS)

El Kenawy, Ahmed M.; Lopez-Moreno, Juan I.; McCabe, Matthew F.; Vicente-Serrano, Sergio M.

2015-10-01

The performance of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA)-3B42 version 7 product is assessed over north-eastern Iberia, a region with considerable topographical gradients and complexity. Precipitation characteristics from a dense network of 656 rain gauges, spanning the period from 1998 to 2009, are used to evaluate TMPA-3B42 estimates on a daily scale. A set of accuracy estimators, including the relative bias, mean absolute error (MAE), root mean square error (RMSE) and Spearman coefficient was used to evaluate the results. The assessment indicates that TMPA-3B42 product is capable of describing the seasonal characteristics of the observed precipitation over most of the study domain. In particular, TMPA-3B42 precipitation agrees well with in situ measurements, with MAE less than 2.5 mm.day- 1, RMSE of 6.4 mm.day- 1 and Spearman correlation coefficients generally above 0.6. TMPA-3B42 provides improved accuracies in winter and summer, whereas it performs much worse in spring and autumn. Spatially, the retrieval errors show a consistent trend, with a general overestimation in regions of low altitude and underestimation in regions of heterogeneous terrain. TMPA-3B42 generally performs well over inland areas, while showing less skill in the coastal regions. A set of skill metrics, including a false alarm ratio [FAR], frequency bias index [FBI], the probability of detection [POD] and threat score [TS], is also used to evaluate TMPA performance under different precipitation thresholds (1, 5, 10, 25 and 50 mm.day- 1). The results suggest that TMPA-3B42 retrievals perform well in specifying moderate rain events (5-25 mm.day- 1), but show noticeably less skill in producing both light (< 1 mm.day- 1) and heavy rainfall thresholds (more than 50 mm.day- 1). Given the complexity of the terrain and the associated high spatial variability of precipitation in north-eastern Iberia, the results reveal that TMPA-3B42 data provide an informative addition to the spatial and temporal coverage of rain gauges in the domain, offering insights into characteristics of average precipitation and their spatial patterns. However, the satellite-based precipitation data should be used cautiously for monitoring extreme precipitation events, particularly over complex terrain. An improvement in precipitation algorithms is still needed to more accurately reproduce high precipitation events in areas of heterogeneous topography over this region.

Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals.

PubMed

Zhang, Qingxue; Zhou, Dian; Zeng, Xuan

2017-02-06

Long-term continuous systolic blood pressure (SBP) and heart rate (HR) monitors are of tremendous value to medical (cardiovascular, circulatory and cerebrovascular management), wellness (emotional and stress tracking) and fitness (performance monitoring) applications, but face several major impediments, such as poor wearability, lack of widely accepted robust SBP models and insufficient proofing of the generalization ability of calibrated models. This paper proposes a wearable cuff-less electrocardiography (ECG) and photoplethysmogram (PPG)-based SBP and HR monitoring system and many efforts are made focusing on above challenges. Firstly, both ECG/PPG sensors are integrated into a single-arm band to provide a super wearability. A highly convenient but challenging single-lead configuration is proposed for weak single-arm-ECG acquisition, instead of placing the electrodes on the chest, or two wrists. Secondly, to identify heartbeats and estimate HR from the motion artifacts-sensitive weak arm-ECG, a machine learning-enabled framework is applied. Then ECG-PPG heartbeat pairs are determined for pulse transit time (PTT) measurement. Thirdly, a PTT&HR-SBP model is applied for SBP estimation, which is also compared with many PTT-SBP models to demonstrate the necessity to introduce HR information in model establishment. Fourthly, the fitted SBP models are further evaluated on the unseen data to illustrate the generalization ability. A customized hardware prototype was established and a dataset collected from ten volunteers was acquired to evaluate the proof-of-concept system. The semi-customized prototype successfully acquired from the left upper arm the PPG signal, and the weak ECG signal, the amplitude of which is only around 10% of that of the chest-ECG. The HR estimation has a mean absolute error (MAE) and a root mean square error (RMSE) of only 0.21 and 1.20 beats per min, respectively. Through the comparative analysis, the PTT&HR-SBP models significantly outperform the PTT-SBP models. The testing performance is 1.63 ± 4.44, 3.68, 4.71 mmHg in terms of mean error ± standard deviation, MAE and RMSE, respectively, indicating a good generalization ability on the unseen fresh data. The proposed proof-of-concept system is highly wearable, and its robustness is thoroughly evaluated on different modeling strategies and also the unseen data, which are expected to contribute to long-term pervasive hypertension, heart health and fitness management.

Magnetorheological behavior of magnetoactive elastomers filled with bimodal iron and magnetite particles

NASA Astrophysics Data System (ADS)

Sorokin, Vladislav V.; Stepanov, Gennady V.; Shamonin, Mikhail; Monkman, Gareth J.; Kramarenko, Elena Yu

2017-03-01

Magnetoactive elastomers (MAE) based on soft silicone matrices, filled with various proportions of large diameter (approximately 50 μm) iron and small diameter (approximately 0.5 μm) magnetite particles are synthesized. Their rheological behavior in homogeneous magnetic fields up to 600 mT is studied in detail. The addition of small magnetite particles facilitates fabrication of uniformly distributed magnetic elastomer composites by preventing aggregation and sedimentation of large particles during curing. It is shown that using the proposed bimodal filler particles it is possible to tailor various magnetorheological (MR) properties which can be useful for different target applications. In particular, either absolute or relative magnetorheological effects can be tuned. The value of the damping factor as well as the range of deformation amplitudes for the linear viscoelastic regime can be chosen. The interdependencies between different MR properties of bimodal MAEs are considered. The results are discussed in the model framework of particle network formation under the simultaneous influence of external magnetic fields and mechanical deformation.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

PubMed Central

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-01-01

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. PMID:29883381

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

PubMed

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

Prediction of change in protein unfolding rates upon point mutations in two state proteins.

PubMed

Chaudhary, Priyashree; Naganathan, Athi N; Gromiha, M Michael

2016-09-01

Studies on protein unfolding rates are limited and challenging due to the complexity of unfolding mechanism and the larger dynamic range of the experimental data. Though attempts have been made to predict unfolding rates using protein sequence-structure information there is no available method for predicting the unfolding rates of proteins upon specific point mutations. In this work, we have systematically analyzed a set of 790 single mutants and developed a robust method for predicting protein unfolding rates upon mutations (Δlnku) in two-state proteins by combining amino acid properties and knowledge-based classification of mutants with multiple linear regression technique. We obtain a mean absolute error (MAE) of 0.79/s and a Pearson correlation coefficient (PCC) of 0.71 between predicted unfolding rates and experimental observations using jack-knife test. We have developed a web server for predicting protein unfolding rates upon mutation and it is freely available at https://www.iitm.ac.in/bioinfo/proteinunfolding/unfoldingrace.html. Prominent features that determine unfolding kinetics as well as plausible reasons for the observed outliers are also discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating SPT-N Value Based on Soil Resistivity using Hybrid ANN-PSO Algorithm

NASA Astrophysics Data System (ADS)

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

2018-04-01

Standard Penetration Resistance (N value) is used in many empirical geotechnical engineering formulas. Meanwhile, soil resistivity is a measure of soil’s resistance to electrical flow. For a particular site, usually, only a limited N value data are available. In contrast, resistivity data can be obtained extensively. Moreover, previous studies showed evidence of a correlation between N value and resistivity value. Yet, no existing method is able to interpret resistivity data for estimation of N value. Thus, the aim is to develop a method for estimating N-value using resistivity data. This study proposes a hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) method to estimate N value using resistivity data. Five different ANN-PSO models based on five boreholes were developed and analyzed. The performance metrics used were the coefficient of determination, R2 and mean absolute error, MAE. Analysis of result found that this method can estimate N value (R2 best=0.85 and MAEbest=0.54) given that the constraint, Δ {\\bar{l}}ref, is satisfied. The results suggest that ANN-PSO method can be used to estimate N value with good accuracy.

A new approximation of Fermi-Dirac integrals of order 1/2 for degenerate semiconductor devices

NASA Astrophysics Data System (ADS)

AlQurashi, Ahmed; Selvakumar, C. R.

2018-06-01

There had been tremendous growth in the field of Integrated circuits (ICs) in the past fifty years. Scaling laws mandated both lateral and vertical dimensions to be reduced and a steady increase in doping densities. Most of the modern semiconductor devices have invariably heavily doped regions where Fermi-Dirac Integrals are required. Several attempts have been devoted to developing analytical approximations for Fermi-Dirac Integrals since numerical computations of Fermi-Dirac Integrals are difficult to use in semiconductor devices, although there are several highly accurate tabulated functions available. Most of these analytical expressions are not sufficiently suitable to be employed in semiconductor device applications due to their poor accuracy, the requirement of complicated calculations, and difficulties in differentiating and integrating. A new approximation has been developed for the Fermi-Dirac integrals of the order 1/2 by using Prony's method and discussed in this paper. The approximation is accurate enough (Mean Absolute Error (MAE) = 0.38%) and easy enough to be used in semiconductor device equations. The new approximation of Fermi-Dirac Integrals is applied to a more generalized Einstein Relation which is an important relation in semiconductor devices.

Mirage: a visible signature evaluation tool

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.; Meehan, Alaster J.; Shao, Q. T.; Richards, Noel

2017-10-01

This paper presents the Mirage visible signature evaluation tool, designed to provide a visible signature evaluation capability that will appropriately reflect the effect of scene content on the detectability of targets, providing a capability to assess visible signatures in the context of the environment. Mirage is based on a parametric evaluation of input images, assessing the value of a range of image metrics and combining them using the boosted decision tree machine learning method to produce target detectability estimates. It has been developed using experimental data from photosimulation experiments, where human observers search for vehicle targets in a variety of digital images. The images used for tool development are synthetic (computer generated) images, showing vehicles in many different scenes and exhibiting a wide variation in scene content. A preliminary validation has been performed using k-fold cross validation, where 90% of the image data set was used for training and 10% of the image data set was used for testing. The results of the k-fold validation from 200 independent tests show a prediction accuracy between Mirage predictions of detection probability and observed probability of detection of r(262) = 0:63, p < 0:0001 (Pearson correlation) and a MAE = 0:21 (mean absolute error).

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network.

PubMed

Zhao, Guo; Wang, Hui; Liu, Gang

2017-07-03

Abstract : In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd 2+ in the presence of Cu 2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu 2+ concentration on the stripping response to Cd 2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd 2+ in the presence of Cu 2+ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd 2+ and the stripping peak currents of Cu 2+ and Cd 2+ . The factors affecting the SWASV detection of Cd 2+ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd 2+ in soil samples with satisfactory results.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

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.

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

Verification of the NWP models operated at ICM, Poland

NASA Astrophysics Data System (ADS)

Melonek, Malgorzata

2010-05-01

Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw (ICM) started its activity in the field of NWP in May 1997. Since this time the numerical weather forecasts covering Central Europe have been routinely published on our publicly available website. First NWP model used in ICM was hydrostatic Unified Model developed by the UK Meteorological Office. It was a mesoscale version with horizontal resolution of 17 km and 31 levels in vertical. At present two NWP non-hydrostatic models are running in quasi-operational regime. The main new UM model with 4 km horizontal resolution, 38 levels in vertical and forecats range of 48 hours is running four times a day. Second, the COAMPS model (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by the US Naval Research Laboratory, configured with the three nested grids (with coresponding resolutions of 39km, 13km and 4.3km, 30 vertical levels) are running twice a day (for 00 and 12 UTC). The second grid covers Central Europe and has forecast range of 84 hours. Results of the both NWP models, ie. COAMPS computed on 13km mesh resolution and UM, are verified against observations from the Polish synoptic stations. Verification uses surface observations and nearest grid point forcasts. Following meteorological elements are verified: air temperature at 2m, mean sea level pressure, wind speed and wind direction at 10 m and 12 hours accumulated precipitation. There are presented different statistical indices. For continous variables Mean Error(ME), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) in 6 hours intervals are computed. In case of precipitation the contingency tables for different thresholds are computed and some of the verification scores such as FBI, ETS, POD, FAR are graphically presented. The verification sample covers nearly one year.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

[Comparison of three stand-level biomass estimation methods].

PubMed

Dong, Li Hu; Li, Feng Ri

2016-12-01

At present, the forest biomass methods of regional scale attract most of attention of the researchers, and developing the stand-level biomass model is popular. Based on the forestry inventory data of larch plantation (Larix olgensis) in Jilin Province, we used non-linear seemly unrelated regression (NSUR) to estimate the parameters in two additive system of stand-level biomass equations, i.e., stand-level biomass equations including the stand variables and stand biomass equations including the biomass expansion factor (i.e., Model system 1 and Model system 2), listed the constant biomass expansion factor for larch plantation and compared the prediction accuracy of three stand-level biomass estimation methods. The results indicated that for two additive system of biomass equations, the adjusted coefficient of determination (R a 2 ) of the total and stem equations was more than 0.95, the root mean squared error (RMSE), the mean prediction error (MPE) and the mean absolute error (MAE) were smaller. The branch and foliage biomass equations were worse than total and stem biomass equations, and the adjusted coefficient of determination (R a 2 ) was less than 0.95. The prediction accuracy of a constant biomass expansion factor was relatively lower than the prediction accuracy of Model system 1 and Model system 2. Overall, although stand-level biomass equation including the biomass expansion factor belonged to the volume-derived biomass estimation method, and was different from the stand biomass equations including stand variables in essence, but the obtained prediction accuracy of the two methods was similar. The constant biomass expansion factor had the lower prediction accuracy, and was inappropriate. In addition, in order to make the model parameter estimation more effective, the established stand-level biomass equations should consider the additivity in a system of all tree component biomass and total biomass equations.

Generation of synthetic CT data using patient specific daily MR image data and image registration

NASA Astrophysics Data System (ADS)

Melanie Kraus, Kim; Jäkel, Oliver; Niebuhr, Nina I.; Pfaffenberger, Asja

2017-02-01

To fully exploit the advantages of magnetic resonance imaging (MRI) for radiotherapy (RT) treatment planning, a method is required to overcome the problem of lacking electron density information. We aim to establish and evaluate a new method for computed tomography (CT) data generation based on MRI and image registration. The thereby generated CT data is used for dose accumulation. We developed a process flow based on an initial pair of rigidly co-registered CT and T2-weighted MR image representing the same anatomical situation. Deformable image registration using anatomical landmarks is performed between the initial MRI data and daily MR images. The resulting transformation is applied to the initial CT, thus fractional CT data is generated. Furthermore, the dose for a photon intensity modulated RT (IMRT) or intensity modulated proton therapy (IMPT) plan is calculated on the generated fractional CT and accumulated on the initial CT via inverse transformation. The method is evaluated by the use of phantom CT and MRI data. Quantitative validation is performed by evaluation of the mean absolute error (MAE) between the measured and the generated CT. The effect on dose accumulation is examined by means of dose-volume parameters. One patient case is presented to demonstrate the applicability of the method introduced here. Overall, CT data derivation lead to MAEs with a median of 37.0 HU ranging from 29.9 to 66.6 HU for all investigated tissues. The accuracy of image registration showed to be limited in the case of unexpected air cavities and at tissue boundaries. The comparisons of dose distributions based on measured and generated CT data agree well with the published literature. Differences in dose volume parameters kept within 1.6% and 3.2% for photon and proton RT, respectively. The method presented here is particularly suited for application in adaptive RT in current clinical routine, since only minor additional technical equipment is required.

Assessing quality of life in a clinical study on heart rehabilitation patients: how well do value sets based on given or experienced health states reflect patients' valuations?

PubMed

Leidl, Reiner; Schweikert, Bernd; Hahmann, Harry; Steinacker, Juergen M; Reitmeir, Peter

2016-03-22

Quality of life as an endpoint in a clinical study may be sensitive to the value set used to derive a single score. Focusing on patients' actual valuations in a clinical study, we compare different value sets for the EQ-5D-3L and assess how well they reproduce patients' reported results. A clinical study comparing inpatient (n = 98) and outpatient (n = 47) rehabilitation of patients after an acute coronary event is re-analyzed. Value sets include: 1. Given health states and time-trade-off valuation (GHS-TTO) rendering economic utilities; 2. Experienced health states and valuation by visual analog scale (EHS-VAS). Valuations are compared with patient-reported VAS rating. Accuracy is assessed by mean absolute error (MAE) and by Pearson's correlation ρ. External validity is tested by correlation with established MacNew global scores. Drivers of differences between value sets and VAS are analyzed using repeated measures regression. EHS-VAS had smaller MAEs and higher ρ in all patients and in the inpatient group, and correlated best with MacNew global score. Quality-adjusted survival was more accurately reflected by EHS-VAS. Younger, better educated patients reported lower VAS at admission than the EHS-based value set. EHS-based estimates were mostly able to reproduce patient-reported valuation. Economic utility measurement is conceptually different, produced results less strongly related to patients' reports, and resulted in about 20 % longer quality-adjusted survival. Decision makers should take into account the impact of choosing value sets on effectiveness results. For transferring the results of heart rehabilitation patients from another country or from another valuation method, the EHS-based value set offers a promising estimation option for those decision makers who prioritize patient-reported valuation. Yet, EHS-based estimates may not fully reflect patient-reported VAS in all situations.

The simulation of UV spectroscopy and electronic analysis of temozolomide and dacarbazine chemical decomposition to their metabolites.

PubMed

Khalilian, M Hossein; Mirzaei, Saber; Taherpour, Avat Arman

2016-11-01

The electronic features of anti-tumor agent, temozolomide, and its degradation products (MTIC and metabolite AIC) have been traced by means of UV absorption spectroscopy in vacuo and aqueous media. For comparison, electronic spectra of related structures and drugs (e.g., dacarbazine) were also investigated. These investigations were carried out using time-dependent density functional theory (TD-DFT) method while the conductor like screening model (COSMO) were applied for the inclusion of solvent effects in electronic spectra. From functional benchmarking, two methods; B3LYP and O3LYP were selected among several other methods with 6-311+G(2d,p) basis set aiming to get the best results in accord with the experimental values. An assessment of the obtained spectra has shown that O3LYP functional gives a mean absolute error (MAE) from experimental absorption peaks of 4.3 nm compared to the 7.2 nm MAE value at B3LYP level in aqueous media. Furthermore, since the structural and tautomeric conformers affect the electronic spectra, conformational preferences have been analyzed in temozolomide, dacarbazine, and their related structures. Temozolomide structure possesses two rotamers that differ in the orientation of carboxamide moiety with a small energy difference (energy difference of 1.39 kcal mol -1 in vacuo and 0.35 kcal mol -1 in aqueous media at B3LYP/6-311++G(2df,3pd). The more stable and meta-stable TMZ rotamer have shown their absorption maxima at 329-334 nm, respectively, at O3LYP level in aqueous media. Applying statistical calculation according to Boltzmann population formula at 25 °C and computed weighed mean estimates the λ max of temozolomide at 331 nm, which is in notable agreement with the experimental value (330 nm). Moreover, molecular orbital composition analysis has been conducted in order to interpret these findings. Graphical Abstract Temozolomide and dacarbazine.

The Radial Speed-Expansion Speed Relation for Earth-Directed CMEs

NASA Technical Reports Server (NTRS)

Makela, P.; Gopalswamy, N.; Yashiro, S.

2016-01-01

Earth-directed coronal mass ejections (CMEs) are the main drivers of major geomagnetic storms. Therefore, a good estimate of the disturbance arrival time at Earth is required for space weather predictions. The STEREO and SOHO spacecraft were viewing the Sun in near quadrature during January 2010 to September 2012, providing a unique opportunity to study the radial speed (V (sub rad)) to expansion speed(V (sub exp)) relationship of Earth-directed CMEs. This relationship is useful in estimating the V (sub rad) of Earth-directed CMEs, when they are observed from Earth view only. We selected 19 Earth-directed CMEs observed by the Large Angle and Spectrometric Coronagraph (LASCO)/C3 coronagraph on SOHO and the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI)/COR2 coronagraph on STEREO during January 2010 to September 2012. We found that of the three tested geometric CME models the full ice-cream cone model of the CME describes best the V (sub rad) to V (sub exp) relationship, as suggested by earlier investigations. We also tested the prediction accuracy of the empirical shock arrival (ESA) model proposed by Gopalswamy et al.(2005a), while estimating the CME propagation speeds from the CME expansion speeds. If we use STEREO observations to estimate the CME width required to calculate the V (sub rad) from the V (sub exp) measurements, the mean absolute error (MAE) of the shock arrival times of the ESA model is 8.4 hours. If the LASCO measurements are used to estimate the CME width, the MAE still remains below 17 hours. Therefore, by using the simple V (sub rad) to V (sub exp) relationship to estimate the V (sub rad) of the Earth-directed CMEs, the ESA model is able to predict the shock arrival times with accuracy comparable to most other more complex models.

SMOS and AMSR-2 soil moisture evaluation using representative monitoring sites in southern Australia

NASA Astrophysics Data System (ADS)

Walker, J. P.; Mei Sun, M. S.; Rudiger, C.; Parinussa, R.; Koike, T.; Kerr, Y. H.

2016-12-01

The performance of soil moisture products from AMSR-2 and SMOS were evaluated against representative surface soil moisture stations within the Yanco study area in the Murrumbidgee Catchment, in southeast Australia. AMSR-2 Level 3 (L3) soil moisture products retrieved from two sets of brightness temperatures using the Japanese Aerospace exploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithms were included. For the LPRM algorithm, two different parameterization methods were applied. In the case of SMOS, two versions of the SMOS L3 soil moisture product were assessed. Results based on using "random" and representative stations to evaluate the products were contrasted. The latest versions of the JAXA (JX2) and LPRM (LP3) products were found to perform better than the earlier versions (JX1, LP1 and LP2). Moreover, soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used, as opposed to the X-band data. Yet, X-band retrievals were found to perform better than C-band. Inter-comparing AMSR-2 X-band products from different acquisition times showed a better performance for 1:30 pm overpasses whereas SMOS 6:00 am retrievals were found to perform the best. The mean average error (MAE) goal accuracy of the AMSR-2 mission (MAE < 0.08 m3/m3) was met by both versions of the JAXA products, the LPRM X-band products retrieved from the reprocessed version of brightness temperatures, and both versions of SMOS products. Nevertheless, none of the products achieved the SMOS target accuracy of 0.04 m3/m3. Finally, the product performance depended on the statistics used in their evaluation; based on temporal and absolute accuracy JX2 is recommended, whereas LP3 X-band 1:30 pm and SMOS2 6:00 am are recommended based on temporal accuracy alone.

Measuring (subglacial) bedform orientation, length, and longitudinal asymmetry - Method assessment.

PubMed

Jorge, Marco G; Brennand, Tracy A

2017-01-01

Geospatial analysis software provides a range of tools that can be used to measure landform morphometry. Often, a metric can be computed with different techniques that may give different results. This study is an assessment of 5 different methods for measuring longitudinal, or streamlined, subglacial bedform morphometry: orientation, length and longitudinal asymmetry, all of which require defining a longitudinal axis. The methods use the standard deviational ellipse (not previously applied in this context), the longest straight line fitting inside the bedform footprint (2 approaches), the minimum-size footprint-bounding rectangle, and Euler's approximation. We assess how well these methods replicate morphometric data derived from a manually mapped (visually interpreted) longitudinal axis, which, though subjective, is the most typically used reference. A dataset of 100 subglacial bedforms covering the size and shape range of those in the Puget Lowland, Washington, USA is used. For bedforms with elongation > 5, deviations from the reference values are negligible for all methods but Euler's approximation (length). For bedforms with elongation < 5, most methods had small mean absolute error (MAE) and median absolute deviation (MAD) for all morphometrics and thus can be confidently used to characterize the central tendencies of their distributions. However, some methods are better than others. The least precise methods are the ones based on the longest straight line and Euler's approximation; using these for statistical dispersion analysis is discouraged. Because the standard deviational ellipse method is relatively shape invariant and closely replicates the reference values, it is the recommended method. Speculatively, this study may also apply to negative-relief, and fluvial and aeolian bedforms.

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories.

PubMed

Teo, Troy P; Ahmed, Syed Bilal; Kawalec, Philip; Alayoubi, Nadia; Bruce, Neil; Lyn, Ethan; Pistorius, Stephen

2018-02-01

The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learning period so that prediction and adaptation can commence soon after treatment begins, and requires minimal reoptimization for individual patients. Specifically, the feasibility of predicting tumor position using a combination of a generalized (i.e., averaged) neural network, optimized using historical patient data (i.e., tumor trajectories) obtained offline, coupled with the use of real-time online tumor positions (obtained during treatment delivery) was examined. A 3-layer perceptron neural network was implemented to predict tumor motion for a prediction horizon of 650 ms. A backpropagation algorithm and batch gradient descent approach were used to train the model. Twenty-seven 1-min lung tumor motion samples (selected from a CyberKnife patient dataset) were sampled at a rate of 7.5 Hz (0.133 s) to emulate the frame rate of an electronic portal imaging device (EPID). A sliding temporal window was used to sample the data for learning. The sliding window length was set to be equivalent to the first breathing cycle detected from each trajectory. Performing a parametric sweep, an averaged error surface of mean square errors (MSE) was obtained from the prediction responses of seven trajectories used for the training of the model (Group 1). An optimal input data size and number of hidden neurons were selected to represent the generalized model. To evaluate the prediction performance of the generalized model on unseen data, twenty tumor traces (Group 2) that were not involved in the training of the model were used for the leave-one-out cross-validation purposes. An input data size of 35 samples (4.6 s) and 20 hidden neurons were selected for the generalized neural network. An average sliding window length of 28 data samples was used. The average initial learning period prior to the availability of the first predicted tumor position was 8.53 ± 1.03 s. Average mean absolute error (MAE) of 0.59 ± 0.13 mm and 0.56 ± 0.18 mm were obtained from Groups 1 and 2, respectively, giving an overall MAE of 0.57 ± 0.17 mm. Average root-mean-square-error (RMSE) of 0.67 ± 0.36 for all the traces (0.76 ± 0.34 mm, Group 1 and 0.63 ± 0.36 mm, Group 2), is comparable to previously published results. Prediction errors are mainly due to the irregular periodicities between cycles. Since the errors from Groups 1 and 2 are within the same range, it demonstrates that this model can generalize and predict on unseen data. This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data. © 2017 American Association of Physicists in Medicine.

SU-F-303-12: Implementation of MR-Only Simulation for Brain Cancer: A Virtual Clinical Trial

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

Glide-Hurst, C; Zheng, W; Kim, J

2015-06-15

Purpose: To perform a retrospective virtual clinical trial using an MR-only workflow for a variety of brain cancer cases by incorporating novel imaging sequences, tissue segmentation using phase images, and an innovative synthetic CT (synCT) solution. Methods: Ten patients (16 lesions) were evaluated using a 1.0T MR-SIM including UTE-DIXON imaging (TE = 0.144/3.4/6.9ms). Bone-enhanced images were generated from DIXON-water/fat and inverted UTE. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating intersection and Dice similarity coefficients (DSC) using CT-SIM as ground truth. SynCTs were generated using voxel-based weighted summation incorporating T2, FLAIR, UTE1,more » and bone-enhanced images. Mean absolute error (MAE) characterized HU differences between synCT and CT-SIM. Dose was recalculated on synCTs; differences were quantified using planar gamma analysis (2%/2 mm dose difference/distance to agreement) at isocenter. Digitally reconstructed radiographs (DRRs) were compared. Results: On average, air maps intersected 80.8 ±5.5% (range: 71.8–88.8%) between MR-SIM and CT-SIM yielding DSCs of 0.78 ± 0.04 (range: 0.70–0.83). Whole-brain MAE between synCT and CT-SIM was 160.7±8.8 HU, with the largest uncertainty arising from bone (MAE = 423.3±33.2 HU). Gamma analysis revealed pass rates of 99.4 ± 0.04% between synCT and CT-SIM for the cohort. Dose volume histogram analysis revealed that synCT tended to yield slightly higher doses. Organs at risk such as the chiasm and optic nerves were most sensitive due to their proximities to air/bone interfaces. DRRs generated via synCT and CT-SIM were within clinical tolerances. Conclusion: Our approach for MR-only simulation for brain cancer treatment planning yielded clinically acceptable results relative to the CT-based benchmark. While slight dose differences were observed, reoptimization of treatment plans and improved image registration can address this limitation. Future work will incorporate automated registration between setup images (cone-beam CT and kilovoltage images) for synCT and CT-SIM. Submitting institution holds research agreements with Philips HealthCare, Best, Netherlands and Varian Medical Systems, Palo Alto, CA. Research partially sponsored via an Internal Mentored Research Grant.« less

The potential of remotely sensed soil moisture for operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average, compared to assimilation of discharge only. The rank histograms show that the forecast is not biased. The timing errors in the flood predictions are decreased when soil moisture data is used and imminent floods can be forecasted with skill one day earlier. In conclusion, our study shows that assimilation of satellite soil moisture increases the performance of flood forecasting systems for large catchments, like the Upper Danube. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of future soil moisture missions with a higher spatial resolution like SMAP to improve near-real time flood forecasting in large catchments.

Reliable absolute analog code retrieval approach for 3D measurement

NASA Astrophysics Data System (ADS)

Yu, Shuang; Zhang, Jing; Yu, Xiaoyang; Sun, Xiaoming; Wu, Haibin; Chen, Deyun

2017-11-01

The wrapped phase of phase-shifting approach can be unwrapped by using Gray code, but both the wrapped phase error and Gray code decoding error can result in period jump error, which will lead to gross measurement error. Therefore, this paper presents a reliable absolute analog code retrieval approach. The combination of unequal-period Gray code and phase shifting patterns at high frequencies are used to obtain high-frequency absolute analog code, and at low frequencies, the same unequal-period combination patterns are used to obtain the low-frequency absolute analog code. Next, the difference between the two absolute analog codes was employed to eliminate period jump errors, and a reliable unwrapped result can be obtained. Error analysis was used to determine the applicable conditions, and this approach was verified through theoretical analysis. The proposed approach was further verified experimentally. Theoretical analysis and experimental results demonstrate that the proposed approach can perform reliable analog code unwrapping.

Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error

NASA Astrophysics Data System (ADS)

Khair, Ummul; Fahmi, Hasanul; Hakim, Sarudin Al; Rahim, Robbi

2017-12-01

Prediction using a forecasting method is one of the most important things for an organization, the selection of appropriate forecasting methods is also important but the percentage error of a method is more important in order for decision makers to adopt the right culture, the use of the Mean Absolute Deviation and Mean Absolute Percentage Error to calculate the percentage of mistakes in the least square method resulted in a percentage of 9.77% and it was decided that the least square method be worked for time series and trend data.

Sequential microwave superheated water extraction of mannans from spent coffee grounds.

PubMed

Passos, Cláudia P; Moreira, Ana S P; Domingues, M Rosário M; Evtuguin, Dmitry V; Coimbra, Manuel A

2014-03-15

The feasibility of using sequential microwave superheated water extraction (MAE) for the recovery of mannans from spent coffee grounds (SCG) was studied. Due to the high contents of mannose still present in the SCG residue left after two consecutive MAE, the unextracted material was re-suspended in water and submitted to a third microwave irradiation (MAE3) at 200 °C for 3 min. With MAE3, mannose recovery achieved 48%, increasing to 56% by MAE4, and reaching a maximum of 69% with MAE5. Glycosidic-linkage analysis showed that in MAE3 mainly galactomannans were recovered, while debranched galactomannans were recovered with MAE4 and MAE5. With increasing the number of extractions, the average degree of polymerization of the mannans decreased, as observed by size-exclusion chromatography and by methylation analysis. Scanning electron microscopy images showed a decrease on cell walls thickness. After final MAE5, the remaining un-extracted insoluble material, representing 22% of the initial SCG, was composed mainly by cellulose (84%). Copyright © 2013 Elsevier Ltd. All rights reserved.

Historical GIS Data and Changes in Urban Morphological Parameters for the Analysis of Urban Heat Islands in Hong Kong

NASA Astrophysics Data System (ADS)

Peng, F.; Wong, M. S.; Nichol, J. E.; Chan, P. W.

2016-06-01

Rapid urban development between the 1960 and 2010 decades have changed the urban landscape and pattern in the Kowloon Peninsula of Hong Kong. This paper aims to study the changes of urban morphological parameters between the 1985 and 2010 and explore their influences on the urban heat island (UHI) effect. This study applied a mono-window algorithm to retrieve the land surface temperature (LST) using Landsat Thematic Mapper (TM) images from 1987 to 2009. In order to estimate the effects of local urban morphological parameters to LST, the global surface temperature anomaly was analysed. Historical 3D building model was developed based on aerial photogrammetry technique using aerial photographs from 1964 to 2010, in which the urban digital surface models (DSMs) including elevations of infrastructures and buildings have been generated. Then, urban morphological parameters (i.e. frontal area index (FAI), sky view factor (SVF)), vegetation fractional cover (VFC), global solar radiation (GSR), Normalized Difference Built-Up Index (NDBI), wind speed were derived. Finally, a linear regression method in Waikato Environment for Knowledge Analysis (WEKA) was used to build prediction model for revealing LST spatial patterns. Results show that the final apparent surface temperature have uncertainties less than 1 degree Celsius. The comparison between the simulated and actual spatial pattern of LST in 2009 showed that the correlation coefficient is 0.65, mean absolute error (MAE) is 1.24 degree Celsius, and root mean square error (RMSE) is 1.51 degree Celsius of 22,429 pixels.

Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

NASA Astrophysics Data System (ADS)

Kariminia, Shahab; Motamedi, Shervin; Shamshirband, Shahaboddin; Piri, Jamshid; Mohammadi, Kasra; Hashim, Roslan; Roy, Chandrabhushan; Petković, Dalibor; Bonakdari, Hossein

2016-05-01

Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user's behavioural, physiological and psychological aspects. These aspects play critical roles in user's ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature ( T mrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the T mrt.

Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks.

PubMed

Bayram, Adem; Kankal, Murat; Onsoy, Hizir

2012-07-01

Suspended sediment concentration (SSC) is generally determined from the direct measurement of sediment concentration of river or from sediment transport equations. Direct measurement is very costly and cannot be conducted for all river gauge stations. Therefore, correct estimation of suspended sediment amount carried by a river is very important in terms of water pollution, channel navigability, reservoir filling, fish habitat, river aesthetics and scientific interests. This study investigates the feasibility of using turbidity as a surrogate for SSC as in situ turbidity meters are being increasingly used to generate continuous records of SSC in rivers. For this reason, regression analysis (RA) and artificial neural networks (ANNs) were employed to estimate SSC based on in situ turbidity measurements. The SSC was firstly experimentally determined for the surface water samples collected from the six monitoring stations along the main branch of the stream Harsit, Eastern Black Sea Basin, Turkey. There were 144 data for each variable obtained on a fortnightly basis during March 2009 and February 2010. In the ANN method, the used data for training, testing and validation sets are 108, 24 and 12 of total 144 data, respectively. As the results of analyses, the smallest mean absolute error (MAE) and root mean square error (RMSE) values for validation set were obtained from the ANN method with 11.40 and 17.87, respectively. However these were 19.12 and 25.09 for RA. It was concluded that turbidity could be a surrogate for SSC in the streams, and the ANNs method used for the estimation of SSC provided acceptable results.

MR-based synthetic CT generation using a deep convolutional neural network method.

PubMed

Han, Xiao

2017-04-01

Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images. The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion. The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 ± 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 ± 17.8 HU for the atlas-based method. The DCNN method also provided significantly better accuracy when being evaluated using two other metrics: the mean squared error (188.6 ± 33.7 versus 198.3 ± 33.0) and the Pearson correlation coefficient(0.906 ± 0.03 versus 0.896 ± 0.03). Although training a DCNN model can be slow, training only need be done once. Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas-based approach. A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images. © 2017 American Association of Physicists in Medicine.

Closed vessel miniaturized microwave assisted chelating extraction for determination of trace metals in plant materials

NASA Astrophysics Data System (ADS)

Czarnecki, Sezin; Duering, Rolf-Alexander

2013-04-01

In recent years, the use of closed vessel microwave assisted extraction (MAE) for plant samples has shown increasing research interest which will probably substitute conventional procedures in the future due to their general disadvantages including consumption of time and solvents. The objective of this study was to demonstrate an innovative miniaturized closed vessel microwave assisted extraction (µMAE) method under the use of EDTA (µMAE-EDTA) to determine metal contents (Cd, Co, Cu, Mn, Ni, Pb, Zn) in plant samples (Lolio-Cynosuretum) by inductively coupled plasma-optical emission spectrometry (ICP-OES). Validation of the method was done by comparison of the results with another miniaturized closed vessel microwave HNO3 method (µMAE-H) and with two other macro scale MAE procedures (MAE-H and MAE-EDTA) which were applied by using a mixture of nitric acid (HNO3) and hydrogen peroxide (H2O2) (MAE-H) and EDTA (MAE-EDTA), respectively. The already established MAE-H method is taken into consideration as a reference validation MAE method for plant material. A conventional plant extraction (CE) method, based on dry ashing and dissolving of the plant material in HNO3, was used as a confidence comparative method. Certified plant reference materials (CRMs) were used for comparison of recovery rates from different extraction protocols. This allowed the validation of the applicability of the µMAE-EDTA procedure. For 36 real plant samples with triplicates each, µMAE-EDTA showed the same extraction yields as the MAE-H in the determination of Cd, Co, Cu, Mn, Ni, Pb, and Zn contents in plant samples. Analytical parameters in µMAE-EDTA should be further investigated and adapted for other metals of interest. By the reduction and elimination of the use of hazardous chemicals in environmental analysis and thus allowing a better understanding of metal distribution and accumulation process in plants and also the metal transfer from soil to plants and into the food chain, µMAE-EDTA is seen as a promising technique for achieving green chemistry goals.

A new warfarin dosing algorithm including VKORC1 3730 G > A polymorphism: comparison with results obtained by other published algorithms.

PubMed

Cini, Michela; Legnani, Cristina; Cosmi, Benilde; Guazzaloca, Giuliana; Valdrè, Lelia; Frascaro, Mirella; Palareti, Gualtiero

2012-08-01

Warfarin dosing is affected by clinical and genetic variants, but the contribution of the genotype associated with warfarin resistance in pharmacogenetic algorithms has not been well assessed yet. We developed a new dosing algorithm including polymorphisms associated both with warfarin sensitivity and resistance in the Italian population, and its performance was compared with those of eight previously published algorithms. Clinical and genetic data (CYP2C9*2, CYP2C9*3, VKORC1 -1639 G > A, and VKORC1 3730 G > A) were used to elaborate the new algorithm. Derivation and validation groups comprised 55 (58.2% men, mean age 69 years) and 40 (57.5% men, mean age 70 years) patients, respectively, who were on stable anticoagulation therapy for at least 3 months with different oral anticoagulation therapy (OAT) indications. Performance of the new algorithm, evaluated with mean absolute error (MAE) defined as the absolute value of the difference between observed daily maintenance dose and predicted daily dose, correlation with the observed dose and R(2) value, was comparable with or slightly lower than that obtained using the other algorithms. The new algorithm could correctly assign 53.3%, 50.0%, and 57.1% of patients to the low (≤25 mg/week), intermediate (26-44 mg/week) and high (≥ 45 mg/week) dosing range, respectively. Our data showed a significant increase in predictive accuracy among patients requiring high warfarin dose compared with the other algorithms (ranging from 0% to 28.6%). The algorithm including VKORC1 3730 G > A, associated with warfarin resistance, allowed a more accurate identification of resistant patients who require higher warfarin dosage.

Optimization of microwave-assisted extraction with saponification (MAES) for the determination of polybrominated flame retardants in aquaculture samples.

PubMed

Fajar, N M; Carro, A M; Lorenzo, R A; Fernandez, F; Cela, R

2008-08-01

The efficiency of microwave-assisted extraction with saponification (MAES) for the determination of seven polybrominated flame retardants (polybrominated biphenyls, PBBs; and polybrominated diphenyl ethers, PBDEs) in aquaculture samples is described and compared with microwave-assisted extraction (MAE). Chemometric techniques based on experimental designs and desirability functions were used for simultaneous optimization of the operational parameters used in both MAES and MAE processes. Application of MAES to this group of contaminants in aquaculture samples, which had not been previously applied to this type of analytes, was shown to be superior to MAE in terms of extraction efficiency, extraction time and lipid content extracted from complex matrices (0.7% as against 18.0% for MAE extracts). PBBs and PBDEs were determined by gas chromatography with micro-electron capture detection (GC-muECD). The quantification limits for the analytes were 40-750 pg g(-1) (except for BB-15, which was 1.43 ng g(-1)). Precision for MAES-GC-muECD (%RSD < 11%) was significantly better than for MAE-GC-muECD (%RSD < 20%). The accuracy of both optimized methods was satisfactorily demonstrated by analysis of appropriate certified reference material (CRM), WMF-01.

External quality-assurance results for the National Atmospheric Deposition Program/National Trends Network, 2002-03

USGS Publications Warehouse

Wetherbee, Gregory A.; Latysh, Natalie E.; Burke, Kevin P.

2005-01-01

Six external quality-assurance programs were operated by the U.S. Geological Survey (USGS) External Quality-Assurance (QA) Project for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) from 2002 through 2003. Each program measured specific components of the overall error inherent in NADP/NTN wet-deposition measurements. The intersite-comparison program assessed the variability and bias of pH and specific conductance determinations made by NADP/NTN site operators twice per year with respect to accuracy goals. The percentage of site operators that met the pH accuracy goals decreased from 92.0 percent in spring 2002 to 86.3 percent in spring 2003. In these same four intersite-comparison studies, the percentage of site operators that met the accuracy goals for specific conductance ranged from 94.4 to 97.5 percent. The blind-audit program and the sample-handling evaluation (SHE) program evaluated the effects of routine sample handling, processing, and shipping on the chemistry of weekly NADP/NTN samples. The blind-audit program data indicated that the variability introduced by sample handling might be environmentally significant to data users for sodium, potassium, chloride, and hydrogen ion concentrations during 2002. In 2003, the blind-audit program was modified and replaced by the SHE program. The SHE program was designed to control the effects of laboratory-analysis variability. The 2003 SHE data had less overall variability than the 2002 blind-audit data. The SHE data indicated that sample handling buffers the pH of the precipitation samples and, in turn, results in slightly lower conductivity. Otherwise, the SHE data provided error estimates that were not environmentally significant to data users. The field-audit program was designed to evaluate the effects of onsite exposure, sample handling, and shipping on the chemistry of NADP/NTN precipitation samples. Field-audit results indicated that exposure of NADP/NTN wet-deposition samples to onsite conditions tended to neutralize the acidity of the samples by less than 1.0 microequivalent per liter. Onsite exposure of the sampling bucket appeared to slightly increase the concentration of most of the analytes but not to an extent that was environmentally significant to NADP data users. An interlaboratory-comparison program was used to estimate the analytical variability and bias of the NADP Central Analytical Laboratory (CAL) during 2002-03. Bias was identified in the CAL data for calcium, magnesium, sodium, potassium, ammonium, chloride, nitrate, sulfate, hydrogen ion, and specific conductance, but the absolute value of the bias was less than analytical minimum detection limits for all constituents except magnesium, nitrate, sulfate, and specific conductance. Control charts showed that CAL results were within statistical control approximately 90 percent of the time. Data for the analysis of ultrapure deionized-water samples indicated that CAL did not have problems with laboratory contamination. During 2002-03, the overall variability of data from the NADP/NTN precipitation-monitoring system was estimated using data from three collocated monitoring sites. Measurement differences of constituent concentration and deposition for paired samples from the collocated samplers were evaluated to compute error terms. The medians of the absolute percentage errors (MAEs) for the paired samples generally were larger for cations (approximately 8 to 50 percent) than for anions (approximately 3 to 33 percent). MAEs were approximately 16 to 30 percent for hydrogen-ion concentration, less than 10 percent for specific conductance, less than 5 percent for sample volume, and less than 8 percent for precipitation depth. The variability attributed to each component of the sample-collection and analysis processes, as estimated by USGS quality-assurance programs, varied among analytes. Laboratory analysis variability accounted for approximately 2 percent of the

Observational insights into aerosol formation from isoprene.

PubMed

Worton, David R; Surratt, Jason D; Lafranchi, Brian W; Chan, Arthur W H; Zhao, Yunliang; Weber, Robin J; Park, Jeong-Hoo; Gilman, Jessica B; de Gouw, Joost; Park, Changhyoun; Schade, Gunnar; Beaver, Melinda; Clair, Jason M St; Crounse, John; Wennberg, Paul; Wolfe, Glenn M; Harrold, Sara; Thornton, Joel A; Farmer, Delphine K; Docherty, Kenneth S; Cubison, Michael J; Jimenez, Jose-Luis; Frossard, Amanda A; Russell, Lynn M; Kristensen, Kasper; Glasius, Marianne; Mao, Jingqiu; Ren, Xinrong; Brune, William; Browne, Eleanor C; Pusede, Sally E; Cohen, Ronald C; Seinfeld, John H; Goldstein, Allen H

2013-10-15

Atmospheric photooxidation of isoprene is an important source of secondary organic aerosol (SOA) and there is increasing evidence that anthropogenic oxidant emissions can enhance this SOA formation. In this work, we use ambient observations of organosulfates formed from isoprene epoxydiols (IEPOX) and methacrylic acid epoxide (MAE) and a broad suite of chemical measurements to investigate the relative importance of nitrogen oxide (NO/NO2) and hydroperoxyl (HO2) SOA formation pathways from isoprene at a forested site in California. In contrast to IEPOX, the calculated production rate of MAE was observed to be independent of temperature. This is the result of the very fast thermolysis of MPAN at high temperatures that affects the distribution of the MPAN reservoir (MPAN / MPA radical) reducing the fraction that can react with OH to form MAE and subsequently SOA (F(MAE formation)). The strong temperature dependence of F(MAE formation) helps to explain our observations of similar concentrations of IEPOX-derived organosulfates (IEPOX-OS; ~1 ng m(-3)) and MAE-derived organosulfates (MAE-OS; ~1 ng m(-3)) under cooler conditions (lower isoprene concentrations) and much higher IEPOX-OS (~20 ng m(-3)) relative to MAE-OS (<0.0005 ng m(-3)) at higher temperatures (higher isoprene concentrations). A kinetic model of IEPOX and MAE loss showed that MAE forms 10-100 times more ring-opening products than IEPOX and that both are strongly dependent on aerosol water content when aerosol pH is constant. However, the higher fraction of MAE ring opening products does not compensate for the lower MAE production under warmer conditions (higher isoprene concentrations) resulting in lower formation of MAE-derived products relative to IEPOX at the surface. In regions of high NOx, high isoprene emissions and strong vertical mixing the slower MPAN thermolysis rate aloft could increase the fraction of MPAN that forms MAE resulting in a vertically varying isoprene SOA source.

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.

Nondestructive evaluation of fatigue damage on low alloy steel by magnetomechanical acoustic emission technique

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

Hiraasawa, T.; Saito, K.; Komura, I.

1995-08-01

A modified magnetomechanical acoustic emission (MAE) technique, denoted Pulse-MAE, in which the magnetization by current pulse was adopted, was newly developed and its applicability was assessed for the nondestructive detection and evaluation of fatigue damage in reactor pressure vessel steel SFVV2 and SA508 class2. MAE signals were measured with both conventional MAE and Pulse-MAE technique for fatigue damaged specimens having several damage fractions, and peak voltage ratio Vp/Vo, where Vp and Vo were the peak voltage for damaged and undamaged specimen respectively, was chosen as a measure. Vp/Vo was found to increase monotonously at the early stage of fatigue processmore » and the rate of increase in Vp/Vo during the fatigue process was larger in Pulse-MAE than conventional MAE. Therefore, Pulse-MAE technique proved to have higher sensitivity for the detection of fatigue damage compared with the conventional MAE and to have the potential of a practical technique for nondestructive detection and evaluation of fatigue damage in actual components.« less

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

Astigmatism error modification for absolute shape reconstruction using Fourier transform method

NASA Astrophysics Data System (ADS)

He, Yuhang; Li, Qiang; Gao, Bo; Liu, Ang; Xu, Kaiyuan; Wei, Xiaohong; Chai, Liqun

2014-12-01

A method is proposed to modify astigmatism errors in absolute shape reconstruction of optical plane using Fourier transform method. If a transmission and reflection flat are used in an absolute test, two translation measurements lead to obtain the absolute shapes by making use of the characteristic relationship between the differential and original shapes in spatial frequency domain. However, because the translation device cannot guarantee the test and reference flats rigidly parallel to each other after the translations, a tilt error exists in the obtained differential data, which caused power and astigmatism errors in the reconstructed shapes. In order to modify the astigmatism errors, a rotation measurement is added. Based on the rotation invariability of the form of Zernike polynomial in circular domain, the astigmatism terms are calculated by solving polynomial coefficient equations related to the rotation differential data, and subsequently the astigmatism terms including error are modified. Computer simulation proves the validity of the proposed method.

The surface and deep structure of the waterfall illusion.

PubMed

Wade, Nicholas J; Ziefle, Martina

2008-11-01

The surface structure of the waterfall illusion or motion aftereffect (MAE) is its phenomenal visibility. Its deep structure will be examined in the context of a model of space and motion perception. The MAE can be observed following protracted observation of a pattern that is translating, rotating, or expanding/contracting, a static pattern appears to move in the opposite direction. The phenomenon has long been known, and it continues to present novel properties. One of the novel features of MAEs is that they can provide an ideal visual assay for distinguishing local from global processes. Motion during adaptation can be induced in a static central grating by moving surround gratings; the MAE is observed in the static central grating but not in static surrounds. The adaptation phase is local and the test phase is global. That is, localised adaptation can be expressed in different ways depending on the structure of the test display. These aspects of MAEs can be exploited to determine a variety of local/global interactions. Six experiments on MAEs are reported. The results indicated that relational motion is required to induce an MAE; the region adapted extends beyond that stimulated; storage can be complete when the MAE is not seen during the storage period; interocular transfer (IOT) is around 30% of monocular MAEs with phase alternation; large field spiral patterns yield MAEs with characteristic monocular and binocular interactions.

A new bio-optical model to estimate phytoplankton primary production: An application in the eastern Mediterranean Sea

NASA Astrophysics Data System (ADS)

Stefanì, Chiara; Bonamano, Simone; Melchiorri, Cristiano; Piermattei, Viviana; Fani, Fabiola; Lazzara, Luigi; Marcelli, Marco

2015-04-01

The estimation of phytoplankton primary production provides basic input for the quantification of carbon flux in the ocean because of the strong relationship between available photosynthetic energy at the ocean surface and energy storage by algal photosynthesis. We used a new version of PhytoVFP (Variable Fluorescence Phytoplankton Production) bio-optical model to calculate phytoplankton primary production (PP) in the euphotic zone. PhytoVFP is classified as a Wavelength- and Depth-resolved (WRDR) model and is based on the implementation of photosynthetic efficiency (Fv / Fmax), measured in-situ by the PrimProd probe. An innovation of the model is the reproduction of the daily photoacclimation process by varying photosynthetic parameters (Ek, alfa and Pbmax ) along the water column as a function of stratification. The PhytoVFP model is structured into three main modules: (1) "PAR estimation ";- (2) "Photo-acclimation of marine phytoplankton"; - (3) "Phytoplankton primary production estimation". The performance of the PhytoVFP model was evaluated using PAR and 14C primary production measures collected during the SAMCA3 and SAMCA4 oceanographic cruises. The comparison between the measured and calculated radiation showed a good correlation, both in the surface and along the water column (R2 = 0.8992 in the presence, and R2 = 0.8747 in the absence, of clouds) Sensitivity tests, carried out on phie (photosynthetic quantum yield) and beta (photoinhibition parameter), allowed us to identify the best model parametrization which minimized the MAE (Mean Absolute Error). The values assigned to these parameters allowed to have a good correlation between the measured and estimated primary production values (R² = 0.808923). The results of PhytoVFP model have been also compared with its older version and the Morel (1991) model showing that the MAE of the new version is lower than the other models. The PhytoVFP model was applied on Primprod data collected during MedGOOS12 cruise in order to analyse the vertical distribution of phytoplankton primary production in the eastern Mediterranean sea.

Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study

PubMed Central

Suleder, Julian; Zowalla, Richard

2017-01-01

Background Today, runners use wearable technology such as global positioning system (GPS)–enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors. Objective The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially “smart” devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices. Methods A pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other. Results A total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event. Conclusions A great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances. PMID:28246070

Spatial and temporal variability of the overall error of National Atmospheric Deposition Program measurements determined by the USGS collocated-sampler program, water years 1989-2001

USGS Publications Warehouse

Wetherbee, G.A.; Latysh, N.E.; Gordon, J.D.

2005-01-01

Data from the U.S. Geological Survey (USGS) collocated-sampler program for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) are used to estimate the overall error of NADP/NTN measurements. Absolute errors are estimated by comparison of paired measurements from collocated instruments. Spatial and temporal differences in absolute error were identified and are consistent with longitudinal distributions of NADP/NTN measurements and spatial differences in precipitation characteristics. The magnitude of error for calcium, magnesium, ammonium, nitrate, and sulfate concentrations, specific conductance, and sample volume is of minor environmental significance to data users. Data collected after a 1994 sample-handling protocol change are prone to less absolute error than data collected prior to 1994. Absolute errors are smaller during non-winter months than during winter months for selected constituents at sites where frozen precipitation is common. Minimum resolvable differences are estimated for different regions of the USA to aid spatial and temporal watershed analyses.

Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures

DOE PAGES

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2018-01-01

The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentallymore » obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Finally and furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.« less

31 CFR 354.9 - Liability of Sallie Mae and Federal Reserve Banks.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Reserve Banks. 354.9 Section 354.9 Money and Finance: Treasury Regulations Relating to Money and Finance...-ENTRY SECURITIES OF THE STUDENT LOAN MARKETING ASSOCIATION (SALLIE MAE) § 354.9 Liability of Sallie Mae and Federal Reserve Banks. Sallie Mae and the Federal Reserve Banks may rely on the information...

[Influence of electromagnetic emission at the frequencies of molecular absorption and emission spectra of oxygen and nitrogen oxide on the adhesion and formation of Pseudomonas aeruginosa biofilm].

PubMed

Pronina, E A; Shvidenko, I G; Shub, G M; Shapoval, O G

2011-01-01

Evaluate the influence of electromagnetic emission (EME) at the frequencies of molecular absorption and emission spectra of atmospheric oxygen and nitrogen oxide (MAES 02 and MAES NO respectively) on the adhesion, population progress and biofilm formation of Pseudomonas aeruginosa. Adhesive activity was evaluated by mean adhesion index (MAI) of bacteria on human erythrocytes. Population growth dynamic was assessed by optical density index of broth cultures; biofilm formation--by values of optical density of the cells attached to the surface of polystyrol wells. P.aeruginosa bacteria had high adhesive properties that have increased under the influence of MAES 02 frequency emission and have not changed under the influence of MAES NO frequency. Exposure of bacteria to MAES NO frequency did not influence the population progress; exposure to MAES 02 frequency stimulated the biofilm formation ability of the bacteria, and MAES NO--decreased this ability. EME at MAES NO frequency can be used to suppress bacterial biofilm formation by pseudomonas.

Antiplatelet Activity of Morus alba Leaves Extract, Mediated via Inhibiting Granule Secretion and Blocking the Phosphorylation of Extracellular-Signal-Regulated Kinase and Akt

PubMed Central

Rhee, Man Hee; Sung, Yoon-Young; Yang, Won-Kyung; Kim, Seung Hyung; Kim, Ho-Kyoung

2014-01-01

Ethnopharmacological Relevance. Morus alba L. leaves (MAE) have been used in fork medicine for the treatment of beriberi, edema, diabetes, hypertension, and atherosclerosis. However, underlying mechanism of MAE on cardiovascular protection remains to be elucidated. Therefore, we investigated whether MAE affect platelet aggregation and thrombosis. Materials and Methods. The anti-platelet activity of MAE was studied using rat platelets. The extent of anti-platelet activity of MAE was assayed in collagen-induced platelet aggregation. ATP and serotonin release was carried out. The activation of integrin α IIb β 3 and phosphorylation of signaling molecules, including MAPK and Akt, were investigated with cytofluorometer and immunoblotting, respectively. The thrombus formation in vivo was also evaluated in arteriovenous shunt model of rats. Results. HPLC chromatographic analysis revealed that MAE contained rutin and isoquercetin. MAE dose-dependently inhibited collagen-induced platelet aggregation. MAE also attenuated serotonin secretion and thromboxane A2 formation. In addition, the extract in vivo activity showed that MAE at 100, 200, and 400 mg/kg significantly and dose-dependently attenuated thrombus formation in rat arterio-venous shunt model by 52.3% (P < 0.001), 28.3% (P < 0.01), and 19.1% (P < 0.05), respectively. Conclusions. MAE inhibit platelet activation, TXB2 formation, serotonin secretion, aggregation, and thrombus formation. The plant extract could be considered as a candidate to anti-platelet and antithrombotic agent. PMID:24701244

Soot and SO2 contribution to the supersites in the MILAGRO campaign from elevated flares in the Tula Refinery

NASA Astrophysics Data System (ADS)

Almanza, V. H.; Molina, L. T.; Sosa, G.

2012-11-01

This work presents a simulation of the plume trajectory emitted by flaring activities of the Miguel Hidalgo Refinery in Mexico. The flame of a representative sour gas flare is modeled with a CFD combustion code in order to estimate emission rates of combustion by-products of interest for air quality: acetylene, ethylene, nitrogen oxides, carbon monoxide, soot and sulfur dioxide. The emission rates of NO2 and SO2 were compared with measurements obtained at Tula as part of MILAGRO field campaign. The rates of soot, VOCs and CO emissions were compared with estimates obtained by Instituto Mexicano del Petróleo (IMP). The emission rates of these species were further included in WRF-Chem model to simulate the chemical transport of the plume from 22 to 27 March of 2006. The model presents reliable performance of the resolved meteorology, with respect to the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), mean bias (BIAS), vector RMSE and Index of Agreement (IOA). WRF-Chem outputs of SO2 and soot were compared with surface measurements obtained at the three supersites of MILAGRO campaign. The results suggest a contribution of Tula flaring activities to the total SO2 levels of 18% to 27% at the urban supersite (T0), and of 10% to 18% at the suburban supersite (T1). For soot, the model predicts low contribution at the three supersites, with less than 0.1% at three supersites. According to the model, the greatest contribution of both pollutants to the three supersites occurred on 23 March, which coincides with the third cold surge event reported during the campaign.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Monthly evaporation forecasting using artificial neural networks and support vector machines

NASA Astrophysics Data System (ADS)

Tezel, Gulay; Buyukyildiz, Meral

2016-04-01

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

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

78 FR 21393 - Notice of Submission of Proposed Information Collection to OMB Ginnie Mae Multiclass Securities...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-10

..., allowing the private sector to combine and restructure cash flows from Ginnie Mae Single Class MBS into... program, Ginnie Mae guarantees, with the full faith and credit of the United States, the timely payment of... combine and restructure cash flows from Ginnie Mae Single Class MBS into securities that meet unique...

Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K. V.

2015-12-01

Taiwan is a populated island with a majority of residents settled in the western plains where soils are suitable for rice cultivation. Rice is not only the most important commodity, but also plays a critical role for agricultural and food marketing. Information of rice production is thus important for policymakers to devise timely plans for ensuring sustainably socioeconomic development. Because rice fields in Taiwan are generally small and yet crop monitoring requires information of crop phenology associating with the spatiotemporal resolution of satellite data, this study used Landsat-MODIS fusion data for rice yield modeling in Taiwan. We processed the data for the first crop (Feb-Mar to Jun-Jul) and the second (Aug-Sep to Nov-Dec) in 2014 through five main steps: (1) data pre-processing to account for geometric and radiometric errors of Landsat data, (2) Landsat-MODIS data fusion using using the spatial-temporal adaptive reflectance fusion model, (3) construction of the smooth time-series enhanced vegetation index 2 (EVI2), (4) rice yield modeling using EVI2-based crop phenology metrics, and (5) error verification. The fusion results by a comparison bewteen EVI2 derived from the fusion image and that from the reference Landsat image indicated close agreement between the two datasets (R2 > 0.8). We analysed smooth EVI2 curves to extract phenology metrics or phenological variables for establishment of rice yield models. The results indicated that the established yield models significantly explained more than 70% variability in the data (p-value < 0.001). The comparison results between the estimated yields and the government's yield statistics for the first and second crops indicated a close significant relationship between the two datasets (R2 > 0.8), in both cases. The root mean square error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed the consistency between the estimated yields and the government's yield statistics. This study demonstrates advantages of using EVI2-based phenology metrics (derived from Landsat-MODIS fusion data) for rice yield estimation in Taiwan prior to the harvest period.

Marine Jurassic lithostratigraphy of Thailand

NASA Astrophysics Data System (ADS)

Meesook, A.; Grant-Mackie, J. A.

Marine Jurassic rocks of Thailand are well-exposed in the Mae Sot and Umphang areas and less extensively near Mae Hong Son, Kanchanaburi, Chumphon and Nakhon Si Thammarat, in the north, west, and south respectively. They are generally underlain unconformably by Triassic and overlain by Quaternary strata. Based mainly on five measured sections, fourteen new lithostratigraphic units are established: (in ascending order) Pa Lan, Mai Hung and Kong Mu Formations of the Huai Pong Group in the Mae Hong Son area; Khun Huai, Doi Yot and Pha De Formations of the Hua Fai Group in the Mae Sot area; Klo Tho, Ta Sue Kho, Pu Khloe Khi and Lu Kloc Tu Formations of the Umphang Group in the Umphang area; and the Khao Lak Formation in the Chumphon area. Mudstone, siltstone, sandstone, limestone and marl are the dominant lithologies. Mudstones, siltstones and sandstones are widespread; limestones are confined to the Mae Sot, Umphang, Kanchanaburi and Mae Hong Son areas; marls are found only in Mae Sot. The sequences are approximately 900 m thick in Mae Sot and 450 m thick in Umphang and are rather thinner in the other areas, particularly in the south. Based on ammonites, with additional data from bivalves and foraminifera, the marine Jurassic is largely Toarcian-Aalenian plus some Bajocian. Late Jurassic ages given previously for strata in the Mae Sot and Umphang areas have not been confirmed.

Motion mechanisms with different spatiotemporal characteristics identified by an MAE technique with superimposed gratings.

PubMed

Shioiri, Satoshi; Matsumiya, Kazumichi

2009-05-29

We investigated spatiotemporal characteristics of motion mechanisms using a new type of motion aftereffect (MAE) we found. Our stimulus comprised two superimposed sinusoidal gratings with different spatial frequencies. After exposure to the moving stimulus, observers perceived the MAE in the static test in the direction opposite to that of the high spatial frequency grating even when low spatial frequency motion was perceived during adaptation. In contrast, in the flicker test, the MAE was perceived in the direction opposite to that of the low spatial frequency grating. These MAEs indicate that two different motion systems contribute to motion perception and can be isolated by using different test stimuli. Using a psychophysical technique based on the MAE, we investigated the differences between the two motion mechanisms. The results showed that the static MAE is the aftereffect of the motion system with a high spatial and low temporal frequency tuning (slow motion detector) and the flicker MAE is the aftereffect of the motion system with a low spatial and high temporal frequency tuning (fast motion detector). We also revealed that the two motion detectors differ in orientation tuning, temporal frequency tuning, and sensitivity to relative motion.

Spline-based high-accuracy piecewise-polynomial phase-to-sinusoid amplitude converters.

PubMed

Petrinović, Davor; Brezović, Marko

2011-04-01

We propose a method for direct digital frequency synthesis (DDS) using a cubic spline piecewise-polynomial model for a phase-to-sinusoid amplitude converter (PSAC). This method offers maximum smoothness of the output signal. Closed-form expressions for the cubic polynomial coefficients are derived in the spectral domain and the performance analysis of the model is given in the time and frequency domains. We derive the closed-form performance bounds of such DDS using conventional metrics: rms and maximum absolute errors (MAE) and maximum spurious free dynamic range (SFDR) measured in the discrete time domain. The main advantages of the proposed PSAC are its simplicity, analytical tractability, and inherent numerical stability for high table resolutions. Detailed guidelines for a fixed-point implementation are given, based on the algebraic analysis of all quantization effects. The results are verified on 81 PSAC configurations with the output resolutions from 5 to 41 bits by using a bit-exact simulation. The VHDL implementation of a high-accuracy DDS based on the proposed PSAC with 28-bit input phase word and 32-bit output value achieves SFDR of its digital output signal between 180 and 207 dB, with a signal-to-noise ratio of 192 dB. Its implementation requires only one 18 kB block RAM and three 18-bit embedded multipliers in a typical field-programmable gate array (FPGA) device. © 2011 IEEE

75 FR 44804 - Privacy Act of 1974; Notice of a New Privacy Act System of Records (SORN), Ginnie Mae Mortgage...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-29

...The Department proposes to establish a new Privacy Act SORN subject to the Privacy Act of 1974 (5 U.S.C. 552a), as amended, entitled Ginnie Mae Mortgage-Backed Security Unclaimed Funds System. The new record system will be used to track unclaimed security holder payments. Such unclaimed payments are owed to certificate holders of Ginnie Mae-guaranteed mortgage-backed securities who cannot be located by the Ginnie Mae servicer. Ginnie Mae tracks this information to ensure that security holders are paid properly.

Wavenumber selection method to determine the concentration of cocaine and adulterants in cocaine samples.

PubMed

Kahmann, A; Anzanello, M J; Fogliatto, F S; Marcelo, M C A; Ferrão, M F; Ortiz, R S; Mariotti, K C

2018-04-15

Street cocaine is typically altered with several compounds that increase its harmful health-related side effects, most notably depression, convulsions, and severe damages to the cardiovascular system, lungs, and brain. Thus, determining the concentration of cocaine and adulterants in seized drug samples is important from both health and forensic perspectives. Although FTIR has been widely used to identify the fingerprint and concentration of chemical compounds, spectroscopy datasets are usually comprised of thousands of highly correlated wavenumbers which, when used as predictors in regression models, tend to undermine the predictive performance of multivariate techniques. In this paper, we propose an FTIR wavenumber selection method aimed at identifying FTIR spectra intervals that best predict the concentration of cocaine and adulterants (e.g. caffeine, phenacetin, levamisole, and lidocaine) in cocaine samples. For that matter, the Mutual Information measure is integrated into a Quadratic Programming problem with the objective of minimizing the probability of retaining redundant wavenumbers, while maximizing the relationship between retained wavenumbers and compounds' concentrations. Optimization outputs guide the order of inclusion of wavenumbers in a predictive model, using a forward-based wavenumber selection method. After the inclusion of each wavenumber, parameters of three alternative regression models are estimated, and each model's prediction error is assessed through the Mean Average Error (MAE) measure; the recommended subset of retained wavenumbers is the one that minimizes the prediction error with maximum parsimony. Using our propositions in a dataset of 115 cocaine samples we obtained a best prediction model with average MAE of 0.0502 while retaining only 2.29% of the original wavenumbers, increasing the predictive precision by 0.0359 when compared to a model using the complete set of wavenumbers as predictors. Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of landuse change on the hydrologic regime of the Mae Chaem river basin, NW Thailand

NASA Astrophysics Data System (ADS)

Thanapakpawin, P.; Richey, J.; Thomas, D.; Rodda, S.; Campbell, B.; Logsdon, M.

2007-02-01

SummaryConflicts between upland shifting cultivation, upland commercial crops, and lowland irrigated agriculture cause water resource tension in the Mae Chaem watershed in Chiang Mai, Thailand. In this paper, we assess hydrologic regimes of the Mae Chaem River with landuse change. Three plausible future forest-to-crop expansion scenarios and a scenario of crop-to-forest reversal were developed based on the landcover transition from 1989 to 2000, with emphasis on influences of elevation bands and irrigation diversion. Basin hydrologic responses were simulated using the Distributed Hydrology Soil Vegetation Model (DHSVM). Meteorological data from six weather stations inside and adjacent to the Mae Chaem watershed during the period 1993-2000 were the climate inputs. Computed stream flow was compared to observed discharge at Ban Mae Mu gauge on Mae Mu river, Ban Mae Suk gauge on Mae Suk river, and at Kaeng Ob Luang, located downstream from the district town in Mae Chaem. With current assumptions, expansion of highland crop fields led to slightly higher regulated annual and wet-season water yields compared to similar expansion in the lowland-midland zone. Actual downstream water availability was sensitive to irrigation diversion. This modeling approach can be a useful tool for water allocation for small watersheds undergoing rapid commercialization, because it alerts land managers to the potential range of water supply in wet and dry seasons, and provides information on spatial distribution of basin hydrologic components.

Magneto-acousto-electrical Measurement Based Electrical Conductivity Reconstruction for Tissues.

PubMed

Zhou, Yan; Ma, Qingyu; Guo, Gepu; Tu, Juan; Zhang, Dong

2018-05-01

Based on the interaction of ultrasonic excitation and magnetoelectrical induction, magneto-acousto-electrical (MAE) technology was demonstrated to have the capability of differentiating conductivity variations along the acoustic transmission. By applying the characteristics of the MAE voltage, a simplified algorithm of MAE measurement based conductivity reconstruction was developed. With the analyses of acoustic vibration, ultrasound propagation, Hall effect, and magnetoelectrical induction, theoretical and experimental studies of MAE measurement and conductivity reconstruction were performed. The formula of MAE voltage was derived and simplified for the transducer with strong directivity. MAE voltage was simulated for a three-layer gel phantom and the conductivity distribution was reconstructed using the modified Wiener inverse filter and Hilbert transform, which was also verified by experimental measurements. The experimental results are basically consistent with the simulations, and demonstrate that the wave packets of MAE voltage are generated at tissue interfaces with the amplitudes and vibration polarities representing the values and directions of conductivity variations. With the proposed algorithm, the amplitude and polarity of conductivity gradient can be restored and the conductivity distribution can also be reconstructed accurately. The favorable results demonstrate the feasibility of accurate conductivity reconstruction with improved spatial resolution using MAE measurement for tissues with conductivity variations, especially suitable for nondispersive tissues with abrupt conductivity changes. This study demonstrates that the MAE measurement based conductivity reconstruction algorithm can be applied as a new strategy for nondestructive real-time monitoring of conductivity variations in biomedical engineering.

Effects of Crowding and Attention on High-Levels of Motion Processing and Motion Adaptation

PubMed Central

Pavan, Andrea; Greenlee, Mark W.

2015-01-01

The motion after-effect (MAE) persists in crowding conditions, i.e., when the adaptation direction cannot be reliably perceived. The MAE originating from complex moving patterns spreads into non-adapted sectors of a multi-sector adapting display (i.e., phantom MAE). In the present study we used global rotating patterns to measure the strength of the conventional and phantom MAEs in crowded and non-crowded conditions, and when attention was directed to the adapting stimulus and when it was diverted away from the adapting stimulus. The results show that: (i) the phantom MAE is weaker than the conventional MAE, for both non-crowded and crowded conditions, and when attention was focused on the adapting stimulus and when it was diverted from it, (ii) conventional and phantom MAEs in the crowded condition are weaker than in the non-crowded condition. Analysis conducted to assess the effect of crowding on high-level of motion adaptation suggests that crowding is likely to affect the awareness of the adapting stimulus rather than degrading its sensory representation, (iii) for high-level of motion processing the attentional manipulation does not affect the strength of either conventional or phantom MAEs, neither in the non-crowded nor in the crowded conditions. These results suggest that high-level MAEs do not depend on attention and that at high-level of motion adaptation the effects of crowding are not modulated by attention. PMID:25615577

Determination and error analysis of emittance and spectral emittance measurements by remote sensing

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Kumar, R.

1977-01-01

The author has identified the following significant results. From the theory of remote sensing of surface temperatures, an equation of the upper bound of absolute error of emittance was determined. It showed that the absolute error decreased with an increase in contact temperature, whereas, it increased with an increase in environmental integrated radiant flux density. Change in emittance had little effect on the absolute error. A plot of the difference between temperature and band radiance temperature vs. emittance was provided for the wavelength intervals: 4.5 to 5.5 microns, 8 to 13.5 microns, and 10.2 to 12.5 microns.

Effects of music aerobic exercise on depression and brain-derived neurotrophic factor levels in community dwelling women.

PubMed

Yeh, Shu-Hui; Lin, Li-Wei; Chuang, Yu Kuan; Liu, Cheng-Ling; Tsai, Lu-Jen; Tsuei, Feng-Shiou; Lee, Ming-Tsung; Hsiao, Chiu-Yueh; Yang, Kuender D

2015-01-01

A randomized clinical trial was utilized to compare the improvement of depression and brain-derived neurotrophic factor (BDNF) levels between community women with and without music aerobic exercise (MAE) for 12 weeks. The MAE group involved 47 eligible participants, whereas the comparison group had 59 participants. No significant differences were recorded in the demographic characteristics between the participants in the MAE group and the comparison group. Forty-one participants in the MAE group and 26 in the comparison group completed a pre- and posttest. The MAE group displayed significant improvement in depression scores (p = 0.016), decreased depression symptoms in crying (p = 0.03), appetite (p = 0.006), and fatigue (p = 0.011). The BDNF levels of the participants significantly increased after the 12-week MAE (p = 0.042). The parallel comparison group revealed no significant changes in depression scores or BDNF levels. In summary, the 12-week MAE had a significant impact on the enhancement of BDNF levels and improvement of depression symptoms. Middle-aged community women are encouraged to exercise moderately to improve their depression symptoms and BDNF levels.

Fabrication of a Micro-Needle Array Electrode by Thermal Drawing for Bio-Signals Monitoring

PubMed Central

Ren, Lei; Jiang, Qing; Chen, Keyun; Chen, Zhipeng; Pan, Chengfeng; Jiang, Lelun

2016-01-01

A novel micro-needle array electrode (MAE) fabricated by thermal drawing and coated with Ti/Au film was proposed for bio-signals monitoring. A simple and effective setup was employed to form glassy-state poly (lactic-co-glycolic acid) (PLGA) into a micro-needle array (MA) by the thermal drawing method. The MA was composed of 6 × 6 micro-needles with an average height of about 500 μm. Electrode-skin interface impedance (EII) was recorded as the insertion force was applied on the MAE. The insertion process of the MAE was also simulated by the finite element method. Results showed that MAE could insert into skin with a relatively low compression force and maintain stable contact impedance between the MAE and skin. Bio-signals, including electromyography (EMG), electrocardiography (ECG), and electroencephalograph (EEG) were also collected. Test results showed that the MAE could record EMG, ECG, and EEG signals with good fidelity in shape and amplitude in comparison with the commercial Ag/AgCl electrodes, which proves that MAE is an alternative electrode for bio-signals monitoring. PMID:27322278

Fabrication of a Micro-Needle Array Electrode by Thermal Drawing for Bio-Signals Monitoring.

PubMed

Ren, Lei; Jiang, Qing; Chen, Keyun; Chen, Zhipeng; Pan, Chengfeng; Jiang, Lelun

2016-06-17

A novel micro-needle array electrode (MAE) fabricated by thermal drawing and coated with Ti/Au film was proposed for bio-signals monitoring. A simple and effective setup was employed to form glassy-state poly (lactic-co-glycolic acid) (PLGA) into a micro-needle array (MA) by the thermal drawing method. The MA was composed of 6 × 6 micro-needles with an average height of about 500 μm. Electrode-skin interface impedance (EII) was recorded as the insertion force was applied on the MAE. The insertion process of the MAE was also simulated by the finite element method. Results showed that MAE could insert into skin with a relatively low compression force and maintain stable contact impedance between the MAE and skin. Bio-signals, including electromyography (EMG), electrocardiography (ECG), and electroencephalograph (EEG) were also collected. Test results showed that the MAE could record EMG, ECG, and EEG signals with good fidelity in shape and amplitude in comparison with the commercial Ag/AgCl electrodes, which proves that MAE is an alternative electrode for bio-signals monitoring.

Microwave-assisted extraction of silkworm pupal oil and evaluation of its fatty acid composition, physicochemical properties and antioxidant activities.

PubMed

Hu, Bin; Li, Cheng; Zhang, Zhiqing; Zhao, Qing; Zhu, Yadong; Su, Zhao; Chen, Yizi

2017-09-15

Microwave-assisted extraction (MAE) of oil from silkworm pupae was firstly performed in the present research. The response surface methodology was applied to optimize the parameters for MAE. The yield of oil by MAE was 30.16% under optimal conditions of a mixed solvent consisting of ethanol and n-hexane (1:1, v/v), microwave power (360W), liquid to solid ratio (7.5/1mL/g), microwave time (29min). Moreover, oil extracted by MAE was quantitatively (yield) and qualitatively (fatty acid profile) similar to those obtained using Soxhlet extraction (SE), but oil extracted by MAE exhibited favourable physicochemical properties and oxidation stability. Additionally, oil extracted by MAE had a higher content of total phenolic, and it showed stronger antioxidant activities. Scanning electron microscopy revealed that microwave technique efficiently promoted the release of oil by breaking down the cell structure of silkworm pupae. Therefore, MAE can be an effective method for the silkworm pupal oil extraction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Motion-induced error reduction by combining Fourier transform profilometry with phase-shifting profilometry.

PubMed

Li, Beiwen; Liu, Ziping; Zhang, Song

2016-10-03

We propose a hybrid computational framework to reduce motion-induced measurement error by combining the Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). The proposed method is composed of three major steps: Step 1 is to extract continuous relative phase maps for each isolated object with single-shot FTP method and spatial phase unwrapping; Step 2 is to obtain an absolute phase map of the entire scene using PSP method, albeit motion-induced errors exist on the extracted absolute phase map; and Step 3 is to shift the continuous relative phase maps from Step 1 to generate final absolute phase maps for each isolated object by referring to the absolute phase map with error from Step 2. Experiments demonstrate the success of the proposed computational framework for measuring multiple isolated rapidly moving objects.

Students' Mathematical Work on Absolute Value: Focusing on Conceptions, Errors and Obstacles

ERIC Educational Resources Information Center

Elia, Iliada; Özel, Serkan; Gagatsis, Athanasios; Panaoura, Areti; Özel, Zeynep Ebrar Yetkiner

2016-01-01

This study investigates students' conceptions of absolute value (AV), their performance in various items on AV, their errors in these items and the relationships between students' conceptions and their performance and errors. The Mathematical Working Space (MWS) is used as a framework for studying students' mathematical work on AV and the…

Estimation of Rice Crop Yields Using Random Forests in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. F.; Lin, H. S.; Nguyen, S. T.; Chen, C. R.

2017-12-01

Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2.9%, respectively. Although there are several uncertainties attributed to the data quality of input layers, our study demonstrates the promising application of random forests for estimating rice crop yields at the national level in Taiwan. This approach could be transferable to other regions of the world for improving large-scale estimation of rice crop yields.

Investigation of the component in Artemisia annua L. leading to enhanced antiplasmodial potency of artemisinin via regulation of its metabolism.

PubMed

Cai, Tian-Yu; Zhang, Yun-Rui; Ji, Jian-Bo; Xing, Jie

2017-07-31

The chemical matrix of the herb Artemisia annua L. (A. annua), from which artemisinin (QHS) is isolated, can enhance both the bioavailability and efficacy of QHS. However, the exact mechanism of this synergism remains unknown. The biotransformation of QHS and potential "enzyme inhibitors" in plant matrix could be of great importance in understanding the improved efficacy of QHS in A. annua, which has been limited to the synergism with flavonoid components. To investigate the component in A. annua extracts (MAE) leading to enhanced antiplasmodial potency of QHS via regulation of its metabolism. The efficacy of QHS in combination with the synergistic component was also evaluated. The total MAE extract and its three MAE fractions (MAE-I eluted using 3% methanol, MAE-II eluted using 50% methanol and MAE-III eluted using 85% methanol) were obtained from dry plant materials and prepared after lyophilization. The pharmacokinetic profiles of QHS and its major phase I metabolite monohydroxylated artemisinin (QHS-M) were investigated in healthy rats after a single oral administration of QHS in each MAE extract. Major components isolated from the target MAE fraction were evaluated for their enzyme inhibition. The antimalarial activity of QHS in combination with the potential synergistic component against Plasmodium falciparum was studied in vivo (murine Plasmodium yoelii). The recrudescence and survival time of infected mice were also recorded after drug treatment. Compared to pure QHS, a 2-fold increase in QHS exposure (AUC and C max ) was found in healthy rats after a single oral dose of QHS in the total MAE extract or its fraction MAE-III. In addition, metabolic biotransformation of QHS to the metabolite QHS-M (mediated by CYP3A) was inhibited by MAE or MAE-III. Among nine major components isolated from MAE-III (five sesquiterpenenes, three flavonoids and one phenolic acid), only arteannuin B (AB) showed an inhibition of CYP3A4 (IC 50 1.2μM). The synergism between QHS and AB was supported using in vivo antiplasmodial assay and a pharmacokinetic study in mice. Unfortunately, the synergism cannot reduce the rate of recrudescence. AB was one of main contributors in A. annua leading to enhanced antiplasmodial potency of QHS via regulation of its metabolism. The final recrudescence indicated the careful use of A. annua for malaria treatment unless additional contributing components or antiplasmodial mechanism were found. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Stepped-wedge cluster randomised controlled trial to assess the effectiveness of an electronic medication management system to reduce medication errors, adverse drug events and average length of stay at two paediatric hospitals: a study protocol

PubMed Central

Westbrook, J I; Li, L; Raban, M Z; Baysari, M T; Prgomet, M; Georgiou, A; Kim, T; Lake, R; McCullagh, C; Dalla-Pozza, L; Karnon, J; O'Brien, T A; Ambler, G; Day, R; Cowell, C T; Gazarian, M; Worthington, R; Lehmann, C U; White, L; Barbaric, D; Gardo, A; Kelly, M; Kennedy, P

2016-01-01

Introduction Medication errors are the most frequent cause of preventable harm in hospitals. Medication management in paediatric patients is particularly complex and consequently potential for harms are greater than in adults. Electronic medication management (eMM) systems are heralded as a highly effective intervention to reduce adverse drug events (ADEs), yet internationally evidence of their effectiveness in paediatric populations is limited. This study will assess the effectiveness of an eMM system to reduce medication errors, ADEs and length of stay (LOS). The study will also investigate system impact on clinical work processes. Methods and analysis A stepped-wedge cluster randomised controlled trial (SWCRCT) will measure changes pre-eMM and post-eMM system implementation in prescribing and medication administration error (MAE) rates, potential and actual ADEs, and average LOS. In stage 1, 8 wards within the first paediatric hospital will be randomised to receive the eMM system 1 week apart. In stage 2, the second paediatric hospital will randomise implementation of a modified eMM and outcomes will be assessed. Prescribing errors will be identified through record reviews, and MAEs through direct observation of nurses and record reviews. Actual and potential severity will be assigned. Outcomes will be assessed at the patient-level using mixed models, taking into account correlation of admissions within wards and multiple admissions for the same patient, with adjustment for potential confounders. Interviews and direct observation of clinicians will investigate the effects of the system on workflow. Data from site 1 will be used to develop improvements in the eMM and implemented at site 2, where the SWCRCT design will be repeated (stage 2). Ethics and dissemination The research has been approved by the Human Research Ethics Committee of the Sydney Children's Hospitals Network and Macquarie University. Results will be reported through academic journals and seminar and conference presentations. Trial registration number Australian New Zealand Clinical Trials Registry (ANZCTR) 370325. PMID:27797997

Stepped-wedge cluster randomised controlled trial to assess the effectiveness of an electronic medication management system to reduce medication errors, adverse drug events and average length of stay at two paediatric hospitals: a study protocol.

PubMed

Westbrook, J I; Li, L; Raban, M Z; Baysari, M T; Mumford, V; Prgomet, M; Georgiou, A; Kim, T; Lake, R; McCullagh, C; Dalla-Pozza, L; Karnon, J; O'Brien, T A; Ambler, G; Day, R; Cowell, C T; Gazarian, M; Worthington, R; Lehmann, C U; White, L; Barbaric, D; Gardo, A; Kelly, M; Kennedy, P

2016-10-21

Medication errors are the most frequent cause of preventable harm in hospitals. Medication management in paediatric patients is particularly complex and consequently potential for harms are greater than in adults. Electronic medication management (eMM) systems are heralded as a highly effective intervention to reduce adverse drug events (ADEs), yet internationally evidence of their effectiveness in paediatric populations is limited. This study will assess the effectiveness of an eMM system to reduce medication errors, ADEs and length of stay (LOS). The study will also investigate system impact on clinical work processes. A stepped-wedge cluster randomised controlled trial (SWCRCT) will measure changes pre-eMM and post-eMM system implementation in prescribing and medication administration error (MAE) rates, potential and actual ADEs, and average LOS. In stage 1, 8 wards within the first paediatric hospital will be randomised to receive the eMM system 1 week apart. In stage 2, the second paediatric hospital will randomise implementation of a modified eMM and outcomes will be assessed. Prescribing errors will be identified through record reviews, and MAEs through direct observation of nurses and record reviews. Actual and potential severity will be assigned. Outcomes will be assessed at the patient-level using mixed models, taking into account correlation of admissions within wards and multiple admissions for the same patient, with adjustment for potential confounders. Interviews and direct observation of clinicians will investigate the effects of the system on workflow. Data from site 1 will be used to develop improvements in the eMM and implemented at site 2, where the SWCRCT design will be repeated (stage 2). The research has been approved by the Human Research Ethics Committee of the Sydney Children's Hospitals Network and Macquarie University. Results will be reported through academic journals and seminar and conference presentations. Australian New Zealand Clinical Trials Registry (ANZCTR) 370325. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Effect of atomic monolayer insertions on electric-field-induced rotation of magnetic easy axis

NASA Astrophysics Data System (ADS)

Tsujikawa, M.; Haraguchi, S.; Oda, T.

2012-04-01

We have investigated the electric field (EF) effect on the magnetic anisotropy energy (MAE) in the thin films MgO/M/Fe/Au(001) and MgO/Fe/M(001) (M = Pd, Pt, and Au) by means of first-principles density-functional calculations. We find that the MAE varies linearly with the EF and investigate the change in slope of the MAE as a function of the EF as the buffer layer is changed. We find that a single monatomic buffer layer may be useful for devices that use EF-modified MAE. We simulate the critical EF for easy-axis rotation and discuss interface effects of Mg/Fe and Fe/Au on MAE.

Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data

NASA Astrophysics Data System (ADS)

Wessel, Birgit; Huber, Martin; Wohlfart, Christian; Marschalk, Ursula; Kosmann, Detlev; Roth, Achim

2018-05-01

The primary goal of the German TanDEM-X mission is the generation of a highly accurate and global Digital Elevation Model (DEM) with global accuracies of at least 10 m absolute height error (linear 90% error). The global TanDEM-X DEM acquired with single-pass SAR interferometry was finished in September 2016. This paper provides a unique accuracy assessment of the final TanDEM-X global DEM using two different GPS point reference data sets, which are distributed across all continents, to fully characterize the absolute height error. Firstly, the absolute vertical accuracy is examined by about three million globally distributed kinematic GPS (KGPS) points derived from 19 KGPS tracks covering a total length of about 66,000 km. Secondly, a comparison is performed with more than 23,000 "GPS on Bench Marks" (GPS-on-BM) points provided by the US National Geodetic Survey (NGS) scattered across 14 different land cover types of the US National Land Cover Data base (NLCD). Both GPS comparisons prove an absolute vertical mean error of TanDEM-X DEM smaller than ±0.20 m, a Root Means Square Error (RMSE) smaller than 1.4 m and an excellent absolute 90% linear height error below 2 m. The RMSE values are sensitive to land cover types. For low vegetation the RMSE is ±1.1 m, whereas it is slightly higher for developed areas (±1.4 m) and for forests (±1.8 m). This validation confirms an outstanding absolute height error at 90% confidence level of the global TanDEM-X DEM outperforming the requirement by a factor of five. Due to its extensive and globally distributed reference data sets, this study is of considerable interests for scientific and commercial applications.

Rates of minor adverse events and health resource utilization postcolonoscopy.

PubMed

Marquez Azalgara, Vladimir; Sewitch, Maida J; Joseph, Lawrence; Barkun, Alan N

2014-12-01

Little is known about minor adverse events (MAEs) following outpatient colonoscopies and associated health care resource utilization. To estimate the rates of incident MAE at two, 14 and 30 days postcolonoscopy, and associated health care resource utilization. A secondary aim was to identify factors associated with cumulative 30-day MAE incidence. A longitudinal cohort study was conducted among individuals undergoing an outpatient colonoscopy at the Montreal General Hospital (Montreal, Quebec). Before colonoscopy, consecutive individuals were enrolled and interviewed to obtain data regarding age, sex, comorbidities, use of antiplatelets/anticoagulants and previous symptoms. Endoscopy reports were reviewed for intracolonoscopy procedures (biopsy, polypectomy). Telephone or Internet follow-up was used to obtain data regarding MAEs (abdominal pain, bloating, diarrhea, constipation, nausea, vomiting, blood in the stools, rectal or anal pain, headaches, other) and health resource use (visits to emergency department, primary care doctor, gastroenterologist; consults with nurse, pharmacist or telephone hotline). Rates of incident MAEs and health resources utilization were estimated using Bayesian hierarchical modelling to account for patient clustering within physician practices. Of the 705 individuals approached, 420 (59.6%) were enrolled. Incident MAE rates at the two-, 14- and 30-day follow-ups were 17.3% (95% credible interval [CrI] 8.1% to 30%), 10.5% (95% CrI 2.9% to 23.7%) and 3.2% (95% CrI 0.01% to 19.8%), respectively. The 30-day rate of health resources utilization was 1.7%, with 0.95% of participants seeking the services of a physician. No predictors of the cumulative 30-day incidence of MAEs were identified. The incidence of MAEs was highest in the 48 h following colonoscopy and uncommon after two weeks, supporting the Canadian Association of Gastroenterology's recommendation for assessment of late complications at 14 days. Predictors of new onset of MAEs were not identified, but wide CrIs did not rule out possible associations. Although <1% of participants reported consulting a physician for MAEs, this figure may represent a substantial number of visits given the increasing number of colonoscopies performed annually. Postcolonoscopy MAEs are common, occur mainly in the first two weeks postcolonoscopy and result in little use of health resources.

Assessment of Gamma-Ray Spectra Analysis Method Utilizing the Fireworks Algorithm for various Error Measures

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

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2018-01-01

Significant role in enhancing nuclear nonproliferation plays the analysis of obtained data and the inference of the presence or not of special nuclear materials in them. Among various types of measurements, gamma-ray spectra is the widest used type of data utilized for analysis in nonproliferation. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, with non-zero coefficients expressing the detected signatures. FWA is tested on amore » set of experimentally obtained measurements and various objective functions -MSE, RMSE, Theil-2, MAE, MAPE, MAP- with results exhibiting its potential in providing high accuracy and high precision of detected signatures. Furthermore, FWA is benchmarked against genetic algorithms, and multiple linear regression with results exhibiting its superiority over the rest tested algorithms with respect to precision for MAE, MAPE and MAP measures.« less

Medicines administration for residents with dysphagia in care homes: A small scale observational study to improve practice.

PubMed

Serrano Santos, Jose Manuel; Poland, Fiona; Wright, David; Longmore, Timothy

2016-10-30

In the UK, 69.5% of residents in care homes are exposed to one or more medication errors and 50% have some form of dysphagia. Hospital research identified that nurses frequently crush tablets to facilitate swallowing but this has not been explored in care homes. This project aimed to observe the administration of medicines to patients with dysphagia (PWD) and without in care homes. A convenient sample of general practitioners in North Yorkshire invited care homes with nursing, to participate in the study. A pharmacist specialised in dysphagia observed nurses during drug rounds and compared these practices with national guidelines. Deviations were classified as types of medication administration errors (MAEs). Overall, 738 administrations were observed from 166 patients of which 38 patients (22.9%) had dysphagia. MAE rates were 57.3% and 30.8% for PWD and those without respectively (p<0.001). PWD were more likely to experience inappropriate prescribing (IP). Signs of aspiration were more frequently observed in PWD when IP occurred (p<0.001). Observation of medication administration practices by independent pharmacists may enable the identification of potentially dangerous practices and be used as a method of staff support. Unidentified signs of aspiration suggest that nurses require training in dysphagia and need to communicate its presence to the resident's GP. Further research should explore the design of an effective training for nurses. Copyright © 2016 Elsevier B.V. All rights reserved.

Cybernetic group method of data handling (GMDH) statistical learning for hyperspectral remote sensing inverse problems in coastal ocean optics

NASA Astrophysics Data System (ADS)

Filippi, Anthony Matthew

For complex systems, sufficient a priori knowledge is often lacking about the mathematical or empirical relationship between cause and effect or between inputs and outputs of a given system. Automated machine learning may offer a useful solution in such cases. Coastal marine optical environments represent such a case, as the optical remote sensing inverse problem remains largely unsolved. A self-organizing, cybernetic mathematical modeling approach known as the group method of data handling (GMDH), a type of statistical learning network (SLN), was used to generate explicit spectral inversion models for optically shallow coastal waters. Optically shallow water light fields represent a particularly difficult challenge in oceanographic remote sensing. Several algorithm-input data treatment combinations were utilized in multiple experiments to automatically generate inverse solutions for various inherent optical property (IOP), bottom optical property (BOP), constituent concentration, and bottom depth estimations. The objective was to identify the optimal remote-sensing reflectance Rrs(lambda) inversion algorithm. The GMDH also has the potential of inductive discovery of physical hydro-optical laws. Simulated data were used to develop generalized, quasi-universal relationships. The Hydrolight numerical forward model, based on radiative transfer theory, was used to compute simulated above-water remote-sensing reflectance Rrs(lambda) psuedodata, matching the spectral channels and resolution of the experimental Naval Research Laboratory Ocean PHILLS (Portable Hyperspectral Imager for Low-Light Spectroscopy) sensor. The input-output pairs were for GMDH and artificial neural network (ANN) model development, the latter of which was used as a baseline, or control, algorithm. Both types of models were applied to in situ and aircraft data. Also, in situ spectroradiometer-derived Rrs(lambda) were used as input to an optimization-based inversion procedure. Target variables included bottom depth z b, chlorophyll a concentration [chl- a], spectral bottom irradiance reflectance Rb(lambda), and spectral total absorption a(lambda) and spectral total backscattering bb(lambda) coefficients. When applying the cybernetic and neural models to in situ HyperTSRB-derived Rrs, the difference in the means of the absolute error of the inversion estimates for zb was significant (alpha = 0.05). GMDH yielded significantly better zb than the ANN. The ANN model posted a mean absolute error (MAE) of 0.62214 m, compared with 0.55161 m for GMDH.

Statin-Associated Muscle-Related Adverse Effects: A Case Series of 354 Patients

PubMed Central

Cham, Stephanie; Evans, Marcella A.; Denenberg, Julie O.; Golomb, Beatrice A.

2016-01-01

Study Objective To characterize the properties and natural history of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor (statin)-associated muscle-related adverse effects (MAEs). Design Patient-targeted postmarketing adverse-effect surveillance approach coupling survey design with an open-ended narrative. Setting University-affiliated health care system. Subjects Three hundred fifty-four patients (age range 34–86 yrs) who self-reported muscle-related problems associated with statin therapy. Measurements and Main Results Patients with perceived statin-associated MAEs completed a survey assessing statin drugs and dosages; characteristics of the MAEs; time course of onset, resolution, or recurrence; and impact on quality of life (QOL). Cases were assessed for putative drug adverse-effect causality by using the Naranjo adverse drug reaction probability scale criteria and were evaluated for inclusion in groups for which mortality benefit with statins has been shown. Patients reported muscle pain (93%), fatigue (88%), and weakness (85%). Three hundred patients (85%) met literature criteria for probable or definite drug adverse-effect causality. Ninety-four percent of atorvastatin usages (240/255) generated MAEs versus 61% of lovastatin usages (38/62, p<0.0001). Higher potency statins reproduced MAEs in 100% of 39 rechallenges versus 73% (29/40) with lower potency rechallenges (p<0.01). Time course of onset after statin initiation varied (median 14 wks); some MAEs occurred after long-term symptom-free use. Recurrence with rechallenge had a significantly shorter latency to onset (median 2 wks). The MAEs adversely affected all assessed functional and QOL domains. Most patients with probable or definite MAEs were in categories for which available randomized controlled trial evidence shows no trend to all-cause mortality benefit with statin therapy. Conclusion This study complements available information on the properties and natural history of statin-associated MAEs, affirming dose dependence and strong QOL impact. The data indicating a dose-dependent relationship between MAE risk and recurrence suggest lower potency statins or discontinuation may bear consideration for ameliorating symptoms. PMID:20500044

Statin-associated muscle-related adverse effects: a case series of 354 patients.

PubMed

Cham, Stephanie; Evans, Marcella A; Denenberg, Julie O; Golomb, Beatrice A

2010-06-01

To characterize the properties and natural history of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor (statin)-associated muscle-related adverse effects (MAEs). Patient-targeted postmarketing adverse-effect surveillance approach coupling survey design with an open-ended narrative. University-affiliated health care system. Three hundred fifty-four patients (age range 34-86 yrs) who self-reported muscle-related problems associated with statin therapy. Patients with perceived statin-associated MAEs completed a survey assessing statin drugs and dosages; characteristics of the MAEs; time course of onset, resolution, or recurrence; and impact on quality of life (QOL). Cases were assessed for putative drug adverse-effect causality by using the Naranjo adverse drug reaction probability scale criteria and were evaluated for inclusion in groups for which mortality benefit with statins has been shown. Patients reported muscle pain (93%), fatigue (88%), and weakness (85%). Three hundred patients (85%) met literature criteria for probable or definite drug adverse-effect causality. Ninety-four percent of atorvastatin usages (240/255) generated MAEs versus 61% of lovastatin usages (38/62, p<0.0001). Higher potency statins reproduced MAEs in 100% of 39 rechallenges versus 73% (29/40) with lower potency rechallenges (p<0.01). Time course of onset after statin initiation varied (median 14 wks); some MAEs occurred after long-term symptom-free use. Recurrence with rechallenge had a significantly shorter latency to onset (median 2 wks). The MAEs adversely affected all assessed functional and QOL domains. Most patients with probable or definite MAEs were in categories for which available randomized controlled trial evidence shows no trend to all-cause mortality benefit with statin therapy. This study complements available information on the properties and natural history of statin-associated MAEs, affirming dose dependence and strong QOL impact. The data indicating a dose-dependent relationship between MAE risk and recurrence suggest lower potency statins or discontinuation may bear consideration for ameliorating symptoms.

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.

PubMed

Mutasa, Simukayi; Chang, Peter D; Ruzal-Shapiro, Carrie; Ayyala, Rama

2018-02-05

Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch Dis Child 81.2:172-173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks. Recently, the advent and proliferation of convolutional neural networks (CNNs) has shown promise in a variety of medical imaging applications. There have been at least two published applications of using deep learning for evaluation of bone age (Med Image Anal 36:41-51, 2017; JDI 1-5, 2017). However, current implementations are limited by a combination of both architecture design and relatively small datasets. The purpose of this study is to demonstrate the benefits of a customized neural network algorithm carefully calibrated to the evaluation of bone age utilizing a relatively large institutional dataset. In doing so, this study will aim to show that advanced architectures can be successfully trained from scratch in the medical imaging domain and can generate results that outperform any existing proposed algorithm. The training data consisted of 10,289 images of different skeletal age examinations, 8909 from the hospital Picture Archiving and Communication System at our institution and 1383 from the public Digital Hand Atlas Database. The data was separated into four cohorts, one each for male and female children above the age of 8, and one each for male and female children below the age of 10. The testing set consisted of 20 radiographs of each 1-year-age cohort from 0 to 1 years to 14-15+ years, half male and half female. The testing set included left-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is required for successful implementation given the relatively small datasets in medical imaging. Instead we show that a small, customized architecture incorporating advanced CNN strategies can indeed be trained from scratch, yielding significant improvements in algorithm accuracy. It should be noted that for all four cohorts, testing error outperformed validation error. One reason for this is that our ground truth for our test set was obtained by averaging two pediatric radiologist reads compared to our training data for which only a single read was used. This suggests that despite relatively noisy training data, the algorithm could successfully model the variation between observers and generate estimates that are close to the expected ground truth.

Effects of Music Aerobic Exercise on Depression and Brain-Derived Neurotrophic Factor Levels in Community Dwelling Women

PubMed Central

Yeh, Shu-Hui; Lin, Li-Wei; Chuang, Yu Kuan; Liu, Cheng-Ling; Tsai, Lu-Jen; Tsuei, Feng-Shiou; Lee, Ming-Tsung; Hsiao, Chiu-Yueh; Yang, Kuender D.

2015-01-01

A randomized clinical trial was utilized to compare the improvement of depression and brain-derived neurotrophic factor (BDNF) levels between community women with and without music aerobic exercise (MAE) for 12 weeks. The MAE group involved 47 eligible participants, whereas the comparison group had 59 participants. No significant differences were recorded in the demographic characteristics between the participants in the MAE group and the comparison group. Forty-one participants in the MAE group and 26 in the comparison group completed a pre- and posttest. The MAE group displayed significant improvement in depression scores (p = 0.016), decreased depression symptoms in crying (p = 0.03), appetite (p = 0.006), and fatigue (p = 0.011). The BDNF levels of the participants significantly increased after the 12-week MAE (p = 0.042). The parallel comparison group revealed no significant changes in depression scores or BDNF levels. In summary, the 12-week MAE had a significant impact on the enhancement of BDNF levels and improvement of depression symptoms. Middle-aged community women are encouraged to exercise moderately to improve their depression symptoms and BDNF levels. PMID:26075212

Influence of the extraction process on the rheological and structural properties of agars.

PubMed

Sousa, Ana M M; Borges, João; Silva, A Fernando; Gonçalves, Maria P

2013-07-01

Agars obtained by traditional hot-water (TWE) and microwave-assisted (MAE) extractions were compared in terms of their rheological and physicochemical properties and molecular self-association in solutions of low (0.05%, w/w) and high (1.5%, w/w) polymer concentrations. At low concentration, thin gelled layers were imaged by AFM. Slow or rapid cooling of the solutions influenced structure formation. In each case, TWE and MAE agar structures were different and apparently larger for MAE. At high concentration, progressive structural reinforcement was seen; while TWE agar showed a more open and irregular 3D network, MAE agar gel imaged by cryoSEM was denser and fairly uniform. The rheological (higher thermal stability and consistency) and mechanical (higher gel strength) behaviors of MAE agar seemed consistent with a positive effect of molecular mass and 3,6-anhydro-α-l-galactose content. MAE produced non-degraded agar comparable with commercial ones and if properly monitored, could be a promising alternative to TWE. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fabrication of Flexible Microneedle Array Electrodes for Wearable Bio-Signal Recording.

PubMed

Ren, Lei; Xu, Shujia; Gao, Jie; Lin, Zi; Chen, Zhipeng; Liu, Bin; Liang, Liang; Jiang, Lelun

2018-04-13

Laser-direct writing (LDW) and magneto-rheological drawing lithography (MRDL) have been proposed for the fabrication of a flexible microneedle array electrode (MAE) for wearable bio-signal monitoring. Conductive patterns were directly written onto the flexible polyethylene terephthalate (PET) substrate by LDW. The microneedle array was rapidly drawn and formed from the droplets of curable magnetorheological fluid with the assistance of an external magnetic field by MRDL. A flexible MAE can maintain a stable contact interface with curved human skin due to the flexibility of the PET substrate. Compared with Ag/AgCl electrodes and flexible dry electrodes (FDE), the electrode-skin interface impedance of flexible MAE was the minimum even after a 50-cycle bending test. Flexible MAE can record electromyography (EMG), electroencephalography (EEG) and static electrocardiography (ECG) signals with good fidelity. The main features of the dynamic ECG signal recorded by flexible MAE are the most distinguishable with the least moving artifacts. Flexible MAE is an attractive candidate electrode for wearable bio-signal monitoring.

Fabrication of Flexible Microneedle Array Electrodes for Wearable Bio-Signal Recording

PubMed Central

Ren, Lei; Xu, Shujia; Gao, Jie; Lin, Zi; Chen, Zhipeng; Liu, Bin; Liang, Liang; Jiang, Lelun

2018-01-01

Laser-direct writing (LDW) and magneto-rheological drawing lithography (MRDL) have been proposed for the fabrication of a flexible microneedle array electrode (MAE) for wearable bio-signal monitoring. Conductive patterns were directly written onto the flexible polyethylene terephthalate (PET) substrate by LDW. The microneedle array was rapidly drawn and formed from the droplets of curable magnetorheological fluid with the assistance of an external magnetic field by MRDL. A flexible MAE can maintain a stable contact interface with curved human skin due to the flexibility of the PET substrate. Compared with Ag/AgCl electrodes and flexible dry electrodes (FDE), the electrode–skin interface impedance of flexible MAE was the minimum even after a 50-cycle bending test. Flexible MAE can record electromyography (EMG), electroencephalography (EEG) and static electrocardiography (ECG) signals with good fidelity. The main features of the dynamic ECG signal recorded by flexible MAE are the most distinguishable with the least moving artifacts. Flexible MAE is an attractive candidate electrode for wearable bio-signal monitoring. PMID:29652835

Effects of martial arts exercise on body composition, serum biomarkers and quality of life in overweight/obese premenopausal women: a pilot study

PubMed Central

Chyu, Ming-Chien; Zhang, Yan; Brismée, Jean-Michel; Dagda, Raul Y.; Chaung, Eugene; Von Bergen, Vera; Doctolero, Susan; Shen, Chwan-Li

2013-01-01

Various exercise interventions have been shown to benefit weight control and general health in different populations. However, very few studies have been conducted on martial arts exercise (MAE). The objective of this pilot study is to evaluate the efficacy of 12 weeks of MAE intervention on body composition, serum biomarkers and quality of life (QOL) in overweight/obese premenopausal women. We found that subjects in the MAE group did not lose body weight, while they significantly decreased fat-free mass and muscle mass as compared to those in the control group, who demonstrated an increase in these parameters. The MAE group demonstrated an increase in serum IGF-I concentration, but no change in others. MAE may be a feasible and effective approach to improve body composition and QOL in overweight/obese premenopausal women. Our study underscores the need for further studies using larger samples to establish possible benefits of MAE in various populations. PMID:24665215

Does the Length of Elbow Flexors and Visual Feedback Have Effect on Accuracy of Isometric Muscle Contraction in Men after Stroke?

PubMed Central

Juodzbaliene, Vilma; Darbutas, Tomas; Skurvydas, Albertas

2016-01-01

The aim of the study was to determine the effect of different muscle length and visual feedback information (VFI) on accuracy of isometric contraction of elbow flexors in men after an ischemic stroke (IS). Materials and Methods. Maximum voluntary muscle contraction force (MVMCF) and accurate determinate muscle force (20% of MVMCF) developed during an isometric contraction of elbow flexors in 90° and 60° of elbow flexion were measured by an isokinetic dynamometer in healthy subjects (MH, n = 20) and subjects after an IS during their postrehabilitation period (MS, n = 20). Results. In order to evaluate the accuracy of the isometric contraction of the elbow flexors absolute errors were calculated. The absolute errors provided information about the difference between determinate and achieved muscle force. Conclusions. There is a tendency that greater absolute errors generating determinate force are made by MH and MS subjects in case of a greater elbow flexors length despite presence of VFI. Absolute errors also increase in both groups in case of a greater elbow flexors length without VFI. MS subjects make greater absolute errors generating determinate force without VFI in comparison with MH in shorter elbow flexors length. PMID:27042670

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Band-filling effect on magnetic anisotropy using a Green's function method

DOE PAGES

Ke, Liqin; van Schilfgaarde, Mark

2015-07-28

We use an analytical model to describe the magnetocrystalline anisotropy energy (MAE) in solids as a function of band filling. The MAE is evaluated in second-order perturbation theory, which makes it possible to decompose the MAE into a sum of transitions between occupied and unoccupied pairs. The model enables us to characterize the MAE as a sum of contributions from different, often competing terms. The nitridometalates Li 2[(Li 1–xT x)N], with T= Mn, Fe, Co, Ni, provide a system where the model is very effective because atomiclike orbital characters are preserved and the decomposition is fairly clean. The model resultsmore » are also compared against MAE evaluated directly from first-principles calculations for this system. Good qualitative agreement is found.« less

Rates of minor adverse events and health resource utilization postcolonoscopy

PubMed Central

Azalgara, Vladimir Marquez; Sewitch, Maida J; Joseph, Lawrence; Barkun, Alan N

2014-01-01

BACKGROUND: Little is known about minor adverse events (MAEs) following outpatient colonoscopies and associated health care resource utilization. OBJECTIVE: To estimate the rates of incident MAE at two, 14 and 30 days postcolonoscopy, and associated health care resource utilization. A secondary aim was to identify factors associated with cumulative 30-day MAE incidence. METHODS: A longitudinal cohort study was conducted among individuals undergoing an outpatient colonoscopy at the Montreal General Hospital (Montreal, Quebec). Before colonoscopy, consecutive individuals were enrolled and interviewed to obtain data regarding age, sex, comorbidities, use of antiplatelets/anticoagulants and previous symptoms. Endoscopy reports were reviewed for intracolonoscopy procedures (biopsy, polypectomy). Telephone or Internet follow-up was used to obtain data regarding MAEs (abdominal pain, bloating, diarrhea, constipation, nausea, vomiting, blood in the stools, rectal or anal pain, headaches, other) and health resource use (visits to emergency department, primary care doctor, gastroenterologist; consults with nurse, pharmacist or telephone hotline). Rates of incident MAEs and health resources utilization were estimated using Bayesian hierarchical modelling to account for patient clustering within physician practices. RESULTS: Of the 705 individuals approached, 420 (59.6%) were enrolled. Incident MAE rates at the two-, 14- and 30-day follow-ups were 17.3% (95% credible interval [CrI] 8.1% to 30%), 10.5% (95% CrI 2.9% to 23.7%) and 3.2% (95% CrI 0.01% to 19.8%), respectively. The 30-day rate of health resources utilization was 1.7%, with 0.95% of participants seeking the services of a physician. No predictors of the cumulative 30-day incidence of MAEs were identified. DISCUSSION: The incidence of MAEs was highest in the 48 h following colonoscopy and uncommon after two weeks, supporting the Canadian Association of Gastroenterology’s recommendation for assessment of late complications at 14 days. Predictors of new onset of MAEs were not identified, but wide CrIs did not rule out possible associations. Although <1% of participants reported consulting a physician for MAEs, this figure may represent a substantial number of visits given the increasing number of colonoscopies performed annually. CONCLUSION: Postcolonoscopy MAEs are common, occur mainly in the first two weeks postcolonoscopy and result in little use of health resources. PMID:25575107

SU-D-207A-01: Female Pelvic Synthetic CT Generation Based On Joint Shape and Intensity Analysis

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

Liu, L; Jolly, S; Cao, Y

Purpose: To develop a method for generating female pelvic synthetic CT (MRCT) images from a single MR scan and evaluate its utility in radiotherapy. Methods: Under IRB-approval, an imaging sequence (T1-VIBE-Dixon) was acquired for 10 patients. This sequence yields 3 useful image volumes of different contrast (“in-phase” T1-weighted, fat and water). A previously published pelvic bone shape model was used to generate a rough bone mask for each patient. A modified fuzzy c-means classification was performed on the multi spectral MR data, with a regularization term that utilizes the prior knowledge provided by the bone mask and addresses the intensitymore » overlap between different tissue types. A weighted sum of classification probabilities with attenuation values yielded MRCT volumes. The mean absolute error (MAE) between MRCT and real CT on various regions was calculated following deformable alignment (Velocity). Intensity modulated Treatment plans based on actual CT and MRCT were made and compared. Results: The average/standard deviation of MAE across 10 patients was 10.1/6.7 HU for muscle, 6.7/4.6 HU for fat, 136.9/53.5 HU for bony tissues under 850 HU (97% of total bone volume), 188.9/119.3 HU for bony tissues above 850 HU and 17.3/13.3 HU for intrapelvic soft tissues. Calculated doses were comparable for plans generated on CT and calculated using MRCT densities or vice versa, with differences in PTV D99% (mean/σ) of (–0.1/0.2 Gy) and (0.3/0.2 Gy), PTV D0.5cc of (–0.3/0.2 Gy) and (–0.4/1.7 Gy). OAR differences were similarly small for comparable structures, with differences in bowel V50Gy of (–0.3/0.2%) and (0.0/0.2%), femur V30Gy of (0.7/1.2%) and (0.2/1.2%), sacrum V20GY of (0.0/0.1%) and (–0.1/1.1%) and mean pelvic V20Gy of (0.0/0.1%) and (0.6/1.8%). Conclusion: MRCT based on a single imaging sequence in the female pelvis is feasible, with acceptably small variations in attenuation estimates and calculated doses to target and critical organs. Work supported by NIHR01EB016079.« less

Ab initio study of magnetocrystalline anisotropy, magnetostriction, and Fermi surface of L10 FeNi (tetrataenite)

NASA Astrophysics Data System (ADS)

Werwiński, Mirosław; Marciniak, Wojciech

2017-12-01

We present results of ab initio calculations of several L10 FeNi characteristics, such as the summary of the magnetocrystalline anisotropy energies (MAEs), the full potential calculations of the anisotropy constant K 3, and the combined analysis of the Fermi surface and 3D {k} -resolved MAE. Other calculated parameters are the spin and orbital magnetic moments, the magnetostrictive coefficient λ0 0 1 , the bulk modulus B 0, and the lattice parameters. The MAEs summary shows rather big discrepancies among the experimental MAEs from the literature and also among the calculated MAE’s. The MAEs calculated in this work with the full potential and generalized gradient approximation (GGA) are equal to 0.47 MJ m-3 from WIEN2k, 0.34 MJ m-3 from FPLO, and 0.23 MJ m-3 from FP-SPR-KKR code. These results suggest that the MAE in GGA is below 0.5 MJ m-3 . It is expected that due to the limitations of the GGA, this value is underestimated. The L10 FeNi has further potential to improve its MAE by modifications, like e.g. tetragonal strain or alloying. The presented 3D {k} -resolved map of the MAE combined with the Fermi surface gives a complete picture of the MAE contributions in the Brillouin zone. The obtained, from the full potential FP-SPR-KKR method, magnetocrystalline anisotropy constants K 2 and K 3 are several orders of magnitude smaller than the MAE/K 1 and equal to -2.0 kJ m-3 and 110 J m-3 , respectively. The calculated spin and orbital magnetic moments of the L10 FeNi are equal to 2.72 and 0.054 μB for Fe and 0.53 and 0.039 μB for Ni atoms, respectively. The calculations of geometry optimization lead to a c/a ratio equal to 1.0036, B 0 equal to 194 GPa, and λ0 0 1 equal to 9.4  ×  10-6.

Convective heat transfer in a high aspect ratio minichannel heated on one side

DOE PAGES

Forrest, Eric C.; Hu, Lin -Wen; Buongiorno, Jacopo; ...

2015-10-21

Experimental results are presented for single-phase heat transfer in a narrow rectangular minichannel heated on one side. The aspect ratio and gap thickness of the test channel were 29:1 and 1.96 mm, respectively. Friction pressure drop and Nusselt numbers are reported for the transition and fully turbulent flow regimes, with Prandtl numbers ranging from 2.2 to 5.4. Turbulent friction pressure drop for the high aspect ratio channel is well-correlated by the Blasius solution when a modified Reynolds number, based upon a laminar equivalent diameter, is utilized. The critical Reynolds number for the channel falls between 3500 and 4000, with Nusseltmore » numbers in the transition regime being reasonably predicted by Gnielinski's correlation. The dependence of the heat transfer coefficient on the Prandtl number is larger than that predicted by circular tube correlations, and is likely a result of the asymmetric heating. The problem of asymmetric heating condition is approached theoretically using a boundary layer analysis with a two-region wall layer model, similar to that originally proposed by Prandtl. The analysis clarifies the influence of asymmetric heating on the Nusselt number and correctly predicts the experimentally observed trend with Prandtl number. Furthermore, a semi-analytic correlation is derived from the analysis that accounts for the effect of aspect ratio and asymmetric heating, and is shown to predict the experimental results of this study with a mean absolute error (MAE) of less than 5% for 4000 < Re < 70,000.« less

Computer vision-based method for classification of wheat grains using artificial neural network.

PubMed

Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

2017-06-01

A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Expanding the geography of evapotranspiration: An improved method to quantify land-to-air water fluxes in tropical and subtropical regions

PubMed Central

Jerszurki, Daniela; Souza, Jorge L. M.; Silva, Lucas C. R.

2017-01-01

The development of new reference evapotranspiration (ETo) methods hold significant promise for improving our quantitative understanding of climatic impacts on water loss from the land to the atmosphere. To address the challenge of estimating ETo in tropical and subtropical regions where direct measurements are scarce we tested a new method based on geographical patterns of extraterrestrial radiation (Ra) and atmospheric water potential (Ψair). Our approach consisted of generating daily estimates of ETo across several climate zones in Brazil–as a model system–which we compared with standard EToPM (Penman-Monteith) estimates. In contrast with EToPM, the simplified method (EToMJS) relies solely on Ψair calculated from widely available air temperature (oC) and relative humidity (%) data, which combined with Ra data resulted in reliable estimates of equivalent evaporation (Ee) and ETo. We used regression analyses of Ψair vs EToPM and Ee vs EToPM to calibrate the EToMJS(Ψair) and EToMJS estimates from 2004 to 2014 and between seasons and climatic zone. Finally, we evaluated the performance of the new method based on the coefficient of determination (R2) and correlation (R), index of agreement “d”, mean absolute error (MAE) and mean reason (MR). This evaluation confirmed the suitability of the EToMJS method for application in tropical and subtropical regions, where the climatic information needed for the standard EToPM calculation is absent. PMID:28658324

Expanding the geography of evapotranspiration: An improved method to quantify land-to-air water fluxes in tropical and subtropical regions.

PubMed

Jerszurki, Daniela; Souza, Jorge L M; Silva, Lucas C R

2017-01-01

The development of new reference evapotranspiration (ETo) methods hold significant promise for improving our quantitative understanding of climatic impacts on water loss from the land to the atmosphere. To address the challenge of estimating ETo in tropical and subtropical regions where direct measurements are scarce we tested a new method based on geographical patterns of extraterrestrial radiation (Ra) and atmospheric water potential (Ψair). Our approach consisted of generating daily estimates of ETo across several climate zones in Brazil-as a model system-which we compared with standard EToPM (Penman-Monteith) estimates. In contrast with EToPM, the simplified method (EToMJS) relies solely on Ψair calculated from widely available air temperature (oC) and relative humidity (%) data, which combined with Ra data resulted in reliable estimates of equivalent evaporation (Ee) and ETo. We used regression analyses of Ψair vs EToPM and Ee vs EToPM to calibrate the EToMJS(Ψair) and EToMJS estimates from 2004 to 2014 and between seasons and climatic zone. Finally, we evaluated the performance of the new method based on the coefficient of determination (R2) and correlation (R), index of agreement "d", mean absolute error (MAE) and mean reason (MR). This evaluation confirmed the suitability of the EToMJS method for application in tropical and subtropical regions, where the climatic information needed for the standard EToPM calculation is absent.

Prediction of stream volatilization coefficients

USGS Publications Warehouse

Rathbun, Ronald E.

1990-01-01

Equations are developed for predicting the liquid-film and gas-film reference-substance parameters for quantifying volatilization of organic solutes from streams. Molecular weight and molecular-diffusion coefficients of the solute are used as correlating parameters. Equations for predicting molecular-diffusion coefficients of organic solutes in water and air are developed, with molecular weight and molal volume as parameters. Mean absolute errors of prediction for diffusion coefficients in water are 9.97% for the molecular-weight equation, 6.45% for the molal-volume equation. The mean absolute error for the diffusion coefficient in air is 5.79% for the molal-volume equation. Molecular weight is not a satisfactory correlating parameter for diffusion in air because two equations are necessary to describe the values in the data set. The best predictive equation for the liquid-film reference-substance parameter has a mean absolute error of 5.74%, with molal volume as the correlating parameter. The best equation for the gas-film parameter has a mean absolute error of 7.80%, with molecular weight as the correlating parameter.

Optimized post-operative surveillance of permanent pacemakers by home monitoring: the OEDIPE trial.

PubMed

Halimi, Franck; Clémenty, Jacques; Attuel, Patrick; Dessenne, Xavier; Amara, Walid

2008-12-01

The OEDIPE trial examined the safety and efficacy of an abbreviated hospitalization after implantation or replacement of dual-chamber pacemakers (PM) using a telecardiology-based ambulatory surveillance programme. Patients were randomly assigned to (i) an active group, discharged from the hospital 24 h after a first PM implant or 4-6 h after replacement, and followed for 4 weeks with Home-Monitoring (HM), or (ii) a control group followed for 4 weeks according to usual medical practices. The primary objective was to confirm that the proportion of patients who experienced one or more major adverse events (MAE) was not higher in the active than in the control group. The study included 379 patients. At least one treatment-related MAE was observed in 9.2% of patients (n = 17) assigned to the active group vs. 13.3% of patients (n = 26) in the control group (P = 0.21), a 4.1% absolute risk reduction (95% CI -2.2 to 10.4; P = 0.98). By study design, the mean hospitalization duration was 34% shorter in the active than in the control group (P < 0.001), and HM facilitated the early detection of technical issues and detectable clinical anomalies. Early discharge with HM after PM implantation or replacement was safe and facilitated the monitoring of patients in the month following the procedure.

Optimized post-operative surveillance of permanent pacemakers by home monitoring: the OEDIPE trial

PubMed Central

Halimi, Franck; Clémenty, Jacques; Attuel, Patrick; Dessenne, Xavier; Amara, Walid

2008-01-01

Aims The ŒDIPE trial examined the safety and efficacy of an abbreviated hospitalization after implantation or replacement of dual-chamber pacemakers (PM) using a telecardiology-based ambulatory surveillance programme. Methods and results Patients were randomly assigned to (i) an active group, discharged from the hospital 24 h after a first PM implant or 4–6 h after replacement, and followed for 4 weeks with Home-Monitoring (HM), or (ii) a control group followed for 4 weeks according to usual medical practices. The primary objective was to confirm that the proportion of patients who experienced one or more major adverse events (MAE) was not higher in the active than in the control group. The study included 379 patients. At least one treatment-related MAE was observed in 9.2% of patients (n = 17) assigned to the active group vs. 13.3% of patients (n = 26) in the control group (P = 0.21), a 4.1% absolute risk reduction (95% CI −2.2 to 10.4; P = 0.98). By study design, the mean hospitalization duration was 34% shorter in the active than in the control group (P < 0.001), and HM facilitated the early detection of technical issues and detectable clinical anomalies. Conclusion Early discharge with HM after PM implantation or replacement was safe and facilitated the monitoring of patients in the month following the procedure. PMID:18775878

Vitamin A status of the minority ethnic group of Karen hill tribe children aged 1-6 years in Northern Thailand.

PubMed

Tienboon, Prasong; Wangpakapattanawong, Prasit

2007-01-01

Vitamin A deficiency (VAD) is the most common cause of childhood blindness in the developing world. It is estimated that by giving adequate vitamin A, in vitamin A deficient populations, child mortality from measles can be reduced by 50%, and mortality from diarrheal disease by 40%. Overall mortality in children 6-59 months of age can be reduced by 23%. This paper reported results from a study of vitamin A status and malnutrition of the minority ethnic group of Karen hill tribe children aged 1-6 years in the north of Thailand. All children aged 1-6 years (N = 158; 83 boys, 75 girls) from the three Karen villages (Mae Hae Tai, Mae Yot, Mae Raek) of Mae Chaem district in the north of Thailand were studied. The Karen is the largest mountain ethnic minority ("hill tribe") group in Thailand. All children were examined by a qualified medical doctor and were assessed for their vitamin A intakes using 24 hours dietary recall. Thai food composition table from Ministry of Health, Thailand were used as references. The results were compared with the Thai Recommended Dietary Allowances. Children aged 1-3 years and 4-6 years were separately analysed due to the differences in Thai Recommended Dietary Allowances between the two age groups. A whole blood of 300 microL was obtained by "fingerstick" for determination of serum vitamin A. Community or village's vitamin A status was assessed by using Simplified Dietary Assessment (SDA) method and Helen Keller International (HKI) food frequency method. Descriptive statistics were used to analyse the data. All families of the study boys and girls had income lower than the Thailand poverty line (US $ 1,000/year). On average, 63% of children from Mae Hae Tai village, 1.5% of children from Mae Yot village and none of children from Mae Raek village had serum vitamin A<0.7 micromol/L which indicated VAD. All boys and only girls from Mae Raek village consumed vitamin A more than the Thai RDA but girls from Mae Hae Tai village and Mae Yot village consumed vitamin A less than the Thai RDA. Both boys and girls from Mae Raek village and also girls from Mae Yot village consumed vitamin A more than the Thai RDA. Using SDA and HKI methods to assess vitamin A status of the villages to see whether VAD is a village's nutritional problem, it was found that all children from the three villages were at risk of VAD. In order to improve vitamin A status of the Karen children in Mae Chaem district, recommendations were made as follow: (1) increased use of fat and oil, particularly in areas with high risk of VAD; (2) more general work with Karen communities on how children's diets might be improved in a culturally acceptable manner, so as to bring vitamin A consumption closer to recommended allowance level.

Fringe order correction for the absolute phase recovered by two selected spatial frequency fringe projections in fringe projection profilometry.

PubMed

Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun

2017-08-01

The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.

Application Bayesian Model Averaging method for ensemble system for Poland

NASA Astrophysics Data System (ADS)

Guzikowski, Jakub; Czerwinska, Agnieszka

2014-05-01

The aim of the project is to evaluate methods for generating numerical ensemble weather prediction using a meteorological data from The Weather Research & Forecasting Model and calibrating this data by means of Bayesian Model Averaging (WRF BMA) approach. We are constructing height resolution short range ensemble forecasts using meteorological data (temperature) generated by nine WRF's models. WRF models have 35 vertical levels and 2.5 km x 2.5 km horizontal resolution. The main emphasis is that the used ensemble members has a different parameterization of the physical phenomena occurring in the boundary layer. To calibrate an ensemble forecast we use Bayesian Model Averaging (BMA) approach. The BMA predictive Probability Density Function (PDF) is a weighted average of predictive PDFs associated with each individual ensemble member, with weights that reflect the member's relative skill. For test we chose a case with heat wave and convective weather conditions in Poland area from 23th July to 1st August 2013. From 23th July to 29th July 2013 temperature oscillated below or above 30 Celsius degree in many meteorology stations and new temperature records were added. During this time the growth of the hospitalized patients with cardiovascular system problems was registered. On 29th July 2013 an advection of moist tropical air masses was recorded in the area of Poland causes strong convection event with mesoscale convection system (MCS). MCS caused local flooding, damage to the transport infrastructure, destroyed buildings, trees and injuries and direct threat of life. Comparison of the meteorological data from ensemble system with the data recorded on 74 weather stations localized in Poland is made. We prepare a set of the model - observations pairs. Then, the obtained data from single ensemble members and median from WRF BMA system are evaluated on the basis of the deterministic statistical error Root Mean Square Error (RMSE), Mean Absolute Error (MAE). To evaluation probabilistic data The Brier Score (BS) and Continuous Ranked Probability Score (CRPS) were used. Finally comparison between BMA calibrated data and data from ensemble members will be displayed.

Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

PubMed

Mueller, Silke C; Drewelow, Bernd

2013-05-01

The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam. A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction. A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data

NASA Astrophysics Data System (ADS)

Bibi, Humera; Alam, Khan; Chishtie, Farrukh; Bibi, Samina; Shahid, Imran; Blaschke, Thomas

2015-06-01

This study provides an intercomparison of aerosol optical depth (AOD) retrievals from satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation over Karachi, Lahore, Jaipur, and Kanpur between 2007 and 2013, with validation against AOD observations from the ground-based Aerosol Robotic Network (AERONET). Both MODIS Deep Blue (MODISDB) and MODIS Standard (MODISSTD) products were compared with the AERONET products. The MODISSTD-AERONET comparisons revealed a high degree of correlation for the four investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDB-AERONET comparisons revealed even better correlations, and the MISR-AERONET comparisons also indicated strong correlations, as did the OMI-AERONET comparisons, while the CALIPSO-AERONET comparisons revealed only poor correlations due to the limited number of data points available. We also computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB). Using AERONET data to validate MODISSTD, MODISDB, MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over vegetated locations than over un-vegetated locations, while MISR data was more accurate over areas close to the ocean than over other areas. The MISR instrument performed better than the other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better than those from the other instruments over Lahore and Jaipur. We also computed the expected error bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently outperformed MODISDB in all of the investigated areas. High AOD values were observed by the MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (April-August); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast, high AOD values were observed by the CALIPSO instrument between September and December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the AERONET data, but underestimation by the MISR instrument.

Type of featural attention differentially modulates hMT+ responses to illusory motion aftereffects.

PubMed

Castelo-Branco, Miguel; Kozak, Lajos R; Formisano, Elia; Teixeira, João; Xavier, João; Goebel, Rainer

2009-11-01

Activity in the human motion complex (hMT(+)/V5) is related to the perception of motion, be it either real surface motion or an illusion of motion such as apparent motion (AM) or motion aftereffect (MAE). It is a long-lasting debate whether illusory motion-related activations in hMT(+) represent the motion itself or attention to it. We have asked whether hMT(+) responses to MAEs are present when shifts in arousal are suppressed and attention is focused on concurrent motion versus nonmotion features. Significant enhancement of hMT(+) activity was observed during MAEs when attention was focused either on concurrent spatial angle or color features. This observation was confirmed by direct comparison of adapting (MAE inducing) versus nonadapting conditions. In contrast, this effect was diminished when subjects had to report on concomitant speed changes of superimposed AM. The same finding was observed for concomitant orthogonal real motion (RM), suggesting that selective attention to concurrent illusory or real motion was interfering with the saliency of MAE signals in hMT(+). We conclude that MAE-related changes in the global activity of hMT(+) are present provided selective attention is not focused on an interfering feature such as concurrent motion. Accordingly, there is a genuine MAE-related motion signal in hMT(+) that is neither explained by shifts in arousal nor by selective attention.

31 CFR 354.7 - Withdrawal of eligible Book-entry Sallie Mae Securities for conversion to definitive form.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 2 2011-07-01 2011-07-01 false Withdrawal of eligible Book-entry... PUBLIC DEBT REGULATIONS GOVERNING BOOK-ENTRY SECURITIES OF THE STUDENT LOAN MARKETING ASSOCIATION (SALLIE MAE) § 354.7 Withdrawal of eligible Book-entry Sallie Mae Securities for conversion to definitive form...

24 CFR 350.8 - Withdrawal of Eligible Book-entry Ginnie Mae Securities for Conversion to Definitive Form.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 2 2012-04-01 2012-04-01 false Withdrawal of Eligible Book-entry... ASSOCIATION, DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT BOOK-ENTRY PROCEDURES § 350.8 Withdrawal of Eligible Book-entry Ginnie Mae Securities for Conversion to Definitive Form. (a) Eligible book-entry Ginnie Mae...

24 CFR 350.8 - Withdrawal of Eligible Book-entry Ginnie Mae Securities for Conversion to Definitive Form.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 2 2013-04-01 2013-04-01 false Withdrawal of Eligible Book-entry... ASSOCIATION, DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT BOOK-ENTRY PROCEDURES § 350.8 Withdrawal of Eligible Book-entry Ginnie Mae Securities for Conversion to Definitive Form. (a) Eligible book-entry Ginnie Mae...

31 CFR 354.5 - Obligations of Sallie Mae; no adverse claims.

Code of Federal Regulations, 2010 CFR

2010-07-01

...-ENTRY SECURITIES OF THE STUDENT LOAN MARKETING ASSOCIATION (SALLIE MAE) § 354.5 Obligations of Sallie... a Federal Reserve Bank or otherwise as provided in § 354.4(c)(1), for the purposes of this part 354, Sallie Mae and the Federal Reserve Banks shall treat the Participant to whose Securities Account an...

Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

EPA Science Inventory

Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations...

Morus alba L. suppresses the development of atopic dermatitis induced by the house dust mite in NC/Nga mice

PubMed Central

2014-01-01

Background Morus alba, a medicinal plant in Asia, has been used traditionally to treat diabetes mellitus and hypoglycemia. However, the effects of M. alba extract (MAE) on atopic dermatitis have not been verified scientifically. We investigated the effects of MAE on atopic dermatitis through in vitro and in vivo experiments. Methods We evaluated the effects of MAE on the production of nitric oxide (NO) and prostaglandin E2 (PGE2) in RAW 264.7, as well as thymus and activation-regulated chemokine (TARC/CCL17) in HaCaT cells. In an in vivo experiment, atopic dermatitis was induced by topical application of house dust mites for four weeks, and the protective effects of MAE were investigated by measuring the severity of the skin reaction on the back and ears, the plasma levels of immunoglobulin E (IgE) and histamine, and histopathological changes in the skin on the back and ears. Results MAE suppressed the production of NO and PGE2 in RAW 264.7 cells, as well as TARC in HaCaT cells, in a dose-dependent manner. MAE treatment of NC/Nga mice reduced the severity of dermatitis and the plasma levels of IgE and histamine. MAE also reduced the histological manifestations of atopic dermatitis-like skin lesions such as erosion, hyperplasia of the epidermis and dermis, and inflammatory cell infiltration in the skin on the back and ears. Conclusion Our results suggest that MAE has potent inhibitory effects on atopic dermatitis-like lesion and may be a beneficial natural resource for the treatment of atopic dermatitis. PMID:24755250

Characteristics of different convective parameterization schemes on the simulation of intensity and track of severe extratropical cyclones over North Atlantic

NASA Astrophysics Data System (ADS)

Pradhan, P. K.; Liberato, Margarida L. R.; Ferreira, Juan A.; Dasamsetti, S.; Vijaya Bhaskara Rao, S.

2018-01-01

The role of the convective parameterization schemes (CPSs) in the ARW-WRF (WRF) mesoscale model is examined for extratropical cyclones (ETCs) over the North Atlantic Ocean. The simulation of very severe winter storms such as Xynthia (2010) and Gong (2013) are considered in this study. Most popular CPSs within WRF model, along with Yonsei University (YSU) planetary boundary layer (PBL) and WSM6 microphysical parameterization schemes are incorporated for the model experiments. For each storm, four numerical experiments were carried out using New Kain Fritsch (NKF), Betts-Miller-Janjic (BMJ), Grell 3D Ensemble (Gr3D) and no convection scheme (NCS) respectively. The prime objectives of these experiments were to recognize the best CPS that can forecast the intensity, track, and landfall over the Iberian Peninsula in advance of two days. The WRF model results such as central sea level pressure (CSLP), wind field, moisture flux convergence, geopotential height, jet stream, track and precipitation have shown sensitivity CPSs. The 48-hour lead simulations with BMJ schemes produce the best simulations both regarding ETCs intensity and track than Gr3D and NKF schemes. The average MAE and RMSE of intensities are least that (6.5 hPa in CSLP and 3.4 ms- 1 in the 10-m wind) found in BMJ scheme. The MAE and RMSE for and intensity and track error have revealed that NCS produces large errors than other CPSs experiments. However, for track simulation of these ETCs, at 72-, 48- and 24-hour means track errors were 440, 390 and 158 km respectively. In brevity, BMJ and Gr3D schemes can be used for short and medium range predictions of the ETCs over North Atlantic. For the evaluation of precipitation distributions using Gr3D scheme are good agreement with TRMM satellite than other CPSs.

Sensorimotor Grounding of Musical Embodiment and the Role of Prediction: A Review

PubMed Central

Maes, Pieter-Jan

2016-01-01

In a previous article, we reviewed empirical evidence demonstrating action-based effects on music perception to substantiate the musical embodiment thesis (Maes et al., 2014). Evidence was largely based on studies demonstrating that music perception automatically engages motor processes, or that body states/movements influence music perception. Here, we argue that more rigorous evidence is needed before any decisive conclusion in favor of a “radical” musical embodiment thesis can be posited. In the current article, we provide a focused review of recent research to collect further evidence for the “radical” embodiment thesis that music perception is a dynamic process firmly rooted in the natural disposition of sounds and the human auditory and motor system. Though, we emphasize that, on top of these natural dispositions, long-term processes operate, rooted in repeated sensorimotor experiences and leading to learning, prediction, and error minimization. This approach sheds new light on the development of musical repertoires, and may refine our understanding of action-based effects on music perception as discussed in our previous article (Maes et al., 2014). Additionally, we discuss two of our recent empirical studies demonstrating that music performance relies on similar principles of sensorimotor dynamics and predictive processing. PMID:26973587

Sensorimotor Grounding of Musical Embodiment and the Role of Prediction: A Review.

PubMed

Maes, Pieter-Jan

2016-01-01

In a previous article, we reviewed empirical evidence demonstrating action-based effects on music perception to substantiate the musical embodiment thesis (Maes et al., 2014). Evidence was largely based on studies demonstrating that music perception automatically engages motor processes, or that body states/movements influence music perception. Here, we argue that more rigorous evidence is needed before any decisive conclusion in favor of a "radical" musical embodiment thesis can be posited. In the current article, we provide a focused review of recent research to collect further evidence for the "radical" embodiment thesis that music perception is a dynamic process firmly rooted in the natural disposition of sounds and the human auditory and motor system. Though, we emphasize that, on top of these natural dispositions, long-term processes operate, rooted in repeated sensorimotor experiences and leading to learning, prediction, and error minimization. This approach sheds new light on the development of musical repertoires, and may refine our understanding of action-based effects on music perception as discussed in our previous article (Maes et al., 2014). Additionally, we discuss two of our recent empirical studies demonstrating that music performance relies on similar principles of sensorimotor dynamics and predictive processing.

Development and Change in Swedish Municipal Adult Education: Occupational Life History Studies and Four Genealogies of Context

ERIC Educational Resources Information Center

Loeb, Ingrid Henning

2007-01-01

This article is based on the author's dissertation work on development and change in Swedish municipal adult education (MAE), investigated through occupational life history studies of four teachers in different municipalities who have worked in MAE since the mid 1970s. Three periods of development--three "eras"--in MAE have been…

78 FR 77450 - Fannie Mae and Freddie Mac Loan Purchase Limits: Request for Public Input on Implementation Issues

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-23

... reductions in Freddie Mac's and Fannie Mae's loan purchase limits. In short, no final decision on loan... decision to direct the setting of new and lower loan purchase limits by the Enterprises. A Plan for Setting... FEDERAL HOUSING FINANCE AGENCY [No. 2013-N-18] Fannie Mae and Freddie Mac Loan Purchase Limits...

Despite a Settlement, Sallie Mae Still Plays Host to College Student-Aid Sites

ERIC Educational Resources Information Center

Hermes, J. J.

2008-01-01

Last April, as part of a $2-million settlement with New York's attorney general, the nation's largest student-loan company, Sallie Mae, agreed to stop providing staff members for colleges' financial-aid offices and call centers at no cost to the institutions. But one year later, Sallie Mae still plays host to the entire online presence for the…

77 FR 74022 - Notice of Proposed Information Collection: Comment Request; Ginnie Mae Mortgage-Backed Securities...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-12

... multiple Issuer MBS is structured so that small issuers, who do not meet the minimum number of loans and... program, securities are backed by single-family or multifamily loans. Under the Ginnie Mae II program, securities are only backed by single family loans. Both the Ginnie Mae I and II MBS are modified pass-through...

A new method for microwave assisted ethanolic extraction of Mentha rotundifolia bioactive terpenoids.

PubMed

García-Sarrió, María Jesús; Sanz, María Luz; Sanz, Jesús; González-Coloma, Azucena; Cristina Soria, Ana

2018-04-14

A new microwave-assisted extraction (MAE) method using ethanol as solvent has been optimized by means of a Box-Behnken experimental design for the enhanced extraction of bioactive terpenoids from Mentha rotundifolia leaves; 100°C, 5 min, 1.125 g dry sample: 10 mL solvent and a single extraction cycle were selected as optimal conditions. Improved performance of MAE method in terms of extraction yield and/or reproducibility over conventional solid-liquid extraction and ultrasound assisted extraction was also previously assessed. A comprehensive characterization of MAE extracts was carried out by GC-MS. A total of 46 compounds, mostly terpenoids, were identified; piperitenone oxide and piperitenone were the major compounds determined. Several neophytadiene isomers were also detected for the first time in MAE extracts. Different procedures (solid-phase extraction and activated charcoal (AC) treatment) were also evaluated for clean-up of MAE extracts, with AC providing the highest enrichment in bioactive terpenoids. Finally, the MAE method here developed is shown as a green, fast, efficient and reproducible liquid extraction methodology to obtain M. rotundifolia bioactive extracts for further application, among others, as food preservatives. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prevalence of Haplorchis taichui and Haplorchoides sp. Metacercariae in Freshwater Fish from Water Reservoirs, Chiang Mai, Thailand

PubMed Central

Nithikathkul, Choosak

2008-01-01

A parasitological investigation on trematode metacercariae was made on 62 freshwater fishes of 13 species in northern Thailand; Cyclocheilichthys apogon, Puntioplites proctozysron, Labiobarbus siamensis, Barbodes gonionotus, Barbodes altus, Henicorhynchus siamensis, Osteochilus hasselti, Notopterus notopterus, Mystacoleucus marginatus, Anabas testudineus, Systomus orphoides, Morulius chrysophykadian, and Hampala macrolepidota. The fish were caught over the summer period (February-May 2007) from 2 Chiang Mai water reservoirs, i.e., the Mae Ngad (UTM 47Q E 503200, 47Q N 2119300) and the Mae Kuang Udomtara (UTM 47Q E 513000, 47Q N 2092600) Reservoirs in Chiang Mai province, Thailand. The prevalence of heterophyid (Haplorchis taichui and Haplorchoides sp.) metacercariae in these fish was 83.9% and 74.2% in the Mae Ngad and Mae Kuang Udomtara Reservoirs, respectively. The highest intensity of heterophyid metacercariae in H. siamensis in the Mae Ngad was 120.4 and that in P. proctozysron in the Mae Kuang Udomtara was 180.0. The fish, A. testudineus, C. apogon, and M. chrysophykadian, were not found to be infected with H. taichui metacercariae. The results show that the freshwater fish in Chiang Mai water reservoirs are heavily infected with H. taichui and Haplorchoides sp. metacercariae. PMID:18552549

Field, laboratory and numerical approaches to studying flow through mangrove pneumatophores

NASA Astrophysics Data System (ADS)

Chua, V. P.

2014-12-01

The circulation of water in riverine mangrove swamps is expected to be influenced by mangrove roots, which in turn affect the nutrients, pollutants and sediments transport in these systems. Field studies were carried out in mangrove areas along the coastline of Singapore where Avicennia marina and Sonneratia alba pneumatophore species are found. Geometrical properties, such as height, diameter and spatial density of the mangrove roots were assessed through the use of photogrammetric methods. Samples of these roots were harvested from mangrove swamps and their material properties, such as bending strength and Young's modulus were determined in the laboratory. It was found that the pneumatophores under hydrodynamic loadings in a mangrove environment could be regarded as fairly rigid. Artificial root models of pneumatophores were fabricated from downscaling based on field observations of mangroves. Flume experiments were performed and measurements of mean flow velocities, Reynolds stress and turbulent kinetic energy were made. The boundary layer formed over the vegetation patch is fully developed after x = 6 m with a linear mean velocity profile. High shear stresses and turbulent kinetic energy were observed at the interface between the top portion of the roots and the upper flow. The experimental data was employed to calibrate and validate three-dimensional simulations of flow in pneumatophores. The simulations were performed with the Delft3D-FLOW model, where the vegetation effect is introduced by adding a depth-distributed resistance force and modifying the k-ɛ turbulence model. The model-predicted profiles for mean velocity, turbulent kinetic energy and concentration were compared with experimental data. The model calibration is performed by adjusting the horizontal and vertical eddy viscosities and diffusivities. A skill assessment of the model is performed using statistical measures that include the Pearson correlation coefficient (r), the mean absolute error (MAE), and the root-mean-squared error (RMSE).

Spatial interpolation of GPS PWV and meteorological variables over the west coast of Peninsular Malaysia during 2013 Klang Valley Flash Flood

NASA Astrophysics Data System (ADS)

Suparta, Wayan; Rahman, Rosnani

2016-02-01

Global Positioning System (GPS) receivers are widely installed throughout the Peninsular Malaysia, but the implementation for monitoring weather hazard system such as flash flood is still not optimal. To increase the benefit for meteorological applications, the GPS system should be installed in collocation with meteorological sensors so the precipitable water vapor (PWV) can be measured. The distribution of PWV is a key element to the Earth's climate for quantitative precipitation improvement as well as flash flood forecasts. The accuracy of this parameter depends on a large extent on the number of GPS receiver installations and meteorological sensors in the targeted area. Due to cost constraints, a spatial interpolation method is proposed to address these issues. In this paper, we investigated spatial distribution of GPS PWV and meteorological variables (surface temperature, relative humidity, and rainfall) by using thin plate spline (tps) and ordinary kriging (Krig) interpolation techniques over the Klang Valley in Peninsular Malaysia (longitude: 99.5°-102.5°E and latitude: 2.0°-6.5°N). Three flash flood cases in September, October, and December 2013 were studied. The analysis was performed using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) to determine the accuracy and reliability of the interpolation techniques. Results at different phases (pre, onset, and post) that were evaluated showed that tps interpolation technique is more accurate, reliable, and highly correlated in estimating GPS PWV and relative humidity, whereas Krig is more reliable for predicting temperature and rainfall during pre-flash flood events. During the onset of flash flood events, both methods showed good interpolation in estimating all meteorological parameters with high accuracy and reliability. The finding suggests that the proposed method of spatial interpolation techniques are capable of handling limited data sources with high accuracy, which in turn can be used to predict future floods.

Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China.

PubMed

Zang, Lin; Mao, Feiyue; Guo, Jianping; Gong, Wei; Wang, Wei; Pan, Zengxin

2018-06-11

Particulate matter with diameter less than 1 μm (PM 1 ) has been found to be closely associated with air quality, climate changes, and even adverse human health. However, a large gap in our knowledge concerning the large-scale distribution and variability of PM 1 remains, which is expected to be bridged with advanced remote-sensing techniques. In this study, a hybrid model called principal component analysis-general regression neural network (PCA-GRNN) is developed to estimate hourly PM 1 concentrations from Himawari-8 aerosol optical depth in combination with coincident ground-based PM 1 measurements in China. Results indicate that the hourly estimated PM 1 concentrations from satellite agree well with the measured values at national scale, with R 2 of 0.65, root-mean-square error (RMSE) of 22.0 μg/m 3 and mean absolute error (MAE) of 13.8 μg/m 3 . On daily and monthly time scales, R 2 increases to 0.70 and 0.81, respectively. Spatially, highly polluted regions of PM 1 are largely located in the North China Plain and Northeast China, in accordance with the distribution of industrialisation and urbanisation. In terms of diurnal variability, PM 1 concentration tends to peak in rush hours during the daytime. PM 1 exhibits distinct seasonality with winter having the largest concentration (31.5±3.5 μg/m 3 ), largely due to peak combustion emissions. We further attempt to estimate PM 2.5 and PM 10 with the proposed method and find that the accuracies of the proposed model for PM 1 and PM 2.5 estimation are significantly higher than that of PM 10 . Our findings suggest that geostationary data is one of the promising data to estimate fine particle concentration on large spatial scale. Copyright © 2018 Elsevier Ltd. All rights reserved.

Estimation of land-atmosphere energy transfer over the Tibetan Plateau by a combination use of geostationary and polar-orbiting satellite data

NASA Astrophysics Data System (ADS)

Zhong, L.; Ma, Y.

2017-12-01

Land-atmosphere energy transfer is of great importance in land-atmosphere interactions and atmospheric boundary layer processes over the Tibetan Plateau (TP). The energy fluxes have high temporal variability, especially in their diurnal cycle, which cannot be acquired by polar-orbiting satellites alone because of their low temporal resolution. Therefore, it's of great practical significance to retrieve land surface heat fluxes by a combination use of geostationary and polar orbiting satellites. In this study, a time series of the hourly LST was estimated from thermal infrared data acquired by the Chinese geostationary satellite FengYun 2C (FY-2C) over the TP. The split window algorithm (SWA) was optimized using a regression method based on the observations from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet) and Tibetan observation and research platform (TORP), the land surface emissivity (LSE) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the water vapor content from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) project. The 10-day composite hourly LST data were generated via the maximum value composite (MVC) method to reduce the cloud effects. The derived LST was validated by the field observations of CAMP/Tibet and TORP. The results show that the retrieved LST and in situ data have a very good correlation (with root mean square error (RMSE), mean bias (MB), mean absolute error (MAE) and correlation coefficient (R) values of 1.99 K, 0.83 K, 1.71 K, and 0.991, respectively). Together with other characteristic parameters derived from polar-orbiting satellites and meteorological forcing data, the energy balance budgets have been retrieved finally. The validation results showed there was a good consistency between estimation results and in-situ measurements over the TP, which prove the robustness of the proposed estimation methodology.

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.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

NASA Astrophysics Data System (ADS)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

IMPROVEMENT OF SMVGEAR II ON VECTOR AND SCALAR MACHINES THROUGH ABSOLUTE ERROR TOLERANCE CONTROL (R823186)

EPA Science Inventory

The computer speed of SMVGEAR II was improved markedly on scalar and vector machines with relatively little loss in accuracy. The improvement was due to a method of frequently recalculating the absolute error tolerance instead of keeping it constant for a given set of chemistry. ...

3D measurement using combined Gray code and dual-frequency phase-shifting approach

NASA Astrophysics Data System (ADS)

Yu, Shuang; Zhang, Jing; Yu, Xiaoyang; Sun, Xiaoming; Wu, Haibin; Liu, Xin

2018-04-01

The combined Gray code and phase-shifting approach is a commonly used 3D measurement technique. In this technique, an error that equals integer multiples of the phase-shifted fringe period, i.e. period jump error, often exists in the absolute analog code, which can lead to gross measurement errors. To overcome this problem, the present paper proposes 3D measurement using a combined Gray code and dual-frequency phase-shifting approach. Based on 3D measurement using the combined Gray code and phase-shifting approach, one set of low-frequency phase-shifted fringe patterns with an odd-numbered multiple of the original phase-shifted fringe period is added. Thus, the absolute analog code measured value can be obtained by the combined Gray code and phase-shifting approach, and the low-frequency absolute analog code measured value can also be obtained by adding low-frequency phase-shifted fringe patterns. Then, the corrected absolute analog code measured value can be obtained by correcting the former by the latter, and the period jump errors can be eliminated, resulting in reliable analog code unwrapping. For the proposed approach, we established its measurement model, analyzed its measurement principle, expounded the mechanism of eliminating period jump errors by error analysis, and determined its applicable conditions. Theoretical analysis and experimental results show that the proposed approach can effectively eliminate period jump errors, reliably perform analog code unwrapping, and improve the measurement accuracy.

Error Budget for a Calibration Demonstration System for the Reflected Solar Instrument for the Climate Absolute Radiance and Refractivity Observatory

NASA Technical Reports Server (NTRS)

Thome, Kurtis; McCorkel, Joel; McAndrew, Brendan

2013-01-01

A goal of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is to observe highaccuracy, long-term climate change trends over decadal time scales. The key to such a goal is to improving the accuracy of SI traceable absolute calibration across infrared and reflected solar wavelengths allowing climate change to be separated from the limit of natural variability. The advances required to reach on-orbit absolute accuracy to allow climate change observations to survive data gaps exist at NIST in the laboratory, but still need demonstration that the advances can move successfully from to NASA and/or instrument vendor capabilities for spaceborne instruments. The current work describes the radiometric calibration error budget for the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. The goal of the CDS is to allow the testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The resulting SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climatequality data collections is given. Key components in the error budget are geometry differences between the solar and earth views, knowledge of attenuator behavior when viewing the sun, and sensor behavior such as detector linearity and noise behavior. Methods for demonstrating this error budget are also presented.

Proprioceptive deficit in individuals with unilateral tearing of the anterior cruciate ligament after active evaluation of the sense of joint position.

PubMed

Cossich, Victor; Mallrich, Frédéric; Titonelli, Victor; de Sousa, Eduardo Branco; Velasques, Bruna; Salles, José Inácio

2014-01-01

To ascertain whether the proprioceptive deficit in the sense of joint position continues to be present when patients with a limb presenting a deficient anterior cruciate ligament (ACL) are assessed by testing their active reproduction of joint position, in comparison with the contralateral limb. Twenty patients with unilateral ACL tearing participated in the study. Their active reproduction of joint position in the limb with the deficient ACL and in the healthy contralateral limb was tested. Meta-positions of 20% and 50% of the maximum joint range of motion were used. Proprioceptive performance was determined through the values of the absolute error, variable error and constant error. Significant differences in absolute error were found at both of the positions evaluated, and in constant error at 50% of the maximum joint range of motion. When evaluated in terms of absolute error, the proprioceptive deficit continues to be present even when an active evaluation of the sense of joint position is made. Consequently, this sense involves activity of both intramuscular and tendon receptors.

Absolute calibration of optical flats

DOEpatents

Sommargren, Gary E.

2005-04-05

The invention uses the phase shifting diffraction interferometer (PSDI) to provide a true point-by-point measurement of absolute flatness over the surface of optical flats. Beams exiting the fiber optics in a PSDI have perfect spherical wavefronts. The measurement beam is reflected from the optical flat and passed through an auxiliary optic to then be combined with the reference beam on a CCD. The combined beams include phase errors due to both the optic under test and the auxiliary optic. Standard phase extraction algorithms are used to calculate this combined phase error. The optical flat is then removed from the system and the measurement fiber is moved to recombine the two beams. The newly combined beams include only the phase errors due to the auxiliary optic. When the second phase measurement is subtracted from the first phase measurement, the absolute phase error of the optical flat is obtained.

Optimum Antenna Configuration for Maximizing Access Point Range of an IEEE 802.11 Wireless Mesh Network in Support of Multi-Mission Operations Relative to Hastily Formed Scalable Deployments

DTIC Science & Technology

2007-09-01

Configuration Consideration ...........................54 C. MAE NGAT DAM, CHIANG MAI , THAILAND, FIELD EXPERIMENT...2006 802.11 Network Topology Mae Ngat Dam, Chiang Mai , Thailand.......................39 Figure 31. View of COASTS 2006 802.11 Topology...Requirements (Background From Google Earth).....62 Figure 44. Mae Ngat Dam, Chiang Mai , Thailand (From Google Earth

Moderate physical activity of music aerobic exercise increases lymphocyte counts, specific subsets, and differentiation.

PubMed

Yeh, Shu-Hui; Lai, Hsiu-Ling; Hsiao, Chiu-Yueh; Lin, Li-Wei; Chuang, Yu-Kuan; Yang, Yu-Yeng; Yang, Kuender D

2014-09-01

Moderate physical activity has been shown to promote immunity. Different moderate physical activities may have different effects on immunity. This study investigated the impacts of a 12-week regular music aerobic exercise (MAE) program on leukocyte distribution, lymphocyte subsets, and lymphocyte polarization. The study used a case-control design with pretest and posttest. Forty-seven middle-age women were recruited for this study. Three participants dropped out, 22 completed the 12-week MAE program, and the other 22 participants who had heat-intolerance or limited schedule eligibility were enrolled as the control group without the MAE exercise. Results showed that the MAE exercise for 12 weeks didn't change red blood cells or total leukocytes but increased lymphocyte counts. The women in MAE group revealed significant increases (P ≤ 0.01) of CD3CD4, CD3CD8, and CD4CD25 cells, associated with Treg polarization showing enhanced FoxP3 but not T-bet, Gata-3, or RORγT expression (P < .01). The control group without exercise revealed insignificant change of lymphocyte subsets or lymphocyte polarization. This study shows that MAE increases specific lymphocyte subsets and enhances Treg cell differentiation. It is suggested to encourage moderate physical activity of music aerobic exercise to enhance lymphocyte function of middle-aged women.

Relative and Absolute Error Control in a Finite-Difference Method Solution of Poisson's Equation

ERIC Educational Resources Information Center

Prentice, J. S. C.

2012-01-01

An algorithm for error control (absolute and relative) in the five-point finite-difference method applied to Poisson's equation is described. The algorithm is based on discretization of the domain of the problem by means of three rectilinear grids, each of different resolution. We discuss some hardware limitations associated with the algorithm,…

Assessing Suturing Skills in a Self-Guided Learning Setting: Absolute Symmetry Error

ERIC Educational Resources Information Center

Brydges, Ryan; Carnahan, Heather; Dubrowski, Adam

2009-01-01

Directed self-guidance, whereby trainees independently practice a skill-set in a structured setting, may be an effective technique for novice training. Currently, however, most evaluation methods require an expert to be present during practice. The study aim was to determine if absolute symmetry error, a clinically important measure that can be…

Vegetation Canopy Structure from NASA EOS Multiangle Imaging

NASA Astrophysics Data System (ADS)

Chopping, M.; Martonchik, J. V.; Bull, M.; Rango, A.; Schaaf, C. B.; Zhao, F.; Wang, Z.

2008-12-01

We used red band bidirectional reflectance data from the NASA Multiangle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS) mapped onto a 250 m grid in a multiangle approach to obtain estimates of woody plant fractional cover and crown height through adjustment of the mean radius and mean crown aspect ratio parameters of an hybrid geometric-optical (GO) model. We used a technique to rapidly obtain MISR surface reflectance estimates at 275 m resolution through regression on 1 km MISR land surface estimates previously corrected for atmospheric attenuation using MISR aerosol estimates. MISR data were used to make end of dry season maps from 2000-2007 for parts of southern New Mexico, while MODIS data were used to replicate previous results obtained using MISR for June 2002 over large parts of New Mexico and Arizona. We also examined the applicability of this method in Alaskan tundra and forest by adjusting the GO model against MISR data for winter (March 2000) and summer (August 2008) scenes. We found that the GO model crown aspect ratio from MISR followed dominant shrub species distributions in the USDA, ARS Jornada Experimental Range, enabling differentiation of the more spherical crowns of creosotebush (Larrea tridentata) from the more prolate crowns of honey mesquite (Prosopis glandulosa). The measurement limits determined from 2000-2007 maps for a large part of southern New Mexico are ~0.1 in fractional shrub crown cover and ~3 m in mean canopy height (results obtained using data acquired shortly after precipitation events that radically darkened and altered the structure and angular response of the background). Typical standard deviations over the period for 12 sites covering a range of cover types are on the order of 0.05 in crown cover and 2 m in mean canopy height. We found that the GO model can be inverted to retrieve reasonable distributions of canopy parameters in southwestern environments using MODIS V005 red band surface reflectance estimates at ~250 m spatial resolution accumulated over 16 day periods. The MODIS (N=895) and MISR (N=576) estimates of forest height and cover both showed agreement with USDA, Forest Service estimates, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively; and MISR MAE of 0.10 and 2.2 m, respectively, noting that a sub-optimal background was used for the MODIS inversions. The MODIS and MISR MAE for estimates of aboveground woody biomass via regression against Forest Service estimates were both 10.1 Mg.ha-1. We found that red band MISR data for central Alaska can be used to obtain first-order estimates of forest cover and height using a snow-free summer scene and shrub cover using a winter scene with full snow cover. The GO model inversion results are often physically unrealistic but spatial distributions correspond to high resolution images and reflect the potential for the multiangle/GO method to retrieve meaningful information that is qualitatively different to that obtained using vegetation indices.

Supercritical Carbon Dioxide and Microwave-Assisted Extraction of Functional Lipophilic Compounds from Arthrospira platensis

PubMed Central

Esquivel-Hernández, Diego A.; López, Víctor H.; Rodríguez-Rodríguez, José; Alemán-Nava, Gibrán S.; Cuéllar-Bermúdez, Sara P.; Rostro-Alanis, Magdalena; Parra-Saldívar, Roberto

2016-01-01

Arthrospira platensis biomass was used in order to obtain functional lipophilic compounds through green extraction technologies such as supercritical carbon dioxide fluid extraction (SFE) and microwave-assisted extraction (MAE). The temperature (T) factor was evaluated for MAE, while for SFE, pressure (P), temperature (T), and co-solvent (ethanol) (CS) were evaluated. The maximum extraction yield of the obtained oleoresin was (4.07% ± 0.14%) and (4.27% ± 0.10%) for SFE and MAE, respectively. Extracts were characterized by gas chromatography mass spectrometry (GC-MS) and gas chromatography flame ionization detector (GC-FID). The maximum contents of functional lipophilic compounds in the SFE and MAE extracts were: for carotenoids 283 ± 0.10 μg/g and 629 ± 0.13 μg/g, respectively; for tocopherols 5.01 ± 0.05 μg/g and 2.46 ± 0.09 μg/g, respectively; and for fatty acids 34.76 ± 0.08 mg/g and 15.88 ± 0.06 mg/g, respectively. In conclusion, the SFE process at P 450 bar, T 60 °C and CS 53.33% of CO2 produced the highest yield of tocopherols, carotenoids and fatty acids. The MAE process at 400 W and 50 °C gives the best extracts in terms of tocopherols and carotenoids. For yield and fatty acids, the MAE process at 400 W and 70 °C produced the highest values. Both SFE and MAE showed to be suitable green extraction technologies for obtaining functional lipophilic compounds from Arthrospira platensis. PMID:27164081

Specificity of early movie effects on adolescent sexual behavior and alcohol use.

PubMed

O'Hara, Ross E; Gibbons, Frederick X; Li, Zhigang; Gerrard, Meg; Sargent, James D

2013-11-01

Adolescents' movie sex exposure (MSE) and movie alcohol exposure (MAE) have been shown to influence later sexual behavior and drinking, respectively. No study to date, however, has tested whether these effects generalize across behaviors. This study examined the concurrent influences of early (i.e., before age 16) MSE and MAE on subsequent risky sex and alcohol use among a national sample of 1228 U.S. adolescents. Participants reported their health behaviors and movie viewing up to six times between 2003 and 2009 in telephone interviews. The Beach method was used to create a population-based estimate of each participant's MSE and MAE, which were then entered into a structural equation model (SEM) to predict lifetime risky sex and past month alcohol use at ages 18-21. For both men and women, MAE predicted alcohol use, mediated by age of initiation of heavy episodic drinking (HED) and age of sexual debut; MAE also predicted risky sex via age of sexual debut. Among men only, MSE indirectly predicted risky sex and alcohol use. Findings indicated that early exposure to risk content from movies had both specific and general effects on later risk-taking, but gender differences were evident: for men, MSE was a stronger predictor than MAE, but for women, only MAE predicted later risk behavior. These results have implications for future media research, prevention programs for adolescent sex and alcohol use, and movie ratings that can guide parents' decisions as to which movies are appropriate for their children. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimization of microwave-assisted extraction (MAE) of coriander phenolic antioxidants - response surface methodology approach.

PubMed

Zeković, Zoran; Vladić, Jelena; Vidović, Senka; Adamović, Dušan; Pavlić, Branimir

2016-10-01

Microwave-assisted extraction (MAE) of polyphenols from coriander seeds was optimized by simultaneous maximization of total phenolic (TP) and total flavonoid (TF) yields, as well as maximized antioxidant activity determined by 1,1-diphenyl-2-picrylhydrazyl and reducing power assays. Box-Behnken experimental design with response surface methodology (RSM) was used for optimization of MAE. Extraction time (X1 , 15-35 min), ethanol concentration (X2 , 50-90% w/w) and irradiation power (X3 , 400-800 W) were investigated as independent variables. Experimentally obtained values of investigated responses were fitted to a second-order polynomial model, and multiple regression analysis and analysis of variance were used to determine fitness of the model and optimal conditions. The optimal MAE conditions for simultaneous maximization of polyphenol yield and increased antioxidant activity were an extraction time of 19 min, an ethanol concentration of 63% and an irradiation power of 570 W, while predicted values of TP, TF, IC50 and EC50 at optimal MAE conditions were 311.23 mg gallic acid equivalent per 100 g dry weight (DW), 213.66 mg catechin equivalent per 100 g DW, 0.0315 mg mL(-1) and 0.1311 mg mL(-1) respectively. RSM was successfully used for multi-response optimization of coriander seed polyphenols. Comparison of optimized MAE with conventional extraction techniques confirmed that MAE provides significantly higher polyphenol yields and extracts with increased antioxidant activity. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

The mixed alkali effect in ionically conducting glasses revisited: a study by molecular dynamics simulation.

PubMed

Habasaki, Junko; Ngai, Kia L

2007-09-07

When more than two kinds of mobile ions are mixed in ionic conducting glasses and crystals, there is a non-linear decrease of the transport coefficients of either type of ion. This phenomenon is known as the mixed mobile ion effect or Mixed Alkali Effect (MAE), and remains an unsolved problem. We use molecular dynamics simulation to study the complex ion dynamics in ionically conducting glasses including the MAE. In the mixed alkali lithium-potassium silicate glasses and related systems, a distinct part of the van Hove functions reveals that jumps from one kind of site to another are suppressed. Although, consensus for the existence of preferential jump paths for each kind of mobile ions seems to have been reached amongst researchers, the role of network formers and the number of unoccupied ion sites remain controversial in explaining the MAE. In principle, these factors when incorporated into a theory can generate the MAE, but in reality they are not essential for a viable explanation of the ion dynamics and the MAE. Instead, dynamical heterogeneity and "cooperativity blockage" originating from ion-ion interaction and correlation are fundamental for the observed ion dynamics and the MAE. Suppression of long range motion with increased back-correlated motions is shown to be a cause of the large decrease of the diffusivity especially in dilute foreign alkali regions. Support for our conclusion also comes from the fact that these features of ion dynamics are common to other ionic conductors, which have no glassy networks, and yet they all exhibit the MAE.

Specificity of Early Movie Effects on Adolescent Sexual Behavior and Alcohol Use

PubMed Central

O’Hara, Ross E.; Gibbons, Frederick X.; Li, Zhigang; Gerrard, Meg; Sargent, James D.

2013-01-01

Adolescents’ movie sex exposure (MSE) and movie alcohol exposure (MAE) have been shown to influence later sexual behavior and drinking, respectively. No study to date, however, has tested whether these effects generalize across behaviors. This study examined the concurrent influences of early (i.e., before age 16) MSE and MAE on subsequent risky sex and alcohol use among a national sample of 1,228 U.S. adolescents. Participants reported their health behaviors and movie viewing up to six times between 2003 and 2009 in telephone interviews. The Beach method was used to create a population-based estimate of each participant’s MSE and MAE, which were then entered into a structural equation model (SEM) to predict lifetime risky sex and past month alcohol use at ages 18–21. For both men and women, MAE predicted alcohol use, mediated by age of initiation of heavy episodic drinking (HED) and age of sexual debut; MAE also predicted risky sex via age of sexual debut. Among men only, MSE indirectly predicted risky sex and alcohol use. Findings indicated that early exposure to risk content from movies had both specific and general effects on later risk-taking, but gender differences were evident: for men, MSE was a stronger predictor than MAE, but for women, only MAE predicted later risk behavior. These results have implications for future media research, prevention programs for adolescent sex and alcohol use, and movie ratings that can guide parents’ decisions as to which movies are appropriate for their children. PMID:24034968

Supercritical Carbon Dioxide and Microwave-Assisted Extraction of Functional Lipophilic Compounds from Arthrospira platensis.

PubMed

Esquivel-Hernández, Diego A; López, Víctor H; Rodríguez-Rodríguez, José; Alemán-Nava, Gibrán S; Cuéllar-Bermúdez, Sara P; Rostro-Alanis, Magdalena; Parra-Saldívar, Roberto

2016-05-05

Arthrospira platensis biomass was used in order to obtain functional lipophilic compounds through green extraction technologies such as supercritical carbon dioxide fluid extraction (SFE) and microwave-assisted extraction (MAE). The temperature (T) factor was evaluated for MAE, while for SFE, pressure (P), temperature (T), and co-solvent (ethanol) (CS) were evaluated. The maximum extraction yield of the obtained oleoresin was (4.07% ± 0.14%) and (4.27% ± 0.10%) for SFE and MAE, respectively. Extracts were characterized by gas chromatography mass spectrometry (GC-MS) and gas chromatography flame ionization detector (GC-FID). The maximum contents of functional lipophilic compounds in the SFE and MAE extracts were: for carotenoids 283 ± 0.10 μg/g and 629 ± 0.13 μg/g, respectively; for tocopherols 5.01 ± 0.05 μg/g and 2.46 ± 0.09 μg/g, respectively; and for fatty acids 34.76 ± 0.08 mg/g and 15.88 ± 0.06 mg/g, respectively. In conclusion, the SFE process at P 450 bar, T 60 °C and CS 53.33% of CO₂ produced the highest yield of tocopherols, carotenoids and fatty acids. The MAE process at 400 W and 50 °C gives the best extracts in terms of tocopherols and carotenoids. For yield and fatty acids, the MAE process at 400 W and 70 °C produced the highest values. Both SFE and MAE showed to be suitable green extraction technologies for obtaining functional lipophilic compounds from Arthrospira platensis.

Interannual variability in the extent and intensity of tropical dry forest deciduousness in the Mexican Yucatan (2000-2016): Drivers and Links to Regional Atmospheric Conditions

NASA Astrophysics Data System (ADS)

Cuba, Nicholas Joseph

The dry topical forests of the southern Yucatan Peninsula experience multiple natural and anthropogenic disturbances, as well as substantial interannual climate variability that can result in stark interannual differences in vegetation phenology. Dry season deciduousness is a typical response to limit tree water loss during prolonged periods of hot and dry conditions, and this behavior has both direct implications for ecosystem functioning, and the potential to indicate climate conditions when observed using remotely-sensed data. The first research paper of this dissertation advances methods to assess the accuracy of remotely-sensed measurements of canopy conditions using in-situ observations. Linear regression models show the highest correlation (R2 = 0.751) between in-situ canopy gap fraction and Landsat NDWISWIR2. MODIS time series NDWISWIR2 are created for the period March 2000-February 2011, and exhibit stronger correlation with time series of TRMM precipitation data than do MODIS EVI time series (R2= 0.48 vs. R2 = 0.43 in deciduous forest areas). The second paper examines differences between the deciduous phenology of young forest stands and older forest stands. Land-cover maps are overlaid to determine whether forested areas are greater than or less than 22 years old in 2010, and metrics related to deciduous phenology are derived from MODIS EVI2 time series in three years, 2008 to 2011. Statistical tests that compare matched pairs of young (12-22 years) and older (>22 years) forest stand age class samples are used to detect significant differences in metrics related to the intensity and timing of deciduousness. In all three years, younger forests exhibit significantly more intense deciduousness, measured as total seasonal change of EVI2 normalized by annual maximum EVI2 (p<0.001), and exhibit larger EVI2 declines at successive 32-day periods during dry season months (p<0.02), than nearby older forests that are assumed to share similar environmental conditions. explores how deciduousness influences the relationship between land-clearing and regional atmospheric conditions. Two sets of bottom-up estimates of Organic and Black Carbon (OCBC) emissions are derived from MODIS fire and land-cover data in the greater Yucatan region during the burning seasons of years 2003-2013: a control series in which estimated emissions from fires in deciduous forest and non-deciduous forest were modeled in the same way, and a "deciduous-adjusted" series in which the emissions from fires in deciduous forest were estimated to increase throughout the burn season as a result of accumulated leaf litter fuel and increasingly hot and dry understory conditions. The two sets of estimated OCBC emission were compared to top-down modeled values of OCBC from MERRA-2 global reanalysis and a comparison of residual differences measured as Mean Absolute Error (MAE) was made to determine the effect of the deciduous-adjustment on bottom-up estimates. The deciduous-adjustment is shown to decrease MAE relative to the control series for annual total estimates (31% vs. 26%), monthly average values (32% to 21%), and monthly values (39% to 34%) with respect to MERRA-2 OCBC. The largest MAE for annual total values were observed in the years 2009 to 2013, when both bottom-up series substantially underestimated MERRA-2 OCBC. This distribution of error is accounted for in part by the comparatively low amount of early dry-season rainfall during these years, increasing the rate of desiccation of fuel load, and may arise from the large increases to non-standing dead biomass resulting from the damage of category-5 Hurricane Dean in August 2007. These papers together provide a better understanding of the climate conditions and mediating environmental factors that drive the spatial and temporal variability in the intensity of deciduousness, and point toward analyzing deciduousness to reveal information about other environmental phenomena of interest with which it is correlated.

Mass absorption efficiency of elemental carbon over Van Vihar National Park, Bhopal, India: Temporal variability and implications to estimates of black carbon radiative forcing

NASA Astrophysics Data System (ADS)

Samiksha, S.; Raman, R. S.; Singh, A.

2016-12-01

It is now well recognized that black carbon (a component of aerosols that is similar but not identical to elemental carbon) is an important contributor to global warming, second only to CO2.However, the most popular methods for estimation of black carbon rely on accurate estimates of its mass absorption efficiency (MAE) to convert optical attenuation measurements to black carbon concentrations. Often a constant manufacturer specified MAE is used for this purposes. Recent literature has unequivocally established that MAE shows large spatio-temporal heterogeneities. This is so because MAE depends on emission sources, chemical composition, and mixing state of aerosols. In this study, ambient PM2.5 samples were collected over an ecologically sensitive zone (Van Vihar National Park) in Bhopal, Central India for two years (01 January, 2012 to 31 December, 2013). Samples were collected on Teflon, Nylon, and Tissue quartz filter substrates. Punches of quartz fibre filter were analysed for organic and elemental carbon (OC/EC) by a thermal-optical-transmittance/reflectance (TOT-TOR) analyser operating with a 632 nm laser diode. Teflon filters were also used to interdependently measure PM2.5 attenuation (at 370 nm and 800 nm) by transmissometry. Site-specific mass absorption efficiency (MAE) for elemental carbon over the study site will be derived using a combination of measurements from the TOT/TOR analyser and transmissometer. An assessment of site-specific MAE values, its temporal variability and implications to black carbon radiative forcing will be discussed. It is now well recognized that black carbon (a component of aerosols that is similar but not identical to elemental carbon) is an important contributor to global warming, second only to CO2. However, the most popular methods for estimation of black carbon rely on accurate estimates of its mass absorption efficiency (MAE) to convert optical attenuation measurements to black carbon concentrations. Often a constant manufacturer specified MAE is used for this purposes. Recent literature has unequivocally established that MAE shows large spatio-temporal heterogeneities. This is so because MAE depends on emission sources, chemical composition, and mixing state of aerosols. In this study, ambient PM2.5 samples were collected over an ecologically sensitive zone (Van Vihar National Park) in Bhopal, Central India for two years (01 January, 2012 to 31 December, 2013). Samples were collected on Teflon, Nylon, and Tissue quartz filter substrates. Punches of quartz fibre filter were analysed for organic and elemental carbon (OC/EC) by a thermal-optical-transmittance/reflectance (TOT-TOR) analyser operating with a 632 nm laser diode. Teflon filters were also used to interdependently measure PM2.5 attenuation (at 370 nm and 800 nm) by transmissometry. Site-specific mass absorption efficiency (MAE) for elemental carbon over the study site will be derived using a combination of measurements from the TOT/TOR analyser and transmissometer. An assessment of site-specific MAE values, its temporal variability and implications to black carbon radiative forcing will be discussed.

Oversight Hearing on Student Loan Marketing Associations. Hearing before the Subcommittee on Postsecondary Education of the Committee on Education and Labor. House of Representatives, Ninety-Eighth Congress, First Session.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. House Committee on Education and Labor.

Oversight hearings on the Student Loan Marketing Association (Sallie Mae) are presented. Sallie Mae was established by the Education Amendments of 1972 to provide liquidity for Guaranteed Student Loan (GSL) lenders by purchasing GSL portfolios from lenders or making loans on GSL loans held by lenders. In 1982, Sallie Mae had total cumulative…

Estimation of effect of hydrogen on the parameters of magnetoacoustic emission signals

NASA Astrophysics Data System (ADS)

Skalskyi, Valentyn; Stankevych, Olena; Dubytskyi, Olexandr

2018-05-01

The features of the magnetoacoustic emission (MAE) signals during magnetization of structural steels with the different degree of hydrogenating were investigated by the wavelet transform. The dominant frequency ranges of MAE signals for the different magnetic field strength were determined using Discrete Wavelet Transform (DWT), and the energy and spectral parameters of MAE signals were determined using Continuous Wavelet Transform (CWT). The characteristic differences of the local maximums of signals according to energy, bandwidth, duration and frequency were found. The methodology of estimation of state of local degradation of materials by parameters of wavelet transform of MAE signals was proposed. This methodology was approbated for investigate of state of long-time exploitations structural steels of oil and gas pipelines.

Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

NASA Astrophysics Data System (ADS)

Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

2015-12-01

Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.

Similarity-based multi-model ensemble approach for 1-15-day advance prediction of monsoon rainfall over India

NASA Astrophysics Data System (ADS)

Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati

2018-04-01

The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1-5, 6-10 and 11-15 days were generated using the newly developed approach for each pentad of June-September during the years 2008-2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson's correlation coefficient and Nash-Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1-5, 6-10 and 11-15 days.

Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns.

PubMed

Kolehmainen, V; Vauhkonen, M; Karjalainen, P A; Kaipio, J P

1997-11-01

In electrical impedance tomography (EIT), difference imaging is often preferred over static imaging. This is because of the many unknowns in the forward modelling which make it difficult to obtain reliable absolute resistivity estimates. However, static imaging and absolute resistivity values are needed in some potential applications of EIT. In this paper we demonstrate by simulation the effects of different error components that are included in the reconstruction of static EIT images. All simulations are carried out in two dimensions with the so-called complete electrode model. Errors that are considered are the modelling error in the boundary shape of an object, errors in the electrode sizes and localizations and errors in the contact impedances under the electrodes. Results using both adjacent and trigonometric current patterns are given.

Phantom motion after effects--evidence of detectors for the analysis of optic flow.

PubMed

Snowden, R J; Milne, A B

1997-10-01

Electrophysiological recording from the extrastriate cortex of non-human primates has revealed neurons that have large receptive fields and are sensitive to various components of object or self movement, such as translations, rotations and expansion/contractions. If these mechanisms exist in human vision, they might be susceptible to adaptation that generates motion aftereffects (MAEs). Indeed, it might be possible to adapt the mechanism in one part of the visual field and reveal what we term a 'phantom MAE' in another part. The existence of phantom MAEs was probed by adapting to a pattern that contained motion in only two non-adjacent 'quarter' segments and then testing using patterns that had elements in only the other two segments. We also tested for the more conventional 'concrete' MAE by testing in the same two segments that had adapted. The strength of each MAE was quantified by measuring the percentage of dots that had to be moved in the opposite direction to the MAE in order to nullify it. Four experiments tested rotational motion, expansion/contraction motion, translational motion and a 'rotation' that consisted simply of the two segments that contained only translational motions of opposing direction. Compared to a baseline measurement where no adaptation took place, all subjects in all experiments exhibited both concrete and phantom MAEs, with the size of the latter approximately half that of the former. Adaptation to two segments that contained upward and downward motion induced the perception of leftward and rightward motion in another part of the visual field. This strongly suggests there are mechanisms in human vision that are sensitive to complex motions such as rotations.

Optimization of microwave-assisted extraction of polyphenols from Myrtus communis L. leaves.

PubMed

Dahmoune, Farid; Nayak, Balunkeswar; Moussi, Kamal; Remini, Hocine; Madani, Khodir

2015-01-01

Phytochemicals, such as phenolic compounds, are of great interest due to their health-benefitting antioxidant properties and possible protection against inflammation, cardiovascular diseases and certain types of cancer. Maximum retention of these phytochemicals during extraction requires optimised process parameter conditions. A microwave-assisted extraction (MAE) method was investigated for extraction of total phenolics from Myrtus communis leaves. The total phenolic capacity (TPC) of leaf extracts at optimised MAE conditions was compared with ultrasound-assisted extraction (UAE) and conventional solvent extraction (CSE). The influence of extraction parameters including ethanol concentration, microwave power, irradiation time and solvent-to-solid ratio on the extraction of TPC was modeled by using a second-order regression equation. The optimal MAE conditions were 42% ethanol concentration, 500 W microwave power, 62 s irradiation time and 32 mL/g solvent to material ratio. Ethanol concentration and liquid-to-solid ratio were the significant parameters for the extraction process (p<0.01). Under the MAE optimised conditions, the recovery of TPC was 162.49 ± 16.95 mg gallic acidequivalent/gdry weight(DW), approximating the predicted content (166.13 mg GAE/g DW). When bioactive phytochemicals extracted from Myrtus leaves using MAE compared with UAE and CSE, it was also observed that tannins (32.65 ± 0.01 mg/g), total flavonoids (5.02 ± 0.05 mg QE/g) and antioxidant activities (38.20 ± 1.08 μg GAE/mL) in MAE extracts were higher than the other two extracts. These findings further illustrate that extraction of bioactive phytochemicals from plant materials using MAE method consumes less extraction solvent and saves time. Copyright © 2014 Elsevier Ltd. All rights reserved.

Pyrometer with tracking balancing

NASA Astrophysics Data System (ADS)

Ponomarev, D. B.; Zakharenko, V. A.; Shkaev, A. G.

2018-04-01

Currently, one of the main metrological noncontact temperature measurement challenges is the emissivity uncertainty. This paper describes a pyrometer with emissivity effect diminishing through the use of a measuring scheme with tracking balancing in which the radiation receiver is a null-indicator. In this paper the results of the prototype pyrometer absolute error study in surfaces temperature measurement of aluminum and nickel samples are presented. There is absolute error calculated values comparison considering the emissivity table values with errors on the results of experimental measurements by the proposed method. The practical implementation of the proposed technical solution has allowed two times to reduce the error due to the emissivity uncertainty.

Neural Network Autopilot System for a Mathematical Model of the Boeing 747

DTIC Science & Technology

1998-08-04

the NASA/Aurora Theseus ", Thesis WVU MAE Dept., Morgantown, WV, June 1996. [9] Napolitano, M.R., Neppach, C, Casdorph, V., Naylor, S. "On-Line...Validation Schemes for Implementation on the NASA/Aurora Theseus ", Thesis WVU MAE Dept., Morgantown, WV, June 1996. [9] Napolitano, M.R., Neppach, C...Schemes for Implementation on the NASA/Aurora Theseus ", Thesis WVU MAE Dept., Morgantown, WV, June 1996. [9] Napolitano, M.R., Neppach, C, Casdorph, V

Ultra-Soft PDMS-Based Magnetoactive Elastomers as Dynamic Cell Culture Substrata

PubMed Central

Mayer, Matthias; Rabindranath, Raman; Börner, Juliane; Hörner, Eva; Bentz, Alexander; Salgado, Josefina; Han, Hong; Böse, Holger; Probst, Jörn; Shamonin, Mikhail; Monkman, Gareth J.; Schlunck, Günther

2013-01-01

Mechanical cues such as extracellular matrix stiffness and movement have a major impact on cell differentiation and function. To replicate these biological features in vitro, soft substrata with tunable elasticity and the possibility for controlled surface translocation are desirable. Here we report on the use of ultra-soft (Young’s modulus <100 kPa) PDMS-based magnetoactive elastomers (MAE) as suitable cell culture substrata. Soft non-viscous PDMS (<18 kPa) is produced using a modified extended crosslinker. MAEs are generated by embedding magnetic microparticles into a soft PDMS matrix. Both substrata yield an elasticity-dependent (14 vs. 100 kPa) modulation of α-smooth muscle actin expression in primary human fibroblasts. To allow for static or dynamic control of MAE material properties, we devise low magnetic field (≈40 mT) stimulation systems compatible with cell-culture environments. Magnetic field-instigated stiffening (14 to 200 kPa) of soft MAE enhances the spreading of primary human fibroblasts and decreases PAX-7 transcription in human mesenchymal stem cells. Pulsatile MAE movements are generated using oscillating magnetic fields and are well tolerated by adherent human fibroblasts. This MAE system provides spatial and temporal control of substratum material characteristics and permits novel designs when used as dynamic cell culture substrata or cell culture-coated actuator in tissue engineering applications or biomedical devices. PMID:24204603

Uncertainty analysis technique for OMEGA Dante measurements

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

May, M. J.; Widmann, K.; Sorce, C.

2010-10-15

The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determinedmore » flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.« less

Uncertainty Analysis Technique for OMEGA Dante Measurements

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

May, M J; Widmann, K; Sorce, C

2010-05-07

The Dante is an 18 channel X-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g. hohlraums, etc.) at X-ray energies between 50 eV to 10 keV. It is a main diagnostics installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the X-ray diodes, filters and mirrors and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determinedmore » flux using a Monte-Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.« less

Mapping the absolute magnetic field and evaluating the quadratic Zeeman-effect-induced systematic error in an atom interferometer gravimeter

NASA Astrophysics Data System (ADS)

Hu, Qing-Qing; Freier, Christian; Leykauf, Bastian; Schkolnik, Vladimir; Yang, Jun; Krutzik, Markus; Peters, Achim

2017-09-01

Precisely evaluating the systematic error induced by the quadratic Zeeman effect is important for developing atom interferometer gravimeters aiming at an accuracy in the μ Gal regime (1 μ Gal =10-8m /s2 ≈10-9g ). This paper reports on the experimental investigation of Raman spectroscopy-based magnetic field measurements and the evaluation of the systematic error in the gravimetric atom interferometer (GAIN) due to quadratic Zeeman effect. We discuss Raman duration and frequency step-size-dependent magnetic field measurement uncertainty, present vector light shift and tensor light shift induced magnetic field measurement offset, and map the absolute magnetic field inside the interferometer chamber of GAIN with an uncertainty of 0.72 nT and a spatial resolution of 12.8 mm. We evaluate the quadratic Zeeman-effect-induced gravity measurement error in GAIN as 2.04 μ Gal . The methods shown in this paper are important for precisely mapping the absolute magnetic field in vacuum and reducing the quadratic Zeeman-effect-induced systematic error in Raman transition-based precision measurements, such as atomic interferometer gravimeters.

A new accuracy measure based on bounded relative error for time series forecasting

PubMed Central

Twycross, Jamie; Garibaldi, Jonathan M.

2017-01-01

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred. PMID:28339480

A new accuracy measure based on bounded relative error for time series forecasting.

PubMed

Chen, Chao; Twycross, Jamie; Garibaldi, Jonathan M

2017-01-01

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred.

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

Morley, Steven

The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient ofmore » variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.« less

Magnetic anisotropy of metal functionalized phthalocyanine 2D networks

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

Zhu, Guojun; Zhang, Yun; Xiao, Huaping, E-mail: hpxiao@xtu.edu.cn

2016-06-15

The magnetic anisotropy of metal including Cr, Mn, Fe, Co, Mo, Tc, Ru, Rh, W, Re, Os, Ir atoms functionalized phthalocyanine networks have been investigated with first-principles calculations. The magnetic moments can be expressed as 8-n μ{sub B} with n the electronic number of outmost d shell in the transition metals. The huge magnetocrystalline anisotropy energy (MAE) is obtained by torque method. Especially, the MAE of Re functionalized phthalocyanine network is about 20 meV with an easy axis perpendicular to the plane of phthalocyanine network. The MAE is further manipulated by applying the external biaxial strain. It is found thatmore » the MAE is linear increasing with the external strain in the range of −2% to 2%. Our results indicate an effective approach to modulate the MAE for practical application. - Graphical abstract: The charge density redistribution (ρ{sub MPc}-ρ{sub M}-ρ{sub Pc}) and spin density of the CoPc molecule, from top- and side-views. Purple and green isosurfaces indicate charge depletion and accumulation, respectively. Display Omitted.« less

Giant magnetic anisotropy of heavy p-elements on high-symmetry substrates: a new paradigm for supported nanostructures

NASA Astrophysics Data System (ADS)

Pang, Rui; Deng, Bei; Shi, Xingqiang; Zheng, Xiaohong

2018-04-01

Nanostructures with giant magnetic anisotropy energies (MAEs) are desired in designing miniaturized magnetic storage and quantum computing devices. Previous works focused mainly on materials or elements with d electrons. Here, by taking Bi–X(X = In, Tl, Ge, Sn, Pb) adsorbed on nitrogenized divacancy of graphene and Bi atoms adsorbed on MgO(100) as examples, through ab initio and model calculations, we propose that special p-element dimers and single-adatoms on symmetry-matched substrates possess giant atomic MAEs of 72–200 meV, and has room temperature structural stability. The huge MAEs originate from the p-orbital degeneracy around the Fermi level in a symmetry-matched surface ligand field and the lifting of this degeneracy when spin–orbit interaction (SOI) is taken into account. Especially, we developed a simplified quantum mechanical model for the design principles of giant MAEs of supported magnetic adatoms and dimers. Thus, our discoveries and mechanisms provide a new paradigm to design giant atomic MAE of p electrons in supported nanostructures.

Influence of the geometry on magnetic interactions in a retina fixator based on a magnetoactive elastomer seal

NASA Astrophysics Data System (ADS)

Nadzharyan, T. A.; Makarova, L. A.; Kazimirova, E. G.; Perov, N. S.; Kramarenko, E. Yu

2018-03-01

We study the effects the geometric configuration has on magnetic interactions between a magnetoactive elastomer (MAE) sample and various systems of permanent magnets for problems with both flat and curved geometry. MAEs consist of a silicone polymer matrix and iron filler microparticles embedded in it. Permanent magnets are cylindrical neodymium magnets arranged in a line on a flat or curved solid surfaces. We use computer simulations, namely the finite element method, in order to study the interaction force and magnetic pressure in a system with an MAE sample and permanent magnets. The model is based on classical Maxwell magnetostatics and two factors taking into account field dependence of MAE’s magnetic properties and inhomogeneities caused by local demagnetization. We calculate magnetic pressure dependences on various geometric parameters of the system, namely, the diameter and the height of permanent magnets, the distance between the magnets and dimensions of MAE samples. This research aims to create a set of guidelines for choosing the geometric configuration of a retina fixator based on MAE seals to be used in eye surgery for retinal detachment treatment.

Determination of Magneto-crystalline Anisotropy Energy (MAE) Of ordered L10 CoPt and FePt nanoparticles

NASA Astrophysics Data System (ADS)

Alsaad, A.; Ahmad, A. A.; Shukri, A. A.; Bani-Younes, O. A.

2018-02-01

The structural and magnetic properties of both L10 ordered FePt and CoPt nanoparticles make them potential candidates for optical-electronic and magneto-optical devices. First, we carried out an ab initio total energy minimization study to find the geometrical optimization of both L10 phases of FePt and CoPt nanoparticles. Then, we investigated the magnetocrystalline anisotropy energy (MAE) of both systems along special line joining the points of high symmetry (A,B and C points) using super-cell slap approach with alternating layers Fe/Co and Pt along the (001) direction. We found that the point (A) has the highest MAE value for both systems, where the value of MAE in FePt is 8.89 × 107 erg/cm3 and in CoPt is 6.40 × 107 erg/cm3. Our spin density based calculations indicate that large spin-orbit interaction and the hybridization between Pt 5d states and Fe/Co 3d states are the dominant factors in determining the MAE in both systems.

Optimization of microwave-assisted extraction for the characterization of olive leaf phenolic compounds by using HPLC-ESI-TOF-MS/IT-MS(2).

PubMed

Taamalli, Amani; Arráez-Román, David; Ibañez, Elena; Zarrouk, Mokhtar; Segura-Carretero, Antonio; Fernández-Gutiérrez, Alberto

2012-01-25

In the present work, a simple and rapid method for the extraction of phenolic compounds from olive leaves, using microwave-assisted extraction (MAE) technique, has been developed. The experimental variables that affect the MAE process, such as the solvent type and composition, microwave temperature, and extraction time, were optimized using a univariate method. The obtained extracts were analyzed by using high-performance liquid chromatography (HPLC) coupled to electrospray time-of-flight mass spectrometry (ESI-TOF-MS) and electrospray ion trap tandem mass spectrometry (ESI-IT-MS(2)) to prove the MAE extraction efficiency. The optimal MAE conditions were methanol:water (80:20, v/v) as extracting solvent, at a temperature equal to 80 °C for 6 min. Under these conditions, several phenolic compounds could be characterized by HPLC-ESI-MS/MS(2). As compared to the conventional method, MAE can be used as an alternative extraction method for the characterization of phenolic compounds from olive leaves due to its efficiency and speed.

Second-order motions contribute to vection.

PubMed

Gurnsey, R; Fleet, D; Potechin, C

1998-09-01

First- and second-order motions differ in their ability to induce motion aftereffects (MAEs) and the kinetic depth effect (KDE). To test whether second-order stimuli support computations relating to motion-in-depth we examined the vection illusion (illusory self motion induced by image flow) using a vection stimulus (V, expanding concentric rings) that depicted a linear path through a circular tunnel. The set of vection stimuli contained differing amounts of first- and second-order motion energy (ME). Subjects reported the duration of the perceived MAEs and the duration of their vection percept. In Experiment 1 both MAEs and vection durations were longest when the first-order (Fourier) components of V were present in the stimulus. In Experiment 2, V was multiplicatively combined with static noise carriers having different check sizes. The amount of first-order ME associated with V increases with check size. MAEs were found to increase with check size but vection durations were unaffected. In general MAEs depend on the amount of first-order ME present in the signal. Vection, on the other hand, appears to depend on a representation of image flow that combines first- and second-order ME.

Sub-nanometer periodic nonlinearity error in absolute distance interferometers

NASA Astrophysics Data System (ADS)

Yang, Hongxing; Huang, Kaiqi; Hu, Pengcheng; Zhu, Pengfei; Tan, Jiubin; Fan, Zhigang

2015-05-01

Periodic nonlinearity which can result in error in nanometer scale has become a main problem limiting the absolute distance measurement accuracy. In order to eliminate this error, a new integrated interferometer with non-polarizing beam splitter is developed. This leads to disappearing of the frequency and/or polarization mixing. Furthermore, a strict requirement on the laser source polarization is highly reduced. By combining retro-reflector and angel prism, reference and measuring beams can be spatially separated, and therefore, their optical paths are not overlapped. So, the main cause of the periodic nonlinearity error, i.e., the frequency and/or polarization mixing and leakage of beam, is eliminated. Experimental results indicate that the periodic phase error is kept within 0.0018°.

Demand Controlled Economizer Cycles: A Direct Digital Control Scheme for Heating, Ventilating, and Air Conditioning Systems,

DTIC Science & Technology

1984-05-01

Control Ignored any error of 1/10th degree or less. This was done by setting the error term E and the integral sum PREINT to zero If then absolute value of...signs of two errors jeq tdiff if equal, jump clr @preint else zero integal sum tdiff mov @diff,rl fetch absolute value of OAT-RAT ci rl,25 is...includes a heating coil and thermostatic control to maintain the air in this path at an elevated temperature, typically around 80 degrees Farenheit (80 F

On the enhancement of magnetic anisotropy in cobalt clusters via non-magnetic doping.

PubMed

Islam, M Fhokrul; Khanna, Shiv N

2014-03-26

We show that the magnetic anisotropy energy (MAE) in cobalt clusters can be significantly enhanced by doping them with group IV elements. Our first-principles electronic structure calculations show that Co4C2 and Co12C4 clusters have MAEs of 25 K and 61 K, respectively. The large MAE is due to controlled mixing between Co d- and C p-states and can be further tuned by replacing C by Si. Larger assemblies of such primitive units are shown to be stable with MAEs exceeding 100 K in units as small as 1.2 nm, in agreement with the recent observation of large coercivity. These results may pave the way for the use of nano-clusters in high density magnetic memory devices for spintronics applications.

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Accurate electronic and chemical properties of 3d transition metal oxides using a calculated linear response U and a DFT + U(V) method.

PubMed

Xu, Zhongnan; Joshi, Yogesh V; Raman, Sumathy; Kitchin, John R

2015-04-14

We validate the usage of the calculated, linear response Hubbard U for evaluating accurate electronic and chemical properties of bulk 3d transition metal oxides. We find calculated values of U lead to improved band gaps. For the evaluation of accurate reaction energies, we first identify and eliminate contributions to the reaction energies of bulk systems due only to changes in U and construct a thermodynamic cycle that references the total energies of unique U systems to a common point using a DFT + U(V) method, which we recast from a recently introduced DFT + U(R) method for molecular systems. We then introduce a semi-empirical method based on weighted DFT/DFT + U cohesive energies to calculate bulk oxidation energies of transition metal oxides using density functional theory and linear response calculated U values. We validate this method by calculating 14 reactions energies involving V, Cr, Mn, Fe, and Co oxides. We find up to an 85% reduction of the mean average error (MAE) compared to energies calculated with the Perdew-Burke-Ernzerhof functional. When our method is compared with DFT + U with empirically derived U values and the HSE06 hybrid functional, we find up to 65% and 39% reductions in the MAE, respectively.

External quality-assurance results for the national atmospheric deposition program/national trends network, 2000-2001

USGS Publications Warehouse

Wetherbee, Gregory A.; Latysh, Natalie E.; Gordon, John D.

2004-01-01

Five external quality-assurance programs were operated by the U.S. Geological Survey for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) from 2000 through 2001 (study period): the intersite-comparison program, the blind-audit program, the field-audit program, the interlaboratory-comparison program, and the collocated-sampler program. Each program is designed to measure specific components of the total error inherent in NADP/NTN wet-deposition measurements. The intersite-comparison program assesses the variability and bias of pH and specific-conductance determinations made by NADP/NTN site operators with respect to accuracy goals. The accuracy goals are statistically based using the median of all of the measurements obtained for each of four intersite-comparison studies. The percentage of site operators responding on time that met the pH accuracy goals ranged from 84.2 to 90.5 percent. In these same four intersite-comparison studies, 88.9 to 99.0 percent of the site operators met the accuracy goals for specific conductance. The blind-audit program evaluates the effects of routine sample handling, processing, and shipping on the chemistry of weekly precipitation samples. The blind-audit data for the study period indicate that sample handling introduced a small amount of sulfate contamination and slight changes to hydrogen-ion content of the precipitation samples. The magnitudes of the paired differences are not environmentally significant to NADP/NTN data users. The field-audit program (also known as the 'field-blank program') was designed to measure the effects of field exposure, handling, and processing on the chemistry of NADP/NTN precipitation samples. The results indicate potential low-level contamination of NADP/NTN samples with calcium, ammonium, chloride, and nitrate. Less sodium contamination was detected by the field-audit data than in previous years. Statistical analysis of the paired differences shows that contaminant ions are entrained into the solutions from the field-exposed buckets, but the positive bias that results from the minor amount of contamination appears to affect the analytical results by less than 6 percent. An interlaboratory-comparison program is used to estimate the analytical variability and bias of participating laboratories, especially the NADP Central Analytical Laboratory (CAL). Statistical comparison of the analytical results of participating laboratories implies that analytical data from the various monitoring networks can be compared. Bias was identified in the CAL data for ammonium, chloride, nitrate, sulfate, hydrogen-ion, and specific-conductance measurements, but the absolute value of the bias was less than analytical minimum reporting limits for all constituents except ammonium and sulfate. Control charts show brief time periods when the CAL's analytical precision for sodium, ammonium, and chloride was not within the control limits. Data for the analysis of ultrapure deionized-water samples indicated that the laboratories are maintaining good control of laboratory contamination. Estimated analytical precision among the laboratories indicates that the magnitudes of chemical-analysis errors are not environmentally significant to NADP data users. Overall precision of the precipitation-monitoring system used by the NADP/NTN was estimated by evaluation of samples from collocated monitoring sites at CA99, CO08, and NH02. Precision defined by the median of the absolute percent difference (MAE) was estimated to be approximately 10 percent or less for calcium, magnesium, sodium, chloride, nitrate, sulfate, specific conductance, and sample volume. The MAE values for ammonium and hydrogen-ion concentrations were estimated to be less than 10 percent for CA99 and NH02 but nearly 20 percent for ammonium concentration and about 17 percent for hydrogen-ion concentration for CO08. As in past years, the variability in the collocated-site data for sam

Analysis of forecasting and inventory control of raw material supplies in PT INDAC INT’L

NASA Astrophysics Data System (ADS)

Lesmana, E.; Subartini, B.; Riaman; Jabar, D. A.

2018-03-01

This study discusses the data forecasting sales of carbon electrodes at PT. INDAC INT L uses winters and double moving average methods, while for predicting the amount of inventory and cost required in ordering raw material of carbon electrode next period using Economic Order Quantity (EOQ) model. The result of error analysis shows that winters method for next period gives result of MAE, MSE, and MAPE, the winters method is a better forecasting method for forecasting sales of carbon electrode products. So that PT. INDAC INT L is advised to provide products that will be sold following the sales amount by the winters method.

Optical Coherence Tomography–Based Corneal Power Measurement and Intraocular Lens Power Calculation Following Laser Vision Correction (An American Ophthalmological Society Thesis)

PubMed Central

Huang, David; Tang, Maolong; Wang, Li; Zhang, Xinbo; Armour, Rebecca L.; Gattey, Devin M.; Lombardi, Lorinna H.; Koch, Douglas D.

2013-01-01

Purpose: To use optical coherence tomography (OCT) to measure corneal power and improve the selection of intraocular lens (IOL) power in cataract surgeries after laser vision correction. Methods: Patients with previous myopic laser vision corrections were enrolled in this prospective study from two eye centers. Corneal thickness and power were measured by Fourier-domain OCT. Axial length, anterior chamber depth, and automated keratometry were measured by a partial coherence interferometer. An OCT-based IOL formula was developed. The mean absolute error of the OCT-based formula in predicting postoperative refraction was compared to two regression-based IOL formulae for eyes with previous laser vision correction. Results: Forty-six eyes of 46 patients all had uncomplicated cataract surgery with monofocal IOL implantation. The mean arithmetic prediction error of postoperative refraction was 0.05 ± 0.65 diopter (D) for the OCT formula, 0.14 ± 0.83 D for the Haigis-L formula, and 0.24 ± 0.82 D for the no-history Shammas-PL formula. The mean absolute error was 0.50 D for OCT compared to a mean absolute error of 0.67 D for Haigis-L and 0.67 D for Shammas-PL. The adjusted mean absolute error (average prediction error removed) was 0.49 D for OCT, 0.65 D for Haigis-L (P=.031), and 0.62 D for Shammas-PL (P=.044). For OCT, 61% of the eyes were within 0.5 D of prediction error, whereas 46% were within 0.5 D for both Haigis-L and Shammas-PL (P=.034). Conclusions: The predictive accuracy of OCT-based IOL power calculation was better than Haigis-L and Shammas-PL formulas in eyes after laser vision correction. PMID:24167323

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand Intensive Care Adult Patient Data-Base, 2008-2009.

PubMed

Moran, John L; Solomon, Patricia J

2012-05-16

For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator. The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function. For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis

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

Andreasen, Daniel, E-mail: dana@dtu.dk

Purpose: In radiotherapy based only on magnetic resonance imaging (MRI), knowledge about tissue electron densities must be derived from the MRI. This can be achieved by converting the MRI scan to the so-called pseudo-computed tomography (pCT). An obstacle is that the voxel intensities in conventional MRI scans are not uniquely related to electron density. The authors previously demonstrated that a patch-based method could produce accurate pCTs of the brain using conventional T{sub 1}-weighted MRI scans. The method was driven mainly by local patch similarities and relied on simple affine registrations between an atlas database of the co-registered MRI/CT scan pairsmore » and the MRI scan to be converted. In this study, the authors investigate the applicability of the patch-based approach in the pelvis. This region is challenging for a method based on local similarities due to the greater inter-patient variation. The authors benchmark the method against a baseline pCT strategy where all voxels inside the body contour are assigned a water-equivalent bulk density. Furthermore, the authors implement a parallelized approximate patch search strategy to speed up the pCT generation time to a more clinically relevant level. Methods: The data consisted of CT and T{sub 1}-weighted MRI scans of 10 prostate patients. pCTs were generated using an approximate patch search algorithm in a leave-one-out fashion and compared with the CT using frequently described metrics such as the voxel-wise mean absolute error (MAE{sub vox}) and the deviation in water-equivalent path lengths. Furthermore, the dosimetric accuracy was tested for a volumetric modulated arc therapy plan using dose–volume histogram (DVH) point deviations and γ-index analysis. Results: The patch-based approach had an average MAE{sub vox} of 54 HU; median deviations of less than 0.4% in relevant DVH points and a γ-index pass rate of 0.97 using a 1%/1 mm criterion. The patch-based approach showed a significantly better performance than the baseline water pCT in almost all metrics. The approximate patch search strategy was 70x faster than a brute-force search, with an average prediction time of 20.8 min. Conclusions: The authors showed that a patch-based method based on affine registrations and T{sub 1}-weighted MRI could generate accurate pCTs of the pelvis. The main source of differences between pCT and CT was positional changes of air pockets and body outline.« less

Noise-Enhanced Eversion Force Sense in Ankles With or Without Functional Instability.

PubMed

Ross, Scott E; Linens, Shelley W; Wright, Cynthia J; Arnold, Brent L

2015-08-01

Force sense impairments are associated with functional ankle instability. Stochastic resonance stimulation (SRS) may have implications for correcting these force sense deficits. To determine if SRS improved force sense. Case-control study. Research laboratory. Twelve people with functional ankle instability (age = 23 ± 3 years, height = 174 ± 8 cm, mass = 69 ± 10 kg) and 12 people with stable ankles (age = 22 ± 2 years, height = 170 ± 7 cm, mass = 64 ± 10 kg). The eversion force sense protocol required participants to reproduce a targeted muscle tension (10% of maximum voluntary isometric contraction). This protocol was assessed under SRSon and SRSoff (control) conditions. During SRSon, random subsensory mechanical noise was applied to the lower leg at a customized optimal intensity for each participant. Constant error, absolute error, and variable error measures quantified accuracy, overall performance, and consistency of force reproduction, respectively. With SRS, we observed main effects for force sense absolute error (SRSoff = 1.01 ± 0.67 N, SRSon = 0.69 ± 0.42 N) and variable error (SRSoff = 1.11 ± 0.64 N, SRSon = 0.78 ± 0.56 N) (P < .05). No other main effects or treatment-by-group interactions were found (P > .05). Although SRS reduced the overall magnitude (absolute error) and variability (variable error) of force sense errors, it had no effect on the directionality (constant error). Clinically, SRS may enhance muscle tension ability, which could have treatment implications for ankle stability.

Relevant reduction effect with a modified thermoplastic mask of rotational error for glottic cancer in IMRT

NASA Astrophysics Data System (ADS)

Jung, Jae Hong; Jung, Joo-Young; Cho, Kwang Hwan; Ryu, Mi Ryeong; Bae, Sun Hyun; Moon, Seong Kwon; Kim, Yong Ho; Choe, Bo-Young; Suh, Tae Suk

2017-02-01

The purpose of this study was to analyze the glottis rotational error (GRE) by using a thermoplastic mask for patients with the glottic cancer undergoing intensity-modulated radiation therapy (IMRT). We selected 20 patients with glottic cancer who had received IMRT by using the tomotherapy. The image modalities with both kilovoltage computed tomography (planning kVCT) and megavoltage CT (daily MVCT) images were used for evaluating the error. Six anatomical landmarks in the image were defined to evaluate a correlation between the absolute GRE (°) and the length of contact with the underlying skin of the patient by the mask (mask, mm). We also statistically analyzed the results by using the Pearson's correlation coefficient and a linear regression analysis ( P <0.05). The mask and the absolute GRE were verified to have a statistical correlation ( P < 0.01). We found a statistical significance for each parameter in the linear regression analysis (mask versus absolute roll: P = 0.004 [ P < 0.05]; mask versus 3D-error: P = 0.000 [ P < 0.05]). The range of the 3D-errors with contact by the mask was from 1.2% - 39.7% between the maximumand no-contact case in this study. A thermoplastic mask with a tight, increased contact area may possibly contribute to the uncertainty of the reproducibility as a variation of the absolute GRE. Thus, we suggest that a modified mask, such as one that covers only the glottis area, can significantly reduce the patients' setup errors during the treatment.

Online absolute pose compensation and steering control of industrial robot based on six degrees of freedom laser measurement

NASA Astrophysics Data System (ADS)

Yang, Juqing; Wang, Dayong; Fan, Baixing; Dong, Dengfeng; Zhou, Weihu

2017-03-01

In-situ intelligent manufacturing for large-volume equipment requires industrial robots with absolute high-accuracy positioning and orientation steering control. Conventional robots mainly employ an offline calibration technology to identify and compensate key robotic parameters. However, the dynamic and static parameters of a robot change nonlinearly. It is not possible to acquire a robot's actual parameters and control the absolute pose of the robot with a high accuracy within a large workspace by offline calibration in real-time. This study proposes a real-time online absolute pose steering control method for an industrial robot based on six degrees of freedom laser tracking measurement, which adopts comprehensive compensation and correction of differential movement variables. First, the pose steering control system and robot kinematics error model are constructed, and then the pose error compensation mechanism and algorithm are introduced in detail. By accurately achieving the position and orientation of the robot end-tool, mapping the computed Jacobian matrix of the joint variable and correcting the joint variable, the real-time online absolute pose compensation for an industrial robot is accurately implemented in simulations and experimental tests. The average positioning error is 0.048 mm and orientation accuracy is better than 0.01 deg. The results demonstrate that the proposed method is feasible, and the online absolute accuracy of a robot is sufficiently enhanced.

Spinal intra-operative three-dimensional navigation with infra-red tool tracking: correlation between clinical and absolute engineering accuracy

NASA Astrophysics Data System (ADS)

Guha, Daipayan; Jakubovic, Raphael; Gupta, Shaurya; Yang, Victor X. D.

2017-02-01

Computer-assisted navigation (CAN) may guide spinal surgeries, reliably reducing screw breach rates. Definitions of screw breach, if reported, vary widely across studies. Absolute quantitative error is theoretically a more precise and generalizable metric of navigation accuracy, but has been computed variably and reported in fewer than 25% of clinical studies of CAN-guided pedicle screw accuracy. We reviewed a prospectively-collected series of 209 pedicle screws placed with CAN guidance to characterize the correlation between clinical pedicle screw accuracy, based on postoperative imaging, and absolute quantitative navigation accuracy. We found that acceptable screw accuracy was achieved for significantly fewer screws based on 2mm grade vs. Heary grade, particularly in the lumbar spine. Inter-rater agreement was good for the Heary classification and moderate for the 2mm grade, significantly greater among radiologists than surgeon raters. Mean absolute translational/angular accuracies were 1.75mm/3.13° and 1.20mm/3.64° in the axial and sagittal planes, respectively. There was no correlation between clinical and absolute navigation accuracy, in part because surgeons appear to compensate for perceived translational navigation error by adjusting screw medialization angle. Future studies of navigation accuracy should therefore report absolute translational and angular errors. Clinical screw grades based on post-operative imaging, if reported, may be more reliable if performed in multiple by radiologist raters.

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

The PMA Catalogue: 420 million positions and absolute proper motions

NASA Astrophysics Data System (ADS)

Akhmetov, V. S.; Fedorov, P. N.; Velichko, A. B.; Shulga, V. M.

2017-07-01

We present a catalogue that contains about 420 million absolute proper motions of stars. It was derived from the combination of positions from Gaia DR1 and 2MASS, with a mean difference of epochs of about 15 yr. Most of the systematic zonal errors inherent in the 2MASS Catalogue were eliminated before deriving the absolute proper motions. The absolute calibration procedure (zero-pointing of the proper motions) was carried out using about 1.6 million positions of extragalactic sources. The mean formal error of the absolute calibration is less than 0.35 mas yr-1. The derived proper motions cover the whole celestial sphere without gaps for a range of stellar magnitudes from 8 to 21 mag. In the sky areas where the extragalactic sources are invisible (the avoidance zone), a dedicated procedure was used that transforms the relative proper motions into absolute ones. The rms error of proper motions depends on stellar magnitude and ranges from 2-5 mas yr-1 for stars with 10 mag < G < 17 mag to 5-10 mas yr-1 for faint ones. The present catalogue contains the Gaia DR1 positions of stars for the J2015 epoch. The system of the PMA proper motions does not depend on the systematic errors of the 2MASS positions, and in the range from 14 to 21 mag represents an independent realization of a quasi-inertial reference frame in the optical and near-infrared wavelength range. The Catalogue also contains stellar magnitudes taken from the Gaia DR1 and 2MASS catalogues. A comparison of the PMA proper motions of stars with similar data from certain recent catalogues has been undertaken.

Medication errors in chemotherapy preparation and administration: a survey conducted among oncology nurses in Turkey.

PubMed

Ulas, Arife; Silay, Kamile; Akinci, Sema; Dede, Didem Sener; Akinci, Muhammed Bulent; Sendur, Mehmet Ali Nahit; Cubukcu, Erdem; Coskun, Hasan Senol; Degirmenci, Mustafa; Utkan, Gungor; Ozdemir, Nuriye; Isikdogan, Abdurrahman; Buyukcelik, Abdullah; Inanc, Mevlude; Bilici, Ahmet; Odabasi, Hatice; Cihan, Sener; Avci, Nilufer; Yalcin, Bulent

2015-01-01

Medication errors in oncology may cause severe clinical problems due to low therapeutic indices and high toxicity of chemotherapeutic agents. We aimed to investigate unintentional medication errors and underlying factors during chemotherapy preparation and administration based on a systematic survey conducted to reflect oncology nurses experience. This study was conducted in 18 adult chemotherapy units with volunteer participation of 206 nurses. A survey developed by primary investigators and medication errors (MAEs) defined preventable errors during prescription of medication, ordering, preparation or administration. The survey consisted of 4 parts: demographic features of nurses; workload of chemotherapy units; errors and their estimated monthly number during chemotherapy preparation and administration; and evaluation of the possible factors responsible from ME. The survey was conducted by face to face interview and data analyses were performed with descriptive statistics. Chi-square or Fisher exact tests were used for a comparative analysis of categorical data. Some 83.4% of the 210 nurses reported one or more than one error during chemotherapy preparation and administration. Prescribing or ordering wrong doses by physicians (65.7%) and noncompliance with administration sequences during chemotherapy administration (50.5%) were the most common errors. The most common estimated average monthly error was not following the administration sequence of the chemotherapeutic agents (4.1 times/month, range 1-20). The most important underlying reasons for medication errors were heavy workload (49.7%) and insufficient number of staff (36.5%). Our findings suggest that the probability of medication error is very high during chemotherapy preparation and administration, the most common involving prescribing and ordering errors. Further studies must address the strategies to minimize medication error in chemotherapy receiving patients, determine sufficient protective measures and establishing multistep control mechanisms.

Epoxide as a precursor to secondary organic aerosol formation from isoprene photooxidation in the presence of nitrogen oxides

PubMed Central

Lin, Ying-Hsuan; Zhang, Haofei; Pye, Havala O. T.; Zhang, Zhenfa; Marth, Wendy J.; Park, Sarah; Arashiro, Maiko; Cui, Tianqu; Budisulistiorini, Sri Hapsari; Sexton, Kenneth G.; Vizuete, William; Xie, Ying; Luecken, Deborah J.; Piletic, Ivan R.; Edney, Edward O.; Bartolotti, Libero J.; Gold, Avram; Surratt, Jason D.

2013-01-01

Isoprene is a substantial contributor to the global secondary organic aerosol (SOA) burden, with implications for public health and the climate system. The mechanism by which isoprene-derived SOA is formed and the influence of environmental conditions, however, remain unclear. We present evidence from controlled smog chamber experiments and field measurements that in the presence of high levels of nitrogen oxides (NOx = NO + NO2) typical of urban atmospheres, 2-methyloxirane-2-carboxylic acid (methacrylic acid epoxide, MAE) is a precursor to known isoprene-derived SOA tracers, and ultimately to SOA. We propose that MAE arises from decomposition of the OH adduct of methacryloylperoxynitrate (MPAN). This hypothesis is supported by the similarity of SOA constituents derived from MAE to those from photooxidation of isoprene, methacrolein, and MPAN under high-NOx conditions. Strong support is further derived from computational chemistry calculations and Community Multiscale Air Quality model simulations, yielding predictions consistent with field observations. Field measurements taken in Chapel Hill, North Carolina, considered along with the modeling results indicate the atmospheric significance and relevance of MAE chemistry across the United States, especially in urban areas heavily impacted by isoprene emissions. Identification of MAE implies a major role of atmospheric epoxides in forming SOA from isoprene photooxidation. Updating current atmospheric modeling frameworks with MAE chemistry could improve the way that SOA has been attributed to isoprene based on ambient tracer measurements, and lead to SOA parameterizations that better capture the dependency of yield on NOx. PMID:23553832

Epoxide as a precursor to secondary organic aerosol formation from isoprene photooxidation in the presence of nitrogen oxides.

PubMed

Lin, Ying-Hsuan; Zhang, Haofei; Pye, Havala O T; Zhang, Zhenfa; Marth, Wendy J; Park, Sarah; Arashiro, Maiko; Cui, Tianqu; Budisulistiorini, Sri Hapsari; Sexton, Kenneth G; Vizuete, William; Xie, Ying; Luecken, Deborah J; Piletic, Ivan R; Edney, Edward O; Bartolotti, Libero J; Gold, Avram; Surratt, Jason D

2013-04-23

Isoprene is a substantial contributor to the global secondary organic aerosol (SOA) burden, with implications for public health and the climate system. The mechanism by which isoprene-derived SOA is formed and the influence of environmental conditions, however, remain unclear. We present evidence from controlled smog chamber experiments and field measurements that in the presence of high levels of nitrogen oxides (NO(x) = NO + NO2) typical of urban atmospheres, 2-methyloxirane-2-carboxylic acid (methacrylic acid epoxide, MAE) is a precursor to known isoprene-derived SOA tracers, and ultimately to SOA. We propose that MAE arises from decomposition of the OH adduct of methacryloylperoxynitrate (MPAN). This hypothesis is supported by the similarity of SOA constituents derived from MAE to those from photooxidation of isoprene, methacrolein, and MPAN under high-NOx conditions. Strong support is further derived from computational chemistry calculations and Community Multiscale Air Quality model simulations, yielding predictions consistent with field observations. Field measurements taken in Chapel Hill, North Carolina, considered along with the modeling results indicate the atmospheric significance and relevance of MAE chemistry across the United States, especially in urban areas heavily impacted by isoprene emissions. Identification of MAE implies a major role of atmospheric epoxides in forming SOA from isoprene photooxidation. Updating current atmospheric modeling frameworks with MAE chemistry could improve the way that SOA has been attributed to isoprene based on ambient tracer measurements, and lead to SOA parameterizations that better capture the dependency of yield on NO(x).

Error Analysis of Wind Measurements for the University of Illinois Sodium Doppler Temperature System

NASA Technical Reports Server (NTRS)

Pfenninger, W. Matthew; Papen, George C.

1992-01-01

Four-frequency lidar measurements of temperature and wind velocity require accurate frequency tuning to an absolute reference and long term frequency stability. We quantify frequency tuning errors for the Illinois sodium system, to measure absolute frequencies and a reference interferometer to measure relative frequencies. To determine laser tuning errors, we monitor the vapor cell and interferometer during lidar data acquisition and analyze the two signals for variations as functions of time. Both sodium cell and interferometer are the same as those used to frequency tune the laser. By quantifying the frequency variations of the laser during data acquisition, an error analysis of temperature and wind measurements can be calculated. These error bounds determine the confidence in the calculated temperatures and wind velocities.

Opposite effects of high- and low-frequency transcranial random noise stimulation probed with visual motion adaptation

PubMed Central

Campana, Gianluca; Camilleri, Rebecca; Moret, Beatrice; Ghin, Filippo; Pavan, Andrea

2016-01-01

Transcranial random noise stimulation (tRNS) is a recent neuro-modulation technique whose effects at both behavioural and neural level are still debated. Here we employed the well-known phenomenon of motion after-effect (MAE) in order to investigate the effects of high- vs. low-frequency tRNS on motion adaptation and recovery. Participants were asked to estimate the MAE duration following prolonged adaptation (20 s) to a complex moving pattern, while being stimulated with either sham or tRNS across different blocks. Different groups were administered with either high- or low-frequency tRNS. Stimulation sites were either bilateral human MT complex (hMT+) or frontal areas. The results showed that, whereas no effects on MAE duration were induced by stimulating frontal areas, when applied to the bilateral hMT+, high-frequency tRNS caused a significant decrease in MAE duration whereas low-frequency tRNS caused a significant corresponding increase in MAE duration. These findings indicate that high- and low-frequency tRNS have opposed effects on the adaptation-dependent unbalance between neurons tuned to opposite motion directions, and thus on neuronal excitability. PMID:27934947

On the possibility of non-invasive multilayer temperature estimation using soft-computing methods.

PubMed

Teixeira, C A; Pereira, W C A; Ruano, A E; Ruano, M Graça

2010-01-01

This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics (e.g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar-agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities (I(SATA)): 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2.0W/cm(2). Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 degrees C. The best-fitted estimator presents a MAE of only 0.4 degrees C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 degrees C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time. A non-invasive temperature estimation model, based on soft-computing technique, was proposed for a three-layered phantom. The best-achieved estimator models presented an appropriate error performance regardless of the spatial point considered (inside or at the interface of the layers) and of the intensity applied. Other methodologies published so far, estimate temperature only in homogeneous media. The main drawback of the proposed methodology is the necessity of a-priory knowledge of the temperature behavior. Data used for training and optimisation should be representative, i.e., they should cover all possible physical situations of the estimation environment.

Validation of the H-SAF precipitation product H03 over Greece using rain gauge data

NASA Astrophysics Data System (ADS)

Feidas, H.; Porcu, F.; Puca, S.; Rinollo, A.; Lagouvardos, C.; Kotroni, V.

2018-01-01

This paper presents an extensive validation of the combined infrared/microwave H-SAF (EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management) precipitation product H03, for a 1-year period, using gauge observations from a relatively dense network of 233 stations over Greece. First, the quality of the interpolated data used to validate the precipitation product is assessed and a quality index is constructed based on parameters such as the density of the station network and the orography. Then, a validation analysis is conducted based on comparisons of satellite (H03) with interpolated rain gauge data to produce continuous and multi-categorical statistics at monthly and annual timescales by taking into account the different geophysical characteristics of the terrain (land, coast, sea, elevation). Finally, the impact of the quality of interpolated data on the validation statistics is examined in terms of different configurations of the interpolation model and the rain gauge network characteristics used in the interpolation. The possibility of using a quality index of the interpolated data as a filter in the validation procedure is also investigated. The continuous validation statistics show yearly root mean squared error (RMSE) and mean absolute error (MAE) corresponding to the 225 and 105 % of the mean rain rate, respectively. Mean error (ME) indicates a slight overall tendency for underestimation of the rain gauge rates, which takes large values for the high rain rates. In general, the H03 algorithm cannot retrieve very well the light (< 1 mm/h) and the convective type (>10 mm/h) precipitation. The poor correlation between satellite and gauge data points to algorithm problems in co-locating precipitation patterns. Seasonal comparison shows that retrieval errors are lower for cold months than in the summer months of the year. The multi-categorical statistics indicate that the H03 algorithm is able to discriminate efficiently the rain from the no rain events although a large number of rain events are missed. The most prominent feature is the very high false alarm ratio (FAR) (more than 70 %), the relatively low probability of detection (POD) (less than 40 %), and the overestimation of the rainy pixels. Although the different geophysical features of the terrain (land, coast, sea, elevation) and the quality of the interpolated data have an effect on the validation statistics, this, in general, is not significant and seems to be more distinct in the categorical than in the continuous statistics.

Estimation of Sub Hourly Glacier Albedo Values Using Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Moya Quiroga, Vladimir; Mano, Akira; Asaoka, Yoshihiro; Udo, Keiko; Kure, Shuichi; Mendoza, Javier

2013-04-01

Glaciers are the most important fresh water reservoirs storing about 67% of total fresh water. Unfortunately, they are retreating and some small glaciers have already disappeared. Thus, snow glacier melt (SGM) estimation plays an important role in water resources management. Whether SGM is estimated by complete energy balance or a simplified method, albedo is an important data present in most of the methods. However, this is a variable value depending on the ground surface and local conditions. The present research presents a new approach for estimating sub hourly albedo values using different artificial intelligence techniques such as artificial neural networks and decision trees along with measured and easy to obtain data. . The models were developed using measured data from the Zongo-Ore station located in the Bolivian tropical glacier Zongo (68°10' W, 16°15' S). This station automatically records every 30 minutes several meteorological parameters such as incoming short wave radiation, outgoing short wave radiation, temperature or relative humidity. The ANN model used was the Multi Layer Perceptron, while the decision tree used was the M5 model. Both models were trained using the WEKA software and validated using the cross validation method. After analysing the model performances, it was concluded that the decision tree models have a better performance. The model with the best performance was then validated with measured data from the Equatorian tropical glacier Antizana (78°09'W, 0°28'S). The model predicts the sub hourly albedo with an overall mean absolute error of 0.103. The highest errors occur for albedo measured values higher than 0.9. Considering that this is an extreme value coincident with low measured values of incoming short wave radiation, it is reasonable to assume that such values include errors due to censored data. Assuming a maximum albedo of 0.9 improved the accuracy of the model reducing the MAE to less than 0.1. Considering that the model was successfully verified both in the inner tropics and the outer tropics, this model is a valuable contribution that may be used to project future scenarios in tropical glaciers. This research is developed within the GRANDE project (Glacier Retreat impact Assessment and National policy Development), financed by SATREPS from JST-JICA.

Predictability of the Arctic sea ice edge

NASA Astrophysics Data System (ADS)

Goessling, H. F.; Tietsche, S.; Day, J. J.; Hawkins, E.; Jung, T.

2016-02-01

Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the "truth" disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.

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

Ellefson, S; Department of Human Oncology, University of Wisconsin, Madison, WI; Culberson, W

Purpose: Discrepancies in absolute dose values have been detected between the ViewRay treatment planning system and ArcCHECK readings when performing delivery quality assurance on the ViewRay system with the ArcCHECK-MR diode array (SunNuclear Corporation). In this work, we investigate whether these discrepancies are due to errors in the ViewRay planning and/or delivery system or due to errors in the ArcCHECK’s readings. Methods: Gamma analysis was performed on 19 ViewRay patient plans using the ArcCHECK. Frequency analysis on the dose differences was performed. To investigate whether discrepancies were due to measurement or delivery error, 10 diodes in low-gradient dose regions weremore » chosen to compare with ion chamber measurements in a PMMA phantom with the same size and shape as the ArcCHECK, provided by SunNuclear. The diodes chosen all had significant discrepancies in absolute dose values compared to the ViewRay TPS. Absolute doses to PMMA were compared between the ViewRay TPS calculations, ArcCHECK measurements, and measurements in the PMMA phantom. Results: Three of the 19 patient plans had 3%/3mm gamma passing rates less than 95%, and ten of the 19 plans had 2%/2mm passing rates less than 95%. Frequency analysis implied a non-random error process. Out of the 10 diode locations measured, ion chamber measurements were all within 2.2% error relative to the TPS and had a mean error of 1.2%. ArcCHECK measurements ranged from 4.5% to over 15% error relative to the TPS and had a mean error of 8.0%. Conclusion: The ArcCHECK performs well for quality assurance on the ViewRay under most circumstances. However, under certain conditions the absolute dose readings are significantly higher compared to the planned doses. As the ion chamber measurements consistently agree with the TPS, it can be concluded that the discrepancies are due to ArcCHECK measurement error and not TPS or delivery system error. This work was funded by the Bhudatt Paliwal Professorship and the University of Wisconsin Medical Radiation Research Center.« less

Effect of stacking faults on the magnetocrystalline anisotropy of hcp Co: a first-principles study.

PubMed

Aas, C J; Szunyogh, L; Evans, R F L; Chantrell, R W

2013-07-24

In terms of the fully relativistic screened Korringa-Kohn-Rostoker method we investigate the effect of stacking faults on the magnetic properties of hexagonal close-packed (hcp) cobalt. In particular, we consider the formation energy and the effect on the magnetocrystalline anisotropy energy (MAE) of four different stacking faults in hcp cobalt-an intrinsic growth fault, an intrinsic deformation fault, an extrinsic fault and a twin-like fault. We find that the intrinsic growth fault has the lowest formation energy, in good agreement with previous first-principles calculations. With the exception of the intrinsic deformation fault which has a positive impact on the MAE, we find that the presence of a stacking fault generally reduces the MAE of bulk Co. Finally, we consider a pair of intrinsic growth faults and find that their effect on the MAE is not additive, but synergic.

Experiments on the applicability of MAE techniques for predicting sound diffraction by irregular terrains. [Matched Asymptotic Expansion

NASA Technical Reports Server (NTRS)

Berthelot, Yves H.; Pierce, Allan D.; Kearns, James A.

1987-01-01

The sound field diffracted by a single smooth hill of finite impedance is studied both analytically, within the context of the theory of Matched Asymptotic Expansions (MAE), and experimentally, under laboratory scale modeling conditions. Special attention is given to the sound field on the diffracting surface and throughout the transition region between the illuminated and the shadow zones. The MAE theory yields integral equations that are amenable to numerical computations. Experimental results are obtained with a spark source producing a pulse of 42 microsec duration and about 130 Pa at 1 m. The insertion loss of the hill is inferred from measurements of the acoustic signals at two locations in the field, with subsequent Fourier analysis on an IBM PC/AT. In general, experimental results support the predictions of the MAE theory, and provide a basis for the analysis of more complicated geometries.

Systematic errors of EIT systems determined by easily-scalable resistive phantoms.

PubMed

Hahn, G; Just, A; Dittmar, J; Hellige, G

2008-06-01

We present a simple method to determine systematic errors that will occur in the measurements by EIT systems. The approach is based on very simple scalable resistive phantoms for EIT systems using a 16 electrode adjacent drive pattern. The output voltage of the phantoms is constant for all combinations of current injection and voltage measurements and the trans-impedance of each phantom is determined by only one component. It can be chosen independently from the input and output impedance, which can be set in order to simulate measurements on the human thorax. Additional serial adapters allow investigation of the influence of the contact impedance at the electrodes on resulting errors. Since real errors depend on the dynamic properties of an EIT system, the following parameters are accessible: crosstalk, the absolute error of each driving/sensing channel and the signal to noise ratio in each channel. Measurements were performed on a Goe-MF II EIT system under four different simulated operational conditions. We found that systematic measurement errors always exceeded the error level of stochastic noise since the Goe-MF II system had been optimized for a sufficient signal to noise ratio but not for accuracy. In time difference imaging and functional EIT (f-EIT) systematic errors are reduced to a minimum by dividing the raw data by reference data. This is not the case in absolute EIT (a-EIT) where the resistivity of the examined object is determined on an absolute scale. We conclude that a reduction of systematic errors has to be one major goal in future system design.

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Evaluation of extraction protocols for anti-diabetic phytochemical substances from medicinal plants.

PubMed

Okoduwa, Stanley Irobekhian Reuben; Umar, Ismaila A; James, Dorcas B; Inuwa, Hajara M; Habila, James D

2016-12-15

To examine the efficacy of three extraction techniques: Soxhlet-extraction (SE), cold-maceration (CM) and microwave-assisted-extraction (MAE) using 80% methanol as solvent. The study was performed on each of 50 g of Vernonia amygdalina (VA) and Occimum gratissimum (OG) leaves respectively. The percentage yield, duration of extraction, volume of solvent used, qualitative and quantitative phytoconstituents present was compared. The biological activities (hypoglycemic effect) were investigated using albino wistar rat model of diabetes mellitus ( n = 36) with a combined dose (1:1) of the two plants leaf extracts (250 mg/kg b.w.) from the three methods. The extracts were administered orally, once daily for 21 d. In this report, the percentage VA extract yield from MAE was highest (20.9% ± 1.05%) within 39 min using 250 mL of solvent, when compared to the CM (14.35% ± 0.28%) within 4320 min using 900 mL of solvent and SE (15.75% ± 0.71%) within 265 min using 500 mL of solvent. The percentage differences in OG extract yield between: MAE vs SE was 41.05%; MAE vs CM was 46.81% and SE vs CM was 9.77%. The qualitative chemical analysis of the two plants showed no difference in the various phytoconstituents tested, but differs quantitatively in the amount of the individual phytoconstituents, as MAE had significantly high yield ( P > 0.05) on phenolics, saponins and tannins. SE technique gave significantly high yield ( P > 0.05) on alkaloid, while CM gave significant high yield on flavonoids. The extracts from CM exhibited a significantly ( P > 0.05) better hypoglycemic activity within the first 14-d of treatment (43.3% ± 3.62%) when compared to MAE (36.5% ± 0.08%) and SE methods (33.3% ± 1.60%). However, the percentage hypoglycemic activity, 21 d post-treatment with 250 mg/kg b.w. extract from MAE was 72.6% ± 1.03% and it was more comparable to 10 mg/kg b.w. glibenclamide treated group (75.0% ± 0.73%), unlike the SE (69.5% ± 0.71%) and CM (69.1% ± 1.03%). CM technique produces extract with better hypoglycemic activity, whereas; MAE is a better option for high yield of phytoconstituents using less solvent within a short time.

Evaluation of extraction protocols for anti-diabetic phytochemical substances from medicinal plants

PubMed Central

Okoduwa, Stanley Irobekhian Reuben; Umar, Ismaila A; James, Dorcas B; Inuwa, Hajara M; Habila, James D

2016-01-01

AIM To examine the efficacy of three extraction techniques: Soxhlet-extraction (SE), cold-maceration (CM) and microwave-assisted-extraction (MAE) using 80% methanol as solvent. METHODS The study was performed on each of 50 g of Vernonia amygdalina (VA) and Occimum gratissimum (OG) leaves respectively. The percentage yield, duration of extraction, volume of solvent used, qualitative and quantitative phytoconstituents present was compared. The biological activities (hypoglycemic effect) were investigated using albino wistar rat model of diabetes mellitus (n = 36) with a combined dose (1:1) of the two plants leaf extracts (250 mg/kg b.w.) from the three methods. The extracts were administered orally, once daily for 21 d. RESULTS In this report, the percentage VA extract yield from MAE was highest (20.9% ± 1.05%) within 39 min using 250 mL of solvent, when compared to the CM (14.35% ± 0.28%) within 4320 min using 900 mL of solvent and SE (15.75% ± 0.71%) within 265 min using 500 mL of solvent. The percentage differences in OG extract yield between: MAE vs SE was 41.05%; MAE vs CM was 46.81% and SE vs CM was 9.77%. The qualitative chemical analysis of the two plants showed no difference in the various phytoconstituents tested, but differs quantitatively in the amount of the individual phytoconstituents, as MAE had significantly high yield (P > 0.05) on phenolics, saponins and tannins. SE technique gave significantly high yield (P > 0.05) on alkaloid, while CM gave significant high yield on flavonoids. The extracts from CM exhibited a significantly (P > 0.05) better hypoglycemic activity within the first 14-d of treatment (43.3% ± 3.62%) when compared to MAE (36.5% ± 0.08%) and SE methods (33.3% ± 1.60%). However, the percentage hypoglycemic activity, 21 d post-treatment with 250 mg/kg b.w. extract from MAE was 72.6% ± 1.03% and it was more comparable to 10 mg/kg b.w. glibenclamide treated group (75.0% ± 0.73%), unlike the SE (69.5% ± 0.71%) and CM (69.1% ± 1.03%). CONCLUSION CM technique produces extract with better hypoglycemic activity, whereas; MAE is a better option for high yield of phytoconstituents using less solvent within a short time. PMID:28031778

High-speed homogenization coupled with microwave-assisted extraction followed by liquid chromatography-tandem mass spectrometry for the direct determination of alkaloids and flavonoids in fresh Isatis tinctoria L. hairy root cultures.

PubMed

Jiao, Jiao; Gai, Qing-Yan; Zhang, Lin; Wang, Wei; Luo, Meng; Zu, Yuan-Gang; Fu, Yu-Jie

2015-06-01

A new, simple and efficient analysis method for fresh plant in vitro cultures-namely, high-speed homogenization coupled with microwave-assisted extraction (HSH-MAE) followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS)-was developed for simultaneous determination of six alkaloids and eight flavonoids in Isatis tinctoria hairy root cultures (ITHRCs). Compared with traditional methods, the proposed HSH-MAE offers the advantages of easy manipulation, higher efficiency, energy saving, and reduced waste. Cytohistological studies were conducted to clarify the mechanism of HSH-MAE at cellular/tissue levels. Moreover, the established LC-MS/MS method showed excellent linearity, precision, repeatability, and reproducibility. The HSH-MAE-LC-MS/MS method was also successfully applied for screening high-productivity ITHRCs. Overall, this study opened up a new avenue for the direct determination of secondary metabolic profiles from fresh plant in vitro cultures, which is valuable for improving quality control of plant cell/organ cultures and sheds light on the metabolomic analysis of biological samples. Graphical Abstract HSH-MAE-LC-MS/MS opened up a new avenue for the direct determination of alkaloids and flavonoids in fresh Isatis tinctoria hairy root cultures.

Design and application of permanent magnet flux sources for mechanical testing of magnetoactive elastomers at variable field directions.

PubMed

Hiptmair, F; Major, Z; Haßlacher, R; Hild, S

2015-08-01

Magnetoactive elastomers (MAEs) are a class of smart materials whose mechanical properties can be rapidly and reversibly changed by an external magnetic field. Due to this tunability, they are useable for actuators or in active vibration control applications. An extensive magnetomechanical characterization is necessary for MAE material development and requires experiments under cyclic loading in uniform but variable magnetic fields. MAE testing apparatus typically rely on fields of adjustable strength, but fixed (transverse) direction, often provided by electromagnets. In this work, two permanent magnet flux sources were developed as an add-on for a modular test stand, to allow for mechanical testing in uniform fields of variable direction. MAE specimens, based on a silicone matrix with isotropic and anisotropic carbonyl iron particle distributions, were subjected to dynamic mechanical analysis under different field and loading configurations. The magneto-induced increase of stiffness and energy dissipation was determined by the change of the hysteresis loop area and dynamic modulus values. A distinct influence of the composite microstructure and the loading state was observed. Due to the very soft and flexible matrix used for preparing the MAE samples, the material stiffness and damping behavior could be varied over a wide range via the applied field direction and intensity.

Ionic liquid-based microwave-assisted extraction of flavonoids from Bauhinia championii (Benth.) Benth.

PubMed

Xu, Wei; Chu, Kedan; Li, Huang; Zhang, Yuqin; Zheng, Haiyin; Chen, Ruilan; Chen, Lidian

2012-12-03

An ionic liquids (IL)-based microwave-assisted approach for extraction and determination of flavonoids from Bauhinia championii (Benth.) Benth. was proposed for the first time. Several ILs with different cations and anions and the microwave-assisted extraction (MAE) conditions, including sample particle size, extraction time and liquid-solid ratio, were investigated. Two M 1-butyl-3-methylimidazolium bromide ([bmim] Br) solution with 0.80 M HCl was selected as the optimal solvent. Meanwhile the optimized conditions a ratio of liquid to material of 30:1, and the extraction for 10 min at 70 °C. Compared with conventional heat-reflux extraction (CHRE) and the regular MAE, IL-MAE exhibited a higher extraction yield and shorter extraction time (from 1.5 h to 10 min). The optimized extraction samples were analysed by LC-MS/MS. IL extracts of Bauhinia championii (Benth.)Benth consisted mainly of flavonoids, among which myricetin, quercetin and kaempferol, β-sitosterol, triacontane and hexacontane were identified. The study indicated that IL-MAE was an efficient and rapid method with simple sample preparation. LC-MS/MS was also used to determine the chemical composition of the ethyl acetate/MAE extract of Bauhinia championii (Benth.) Benth, and it maybe become a rapid method to determine the composition of new plant extracts.

Wideband Arrhythmia-Insensitive-Rapid (AIR) Pulse Sequence for Cardiac T1 mapping without Image Artifacts induced by ICD

PubMed Central

Hong, KyungPyo; Jeong, Eun-Kee; Wall, T. Scott; Drakos, Stavros G.; Kim, Daniel

2015-01-01

Purpose To develop and evaluate a wideband arrhythmia-insensitive-rapid (AIR) pulse sequence for cardiac T1 mapping without image artifacts induced by implantable-cardioverter-defibrillator (ICD). Methods We developed a wideband AIR pulse sequence by incorporating a saturation pulse with wide frequency bandwidth (8.9 kHz), in order to achieve uniform T1 weighting in the heart with ICD. We tested the performance of original and “wideband” AIR cardiac T1 mapping pulse sequences in phantom and human experiments at 1.5T. Results In 5 phantoms representing native myocardium and blood and post-contrast blood/tissue T1 values, compared with the control T1 values measured with an inversion-recovery pulse sequence without ICD, T1 values measured with original AIR with ICD were considerably lower (absolute percent error >29%), whereas T1 values measured with wideband AIR with ICD were similar (absolute percent error <5%). Similarly, in 11 human subjects, compared with the control T1 values measured with original AIR without ICD, T1 measured with original AIR with ICD was significantly lower (absolute percent error >10.1%), whereas T1 measured with wideband AIR with ICD was similar (absolute percent error <2.0%). Conclusion This study demonstrates the feasibility of a wideband pulse sequence for cardiac T1 mapping without significant image artifacts induced by ICD. PMID:25975192

Absolute color scale for improved diagnostics with wavefront error mapping.

PubMed

Smolek, Michael K; Klyce, Stephen D

2007-11-01

Wavefront data are expressed in micrometers and referenced to the pupil plane, but current methods to map wavefront error lack standardization. Many use normalized or floating scales that may confuse the user by generating ambiguous, noisy, or varying information. An absolute scale that combines consistent clinical information with statistical relevance is needed for wavefront error mapping. The color contours should correspond better to current corneal topography standards to improve clinical interpretation. Retrospective analysis of wavefront error data. Historic ophthalmic medical records. Topographic modeling system topographical examinations of 120 corneas across 12 categories were used. Corneal wavefront error data in micrometers from each topography map were extracted at 8 Zernike polynomial orders and for 3 pupil diameters expressed in millimeters (3, 5, and 7 mm). Both total aberrations (orders 2 through 8) and higher-order aberrations (orders 3 through 8) were expressed in the form of frequency histograms to determine the working range of the scale across all categories. The standard deviation of the mean error of normal corneas determined the map contour resolution. Map colors were based on corneal topography color standards and on the ability to distinguish adjacent color contours through contrast. Higher-order and total wavefront error contour maps for different corneal conditions. An absolute color scale was produced that encompassed a range of +/-6.5 microm and a contour interval of 0.5 microm. All aberrations in the categorical database were plotted with no loss of clinical information necessary for classification. In the few instances where mapped information was beyond the range of the scale, the type and severity of aberration remained legible. When wavefront data are expressed in micrometers, this absolute scale facilitates the determination of the severity of aberrations present compared with a floating scale, particularly for distinguishing normal from abnormal levels of wavefront error. The new color palette makes it easier to identify disorders. The corneal mapping method can be extended to mapping whole eye wavefront errors. When refraction data are expressed in diopters, the previously published corneal topography scale is suggested.

Reliability study of biometrics "do not contact" in myopia.

PubMed

Migliorini, R; Fratipietro, M; Comberiati, A M; Pattavina, L; Arrico, L

The aim of the study is a comparison between the actually achieved after surgery condition versus the expected refractive condition of the eye as calculated via a biometer. The study was conducted in a random group of 38 eyes of patients undergoing surgery by phacoemulsification. The mean absolute error was calculated between the predicted values from the measurements with the optical biometer and those obtained in the post-operative error which was at around 0.47% Our study shows results not far from those reported in the literature, and in relation, to the mean absolute error is among the lowest values at 0.47 ± 0.11 SEM.

Learning to predict where human gaze is using quaternion DCT based regional saliency detection

NASA Astrophysics Data System (ADS)

Li, Ting; Xu, Yi; Zhang, Chongyang

2014-09-01

Many current visual attention approaches used semantic features to accurately capture human gaze. However, these approaches demand high computational cost and can hardly be applied to daily use. Recently, some quaternion-based saliency detection models, such as PQFT (phase spectrum of Quaternion Fourier Transform), QDCT (Quaternion Discrete Cosine Transform), have been proposed to meet real-time requirement of human gaze tracking tasks. However, current saliency detection methods used global PQFT and QDCT to locate jump edges of the input, which can hardly detect the object boundaries accurately. To address the problem, we improved QDCT-based saliency detection model by introducing superpixel-wised regional saliency detection mechanism. The local smoothness of saliency value distribution is emphasized to distinguish noises of background from salient regions. Our algorithm called saliency confidence can distinguish the patches belonging to the salient object and those of the background. It decides whether the image patches belong to the same region. When an image patch belongs to a region consisting of other salient patches, this patch should be salient as well. Therefore, we use saliency confidence map to get background weight and foreground weight to do the optimization on saliency map obtained by QDCT. The optimization is accomplished by least square method. The optimization approach we proposed unifies local and global saliency by combination of QDCT and measuring the similarity between each image superpixel. We evaluate our model on four commonly-used datasets (Toronto, MIT, OSIE and ASD) using standard precision-recall curves (PR curves), the mean absolute error (MAE) and area under curve (AUC) measures. In comparison with most state-of-art models, our approach can achieve higher consistency with human perception without training. It can get accurate human gaze even in cluttered background. Furthermore, it achieves better compromise between speed and accuracy.

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

PubMed Central

Song, Jiangning; Tan, Hao; Wang, Mingjun; Webb, Geoffrey I.; Akutsu, Tatsuya

2012-01-01

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/. PMID:22319565

Climate-scale modelling of suspended sediment load in an Alpine catchment debris flow (Rio Cordon-northeastern Italy)

NASA Astrophysics Data System (ADS)

Diodato, Nazzareno; Mao, Luca; Borrelli, Pasquale; Panagos, Panos; Fiorillo, Francesco; Bellocchi, Gianni

2018-05-01

Pulsing storms and prolonged rainfall can drive hydrological damaging events in mountain regions with soil erosion and debris flow in river catchments. The paper presents a parsimonious model for estimating climate forcing on sediment loads in an Alpine catchment (Rio Cordon, northeastern Italian Alps). Hydroclimatic forcing was interpreted by the novel CliSMSSL (Climate-Scale Modelling of Suspended Sediment Load) model to estimate annual sediment loads. We used annual data on suspended-solid loads monitored at an experimental station from 1987 to 2001 and on monthly precipitation data. The quality of sediment load data was critically examined, and one outlying year was identified and removed from further analyses. This outlier revealed that our model underestimates exceptionally high sediment loads in years characterized by a severe flood event. For all other years, the CliSMSSL performed well, with a determination coefficient (R2) equal to 0.67 and a mean absolute error (MAE) of 129 Mg y-1. The calibrated model for the period 1986-2010 was used to reconstruct sediment loads in the river catchment for historical times when detailed precipitation records are not available. For the period 1810-2010, the model results indicate that the past centuries have been characterized by large interannual to interdecadal fluctuations in the conditions affecting sediment loads. This paper argues that climate-induced erosion processes in Alpine areas and their impact on environment should be given more attention in discussions about climate-driven strategies. Future work should focus on delineating the extents of these findings (e.g., at other catchments of the European Alpine belt) as well as investigating the dynamics for the formation of sediment loads.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

PubMed

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

Automated estimation of abdominal effective diameter for body size normalization of CT dose.

PubMed

Cheng, Phillip M

2013-06-01

Most CT dose data aggregation methods do not currently adjust dose values for patient size. This work proposes a simple heuristic for reliably computing an effective diameter of a patient from an abdominal CT image. Evaluation of this method on 106 patients scanned on Philips Brilliance 64 and Brilliance Big Bore scanners demonstrates close correspondence between computed and manually measured patient effective diameters, with a mean absolute error of 1.0 cm (error range +2.2 to -0.4 cm). This level of correspondence was also demonstrated for 60 patients on Siemens, General Electric, and Toshiba scanners. A calculated effective diameter in the middle slice of an abdominal CT study was found to be a close approximation of the mean calculated effective diameter for the study, with a mean absolute error of approximately 1.0 cm (error range +3.5 to -2.2 cm). Furthermore, the mean absolute error for an adjusted mean volume computed tomography dose index (CTDIvol) using a mid-study calculated effective diameter, versus a mean per-slice adjusted CTDIvol based on the calculated effective diameter of each slice, was 0.59 mGy (error range 1.64 to -3.12 mGy). These results are used to calculate approximate normalized dose length product values in an abdominal CT dose database of 12,506 studies.

Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios

NASA Astrophysics Data System (ADS)

Sanikhani, Hadi; Kisi, Ozgur; Maroufpoor, Eisa; Yaseen, Zaher Mundher

2018-02-01

The establishment of an accurate computational model for predicting reference evapotranspiration (ET0) process is highly essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, six artificial intelligence (AI) models were investigated for modeling ET0 using a small number of climatic data generated from the minimum and maximum temperatures of the air and extraterrestrial radiation. The investigated models were multilayer perceptron (MLP), generalized regression neural networks (GRNN), radial basis neural networks (RBNN), integrated adaptive neuro-fuzzy inference systems with grid partitioning and subtractive clustering (ANFIS-GP and ANFIS-SC), and gene expression programming (GEP). The implemented monthly time scale data set was collected at the Antalya and Isparta stations which are located in the Mediterranean Region of Turkey. The Hargreaves-Samani (HS) equation and its calibrated version (CHS) were used to perform a verification analysis of the established AI models. The accuracy of validation was focused on multiple quantitative metrics, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R 2), coefficient of residual mass (CRM), and Nash-Sutcliffe efficiency coefficient (NS). The results of the conducted models were highly practical and reliable for the investigated case studies. At the Antalya station, the performance of the GEP and GRNN models was better than the other investigated models, while the performance of the RBNN and ANFIS-SC models was best compared to the other models at the Isparta station. Except for the MLP model, all the other investigated models presented a better performance accuracy compared to the HS and CHS empirical models when applied in a cross-station scenario. A cross-station scenario examination implies the prediction of the ET0 of any station using the input data of the nearby station. The performance of the CHS models in the modeling the ET0 was better in all the cases when compared to that of the original HS.

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

PubMed

Zhou, Qingtao; Flores, Alejandro; Glenn, Nancy F; Walters, Reggie; Han, Bangshuai

2017-01-01

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3-0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling.

Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)

NASA Astrophysics Data System (ADS)

Jing, Miao; Heße, Falk; Kumar, Rohini; Wang, Wenqing; Fischer, Thomas; Walther, Marc; Zink, Matthias; Zech, Alraune; Samaniego, Luis; Kolditz, Olaf; Attinger, Sabine

2018-06-01

Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river-groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM-OGS v1.0) is evaluated by a case study in the central European mesoscale river basin - Nägelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash-Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems.

Estimation of water table level and nitrate pollution based on geostatistical and multiple mass transport models

NASA Astrophysics Data System (ADS)

Matiatos, Ioannis; Varouhakis, Emmanouil A.; Papadopoulou, Maria P.

2015-04-01

As the sustainable use of groundwater resources is a great challenge for many countries in the world, groundwater modeling has become a very useful and well established tool for studying groundwater management problems. Based on various methods used to numerically solve algebraic equations representing groundwater flow and contaminant mass transport, numerical models are mainly divided into Finite Difference-based and Finite Element-based models. The present study aims at evaluating the performance of a finite difference-based (MODFLOW-MT3DMS), a finite element-based (FEFLOW) and a hybrid finite element and finite difference (Princeton Transport Code-PTC) groundwater numerical models simulating groundwater flow and nitrate mass transport in the alluvial aquifer of Trizina region in NE Peloponnese, Greece. The calibration of groundwater flow in all models was performed using groundwater hydraulic head data from seven stress periods and the validation was based on a series of hydraulic head data for two stress periods in sufficient numbers of observation locations. The same periods were used for the calibration of nitrate mass transport. The calibration and validation of the three models revealed that the simulated values of hydraulic heads and nitrate mass concentrations coincide well with the observed ones. The models' performance was assessed by performing a statistical analysis of these different types of numerical algorithms. A number of metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash Sutcliffe Model Efficiency (NSE) and Reliability Index (RI) were used allowing the direct comparison of models' performance. Spatiotemporal Kriging (STRK) was also applied using separable and non-separable spatiotemporal variograms to predict water table level and nitrate concentration at each sampling station for two selected hydrological stress periods. The predictions were validated using the respective measured values. Maps of water table level and nitrate concentrations were produced and compared with those obtained from groundwater and mass transport numerical models. Preliminary results showed similar efficiency of the spatiotemporal geostatistical method with the numerical models. However data requirements of the former model were significantly less. Advantages and disadvantages of the methods performance were analysed and discussed indicating the characteristics of the different approaches.

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

PubMed Central

Flores, Alejandro; Glenn, Nancy F.; Walters, Reggie; Han, Bangshuai

2017-01-01

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3–0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling. PMID:28777811

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.

Low plasma vitamin D levels and muscle-related adverse effects in statin users.

PubMed

Eisen, Alon; Lev, Eli; Iakobishvilli, Zaza; Porter, Avital; Brosh, David; Hasdai, David; Mager, Aviv

2014-01-01

Treatment with HMG-CoA reductase inhibitors (statins) is often complicated by muscle-related adverse effects (MAEs). Studies of the association between low plasma vitamin D levels and MAEs have yielded conflicting results. To determine if low plasma vitamin D level is a risk factorfor MAEs in statin users. Plasma levels of 25(OH) vitamin D were measured as part of the routine evaluation of unselected statin-treated patients attending the coronary and lipid clinics at our hospital during the period 2007-2010. Medical data on muscle complaints and statin use were retrieved from the medical files. Creatine kinase (CK) levels were derived from the hospital laboratory database. The sample included 272 patients (141 men) aged 33-89 years. Mean vitamin D level was 48.04 nmol/L. Levels were higher in men (51.0 +/- 20.5 versus 44.7 +/- 18.9 nmol/L, P = 0.001) and were unaffected by age. MAEs were observed in 106 patients (39%): myalgia in 95 (35%) and CK elevation in 20 (7%); 9 patients (3%) had both. There was no difference in plasma vitamin D levels between patients with and without myalgia (46.3 +/- 17.7 versus 48.9 +/- 21.0 nmol/L, P = 0.31), with and without CK elevation (50.2 +/- 14.6 versus 47.8 +/- 20.3 nmol/L, P = 0.60), or with or without any MAE (50.4 +/- 15.0 versus 47.8 +/- 10.2 nmol/L, P = 0.27). These findings were consistent when analyzed by patient gender and presence/absence of coronary artery disease, and when using a lower vitamin D cutoff (< 25 nmol/L). There is apparently no relationship between plasma vitamin D level and risk of MAEs in statin users.

The relationship between elevated red cell distribution width and long-term outcomes among patients with atrial fibrillation.

PubMed

Wan, Huaibin; Yang, Yanmin; Zhu, Jun; Huang, Bi; Wang, Juan; Wu, Shuang; Shao, Xinghui; Zhang, Han

2015-08-01

Red cell distribution width (RDW) is associated with the incidence of atrial fibrillation (AF). The aim of this study was to evaluate the relationship between elevated RDW and long-term clinical outcomes among patients with AF. We prospectively observed 300 consecutive patients with AF (50.3% males, mean age 62.6 ± 12.9 years) between February 2009 and October 2011. Baseline RDW levels and clinical data were collected. The primary clinical outcomes of interest included all-cause mortality and the incidence of major adverse events (MAEs). During a median follow-up period of 3.2 years, 60 deaths and 92 MAEs were recorded. From the lowest to the highest RDW quartile, an increased risk of mortality (2.76, 3.98, 8.40 and 13.77 per 100 person-years, respectively) and an incidence of MAEs (6.46, 8.18, 13.79 and 20.27 per 100 person-years, respectively) were noted. In a multivariate Cox regression analysis, RDW was independently associated with both all-cause mortality (hazard ratio (HR): 1.024; 95% confidence interval (CI): 1.012-1.036, P < 0.001) and MAEs (HR: 1.012; 95% CI: 1.002-1.023, P = 0.023). A receiver operating characteristic (ROC) analysis revealed that RDW predicted both mortality and MAEs with areas under the ROC curves (AUCs) of 0.682 (P < 0.001) and 0.617 (P = 0.001); the best cutoff points were 13.85% and 13.55%, respectively. Elevated RDW is an independent predictor of long-term adverse clinical outcomes, including all-cause mortality and MAEs, among patients with AF. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Oscillatory shear stress stimulates endothelial production of O2- from p47phox-dependent NAD(P)H oxidases, leading to monocyte adhesion

NASA Technical Reports Server (NTRS)

Hwang, Jinah; Saha, Aniket; Boo, Yong Chool; Sorescu, George P.; McNally, J. Scott; Holland, Steven M.; Dikalov, Sergei; Giddens, Don P.; Griendling, Kathy K.; Harrison, David G.;

Clinical Outcomes of Patients Undergoing Rotational Atherectomy Followed by Drug-eluting Stent Implantation: A Single-center Real-world Experience

PubMed Central

Cuenza, Lucky R.; Jayme, Ada Cherryl; Khe Sui, James Ho

2017-01-01

Background: Rotational atherectomy (RA) is used to improve procedural success of percutaneous catheter interventions (PCIs) of complex and heavily calcified coronary lesions. We report the clinical experience and outcomes in our institution with the use of RA, followed by drug-eluting stent implantation. Materials and Methods: Data of 81 patients treated with PCI and adjunctive RA were analyzed. Clinical follow-up for the occurrence of major adverse events (MAEs) was obtained in all patients and correlated with significant variables using multivariate Cox proportional hazards analysis. Results: Mean age was 67.9 ± 9.2 years, 61.7% had diabetes, 20.9% had chronic kidney disease, and 48.1% had previous acute coronary syndrome (ACS). Mean SYNTAX score was 29.8 ± 12.2, with a 92.5% angiographic success rate achieved. In-hospital MAEs rate was 7.4% while mortality rate was 8.6%. On median follow-up of 12.2 months, incidence of MAEs of 13.5% with a 75% free incidence from MAEs at 34 months. Multivariate analysis revealed that a history of previous ACS, ejection fraction, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, SYNTAX score, burr to artery ratio, and attainment of angiographic success were significant predictors of MAEs. Conclusion: RA followed by drug-eluting stent implantation is a safe and effective method in improving procedural success as well as short- and long-term outcomes of PCI in our center. A combination of clinical and procedural factors is predictive for the occurrence of MAEs and should be taken into account in the application of this technique. PMID:29326773

Intensification of marrubiin concentration by optimization of microwave-assisted (low CO2 yielding) extraction process for Marrubium vulgare using central composite design and antioxidant evaluation.

PubMed

Mittal, Vineet; Nanda, Arun

2017-12-01

Marrubium vulgare Linn (Lamiaceae) was generally extracted by conventional methods with low yield of marrubiin; these processes were not considered environment friendly. This study extracts the whole plant of M. vulgare by microwave assisted extraction (MAE) and optimizes the effect of various extraction parameters on the marrubiin yield by using Central Composite Design (CCD). The selected medicinal plant was extracted using ethanol: water (1:1) as solvent by MAE. The plant material was also extracted using a Soxhlet and the various extracts were analyzed by HPTLC to quantify the marrubiin concentration. The optimized conditions for the microwave-assisted extraction of selected medicinal plant was microwave power of 539 W, irradiation time of 373 s and solvent to drug ratio, 32 mL per g of the drug. The marrubiin concentration in MAE almost doubled relative to the traditional method (0.69 ± 0.08 to 1.35 ± 0.04%). The IC 50 for DPPH was reduced to 66.28 ± 0.6 μg/mL as compared to conventional extract (84.14 ± 0.7 μg/mL). The scanning electron micrographs of the treated and untreated drug samples further support the results. The CCD can be successfully applied to optimize the extraction parameters (MAE) for M. vulgare. Moreover, in terms of environmental impact, the MAE technique could be assumed as a 'Green approach' because the MAE approach for extraction of plant released only 92.3 g of CO 2 as compared to 3207.6 g CO 2 using the Soxhlet method of extraction.

Development of a microwave assisted extraction method for the analysis of 2,4,6-trichloroanisole in cork stoppers by SIDA-SBSE-GC-MS.

PubMed

Vestner, Jochen; Fritsch, Stefanie; Rauhut, Doris

2010-02-15

The aim of this research work was focused on the replacement of the time-consuming soaking of cork stoppers which is mainly used as screening method for cork lots in connection with sensory analysis and/or analytical methods to detect releasable 2,4,6-trichloroanisole (TCA) of natural cork stoppers. Releasable TCA from whole cork stoppers was analysed with the application of a microwave assisted extraction method (MAE) in combination with stir bar sorptive extraction (SBSE). The soaking of corks (SOAK) was used as a reference method to optimise MAE parameters. Cork lots of different quality and TCA contamination levels were used to adapt MAE. Pre-tests indicated that an MAE at 40 degrees C for 120 min with 90 min of cooling time are suitable conditions to avoid an over-extraction of TCA of low and medium tainted cork stoppers in comparison to SOAK. These MAE parameters allow the measuring of almost the same amounts of releasable TCA as with the application of the soaking procedure in the relevant range (<25 ng L(-1) releasable TCA from one cork) to evaluate the TCA level of cork stoppers. Stable isotope dilution assay (SIDA) was applied to optimise quantification of the released TCA with deuterium-labelled TCA (TCA-d(5)) using a time-saving GC-MS technique in single ion monitoring (SIM) mode. The developed MAE method allows the measuring of releasable TCA from the whole cork stopper under improved conditions and in connection with a low use of solvent and a higher sample throughput. Copyright 2009 Elsevier B.V. All rights reserved.

Prediction of long-term prognosis by heteroplasmy levels of the m.3243A>G mutation in patients with the mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes syndrome.

PubMed

Fayssoil, A; Laforêt, P; Bougouin, W; Jardel, C; Lombès, A; Bécane, H M; Berber, N; Stojkovic, T; Béhin, A; Eymard, B; Duboc, D; Wahbi, K

2017-02-01

Our aim was to determine the prognostic value of urine and blood heteroplasmy in patients with the m.3243A>G mutation. Adults with the m.3243A>G mutation referred to our institution between January 2000 and May 2014 were retrospectively included. The relationship between their baseline clinical characteristics, their mutation load in urine and blood, and major adverse events (MAEs) during follow-up, defined as medical complications requiring a hospitalization or complicated by death, was studied. Of the 43 patients (age 45.6 ± 13.3 years) included in the study, 36 patients were symptomatic, including nine with evidence of focal brain involvement, and seven were asymptomatic. Over a 5.5 ± 4.0 year mean follow-up duration, 14 patients (33%) developed MAEs. Patients with MAEs had a higher mutation load than others in urine (60.1% ± 13.8% vs. 40.6% ± 26.2%, P = 0.01) and in blood (26.9% ± 18.4% vs. 16.0% ± 12.1%, P = 0.03). Optimal cutoff values for the prediction of MAEs were 45% for urine and 35% for blood. In multivariate analysis, mutation load in urine ≥45% [odds ratio 25.3; 95% confidence interval (CI) 1.1-567.8; P = 0.04], left ventricular hypertrophy (odds ratio 16.7; 95% CI 1.3- 222.5; P = 0.03) and seizures (odds ratio 48.3; 95% CI 2.5-933; P = 0.01) were associated with MAEs. Patients with the m.3243A>G mutation are at high risk of MAEs, which can be independently predicted by mutation load in urine ≥45%, a personal history of seizures, and left ventricular hypertrophy. © 2016 EAN.

Suivi thérapeutique pharmacologique de trois médicaments antiépileptiques: retour sur vingt années d’expérience

PubMed Central

Serragui, Samira; Zalagh, Fatima; Tanani, Driss Soussi; Ouammi, Lahcen; Moussa, Latifa Ait; Badrane, Narjis; Bencheikh, Rachida Soulaymani

2016-01-01

Introduction Le suivi thérapeutique pharmacologique (STP) des médicaments antiépileptiques (MAE) est un outil très utilisé dans la gestion de l'épilepsie. Au Maroc, ce dosage est réalisé au Centre Anti Poison et de Pharmacovigilance du Maroc (CAPM) depuis Avril 1995. Méthodes Il s'agit d'une étude rétrospective s'étalant sur 20 ans. Elle concerne le STP du Phénobarbital (PB), de la Carbamazépine (CBZ) et de l'Acide Valproique (AVP). Résultats Le STP des 3 MAE représentaient 58,85% de l'ensemble des demandes de STP reçue par le CAPM. Le dosage du PB était classé en première position suivi par celui de la CBZ et enfin par l'AVP. La faible demande de STP au Maroc pouvait être expliquée par le faible nombre de neurologues auquel s'ajoutaient des facteurs sociaux. Grâce à son prix très accessible par les patients, le PB est le MAE le plus prescrit dans notre pays expliquant ainsi la demande élevée de son dosage. Quant aux motifs de STP des 3 MAE, ils étaient essentiellement liés à l'âge, à l'apparition d'effets indésirables, à l'association de MAE ou dans le cas de vérification de l'observance des malades. Conclusion Des efforts sont à fournir pour promouvoir l'intérêt du STP des MAE dans la prise en charge de l'épilepsie au Maroc. PMID:28154702

Comparison of the Pentacam equivalent keratometry reading and IOL Master keratometry measurement in intraocular lens power calculations.

PubMed

Karunaratne, Nicholas

2013-12-01

To compare the accuracy of the Pentacam Holladay equivalent keratometry readings with the IOL Master 500 keratometry in calculating intraocular lens power. Non-randomized, prospective clinical study conducted in private practice. Forty-five consecutive normal patients undergoing cataract surgery. Forty-five consecutive patients had Pentacam equivalent keratometry readings at the 2-, 3 and 4.5-mm corneal zone and IOL Master keratometry measurements prior to cataract surgery. For each Pentacam equivalent keratometry reading zone and IOL Master measurement the difference between the observed and expected refractive error was calculated using the Holladay 2 and Sanders, Retzlaff and Kraff theoretic (SRKT) formulas. Mean keratometric value and mean absolute refractive error. There was a statistically significantly difference between the mean keratometric values of the IOL Master, Pentacam equivalent keratometry reading 2-, 3- and 4.5-mm measurements (P < 0.0001, analysis of variance). There was no statistically significant difference between the mean absolute refraction error for the IOL Master and equivalent keratometry readings 2 mm, 3 mm and 4.5 mm zones for either the Holladay 2 formula (P = 0.14) or SRKT formula (P = 0.47). The lowest mean absolute refraction error for Holladay 2 equivalent keratometry reading was the 4.5 mm zone (mean 0.25 D ± 0.17 D). The lowest mean absolute refraction error for SRKT equivalent keratometry reading was the 4.5 mm zone (mean 0.25 D ± 0.19 D). Comparing the absolute refraction error of IOL Master and Pentacam equivalent keratometry reading, best agreement was with Holladay 2 and equivalent keratometry reading 4.5 mm, with mean of the difference of 0.02 D and 95% limits of agreement of -0.35 and 0.39 D. The IOL Master keratometry and Pentacam equivalent keratometry reading were not equivalent when used only for corneal power measurements. However, the keratometry measurements of the IOL Master and Pentacam equivalent keratometry reading 4.5 mm may be similarly effective when used in intraocular lens power calculation formulas, following constant optimization. © 2013 Royal Australian and New Zealand College of Ophthalmologists.

24 CFR 81.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... SECRETARY OF HUD'S REGULATION OF THE FEDERAL NATIONAL MORTGAGE ASSOCIATION (FANNIE MAE) AND THE FEDERAL HOME... Management and Budget of the Executive Office of the President. Charter Act means the Federal National... individuals who occupy the same dwelling unit. Fannie Mae means the Federal National Mortgage Association and...

24 CFR 81.2 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-04-01

... SECRETARY OF HUD'S REGULATION OF THE FEDERAL NATIONAL MORTGAGE ASSOCIATION (FANNIE MAE) AND THE FEDERAL HOME... Management and Budget of the Executive Office of the President. Charter Act means the Federal National... individuals who occupy the same dwelling unit. Fannie Mae means the Federal National Mortgage Association and...

24 CFR 81.2 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-04-01

... SECRETARY OF HUD'S REGULATION OF THE FEDERAL NATIONAL MORTGAGE ASSOCIATION (FANNIE MAE) AND THE FEDERAL HOME... Management and Budget of the Executive Office of the President. Charter Act means the Federal National... individuals who occupy the same dwelling unit. Fannie Mae means the Federal National Mortgage Association and...

Magnetocrystalline anisotropy in cobalt based magnets: a choice of correlation parameters and the relativistic effects

DOE PAGES

Nguyen, Manh Cuong; Yao, Yongxin; Wang, Cai-Zhuang; ...

2018-05-16

The dependence of the magnetocrystalline anisotropy energy (MAE) in MCo 5 (M = Y, La, Ce, Gd) and CoPt on the Coulomb correlations and strength of spin orbit (SO) interaction within the GGA + U scheme is investigated. A range of parameters suitable for the satisfactory description of key magnetic properties is determined. We show that for a large variation of SO interaction the MAE in these materials can be well described by the traditional second order perturbation theory. We also show that in these materials the MAE can be both proportional and negatively proportional to the orbital moment anisotropymore » (OMA) of Co atoms. Dependence of relativistic effects on Coulomb correlations, applicability of the second order perturbation theory for the description of MAE, and effective screening of the SO interaction in these systems are discussed using a generalized virial theorem. Finally, such determined sets of parameters of Coulomb correlations can be used in much needed large scale atomistic simulations.« less

Magnetocrystalline anisotropy in cobalt based magnets: a choice of correlation parameters and the relativistic effects

NASA Astrophysics Data System (ADS)

Nguyen, Manh Cuong; Yao, Yongxin; Wang, Cai-Zhuang; Ho, Kai-Ming; Antropov, Vladimir P.

2018-05-01

The dependence of the magnetocrystalline anisotropy energy (MAE) in MCo5 (M  =  Y, La, Ce, Gd) and CoPt on the Coulomb correlations and strength of spin orbit (SO) interaction within the GGA  +  U scheme is investigated. A range of parameters suitable for the satisfactory description of key magnetic properties is determined. We show that for a large variation of SO interaction the MAE in these materials can be well described by the traditional second order perturbation theory. We also show that in these materials the MAE can be both proportional and negatively proportional to the orbital moment anisotropy (OMA) of Co atoms. Dependence of relativistic effects on Coulomb correlations, applicability of the second order perturbation theory for the description of MAE, and effective screening of the SO interaction in these systems are discussed using a generalized virial theorem. Such determined sets of parameters of Coulomb correlations can be used in much needed large scale atomistic simulations.

Influence of antisite defects and stacking faults on the magnetocrystalline anisotropy of FePt

NASA Astrophysics Data System (ADS)

Wolloch, M.; Suess, D.; Mohn, P.

2017-09-01

We present density functional theory (DFT) calculations of the magnetic anisotropy energy (MAE) of FePt, which is of great interest for magnetic recording applications. Our data, and the majority of previously calculated results for perfectly ordered crystals, predict a MAE of ˜3.0 meV per formula unit, which is significantly larger than experimentally measured values. Analyzing the effects of disorder by introducing stacking faults (SFs) and antisite defects (ASDs) in varying concentrations we are able to reconcile calculations with experimental data and show that even a low concentration of ASDs are able to reduce the MAE of FePt considerably. Investigating the effect of exact exchange and electron correlation within the adiabatic-connection dissipation fluctuation theorem in the random phase approximation (ACDFT-RPA) reveals a significantly smaller influence on the MAE. Thus the effect of disorder, and more specifically ASDs, is the crucial factor in explaining the deviation of common DFT calculations of FePt to experimental measurements.

Urinary cadmium level in children between nine to fifteen years old in three Sub-districts of Tak Province in Thailand

NASA Astrophysics Data System (ADS)

Chaiwong, S.; Sthiannopkao, S.; Kim, K. W.; Chuenchoojit, S.; Poopatpiboon, K.; Poodendean, C.; Supanpaiboon, W.

2009-07-01

Urinary cadmium (UCd) is an indicator of the long term exposure of human health. The objective of this research was to study UCd of people aged between 9 to 12 and 13 to 15 years old in both sexes in Prathadpadeang, in Mae Tao and Mae Ku. 849 urines were collected, and determined by using the ICP-MS. The results revealed that 64.30% had UCd less than 1 μg/gCr. XUCd in 3 Sub-districts were 0.132 μg/gCr in Prathadpadeang, 0.141 μg/gCr in Mae Tao, and 0.105 μg/gCr in Mae Ku. The difference in the 3 Sub-districts was significant. XUCd were 0.125 μg/gCr and 0.129 μg/gCr in boys and girls, and 0.119 μg/gCr and 0.135 μg/gCr in age group 9-12 and 13-15 years old.

Magnetocrystalline anisotropy in cobalt based magnets: a choice of correlation parameters and the relativistic effects

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

Nguyen, Manh Cuong; Yao, Yongxin; Wang, Cai-Zhuang

The dependence of the magnetocrystalline anisotropy energy (MAE) in MCo 5 (M = Y, La, Ce, Gd) and CoPt on the Coulomb correlations and strength of spin orbit (SO) interaction within the GGA + U scheme is investigated. A range of parameters suitable for the satisfactory description of key magnetic properties is determined. We show that for a large variation of SO interaction the MAE in these materials can be well described by the traditional second order perturbation theory. We also show that in these materials the MAE can be both proportional and negatively proportional to the orbital moment anisotropymore » (OMA) of Co atoms. Dependence of relativistic effects on Coulomb correlations, applicability of the second order perturbation theory for the description of MAE, and effective screening of the SO interaction in these systems are discussed using a generalized virial theorem. Finally, such determined sets of parameters of Coulomb correlations can be used in much needed large scale atomistic simulations.« less

Modeling and prediction of extraction profile for microwave-assisted extraction based on absorbed microwave energy.

PubMed

Chan, Chung-Hung; Yusoff, Rozita; Ngoh, Gek-Cheng

2013-09-01

A modeling technique based on absorbed microwave energy was proposed to model microwave-assisted extraction (MAE) of antioxidant compounds from cocoa (Theobroma cacao L.) leaves. By adapting suitable extraction model at the basis of microwave energy absorbed during extraction, the model can be developed to predict extraction profile of MAE at various microwave irradiation power (100-600 W) and solvent loading (100-300 ml). Verification with experimental data confirmed that the prediction was accurate in capturing the extraction profile of MAE (R-square value greater than 0.87). Besides, the predicted yields from the model showed good agreement with the experimental results with less than 10% deviation observed. Furthermore, suitable extraction times to ensure high extraction yield at various MAE conditions can be estimated based on absorbed microwave energy. The estimation is feasible as more than 85% of active compounds can be extracted when compared with the conventional extraction technique. Copyright © 2013 Elsevier Ltd. All rights reserved.

Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd(ii) removal from a binary aqueous solution by natural walnut carbon.

PubMed

Mazaheri, H; Ghaedi, M; Ahmadi Azqhandi, M H; Asfaram, A

2017-05-10

Analytical chemists apply statistical methods for both the validation and prediction of proposed models. Methods are required that are adequate for finding the typical features of a dataset, such as nonlinearities and interactions. Boosted regression trees (BRTs), as an ensemble technique, are fundamentally different to other conventional techniques, with the aim to fit a single parsimonious model. In this work, BRT, artificial neural network (ANN) and response surface methodology (RSM) models have been used for the optimization and/or modeling of the stirring time (min), pH, adsorbent mass (mg) and concentrations of MB and Cd 2+ ions (mg L -1 ) in order to develop respective predictive equations for simulation of the efficiency of MB and Cd 2+ adsorption based on the experimental data set. Activated carbon, as an adsorbent, was synthesized from walnut wood waste which is abundant, non-toxic, cheap and locally available. This adsorbent was characterized using different techniques such as FT-IR, BET, SEM, point of zero charge (pH pzc ) and also the determination of oxygen containing functional groups. The influence of various parameters (i.e. pH, stirring time, adsorbent mass and concentrations of MB and Cd 2+ ions) on the percentage removal was calculated by investigation of sensitive function, variable importance rankings (BRT) and analysis of variance (RSM). Furthermore, a central composite design (CCD) combined with a desirability function approach (DFA) as a global optimization technique was used for the simultaneous optimization of the effective parameters. The applicability of the BRT, ANN and RSM models for the description of experimental data was examined using four statistical criteria (absolute average deviation (AAD), mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 )). All three models demonstrated good predictions in this study. The BRT model was more precise compared to the other models and this showed that BRT could be a powerful tool for the modeling and optimizing of removal of MB and Cd(ii). Sensitivity analysis (calculated from the weight of neurons in ANN) confirmed that the adsorbent mass and pH were the essential factors affecting the removal of MB and Cd(ii), with relative importances of 28.82% and 38.34%, respectively. A good agreement (R 2 > 0.960) between the predicted and experimental values was obtained. Maximum removal (R% > 99) was achieved at an initial dye concentration of 15 mg L -1 , a Cd 2+ concentration of 20 mg L -1 , a pH of 5.2, an adsorbent mass of 0.55 g and a time of 35 min.

Exploiting data representation for fault tolerance

DOE PAGES

Hoemmen, Mark Frederick; Elliott, J.; Sandia National Lab.; ...

2015-01-06

Incorrect computer hardware behavior may corrupt intermediate computations in numerical algorithms, possibly resulting in incorrect answers. Prior work models misbehaving hardware by randomly flipping bits in memory. We start by accepting this premise, and present an analytic model for the error introduced by a bit flip in an IEEE 754 floating-point number. We then relate this finding to the linear algebra concepts of normalization and matrix equilibration. In particular, we present a case study illustrating that normalizing both vector inputs of a dot product minimizes the probability of a single bit flip causing a large error in the dot product'smore » result. Moreover, the absolute error is either less than one or very large, which allows detection of large errors. Then, we apply this to the GMRES iterative solver. We count all possible errors that can be introduced through faults in arithmetic in the computationally intensive orthogonalization phase of GMRES, and show that when the matrix is equilibrated, the absolute error is bounded above by one.« less

Search for giant magnetic anisotropy in transition-metal dimers on defected hexagonal boron nitride sheet

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

Li, J.; Wang, H.; Wu, R. Q., E-mail: wur@uci.edu

2016-05-28

Structural and magnetic properties of many transition-metal dimers embedded in a defected hexagonal boron nitride monolayer are investigated through density functional calculations to search for systems with magnetic anisotropy energies (MAEs) larger than 30meV. In particular, Ir–Ir@Dh–BN is found to have both large MAE (∼126 meV) and high structural stability against dissociation and diffusion, and it hence can serve as magnetic unit in spintronics and quantum computing devices. This giant MAE mainly results from the spin orbit coupling and the magnetization of the upper Ir atom, which is in a rather isolated environment.

Sallie Mae Eyes Expansion beyond Its Charter.

ERIC Educational Resources Information Center

Zook, Jim

1995-01-01

The Student Loan Marketing Association (Sallie Mae) and the Clinton Administration are preparing legislation to transform the federally sponsored corporation into a private business but must negotiate complex political and financial issues. Destabilization of the private student-loan industry and conflict over direct-lending policies are central…

24 CFR 350.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Security) maintained in the Book-entry System, as set forth in Federal Reserve Bank Operating Circulars. (b...: Book-entry Ginnie Mae Security. A Ginnie Mae Security issued or maintained in the Book-entry System... Reserve Banks. Book-entry System. The automated book-entry system operated by the Federal Reserve Banks...

Dialect Variation and Reading: Is Change in Nonmainstream American English Use Related to Reading Achievement in First and Second Grades?

PubMed Central

Terry, Nicole Patton; Connor, Carol McDonald; Petscher, Yaacov; Conlin, Catherine Ross

2015-01-01

Purpose In this study, we examined (a) whether children who spoke Nonmainstream American English (NMAE) frequently in school at the beginning of 1st grade increased their use of Mainstream American English (MAE) through the end of 2nd grade, and whether increasing MAE use was associated with (b) language and reading skills and school context and (c) greater gains in reading skills. Method A longitudinal design was implemented with 49 children who spoke NMAE moderately to strongly. Spoken production of NMAE forms, word reading, and reading comprehension were measured at the beginning, middle, and end of 1st and 2nd grades. Various oral language skills were also measured at the beginning of 1st grade. Results Results indicate that most children increased their MAE production during 1st grade and maintained these levels in 2nd grade. Increasing MAE use was predicted by children’s expressive vocabulary and nonword repetition skills at the beginning of 1st grade. Finally, the more children increased their MAE production, the greater were their reading gains from 1st grade through 2nd grade. Conclusions The findings extend previous reports of a significant association between NMAE use and specific reading skills among young children and have implications for theory, educational practice, and future research. PMID:22199203

Microwave-assisted extraction with water for fast extraction and simultaneous RP-HPLC determination of phenolic acids in radix Salviae Miltiorrhizae.

PubMed

Fang, Xinsheng; Wang, Jianhua; Zhou, Hongying; Jiang, Xingkai; Zhu, Lixiang; Gao, Xin

2009-07-01

An optimized microwave-assisted extraction method using water (MAE-W) as the extractant and an efficient HPLC analysis method were first developed for the fast extraction and simultaneous determination of D(+)-(3,4-dihydroxyphenyl) lactic acid (Dla), salvianolic acid B (SaB), and lithospermic acid (La) in radix Salviae Miltiorrhizae. The key parameters of MAE-W were optimized. It was found that the degradation of SaB was inhibited when using the optimized MAE-W and the stable content of Dla, La, and SaB in danshen was obtained. Furthermore, compared to the conventional extraction methods, the proposed MAE-W is a more rapid method with higher yield and lower solvent consumption with a reproducibility (RSD <6%). In addition, using water as extractant is safe and helpful for environment protection, which could be referred to as green extraction. The separation and quantitative determination of the three compounds was carried out by a developed reverse-phase high-performance liquid chromatographic (RP-HPLC) method with UV detection. Highly efficient separation was obtained using gradient solvent system. The optimized HPLC analysis method was validated to have specificity, linearity, precision, and accuracy. The results indicated that MAE-W followed by HPLC-UV determination is an appropriate alternative to previously proposed method for quality control of radix Salviae Miltiorrhizae.

Microwave-assisted extraction of rutin and quercetin from the stalks of Euonymus alatus (Thunb.) Sieb.

PubMed

Zhang, Fan; Yang, Yi; Su, Ping; Guo, Zhenku

2009-01-01

Euonymus alatus (Thunb.) has been used as one of traditional Chinese medicines for several thousand years. Conventional methods for the extraction of rutin and quercetin from E. alatus, including solvent extraction, Soxhlet extraction and heat reflux extraction are characterised by long extraction times and consumption of large amounts of solvents. To develop a simple and rapid method for the extraction of rutin and quercetin from the stalks of Euonymus alatus (Thunb.) Sieb using microwave-assisted extraction (MAE) technique. MAE experiments were performed with a multimode microwave extraction system. The experimental variables that affect the MAE process, such as the concentration of ethanol solution, extractant volume, microwave power and extraction time were optimised. Yields were determined by HPLC. The results were compared with that obtained by classical Soxhlet and ultrasonic-assisted extraction (UAE). From the optimised conditions for MAE of rutin and quercetin it can be concluded that the solvent is 50% ethanol (v/v) solution, the extractant volume is 40 mL, microwave power is 170 W and irradiation time is 6 min. Compared with Soxhlet extraction and ultrasonic extraction, microwave extraction is a rapid method with a higher yield and lower solvent consumption. The results showed that MAE can be used as an efficient and rapid method for the extraction of the active components from plants.

Determination of parabens and endocrine-disrupting alkylphenols in soil by gas chromatography-mass spectrometry following matrix solid-phase dispersion or in-column microwave-assisted extraction: a comparative study.

PubMed

Pérez, R A; Albero, B; Miguel, E; Sánchez-Brunete, C

2012-03-01

Two rapid methods were evaluated for the simultaneous extraction of seven parabens and two alkylphenols from soil based on matrix solid-phase dispersion (MSPD) and microwave-assisted extraction (MAE). Soil extracts were derivatized with N,O-bis(trimethylsilyl)trifluoroacetamide and analyzed by gas chromatography with mass spectrometry. Extraction and clean-up of samples were carried out by both methods in a single step. A glass sample holder, inside the microwave cell, was used in MAE to allow the simultaneous extraction and clean-up of samples and shorten the MAE procedure. The detection limits achieved by MSPD were lower than those obtained by MAE because the presence of matrix interferences increased with this extraction method. The extraction yields obtained by MSPD and MAE for three different types of soils were compared. Both procedures showed good recoveries and sensitivity for the determination of parabens and alkylphenols in two of the soils assayed, however, only MSPD yielded good recoveries with the other soil. Finally, MSPD was applied to the analysis of soils collected in different sites of Spain. In most of the samples analyzed, methylparaben and butylparaben were detected at levels ranging from 1.21 to 8.04 ng g(-1) dry weight and 0.48 to 1.02 ng g(-1) dry weight, respectively.