Rapid Detection of Volatile Oil in Mentha haplocalyx by Near-Infrared Spectroscopy and Chemometrics.

PubMed

Yan, Hui; Guo, Cheng; Shao, Yang; Ouyang, Zhen

2017-01-01

Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient ( R ) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx . The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx . Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx . Abbreviations used: 1 st der: First-order derivative; 2 nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection.

Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods

USGS Publications Warehouse

Linhart, S. Mike; Nania, Jon F.; Sanders, Curtis L.; Archfield, Stacey A.

2012-01-01

The U.S. Geological Survey (USGS) maintains approximately 148 real-time streamgages in Iowa for which daily mean streamflow information is available, but daily mean streamflow data commonly are needed at locations where no streamgages are present. Therefore, the USGS conducted a study as part of a larger project in cooperation with the Iowa Department of Natural Resources to develop methods to estimate daily mean streamflow at locations in ungaged watersheds in Iowa by using two regression-based statistical methods. The regression equations for the statistical methods were developed from historical daily mean streamflow and basin characteristics from streamgages within the study area, which includes the entire State of Iowa and adjacent areas within a 50-mile buffer of Iowa in neighboring states. Results of this study can be used with other techniques to determine the best method for application in Iowa and can be used to produce a Web-based geographic information system tool to compute streamflow estimates automatically. The Flow Anywhere statistical method is a variation of the drainage-area-ratio method, which transfers same-day streamflow information from a reference streamgage to another location by using the daily mean streamflow at the reference streamgage and the drainage-area ratio of the two locations. The Flow Anywhere method modifies the drainage-area-ratio method in order to regionalize the equations for Iowa and determine the best reference streamgage from which to transfer same-day streamflow information to an ungaged location. Data used for the Flow Anywhere method were retrieved for 123 continuous-record streamgages located in Iowa and within a 50-mile buffer of Iowa. The final regression equations were computed by using either left-censored regression techniques with a low limit threshold set at 0.1 cubic feet per second (ft3/s) and the daily mean streamflow for the 15th day of every other month, or by using an ordinary-least-squares multiple linear regression method and the daily mean streamflow for the 15th day of every other month. The Flow Duration Curve Transfer method was used to estimate unregulated daily mean streamflow from the physical and climatic characteristics of gaged basins. For the Flow Duration Curve Transfer method, daily mean streamflow quantiles at the ungaged site were estimated with the parameter-based regression model, which results in a continuous daily flow-duration curve (the relation between exceedance probability and streamflow for each day of observed streamflow) at the ungaged site. By the use of a reference streamgage, the Flow Duration Curve Transfer is converted to a time series. Data used in the Flow Duration Curve Transfer method were retrieved for 113 continuous-record streamgages in Iowa and within a 50-mile buffer of Iowa. The final statewide regression equations for Iowa were computed by using a weighted-least-squares multiple linear regression method and were computed for the 0.01-, 0.05-, 0.10-, 0.15-, 0.20-, 0.30-, 0.40-, 0.50-, 0.60-, 0.70-, 0.80-, 0.85-, 0.90-, and 0.95-exceedance probability statistics determined from the daily mean streamflow with a reporting limit set at 0.1 ft3/s. The final statewide regression equation for Iowa computed by using left-censored regression techniques was computed for the 0.99-exceedance probability statistic determined from the daily mean streamflow with a low limit threshold and a reporting limit set at 0.1 ft3/s. For the Flow Anywhere method, results of the validation study conducted by using six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 1,016 to 138 ft3/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 1,690 to 237 ft3/s. Values of the percent root-mean-square error ranged from 115 percent to 26.2 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 13.0 to 5.3 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.80 to 0.40. Percent-bias values ranged from 25.4 to 4.0 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.35. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.86 to 0.56. For the streamgage with the best agreement between observed and estimated streamflow, higher streamflows appear to be underestimated. For the streamgage with the worst agreement between observed and estimated streamflow, low flows appear to be overestimated whereas higher flows seem to be underestimated. Estimated cumulative streamflows for the period October 1, 2004, to September 30, 2009, are underestimated by -25.8 and -7.4 percent for the closest and poorest comparisons, respectively. For the Flow Duration Curve Transfer method, results of the validation study conducted by using the same six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 437 to 93.9 ft3/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 906 to 169 ft3/s. Values of the percent root-mean-square-error ranged from 67.0 to 25.6 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 12.5 to 4.4 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.79 to 0.40. Percent-bias values ranged from 22.7 to 0.94 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.38. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.89 to 0.48. For the streamgage with the closest agreement between observed and estimated streamflow, there is relatively good agreement between observed and estimated streamflows. For the streamgage with the poorest agreement between observed and estimated streamflow, streamflows appear to be substantially underestimated for much of the time period. Estimated cumulative streamflow for the period October 1, 2004, to September 30, 2009, are underestimated by -9.3 and -22.7 percent for the closest and poorest comparisons, respectively.

Rovibrational spectra of ammonia. I. Unprecedented accuracy of a potential energy surface used with nonadiabatic corrections.

PubMed

Huang, Xinchuan; Schwenke, David W; Lee, Timothy J

2011-01-28

In this work, we build upon our previous work on the theoretical spectroscopy of ammonia, NH(3). Compared to our 2008 study, we include more physics in our rovibrational calculations and more experimental data in the refinement procedure, and these enable us to produce a potential energy surface (PES) of unprecedented accuracy. We call this the HSL-2 PES. The additional physics we include is a second-order correction for the breakdown of the Born-Oppenheimer approximation, and we find it to be critical for improved results. By including experimental data for higher rotational levels in the refinement procedure, we were able to greatly reduce our systematic errors for the rotational dependence of our predictions. These additions together lead to a significantly improved total angular momentum (J) dependence in our computed rovibrational energies. The root-mean-square error between our predictions using the HSL-2 PES and the reliable energy levels from the HITRAN database for J = 0-6 and J = 7∕8 for (14)NH(3) is only 0.015 cm(-1) and 0.020∕0.023 cm(-1), respectively. The root-mean-square errors for the characteristic inversion splittings are approximately 1∕3 smaller than those for energy levels. The root-mean-square error for the 6002 J = 0-8 transition energies is 0.020 cm(-1). Overall, for J = 0-8, the spectroscopic data computed with HSL-2 is roughly an order of magnitude more accurate relative to our previous best ammonia PES (denoted HSL-1). These impressive numbers are eclipsed only by the root-mean-square error between our predictions for purely rotational transition energies of (15)NH(3) and the highly accurate Cologne database (CDMS): 0.00034 cm(-1) (10 MHz), in other words, 2 orders of magnitude smaller. In addition, we identify a deficiency in the (15)NH(3) energy levels determined from a model of the experimental data.

Diffuse-flow conceptualization and simulation of the Edwards aquifer, San Antonio region, Texas

USGS Publications Warehouse

Lindgren, R.J.

2006-01-01

A numerical ground-water-flow model (hereinafter, the conduit-flow Edwards aquifer model) of the karstic Edwards aquifer in south-central Texas was developed for a previous study on the basis of a conceptualization emphasizing conduit development and conduit flow, and included simulating conduits as one-cell-wide, continuously connected features. Uncertainties regarding the degree to which conduits pervade the Edwards aquifer and influence ground-water flow, as well as other uncertainties inherent in simulating conduits, raised the question of whether a model based on the conduit-flow conceptualization was the optimum model for the Edwards aquifer. Accordingly, a model with an alternative hydraulic conductivity distribution without conduits was developed in a study conducted during 2004-05 by the U.S. Geological Survey, in cooperation with the San Antonio Water System. The hydraulic conductivity distribution for the modified Edwards aquifer model (hereinafter, the diffuse-flow Edwards aquifer model), based primarily on a conceptualization in which flow in the aquifer predominantly is through a network of numerous small fractures and openings, includes 38 zones, with hydraulic conductivities ranging from 3 to 50,000 feet per day. Revision of model input data for the diffuse-flow Edwards aquifer model was limited to changes in the simulated hydraulic conductivity distribution. The root-mean-square error for 144 target wells for the calibrated steady-state simulation for the diffuse-flow Edwards aquifer model is 20.9 feet. This error represents about 3 percent of the total head difference across the model area. The simulated springflows for Comal and San Marcos Springs for the calibrated steady-state simulation were within 2.4 and 15 percent of the median springflows for the two springs, respectively. The transient calibration period for the diffuse-flow Edwards aquifer model was 1947-2000, with 648 monthly stress periods, the same as for the conduit-flow Edwards aquifer model. The root-mean-square error for a period of drought (May-November 1956) for the calibrated transient simulation for 171 target wells is 33.4 feet, which represents about 5 percent of the total head difference across the model area. The root-mean-square error for a period of above-normal rainfall (November 1974-July 1975) for the calibrated transient simulation for 169 target wells is 25.8 feet, which represents about 4 percent of the total head difference across the model area. The root-mean-square error ranged from 6.3 to 30.4 feet in 12 target wells with long-term water-level measurements for varying periods during 1947-2000 for the calibrated transient simulation for the diffuse-flow Edwards aquifer model, and these errors represent 5.0 to 31.3 percent of the range in water-level fluctuations of each of those wells. The root-mean-square errors for the five major springs in the San Antonio segment of the aquifer for the calibrated transient simulation, as a percentage of the range of discharge fluctuations measured at the springs, varied from 7.2 percent for San Marcos Springs and 8.1 percent for Comal Springs to 28.8 percent for Leona Springs. The root-mean-square errors for hydraulic heads for the conduit-flow Edwards aquifer model are 27, 76, and 30 percent greater than those for the diffuse-flow Edwards aquifer model for the steady-state, drought, and above-normal rainfall synoptic time periods, respectively. The goodness-of-fit between measured and simulated springflows is similar for Comal, San Marcos, and Leona Springs for the diffuse-flow Edwards aquifer model and the conduit-flow Edwards aquifer model. The root-mean-square errors for Comal and Leona Springs were 15.6 and 21.3 percent less, respectively, whereas the root-mean-square error for San Marcos Springs was 3.3 percent greater for the diffuse-flow Edwards aquifer model compared to the conduit-flow Edwards aquifer model. The root-mean-square errors for San Antonio and San Pedro Springs were appreciably greater, 80.2 and 51.0 percent, respectively, for the diffuse-flow Edwards aquifer model. The simulated water budgets for the diffuse-flow Edwards aquifer model are similar to those for the conduit-flow Edwards aquifer model. Differences in percentage of total sources or discharges for a budget component are 2.0 percent or less for all budget components for the steady-state and transient simulations. The largest difference in terms of the magnitude of water budget components for the transient simulation for 1956 was a decrease of about 10,730 acre-feet per year (about 2 per-cent) in springflow for the diffuse-flow Edwards aquifer model compared to the conduit-flow Edwards aquifer model. This decrease in springflow (a water budget discharge) was largely offset by the decreased net loss of water from storage (a water budget source) of about 10,500 acre-feet per year.

Spectral combination of spherical gravitational curvature boundary-value problems

NASA Astrophysics Data System (ADS)

PitoÅák, Martin; Eshagh, Mehdi; Šprlák, Michal; Tenzer, Robert; Novák, Pavel

2018-04-01

Four solutions of the spherical gravitational curvature boundary-value problems can be exploited for the determination of the Earth's gravitational potential. In this article we discuss the combination of simulated satellite gravitational curvatures, i.e., components of the third-order gravitational tensor, by merging these solutions using the spectral combination method. For this purpose, integral estimators of biased- and unbiased-types are derived. In numerical studies, we investigate the performance of the developed mathematical models for the gravitational field modelling in the area of Central Europe based on simulated satellite measurements. Firstly, we verify the correctness of the integral estimators for the spectral downward continuation by a closed-loop test. Estimated errors of the combined solution are about eight orders smaller than those from the individual solutions. Secondly, we perform a numerical experiment by considering the Gaussian noise with the standard deviation of 6.5× 10-17 m-1s-2 in the input data at the satellite altitude of 250 km above the mean Earth sphere. This value of standard deviation is equivalent to a signal-to-noise ratio of 10. Superior results with respect to the global geopotential model TIM-r5 are obtained by the spectral downward continuation of the vertical-vertical-vertical component with the standard deviation of 2.104 m2s-2, but the root mean square error is the largest and reaches 9.734 m2s-2. Using the spectral combination of all gravitational curvatures the root mean square error is more than 400 times smaller but the standard deviation reaches 17.234 m2s-2. The combination of more components decreases the root mean square error of the corresponding solutions while the standard deviations of the combined solutions do not improve as compared to the solution from the vertical-vertical-vertical component. The presented method represents a weight mean in the spectral domain that minimizes the root mean square error of the combined solutions and improves standard deviation of the solution based only on the least accurate components.

Red Wine Age Estimation by the Alteration of Its Color Parameters: Fourier Transform Infrared Spectroscopy as a Tool to Monitor Wine Maturation Time

PubMed Central

Basalekou, M.; Pappas, C.; Kotseridis, Y.; Tarantilis, P. A.; Kontaxakis, E.

2017-01-01

Color, phenolic content, and chemical age values of red wines made from Cretan grape varieties (Kotsifali, Mandilari) were evaluated over nine months of maturation in different containers for two vintages. The wines differed greatly on their anthocyanin profiles. Mid-IR spectra were also recorded with the use of a Fourier Transform Infrared Spectrophotometer in ZnSe disk mode. Analysis of Variance was used to explore the parameter's dependency on time. Determination models were developed for the chemical age indexes using Partial Least Squares (PLS) (TQ Analyst software) considering the spectral region 1830–1500 cm−1. The correlation coefficients (r) for chemical age index i were 0.86 for Kotsifali (Root Mean Square Error of Calibration (RMSEC) = 0.067, Root Mean Square Error of Prediction (RMSEP) = 0,115, and Root Mean Square Error of Validation (RMSECV) = 0.164) and 0.90 for Mandilari (RMSEC = 0.050, RMSEP = 0.040, and RMSECV = 0.089). For chemical age index ii the correlation coefficients (r) were 0.86 and 0.97 for Kotsifali (RMSEC 0.044, RMSEP = 0.087, and RMSECV = 0.214) and Mandilari (RMSEC = 0.024, RMSEP = 0.033, and RMSECV = 0.078), respectively. The proposed method is simpler, less time consuming, and more economical and does not require chemical reagents. PMID:29225994

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

[NIR Assignment of Magnolol by 2D-COS Technology and Model Application Huoxiangzhengqi Oral Liduid].

PubMed

Pei, Yan-ling; Wu, Zhi-sheng; Shi, Xin-yuan; Pan, Xiao-ning; Peng, Yan-fang; Qiao, Yan-jiang

2015-08-01

Near infrared (NIR) spectroscopy assignment of Magnolol was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. According to the synchronous spectra of deuterated chloroform solvent and Magnolol, 1365~1455, 1600~1720, 2000~2181 and 2275~2465 nm were the characteristic absorption of Magnolol. Connected with the structure of Magnolol, 1440 nm was the stretching vibration of phenolic group O-H, 1679 nm was the stretching vibration of aryl and methyl which connected with aryl, 2117, 2304, 2339 and 2370 nm were the combination of the stretching vibration, bending vibration and deformation vibration for aryl C-H, 2445 nm were the bending vibration of methyl which linked with aryl group, these bands attribut to the characteristics of Magnolol. Huoxiangzhengqi Oral Liduid was adopted to study the Magnolol, the characteristic band by spectral assignment and the band by interval Partial Least Squares (iPLS) and Synergy interval Partial Least Squares (SiPLS) were used to establish Partial Least Squares (PLS) quantitative model, the coefficient of determination Rcal(2) and Rpre(2) were greater than 0.99, the Root Mean of Square Error of Calibration (RM-SEC), Root Mean of Square Error of Cross Validation (RMSECV) and Root Mean of Square Error of Prediction (RMSEP) were very small. It indicated that the characteristic band by spectral assignment has the same results with the Chemometrics in PLS model. It provided a reference for NIR spectral assignment of chemical compositions in Chinese Materia Medica, and the band filters of NIR were interpreted.

Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Tao, Yang

2005-09-01

This research evaluated the feasibility of using Fourier-transform near-infrared (FT-NIR) spectroscopy to quantify the soluble-solids content (SSC) and the available acidity (VA) in intact apples. Partial least-squares calibration models, obtained from several preprocessing techniques (smoothing, derivative, etc.) in several wave-number ranges were compared. The best models were obtained with the high coefficient determination (r) 0.940 for the SSC and a moderate r of 0.801 for the VA, root-mean-square errors of prediction of 0.272% and 0.053%, and root-mean-square errors of calibration of 0.261% and 0.046%, respectively. The results indicate that the FT-NIR spectroscopy yields good predictions of the SSC and also showed the feasibility of using it to predict the VA of apples.

RM2: rms error comparisons

NASA Technical Reports Server (NTRS)

Rice, R. F.

1976-01-01

The root-mean-square error performance measure is used to compare the relative performance of several widely known source coding algorithms with the RM2 image data compression system. The results demonstrate that RM2 has a uniformly significant performance advantage.

Cervicocephalic kinesthetic sensibility in young and middle-aged adults with or without a history of mild neck pain.

PubMed

Teng, C-C; Chai, H; Lai, D-M; Wang, S-F

2007-02-01

Previous research has shown that there is no significant relationship between the degree of structural degeneration of the cervical spine and neck pain. We therefore sought to investigate the potential role of sensory dysfunction in chronic neck pain. Cervicocephalic kinesthetic sensibility, expressed by how accurately an individual can reposition the head, was studied in three groups of individuals, a control group of 20 asymptomatic young adults and two groups of middle-aged adults (20 subjects in each group) with or without a history of mild neck pain. An ultrasound-based three-dimensional coordinate measuring system was used to measure the position of the head and to test the accuracy of repositioning. Constant error (indicating that the subject overshot or undershot the intended position) and root mean square errors (representing total errors of accuracy and variability) were measured during repositioning of the head to the neutral head position (Head-to-NHP) and repositioning of the head to the target (Head-to-Target) in three cardinal planes (sagittal, transverse, and frontal). Analysis of covariance (ANCOVA) was used to test the group effect, with age used as a covariate. The constant errors during repositioning from a flexed position and from an extended position to the NHP were significantly greater in the middle-aged subjects than in the control group (beta=0.30 and beta=0.60, respectively; P<0.05 for both). In addition, the root mean square errors during repositioning from a flexed or extended position to the NHP were greater in the middle-aged subjects than in the control group (beta=0.27 and beta=0.49, respectively; P<0.05 for both). The root mean square errors also increased during Head-to-Target in left rotation (beta=0.24;P<0.05), but there was no difference in the constant errors or root mean square errors during Head-to-NHP repositioning from other target positions (P>0.05). The results indicate that, after controlling for age as a covariate, there was no group effect. Thus, age appears to have a profound effect on an individual's ability to accurately reposition the head toward the neutral position in the sagittal plane and repositioning the head toward left rotation. A history of mild chronic neck pain alone had no significant effect on cervicocephalic kinesthetic sensibility.

Force and Directional Force Modulation Effects on Accuracy and Variability in Low-Level Pinch Force Tracking.

PubMed

Park, Sangsoo; Spirduso, Waneen; Eakin, Tim; Abraham, Lawrence

2018-01-01

The authors investigated how varying the required low-level forces and the direction of force change affect accuracy and variability of force production in a cyclic isometric pinch force tracking task. Eighteen healthy right-handed adult volunteers performed the tracking task over 3 different force ranges. Root mean square error and coefficient of variation were higher at lower force levels and during minimum reversals compared with maximum reversals. Overall, the thumb showed greater root mean square error and coefficient of variation scores than did the index finger during maximum reversals, but not during minimum reversals. The observed impaired performance during minimum reversals might originate from history-dependent mechanisms of force production and highly coupled 2-digit performance.

Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods.

PubMed

Durakli Velioglu, Serap; Ercioglu, Elif; Boyaci, Ismail Hakki

2017-05-01

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400-550 nm excitation range with Δλ of 10-100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

Quantitative determination of additive Chlorantraniliprole in Abamectin preparation: Investigation of bootstrapping soft shrinkage approach by mid-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Yan, Hong; Song, Xiangzhong; Tian, Kuangda; Chen, Yilin; Xiong, Yanmei; Min, Shungeng

2018-02-01

A novel method, mid-infrared (MIR) spectroscopy, which enables the determination of Chlorantraniliprole in Abamectin within minutes, is proposed. We further evaluate the prediction ability of four wavelength selection methods, including bootstrapping soft shrinkage approach (BOSS), Monte Carlo uninformative variable elimination (MCUVE), genetic algorithm partial least squares (GA-PLS) and competitive adaptive reweighted sampling (CARS) respectively. The results showed that BOSS method obtained the lowest root mean squared error of cross validation (RMSECV) (0.0245) and root mean squared error of prediction (RMSEP) (0.0271), as well as the highest coefficient of determination of cross-validation (Qcv2) (0.9998) and the coefficient of determination of test set (Q2test) (0.9989), which demonstrated that the mid infrared spectroscopy can be used to detect Chlorantraniliprole in Abamectin conveniently. Meanwhile, a suitable wavelength selection method (BOSS) is essential to conducting a component spectral analysis.

Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy

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

Ying Yibin; Liu Yande; Tao Yang

2005-09-01

This research evaluated the feasibility of using Fourier-transform near-infrared (FT-NIR) spectroscopy to quantify the soluble-solids content (SSC) and the available acidity (VA) in intact apples. Partial least-squares calibration models, obtained from several preprocessing techniques (smoothing, derivative, etc.) in several wave-number ranges were compared. The best models were obtained with the high coefficient determination (r{sup 2}) 0.940 for the SSC and a moderate r{sup 2} of 0.801 for the VA, root-mean-square errors of prediction of 0.272% and 0.053%, and root-mean-square errors of calibration of 0.261% and 0.046%, respectively. The results indicate that the FT-NIR spectroscopy yields good predictions of the SSCmore » and also showed the feasibility of using it to predict the VA of apples.« less

Modeling Water Temperature in the Yakima River, Washington, from Roza Diversion Dam to Prosser Dam, 2005-06

USGS Publications Warehouse

Voss, Frank D.; Curran, Christopher A.; Mastin, Mark C.

2008-01-01

A mechanistic water-temperature model was constructed by the U.S. Geological Survey for use by the Bureau of Reclamation for studying the effect of potential water management decisions on water temperature in the Yakima River between Roza and Prosser, Washington. Flow and water temperature data for model input were obtained from the Bureau of Reclamation Hydromet database and from measurements collected by the U.S. Geological Survey during field trips in autumn 2005. Shading data for the model were collected by the U.S. Geological Survey in autumn 2006. The model was calibrated with data collected from April 1 through October 31, 2005, and tested with data collected from April 1 through October 31, 2006. Sensitivity analysis results showed that for the parameters tested, daily maximum water temperature was most sensitive to changes in air temperature and solar radiation. Root mean squared error for the five sites used for model calibration ranged from 1.3 to 1.9 degrees Celsius (?C) and mean error ranged from ?1.3 to 1.6?C. The root mean squared error for the five sites used for testing simulation ranged from 1.6 to 2.2?C and mean error ranged from 0.1 to 1.3?C. The accuracy of the stream temperatures estimated by the model is limited by four errors (model error, data error, parameter error, and user error).

Evaluation of LiDAR-Acquired Bathymetric and Topographic Data Accuracy in Various Hydrogeomorphic Settings in the Lower Boise River, Southwestern Idaho, 2007

USGS Publications Warehouse

Skinner, Kenneth D.

2009-01-01

Elevation data in riverine environments can be used in various applications for which different levels of accuracy are required. The Experimental Advanced Airborne Research LiDAR (Light Detection and Ranging) - or EAARL - system was used to obtain topographic and bathymetric data along the lower Boise River, southwestern Idaho, for use in hydraulic and habitat modeling. The EAARL data were post-processed into bare earth and bathymetric raster and point datasets. Concurrently with the EAARL data collection, real-time kinetic global positioning system and total station ground-survey data were collected in three areas within the lower Boise River basin to assess the accuracy of the EAARL elevation data in different hydrogeomorphic settings. The accuracies of the EAARL-derived elevation data, determined in open, flat terrain, to provide an optimal vertical comparison surface, had root mean square errors ranging from 0.082 to 0.138 m. Accuracies for bank, floodplain, and in-stream bathymetric data had root mean square errors ranging from 0.090 to 0.583 m. The greater root mean square errors for the latter data are the result of high levels of turbidity in the downstream ground-survey area, dense tree canopy, and horizontal location discrepancies between the EAARL and ground-survey data in steeply sloping areas such as riverbanks. The EAARL point to ground-survey comparisons produced results similar to those for the EAARL raster to ground-survey comparisons, indicating that the interpolation of the EAARL points to rasters did not introduce significant additional error. The mean percent error for the wetted cross-sectional areas of the two upstream ground-survey areas was 1 percent. The mean percent error increases to -18 percent if the downstream ground-survey area is included, reflecting the influence of turbidity in that area.

Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems.

PubMed

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng

2018-05-31

For a nonlinear system, the cubature Kalman filter (CKF) and its square-root version are useful methods to solve the state estimation problems, and both can obtain good performance in Gaussian noises. However, their performances often degrade significantly in the face of non-Gaussian noises, particularly when the measurements are contaminated by some heavy-tailed impulsive noises. By utilizing the maximum correntropy criterion (MCC) to improve the robust performance instead of traditional minimum mean square error (MMSE) criterion, a new square-root nonlinear filter is proposed in this study, named as the maximum correntropy square-root cubature Kalman filter (MCSCKF). The new filter not only retains the advantage of square-root cubature Kalman filter (SCKF), but also exhibits robust performance against heavy-tailed non-Gaussian noises. A judgment condition that avoids numerical problem is also given. The results of two illustrative examples, especially the SINS/GPS integrated systems, demonstrate the desirable performance of the proposed filter. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

Prediction of ethanol in bottled Chinese rice wine by NIR spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Yu, Haiyan; Pan, Xingxiang; Lin, Tao

2006-10-01

To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (R cal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The R cal and the correlation coefficient in validation (R val) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v -1) and 0.177 (%, v v -1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

PubMed

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

A Comparison of Normal and Elliptical Estimation Methods in Structural Equation Models.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Cheevatanarak, Suchittra

Monte Carlo simulation compared chi-square statistics, parameter estimates, and root mean square error of approximation values using normal and elliptical estimation methods. Three research conditions were imposed on the simulated data: sample size, population contamination percent, and kurtosis. A Bentler-Weeks structural model established the…

An Examination of Statistical Power in Multigroup Dynamic Structural Equation Models

ERIC Educational Resources Information Center

Prindle, John J.; McArdle, John J.

2012-01-01

This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of…

Validation of Core Temperature Estimation Algorithm

DTIC Science & Technology

2016-01-29

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

[Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

PubMed

Li, Wen-bing; Yao, Lin-tao; Liu, Mu-hua; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; He, Xiu-wen; Yang, Ping; Hu, Hui-qin; Nie, Jiang-hui

2015-05-01

Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

Geospatial distribution modeling and determining suitability of groundwater quality for irrigation purpose using geospatial methods and water quality index (WQI) in Northern Ethiopia

NASA Astrophysics Data System (ADS)

Gidey, Amanuel

2018-06-01

Determining suitability and vulnerability of groundwater quality for irrigation use is a key alarm and first aid for careful management of groundwater resources to diminish the impacts on irrigation. This study was conducted to determine the overall suitability of groundwater quality for irrigation use and to generate their spatial distribution maps in Elala catchment, Northern Ethiopia. Thirty-nine groundwater samples were collected to analyze and map the water quality variables. Atomic absorption spectrophotometer, ultraviolet spectrophotometer, titration and calculation methods were used for laboratory groundwater quality analysis. Arc GIS, geospatial analysis tools, semivariogram model types and interpolation methods were used to generate geospatial distribution maps. Twelve and eight water quality variables were used to produce weighted overlay and irrigation water quality index models, respectively. Root-mean-square error, mean square error, absolute square error, mean error, root-mean-square standardized error, measured values versus predicted values were used for cross-validation. The overall weighted overlay model result showed that 146 km2 areas are highly suitable, 135 km2 moderately suitable and 60 km2 area unsuitable for irrigation use. The result of irrigation water quality index confirms 10.26% with no restriction, 23.08% with low restriction, 20.51% with moderate restriction, 15.38% with high restriction and 30.76% with the severe restriction for irrigation use. GIS and irrigation water quality index are better methods for irrigation water resources management to achieve a full yield irrigation production to improve food security and to sustain it for a long period, to avoid the possibility of increasing environmental problems for the future generation.

Four Types of Pulse Oximeters Accurately Detect Hypoxia during Low Perfusion and Motion.

PubMed

Louie, Aaron; Feiner, John R; Bickler, Philip E; Rhodes, Laura; Bernstein, Michael; Lucero, Jennifer

2018-03-01

Pulse oximeter performance is degraded by motion artifacts and low perfusion. Manufacturers developed algorithms to improve instrument performance during these challenges. There have been no independent comparisons of these devices. We evaluated the performance of four pulse oximeters (Masimo Radical-7, USA; Nihon Kohden OxyPal Neo, Japan; Nellcor N-600, USA; and Philips Intellivue MP5, USA) in 10 healthy adult volunteers. Three motions were evaluated: tapping, pseudorandom, and volunteer-generated rubbing, adjusted to produce photoplethsmogram disturbance similar to arterial pulsation amplitude. During motion, inspired gases were adjusted to achieve stable target plateaus of arterial oxygen saturation (SaO2) at 75%, 88%, and 100%. Pulse oximeter readings were compared with simultaneous arterial blood samples to calculate bias (oxygen saturation measured by pulse oximetry [SpO2] - SaO2), mean, SD, 95% limits of agreement, and root mean square error. Receiver operating characteristic curves were determined to detect mild (SaO2 < 90%) and severe (SaO2 < 80%) hypoxemia. Pulse oximeter readings corresponding to 190 blood samples were analyzed. All oximeters detected hypoxia but motion and low perfusion degraded performance. Three of four oximeters (Masimo, Nellcor, and Philips) had root mean square error greater than 3% for SaO2 70 to 100% during any motion, compared to a root mean square error of 1.8% for the stationary control. A low perfusion index increased error. All oximeters detected hypoxemia during motion and low-perfusion conditions, but motion impaired performance at all ranges, with less accuracy at lower SaO2. Lower perfusion degraded performance in all but the Nihon Kohden instrument. We conclude that different types of pulse oximeters can be similarly effective in preserving sensitivity to clinically relevant hypoxia.

Performance of statistical models to predict mental health and substance abuse cost.

PubMed

Montez-Rath, Maria; Christiansen, Cindy L; Ettner, Susan L; Loveland, Susan; Rosen, Amy K

2006-10-26

Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help deal with the high comorbidity of this population by introducing a form of interaction. The Gamma distribution helps with the long tail of the distribution. However, the Normal distribution is suitable if the correct transformation of the outcome is used.

An algorithm for propagating the square-root covariance matrix in triangular form

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Choe, C. Y.

1976-01-01

A method for propagating the square root of the state error covariance matrix in lower triangular form is described. The algorithm can be combined with any triangular square-root measurement update algorithm to obtain a triangular square-root sequential estimation algorithm. The triangular square-root algorithm compares favorably with the conventional sequential estimation algorithm with regard to computation time.

Synchronous front-face fluorescence spectroscopy for authentication of the adulteration of edible vegetable oil with refined used frying oil.

PubMed

Tan, Jin; Li, Rong; Jiang, Zi-Tao; Tang, Shu-Hua; Wang, Ying; Shi, Meng; Xiao, Yi-Qian; Jia, Bin; Lu, Tian-Xiang; Wang, Hao

2017-02-15

Synchronous front-face fluorescence spectroscopy has been developed for the discrimination of used frying oil (UFO) from edible vegetable oil (EVO), the estimation of the using time of UFO, and the determination of the adulteration of EVO with UFO. Both the heating time of laboratory prepared UFO and the adulteration of EVO with UFO could be determined by partial least squares regression (PLSR). To simulate the EVO adulteration with UFO, for each kind of oil, fifty adulterated samples at the adulterant amounts range of 1-50% were prepared. PLSR was then adopted to build the model and both full (leave-one-out) cross-validation and external validation were performed to evaluate the predictive ability. Under the optimum condition, the plots of observed versus predicted values exhibited high linearity (R(2)>0.96). The root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were both lower than 3%. Copyright © 2016 Elsevier Ltd. All rights reserved.

Feasibility of using near infrared spectroscopy to detect and quantify an adulterant in high quality sandalwood oil.

PubMed

Kuriakose, Saji; Joe, I Hubert

2013-11-01

Determination of the authenticity of essential oils has become more significant, in recent years, following some illegal adulteration and contamination scandals. The present investigative study focuses on the application of near infrared spectroscopy to detect sample authenticity and quantify economic adulteration of sandalwood oils. Several data pre-treatments are investigated for calibration and prediction using partial least square regression (PLSR). The quantitative data analysis is done using a new spectral approach - full spectrum or sequential spectrum. The optimum number of PLS components is obtained according to the lowest root mean square error of calibration (RMSEC=0.00009% v/v). The lowest root mean square error of prediction (RMSEP=0.00016% v/v) in the test set and the highest coefficient of determination (R(2)=0.99989) are used as the evaluation tools for the best model. A nonlinear method, locally weighted regression (LWR), is added to extract nonlinear information and to compare with the linear PLSR model. Copyright © 2013 Elsevier B.V. All rights reserved.

Feasibility of using near infrared spectroscopy to detect and quantify an adulterant in high quality sandalwood oil

NASA Astrophysics Data System (ADS)

Kuriakose, Saji; Joe, I. Hubert

2013-11-01

Determination of the authenticity of essential oils has become more significant, in recent years, following some illegal adulteration and contamination scandals. The present investigative study focuses on the application of near infrared spectroscopy to detect sample authenticity and quantify economic adulteration of sandalwood oils. Several data pre-treatments are investigated for calibration and prediction using partial least square regression (PLSR). The quantitative data analysis is done using a new spectral approach - full spectrum or sequential spectrum. The optimum number of PLS components is obtained according to the lowest root mean square error of calibration (RMSEC = 0.00009% v/v). The lowest root mean square error of prediction (RMSEP = 0.00016% v/v) in the test set and the highest coefficient of determination (R2 = 0.99989) are used as the evaluation tools for the best model. A nonlinear method, locally weighted regression (LWR), is added to extract nonlinear information and to compare with the linear PLSR model.

Quality assessment of gasoline using comprehensive two-dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration.

PubMed

Parastar, Hadi; Mostafapour, Sara; Azimi, Gholamhasan

2016-01-01

Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

NASA Astrophysics Data System (ADS)

Bunai, Tasya; Rokhmatuloh; Wibowo, Adi

2018-05-01

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

Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection

NASA Astrophysics Data System (ADS)

Sun, Li-wei; Ye, Xin; Fang, Wei; He, Zhen-lei; Yi, Xiao-long; Wang, Yu-peng

2017-11-01

Hyper-spectral imaging spectrometer has high spatial and spectral resolution. Its radiometric calibration needs the knowledge of the sources used with high spectral resolution. In order to satisfy the requirement of source, an on-orbit radiometric calibration method is designed in this paper. This chain is based on the spectral inversion accuracy of the calibration light source. We compile the genetic algorithm progress which is used to optimize the channel design of the transfer radiometer and consider the degradation of the halogen lamp, thus realizing the high accuracy inversion of spectral curve in the whole working time. The experimental results show the average root mean squared error is 0.396%, the maximum root mean squared error is 0.448%, and the relative errors at all wavelengths are within 1% in the spectral range from 500 nm to 900 nm during 100 h operating time. The design lays a foundation for the high accuracy calibration of imaging spectrometer.

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.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method].

PubMed

Wang, L; Qin, X C; Lin, H C; Deng, K F; Luo, Y W; Sun, Q R; Du, Q X; Wang, Z Y; Tuo, Y; Sun, J H

2018-02-01

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method. Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method. The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （ R ²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R ² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h. The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

2014-12-01

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies-both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet-based performance measures, namely the MNSC (Multiscale Nash-Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash-Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

pKa prediction of monoprotic small molecules the SMARTS way.

PubMed

Lee, Adam C; Yu, Jing-Yu; Crippen, Gordon M

2008-10-01

Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.

Sensitivity of Fit Indices to Misspecification in Growth Curve Models

ERIC Educational Resources Information Center

Wu, Wei; West, Stephen G.

2010-01-01

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…

Quantified choice of root-mean-square errors of approximation for evaluation and power analysis of small differences between structural equation models.

PubMed

Li, Libo; Bentler, Peter M

2011-06-01

MacCallum, Browne, and Cai (2006) proposed a new framework for evaluation and power analysis of small differences between nested structural equation models (SEMs). In their framework, the null and alternative hypotheses for testing a small difference in fit and its related power analyses were defined by some chosen root-mean-square error of approximation (RMSEA) pairs. In this article, we develop a new method that quantifies those chosen RMSEA pairs and allows a quantitative comparison of them. Our method proposes the use of single RMSEA values to replace the choice of RMSEA pairs for model comparison and power analysis, thus avoiding the differential meaning of the chosen RMSEA pairs inherent in the approach of MacCallum et al. (2006). With this choice, the conventional cutoff values in model overall evaluation can directly be transferred and applied to the evaluation and power analysis of model differences. © 2011 American Psychological Association

Three filters for visualization of phase objects with large variations of phase gradients

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

Sagan, Arkadiusz; Antosiewicz, Tomasz J.; Szoplik, Tomasz

2009-02-20

We propose three amplitude filters for visualization of phase objects. They interact with the spectra of pure-phase objects in the frequency plane and are based on tangent and error functions as well as antisymmetric combination of square roots. The error function is a normalized form of the Gaussian function. The antisymmetric square-root filter is composed of two square-root filters to widen its spatial frequency spectral range. Their advantage over other known amplitude frequency-domain filters, such as linear or square-root graded ones, is that they allow high-contrast visualization of objects with large variations of phase gradients.

Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

PubMed

Sila, Andrew M; Shepherd, Keith D; Pokhariyal, Ganesh P

2016-04-15

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

Online measurement of urea concentration in spent dialysate during hemodialysis.

PubMed

Olesberg, Jonathon T; Arnold, Mark A; Flanigan, Michael J

2004-01-01

We describe online optical measurements of urea in the effluent dialysate line during regular hemodialysis treatment of several patients. Monitoring urea removal can provide valuable information about dialysis efficiency. Spectral measurements were performed with a Fourier-transform infrared spectrometer equipped with a flow-through cell. Spectra were recorded across the 5000-4000 cm(-1) (2.0-2.5 microm) wavelength range at 1-min intervals. Savitzky-Golay filtering was used to remove baseline variations attributable to the temperature dependence of the water absorption spectrum. Urea concentrations were extracted from the filtered spectra by use of partial least-squares regression and the net analyte signal of urea. Urea concentrations predicted by partial least-squares regression matched concentrations obtained from standard chemical assays with a root mean square error of 0.30 mmol/L (0.84 mg/dL urea nitrogen) over an observed concentration range of 0-11 mmol/L. The root mean square error obtained with the net analyte signal of urea was 0.43 mmol/L with a calibration based only on a set of pure-component spectra. The error decreased to 0.23 mmol/L when a slope and offset correction were used. Urea concentrations can be continuously monitored during hemodialysis by near-infrared spectroscopy. Calibrations based on the net analyte signal of urea are particularly appealing because they do not require a training step, as do statistical multivariate calibration procedures such as partial least-squares regression.

[Study on predicting sugar content and valid acidity of apples by near infrared diffuse reflectance technique].

PubMed

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-11-01

The nondestructive method for quantifying sugar content (SC) and available acid (VA) of intact apples using diffuse near infrared reflectance and optical fiber sensing techniques were explored in the present research. The standard sample sets and prediction models were established by partial least squares analysis (PLS). A total of 120 Shandong Fuji apples were tested in the wave number of 12,500 - 4000 cm(-1) using Fourier transform near infrared spectroscopy. The results of the research indicated that the nondestructive quantification of SC and VA, gave a high correlation coefficient 0.970 and 0.906, a low root mean square error of prediction (RMSEP) 0.272 and 0.056 2, a low root mean square error of calibration (RMSEC) 0.261 and 0.0677, and a small difference between RMSEP and RMSEC 0.011 a nd 0.0115. It was suggested that the diffuse nearinfrared reflectance technique be feasible for nondestructive determination of apple sugar content in the wave number range of 10,341 - 5461 cm(-1) and for available acid in the wave number range of 10,341 - 3818 cm(-1).

Analysis of Lard in Lipstick Formulation Using FTIR Spectroscopy and Multivariate Calibration: A Comparison of Three Extraction Methods.

PubMed

Waskitho, Dri; Lukitaningsih, Endang; Sudjadi; Rohman, Abdul

2016-01-01

Analysis of lard extracted from lipstick formulation containing castor oil has been performed using FTIR spectroscopic method combined with multivariate calibration. Three different extraction methods were compared, namely saponification method followed by liquid/liquid extraction with hexane/dichlorometane/ethanol/water, saponification method followed by liquid/liquid extraction with dichloromethane/ethanol/water, and Bligh & Dyer method using chloroform/methanol/water as extracting solvent. Qualitative and quantitative analysis of lard were performed using principle component (PCA) and partial least square (PLS) analysis, respectively. The results showed that, in all samples prepared by the three extraction methods, PCA was capable of identifying lard at wavelength region of 1200-800 cm -1 with the best result was obtained by Bligh & Dyer method. Furthermore, PLS analysis at the same wavelength region used for qualification showed that Bligh and Dyer was the most suitable extraction method with the highest determination coefficient (R 2 ) and the lowest root mean square error of calibration (RMSEC) as well as root mean square error of prediction (RMSEP) values.

Detection of Butter Adulteration with Lard by Employing (1)H-NMR Spectroscopy and Multivariate Data Analysis.

PubMed

Fadzillah, Nurrulhidayah Ahmad; Man, Yaakob bin Che; Rohman, Abdul; Rosman, Arieff Salleh; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2015-01-01

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PubMed

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.

Model assessment using a multi-metric ranking technique

NASA Astrophysics Data System (ADS)

Fitzpatrick, P. J.; Lau, Y.; Alaka, G.; Marks, F.

2017-12-01

Validation comparisons of multiple models presents challenges when skill levels are similar, especially in regimes dominated by the climatological mean. Assessing skill separation will require advanced validation metrics and identifying adeptness in extreme events, but maintain simplicity for management decisions. Flexibility for operations is also an asset. This work postulates a weighted tally and consolidation technique which ranks results by multiple types of metrics. Variables include absolute error, bias, acceptable absolute error percentages, outlier metrics, model efficiency, Pearson correlation, Kendall's Tau, reliability Index, multiplicative gross error, and root mean squared differences. Other metrics, such as root mean square difference and rank correlation were also explored, but removed when the information was discovered to be generally duplicative to other metrics. While equal weights are applied, weights could be altered depending for preferred metrics. Two examples are shown comparing ocean models' currents and tropical cyclone products, including experimental products. The importance of using magnitude and direction for tropical cyclone track forecasts instead of distance, along-track, and cross-track are discussed. Tropical cyclone intensity and structure prediction are also assessed. Vector correlations are not included in the ranking process, but found useful in an independent context, and will be briefly reported.

Static Scene Statistical Non-Uniformity Correction

DTIC Science & Technology

2015-03-01

Error NUC Non-Uniformity Correction RMSE Root Mean Squared Error RSD Relative Standard Deviation S3NUC Static Scene Statistical Non-Uniformity...Deviation ( RSD ) which normalizes the standard deviation, σ, to the mean estimated value, µ using the equation RS D = σ µ × 100. The RSD plot of the gain...estimates is shown in Figure 4.1(b). The RSD plot shows that after a sample size of approximately 10, the different photocount values and the inclusion

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.

Precision of intraoral digital dental impressions with iTero and extraoral digitization with the iTero and a model scanner.

PubMed

Flügge, Tabea V; Schlager, Stefan; Nelson, Katja; Nahles, Susanne; Metzger, Marc C

2013-09-01

Digital impression devices are used alternatively to conventional impression techniques and materials. The aims of this study were to evaluate the precision of digital intraoral scanning under clinical conditions (iTero; Align Technologies, San Jose, Calif) and to compare it with the precision of extraoral digitization. One patient received 10 full-arch intraoral scans with the iTero and conventional impressions with a polyether impression material (Impregum Penta; 3M ESPE, Seefeld, Germany). Stone cast models manufactured from the impressions were digitized 10 times with an extraoral scanner (D250; 3Shape, Copenhagen, Denmark) and 10 times with the iTero. Virtual models provided by each method were roughly aligned, and the model edges were trimmed with cutting planes to create common borders (Rapidform XOR; Inus Technologies, Seoul, Korea). A second model alignment was then performed along the closest distances of the surfaces (Artec Studio software; Artec Group, Luxembourg, Luxembourg). To assess precision, deviations between corresponding models were compared. Repeated intraoral scanning was evaluated in group 1, repeated extraoral model scanning with the iTero was assessed in group 2, and repeated model scanning with the D250 was assessed in group 3. Deviations between models were measured and expressed as maximums, means, medians, and root mean square errors for quantitative analysis. Color-coded displays of the deviations allowed qualitative visualization of the deviations. The greatest deviations and therefore the lowest precision were in group 1, with mean deviations of 50 μm, median deviations of 37 μm, and root mean square errors of 73 μm. Group 2 showed a higher precision, with mean deviations of 25 μm, median deviations of 18 μm, and root mean square errors of 51 μm. Scanning with the D250 had the highest precision, with mean deviations of 10 μm, median deviations of 5 μm, and root mean square errors of 20 μm. Intraoral and extraoral scanning with the iTero resulted in deviations at the facial surfaces of the anterior teeth and the buccal molar surfaces. Scanning with the iTero is less accurate than scanning with the D250. Intraoral scanning with the iTero is less accurate than model scanning with the iTero, suggesting that the intraoral conditions (saliva, limited spacing) contribute to the inaccuracy of a scan. For treatment planning and manufacturing of tooth-supported appliances, virtual models created with the iTero can be used. An extended scanning protocol could improve the scanning results in some regions. Copyright © 2013 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Evaluation of LiDAR-acquired bathymetric and topographic data accuracy in various hydrogeomorphic settings in the Deadwood and South Fork Boise Rivers, West-Central Idaho, 2007

USGS Publications Warehouse

Skinner, Kenneth D.

2011-01-01

High-quality elevation data in riverine environments are important for fisheries management applications and the accuracy of such data needs to be determined for its proper application. The Experimental Advanced Airborne Research LiDAR (Light Detection and Ranging)-or EAARL-system was used to obtain topographic and bathymetric data along the Deadwood and South Fork Boise Rivers in west-central Idaho. The EAARL data were post-processed into bare earth and bathymetric raster and point datasets. Concurrently with the EAARL surveys, real-time kinematic global positioning system surveys were made in three areas along each of the rivers to assess the accuracy of the EAARL elevation data in different hydrogeomorphic settings. The accuracies of the EAARL-derived raster elevation values, determined in open, flat terrain, to provide an optimal vertical comparison surface, had root mean square errors ranging from 0.134 to 0.347 m. Accuracies in the elevation values for the stream hydrogeomorphic settings had root mean square errors ranging from 0.251 to 0.782 m. The greater root mean square errors for the latter data are the result of complex hydrogeomorphic environments within the streams, such as submerged aquatic macrophytes and air bubble entrainment; and those along the banks, such as boulders, woody debris, and steep slopes. These complex environments reduce the accuracy of EAARL bathymetric and topographic measurements. Steep banks emphasize the horizontal location discrepancies between the EAARL and ground-survey data and may not be good representations of vertical accuracy. The EAARL point to ground-survey comparisons produced results with slightly higher but similar root mean square errors than those for the EAARL raster to ground-survey comparisons, emphasizing the minimized horizontal offset by using interpolated values from the raster dataset at the exact location of the ground-survey point as opposed to an actual EAARL point within a 1-meter distance. The average error for the wetted stream channel surface areas was -0.5 percent, while the average error for the wetted stream channel volume was -8.3 percent. The volume of the wetted river channel was underestimated by an average of 31 percent in half of the survey areas, and overestimated by an average of 14 percent in the remainder of the survey areas. The EAARL system is an efficient way to obtain topographic and bathymetric data in large areas of remote terrain. The elevation accuracy of the EAARL system varies throughout the area depending upon the hydrogeomorphic setting, preventing the use of a single accuracy value to describe the EAARL system. The elevation accuracy variations should be kept in mind when using the data, such as for hydraulic modeling or aquatic habitat assessments.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Accuracy of a pulse-coherent acoustic Doppler profiler in a wave-dominated flow

USGS Publications Warehouse

Lacy, J.R.; Sherwood, C.R.

2004-01-01

The accuracy of velocities measured by a pulse-coherent acoustic Doppler profiler (PCADP) in the bottom boundary layer of a wave-dominated inner-shelf environment is evaluated. The downward-looking PCADP measured velocities in eight 10-cm cells at 1 Hz. Velocities measured by the PCADP are compared to those measured by an acoustic Doppler velocimeter for wave orbital velocities up to 95 cm s-1 and currents up to 40 cm s-1. An algorithm for correcting ambiguity errors using the resolution velocities was developed. Instrument bias, measured as the average error in burst mean speed, is -0.4 cm s-1 (standard deviation = 0.8). The accuracy (root-mean-square error) of instantaneous velocities has a mean of 8.6 cm s-1 (standard deviation = 6.5) for eastward velocities (the predominant direction of waves), 6.5 cm s-1 (standard deviation = 4.4) for northward velocities, and 2.4 cm s-1 (standard deviation = 1.6) for vertical velocities. Both burst mean and root-mean-square errors are greater for bursts with ub ??? 50 cm s-1. Profiles of burst mean speeds from the bottom five cells were fit to logarithmic curves: 92% of bursts with mean speed ??? 5 cm s-1 have a correlation coefficient R2 > 0.96. In cells close to the transducer, instantaneous velocities are noisy, burst mean velocities are biased low, and bottom orbital velocities are biased high. With adequate blanking distances for both the profile and resolution velocities, the PCADP provides sufficient accuracy to measure velocities in the bottom boundary layer under moderately energetic inner-shelf conditions.

Augmented Reality Using Transurethral Ultrasound for Laparoscopic Radical Prostatectomy: Preclinical Evaluation.

PubMed

Lanchon, Cecilia; Custillon, Guillaume; Moreau-Gaudry, Alexandre; Descotes, Jean-Luc; Long, Jean-Alexandre; Fiard, Gaelle; Voros, Sandrine

2016-07-01

To guide the surgeon during laparoscopic or robot-assisted radical prostatectomy an innovative laparoscopic/ultrasound fusion platform was developed using a motorized 3-dimensional transurethral ultrasound probe. We present what is to our knowledge the first preclinical evaluation of 3-dimensional prostate visualization using transurethral ultrasound and the preliminary results of this new augmented reality. The transurethral probe and laparoscopic/ultrasound registration were tested on realistic prostate phantoms made of standard polyvinyl chloride. The quality of transurethral ultrasound images and the detection of passive markers placed on the prostate surface were evaluated on 2-dimensional dynamic views and 3-dimensional reconstructions. The feasibility, precision and reproducibility of laparoscopic/transurethral ultrasound registration was then determined using 4, 5, 6 and 7 markers to assess the optimal amount needed. The root mean square error was calculated for each registration and the median root mean square error and IQR were calculated according to the number of markers. The transurethral ultrasound probe was easy to manipulate and the prostatic capsule was well visualized in 2 and 3 dimensions. Passive markers could precisely be localized in the volume. Laparoscopic/transurethral ultrasound registration procedures were performed on 74 phantoms of various sizes and shapes. All were successful. The median root mean square error of 1.1 mm (IQR 0.8-1.4) was significantly associated with the number of landmarks (p = 0.001). The highest accuracy was achieved using 6 markers. However, prostate volume did not affect registration precision. Transurethral ultrasound provided high quality prostate reconstruction and easy marker detection. Laparoscopic/ultrasound registration was successful with acceptable mm precision. Further investigations are necessary to achieve sub mm accuracy and assess feasibility in a human model. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Near-infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Parching Process of Traditional Chinese Medicine Based on Two Kinds of Chemical Indicators.

PubMed

Li, Kaiyue; Wang, Weiying; Liu, Yanping; Jiang, Su; Huang, Guo; Ye, Liming

2017-01-01

The active ingredients and thus pharmacological efficacy of traditional Chinese medicine (TCM) at different degrees of parching process vary greatly. Near-infrared spectroscopy (NIR) was used to develop a new method for rapid online analysis of TCM parching process, using two kinds of chemical indicators (5-(hydroxymethyl) furfural [5-HMF] content and 420 nm absorbance) as reference values which were obviously observed and changed in most TCM parching process. Three representative TCMs, Areca ( Areca catechu L.), Malt ( Hordeum Vulgare L.), and Hawthorn ( Crataegus pinnatifida Bge.), were used in this study. With partial least squares regression, calibration models of NIR were generated based on two kinds of reference values, i.e. 5-HMF contents measured by high-performance liquid chromatography (HPLC) and 420 nm absorbance measured by ultraviolet-visible spectroscopy (UV/Vis), respectively. In the optimized models for 5-HMF, the root mean square errors of prediction (RMSEP) for Areca, Malt, and Hawthorn was 0.0192, 0.0301, and 0.2600 and correlation coefficients ( R cal ) were 99.86%, 99.88%, and 99.88%, respectively. Moreover, in the optimized models using 420 nm absorbance as reference values, the RMSEP for Areca, Malt, and Hawthorn was 0.0229, 0.0096, and 0.0409 and R cal were 99.69%, 99.81%, and 99.62%, respectively. NIR models with 5-HMF content and 420 nm absorbance as reference values can rapidly and effectively identify three kinds of TCM in different parching processes. This method has great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online analysis and quality control in TCM industrial manufacturing process. Near-infrared spectroscopy.(NIR) was used to develop a new method for online analysis of traditional Chinese medicine.(TCM) parching processCalibration and validation models of Areca, Malt, and Hawthorn were generated by partial least squares regression using 5.(hydroxymethyl) furfural contents and 420.nm absorbance as reference values, respectively, which were main indicator components during parching process of most TCMThe established NIR models of three TCMs had low root mean square errors of prediction and high correlation coefficientsThe NIR method has great promise for use in TCM industrial manufacturing processes for rapid online analysis and quality control. Abbreviations used: NIR: Near-infrared Spectroscopy; TCM: Traditional Chinese medicine; Areca: Areca catechu L.; Hawthorn: Crataegus pinnatifida Bge.; Malt: Hordeum vulgare L.; 5-HMF: 5-(hydroxymethyl) furfural; PLS: Partial least squares; D: Dimension faction; SLS: Straight line subtraction, MSC: Multiplicative scatter correction; VN: Vector normalization; RMSECV: Root mean square errors of cross-validation; RMSEP: Root mean square errors of validation; R cal : Correlation coefficients; RPD: Residual predictive deviation; PAT: Process analytical technology; FDA: Food and Drug Administration; ICH: International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use.

[Adaptability of APSIM model in Southwestern China: A case study of winter wheat in Chongqing City].

PubMed

Dai, Tong; Wang, Jing; He, Di; Zhang, Jian-ping; Wang, Na

2015-04-01

Field experimental data of winter wheat and parallel daily meteorological data at four typical stations in Chongqing City were used to calibrate and validate APSIM-wheat model and determine the genetic parameters for 12 varieties of winter wheat. The results showed that there was a good agreement between the simulated and observed growth periods from sowing to emergence, flowering and maturity of wheat. Root mean squared errors (RMSEs) between simulated and observed emergence, flowering and maturity were 0-3, 1-8, and 0-8 d, respectively. Normalized root mean squared errors (NRMSEs) between simulated and observed above-ground biomass for 12 study varieties were less than 30%. NRMSE between simulated and observed yields for 10 varieties out of 12 study varieties were less than 30%. APSIM-wheat model performed well in simulating phenology, aboveground biomass and yield of winter wheat in Chongqing City, which could provide a foundational support for assessing the impact of climate change on wheat production in the study area based on the model.

Comparison of flood frequency estimates from synthetic and observed data on small drainage areas in Mississippi

USGS Publications Warehouse

Colson, B.E.

1986-01-01

In 1964 the U.S. Geological Survey in Mississippi expanded the small stream gaging network for collection of rainfall and runoff data to 92 stations. To expedite availability of flood frequency information a rainfall-runoff model using available long-term rainfall data was calibrated to synthesize flood peaks. Results obtained from observed annual peak flow data for 51 sites having 16 yr to 30 yr of annual peaks are compared with the synthetic results. Graphical comparison of the 2, 5, 10, 25, 50, and 100-year flood discharges indicate good agreement. The root mean square error ranges from 27% to 38% and the synthetic record bias from -9% to -18% in comparison with the observed record. The reduced variance in the synthetic results is attributed to use of only four long-term rainfall records and model limitations. The root mean square error and bias is within the accuracy considered to be satisfactory. (Author 's abstract)

Radon-222 concentrations in ground water and soil gas on Indian reservations in Wisconsin

USGS Publications Warehouse

DeWild, John F.; Krohelski, James T.

1995-01-01

For sites with wells finished in the sand and gravel aquifer, the coefficient of determination (R2) of the regression of concentration of radon-222 in ground water as a function of well depth is 0.003 and the significance level is 0.32, which indicates that there is not a statistically significant relation between radon-222 concentrations in ground water and well depth. The coefficient of determination of the regression of radon-222 in ground water and soil gas is 0.19 and the root mean square error of the regression line is 271 picocuries per liter. Even though the significance level (0.036) indicates a statistical relation, the root mean square error of the regression is so large that the regression equation would not give reliable predictions. Because of an inadequate number of samples, similar statistical analyses could not be performed for sites with wells finished in the crystalline and sedimentary bedrock aquifers.

A nonlinear model of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Ramli, Norashikin; Muda, Nora; Umor, Mohd Rozi

2014-06-01

Malaysia is a country which is rich in natural resources and one of it is a gold. Gold has already become an important national commodity. This study is conducted to determine a model that can be well fitted with the gold production in Malaysia from the year 1995-2010. Five nonlinear models are presented in this study which are Logistic model, Gompertz, Richard, Weibull and Chapman-Richard model. These model are used to fit the cumulative gold production in Malaysia. The best model is then selected based on the model performance. The performance of the fitted model is measured by sum squares error, root mean squares error, coefficient of determination, mean relative error, mean absolute error and mean absolute percentage error. This study has found that a Weibull model is shown to have significantly outperform compare to the other models. To confirm that Weibull is the best model, the latest data are fitted to the model. Once again, Weibull model gives the lowest readings at all types of measurement error. We can concluded that the future gold production in Malaysia can be predicted according to the Weibull model and this could be important findings for Malaysia to plan their economic activities.

Quantitative Modelling of Trace Elements in Hard Coal.

PubMed

Smoliński, Adam; Howaniec, Natalia

2016-01-01

The significance of coal in the world economy remains unquestionable for decades. It is also expected to be the dominant fossil fuel in the foreseeable future. The increased awareness of sustainable development reflected in the relevant regulations implies, however, the need for the development and implementation of clean coal technologies on the one hand, and adequate analytical tools on the other. The paper presents the application of the quantitative Partial Least Squares method in modeling the concentrations of trace elements (As, Ba, Cd, Co, Cr, Cu, Mn, Ni, Pb, Rb, Sr, V and Zn) in hard coal based on the physical and chemical parameters of coal, and coal ash components. The study was focused on trace elements potentially hazardous to the environment when emitted from coal processing systems. The studied data included 24 parameters determined for 132 coal samples provided by 17 coal mines of the Upper Silesian Coal Basin, Poland. Since the data set contained outliers, the construction of robust Partial Least Squares models for contaminated data set and the correct identification of outlying objects based on the robust scales were required. These enabled the development of the correct Partial Least Squares models, characterized by good fit and prediction abilities. The root mean square error was below 10% for all except for one the final Partial Least Squares models constructed, and the prediction error (root mean square error of cross-validation) exceeded 10% only for three models constructed. The study is of both cognitive and applicative importance. It presents the unique application of the chemometric methods of data exploration in modeling the content of trace elements in coal. In this way it contributes to the development of useful tools of coal quality assessment.

Quantitative Modelling of Trace Elements in Hard Coal

PubMed Central

Smoliński, Adam; Howaniec, Natalia

2016-01-01

The significance of coal in the world economy remains unquestionable for decades. It is also expected to be the dominant fossil fuel in the foreseeable future. The increased awareness of sustainable development reflected in the relevant regulations implies, however, the need for the development and implementation of clean coal technologies on the one hand, and adequate analytical tools on the other. The paper presents the application of the quantitative Partial Least Squares method in modeling the concentrations of trace elements (As, Ba, Cd, Co, Cr, Cu, Mn, Ni, Pb, Rb, Sr, V and Zn) in hard coal based on the physical and chemical parameters of coal, and coal ash components. The study was focused on trace elements potentially hazardous to the environment when emitted from coal processing systems. The studied data included 24 parameters determined for 132 coal samples provided by 17 coal mines of the Upper Silesian Coal Basin, Poland. Since the data set contained outliers, the construction of robust Partial Least Squares models for contaminated data set and the correct identification of outlying objects based on the robust scales were required. These enabled the development of the correct Partial Least Squares models, characterized by good fit and prediction abilities. The root mean square error was below 10% for all except for one the final Partial Least Squares models constructed, and the prediction error (root mean square error of cross–validation) exceeded 10% only for three models constructed. The study is of both cognitive and applicative importance. It presents the unique application of the chemometric methods of data exploration in modeling the content of trace elements in coal. In this way it contributes to the development of useful tools of coal quality assessment. PMID:27438794

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

New equations improve NIR prediction of body fat among high school wrestlers.

PubMed

Oppliger, R A; Clark, R R; Nielsen, D H

2000-09-01

Methodologic study to derive prediction equations for percent body fat (%BF). To develop valid regression equations using NIR to assess body composition among high school wrestlers. Clinicians need a portable, fast, and simple field method for assessing body composition among wrestlers. Near-infrared photospectrometry (NIR) meets these criteria, but its efficacy has been challenged. Subjects were 150 high school wrestlers from 2 Midwestern states with mean +/- SD age of 16.3 +/- 1.1 yrs, weight of 69.5 +/- 11.7 kg, and height of 174.4 +/- 7.0 cm. Relative body fatness (%BF) determined from hydrostatic weighing was the criterion measure, and NIR optical density (OD) measurements at multiple sites, plus height, weight, and body mass index (BMI) were the predictor variables. Four equations were developed with multiple R2s that varied from .530 to .693, root mean squared errors varied from 2.8% BF to 3.4% BF, and prediction errors varied from 2.9% BF to 3.1% BF. The best equation used OD measurements at the biceps, triceps, and thigh sites, BMI, and age. The root mean squared error and prediction error for all 4 equations were equal to or smaller than for a skinfold equation commonly used with wrestlers. The results substantiate the validity of NIR for predicting % BF among high school wrestlers. Cross-validation of these equations is warranted.

Information and complexity measures for hydrologic model evaluation

USDA-ARS?s Scientific Manuscript database

Hydrological models are commonly evaluated through the residual-based performance measures such as the root-mean square error or efficiency criteria. Such measures, however, do not evaluate the degree of similarity of patterns in simulated and measured time series. The objective of this study was to...

Performance Metrics for Soil Moisture Retrievals and Applications Requirements

USDA-ARS?s Scientific Manuscript database

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

[Rapid discriminating hogwash oil and edible vegetable oil using near infrared optical fiber spectrometer technique].

PubMed

Zhang, Bing-Fang; Yuan, Li-Bo; Kong, Qing-Ming; Shen, Wei-Zheng; Zhang, Bing-Xiu; Liu, Cheng-Hai

2014-10-01

In the present study, a new method using near infrared spectroscopy combined with optical fiber sensing technology was applied to the analysis of hogwash oil in blended oil. The 50 samples were a blend of frying oil and "nine three" soybean oil according to a certain volume ratio. The near infrared transmission spectroscopies were collected and the quantitative analysis model of frying oil was established by partial least squares (PLS) and BP artificial neural network The coefficients of determina- tion of calibration sets were 0.908 and 0.934 respectively. The coefficients of determination of validation sets were 0.961 and 0.952, the root mean square error of calibrations (RMSEC) was 0.184 and 0.136, and the root mean square error of predictions (RMSEP) was all 0.111 6. They conform to the model application requirement. At the same time, frying oil and qualified edible oil were identified with the principal component analysis (PCA), and the accurate rate was 100%. The experiment proved that near infrared spectral technology not only can quickly and accurately identify hogwash oil, but also can quantitatively detect hog- wash oil. This method has a wide application prospect in the detection of oil.

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy.

PubMed

Jiang, B; Huang, Y D

2008-05-26

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39-14.14mgcm(-2). The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R(2)), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20s without sample destruction.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.

PubMed

Liu, Yan-De; Ying, Yi-Bin; Fu, Xia-Ping

2005-03-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy*

PubMed Central

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-01-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r 2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way. PMID:15682498

SMAP Level 4 Surface and Root Zone Soil Moisture

NASA Technical Reports Server (NTRS)

Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.

2017-01-01

The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Accuracy of a Radiological Evaluation Method for Thoracic and Lumbar Spinal Curvatures Using Spinous Processes.

PubMed

Marchetti, Bárbara V; Candotti, Cláudia T; Raupp, Eduardo G; Oliveira, Eduardo B C; Furlanetto, Tássia S; Loss, Jefferson F

The purpose of this study was to assess a radiographic method for spinal curvature evaluation in children, based on spinous processes, and identify its normality limits. The sample consisted of 90 radiographic examinations of the spines of children in the sagittal plane. Thoracic and lumbar curvatures were evaluated using angular (apex angle [AA]) and linear (sagittal arrow [SA]) measurements based on the spinous processes. The same curvatures were also evaluated using the Cobb angle (CA) method, which is considered the gold standard. For concurrent validity (AA vs CA), Pearson's product-moment correlation coefficient, root-mean-square error, Pitman- Morgan test, and Bland-Altman analysis were used. For reproducibility (AA, SA, and CA), the intraclass correlation coefficient, standard error of measurement, and minimal detectable change measurements were used. A significant correlation was found between CA and AA measurements, as was a low root-mean-square error. The mean difference between the measurements was 0° for thoracic and lumbar curvatures, and the mean standard deviations of the differences were ±5.9° and 6.9°, respectively. The intraclass correlation coefficients of AA and SA were similar to or higher than the gold standard (CA). The standard error of measurement and minimal detectable change of the AA were always lower than the CA. This study determined the concurrent validity, as well as intra- and interrater reproducibility, of the radiographic measurements of kyphosis and lordosis in children. Copyright © 2017. Published by Elsevier Inc.

Analysis of low flows and selected methods for estimating low-flow characteristics at partial-record and ungaged stream sites in western Washington

USGS Publications Warehouse

Curran, Christopher A.; Eng, Ken; Konrad, Christopher P.

2012-01-01

Regional low-flow regression models for estimating Q7,10 at ungaged stream sites are developed from the records of daily discharge at 65 continuous gaging stations (including 22 discontinued gaging stations) for the purpose of evaluating explanatory variables. By incorporating the base-flow recession time constant τ as an explanatory variable in the regression model, the root-mean square error for estimating Q7,10 at ungaged sites can be lowered to 72 percent (for known values of τ), which is 42 percent less than if only basin area and mean annual precipitation are used as explanatory variables. If partial-record sites are included in the regression data set, τ must be estimated from pairs of discharge measurements made during continuous periods of declining low flows. Eight measurement pairs are optimal for estimating τ at partial-record sites, and result in a lowering of the root-mean square error by 25 percent. A low-flow survey strategy that includes paired measurements at partial-record sites requires additional effort and planning beyond a standard strategy, but could be used to enhance regional estimates of τ and potentially reduce the error of regional regression models for estimating low-flow characteristics at ungaged sites.

Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration.

PubMed

Wood, Clive; Alwati, Abdolati; Halsey, Sheelagh; Gough, Tim; Brown, Elaine; Kelly, Adrian; Paradkar, Anant

2016-09-10

The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen-nicotinamide (IBU-NIC) and 1:1 carbamazepine-nicotinamide (CBZ-NIC) has been evaluated. A partial least squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Prevalence of technical errors and periapical lesions in a sample of endodontically treated teeth: a CBCT analysis.

PubMed

Nascimento, Eduarda Helena Leandro; Gaêta-Araujo, Hugo; Andrade, Maria Fernanda Silva; Freitas, Deborah Queiroz

2018-01-21

The aims of this study are to identify the most frequent technical errors in endodontically treated teeth and to determine which root canals were most often associated with those errors, as well as to relate endodontic technical errors and the presence of coronal restorations with periapical status by means of cone-beam computed tomography images. Six hundred eighteen endodontically treated teeth (1146 root canals) were evaluated for the quality of their endodontic treatment and for the presence of coronal restorations and periapical lesions. Each root canal was classified according to dental groups, and the endodontic technical errors were recorded. Chi-square's test and descriptive analyses were performed. Six hundred eighty root canals (59.3%) had periapical lesions. Maxillary molars and anterior teeth showed higher prevalence of periapical lesions (p < 0.05). Endodontic treatment quality and coronal restoration were associated with periapical status (p < 0.05). Underfilling was the most frequent technical error in all root canals, except for the second mesiobuccal root canal of maxillary molars and the distobuccal root canal of mandibular molars, which were non-filled in 78.4 and 30% of the cases, respectively. There is a high prevalence of apical radiolucencies, which increased in the presence of poor coronal restorations, endodontic technical errors, and when both conditions were concomitant. Underfilling was the most frequent technical error, followed by non-homogeneous and non-filled canals. Evaluation of endodontic treatment quality that considers every single root canal aims on warning dental practitioners of the prevalence of technical errors that could be avoided with careful treatment planning and execution.

Highly Efficient Compression Algorithms for Multichannel EEG.

PubMed

Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

2018-05-01

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

Month-to-month and year-to-year reproducibility of high frequency QRS ECG signals

NASA Technical Reports Server (NTRS)

Batdorf, Niles J.; Feiveson, Alan H.; Schlegel, Todd T.

2004-01-01

High frequency electrocardiography analyzing the entire QRS complex in the frequency range of 150 to 250 Hz may prove useful in the detection of coronary artery disease, yet the long-term stability of these waveforms has not been fully characterized. Therefore, we prospectively investigated the reproducibility of the root mean squared voltage, kurtosis, and the presence versus absence of reduced amplitude zones in signal averaged 12-lead high frequency QRS recordings acquired in the supine position one month apart in 16 subjects and one year apart in 27 subjects. Reproducibility of root mean squared voltage and kurtosis was excellent over these time intervals in the limb leads, and acceptable in the precordial leads using both the V-lead and CR-lead derivations. The relative error of root mean squared voltage was 12% month-to-month and 16% year-to-year in the serial recordings when averaged over all 12 leads. Reduced amplitude zones were also reproducible up to a rate of 87% and 81%, respectively, for the month-to-month and year-to-year recordings. We conclude that 12-lead high frequency QRS electrocardiograms are sufficiently reproducible for clinical use.

Has the Performance of Regional-Scale Photochemical Modelling Systems Changed over the Past Decade?

EPA Science Inventory

This study analyzed summertime ozone concentrations that have been simulated by various regional-scale photochemical modelling systems over the Eastern U.S. as part of more than ten independent studies. Results indicate that there has been a reduction of root mean square errors ...

Development of a transformation model to derive general population-based utility: Mapping the pruritus-visual analog scale (VAS) to the EQ-5D utility.

PubMed

Park, Sun-Young; Park, Eun-Ja; Suh, Hae Sun; Ha, Dongmun; Lee, Eui-Kyung

2017-08-01

Although nonpreference-based disease-specific measures are widely used in clinical studies, they cannot generate utilities for economic evaluation. A solution to this problem is to estimate utilities from disease-specific instruments using the mapping function. This study aimed to develop a transformation model for mapping the pruritus-visual analog scale (VAS) to the EuroQol 5-Dimension 3-Level (EQ-5D-3L) utility index in pruritus. A cross-sectional survey was conducted with a sample (n = 268) drawn from the general population of South Korea. Data were randomly divided into 2 groups, one for estimating and the other for validating mapping models. To select the best model, we developed and compared 3 separate models using demographic information and the pruritus-VAS as independent variables. The predictive performance was assessed using the mean absolute deviation and root mean square error in a separate dataset. Among the 3 models, model 2 using age, age squared, sex, and the pruritus-VAS as independent variables had the best performance based on the goodness of fit and model simplicity, with a log likelihood of 187.13. The 3 models had similar precision errors based on mean absolute deviation and root mean square error in the validation dataset. No statistically significant difference was observed between the mean observed and predicted values in all models. In conclusion, model 2 was chosen as the preferred mapping model. Outcomes measured as the pruritus-VAS can be transformed into the EQ-5D-3L utility index using this mapping model, which makes an economic evaluation possible when only pruritus-VAS data are available. © 2017 John Wiley & Sons, Ltd.

A passive microwave technique for estimating rainfall and vertical structure information from space. Part 2: Applications to SSM/I data

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Giglio, Louis

1994-01-01

A multi channel physical approach for retrieving rainfall and its vertical structure from Special Sensor Microwave/Imager (SSM/I) observations is examined. While a companion paper was devoted exclusively to the description of the algorithm, its strengths, and its limitations, the main focus of this paper is to report on the results, applicability, and expected accuraciesfrom this algorithm. Some examples are given that compare retrieved results with ground-based radar data from different geographical regions to illustrate the performance and utility of the algorithm under distinct rainfall conditions. More quantitative validation is accomplished using two months of radar data from Darwin, Australia, and the radar network over Japan. Instantaneous comparisons at Darwin indicate that root-mean-square errors for 1.25 deg areas over water are 0.09 mm/h compared to the mean rainfall value of 0.224 mm/h while the correlation exceeds 0.9. Similar results are obtained over the Japanese validation site with rms errors of 0.615 mm/h compared to the mean of 0.0880 mm/h and a correlation of 0.9. Results are less encouraging over land with root-mean-square errors somewhat larger than the mean rain rates and correlations of only 0.71 and 0.62 for Darwin and Japan, respectively. These validation studies are further used in combination with the theoretical treatment of expected accuracies developed in the companion paper to define error estimates on a broader scale than individual radar sites from which the errors may be analyzed. Comparisons with simpler techniques that are based on either emission or scattering measurements are used to illustrate the fact that the current algorithm, while better correlated with the emission methods over water, cannot be reduced to either of these simpler methods.

Systemic inflammatory markers and sources of social support among older adults in the Memory Research Unit cohort.

PubMed

McHugh Power, Joanna; Carney, Sile; Hannigan, Caoimhe; Brennan, Sabina; Wolfe, Hannah; Lynch, Marina; Kee, Frank; Lawlor, Brian

2016-11-01

Potential associations between systemic inflammation and social support received by a sample of 120 older adults were examined here. Inflammatory markers, cognitive function, social support and psychosocial wellbeing were evaluated. A structural equation modelling approach was used to analyse the data. The model was a good fit [Formula: see text], p < 0.001; comparative fit index = 0.973; Tucker-Lewis Index = 0.962; root mean square error of approximation = 0.021; standardised root mean-square residual = 0.074). Chemokine levels were associated with increased age ( β = 0.276), receipt of less social support from friends ( β = -0.256) and body mass index ( β = -0.256). Results are discussed in relation to social signal transduction theory.

Evaluation of Fast-Time Wake Vortex Prediction Models

NASA Technical Reports Server (NTRS)

Proctor, Fred H.; Hamilton, David W.

2009-01-01

Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.

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

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

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

Performance metrics for the assessment of satellite data products: an ocean color case study

PubMed Central

Seegers, Bridget N.; Stumpf, Richard P.; Schaeffer, Blake A.; Loftin, Keith A.; Werdell, P. Jeremy

2018-01-01

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities. PMID:29609296

New method for propagating the square root covariance matrix in triangular form. [using Kalman-Bucy filter

NASA Technical Reports Server (NTRS)

Choe, C. Y.; Tapley, B. D.

1975-01-01

A method proposed by Potter of applying the Kalman-Bucy filter to the problem of estimating the state of a dynamic system is described, in which the square root of the state error covariance matrix is used to process the observations. A new technique which propagates the covariance square root matrix in lower triangular form is given for the discrete observation case. The technique is faster than previously proposed algorithms and is well-adapted for use with the Carlson square root measurement algorithm.

Validation of the Family Inpatient Communication Survey.

PubMed

Torke, Alexia M; Monahan, Patrick; Callahan, Christopher M; Helft, Paul R; Sachs, Greg A; Wocial, Lucia D; Slaven, James E; Montz, Kianna; Inger, Lev; Burke, Emily S

2017-01-01

Although many family members who make surrogate decisions report problems with communication, there is no validated instrument to accurately measure surrogate/clinician communication for older adults in the acute hospital setting. The objective of this study was to validate a survey of surrogate-rated communication quality in the hospital that would be useful to clinicians, researchers, and health systems. After expert review and cognitive interviewing (n = 10 surrogates), we enrolled 350 surrogates (250 development sample and 100 validation sample) of hospitalized adults aged 65 years and older from three hospitals in one metropolitan area. The communication survey and a measure of decision quality were administered within hospital days 3 and 10. Mental health and satisfaction measures were administered six to eight weeks later. Factor analysis showed support for both one-factor (Total Communication) and two-factor models (Information and Emotional Support). Item reduction led to a final 30-item scale. For the validation sample, internal reliability (Cronbach's alpha) was 0.96 (total), 0.94 (Information), and 0.90 (Emotional Support). Confirmatory factor analysis fit statistics were adequate (one-factor model, comparative fit index = 0.981, root mean square error of approximation = 0.62, weighted root mean square residual = 1.011; two-factor model comparative fit index = 0.984, root mean square error of approximation = 0.055, weighted root mean square residual = 0.930). Total score and subscales showed significant associations with the Decision Conflict Scale (Pearson correlation -0.43, P < 0.001 for total score). Emotional Support was associated with improved mental health outcomes at six to eight weeks, such as anxiety (-0.19 P < 0.001), and Information was associated with satisfaction with the hospital stay (0.49, P < 0.001). The survey shows high reliability and validity in measuring communication experiences for hospital surrogates. The scale has promise for measurement of communication quality and is predictive of important outcomes, such as surrogate satisfaction and well-being. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

NASA Technical Reports Server (NTRS)

Draper, Clara S.; Reichle, Rolf; de Jeu, Richard; Naeimi, Vahid; Parinussa, Robert; Wagner, Wolfgang

2013-01-01

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

NASA Astrophysics Data System (ADS)

Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

2017-03-01

Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

Computing Robust, Bootstrap-Adjusted Fit Indices for Use with Nonnormal Data

ERIC Educational Resources Information Center

Walker, David A.; Smith, Thomas J.

2017-01-01

Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker-Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the…

A Strategy for Replacing Sum Scoring

ERIC Educational Resources Information Center

Ramsay, James O.; Wiberg, Marie

2017-01-01

This article promotes the use of modern test theory in testing situations where sum scores for binary responses are now used. It directly compares the efficiencies and biases of classical and modern test analyses and finds an improvement in the root mean squared error of ability estimates of about 5% for two designed multiple-choice tests and…

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

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

PubMed Central

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

2011-01-01

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

Achievable accuracy of hip screw holding power estimation by insertion torque measurement.

PubMed

Erani, Paolo; Baleani, Massimiliano

2018-02-01

To ensure stability of proximal femoral fractures, the hip screw must firmly engage into the femoral head. Some studies suggested that screw holding power into trabecular bone could be evaluated, intraoperatively, through measurement of screw insertion torque. However, those studies used synthetic bone, instead of trabecular bone, as host material or they did not evaluate accuracy of predictions. We determined prediction accuracy, also assessing the impact of screw design and host material. We measured, under highly-repeatable experimental conditions, disregarding clinical procedure complexities, insertion torque and pullout strength of four screw designs, both in 120 synthetic and 80 trabecular bone specimens of variable density. For both host materials, we calculated the root-mean-square error and the mean-absolute-percentage error of predictions based on the best fitting model of torque-pullout data, in both single-screw and merged dataset. Predictions based on screw-specific regression models were the most accurate. Host material impacts on prediction accuracy: the replacement of synthetic with trabecular bone decreased both root-mean-square errors, from 0.54 ÷ 0.76 kN to 0.21 ÷ 0.40 kN, and mean-absolute-percentage errors, from 14 ÷ 21% to 10 ÷ 12%. However, holding power predicted on low insertion torque remained inaccurate, with errors up to 40% for torques below 1 Nm. In poor-quality trabecular bone, tissue inhomogeneities likely affect pullout strength and insertion torque to different extents, limiting the predictive power of the latter. This bias decreases when the screw engages good-quality bone. Under this condition, predictions become more accurate although this result must be confirmed by close in-vitro simulation of the clinical procedure. Copyright © 2018 Elsevier Ltd. All rights reserved.

Estimating random errors due to shot noise in backscatter lidar observations.

PubMed

Liu, Zhaoyan; Hunt, William; Vaughan, Mark; Hostetler, Chris; McGill, Matthew; Powell, Kathleen; Winker, David; Hu, Yongxiang

2006-06-20

We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson- distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment.

Estimating Random Errors Due to Shot Noise in Backscatter Lidar Observations

NASA Technical Reports Server (NTRS)

Liu, Zhaoyan; Hunt, William; Vaughan, Mark A.; Hostetler, Chris A.; McGill, Matthew J.; Powell, Kathy; Winker, David M.; Hu, Yongxiang

2006-01-01

In this paper, we discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root-mean-square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF uncertainties can be reliably calculated from/for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar and tested using data from the Lidar In-space Technology Experiment (LITE). OCIS Codes:

Increasing point-count duration increases standard error

USGS Publications Warehouse

Smith, W.P.; Twedt, D.J.; Hamel, P.B.; Ford, R.P.; Wiedenfeld, D.A.; Cooper, R.J.

1998-01-01

We examined data from point counts of varying duration in bottomland forests of west Tennessee and the Mississippi Alluvial Valley to determine if counting interval influenced sampling efficiency. Estimates of standard error increased as point count duration increased both for cumulative number of individuals and species in both locations. Although point counts appear to yield data with standard errors proportional to means, a square root transformation of the data may stabilize the variance. Using long (>10 min) point counts may reduce sample size and increase sampling error, both of which diminish statistical power and thereby the ability to detect meaningful changes in avian populations.

Prediction of Soil pH Hyperspectral Spectrum in Guanzhong Area of Shaanxi Province Based on PLS

NASA Astrophysics Data System (ADS)

Liu, Jinbao; Zhang, Yang; Wang, Huanyuan; Cheng, Jie; Tong, Wei; Wei, Jing

2017-12-01

The soil pH of Fufeng County, Yangling County and Wugong County in Shaanxi Province was studied. The spectral reflectance was measured by ASD Field Spec HR portable terrain spectrum, and its spectral characteristics were analyzed. The first deviation of the original spectral reflectance of the soil, the second deviation, the logarithm of the reciprocal logarithm, the first order differential of the reciprocal logarithm and the second order differential of the reciprocal logarithm were used to establish the soil pH Spectral prediction model. The results showed that the correlation between the reflectance spectra after SNV pre-treatment and the soil pH was significantly improved. The optimal prediction model of soil pH established by partial least squares method was a prediction model based on the first order differential of the reciprocal logarithm of spectral reflectance. The principal component factor was 10, the decision coefficient Rc2 = 0.9959, the model root means square error RMSEC = 0.0076, the correction deviation SEC = 0.0077; the verification decision coefficient Rv2 = 0.9893, the predicted root mean square error RMSEP = 0.0157, The deviation of SEP = 0.0160, the model was stable, the fitting ability and the prediction ability were high, and the soil pH can be measured quickly.

Detection and quantification of adulteration in sandalwood oil through near infrared spectroscopy.

PubMed

Kuriakose, Saji; Thankappan, Xavier; Joe, Hubert; Venkataraman, Venkateswaran

2010-10-01

The confirmation of authenticity of essential oils and the detection of adulteration are problems of increasing importance in the perfumes, pharmaceutical, flavor and fragrance industries. This is especially true for 'value added' products like sandalwood oil. A methodical study is conducted here to demonstrate the potential use of Near Infrared (NIR) spectroscopy along with multivariate calibration models like principal component regression (PCR) and partial least square regression (PLSR) as rapid analytical techniques for the qualitative and quantitative determination of adulterants in sandalwood oil. After suitable pre-processing of the NIR raw spectral data, the models are built-up by cross-validation. The lowest Root Mean Square Error of Cross-Validation and Calibration (RMSECV and RMSEC % v/v) are used as a decision supporting system to fix the optimal number of factors. The coefficient of determination (R(2)) and the Root Mean Square Error of Prediction (RMSEP % v/v) in the prediction sets are used as the evaluation parameters (R(2) = 0.9999 and RMSEP = 0.01355). The overall result leads to the conclusion that NIR spectroscopy with chemometric techniques could be successfully used as a rapid, simple, instant and non-destructive method for the detection of adulterants, even 1% of the low-grade oils, in the high quality form of sandalwood oil.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm

NASA Astrophysics Data System (ADS)

Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan

2012-11-01

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV = 0.0776, Rc = 0.9777, RMSEP = 0.0963, and Rp = 0.9686 for pH model; RMSECV = 1.3544% w/w, Rc = 0.8871, RMSEP = 1.4946% w/w, and Rp = 0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.

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.

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

PubMed

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

2017-09-26

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

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

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Mohamed, Heba M.

2016-01-01

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference.

Optimizing UV Index determination from broadband irradiances

NASA Astrophysics Data System (ADS)

Tereszchuk, Keith A.; Rochon, Yves J.; McLinden, Chris A.; Vaillancourt, Paul A.

2018-03-01

A study was undertaken to improve upon the prognosticative capability of Environment and Climate Change Canada's (ECCC) UV Index forecast model. An aspect of that work, and the topic of this communication, was to investigate the use of the four UV broadband surface irradiance fields generated by ECCC's Global Environmental Multiscale (GEM) numerical prediction model to determine the UV Index. The basis of the investigation involves the creation of a suite of routines which employ high-spectral-resolution radiative transfer code developed to calculate UV Index fields from GEM forecasts. These routines employ a modified version of the Cloud-J v7.4 radiative transfer model, which integrates GEM output to produce high-spectral-resolution surface irradiance fields. The output generated using the high-resolution radiative transfer code served to verify and calibrate GEM broadband surface irradiances under clear-sky conditions and their use in providing the UV Index. A subsequent comparison of irradiances and UV Index under cloudy conditions was also performed. Linear correlation agreement of surface irradiances from the two models for each of the two higher UV bands covering 310.70-330.0 and 330.03-400.00 nm is typically greater than 95 % for clear-sky conditions with associated root-mean-square relative errors of 6.4 and 4.0 %. However, underestimations of clear-sky GEM irradiances were found on the order of ˜ 30-50 % for the 294.12-310.70 nm band and by a factor of ˜ 30 for the 280.11-294.12 nm band. This underestimation can be significant for UV Index determination but would not impact weather forecasting. Corresponding empirical adjustments were applied to the broadband irradiances now giving a correlation coefficient of unity. From these, a least-squares fitting was derived for the calculation of the UV Index. The resultant differences in UV indices from the high-spectral-resolution irradiances and the resultant GEM broadband irradiances are typically within 0.2-0.3 with a root-mean-square relative error in the scatter of ˜ 6.6 % for clear-sky conditions. Similar results are reproduced under cloudy conditions with light to moderate clouds, with a relative error comparable to the clear-sky counterpart; under strong attenuation due to clouds, a substantial increase in the root-mean-square relative error of up to 35 % is observed due to differing cloud radiative transfer models.

Quantitative coronary angiography using image recovery techniques for background estimation in unsubtracted images

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

Wong, Jerry T.; Kamyar, Farzad; Molloi, Sabee

2007-10-15

Densitometry measurements have been performed previously using subtracted images. However, digital subtraction angiography (DSA) in coronary angiography is highly susceptible to misregistration artifacts due to the temporal separation of background and target images. Misregistration artifacts due to respiration and patient motion occur frequently, and organ motion is unavoidable. Quantitative densitometric techniques would be more clinically feasible if they could be implemented using unsubtracted images. The goal of this study is to evaluate image recovery techniques for densitometry measurements using unsubtracted images. A humanoid phantom and eight swine (25-35 kg) were used to evaluate the accuracy and precision of the followingmore » image recovery techniques: Local averaging (LA), morphological filtering (MF), linear interpolation (LI), and curvature-driven diffusion image inpainting (CDD). Images of iodinated vessel phantoms placed over the heart of the humanoid phantom or swine were acquired. In addition, coronary angiograms were obtained after power injections of a nonionic iodinated contrast solution in an in vivo swine study. Background signals were estimated and removed with LA, MF, LI, and CDD. Iodine masses in the vessel phantoms were quantified and compared to known amounts. Moreover, the total iodine in left anterior descending arteries was measured and compared with DSA measurements. In the humanoid phantom study, the average root mean square errors associated with quantifying iodine mass using LA and MF were approximately 6% and 9%, respectively. The corresponding average root mean square errors associated with quantifying iodine mass using LI and CDD were both approximately 3%. In the in vivo swine study, the root mean square errors associated with quantifying iodine in the vessel phantoms with LA and MF were approximately 5% and 12%, respectively. The corresponding average root mean square errors using LI and CDD were both 3%. The standard deviations in the differences between measured iodine mass in left anterior descending arteries using DSA and LA, MF, LI, or CDD were calculated. The standard deviations in the DSA-LA and DSA-MF differences (both {approx}21 mg) were approximately a factor of 3 greater than that of the DSA-LI and DSA-CDD differences (both {approx}7 mg). Local averaging and morphological filtering were considered inadequate for use in quantitative densitometry. Linear interpolation and curvature-driven diffusion image inpainting were found to be effective techniques for use with densitometry in quantifying iodine mass in vitro and in vivo. They can be used with unsubtracted images to estimate background anatomical signals and obtain accurate densitometry results. The high level of accuracy and precision in quantification associated with using LI and CDD suggests the potential of these techniques in applications where background mask images are difficult to obtain, such as lumen volume and blood flow quantification using coronary arteriography.« less

Foveal Curvature and Asymmetry Assessed Using Optical Coherence Tomography.

PubMed

VanNasdale, Dean A; Eilerman, Amanda; Zimmerman, Aaron; Lai, Nicky; Ramsey, Keith; Sinnott, Loraine T

2017-06-01

The aims of this study were to use cross-sectional optical coherence tomography imaging and custom curve fitting software to evaluate and model the foveal curvature as a spherical surface and to compare the radius of curvature in the horizontal and vertical meridians and test the sensitivity of this technique to anticipated meridional differences. Six 30-degree foveal-centered radial optical coherence tomography cross-section scans were acquired in the right eye of 20 clinically normal subjects. Cross sections were manually segmented, and custom curve fitting software was used to determine foveal pit radius of curvature using the central 500, 1000, and 1500 μm of the foveal contour. Radius of curvature was compared across different fitting distances. Root mean square error was used to determine goodness of fit. The radius of curvature was compared between the horizontal and vertical meridians for each fitting distance. There radius of curvature was significantly different when comparing each of the three fitting distances (P < .01 for each comparison). The average radii of curvature were 970 μm (95% confidence interval [CI], 913 to 1028 μm), 1386 μm (95% CI, 1339 to 1439 μm), and 2121 μm (95% CI, 2066 to 2183) for the 500-, 1000-, and 1500-μm fitting distances, respectively. Root mean square error was also significantly different when comparing each fitting distance (P < .01 for each comparison). The average root mean square errors were 2.48 μm (95% CI, 2.41 to 2.53 μm), 6.22 μm (95% CI, 5.77 to 6.60 μm), and 13.82 μm (95% CI, 12.93 to 14.58 μm) for the 500-, 1000-, and 1500-μm fitting distances, respectively. The radius of curvature between the horizontal and vertical meridian radii was statistically different only in the 1000- and 1500-μm fitting distances (P < .01 for each), with the horizontal meridian being flatter than the vertical. The foveal contour can be modeled as a sphere with low curve fitting error over a limited distance and capable of detecting subtle foveal contour differences between meridians.

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

PubMed

Darajeh, Negisa; Idris, Azni; Fard Masoumi, Hamid Reza; Nourani, Abolfazl; Truong, Paul; Rezania, Shahabaldin

2017-05-04

Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.

In vivo validation of a new technique that compensates for soft tissue artefact in the upper-arm: preliminary results.

PubMed

Cutti, Andrea Giovanni; Cappello, Angelo; Davalli, Angelo

2006-01-01

Soft tissue artefact is the dominant error source for upper extremity motion analyses that use skin-mounted markers, especially in humeral axial rotation. A new in vivo technique is presented that is based on the definition of a humerus bone-embedded frame almost "artefact free" but influenced by the elbow orientation in the measurement of the humeral axial rotation, and on an algorithm designed to solve this kinematic coupling. The technique was validated in vivo in a study of six healthy subjects who performed five arm-movement tasks. For each task the similarity between a gold standard pattern and the axial rotation pattern before and after the application of the compensation algorithm was evaluated in terms of explained variance, gain, phase and offset. In addition the root mean square error between the patterns was used as a global similarity estimator. After the application, for four out of five tasks, patterns were highly correlated, in phase, with almost equal gain and limited offset; the root mean square error decreased from the original 9 degrees to 3 degrees . The proposed technique appears to help compensate for the soft tissue artefact affecting axial rotation. A further development is also proposed to make the technique effective also for the pure prono-supination task.

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.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014

USGS Publications Warehouse

Smith, Erik A.; Kiesling, Richard L.; Ziegeweid, Jeffrey R.

2017-07-20

Fish habitat can degrade in many lakes due to summer blue-green algal blooms. Predictive models are needed to better manage and mitigate loss of fish habitat due to these changes. The U.S. Geological Survey (USGS), in cooperation with the Minnesota Department of Natural Resources, developed predictive water-quality models for two agricultural land-use dominated lakes in Minnesota—Madison Lake and Pearl Lake, which are part of Minnesota’s sentinel lakes monitoring program—to assess algal community dynamics, water quality, and fish habitat suitability of these two lakes under recent (2014) meteorological conditions. The interaction of basin processes to these two lakes, through the delivery of nutrient loads, were simulated using CE-QUAL-W2, a carbon-based, laterally averaged, two-dimensional water-quality model that predicts distribution of temperature and oxygen from interactions between nutrient cycling, primary production, and trophic dynamics.The CE-QUAL-W2 models successfully predicted water temperature and dissolved oxygen on the basis of the two metrics of mean absolute error and root mean square error. For Madison Lake, the mean absolute error and root mean square error were 0.53 and 0.68 degree Celsius, respectively, for the vertical temperature profile comparisons; for Pearl Lake, the mean absolute error and root mean square error were 0.71 and 0.95 degree Celsius, respectively, for the vertical temperature profile comparisons. Temperature and dissolved oxygen were key metrics for calibration targets. These calibrated lake models also simulated algal community dynamics and water quality. The model simulations presented potential explanations for persistently large total phosphorus concentrations in Madison Lake, key differences in nutrient concentrations between these lakes, and summer blue-green algal bloom persistence.Fish habitat suitability simulations for cool-water and warm-water fish indicated that, in general, both lakes contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer, without any periods of lethal oxythermal habitat. For Madison and Pearl Lakes, examples of important cool-water fish, particularly game fish, include northern pike (Esox lucius), walleye (Sander vitreus), and black crappie (Pomoxis nigromaculatus); examples of important warm-water fish include bluegill (Lepomis macrochirus), largemouth bass (Micropterus salmoides), and smallmouth bass (Micropterus dolomieu). Sensitivity analyses were completed to understand lake response effects through the use of controlled departures on certain calibrated model parameters and input nutrient loads. These sensitivity analyses also operated as land-use change scenarios because alterations in agricultural practices, for example, could potentially increase or decrease nutrient loads.

Improvement of GPS radio occultation retrieval error of E region electron density: COSMIC measurement and IRI model simulation

NASA Astrophysics Data System (ADS)

Wu, Kang-Hung; Su, Ching-Lun; Chu, Yen-Hsyang

2015-03-01

In this article, we use the International Reference Ionosphere (IRI) model to simulate temporal and spatial distributions of global E region electron densities retrieved by the FORMOSAT-3/COSMIC satellites by means of GPS radio occultation (RO) technique. Despite regional discrepancies in the magnitudes of the E region electron density, the IRI model simulations can, on the whole, describe the COSMIC measurements in quality and quantity. On the basis of global ionosonde network and the IRI model, the retrieval errors of the global COSMIC-measured E region peak electron density (NmE) from July 2006 to July 2011 are examined and simulated. The COSMIC measurement and the IRI model simulation both reveal that the magnitudes of the percentage error (PE) and root mean-square-error (RMSE) of the relative RO retrieval errors of the NmE values are dependent on local time (LT) and geomagnetic latitude, with minimum in the early morning and at high latitudes and maximum in the afternoon and at middle latitudes. In addition, the seasonal variation of PE and RMSE values seems to be latitude dependent. After removing the IRI model-simulated GPS RO retrieval errors from the original COSMIC measurements, the average values of the annual and monthly mean percentage errors of the RO retrieval errors of the COSMIC-measured E region electron density are, respectively, substantially reduced by a factor of about 2.95 and 3.35, and the corresponding root-mean-square errors show averaged decreases of 15.6% and 15.4%, respectively. It is found that, with this process, the largest reduction in the PE and RMSE of the COSMIC-measured NmE occurs at the equatorial anomaly latitudes 10°N-30°N in the afternoon from 14 to 18 LT, with a factor of 25 and 2, respectively. Statistics show that the residual errors that remained in the corrected COSMIC-measured NmE vary in a range of -20% to 38%, which are comparable to or larger than the percentage errors of the IRI-predicted NmE fluctuating in a range of -6.5% to 20%.

A new validation technique for estimations of body segment inertia tensors: Principal axes of inertia do matter.

PubMed

Rossi, Marcel M; Alderson, Jacqueline; El-Sallam, Amar; Dowling, James; Reinbolt, Jeffrey; Donnelly, Cyril J

2016-12-08

The aims of this study were to: (i) establish a new criterion method to validate inertia tensor estimates by setting the experimental angular velocity data of an airborne objects as ground truth against simulations run with the estimated tensors, and (ii) test the sensitivity of the simulations to changes in the inertia tensor components. A rigid steel cylinder was covered with reflective kinematic markers and projected through a calibrated motion capture volume. Simulations of the airborne motion were run with two models, using inertia tensor estimated with geometric formula or the compound pendulum technique. The deviation angles between experimental (ground truth) and simulated angular velocity vectors and the root mean squared deviation angle were computed for every simulation. Monte Carlo analyses were performed to assess the sensitivity of simulations to changes in magnitude of principal moments of inertia within ±10% and to changes in orientation of principal axes of inertia within ±10° (of the geometric-based inertia tensor). Root mean squared deviation angles ranged between 2.9° and 4.3° for the inertia tensor estimated geometrically, and between 11.7° and 15.2° for the compound pendulum values. Errors up to 10% in magnitude of principal moments of inertia yielded root mean squared deviation angles ranging between 3.2° and 6.6°, and between 5.5° and 7.9° when lumped with errors of 10° in principal axes of inertia orientation. The proposed technique can effectively validate inertia tensors from novel estimation methods of body segment inertial parameter. Principal axes of inertia orientation should not be neglected when modelling human/animal mechanics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules.

PubMed

Anandakrishnan, Ramu; Onufriev, Alexey

2008-03-01

In statistical mechanics, the equilibrium properties of a physical system of particles can be calculated as the statistical average over accessible microstates of the system. In general, these calculations are computationally intractable since they involve summations over an exponentially large number of microstates. Clustering algorithms are one of the methods used to numerically approximate these sums. The most basic clustering algorithms first sub-divide the system into a set of smaller subsets (clusters). Then, interactions between particles within each cluster are treated exactly, while all interactions between different clusters are ignored. These smaller clusters have far fewer microstates, making the summation over these microstates, tractable. These algorithms have been previously used for biomolecular computations, but remain relatively unexplored in this context. Presented here, is a theoretical analysis of the error and computational complexity for the two most basic clustering algorithms that were previously applied in the context of biomolecular electrostatics. We derive a tight, computationally inexpensive, error bound for the equilibrium state of a particle computed via these clustering algorithms. For some practical applications, it is the root mean square error, which can be significantly lower than the error bound, that may be more important. We how that there is a strong empirical relationship between error bound and root mean square error, suggesting that the error bound could be used as a computationally inexpensive metric for predicting the accuracy of clustering algorithms for practical applications. An example of error analysis for such an application-computation of average charge of ionizable amino-acids in proteins-is given, demonstrating that the clustering algorithm can be accurate enough for practical purposes.

Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

ERIC Educational Resources Information Center

Leite, Walter L.; Stapleton, Laura M.

2011-01-01

In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA

ERIC Educational Resources Information Center

Brosseau-Liard, Patricia E.; Savalei, Victoria; Li, Libo

2012-01-01

The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have…

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

ERIC Educational Resources Information Center

Lee, Wooyeol; Cho, Sun-Joo

2017-01-01

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

Validity of the Aberrant Behavior Checklist in Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Kaat, Aaron J.; Lecavalier, Luc; Aman, Michael G.

2014-01-01

The Aberrant Behavior Checklist (ABC) is a widely used measure in autism spectrum disorder (ASD) treatment studies. We conducted confirmatory and exploratory factor analyses of the ABC in 1,893 children evaluated as part of the Autism Treatment Network. The root mean square error of approximation was .086 for the standard item assignment, and in…

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

A comparative study of optimum and suboptimum direct-detection laser ranging receivers

NASA Technical Reports Server (NTRS)

Abshire, J. B.

1978-01-01

A summary of previously proposed receiver strategies for direct-detection laser ranging receivers is presented. Computer simulations are used to compare performance of candidate implementation strategies in the 1- to 100-photoelectron region. Under the condition of no background radiation, the maximum-likelihood and minimum mean-square error estimators were found to give the same performance for both bell-shaped and rectangular optical-pulse shapes. For signal energies greater than 100 photoelectrons, the root-mean-square range error is shown to decrease as Q to the -1/2 power for bell-shaped pulses and Q to the -1 power for rectangular pulses, where Q represents the average pulse energy. Of several receiver implementations presented, the matched-filter peak detector was found to be preferable. A similar configuration, using a constant-fraction discriminator, exhibited a signal-level dependent time bias.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

[Near infrared spectroscopy study on water content in turbine oil].

PubMed

Chen, Bin; Liu, Ge; Zhang, Xian-Ming

2013-11-01

Near infrared (NIR) spectroscopy combined with successive projections algorithm (SPA) was investigated for determination of water content in turbine oil. Through the 57 samples of different water content in turbine oil scanned applying near infrared (NIR) spectroscopy, with the water content in the turbine oil of 0-0.156%, different pretreatment methods such as the original spectra, first derivative spectra and differential polynomial least squares fitting algorithm Savitzky-Golay (SG), and successive projections algorithm (SPA) were applied for the extraction of effective wavelengths, the correlation coefficient (R) and root mean square error (RMSE) were used as the model evaluation indices, accordingly water content in turbine oil was investigated. The results indicated that the original spectra with different water content in turbine oil were pretreated by the performance of first derivative + SG pretreatments, then the selected effective wavelengths were used as the inputs of least square support vector machine (LS-SVM). A total of 16 variables selected by SPA were employed to construct the model of SPA and least square support vector machine (SPA-LS-SVM). There is 9 as The correlation coefficient was 0.975 9 and the root of mean square error of validation set was 2.655 8 x 10(-3) using the model, and it is feasible to determine the water content in oil using near infrared spectroscopy and SPA-LS-SVM, and an excellent prediction precision was obtained. This study supplied a new and alternative approach to the further application of near infrared spectroscopy in on-line monitoring of contamination such as water content in oil.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy.

PubMed

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

PubMed Central

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation. PMID:23840459

Precise calibration of spatial phase response nonuniformity arising in liquid crystal on silicon.

PubMed

Xu, Jingquan; Qin, SiYi; Liu, Chen; Fu, Songnian; Liu, Deming

2018-06-15

In order to calibrate the spatial phase response nonuniformity of liquid crystal on silicon (LCoS), we propose to use a Twyman-Green interferometer to characterize the wavefront distortion, due to the inherent curvature of the device. During the characterization, both the residual carrier frequency introduced by the Fourier transform evaluation method and the lens aberration are error sources. For the tilted phase error introduced by residual carrier frequency, the least mean square fitting method is used to obtain the tilted phase error. Meanwhile, we use Zernike polynomials fitting based on plane mirror calibration to mitigate the lens aberration. For a typical LCoS with 1×12,288 pixels after calibration, the peak-to-valley value of the inherent wavefront distortion is approximately 0.25λ at 1550 nm, leading to a half-suppression of wavefront distortion. All efforts can suppress the root mean squares value of the inherent wavefront distortion to approximately λ/34.

Research on the infiltration processes of lawn soils of the Babao River in the Qilian Mountain.

PubMed

Li, GuangWen; Feng, Qi; Zhang, FuPing; Cheng, AiFang

2014-01-01

Using a Guelph Permeameter, the soil water infiltration processes were analyzed in the Babao River of the Qilian Mountain in China. The results showed that the average soil initial infiltration and the steady infiltration rates in the upstream reaches of the Babao River are 1.93 and 0.99 cm/min, whereas those of the middle area are 0.48 cm/min and 0.21 cm/min, respectively. The infiltration processes can be divided into three stages: the rapidly changing stage (0-10 min), the slowly changing stage (10-30 min) and the stabilization stage (after 30 min). We used field data collected from lawn soils and evaluated the performances of the infiltration models of Philip, Kostiakov and Horton with the sum of squared error, the root mean square error, the coefficient of determination, the mean error, the model efficiency and Willmott's index of agreement. The results indicated that the Kostiakov model was most suitable for studying the infiltration process in the alpine lawn soils.

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol.

PubMed

Yehia, Ali M; Mohamed, Heba M

2016-01-05

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate

NASA Astrophysics Data System (ADS)

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

2018-06-01

Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaïa region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient ( r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.

Automated body weight prediction of dairy cows using 3-dimensional vision.

PubMed

Song, X; Bokkers, E A M; van der Tol, P P J; Groot Koerkamp, P W G; van Mourik, S

2018-05-01

The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

NASA Astrophysics Data System (ADS)

Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

2013-04-01

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

[Application of wavelet neural networks model to forecast incidence of syphilis].

PubMed

Zhou, Xian-Feng; Feng, Zi-Jian; Yang, Wei-Zhong; Li, Xiao-Song

2011-07-01

To apply Wavelet Neural Networks (WNN) model to forecast incidence of Syphilis. Back Propagation Neural Network (BPNN) and WNN were developed based on the monthly incidence of Syphilis in Sichuan province from 2004 to 2008. The accuracy of forecast was compared between the two models. In the training approximation, the mean absolute error (MAE), rooted mean square error (RMSE) and mean absolute percentage error (MAPE) were 0.0719, 0.0862 and 11.52% respectively for WNN, and 0.0892, 0.1183 and 14.87% respectively for BPNN. The three indexes for generalization of models were 0.0497, 0.0513 and 4.60% for WNN, and 0.0816, 0.1119 and 7.25% for BPNN. WNN is a better model for short-term forecasting of Syphilis.

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

Treesearch

K.P. Poudel; H. Temesgen

2016-01-01

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

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

DTIC Science & Technology

2011-02-01

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

The relationship between purely stochastic sampling error and the number of technical replicates used to estimate concentration at an extreme dilution

USDA-ARS?s Scientific Manuscript database

For any analytical system the population mean (mu) number of entities (e.g., cells or molecules) per tested volume, surface area, or mass also defines the population standard deviation (sigma = square root of mu ). For a preponderance of analytical methods, sigma is very small relative to mu due to...

Retrieving air humidity, global solar radiation, and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part I: humidity

NASA Astrophysics Data System (ADS)

Lobit, P.; López Pérez, L.; Lhomme, J. P.; Gómez Tagle, A.

2017-07-01

This study evaluates the dew point method (Allen et al. 1998) to estimate atmospheric vapor pressure from minimum temperature, and proposes an improved model to estimate it from maximum and minimum temperature. Both methods were evaluated on 786 weather stations in Mexico. The dew point method induced positive bias in dry areas but also negative bias in coastal areas, and its average root mean square error for all evaluated stations was 0.38 kPa. The improved model assumed a bi-linear relation between estimated vapor pressure deficit (difference between saturated vapor pressure at minimum and average temperature) and measured vapor pressure deficit. The parameters of these relations were estimated from historical annual median values of relative humidity. This model removed bias and allowed for a root mean square error of 0.31 kPa. When no historical measurements of relative humidity were available, empirical relations were proposed to estimate it from latitude and altitude, with only a slight degradation on the model accuracy (RMSE = 0.33 kPa, bias = -0.07 kPa). The applicability of the method to other environments is discussed.

Solar Irradiance from GOES Albedo performance in a Hydrologic Model Simulation of Snowmelt Runoff

NASA Astrophysics Data System (ADS)

Sumargo, E.; Cayan, D. R.; McGurk, B. J.

2015-12-01

In many hydrologic modeling applications, solar radiation has been parameterized using commonly available measures, such as the daily temperature range, due to scarce in situ solar radiation measurement network. However, these parameterized estimates often produce significant biases. Here we test hourly solar irradiance derived from the Geostationary Operational Environmental Satellite (GOES) visible albedo product, using several established algorithms. Focusing on the Sierra Nevada and White Mountain in California, we compared the GOES irradiance and that from a traditional temperature-based algorithm with incoming irradiance from pyranometers at 19 stations. The GOES based estimates yielded 21-27% reduction in root-mean-squared error (average over 19 sites). The derived irradiance is then prescribed as an input to Precipitation-Runoff Modeling System (PRMS). We constrain our experiment to the Tuolumne River watershed and focus our attention on the winter and spring of 1996-2014. A root-mean-squared error reduction of 2-6% in daily inflow to Hetch Hetchy at the lower end of the Tuolumne catchment was achieved by incorporating the insolation estimates at only 8 out of 280 Hydrologic Response Units (HRUs) within the basin. Our ongoing work endeavors to apply satellite-derived irradiance at each individual HRU.

Breast Tissue Characterization with Photon-counting Spectral CT Imaging: A Postmortem Breast Study

PubMed Central

Ding, Huanjun; Klopfer, Michael J.; Ducote, Justin L.; Masaki, Fumitaro

2014-01-01

Purpose To investigate the feasibility of breast tissue characterization in terms of water, lipid, and protein contents with a spectral computed tomographic (CT) system based on a cadmium zinc telluride (CZT) photon-counting detector by using postmortem breasts. Materials and Methods Nineteen pairs of postmortem breasts were imaged with a CZT-based photon-counting spectral CT system with beam energy of 100 kVp. The mean glandular dose was estimated to be in the range of 1.8–2.2 mGy. The images were corrected for pulse pile-up and other artifacts by using spectral distortion corrections. Dual-energy decomposition was then applied to characterize each breast into water, lipid, and protein contents. The precision of the three-compartment characterization was evaluated by comparing the composition of right and left breasts, where the standard error of the estimations was determined. The results of dual-energy decomposition were compared by using averaged root mean square to chemical analysis, which was used as the reference standard. Results The standard errors of the estimations of the right-left correlations obtained from spectral CT were 7.4%, 6.7%, and 3.2% for water, lipid, and protein contents, respectively. Compared with the reference standard, the average root mean square error in breast tissue composition was 2.8%. Conclusion Spectral CT can be used to accurately quantify the water, lipid, and protein contents in breast tissue in a laboratory study by using postmortem specimens. © RSNA, 2014 PMID:24814180

Quality evaluation of frozen guava and yellow passion fruit pulps by NIR spectroscopy and chemometrics.

PubMed

Alamar, Priscila D; Caramês, Elem T S; Poppi, Ronei J; Pallone, Juliana A L

2016-07-01

The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy.

PubMed

Rohman, A; Sismindari; Erwanto, Y; Che Man, Yaakob B

2011-05-01

Meatball is one of the favorite foods in Indonesia. The adulteration of pork in beef meatball is frequently occurring. This study was aimed to develop a fast and non destructive technique for the detection and quantification of pork in beef meatball using Fourier transform infrared (FTIR) spectroscopy and partial least square (PLS) calibration. The spectral bands associated with pork fat (PF), beef fat (BF), and their mixtures in meatball formulation were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure PF and BF. For quantitative analysis, PLS regression was used to develop a calibration model at the selected fingerprint regions of 1200-1000 cm(-1). The equation obtained for the relationship between actual PF value and FTIR predicted values in PLS calibration model was y = 0.999x + 0.004, with coefficient of determination (R(2)) and root mean square error of calibration are 0.999 and 0.442, respectively. The PLS calibration model was subsequently used for the prediction of independent samples using laboratory made meatball samples containing the mixtures of BF and PF. Using 4 principal components, root mean square error of prediction is 0.742. The results showed that FTIR spectroscopy can be used for the detection and quantification of pork in beef meatball formulation for Halal verification purposes. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm.

PubMed

Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan

2012-11-01

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry. Copyright © 2012 Elsevier B.V. All rights reserved.

a New Method for Calculating the Fractal Dimension of Surface Topography

NASA Astrophysics Data System (ADS)

Zuo, Xue; Zhu, Hua; Zhou, Yuankai; Li, Yan

2015-06-01

A new method termed as three-dimensional root-mean-square (3D-RMS) method, is proposed to calculate the fractal dimension (FD) of machined surfaces. The measure of this method is the root-mean-square value of surface data, and the scale is the side length of square in the projection plane. In order to evaluate the calculation accuracy of the proposed method, the isotropic surfaces with deterministic FD are generated based on the fractional Brownian function and Weierstrass-Mandelbrot (WM) fractal function, and two kinds of anisotropic surfaces are generated by stretching or rotating a WM fractal curve. Their FDs are estimated by the proposed method, as well as differential boxing-counting (DBC) method, triangular prism surface area (TPSA) method and variation method (VM). The results show that the 3D-RMS method performs better than the other methods with a lower relative error for both isotropic and anisotropic surfaces, especially for the surfaces with dimensions higher than 2.5, since the relative error between the estimated value and its theoretical value decreases with theoretical FD. Finally, the electrodeposited surface, end-turning surface and grinding surface are chosen as examples to illustrate the application of 3D-RMS method on the real machined surfaces. This method gives a new way to accurately calculate the FD from the surface topographic data.

Nutritional Status of Rural Older Adults Is Linked to Physical and Emotional Health.

PubMed

Jung, Seung Eun; Bishop, Alex J; Kim, Minjung; Hermann, Janice; Kim, Giyeon; Lawrence, Jeannine

2017-06-01

Although nutritional status is influenced by multidimensional aspects encompassing physical and emotional well-being, there is limited research on this complex relationship. The purpose of this study was to examine the interplay between indicators of physical health (perceived health status and self-care capacity) and emotional well-being (depressive affect and loneliness) on rural older adults' nutritional status. The cross-sectional study was conducted from June 1, 2007, to June 1, 2008. A total of 171 community-dwelling older adults, aged 65 years and older, residing within nonmetro rural communities in the United States participated in this study. Participants completed validated instruments measuring self-care capacity, perceived health status, loneliness, depressive affect, and nutritional status. Structural equation modeling was employed to investigate the complex interplay of physical and emotional health status with nutritional status among rural older adults. The χ 2 test, comparative fit index, root mean square error of approximation, and standardized root mean square residual were used to assess model fit. The χ 2 test and the other model fit indexes showed the hypothesized structural equation model provided a good fit to the data (χ 2 (2)=2.15; P=0.34; comparative fit index=1.00; root mean square error of approximation=0.02; and standardized root mean square residual=0.03). Self-care capacity was significantly related with depressive affect (γ=-0.11; P=0.03), whereas self-care capacity was not significantly related with loneliness. Perceived health status had a significant negative relationship with both loneliness (γ=-0.16; P=0.03) and depressive affect (γ=-0.22; P=0.03). Although loneliness showed no significant direct relationship with nutritional status, it showed a significant direct relationship with depressive affect (β=.4; P<0.01). Finally, the results demonstrated that depressive affect had a significant negative relationship with nutritional status (β=-.30; P<0.01). The results indicated physical health and emotional indicators have significant multidimensional associations with nutritional status among rural older adults. The present study provides insights into the importance of addressing both physical and emotional well-being together to reduce potential effects of poor emotional well-being on nutritional status, particularly among rural older adults with impaired physical health and self-care capacity. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Prediction of BP reactivity to talking using hybrid soft computing approaches.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

NASA Astrophysics Data System (ADS)

Mandal, Sukomal; Rao, Subba; N., Harish; Lokesha

2012-06-01

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

Gene expression programming approach for the estimation of moisture ratio in herbal plants drying with vacuum heat pump dryer

NASA Astrophysics Data System (ADS)

Dikmen, Erkan; Ayaz, Mahir; Gül, Doğan; Şahin, Arzu Şencan

2017-07-01

The determination of drying behavior of herbal plants is a complex process. In this study, gene expression programming (GEP) model was used to determine drying behavior of herbal plants as fresh sweet basil, parsley and dill leaves. Time and drying temperatures are input parameters for the estimation of moisture ratio of herbal plants. The results of the GEP model are compared with experimental drying data. The statistical values as mean absolute percentage error, root-mean-squared error and R-square are used to calculate the difference between values predicted by the GEP model and the values actually observed from the experimental study. It was found that the results of the GEP model and experimental study are in moderately well agreement. The results have shown that the GEP model can be considered as an efficient modelling technique for the prediction of moisture ratio of herbal plants.

Evaluating the performance of the Lee-Carter method and its variants in modelling and forecasting Malaysian mortality

NASA Astrophysics Data System (ADS)

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

2014-12-01

This study investigated the performance of the Lee-Carter (LC) method and it variants in modeling and forecasting Malaysia mortality. These include the original LC, the Lee-Miller (LM) variant and the Booth-Maindonald-Smith (BMS) variant. These methods were evaluated using Malaysia's mortality data which was measured based on age specific death rates (ASDR) for 1971 to 2009 for overall population while those for 1980-2009 were used in separate models for male and female population. The performance of the variants has been examined in term of the goodness of fit of the models and forecasting accuracy. Comparison was made based on several criteria namely, mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The results indicate that BMS method was outperformed in in-sample fitting for overall population and when the models were fitted separately for male and female population. However, in the case of out-sample forecast accuracy, BMS method only best when the data were fitted to overall population. When the data were fitted separately for male and female, LCnone performed better for male population and LM method is good for female population.

The Effectiveness of Using Limited Gauge Measurements for Bias Adjustment of Satellite-Based Precipitation Estimation over Saudi Arabia

NASA Astrophysics Data System (ADS)

Alharbi, Raied; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

2018-01-01

Precipitation is a key input variable for hydrological and climate studies. Rain gauges are capable of providing reliable precipitation measurements at point scale. However, the uncertainty of rain measurements increases when the rain gauge network is sparse. Satellite -based precipitation estimations appear to be an alternative source of precipitation measurements, but they are influenced by systematic bias. In this study, a method for removing the bias from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping, climate classification, and inverse-weighted distance method. Daily PERSIANN-CCS is selected to test the capability of the method for removing the bias over Saudi Arabia during the period of 2010 to 2016. The first six years (2010 - 2015) are calibrated years and 2016 is used for validation. The results show that the yearly correlation coefficient was enhanced by 12%, the yearly mean bias was reduced by 93% during validated year. Root mean square error was reduced by 73% during validated year. The correlation coefficient, the mean bias, and the root mean square error show that the proposed method removes the bias on PERSIANN-CCS effectively that the method can be applied to other regions where the rain gauge network is sparse.

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

NASA Technical Reports Server (NTRS)

Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

2012-01-01

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

Visualizing the Sample Standard Deviation

ERIC Educational Resources Information Center

Sarkar, Jyotirmoy; Rashid, Mamunur

2017-01-01

The standard deviation (SD) of a random sample is defined as the square-root of the sample variance, which is the "mean" squared deviation of the sample observations from the sample mean. Here, we interpret the sample SD as the square-root of twice the mean square of all pairwise half deviations between any two sample observations. This…

[Application of wavelet transform-radial basis function neural network in NIRS for determination of rifampicin and isoniazide tablets].

PubMed

Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong

2008-06-01

A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

A Gridded Daily Min/Max Temperature Dataset With 0.1° Resolution for the Yangtze River Valley and its Error Estimation

NASA Astrophysics Data System (ADS)

Xiong, Qiufen; Hu, Jianglin

2013-05-01

The minimum/maximum (Min/Max) temperature in the Yangtze River valley is decomposed into the climatic mean and anomaly component. A spatial interpolation is developed which combines the 3D thin-plate spline scheme for climatological mean and the 2D Barnes scheme for the anomaly component to create a daily Min/Max temperature dataset. The climatic mean field is obtained by the 3D thin-plate spline scheme because the relationship between the decreases in Min/Max temperature with elevation is robust and reliable on a long time-scale. The characteristics of the anomaly field tend to be related to elevation variation weakly, and the anomaly component is adequately analyzed by the 2D Barnes procedure, which is computationally efficient and readily tunable. With this hybridized interpolation method, a daily Min/Max temperature dataset that covers the domain from 99°E to 123°E and from 24°N to 36°N with 0.1° longitudinal and latitudinal resolution is obtained by utilizing daily Min/Max temperature data from three kinds of station observations, which are national reference climatological stations, the basic meteorological observing stations and the ordinary meteorological observing stations in 15 provinces and municipalities in the Yangtze River valley from 1971 to 2005. The error estimation of the gridded dataset is assessed by examining cross-validation statistics. The results show that the statistics of daily Min/Max temperature interpolation not only have high correlation coefficient (0.99) and interpolation efficiency (0.98), but also the mean bias error is 0.00 °C. For the maximum temperature, the root mean square error is 1.1 °C and the mean absolute error is 0.85 °C. For the minimum temperature, the root mean square error is 0.89 °C and the mean absolute error is 0.67 °C. Thus, the new dataset provides the distribution of Min/Max temperature over the Yangtze River valley with realistic, successive gridded data with 0.1° × 0.1° spatial resolution and daily temporal scale. The primary factors influencing the dataset precision are elevation and terrain complexity. In general, the gridded dataset has a relatively high precision in plains and flatlands and a relatively low precision in mountainous areas.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).

PubMed

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS. Copyright © 2013 Elsevier B.V. All rights reserved.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

NASA Astrophysics Data System (ADS)

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P.; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

[Determination of Carbaryl in Rice by Using FT Far-IR and THz-TDS Techniques].

PubMed

Sun, Tong; Zhang, Zhuo-yong; Xiang, Yu-hong; Zhu, Ruo-hua

2016-02-01

Determination of carbaryl in rice by using Fourier transform far-infrared (FT- Far-IR) and terahertz time-domain spectroscopy (THz-TDS) combined with chemometrics was studied and the spectral characteristics of carbaryl in terahertz region was investigated. Samples were prepared by mixing carbaryl at different amounts with rice powder, and then a 13 mm diameter, and about 1 mm thick pellet with polyethylene (PE) as matrix was compressed under the pressure of 5-7 tons. Terahertz time domain spectra of the pellets were measured at 0.5~1.5 THz, and the absorption spectra at 1.6. 3 THz were acquired with Fourier transform far-IR spectroscopy. The method of sample preparation is so simple that it does not need separation and enrichment. The absorption peaks in the frequency range of 1.8-6.3 THz have been found at 3.2 and 5.2 THz by Far-IR. There are several weak absorption peaks in the range of 0.5-1.5 THz by THz-TDS. These two kinds of characteristic absorption spectra were randomly divided into calibration set and prediction set by leave-N-out cross-validation, respectively. Finally, the partial least squares regression (PLSR) method was used to establish two quantitative analysis models. The root mean square error (RMSECV), the root mean square errors of prediction (RMSEP) and the correlation coefficient of the prediction are used as a basis for the model of performance evaluation. For the R,, a higher value is better; for the RMSEC and RMSEP, lower is better. The obtained results demonstrated that the predictive accuracy of. the two models with PLSR method were satisfactory. For the FT-Far-IR model, the correlation between actual and predicted values of prediction samples (Rv) was 0.99. The root mean square error of prediction set (RMSEP) was 0.008 6, and for calibration set (RMSECV) was 0.007 7. For the THz-TDS model, R. was 0. 98, RMSEP was 0.004 4, and RMSECV was 0.002 5. Results proved that the technology of FT-Far-IR and THz- TDS can be a feasible tool for quantitative determination of carbaryl in rice. This paper provides a new method for the quantitative determination pesticide in other grain samples.

A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

NASA Astrophysics Data System (ADS)

Shamshirband, Shahaboddin; Mohammadi, Kasra; Tong, Chong Wen; Zamani, Mazdak; Motamedi, Shervin; Ch, Sudheer

2016-07-01

In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination ( R 2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

Demonstration of Inexact Computing Implemented in the JPEG Compression Algorithm using Probabilistic Boolean Logic applied to CMOS Components

DTIC Science & Technology

2015-12-24

Ripple-Carry RCA Ripple-Carry Adder RF Radio Frequency RMS Root-Mean-Square SEU Single Event Upset SIPI Signal and Image Processing Institute SNR...correctness, where 0.5 < p < 1, and a probability (1−p) of error. Errors could be caused by noise, radio frequency (RF) interference, crosstalk...utilized in the Apollo Guidance Computer is the three input NOR Gate. . . At the time that the decision was made to use in- 11 tegrated circuits, the

Demonstration of Inexact Computing Implemented in the JPEG Compression Algorithm Using Probabilistic Boolean Logic Applied to CMOS Components

DTIC Science & Technology

2015-12-24

Ripple-Carry RCA Ripple-Carry Adder RF Radio Frequency RMS Root-Mean-Square SEU Single Event Upset SIPI Signal and Image Processing Institute SNR...correctness, where 0.5 < p < 1, and a probability (1−p) of error. Errors could be caused by noise, radio frequency (RF) interference, crosstalk...utilized in the Apollo Guidance Computer is the three input NOR Gate. . . At the time that the decision was made to use in- 11 tegrated circuits, the

Characterizing the urban temperature trend using seasonal unit root analysis: Hong Kong from 1970 to 2015

NASA Astrophysics Data System (ADS)

To, Wai-Ming; Yu, Tat-Wai

2016-12-01

This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169°C (10 yr)-1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data — much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865°C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.

Using High Spatial Resolution Digital Imagery

DTIC Science & Technology

2005-02-01

digital base maps were high resolution U.S. Geological Survey (USGS) Digital Orthophoto Quarter Quadrangles (DOQQ). The Root Mean Square Errors (RMSE...next step was to assign real world coordinates to the linear im- age. The mosaics were geometrically registered to the panchromatic orthophotos ...useable thematic map from high-resolution imagery. A more practical approach may be to divide the Refuge into a set of smaller areas, or tiles

An Application of M[subscript 2] Statistic to Evaluate the Fit of Cognitive Diagnostic Models

ERIC Educational Resources Information Center

Liu, Yanlou; Tian, Wei; Xin, Tao

2016-01-01

The fit of cognitive diagnostic models (CDMs) to response data needs to be evaluated, since CDMs might yield misleading results when they do not fit the data well. Limited-information statistic M[subscript 2] and the associated root mean square error of approximation (RMSEA[subscript 2]) in item factor analysis were extended to evaluate the fit of…

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

PubMed

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

2014-09-01

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

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

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Three-dimensional relationship between high-order root-mean-square wavefront error, pupil diameter, and aging

PubMed Central

Applegate, Raymond A.; Donnelly, William J.; Marsack, Jason D.; Koenig, Darren E.; Pesudovs, Konrad

2007-01-01

We report root-mean-square (RMS) wavefront error (WFE) for individual aberrations and cumulative high-order (HO) RMS WFE for the normal human eye as a function of age by decade and pupil diameter in 1 mm steps from 3 to 7 mm and determine the relationship among HO RMS WFE, mean age for each decade of life, and luminance for physiologic pupil diameters. Subjects included 146 healthy individuals from 20 to 80 years of age. Ocular aberration was measured on the preferred eye of each subject (for a total of 146 eyes through dilated pupils; computed for 3, 4, 5, 6, and 7 mm pupils; and described with a tenth-radial-order normalized Zernike expansion. We found that HO RMS WFE increases faster with increasing pupil diameter for any given age and pupil diameter than it does with increasing age alone. A planar function accounts for 99% of the variance in the 3-D space defined by mean log HO RMS WFE, mean age for each decade of life, and pupil diameter. When physiologic pupil diameters are used to estimate HO RMS WFE as a function of luminance and age, at low luminance (9 cd/m2) HO RMS WFE decreases with increasing age. This normative data set details (1) the 3-D relationship between HO RMS WFE and age for fixed pupil diameters and (2) the 3-D relationship among HO RMS WFE, age, and luminance for physiologic pupil diameters. PMID:17301847

[Simulation of cropland soil moisture based on an ensemble Kalman filter].

PubMed

Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping

2011-11-01

By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Computational fluid dynamics analysis and experimental study of a low measurement error temperature sensor used in climate observation.

PubMed

Yang, Jie; Liu, Qingquan; Dai, Wei

2017-02-01

To improve the air temperature observation accuracy, a low measurement error temperature sensor is proposed. A computational fluid dynamics (CFD) method is implemented to obtain temperature errors under various environmental conditions. Then, a temperature error correction equation is obtained by fitting the CFD results using a genetic algorithm method. The low measurement error temperature sensor, a naturally ventilated radiation shield, a thermometer screen, and an aspirated temperature measurement platform are characterized in the same environment to conduct the intercomparison. The aspirated platform served as an air temperature reference. The mean temperature errors of the naturally ventilated radiation shield and the thermometer screen are 0.74 °C and 0.37 °C, respectively. In contrast, the mean temperature error of the low measurement error temperature sensor is 0.11 °C. The mean absolute error and the root mean square error between the corrected results and the measured results are 0.008 °C and 0.01 °C, respectively. The correction equation allows the temperature error of the low measurement error temperature sensor to be reduced by approximately 93.8%. The low measurement error temperature sensor proposed in this research may be helpful to provide a relatively accurate air temperature result.

Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy.

PubMed

De Girolamo, A; Lippolis, V; Nordkvist, E; Visconti, A

2009-06-01

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 microm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 microg kg(-1)) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 microg kg(-1)). Coefficients of determination (r(2)) indicated an "approximate to good" level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r(2) = 0.71-0.83), and a "good" discrimination between low and high DON contents in the PLS validation models (r(2) = 0.58-0.63). A "limited to good" practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 microg kg(-1) DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.

A new analytical method for quantification of olive and palm oil in blends with other vegetable edible oils based on the chromatographic fingerprints from the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the quantification of olive oil and palm oil in blends with other vegetable edible oils (canola, safflower, corn, peanut, seeds, grapeseed, linseed, sesame and soybean) using normal phase liquid chromatography, and applying chemometric tools was developed. The procedure for obtaining of chromatographic fingerprint from the methyl-transesterified fraction from each blend is described. The multivariate quantification methods used were Partial Least Square-Regression (PLS-R) and Support Vector Regression (SVR). The quantification results were evaluated by several parameters as the Root Mean Square Error of Validation (RMSEV), Mean Absolute Error of Validation (MAEV) and Median Absolute Error of Validation (MdAEV). It has to be highlighted that the new proposed analytical method, the chromatographic analysis takes only eight minutes and the results obtained showed the potential of this method and allowed quantification of mixtures of olive oil and palm oil with other vegetable oils. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of the mandible on mouthguard measurements of head kinematics.

PubMed

Kuo, Calvin; Wu, Lyndia C; Hammoor, Brad T; Luck, Jason F; Cutcliffe, Hattie C; Lynall, Robert C; Kait, Jason R; Campbell, Kody R; Mihalik, Jason P; Bass, Cameron R; Camarillo, David B

2016-06-14

Wearable sensors are becoming increasingly popular for measuring head motions and detecting head impacts. Many sensors are worn on the skin or in headgear and can suffer from motion artifacts introduced by the compliance of soft tissue or decoupling of headgear from the skull. The instrumented mouthguard is designed to couple directly to the upper dentition, which is made of hard enamel and anchored in a bony socket by stiff ligaments. This gives the mouthguard superior coupling to the skull compared with other systems. However, multiple validation studies have yielded conflicting results with respect to the mouthguard׳s head kinematics measurement accuracy. Here, we demonstrate that imposing different constraints on the mandible (lower jaw) can alter mouthguard kinematic accuracy in dummy headform testing. In addition, post mortem human surrogate tests utilizing the worst-case unconstrained mandible condition yield 40% and 80% normalized root mean square error in angular velocity and angular acceleration respectively. These errors can be modeled using a simple spring-mass system in which the soft mouthguard material near the sensors acts as a spring and the mandible as a mass. However, the mouthguard can be designed to mitigate these disturbances by isolating sensors from mandible loads, improving accuracy to below 15% normalized root mean square error in all kinematic measures. Thus, while current mouthguards would suffer from measurement errors in the worst-case unconstrained mandible condition, future mouthguards should be designed to account for these disturbances and future validation testing should include unconstrained mandibles to ensure proper accuracy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modelling of the batch biosorption system: study on exchange of protons with cell wall-bound mineral ions.

PubMed

Mishra, Vishal

2015-01-01

The interchange of the protons with the cell wall-bound calcium and magnesium ions at the interface of solution/bacterial cell surface in the biosorption system at various concentrations of protons has been studied in the present work. A mathematical model for establishing the correlation between concentration of protons and active sites was developed and optimized. The sporadic limited residence time reactor was used to titrate the calcium and magnesium ions at the individual data point. The accuracy of the proposed mathematical model was estimated using error functions such as nonlinear regression, adjusted nonlinear regression coefficient, the chi-square test, P-test and F-test. The values of the chi-square test (0.042-0.017), P-test (<0.001-0.04), sum of square errors (0.061-0.016), root mean square error (0.01-0.04) and F-test (2.22-19.92) reported in the present research indicated the suitability of the model over a wide range of proton concentrations. The zeta potential of the bacterium surface at various concentrations of protons was observed to validate the denaturation of active sites.

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.

[Main Components of Xinjiang Lavender Essential Oil Determined by Partial Least Squares and Near Infrared Spectroscopy].

PubMed

Liao, Xiang; Wang, Qing; Fu, Ji-hong; Tang, Jun

2015-09-01

This work was undertaken to establish a quantitative analysis model which can rapid determinate the content of linalool, linalyl acetate of Xinjiang lavender essential oil. Totally 165 lavender essential oil samples were measured by using near infrared absorption spectrum (NIR), after analyzing the near infrared spectral absorption peaks of all samples, lavender essential oil have abundant chemical information and the interference of random noise may be relatively low on the spectral intervals of 7100~4500 cm(-1). Thus, the PLS models was constructed by using this interval for further analysis. 8 abnormal samples were eliminated. Through the clustering method, 157 lavender essential oil samples were divided into 105 calibration set samples and 52 validation set samples. Gas chromatography mass spectrometry (GC-MS) was used as a tool to determine the content of linalool and linalyl acetate in lavender essential oil. Then the matrix was established with the GC-MS raw data of two compounds in combination with the original NIR data. In order to optimize the model, different pretreatment methods were used to preprocess the raw NIR spectral to contrast the spectral filtering effect, after analysizing the quantitative model results of linalool and linalyl acetate, the root mean square error prediction (RMSEP) of orthogonal signal transformation (OSC) was 0.226, 0.558, spectrally, it was the optimum pretreatment method. In addition, forward interval partial least squares (FiPLS) method was used to exclude the wavelength points which has nothing to do with determination composition or present nonlinear correlation, finally 8 spectral intervals totally 160 wavelength points were obtained as the dataset. Combining the data sets which have optimized by OSC-FiPLS with partial least squares (PLS) to establish a rapid quantitative analysis model for determining the content of linalool and linalyl acetate in Xinjiang lavender essential oil, numbers of hidden variables of two components were 8 in the model. The performance of the model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP). In the model, RESECV of linalool and linalyl acetate were 0.170 and 0.416, respectively; RM-SEP were 0.188 and 0.364. The results indicated that raw data was pretreated by OSC and FiPLS, the NIR-PLS quantitative analysis model with good robustness, high measurement precision; it could quickly determine the content of linalool and linalyl acetate in lavender essential oil. In addition, the model has a favorable prediction ability. The study also provide a new effective method which could rapid quantitative analysis the major components of Xinjiang lavender essential oil.

Feasibility of the simultaneous determination of polycyclic aromatic hydrocarbons based on two-dimensional fluorescence correlation spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Renjie; Dong, Guimei; Sun, Xueshan; Yang, Yanrong; Yu, Yaping; Liu, Haixue; Zhang, Weiyu

2018-02-01

A new approach for quantitative determination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on two-dimensional (2D) fluorescence correlation spectroscopy in conjunction with multivariate method. 40 mixture solutions of anthracene and pyrene were prepared in the laboratory. Excitation-emission matrix (EEM) fluorescence spectra of all samples were collected. And 2D fluorescence correlation spectra were calculated under the excitation perturbation. The N-way partial least squares (N-PLS) models were developed based on 2D fluorescence correlation spectra, showing a root mean square error of calibration (RMSEC) of 3.50 μg L- 1 and root mean square error of prediction (RMSEP) of 4.42 μg L- 1 for anthracene and of 3.61 μg L- 1 and 4.29 μg L- 1 for pyrene, respectively. Also, the N-PLS models were developed for quantitative analysis of anthracene and pyrene using EEM fluorescence spectra. The RMSEC and RMSEP were 3.97 μg L- 1 and 4.63 μg L- 1 for anthracene, 4.46 μg L- 1 and 4.52 μg L- 1 for pyrene, respectively. It was found that the N-PLS model using 2D fluorescence correlation spectra could provide better results comparing with EEM fluorescence spectra because of its low RMSEC and RMSEP. The methodology proposed has the potential to be an alternative method for detection of PAHs in environment.

Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: model optimisation, calibration transfer and confidence intervals.

PubMed

Boiret, Mathieu; Meunier, Loïc; Ginot, Yves-Michel

2011-02-20

A near infrared (NIR) method was developed for determination of tablet potency of active pharmaceutical ingredient (API) in a complex coated tablet matrix. The calibration set contained samples from laboratory and production scale batches. The reference values were obtained by high performance liquid chromatography (HPLC) and partial least squares (PLS) regression was used to establish a model. The model was challenged by calculating tablet potency of two external test sets. Root mean square errors of prediction were respectively equal to 2.0% and 2.7%. To use this model with a second spectrometer from the production field, a calibration transfer method called piecewise direct standardisation (PDS) was used. After the transfer, the root mean square error of prediction of the first test set was 2.4% compared to 4.0% without transferring the spectra. A statistical technique using bootstrap of PLS residuals was used to estimate confidence intervals of tablet potency calculations. This method requires an optimised PLS model, selection of the bootstrap number and determination of the risk. In the case of a chemical analysis, the tablet potency value will be included within the confidence interval calculated by the bootstrap method. An easy to use graphical interface was developed to easily determine if the predictions, surrounded by minimum and maximum values, are within the specifications defined by the regulatory organisation. Copyright © 2010 Elsevier B.V. All rights reserved.

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

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

Shao Yongni; He Yong; Mao Jingyuan

Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters,such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) ofmore » 0.9451 and root-mean-square error of prediction(RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.« less

Gauging the Effects of Self-efficacy, Social Support, and Coping Style on Self-management Behaviors in Chinese Cancer Survivors.

PubMed

Geng, Zhaohui; Ogbolu, Yolanda; Wang, Jichuan; Hinds, Pamela S; Qian, Huijuan; Yuan, Changrong

2018-02-14

Better self-management control in cancer survivors would benefit their functional status, quality of life, and health service utilization. Factors such as self-efficacy, social support, and coping style are important predictors of self-management behaviors of cancer survivors; however, the impact of these factors on self-management behaviors has not yet been empirically tested in Chinese cancer survivors. The aim of this study was to examine how self-efficacy, social support, and coping style affect specific self-management behaviors. A secondary data analysis was completed from a cross-sectional study. A total of 764 cancer survivors were recruited in the study. Validated instruments were used to assess patients' self-efficacy, social support, and coping style. Structural equation modeling (SEM) was used to test the hypothesis. The SEM model fits the data very well, with root mean square error of approximation (RMSEA) of 0.034; close-fit test cannot reject the hypothesis of root mean square error of approximation of 0.05 or less, comparative fit index of 0.91, Tucker-Lewis index of 0.90, and weighted root mean square residual of 0.82. For the measurement models in the SEM, all items loaded highly on their underlying first-order factors, and the first-order factors loaded highly on their underlying second-order factors (self-efficacy and social support, respectively). The model demonstrated that self-efficacy and social support directly and indirectly, via coping style, affect 3 self-management behaviors (ie, communication, exercise, and information seeking). Our results provide evidence that self-efficacy and social support impose significant direct effects, as well as indirect effects via copying style, on the self-management of cancer survivors. Our findings may help nurses to further improve their care of cancer survivors in terms of their self-management behaviors, specifically communication, exercise, and information seeking.

Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen

2016-02-01

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

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

Treesearch

John R. Brooks; Gary W. Miller

2011-01-01

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

Studies on Radar Sensor Networks

DTIC Science & Technology

2007-08-08

scheme in which 2-D image was created via adding voltages with the appropriate time offset. Simulation results show that our DCT-based scheme works...using RSNs in terms of the probability of miss detection PMD and the root mean square error (RMSE). Simulation results showed that multi-target detection... Simulation results are presented to evaluate the feasibility and effectiveness of the proposed JMIC algorithm in a query surveillance region. 5 SVD-QR and

A partial least squares based spectrum normalization method for uncertainty reduction for laser-induced breakdown spectroscopy measurements

NASA Astrophysics Data System (ADS)

Li, Xiongwei; Wang, Zhe; Lui, Siu-Lung; Fu, Yangting; Li, Zheng; Liu, Jianming; Ni, Weidou

2013-10-01

A bottleneck of the wide commercial application of laser-induced breakdown spectroscopy (LIBS) technology is its relatively high measurement uncertainty. A partial least squares (PLS) based normalization method was proposed to improve pulse-to-pulse measurement precision for LIBS based on our previous spectrum standardization method. The proposed model utilized multi-line spectral information of the measured element and characterized the signal fluctuations due to the variation of plasma characteristic parameters (plasma temperature, electron number density, and total number density) for signal uncertainty reduction. The model was validated by the application of copper concentration prediction in 29 brass alloy samples. The results demonstrated an improvement on both measurement precision and accuracy over the generally applied normalization as well as our previously proposed simplified spectrum standardization method. The average relative standard deviation (RSD), average of the standard error (error bar), the coefficient of determination (R2), the root-mean-square error of prediction (RMSEP), and average value of the maximum relative error (MRE) were 1.80%, 0.23%, 0.992, 1.30%, and 5.23%, respectively, while those for the generally applied spectral area normalization were 3.72%, 0.71%, 0.973, 1.98%, and 14.92%, respectively.

Optical diagnosis of malaria infection in human plasma using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bilal, Muhammad; Saleem, Muhammad; Amanat, Samina Tufail; Shakoor, Huma Abdul; Rashid, Rashad; Mahmood, Arshad; Ahmed, Mushtaq

2015-01-01

We present the prediction of malaria infection in human plasma using Raman spectroscopy. Raman spectra of malaria-infected samples are compared with those of healthy and dengue virus infected ones for disease recognition. Raman spectra were acquired using a laser at 532 nm as an excitation source and 10 distinct spectral signatures that statistically differentiated malaria from healthy and dengue-infected cases were found. A multivariate regression model has been developed that utilized Raman spectra of 20 malaria-infected, 10 non-malarial with fever, 10 healthy, and 6 dengue-infected samples to optically predict the malaria infection. The model yields the correlation coefficient r2 value of 0.981 between the predicted values and clinically known results of trainee samples, and the root mean square error in cross validation was found to be 0.09; both these parameters validated the model. The model was further blindly tested for 30 unknown suspected samples and found to be 86% accurate compared with the clinical results, with the inaccuracy due to three samples which were predicted in the gray region. Standard deviation and root mean square error in prediction for unknown samples were found to be 0.150 and 0.149, which are accepted for the clinical validation of the model.

Quantitative assessment of copper proteinates used as animal feed additives using ATR-FTIR spectroscopy and powder X-ray diffraction (PXRD) analysis.

PubMed

Cantwell, Caoimhe A; Byrne, Laurann A; Connolly, Cathal D; Hynes, Michael J; McArdle, Patrick; Murphy, Richard A

2017-08-01

The aim of the present work was to establish a reliable analytical method to determine the degree of complexation in commercial metal proteinates used as feed additives in the solid state. Two complementary techniques were developed. Firstly, a quantitative attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopic method investigated modifications in vibrational absorption bands of the ligand on complex formation. Secondly, a powder X-ray diffraction (PXRD) method to quantify the amount of crystalline material in the proteinate product was developed. These methods were developed in tandem and cross-validated with each other. Multivariate analysis (MVA) was used to develop validated calibration and prediction models. The FTIR and PXRD calibrations showed excellent linearity (R 2  > 0.99). The diagnostic model parameters showed that the FTIR and PXRD methods were robust with a root mean square error of calibration RMSEC ≤3.39% and a root mean square error of prediction RMSEP ≤7.17% respectively. Comparative statistics show excellent agreement between the MVA packages assessed and between the FTIR and PXRD methods. The methods can be used to determine the degree of complexation in complexes of both protein hydrolysates and pure amino acids.

[Outlier sample discriminating methods for building calibration model in melons quality detecting using NIR spectra].

PubMed

Tian, Hai-Qing; Wang, Chun-Guang; Zhang, Hai-Jun; Yu, Zhi-Hong; Li, Jian-Kang

2012-11-01

Outlier samples strongly influence the precision of the calibration model in soluble solids content measurement of melons using NIR Spectra. According to the possible sources of outlier samples, three methods (predicted concentration residual test; Chauvenet test; leverage and studentized residual test) were used to discriminate these outliers respectively. Nine suspicious outliers were detected from calibration set which including 85 fruit samples. Considering the 9 suspicious outlier samples maybe contain some no-outlier samples, they were reclaimed to the model one by one to see whether they influence the model and prediction precision or not. In this way, 5 samples which were helpful to the model joined in calibration set again, and a new model was developed with the correlation coefficient (r) 0. 889 and root mean square errors for calibration (RMSEC) 0.6010 Brix. For 35 unknown samples, the root mean square errors prediction (RMSEP) was 0.854 degrees Brix. The performance of this model was more better than that developed with non outlier was eliminated from calibration set (r = 0.797, RMSEC= 0.849 degrees Brix, RMSEP = 1.19 degrees Brix), and more representative and stable with all 9 samples were eliminated from calibration set (r = 0.892, RMSEC = 0.605 degrees Brix, RMSEP = 0.862 degrees).

Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements

NASA Astrophysics Data System (ADS)

Chen, Jiang; Zhu, Weining; Tian, Yong Q.; Yu, Qian; Zheng, Yuhan; Huang, Litong

2017-07-01

Colored dissolved organic matter (CDOM) and chlorophyll-a (Chla) are important water quality parameters and play crucial roles in aquatic environment. Remote sensing of CDOM and Chla concentrations for inland lakes is often limited by low spatial resolution. The newly launched Sentinel-2 satellite is equipped with high spatial resolution (10, 20, and 60 m). Empirical band ratio models were developed to derive CDOM and Chla concentrations in Lake Huron. The leave-one-out cross-validation method was used for model calibration and validation. The best CDOM retrieval algorithm is a B3/B5 model with accuracy coefficient of determination (R2)=0.884, root-mean-squared error (RMSE)=0.731 m-1, relative root-mean-squared error (RRMSE)=28.02%, and bias=-0.1 m-1. The best Chla retrieval algorithm is a B5/B4 model with accuracy R2=0.49, RMSE=9.972 mg/m3, RRMSE=48.47%, and bias=-0.116 mg/m3. Neural network models were further implemented to improve inversion accuracy. The applications of the two best band ratio models to Sentinel-2 imagery with 10 m×10 m pixel size presented the high potential of the sensor for monitoring water quality of inland lakes.

Acoustic-articulatory mapping in vowels by locally weighted regression

PubMed Central

McGowan, Richard S.; Berger, Michael A.

2009-01-01

A method for mapping between simultaneously measured articulatory and acoustic data is proposed. The method uses principal components analysis on the articulatory and acoustic variables, and mapping between the domains by locally weighted linear regression, or loess [Cleveland, W. S. (1979). J. Am. Stat. Assoc. 74, 829–836]. The latter method permits local variation in the slopes of the linear regression, assuming that the function being approximated is smooth. The methodology is applied to vowels of four speakers in the Wisconsin X-ray Microbeam Speech Production Database, with formant analysis. Results are examined in terms of (1) examples of forward (articulation-to-acoustics) mappings and inverse mappings, (2) distributions of local slopes and constants, (3) examples of correlations among slopes and constants, (4) root-mean-square error, and (5) sensitivity of formant frequencies to articulatory change. It is shown that the results are qualitatively correct and that loess performs better than global regression. The forward mappings show different root-mean-square error properties than the inverse mappings indicating that this method is better suited for the forward mappings than the inverse mappings, at least for the data chosen for the current study. Some preliminary results on sensitivity of the first two formant frequencies to the two most important articulatory principal components are presented. PMID:19813812

A comparative study of kinetic and connectionist modeling for shelf-life prediction of Basundi mix.

PubMed

Ruhil, A P; Singh, R R B; Jain, D K; Patel, A A; Patil, G R

2011-04-01

A ready-to-reconstitute formulation of Basundi, a popular Indian dairy dessert was subjected to storage at various temperatures (10, 25 and 40 °C) and deteriorative changes in the Basundi mix were monitored using quality indices like pH, hydroxyl methyl furfural (HMF), bulk density (BD) and insolubility index (II). The multiple regression equations and the Arrhenius functions that describe the parameters' dependence on temperature for the four physico-chemical parameters were integrated to develop mathematical models for predicting sensory quality of Basundi mix. Connectionist model using multilayer feed forward neural network with back propagation algorithm was also developed for predicting the storage life of the product employing artificial neural network (ANN) tool box of MATLAB software. The quality indices served as the input parameters whereas the output parameters were the sensorily evaluated flavour and total sensory score. A total of 140 observations were used and the prediction performance was judged on the basis of per cent root mean square error. The results obtained from the two approaches were compared. Relatively lower magnitudes of percent root mean square error for both the sensory parameters indicated that the connectionist models were better fitted than kinetic models for predicting storage life.

Quantification of tumor mobility during the breathing cycle using 3D dynamic MRI

NASA Astrophysics Data System (ADS)

Schoebinger, Max; Plathow, Christian; Wolf, Ivo; Kauczor, Hans-Ulrich; Meinzer, Hans-Peter

2006-03-01

Respiration causes movement and shape changes in thoracic tumors, which has a direct influence on the radio-therapy planning process. Current methods for the estimation of tumor mobility are either two-dimensional (fluoroscopy, 2D dynamic MRI) or based on radiation (3D (+t) CT, implanted gold markers). With current advances in dynamic MRI acquisition, 3D+t image sequences of the thorax can be acquired covering the thorax over the whole breathing cycle. In this work, methods are presented for the interactive segmentation of tumors in dynamic images, the calculation of tumor trajectories, dynamic tumor volumetry and dynamic tumor rotation/deformation based on 3D dynamic MRI. For volumetry calculation, a set of 21 related partial volume correcting volumetry algorithms has been evaluated based on tumor surrogates. Conventional volumetry based on voxel counting yielded a root mean square error of 29% compared to a root mean square error of 11% achieved by the algorithm performing best among the different volumetry methods. The new workflow has been applied to a set of 26 patients. Preliminary results indicate, that 3D dynamic MRI reveals important aspects of tumor behavior during the breathing cycle. This might imply the possibility to further improve high-precision radiotherapy techniques.

Determination of the carmine content based on spectrum fluorescence spectral and PSO-SVM

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Peng, Tao; Cheng, Qi; Wang, Gui-chuan; Kong, De-ming; Wang, Yu-tian

2018-03-01

Carmine is a widely used food pigment in various food and beverage additives. Excessive consumption of synthetic pigment shall do harm to body seriously. The food is generally associated with a variety of colors. Under the simulation context of various food pigments' coexistence, we adopted the technology of fluorescence spectroscopy, together with the PSO-SVM algorithm, so that to establish a method for the determination of carmine content in mixed solution. After analyzing the prediction results of PSO-SVM, we collected a bunch of data: the carmine average recovery rate was 100.84%, the root mean square error of prediction (RMSEP) for 1.03e-04, 0.999 for the correlation coefficient between the model output and the real value of the forecast. Compared with the prediction results of reverse transmission, the correlation coefficient of PSO-SVM was 2.7% higher, the average recovery rate for 0.6%, and the root mean square error was nearly one order of magnitude lower. According to the analysis results, it can effectively avoid the interference caused by pigment with the combination of the fluorescence spectrum technique and PSO-SVM, accurately determining the content of carmine in mixed solution with an effect better than that of BP.

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

[Application of near-infrared spectroscopy technology in extraction and concentration process of Reduning injection].

PubMed

Zhang, Ya-Fei; Zuo, Xiang-Yun; Bi, Yu-An; Wu, Jian-Xiong; Wang, Zhen-Zhong; L, Ping; Xiao, Wei

2014-08-01

To establish a rapid quantitative analysis method for the content of chlorogenic acid and solid content in the extraction liquid concentration process during the production of Reduning injection by using the near-infrared (NIR) spectroscopy, in order to reflect the concentration state in a real-time manner and really realize the quality control of concentrating process of the extraction and concentration process. The samples during the Jinqing extraction liquid concentration process were collected. After the removal of abnormal samples, the spectra pretreatment and the wave band selection, the quantitative calibration model between NIR spectra and chlorogenic acid HPLC analytical value and solid content was established by using PLS algorithm, and unknown samples were predicted. The correlation coefficients between the chlorogenic acid content and the solid content were respectively 0.992 1 and 0.994 0, and the correlation coefficients of the verification model were respectively 0.994 4 and 0.998 4, with the root mean square error of calibration (RMSEC) of 0.814 6 and 2.656 1 and the root mean square error of prediction (RMSEP) of 0.704 6 and 1.876 7 respectively, and the relative standard errors of predictions (RSEP) were 6.01% and 2.93% respectively. The method is simple, rapid, nondestructive, accurate and reliable, thus could be adopted for the fast monitoring of the chlorogenic acid content and the solid content during the concentration process of Reduning injection extraction liquid.

Swing arm profilometer: analytical solutions of misalignment errors for testing axisymmetric optics

NASA Astrophysics Data System (ADS)

Xiong, Ling; Luo, Xiao; Liu, Zhenyu; Wang, Xiaokun; Hu, Haixiang; Zhang, Feng; Zheng, Ligong; Zhang, Xuejun

2016-07-01

The swing arm profilometer (SAP) has been playing a very important role in testing large aspheric optics. As one of most significant error sources that affects the test accuracy, misalignment error leads to low-order errors such as aspherical aberrations and coma apart from power. In order to analyze the effect of misalignment errors, the relation between alignment parameters and test results of axisymmetric optics is presented. Analytical solutions of SAP system errors from tested mirror misalignment, arm length L deviation, tilt-angle θ deviation, air-table spin error, and air-table misalignment are derived, respectively; and misalignment tolerance is given to guide surface measurement. In addition, experiments on a 2-m diameter parabolic mirror are demonstrated to verify the model; according to the error budget, we achieve the SAP test for low-order errors except power with accuracy of 0.1 μm root-mean-square.

Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

NASA Astrophysics Data System (ADS)

Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

2017-03-01

In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

Mapping from disease-specific measures to health-state utility values in individuals with migraine.

PubMed

Gillard, Patrick J; Devine, Beth; Varon, Sepideh F; Liu, Lei; Sullivan, Sean D

2012-05-01

The objective of this study was to develop empirical algorithms that estimate health-state utility values from disease-specific quality-of-life scores in individuals with migraine. Data from a cross-sectional, multicountry study were used. Individuals with episodic and chronic migraine were randomly assigned to training or validation samples. Spearman's correlation coefficients between paired EuroQol five-dimensional (EQ-5D) questionnaire utility values and both Headache Impact Test (HIT-6) scores and Migraine-Specific Quality-of-Life Questionnaire version 2.1 (MSQ) domain scores (role restrictive, role preventive, and emotional function) were examined. Regression models were constructed to estimate EQ-5D questionnaire utility values from the HIT-6 score or the MSQ domain scores. Preferred algorithms were confirmed in the validation samples. In episodic migraine, the preferred HIT-6 and MSQ algorithms explained 22% and 25% of the variance (R(2)) in the training samples, respectively, and had similar prediction errors (root mean square errors of 0.30). In chronic migraine, the preferred HIT-6 and MSQ algorithms explained 36% and 45% of the variance in the training samples, respectively, and had similar prediction errors (root mean square errors 0.31 and 0.29). In episodic and chronic migraine, no statistically significant differences were observed between the mean observed and the mean estimated EQ-5D questionnaire utility values for the preferred HIT-6 and MSQ algorithms in the validation samples. The relationship between the EQ-5D questionnaire and the HIT-6 or the MSQ is adequate to use regression equations to estimate EQ-5D questionnaire utility values. The preferred HIT-6 and MSQ algorithms will be useful in estimating health-state utilities in migraine trials in which no preference-based measure is present. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Medium-range Performance of the Global NWP Model

NASA Astrophysics Data System (ADS)

Kim, J.; Jang, T.; Kim, J.; Kim, Y.

2017-12-01

The medium-range performance of the global numerical weather prediction (NWP) model in the Korea Meteorological Administration (KMA) is investigated. The performance is based on the prediction of the extratropical circulation. The mean square error is expressed by sum of spatial variance of discrepancy between forecasts and observations and the square of the mean error (ME). Thus, it is important to investigate the ME effect in order to understand the model performance. The ME is expressed by the subtraction of an anomaly from forecast difference against the real climatology. It is found that the global model suffers from a severe systematic ME in medium-range forecasts. The systematic ME is dominant in the entire troposphere in all months. Such ME can explain at most 25% of root mean square error. We also compare the extratropical ME distribution with that from other NWP centers. NWP models exhibit similar spatial ME structure each other. It is found that the spatial ME pattern is highly correlated to that of an anomaly, implying that the ME varies with seasons. For example, the correlation coefficient between ME and anomaly ranges from -0.51 to -0.85 by months. The pattern of the extratropical circulation also has a high correlation to an anomaly. The global model has trouble in faithfully simulating extratropical cyclones and blockings in the medium-range forecast. In particular, the model has a hard to simulate an anomalous event in medium-range forecasts. If we choose an anomalous period for a test-bed experiment, we will suffer from a large error due to an anomaly.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Predicting Soil Organic Carbon and Total Nitrogen in the Russian Chernozem from Depth and Wireless Color Sensor Measurements

NASA Astrophysics Data System (ADS)

Mikhailova, E. A.; Stiglitz, R. Y.; Post, C. J.; Schlautman, M. A.; Sharp, J. L.; Gerard, P. D.

2017-12-01

Color sensor technologies offer opportunities for affordable and rapid assessment of soil organic carbon (SOC) and total nitrogen (TN) in the field, but the applicability of these technologies may vary by soil type. The objective of this study was to use an inexpensive color sensor to develop SOC and TN prediction models for the Russian Chernozem (Haplic Chernozem) in the Kursk region of Russia. Twenty-one dried soil samples were analyzed using a Nix Pro™ color sensor that is controlled through a mobile application and Bluetooth to collect CIEL*a*b* (darkness to lightness, green to red, and blue to yellow) color data. Eleven samples were randomly selected to be used to construct prediction models and the remaining ten samples were set aside for cross validation. The root mean squared error (RMSE) was calculated to determine each model's prediction error. The data from the eleven soil samples were used to develop the natural log of SOC (lnSOC) and TN (lnTN) prediction models using depth, L*, a*, and b* for each sample as predictor variables in regression analyses. Resulting residual plots, root mean square errors (RMSE), mean squared prediction error (MSPE) and coefficients of determination ( R 2, adjusted R 2) were used to assess model fit for each of the SOC and total N prediction models. Final models were fit using all soil samples, which included depth and color variables, for lnSOC ( R 2 = 0.987, Adj. R 2 = 0.981, RMSE = 0.003, p-value < 0.001, MSPE = 0.182) and lnTN ( R 2 = 0.980 Adj. R 2 = 0.972, RMSE = 0.004, p-value < 0.001, MSPE = 0.001). Additionally, final models were fit for all soil samples, which included only color variables, for lnSOC ( R 2 = 0.959 Adj. R 2 = 0.949, RMSE = 0.007, p-value < 0.001, MSPE = 0.536) and lnTN ( R 2 = 0.912 Adj. R 2 = 0.890, RMSE = 0.015, p-value < 0.001, MSPE = 0.001). The results suggest that soil color may be used for rapid assessment of SOC and TN in these agriculturally important soils.

Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea

NASA Astrophysics Data System (ADS)

Jeong, Sang-Hun; Kim, Jinah; Heo, Ki-Young; Park, Kwang-Soon

2017-04-01

In winter season, large-height swell-like waves have occurred on the East coast of Korea, causing property damages and loss of human life. It is known that those waves are generated by a local strong wind made by temperate cyclone moving to eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages. In Korea, an operational oceanographic system (KOOS) has been developed by the Korea institute of ocean science and technology (KIOST) and KOOS provides daily basis 72-hours' ocean forecasts such as wind, water elevation, sea currents, water temperature, salinity, and waves which are computed from not only meteorological and hydrodynamic model (WRF, ROMS, MOM, and MOHID) but also wave models (WW-III and SWAN). In order to evaluate the model performance and guarantee a certain level of accuracy of ocean forecasts, a Skill Assessment (SA) system was established as a one of module in KOOS. It has been performed through comparison of model results with in-situ observation data and model errors have been quantified with skill scores. Statistics which are used in skill assessment are including a measure of both errors and correlations such as root-mean-square-error (RMSE), root-mean-square-error percentage (RMSE%), mean bias (MB), correlation coefficient (R), scatter index (SI), circular correlation (CC) and central frequency (CF) that is a frequency with which errors lie within acceptable error criteria. It should be utilized simultaneously not only to quantify an error but also to improve an accuracy of forecasts by providing a feedback interactively. However, in an abnormal phenomena such as high-height swell-like waves in the East coast of Korea, it requires more advanced and optimized error quantification method that allows to predict the abnormal waves well and to improve the accuracy of forecasts by supporting modification of physics and numeric on numerical models through sensitivity test. In this study, we proposed an appropriate method of error quantification especially on abnormal high waves which are occurred by local weather condition. Furthermore, we introduced that how the quantification errors are contributed to improve wind-wave modeling by applying data assimilation and utilizing reanalysis data.

Transmitted wavefront error of a volume phase holographic grating at cryogenic temperature.

PubMed

Lee, David; Taylor, Gordon D; Baillie, Thomas E C; Montgomery, David

2012-06-01

This paper describes the results of transmitted wavefront error (WFE) measurements on a volume phase holographic (VPH) grating operating at a temperature of 120 K. The VPH grating was mounted in a cryogenically compatible optical mount and tested in situ in a cryostat. The nominal root mean square (RMS) wavefront error at room temperature was 19 nm measured over a 50 mm diameter test aperture. The WFE remained at 18 nm RMS when the grating was cooled. This important result demonstrates that excellent WFE performance can be obtained with cooled VPH gratings, as required for use in future cryogenic infrared astronomical spectrometers planned for the European Extremely Large Telescope.

Liquid fuel spray processes in high-pressure gas flow

NASA Technical Reports Server (NTRS)

Ingebo, R. D.

1985-01-01

Atomization of single liquid jets injected downstream in high pressure and high velocity airflow was investigated to determine the effect of airstream pressure on mean drop size as measured with a scanning radiometer. For aerodynamic - wave breakup of liquid jets, the ratio of orifice diameter D sub o to measured mean drop diameter D sub m which is assumed equal to D sub 32 or Sauter mean diameter, was correlated with the product of the Weber and Reynolds numbers WeRe and the dimensionless group G1/square root of c, where G is the gravitational acceleration, 1 the mean free molecular path, and square root of C the root mean square velocity, as follows; D sub o/D sub 32 = 1.2 (WeRe) to the 0.4 (G1/square root of c) to the 0.15 for values of WeRe 1 million and an airstream pressure range of 0.10 to 2.10 MPa.

Liquid fuel spray processes in high-pressure gas flow

NASA Technical Reports Server (NTRS)

Ingebo, R. D.

1986-01-01

Atomization of single liquid jets injected downstream in high pressure and high velocity airflow was investigated to determine the effect of airstream pressure on mean drop size as measured with a scanning radiometer. For aerodynamic - wave breakup of liquid jets, the ratio of orifice diameter D sub o to measured mean drop diameter D sub m which is assumed equal to D sub 32 or Sauter mean diameter, was correlated with the product of the Weber and Reynolds numbers WeRe and the dimensionless group G1/square root of c, where G is the gravitational acceleration, 1 the mean free molecular path, and square root of C the root mean square velocity, as follows; D sub o/D sub 32 = 1.2 (WeRe) to the 0.4 (G1/square root of c) to the 0.15 for values of WeRe 1 million and an airstream pressure range of 0.10 to 2.10 MPa.

The GEOS Ozone Data Assimilation System: Specification of Error Statistics

NASA Technical Reports Server (NTRS)

Stajner, Ivanka; Riishojgaard, Lars Peter; Rood, Richard B.

2000-01-01

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone and the Solar Backscatter Ultraviolet (SBUV) or (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme (PSAS). This system became operational in December 1999. A detailed description of the statistical analysis scheme, and in particular, the forecast and observation error covariance models is given. A new global anisotropic horizontal forecast error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data is sparse. Forecast error variance model is proportional to the ozone field. The forecast error covariance parameters were determined by maximum likelihood estimation. The error covariance models are validated using x squared statistics. The analyzed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and HALOE. There is better than 10% agreement between mean Halogen Occultation Experiment (HALOE) and analysis fields between 70 and 0.2 hPa. The global root-mean-square (RMS) difference between TOMS observed and forecast values is less than 4%. The global RMS difference between SBUV observed and analyzed ozone between 50 and 3 hPa is less than 15%.

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions.

PubMed

Feng, Chao-Hui; Makino, Yoshio; Yoshimura, Masatoshi; Thuyet, Dang Quoc; García-Martín, Juan Francisco

2018-02-01

The potential of hyperspectral imaging with wavelengths of 380 to 1000 nm was used to determine the pH of cooked sausages after different storage conditions (4 °C for 1 d, 35 °C for 1, 3, and 5 d). The mean spectra of the sausages were extracted from the hyperspectral images and partial least squares regression (PLSR) model was developed to relate spectral profiles with the pH of the cooked sausages. Eleven important wavelengths were selected based on the regression coefficient values. The PLSR model established using the optimal wavelengths showed good precision being the prediction coefficient of determination (R p 2 ) 0.909 and the root mean square error of prediction 0.035. The prediction map for illustrating pH indices in sausages was for the first time developed by R statistics. The overall results suggested that hyperspectral imaging combined with PLSR and R statistics are capable to quantify and visualize the sausages pH evolution under different storage conditions. In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high R p 2 (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems. © 2017 Institute of Food Technologists®.

The effect of constraints on the analytical figures of merit achieved by extended multivariate curve resolution-alternating least-squares.

PubMed

Pellegrino Vidal, Rocío B; Allegrini, Franco; Olivieri, Alejandro C

2018-03-20

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

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

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First,more » the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

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

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE PAGES

Zhou, Ping; Wang, Chenyu; Li, Mingjie; ...

2018-01-31

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Dense-HOG-based drift-reduced 3D face tracking for infant pain monitoring

NASA Astrophysics Data System (ADS)

Saeijs, Ronald W. J. J.; Tjon A Ten, Walther E.; de With, Peter H. N.

2017-03-01

This paper presents a new algorithm for 3D face tracking intended for clinical infant pain monitoring. The algorithm uses a cylinder head model and 3D head pose recovery by alignment of dynamically extracted templates based on dense-HOG features. The algorithm includes extensions for drift reduction, using re-registration in combination with multi-pose state estimation by means of a square-root unscented Kalman filter. The paper reports experimental results on videos of moving infants in hospital who are relaxed or in pain. Results show good tracking behavior for poses up to 50 degrees from upright-frontal. In terms of eye location error relative to inter-ocular distance, the mean tracking error is below 9%.

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).

Stabilized high-accuracy correction of ocular aberrations with liquid crystal on silicon spatial light modulator in adaptive optics retinal imaging system.

PubMed

Huang, Hongxin; Inoue, Takashi; Tanaka, Hiroshi

2011-08-01

We studied the long-term optical performance of an adaptive optics scanning laser ophthalmoscope that uses a liquid crystal on silicon spatial light modulator to correct ocular aberrations. The system achieved good compensation of aberrations while acquiring images of fine retinal structures, excepting during sudden eye movements. The residual wavefront aberrations collected over several minutes in several situations were statistically analyzed. The mean values of the root-mean-square residual wavefront errors were 23-30 nm, and for around 91-94% of the effective time the errors were below the Marechal criterion for diffraction limited imaging. The ability to axially shift the imaging plane to different retinal depths was also demonstrated.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

Building on crossvalidation for increasing the quality of geostatistical modeling

USGS Publications Warehouse

Olea, R.A.

2012-01-01

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria. ?? 2011 US Government.

Reconstruction of regional mean temperature for East Asia since 1900s and its uncertainties

NASA Astrophysics Data System (ADS)

Hua, W.

2017-12-01

Regional average surface air temperature (SAT) is one of the key variables often used to investigate climate change. Unfortunately, because of the limited observations over East Asia, there were also some gaps in the observation data sampling for regional mean SAT analysis, which was important to estimate past climate change. In this study, the regional average temperature of East Asia since 1900s is calculated by the Empirical Orthogonal Function (EOF)-based optimal interpolation (OA) method with considering the data errors. The results show that our estimate is more precise and robust than the results from simple average, which provides a better way for past climate reconstruction. In addition to the reconstructed regional average SAT anomaly time series, we also estimated uncertainties of reconstruction. The root mean square error (RMSE) results show that the the error decreases with respect to time, and are not sufficiently large to alter the conclusions on the persist warming in East Asia during twenty-first century. Moreover, the test of influence of data error on reconstruction clearly shows the sensitivity of reconstruction to the size of the data error.

Including sheath effects in the interpretation of planar retarding potential analyzer's low-energy ion data

NASA Astrophysics Data System (ADS)

Fisher, L. E.; Lynch, K. A.; Fernandes, P. A.; Bekkeng, T. A.; Moen, J.; Zettergren, M.; Miceli, R. J.; Powell, S.; Lessard, M. R.; Horak, P.

2016-04-01

The interpretation of planar retarding potential analyzers (RPA) during ionospheric sounding rocket missions requires modeling the thick 3D plasma sheath. This paper overviews the theory of RPAs with an emphasis placed on the impact of the sheath on current-voltage (I-V) curves. It then describes the Petite Ion Probe (PIP) which has been designed to function in this difficult regime. The data analysis procedure for this instrument is discussed in detail. Data analysis begins by modeling the sheath with the Spacecraft Plasma Interaction System (SPIS), a particle-in-cell code. Test particles are traced through the sheath and detector to determine the detector's response. A training set is constructed from these simulated curves for a support vector regression analysis which relates the properties of the I-V curve to the properties of the plasma. The first in situ use of the PIPs occurred during the MICA sounding rocket mission which launched from Poker Flat, Alaska in February of 2012. These data are presented as a case study, providing valuable cross-instrument comparisons. A heritage top-hat thermal ion electrostatic analyzer, called the HT, and a multi-needle Langmuir probe have been used to validate both the PIPs and the data analysis method. Compared to the HT, the PIP ion temperature measurements agree with a root-mean-square error of 0.023 eV. These two instruments agree on the parallel-to-B plasma flow velocity with a root-mean-square error of 130 m/s. The PIP with its field of view aligned perpendicular-to-B provided a density measurement with an 11% error compared to the multi-needle Langmuir Probe. Higher error in the other PIP's density measurement is likely due to simplifications in the SPIS model geometry.

Reliability and validity in measurement of true humeral retroversion by a three-dimensional cylinder fitting method.

PubMed

Saka, Masayuki; Yamauchi, Hiroki; Hoshi, Kenji; Yoshioka, Toru; Hamada, Hidetoshi; Gamada, Kazuyoshi

2015-05-01

Humeral retroversion is defined as the orientation of the humeral head relative to the distal humerus. Because none of the previous methods used to measure humeral retroversion strictly follow this definition, values obtained by these techniques vary and may be biased by morphologic variations of the humerus. The purpose of this study was 2-fold: to validate a method to define the axis of the distal humerus with a virtual cylinder and to establish the reliability of 3-dimensional (3D) measurement of humeral retroversion by this cylinder fitting method. Humeral retroversion in 14 baseball players (28 humeri) was measured by the 3D cylinder fitting method. The root mean square error was calculated to compare values obtained by a single tester and by 2 different testers using the embedded coordinate system. To establish the reliability, intraclass correlation coefficient (ICC) and precision (standard error of measurement [SEM]) were calculated. The root mean square errors for the humeral coordinate system were <1.0 mm/1.0° for comparison of all translations/rotations obtained by a single tester and <1.0 mm/2.0° for comparison obtained by 2 different testers. Assessment of reliability and precision of the 3D measurement of retroversion yielded an intratester ICC of 0.99 (SEM, 1.0°) and intertester ICC of 0.96 (SEM, 2.8°). The error in measurements obtained by a distal humerus cylinder fitting method was small enough not to affect retroversion measurement. The 3D measurement of retroversion by this method provides excellent intratester and intertester reliability. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques

USGS Publications Warehouse

Cuttler, Michael V. W.; Lowe, Ryan J.; Falter, James L.; Buscombe, Daniel D.

2017-01-01

Most techniques for estimating settling velocities of natural particles have been developed for siliciclastic sediments. Therefore, to understand how these techniques apply to bioclastic environments, measured settling velocities of bioclastic sedimentary deposits sampled from a nearshore fringing reef in Western Australia were compared with settling velocities calculated using results from several common grain-size analysis techniques (sieve, laser diffraction and image analysis) and established models. The effects of sediment density and shape were also examined using a range of density values and three different models of settling velocity. Sediment density was found to have a significant effect on calculated settling velocity, causing a range in normalized root-mean-square error of up to 28%, depending upon settling velocity model and grain-size method. Accounting for particle shape reduced errors in predicted settling velocity by 3% to 6% and removed any velocity-dependent bias, which is particularly important for the fastest settling fractions. When shape was accounted for and measured density was used, normalized root-mean-square errors were 4%, 10% and 18% for laser diffraction, sieve and image analysis, respectively. The results of this study show that established models of settling velocity that account for particle shape can be used to estimate settling velocity of irregularly shaped, sand-sized bioclastic sediments from sieve, laser diffraction, or image analysis-derived measures of grain size with a limited amount of error. Collectively, these findings will allow for grain-size data measured with different methods to be accurately converted to settling velocity for comparison. This will facilitate greater understanding of the hydraulic properties of bioclastic sediment which can help to increase our general knowledge of sediment dynamics in these environments.

Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations

PubMed Central

Kish, Nicole E.; Helmuth, Brian; Wethey, David S.

2016-01-01

Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, Mytilus californianus, using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change. PMID:27729979

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.

Stochastic growth logistic model with aftereffect for batch fermentation process

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

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah

2014-06-19

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

Stochastic growth logistic model with aftereffect for batch fermentation process

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

Volatilization of organic compounds from streams

USGS Publications Warehouse

Rathburn, R.E.; Tai, D.Y.

1982-01-01

Mass-transfer coefficients for the volatilization of ethylene and propane were correlated with the hydraulic and geometric properties of seven streams, and predictive equations were developed. The equations were evaluated using a normalized root-mean-square error as the criterion of comparison. The two best equations were a two-variable equation containing the energy dissipated per unit mass per unit time and the average depth of flow and a three-variable equation containing the average velocity, the average depth of flow, and the slope of the stream. Procedures for adjusting the ethylene and propane coefficients for other organic compounds were evaluated. These procedures are based on molecular diffusivity, molecular diameter, or molecular weight. Because of limited data, none of these procedures have been extensively verified. Therefore, until additional data become available, it is suggested that the mass-transfer coefficient be assumed to be inversely proportional to the square root of the molecular weight.

Quantification of Coffea arabica and Coffea canephora var. robusta concentration in blends by means of synchronous fluorescence and UV-Vis spectroscopies.

PubMed

Dankowska, A; Domagała, A; Kowalewski, W

2017-09-01

The potential of fluorescence, UV-Vis spectroscopies as well as the low- and mid-level data fusion of both spectroscopies for the quantification of concentrations of roasted Coffea arabica and Coffea canephora var. robusta in coffee blends was investigated. Principal component analysis was used to reduce data multidimensionality. To calculate the level of undeclared addition, multiple linear regression (PCA-MLR) models were used with lowest root mean square error of calibration (RMSEC) of 3.6% and root mean square error of cross-validation (RMSECV) of 7.9%. LDA analysis was applied to fluorescence intensities and UV spectra of Coffea arabica, canephora samples, and their mixtures in order to examine classification ability. The best performance of PCA-LDA analysis was observed for data fusion of UV and fluorescence intensity measurements at wavelength interval of 60nm. LDA showed that data fusion can achieve over 96% of correct classifications (sensitivity) in the test set and 100% of correct classifications in the training set, with low-level data fusion. The corresponding results for individual spectroscopies ranged from 90% (UV-Vis spectroscopy) to 77% (synchronous fluorescence) in the test set, and from 93% to 97% in the training set. The results demonstrate that fluorescence, UV, and visible spectroscopies complement each other, giving a complementary effect for the quantification of roasted Coffea arabica and Coffea canephora var. robusta concentration in blends. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved volumetric measurement of brain structure with a distortion correction procedure using an ADNI phantom.

PubMed

Maikusa, Norihide; Yamashita, Fumio; Tanaka, Kenichiro; Abe, Osamu; Kawaguchi, Atsushi; Kabasawa, Hiroyuki; Chiba, Shoma; Kasahara, Akihiro; Kobayashi, Nobuhisa; Yuasa, Tetsuya; Sato, Noriko; Matsuda, Hiroshi; Iwatsubo, Takeshi

2013-06-01

Serial magnetic resonance imaging (MRI) images acquired from multisite and multivendor MRI scanners are widely used in measuring longitudinal structural changes in the brain. Precise and accurate measurements are important in understanding the natural progression of neurodegenerative disorders such as Alzheimer's disease. However, geometric distortions in MRI images decrease the accuracy and precision of volumetric or morphometric measurements. To solve this problem, the authors suggest a commercially available phantom-based distortion correction method that accommodates the variation in geometric distortion within MRI images obtained with multivendor MRI scanners. The authors' method is based on image warping using a polynomial function. The method detects fiducial points within a phantom image using phantom analysis software developed by the Mayo Clinic and calculates warping functions for distortion correction. To quantify the effectiveness of the authors' method, the authors corrected phantom images obtained from multivendor MRI scanners and calculated the root-mean-square (RMS) of fiducial errors and the circularity ratio as evaluation values. The authors also compared the performance of the authors' method with that of a distortion correction method based on a spherical harmonics description of the generic gradient design parameters. Moreover, the authors evaluated whether this correction improves the test-retest reproducibility of voxel-based morphometry in human studies. A Wilcoxon signed-rank test with uncorrected and corrected images was performed. The root-mean-square errors and circularity ratios for all slices significantly improved (p < 0.0001) after the authors' distortion correction. Additionally, the authors' method was significantly better than a distortion correction method based on a description of spherical harmonics in improving the distortion of root-mean-square errors (p < 0.001 and 0.0337, respectively). Moreover, the authors' method reduced the RMS error arising from gradient nonlinearity more than gradwarp methods. In human studies, the coefficient of variation of voxel-based morphometry analysis of the whole brain improved significantly from 3.46% to 2.70% after distortion correction of the whole gray matter using the authors' method (Wilcoxon signed-rank test, p < 0.05). The authors proposed a phantom-based distortion correction method to improve reproducibility in longitudinal structural brain analysis using multivendor MRI. The authors evaluated the authors' method for phantom images in terms of two geometrical values and for human images in terms of test-retest reproducibility. The results showed that distortion was corrected significantly using the authors' method. In human studies, the reproducibility of voxel-based morphometry analysis for the whole gray matter significantly improved after distortion correction using the authors' method.

[Application of near infrared spectroscopy in rapid and simultaneous determination of essential components in five varieties of anti-tuberculosis tablets].

PubMed

Teng, Le-sheng; Wang, Di; Song, Jia; Zhang, Yi-bo; Guo, Wei-liang; Teng, Li-rong

2008-08-01

Since 1980s, tuberculosis has become increasingly serious. Rifampicin tablets, isoniazide tablets, pyrazinamide tablets, rifampicin and isoniazide tablets and rifampicin isoniazide and pyrazinamide tablets are currently relatively efficacious antituberculosis drugs. In the present paper, near infrared spectroscopy (NIRS) with partial least squares (PLS) was applied to the simultaneous determination of rifampicin (RMP), isoniazide (INH) and pyrazinamide (PZA) contents in 5 varieties of anti-tuberculosis tablets. As the results showed, all of the models for the determination of RMP, INH and PZA contents applied the original NIR spectra. The most efficacious wavelength range for the determination of RMP contents was 1981-2195 nm, it was 1540-1717 nm and 2086-2197 nm for the determination of INH contents, and it was 1460-1537 nm, 1956-2022 nm and 2268-2393 nm for determination of PZA contents. The root mean square error of the calibration set obtained by cross-validation (RMSECV) of the optimum models for the quantitative analysis of RMP, INH and PZA contents was 0.0494, 0.0257 and 0.0307, respectively. Using these optimum models for the determination of RMP, INH and PZA contents in prediction set, the root mean square error of prediction set (RMSEP) was 0.0182, 0.0166 and 0.0134, respectively. The correlation coefficient (r(p)) between the predicted values and actual values was 0.9864, 0.9989 and 0.9993, respectively. These results demonstrated that this method was precise and reliable, and is significative for in situ measurement and the on-line quality control for anti-tuberculosis tablets production.

Mixed effects versus fixed effects modelling of binary data with inter-subject variability.

PubMed

Murphy, Valda; Dunne, Adrian

2005-04-01

The question of whether or not a mixed effects model is required when modelling binary data with inter-subject variability and within subject correlation was reported in this journal by Yano et al. (J. Pharmacokin. Pharmacodyn. 28:389-412 [2001]). That report used simulation experiments to demonstrate that, under certain circumstances, the use of a fixed effects model produced more accurate estimates of the fixed effect parameters than those produced by a mixed effects model. The Laplace approximation to the likelihood was used when fitting the mixed effects model. This paper repeats one of those simulation experiments, with two binary observations recorded for every subject, and uses both the Laplace and the adaptive Gaussian quadrature approximations to the likelihood when fitting the mixed effects model. The results show that the estimates produced using the Laplace approximation include a small number of extreme outliers. This was not the case when using the adaptive Gaussian quadrature approximation. Further examination of these outliers shows that they arise in situations in which the Laplace approximation seriously overestimates the likelihood in an extreme region of the parameter space. It is also demonstrated that when the number of observations per subject is increased from two to three, the estimates based on the Laplace approximation no longer include any extreme outliers. The root mean squared error is a combination of the bias and the variability of the estimates. Increasing the sample size is known to reduce the variability of an estimator with a consequent reduction in its root mean squared error. The estimates based on the fixed effects model are inherently biased and this bias acts as a lower bound for the root mean squared error of these estimates. Consequently, it might be expected that for data sets with a greater number of subjects the estimates based on the mixed effects model would be more accurate than those based on the fixed effects model. This is borne out by the results of a further simulation experiment with an increased number of subjects in each set of data. The difference in the interpretation of the parameters of the fixed and mixed effects models is discussed. It is demonstrated that the mixed effects model and parameter estimates can be used to estimate the parameters of the fixed effects model but not vice versa.

Patient Perceptions of Deprescribing: Survey Development and Psychometric Assessment.

PubMed

Linsky, Amy; Simon, Steven R; Stolzmann, Kelly; Meterko, Mark

2017-03-01

Although clinicians ultimately decide when to discontinue (deprescribe) medications, patients' perspectives may guide the process. To develop a survey instrument that assesses patients' experience with and attitudes toward deprescribing. We developed a questionnaire with established and newly created items. We used exploratory factor analysis and confirmatory factor analysis (EFA and CFA) to assess the psychometric properties. National sample of 1547 Veterans Affairs patients prescribed ≥5 medications. In the EFA, percent variance, a scree plot, and conceptual coherence determined the number of factors. In the CFA, proposed factor structures were evaluated using standardized root mean square residual, root mean square error of approximation, and comparative fit index. Respondents (n=790; 51% response rate) were randomly assigned to equal derivation and validation groups. EFA yielded credible 4-factor and 5-factor models. The 4 factors were "Medication Concerns," "Provider Knowledge," "Interest in Stopping Medicines," and "Unimportance of Medicines." The 5-factor model added "Patient Involvement in Decision-Making." In the CFA, a modified 5-factor model, with 2 items with marginal loadings moved based upon conceptual fit, had an standardized root mean square residual of 0.06, an RMSEA of 0.07, and a CFI of 0.91. The new scales demonstrated internal consistency reliability, with Cronbach α's of: Concerns, 0.82; Provider Knowledge, 0.86; Interest, 0.77; Involvement, 0.61; and Unimportance, 0.70. The Patient Perceptions of Deprescribing questionnaire is a novel, multidimensional instrument to measure patients' attitudes and experiences related to medication discontinuation that can be used to determine how to best involve patients in deprescribing decisions.

Happiness, Pain Intensity, Pain Interference, and Distress in Individuals with Physical Disabilities.

PubMed

Müller, Rachel; Terrill, Alexandra L; Jensen, Mark P; Molton, Ivan R; Ravesloot, Craig; Ipsen, Catherine

2015-12-01

The aim of this study was to examine how the construct of happiness is related to pain intensity, pain interference, and distress in individuals with physical disabilities. This study involves cross-sectional analyses of 471 individuals with a variety of health conditions reporting at least mild pain. The first hypothesis that happiness mediates the relationship between pain intensity and two outcomes, pain interference and distress, was not supported. The second hypothesis was supported by a good fitting model (χ2(10) = 12.83, P = 0.23, root-mean-square error of approximation = 0.025) and indicated that pain intensity significantly mediated the effect of happiness on pain interference (indirect effect: β = -0.13, P < 0.001) and on distress (indirect effect: β = 0.10, P = 0.01). Happiness showed a significant direct effect on pain intensity (β = -0.20, P < 0.001). A third model exploring the happiness components meaning, pleasure, and engagement fitted well (χ2(4) = 9.65, P = 0.05, root-mean-square error of approximation = 0.055). Pain intensity acted as a significant mediator but only mediated the effect of meaning on pain interference (indirect effect: β = -0.07, P = 0.05) and on distress (indirect effect via pain interference: β = -0.04, P = 0.05). Only meaning (β = -0.10, P = 0.05), but neither pleasure nor engagement, had a significant direct effect on pain intensity. Participants who reported greater happiness reported lower pain interference and distress through happiness' effects on pain intensity. Experiencing meaning and purpose in life seems to be most closely (and negatively) associated with pain intensity, pain interference, and distress. Findings from this study can lay the groundwork for intervention studies to better understand how to more effectively decrease pain intensity, pain interference, and distress.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Energy Performance Measurement and Simulation Modeling of Tactical Soft-Wall Shelters

DTIC Science & Technology

2015-07-01

was too low to measure was on the order of 5 hours. Because the research team did not have access to the site between 1700 and 0500 hours the...Basic for Applications ( VBA ). The objective function was the root mean square (RMS) errors between modeled and measured heating load and the modeled...References Phase Change Energy Solutions. (2013). BioPCM web page, http://phasechange.com/index.php/en/about/our-material. Accessed 16 September

ARGOS Testbed: Study of Multidisciplinary Challenges of Future Spaceborne Interferometric Arrays

DTIC Science & Technology

2004-09-01

optimized ex- tensively by ZEMAX . One drawback of the cemented dou- blet is that it has bonded glasses, therefore if there is a change of temperature, the...residual aberrations @root mean square ~rms! wavefront errors predicted by ZEMAX #. The final FK51- BaK2 design achieves 271.6 mm chromatic focal shift...of ZEMAX , a complete ARGOS optics layout is constructed based on the optical specifications of a subaperture, pyramidal mirror, and the beam combining

Estimating the Mechanical Behavior of the Knee Joint during Crouch Gait: Implications for Real-Time Motor Control of Robotic Knee Orthoses

PubMed Central

Damiano, Diane L.; Bulea, Thomas C.

2016-01-01

Individuals with cerebral palsy frequently exhibit crouch gait, a pathological walking pattern characterized by excessive knee flexion. Knowledge of the knee joint moment during crouch gait is necessary for the design and control of assistive devices used for treatment. Our goal was to 1) develop statistical models to estimate knee joint moment extrema and dynamic stiffness during crouch gait, and 2) use the models to estimate the instantaneous joint moment during weight-acceptance. We retrospectively computed knee moments from 10 children with crouch gait and used stepwise linear regression to develop statistical models describing the knee moment features. The models explained at least 90% of the response value variability: peak moment in early (99%) and late (90%) stance, and dynamic stiffness of weight-acceptance flexion (94%) and extension (98%). We estimated knee extensor moment profiles from the predicted dynamic stiffness and instantaneous knee angle. This approach captured the timing and shape of the computed moment (root-mean-squared error: 2.64 Nm); including the predicted early-stance peak moment as a correction factor improved model performance (root-mean-squared error: 1.37 Nm). Our strategy provides a practical, accurate method to estimate the knee moment during crouch gait, and could be used for real-time, adaptive control of robotic orthoses. PMID:27101612

Four-dimensional data coupled to alternating weighted residue constraint quadrilinear decomposition model applied to environmental analysis: Determination of polycyclic aromatic hydrocarbons

NASA Astrophysics Data System (ADS)

Liu, Tingting; Zhang, Ling; Wang, Shutao; Cui, Yaoyao; Wang, Yutian; Liu, Lingfei; Yang, Zhe

2018-03-01

Qualitative and quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) was carried out by three-dimensional fluorescence spectroscopy combining with Alternating Weighted Residue Constraint Quadrilinear Decomposition (AWRCQLD). The experimental subjects were acenaphthene (ANA) and naphthalene (NAP). Firstly, in order to solve the redundant information of the three-dimensional fluorescence spectral data, the wavelet transform was used to compress data in preprocessing. Then, the four-dimensional data was constructed by using the excitation-emission fluorescence spectra of different concentration PAHs. The sample data was obtained from three solvents that are methanol, ethanol and Ultra-pure water. The four-dimensional spectral data was analyzed by AWRCQLD, then the recovery rate of PAHs was obtained from the three solvents and compared respectively. On one hand, the results showed that PAHs can be measured more accurately by the high-order data, and the recovery rate was higher. On the other hand, the results presented that AWRCQLD can better reflect the superiority of four-dimensional algorithm than the second-order calibration and other third-order calibration algorithms. The recovery rate of ANA was 96.5% 103.3% and the root mean square error of prediction was 0.04 μgL- 1. The recovery rate of NAP was 96.7% 115.7% and the root mean square error of prediction was 0.06 μgL- 1.

Comparison of measured and modeled BRDF of natural targets

NASA Astrophysics Data System (ADS)

Boucher, Yannick; Cosnefroy, Helene; Petit, Alain D.; Serrot, Gerard; Briottet, Xavier

1999-07-01

The Bidirectional Reflectance Distribution Function (BRDF) plays a major role to evaluate or simulate the signatures of natural and artificial targets in the solar spectrum. A goniometer covering a large spectral and directional domain has been recently developed by the ONERA/DOTA. It was designed to allow both laboratory and outside measurements. The spectral domain ranges from 0.40 to 0.95 micrometer, with a resolution of 3 nm. The geometrical domain ranges 0 - 60 degrees for the zenith angle of the source and the sensor, and 0 - 180 degrees for the relative azimuth between the source and the sensor. The maximum target size for nadir measurements is 22 cm. The spatial target irradiance non-uniformity has been evaluated and then used to correct the raw measurements. BRDF measurements are calibrated thanks to a spectralon reference panel. Some BRDF measurements performed on sand and short grass and are presented here. Eight bidirectional models among the most popular models found in the literature have been tested on these measured data set. A code fitting the model parameters to the measured BRDF data has been developed. The comparative evaluation of the model performances is carried out, versus different criteria (root mean square error, root mean square relative error, correlation diagram . . .). The robustness of the models is evaluated with respect to the number of BRDF measurements, noise and interpolation.

Integrated parallel reception, excitation, and shimming (iPRES).

PubMed

Han, Hui; Song, Allen W; Truong, Trong-Kha

2013-07-01

To develop a new concept for a hardware platform that enables integrated parallel reception, excitation, and shimming. This concept uses a single coil array rather than separate arrays for parallel excitation/reception and B0 shimming. It relies on a novel design that allows a radiofrequency current (for excitation/reception) and a direct current (for B0 shimming) to coexist independently in the same coil. Proof-of-concept B0 shimming experiments were performed with a two-coil array in a phantom, whereas B0 shimming simulations were performed with a 48-coil array in the human brain. Our experiments show that individually optimized direct currents applied in each coil can reduce the B0 root-mean-square error by 62-81% and minimize distortions in echo-planar images. The simulations show that dynamic shimming with the 48-coil integrated parallel reception, excitation, and shimming array can reduce the B0 root-mean-square error in the prefrontal and temporal regions by 66-79% as compared with static second-order spherical harmonic shimming and by 12-23% as compared with dynamic shimming with a 48-coil conventional shim array. Our results demonstrate the feasibility of the integrated parallel reception, excitation, and shimming concept to perform parallel excitation/reception and B0 shimming with a unified coil system as well as its promise for in vivo applications. Copyright © 2013 Wiley Periodicals, Inc.

Integrated Parallel Reception, Excitation, and Shimming (iPRES)

PubMed Central

Han, Hui; Song, Allen W.; Truong, Trong-Kha

2013-01-01

Purpose To develop a new concept for a hardware platform that enables integrated parallel reception, excitation, and shimming (iPRES). Theory This concept uses a single coil array rather than separate arrays for parallel excitation/reception and B0 shimming. It relies on a novel design that allows a radiofrequency current (for excitation/reception) and a direct current (for B0 shimming) to coexist independently in the same coil. Methods Proof-of-concept B0 shimming experiments were performed with a two-coil array in a phantom, whereas B0 shimming simulations were performed with a 48-coil array in the human brain. Results Our experiments show that individually optimized direct currents applied in each coil can reduce the B0 root-mean-square error by 62–81% and minimize distortions in echo-planar images. The simulations show that dynamic shimming with the 48-coil iPRES array can reduce the B0 root-mean-square error in the prefrontal and temporal regions by 66–79% as compared to static 2nd-order spherical harmonic shimming and by 12–23% as compared to dynamic shimming with a 48-coil conventional shim array. Conclusion Our results demonstrate the feasibility of the iPRES concept to perform parallel excitation/reception and B0 shimming with a unified coil system as well as its promise for in vivo applications. PMID:23629974

A fast algorithm to compute precise type-2 centroids for real-time control applications.

PubMed

Chakraborty, Sumantra; Konar, Amit; Ralescu, Anca; Pal, Nikhil R

2015-02-01

An interval type-2 fuzzy set (IT2 FS) is characterized by its upper and lower membership functions containing all possible embedded fuzzy sets, which together is referred to as the footprint of uncertainty (FOU). The FOU results in a span of uncertainty measured in the defuzzified space and is determined by the positional difference of the centroids of all the embedded fuzzy sets taken together. This paper provides a closed-form formula to evaluate the span of uncertainty of an IT2 FS. The closed-form formula offers a precise measurement of the degree of uncertainty in an IT2 FS with a runtime complexity less than that of the classical iterative Karnik-Mendel algorithm and other formulations employing the iterative Newton-Raphson algorithm. This paper also demonstrates a real-time control application using the proposed closed-form formula of centroids with reduced root mean square error and computational overhead than those of the existing methods. Computer simulations for this real-time control application indicate that parallel realization of the IT2 defuzzification outperforms its competitors with respect to maximum overshoot even at high sampling rates. Furthermore, in the presence of measurement noise in system (plant) states, the proposed IT2 FS based scheme outperforms its type-1 counterpart with respect to peak overshoot and root mean square error in plant response.

Estimating patient-specific soft-tissue properties in a TKA knee.

PubMed

Ewing, Joseph A; Kaufman, Michelle K; Hutter, Erin E; Granger, Jeffrey F; Beal, Matthew D; Piazza, Stephen J; Siston, Robert A

2016-03-01

Surgical technique is one factor that has been identified as critical to success of total knee arthroplasty. Researchers have shown that computer simulations can aid in determining how decisions in the operating room generally affect post-operative outcomes. However, to use simulations to make clinically relevant predictions about knee forces and motions for a specific total knee patient, patient-specific models are needed. This study introduces a methodology for estimating knee soft-tissue properties of an individual total knee patient. A custom surgical navigation system and stability device were used to measure the force-displacement relationship of the knee. Soft-tissue properties were estimated using a parameter optimization that matched simulated tibiofemoral kinematics with experimental tibiofemoral kinematics. Simulations using optimized ligament properties had an average root mean square error of 3.5° across all tests while simulations using generic ligament properties taken from literature had an average root mean square error of 8.4°. Specimens showed large variability among ligament properties regardless of similarities in prosthetic component alignment and measured knee laxity. These results demonstrate the importance of soft-tissue properties in determining knee stability, and suggest that to make clinically relevant predictions of post-operative knee motions and forces using computer simulations, patient-specific soft-tissue properties are needed. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Low rank alternating direction method of multipliers reconstruction for MR fingerprinting.

PubMed

Assländer, Jakob; Cloos, Martijn A; Knoll, Florian; Sodickson, Daniel K; Hennig, Jürgen; Lattanzi, Riccardo

2018-01-01

The proposed reconstruction framework addresses the reconstruction accuracy, noise propagation and computation time for magnetic resonance fingerprinting. Based on a singular value decomposition of the signal evolution, magnetic resonance fingerprinting is formulated as a low rank (LR) inverse problem in which one image is reconstructed for each singular value under consideration. This LR approximation of the signal evolution reduces the computational burden by reducing the number of Fourier transformations. Also, the LR approximation improves the conditioning of the problem, which is further improved by extending the LR inverse problem to an augmented Lagrangian that is solved by the alternating direction method of multipliers. The root mean square error and the noise propagation are analyzed in simulations. For verification, in vivo examples are provided. The proposed LR alternating direction method of multipliers approach shows a reduced root mean square error compared to the original fingerprinting reconstruction, to a LR approximation alone and to an alternating direction method of multipliers approach without a LR approximation. Incorporating sensitivity encoding allows for further artifact reduction. The proposed reconstruction provides robust convergence, reduced computational burden and improved image quality compared to other magnetic resonance fingerprinting reconstruction approaches evaluated in this study. Magn Reson Med 79:83-96, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Simple method for RF pulse measurement using gradient reversal.

PubMed

Landes, Vanessa L; Nayak, Krishna S

2018-05-01

To develop and evaluate a simple method for measuring the envelope of small-tip radiofrequency (RF) excitation waveforms in MRI, without extra hardware or synchronization. Gradient reversal approach to evaluate RF (GRATER) involves RF excitation with a constant gradient and reversal of that gradient during signal reception to acquire the time-reversed version of an RF envelope. An outer-volume suppression prepulse is used optionally to preselect a uniform volume. GRATER was evaluated in phantom and in vivo experiments. It was compared with the programmed waveform and the traditional pick-up coil method. In uniform phantom experiments, pick-up coil, GRATER, and outer-volume suppression + GRATER matched the programmed waveforms to less than 2.1%, less than 6.1%, and less than 2.4% normalized root mean square error, respectively, for real RF pulses with flip angle less than or equal to 30°, time-bandwidth product 2 to 8, and two to five excitation bands. For flip angles greater than 30°, GRATER measurement error increased as predicted by Bloch simulation. Fat-water phantom and in vivo experiments with outer-volume suppression + GRATER demonstrated less than 6.4% normalized root mean square error. The GRATER sequence measures small-tip RF envelopes without extra hardware or synchronization in just over two times the RF duration. The sequence may be useful in prescan calibration and for measurement and precompensation of RF amplifier nonlinearity. Magn Reson Med 79:2642-2651, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Tourism forecasting using modified empirical mode decomposition and group method of data handling

NASA Astrophysics Data System (ADS)

Yahya, N. A.; Samsudin, R.; Shabri, A.

2017-09-01

In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

Perception of competence in middle school physical education: instrument development and validation.

PubMed

Scrabis-Fletcher, Kristin; Silverman, Stephen

2010-03-01

Perception of Competence (POC) has been studied extensively in physical activity (PA) research with similar instruments adapted for physical education (PE) research. Such instruments do not account for the unique PE learning environment. Therefore, an instrument was developed and the scores validated to measure POC in middle school PE. A multiphase design was used consisting of an intensive theoretical review, elicitation study, prepilot study, pilot study, content validation study, and final validation study (N=1281). Data analysis included a multistep iterative process to identify the best model fit. A three-factor model for POC was tested and resulted in root mean square error of approximation = .09, root mean square residual = .07, goodness offit index = .90, and adjusted goodness offit index = .86 values in the acceptable range (Hu & Bentler, 1999). A two-factor model was also tested and resulted in a good fit (two-factor fit indexes values = .05, .03, .98, .97, respectively). The results of this study suggest that an instrument using a three- or two-factor model provides reliable and valid scores ofPOC measurement in middle school PE.

[Can the scattering of differences from the target refraction be avoided?].

PubMed

Janknecht, P

2008-10-01

We wanted to check how the stochastic error is affected by two lens formulae. The power of the intraocular lens was calculated using the SRK-II formula and the Haigis formula after eye length measurement with ultrasound and the IOL Master. Both lens formulae were partially derived and Gauss error analysis was used for examination of the propagated error. 61 patients with a mean age of 73.8 years were analysed. The postoperative refraction differed from the calculated refraction after ultrasound biometry using the SRK-II formula by 0.05 D (-1.56 to + 1.31, S. D.: 0.59 D; 92 % within +/- 1.0 D), after IOL Master biometry using the SRK-II formula by -0.15 D (-1.18 to + 1.25, S. D.: 0.52 D; 97 % within +/- 1.0 D), and after IOL Master biometry using the Haigis formula by -0.11 D (-1.14 to + 1.14, S. D.: 0.48 D; 95 % within +/- 1.0 D). The results did not differ from one another. The propagated error of the Haigis formula can be calculated according to DeltaP = square root (deltaL x (-4.206))(2) + (deltaVK x 0.9496)(2) + (DeltaDC x (-1.4950))(2). (DeltaL: error measuring axial length, DeltaVK error measuring anterior chamber depth, DeltaDC error measuring corneal power), the propagated error of the SRK-II formula according to DeltaP = square root (DeltaL x (-2.5))(2) + (DeltaDC x (-0.9))(2). The propagated error of the Haigis formula is always larger than the propagated error of the SRK-II formula. Scattering of the postoperative difference from the expected refraction cannot be avoided completely. It is possible to limit the systematic error by developing complicated formulae like the Haigis formula. However, increasing the number of parameters which need to be measured increases the dispersion of the calculated postoperative refraction. A compromise has to be found, and therefore the SRK-II formula is not outdated.

Tantalum films with well-controlled roughness grown by oblique incidence deposition

NASA Astrophysics Data System (ADS)

Rechendorff, K.; Hovgaard, M. B.; Chevallier, J.; Foss, M.; Besenbacher, F.

2005-08-01

We have investigated how tantalum films with well-controlled surface roughness can be grown by e-gun evaporation with oblique angle of incidence between the evaporation flux and the surface normal. Due to a more pronounced shadowing effect the root-mean-square roughness increases from about 2 to 33 nm as grazing incidence is approached. The exponent, characterizing the scaling of the root-mean-square roughness with length scale (α), varies from 0.75 to 0.93, and a clear correlation is found between the angle of incidence and root-mean-square roughness.

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).

Estimating EQ-5D values from the Oswestry Disability Index and numeric rating scales for back and leg pain.

PubMed

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

2014-04-15

Cross-sectional cohort. The purpose of this study is to determine whether the EuroQOL-5D (EQ-5D) can be derived from commonly available low back disease-specific health-related quality of life measures. The Oswestry Disability Index (ODI) and numeric rating scales (0-10) for back pain (BP) and leg pain (LP) are widely used disease-specific measures in patients with lumbar degenerative disorders. Increasingly, the EQ-5D is being used as a measure of utility due to ease of administration and scoring. The EQ-5D, ODI, BP, and LP were prospectively collected in 14,544 patients seen in clinic for lumbar degenerative disorders. Pearson correlation coefficients for paired observations from multiple time points between ODI, BP, LP, and EQ-5D were determined. Regression modeling was done to compute the EQ-5D score from the ODI, BP, and LP. The mean age was 53.3 ± 16.4 years and 41% were male. Correlations between the EQ-5D and the ODI, BP, and LP were statistically significant (P < 0.0001) with correlation coefficients of -0.77, -0.50, and -0.57, respectively. The regression equation: [0.97711 + (-0.00687 × ODI) + (-0.01488 × LP) + (-0.01008 × BP)] to predict EQ-5D, had an R2 of 0.61 and a root mean square error of 0.149. The model using ODI alone had an R2 of 0.57 and a root mean square error of 0.156. The model using the individual ODI items had an R2 of 0.64 and a root mean square error of 0.143. The correlation coefficient between the observed and estimated EQ-5D score was 0.78. There was no statistically significant difference between the actual EQ-5D (0.553 ± 0.238) and the estimated EQ-5D score (0.553 ± 0.186) using the ODI, BP, and LP regression model. However, rounding off the coefficients to less than 5 decimal places produced less accurate results. Unlike previous studies showing a robust relationship between low back-specific measures and the Short Form-6D, a similar relationship was not seen between the ODI, BP, LP, and the EQ-5D. Thus, the EQ-5D cannot be accurately estimated from the ODI, BP, and LP. 2.

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.

Optimal dental age estimation practice in United Arab Emirates' children.

PubMed

Altalie, Salem; Thevissen, Patrick; Fieuws, Steffen; Willems, Guy

2014-03-01

The aim of the study was to detect whether the Willems model, developed on a Belgian reference sample, can be used for age estimations in United Arab Emirates (UAE) children. Furthermore, it was verified that if added third molars development information in children provided more accurate age predictions. On 1900 panoramic radiographs, the development of left mandibular permanent teeth (PT) and third molars (TM) was registered according the Demirjian and the Kohler technique, respectively. The PT data were used to verify the Willems model and to develop a UAE model and to verify it. Multiple regression models with PT, TM, and PT + TM scores as independent and age as dependent factor were developed. Comparing the verified Willems- and the UAE model revealed differences in mean error of -0.01 year, mean absolute error of 0.01 year and root mean squared error of 0.90 year. Neglectable overall decrease in RMSE was detected combining PM and TM developmental information. © 2013 American Academy of Forensic Sciences.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Helical tomotherapy setup variations in canine nasal tumor patients immobilized with a bite block.

PubMed

Kubicek, Lyndsay N; Seo, Songwon; Chappell, Richard J; Jeraj, Robert; Forrest, Lisa J

2012-01-01

The purpose of our study was to compare setup variation in four degrees of freedom (vertical, longitudinal, lateral, and roll) between canine nasal tumor patients immobilized with a mattress and bite block, versus a mattress alone. Our secondary aim was to define a clinical target volume (CTV) to planning target volume (PTV) expansion margin based on our mean systematic error values associated with nasal tumor patients immobilized by a mattress and bite block. We evaluated six parameters for setup corrections: systematic error, random error, patient-patient variation in systematic errors, the magnitude of patient-specific random errors (root mean square [RMS]), distance error, and the variation of setup corrections from zero shift. The variations in all parameters were statistically smaller in the group immobilized by a mattress and bite block. The mean setup corrections in the mattress and bite block group ranged from 0.91 mm to 1.59 mm for the translational errors and 0.5°. Although most veterinary radiation facilities do not have access to Image-guided radiotherapy (IGRT), we identified a need for more rigid fixation, established the value of adding IGRT to veterinary radiation therapy, and define the CTV-PTV setup error margin for canine nasal tumor patients immobilized in a mattress and bite block. © 2012 Veterinary Radiology & Ultrasound.

Enhancing Students' Understanding of Square Roots

ERIC Educational Resources Information Center

Wiesman, Jeff L.

2015-01-01

Students enrolled in a middle school prealgebra or algebra course often struggle to conceptualize and understand the meaning of radical notation when it is introduced. For example, although it is important for students to approximate the decimal value of a number such as [square root of] 30 and estimate the value of a square root in the form of…

Demand Forecasting: An Evaluation of DODs Accuracy Metric and Navys Procedures

DTIC Science & Technology

2016-06-01

inventory management improvement plan, mean of absolute scaled error, lead time adjusted squared error, forecast accuracy, benchmarking, naïve method...Manager JASA Journal of the American Statistical Association LASE Lead-time Adjusted Squared Error LCI Life Cycle Indicator MA Moving Average MAE...Mean Squared Error xvi NAVSUP Naval Supply Systems Command NDAA National Defense Authorization Act NIIN National Individual Identification Number

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.

Evaluation of Non-convective Wind Forecasting Methods in the 15th Operational Weather Squadron Area of Responsibility

DTIC Science & Technology

2012-03-01

Planetary Boundary Layer POD—Probability of Detection RCA—Rossby Centre Regional Atmospheric Model RMSE—Root Mean Square Error RUC—Rapid Update Cycle SWW...SIGNIFICANCE ....................................1  B.  NON-CONVECTIVE WINDS DEFINITIONS AND THRESHOLDS ......4  C .  METEOROLOGY ASSOCIATED WITH NON-CONVECTIVE...19  B.  RESULTS FROM PREVIOUS STUDIES ON THE WGE METHOD ....21  C .  RAPID UPDATE CYCLE (RUC) EMPIRICAL METHOD .....................25  III.  DATA AND

A Gompertzian model with random effects to cervical cancer growth

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

Mazlan, Mazma Syahidatul Ayuni; Rosli, Norhayati

2015-05-15

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

Effects of Microclimate Cooling on Physiology and Performance While Flying the UH-60 Helicopter Simulator in NBC Conditions in a Controlled Heat Environment

DTIC Science & Technology

1992-08-01

including instrumenting and dressing the subjects, monitoring the physiological parameters in the simulator, and collecting and processing data. They...also was decided to extend the recruiting process to include all helicopter aviators, even if not UH-60 qualified. There is little in the flight profile...parameter channels, and the data were processed to produce a single root mean square (RMS) error value for each channel appropriate to each of the 9

Analysis of uncertainties and convergence of the statistical quantities in turbulent wall-bounded flows by means of a physically based criterion

NASA Astrophysics Data System (ADS)

Andrade, João Rodrigo; Martins, Ramon Silva; Thompson, Roney Leon; Mompean, Gilmar; da Silveira Neto, Aristeu

2018-04-01

The present paper provides an analysis of the statistical uncertainties associated with direct numerical simulation (DNS) results and experimental data for turbulent channel and pipe flows, showing a new physically based quantification of these errors, to improve the determination of the statistical deviations between DNSs and experiments. The analysis is carried out using a recently proposed criterion by Thompson et al. ["A methodology to evaluate statistical errors in DNS data of plane channel flows," Comput. Fluids 130, 1-7 (2016)] for fully turbulent plane channel flows, where the mean velocity error is estimated by considering the Reynolds stress tensor, and using the balance of the mean force equation. It also presents how the residual error evolves in time for a DNS of a plane channel flow, and the influence of the Reynolds number on its convergence rate. The root mean square of the residual error is shown in order to capture a single quantitative value of the error associated with the dimensionless averaging time. The evolution in time of the error norm is compared with the final error provided by DNS data of similar Reynolds numbers available in the literature. A direct consequence of this approach is that it was possible to compare different numerical results and experimental data, providing an improved understanding of the convergence of the statistical quantities in turbulent wall-bounded flows.

Application of Near Infrared Spectroscopy Coupled with Fluidized Bed Enrichment and Chemometrics to Detect Low Concentration of β-Naphthalenesulfonic Acid.

PubMed

Li, Wei; Zhang, Xuan; Zheng, Kaiyi; Du, Yiping; Cap, Peng; Sui, Tao; Geng, Jinpei

2015-01-01

A fluidized bed enrichment technique was developed to improve sensitivity of near infrared (NIR) spectroscopy with features of rapidness and large volume solution. D301 resin was used as an adsorption material to preconcentrate β-naphthalenesulfonic acid in solutions in a concentration range of 2.0-100.0 μg/mL, and NIR spectra were measured directly relative to the β-naphthalenesulfonic acid adsorbed on the material. An improved partial least squares (PLS) model was attained with the aid of multiplicative scatter correction pretreatment and stability competitive adaptive reweighted sampling wavenumber selection method. The root mean square error of cross validation was 1.87 μg/mL at PLS factor of 7. An independent test set was used to assess the model, with the relative error (RE) in an acceptable range of 0.46 to 10.03% and mean RE of 3.72%. This study confirmed the viability of the proposed method for the measurement of a low content of β-naphthalenesulfonic acid in water.

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

NASA Astrophysics Data System (ADS)

Saatkamp, Cassiano Junior; de Almeida, Maurício Liberal; Bispo, Jeyse Aliana Martins; Pinheiro, Antonio Luiz Barbosa; Fernandes, Adriana Barrinha; Silveira, Landulfo, Jr.

2016-03-01

Due to their importance in the regulation of metabolites, the kidneys need continuous monitoring to check for correct functioning, mainly by urea and creatinine urinalysis. This study aimed to develop a model to estimate the concentrations of urea and creatinine in urine by means of Raman spectroscopy (RS) that could be used to diagnose kidney disease. Midstream urine samples were obtained from 54 volunteers with no kidney complaints. Samples were subjected to a standard colorimetric assay of urea and creatinine and submitted to spectroscopic analysis by means of a dispersive Raman spectrometer (830 nm, 350 mW, 30 s). The Raman spectra of urine showed peaks related mainly to urea and creatinine. Partial least squares models were developed using selected Raman bands related to urea and creatinine and the biochemical concentrations in urine measured by the colorimetric method, resulting in r=0.90 and 0.91 for urea and creatinine, respectively, with root mean square error of cross-validation (RMSEcv) of 312 and 25.2 mg/dL, respectively. RS may become a technique for rapid urinalysis, with concentration errors suitable for population screening aimed at the prevention of renal diseases.

Region of influence regression for estimating the 50-year flood at ungaged sites

USGS Publications Warehouse

Tasker, Gary D.; Hodge, S.A.; Barks, C.S.

1996-01-01

Five methods of developing regional regression models to estimate flood characteristics at ungaged sites in Arkansas are examined. The methods differ in the manner in which the State is divided into subrogions. Each successive method (A to E) is computationally more complex than the previous method. Method A makes no subdivision. Methods B and C define two and four geographic subrogions, respectively. Method D uses cluster/discriminant analysis to define subrogions on the basis of similarities in watershed characteristics. Method E, the new region of influence method, defines a unique subregion for each ungaged site. Split-sample results indicate that, in terms of root-mean-square error, method E (38 percent error) is best. Methods C and D (42 and 41 percent error) were in a virtual tie for second, and methods B (44 percent error) and A (49 percent error) were fourth and fifth best.

Resolving Mixed Algal Species in Hyperspectral Images

PubMed Central

Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V.

2014-01-01

We investigated a lab-based hyperspectral imaging system's response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system's performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert's law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. PMID:24451451

Sensitivity of thermal inertia calculations to variations in environmental factors. [in mapping of Earth's surface by remote sensing

NASA Technical Reports Server (NTRS)

Kahle, A. B.; Alley, R. E.; Schieldge, J. P.

1984-01-01

The sensitivity of thermal inertia (TI) calculations to errors in the measurement or parameterization of a number of environmental factors is considered here. The factors include effects of radiative transfer in the atmosphere, surface albedo and emissivity, variations in surface turbulent heat flux density, cloud cover, vegetative cover, and topography. The error analysis is based upon data from the Heat Capacity Mapping Mission (HCMM) satellite for July 1978 at three separate test sites in the deserts of the western United States. Results show that typical errors in atmospheric radiative transfer, cloud cover, and vegetative cover can individually cause root-mean-square (RMS) errors of about 10 percent (with atmospheric effects sometimes as large as 30-40 percent) in HCMM-derived thermal inertia images of 20,000-200,000 pixels.

Skin Cooling and Force Replication at the Ankle in Healthy Individuals: A Crossover Randomized Controlled Trial

PubMed Central

Haupenthal, Daniela Pacheco dos Santos; de Noronha, Marcos; Haupenthal, Alessandro; Ruschel, Caroline; Nunes, Guilherme S.

2015-01-01

Context Proprioception of the ankle is determined by the ability to perceive the sense of position of the ankle structures, as well as the speed and direction of movement. Few researchers have investigated proprioception by force-replication ability and particularly after skin cooling. Objective To analyze the ability of the ankle-dorsiflexor muscles to replicate isometric force after a period of skin cooling. Design Randomized controlled clinical trial. Setting Laboratory. Patients or Other Participants Twenty healthy individuals (10 men, 10 women; age = 26.8 ± 5.2 years, height = 171 ± 7 cm, mass = 66.8 ± 10.5 kg). Intervention(s) Skin cooling was carried out using 2 ice applications: (1) after maximal voluntary isometric contraction (MVIC) performance and before data collection for the first target force, maintained for 20 minutes; and (2) before data collection for the second target force, maintained for 10 minutes. We measured skin temperature before and after ice applications to ensure skin cooling. Main Outcome Measure(s) A load cell was placed under an inclined board for data collection, and 10 attempts of force replication were carried out for 2 values of MVIC (20%, 50%) in each condition (ice, no ice). We assessed force sense with absolute and root mean square errors (the difference between the force developed by the dorsiflexors and the target force measured with the raw data and after root mean square analysis, respectively) and variable error (the variance around the mean absolute error score). A repeated-measures multivariate analysis of variance was used for statistical analysis. Results The absolute error was greater for the ice than for the no-ice condition (F1,19 = 9.05, P = .007) and for the target force at 50% of MVIC than at 20% of MVIC (F1,19 = 26.01, P < .001). Conclusions The error was greater in the ice condition and at 50% of MVIC. Skin cooling reduced the proprioceptive ability of the ankle-dorsiflexor muscles to replicate isometric force. PMID:25761136

Quantitative analysis of essential oils in perfume using multivariate curve resolution combined with comprehensive two-dimensional gas chromatography.

PubMed

de Godoy, Luiz Antonio Fonseca; Hantao, Leandro Wang; Pedroso, Marcio Pozzobon; Poppi, Ronei Jesus; Augusto, Fabio

2011-08-05

The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data. Copyright © 2011 Elsevier B.V. All rights reserved.

Rapid investigation of α-glucosidase inhibitory activity of Phaleria macrocarpa extracts using FTIR-ATR based fingerprinting.

PubMed

Easmin, Sabina; Sarker, Md Zaidul Islam; Ghafoor, Kashif; Ferdosh, Sahena; Jaffri, Juliana; Ali, Md Eaqub; Mirhosseini, Hamed; Al-Juhaimi, Fahad Y; Perumal, Vikneswari; Khatib, Alfi

2017-04-01

Phaleria macrocarpa, known as "Mahkota Dewa", is a widely used medicinal plant in Malaysia. This study focused on the characterization of α-glucosidase inhibitory activity of P. macrocarpa extracts using Fourier transform infrared spectroscopy (FTIR)-based metabolomics. P. macrocarpa and its extracts contain thousands of compounds having synergistic effect. Generally, their variability exists, and there are many active components in meager amounts. Thus, the conventional measurement methods of a single component for the quality control are time consuming, laborious, expensive, and unreliable. It is of great interest to develop a rapid prediction method for herbal quality control to investigate the α-glucosidase inhibitory activity of P. macrocarpa by multicomponent analyses. In this study, a rapid and simple analytical method was developed using FTIR spectroscopy-based fingerprinting. A total of 36 extracts of different ethanol concentrations were prepared and tested on inhibitory potential and fingerprinted using FTIR spectroscopy, coupled with chemometrics of orthogonal partial least square (OPLS) at the 4000-400 cm -1 frequency region and resolution of 4 cm -1 . The OPLS model generated the highest regression coefficient with R 2 Y = 0.98 and Q 2 Y = 0.70, lowest root mean square error estimation = 17.17, and root mean square error of cross validation = 57.29. A five-component (1+4+0) predictive model was build up to correlate FTIR spectra with activity, and the responsible functional groups, such as -CH, -NH, -COOH, and -OH, were identified for the bioactivity. A successful multivariate model was constructed using FTIR-attenuated total reflection as a simple and rapid technique to predict the inhibitory activity. Copyright © 2016. Published by Elsevier B.V.

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

PubMed Central

Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei; Tashpolat, Nigara

2017-01-01

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (Rc2), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (Rp2), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance (Rc2 = 0.907, RMSEC = 0.425%, Rp2 = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance (Rc2 = 0.888, RMSEC = 0.446%, Rp2 = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. PMID:28934274

Application of FTIR-ATR spectroscopy coupled with multivariate analysis for rapid estimation of butter adulteration.

PubMed

Fadzlillah, Nurrulhidayah Ahmad; Rohman, Abdul; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2013-01-01

In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.

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

PubMed

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

2015-03-23

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

Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations.

PubMed

da Silva, Fabiana E B; Flores, Érico M M; Parisotto, Graciele; Müller, Edson I; Ferrão, Marco F

2016-03-01

An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration.

PubMed

Dabkiewicz, Vanessa Emídio; de Mello Pereira Abrantes, Shirley; Cassella, Ricardo Jorgensen

2018-08-05

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm -1 region. Copyright © 2018 Elsevier B.V. All rights reserved.

Spectrophotometric determination of ternary mixtures of thiamin, riboflavin and pyridoxal in pharmaceutical and human plasma by least-squares support vector machines.

PubMed

Niazi, Ali; Zolgharnein, Javad; Afiuni-Zadeh, Somaie

2007-11-01

Ternary mixtures of thiamin, riboflavin and pyridoxal have been simultaneously determined in synthetic and real samples by applications of spectrophotometric and least-squares support vector machines. The calibration graphs were linear in the ranges of 1.0 - 20.0, 1.0 - 10.0 and 1.0 - 20.0 microg ml(-1) with detection limits of 0.6, 0.5 and 0.7 microg ml(-1) for thiamin, riboflavin and pyridoxal, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. The concentrations were varied between calibration graph concentrations of vitamins. The simultaneous determination of these vitamin mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares (PLS) modeling and least-squares support vector machines were used for the multivariate calibration of the spectrophotometric data. An excellent model was built using LS-SVM, with low prediction errors and superior performance in relation to PLS. The root mean square errors of prediction (RMSEP) for thiamin, riboflavin and pyridoxal with PLS and LS-SVM were 0.6926, 0.3755, 0.4322 and 0.0421, 0.0318, 0.0457, respectively. The proposed method was satisfactorily applied to the rapid simultaneous determination of thiamin, riboflavin and pyridoxal in commercial pharmaceutical preparations and human plasma samples.

Structural Variation of Alpha-synuclein with Temperature by a Coarse-grained Approach with Knowledge-based Interactions (Postprint)

DTIC Science & Technology

2015-07-01

the radius of gyration in detail as a function FIG. 5. Variation of the root mean square (RMS) displacement of the center of mass of the protein with...depends on the temperature. The global motion can be examined by analyzing the variation of the root mean square displacement (RMS) of the center of...and global physical quantities during the course of simula- tion, including the energy of each residue, its mobility, mean square displacement of the

Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method

NASA Astrophysics Data System (ADS)

Tang, Qingxin; Bo, Yanchen; Zhu, Yuxin

2016-04-01

Merging multisensor aerosol optical depth (AOD) products is an effective way to produce more spatiotemporally complete and accurate AOD products. A spatiotemporal statistical data fusion framework based on a Bayesian maximum entropy (BME) method was developed for merging satellite AOD products in East Asia. The advantages of the presented merging framework are that it not only utilizes the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of the AOD products being merged. The satellite AOD products used for merging are the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Level-2 AOD products (MOD04_L2) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Deep Blue Level 2 AOD products (SWDB_L2). The results show that the average completeness of the merged AOD data is 95.2%,which is significantly superior to the completeness of MOD04_L2 (22.9%) and SWDB_L2 (20.2%). By comparing the merged AOD to the Aerosol Robotic Network AOD records, the results show that the correlation coefficient (0.75), root-mean-square error (0.29), and mean bias (0.068) of the merged AOD are close to those (the correlation coefficient (0.82), root-mean-square error (0.19), and mean bias (0.059)) of the MODIS AOD. In the regions where both MODIS and SeaWiFS have valid observations, the accuracy of the merged AOD is higher than those of MODIS and SeaWiFS AODs. Even in regions where both MODIS and SeaWiFS AODs are missing, the accuracy of the merged AOD is also close to the accuracy of the regions where both MODIS and SeaWiFS have valid observations.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

PubMed

Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

2016-05-15

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

High Predictive Skill of Global Surface Temperature a Year Ahead

NASA Astrophysics Data System (ADS)

Folland, C. K.; Colman, A.; Kennedy, J. J.; Knight, J.; Parker, D. E.; Stott, P.; Smith, D. M.; Boucher, O.

2011-12-01

We discuss the high skill of real-time forecasts of global surface temperature a year ahead issued by the UK Met Office, and their scientific background. Although this is a forecasting and not a formal attribution study, we show that the main instrumental global annual surface temperature data sets since 1891 are structured consistently with a set of five physical forcing factors except during and just after the second World War. Reconstructions use a multiple application of cross validated linear regression to minimise artificial skill allowing time-varying uncertainties in the contribution of each forcing factor to global temperature to be assessed. Mean cross validated reconstructions for the data sets have total correlations in the range 0.93-0.95,interannual correlations in the range 0.72-0.75 and root mean squared errors near 0.06oC, consistent with observational uncertainties.Three transient runs of the HadCM3 coupled model for 1888-2002 demonstrate quite similar reconstruction skill from similar forcing factors defined appropriately for the model, showing that skilful use of our technique is not confined to observations. The observed reconstructions show that the Atlantic Multidecadal Oscillation (AMO) likely contributed to the re-commencement of global warming between 1976 and 2010 and to global cooling observed immediately beforehand in 1965-1976. The slowing of global warming in the last decade is likely to be largely due to a phase-delayed response to the downturn in the solar cycle since 2001-2, with no net ENSO contribution. The much reduced trend in 2001-10 is similar in size to other weak decadal temperature trends observed since global warming resumed in the 1970s. The causes of variations in decadal trends can be mostly explained by variations in the strength of the forcing factors. Eleven real-time forecasts of global mean surface temperature for the year ahead for 2000-2010, based on broadly similar methods, provide an independent test of the ideas of this study. They had the high correlation and root mean square error skill levels compared to observations of 0.74 and 0.07oC respectively. Pseudo-forecasts for the same period reconstructed from somewhat improved forcing data used for this study had the slightly better correlation of 0.80 and root mean squared error of 0.05oC. Finally we compare the statistical forecasts with dynamical hindcasts and forecasts of global surface temperature a year ahead made by the Met Office DePreSys coupled model. The statistical and dynamical forecasts of global surface temperature for 2011 will be compared with preliminary verification data.

Equivalent Linearization Analysis of Geometrically Nonlinear Random Vibrations Using Commercial Finite Element Codes

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Muravyov, Alexander A.

2002-01-01

Two new equivalent linearization implementations for geometrically nonlinear random vibrations are presented. Both implementations are based upon a novel approach for evaluating the nonlinear stiffness within commercial finite element codes and are suitable for use with any finite element code having geometrically nonlinear static analysis capabilities. The formulation includes a traditional force-error minimization approach and a relatively new version of a potential energy-error minimization approach, which has been generalized for multiple degree-of-freedom systems. Results for a simply supported plate under random acoustic excitation are presented and comparisons of the displacement root-mean-square values and power spectral densities are made with results from a nonlinear time domain numerical simulation.

Curve fitting methods for solar radiation data modeling

NASA Astrophysics Data System (ADS)

Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder

2014-10-01

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

An Update on the Role of Systems Modeling in the Design and Verification of the James Webb Space Telescope

NASA Technical Reports Server (NTRS)

Muheim, Danniella; Menzel, Michael; Mosier, Gary; Irish, Sandra; Maghami, Peiman; Mehalick, Kimberly; Parrish, Keith

2010-01-01

The James Web Space Telescope (JWST) is a large, infrared-optimized space telescope scheduled for launch in 2014. System-level verification of critical performance requirements will rely on integrated observatory models that predict the wavefront error accurately enough to verify that allocated top-level wavefront error of 150 nm root-mean-squared (rms) through to the wave-front sensor focal plane is met. The assembled models themselves are complex and require the insight of technical experts to assess their ability to meet their objectives. This paper describes the systems engineering and modeling approach used on the JWST through the detailed design phase.

Curve fitting methods for solar radiation data modeling

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

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

2014-10-24

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

Global optimization method based on ray tracing to achieve optimum figure error compensation

NASA Astrophysics Data System (ADS)

Liu, Xiaolin; Guo, Xuejia; Tang, Tianjin

2017-02-01

Figure error would degrade the performance of optical system. When predicting the performance and performing system assembly, compensation by clocking of optical components around the optical axis is a conventional but user-dependent method. Commercial optical software cannot optimize this clocking. Meanwhile existing automatic figure-error balancing methods can introduce approximate calculation error and the build process of optimization model is complex and time-consuming. To overcome these limitations, an accurate and automatic global optimization method of figure error balancing is proposed. This method is based on precise ray tracing to calculate the wavefront error, not approximate calculation, under a given elements' rotation angles combination. The composite wavefront error root-mean-square (RMS) acts as the cost function. Simulated annealing algorithm is used to seek the optimal combination of rotation angles of each optical element. This method can be applied to all rotational symmetric optics. Optimization results show that this method is 49% better than previous approximate analytical method.

Benchmark fragment-based 1H, 13C, 15N and 17O chemical shift predictions in molecular crystals†

PubMed Central

Hartman, Joshua D.; Kudla, Ryan A.; Day, Graeme M.; Mueller, Leonard J.; Beran, Gregory J. O.

2016-01-01

The performance of fragment-based ab initio 1H, 13C, 15N and 17O chemical shift predictions is assessed against experimental NMR chemical shift data in four benchmark sets of molecular crystals. Employing a variety of commonly used density functionals (PBE0, B3LYP, TPSSh, OPBE, PBE, TPSS), we explore the relative performance of cluster, two-body fragment, and combined cluster/fragment models. The hybrid density functionals (PBE0, B3LYP and TPSSh) generally out-perform their generalized gradient approximation (GGA)-based counterparts. 1H, 13C, 15N, and 17O isotropic chemical shifts can be predicted with root-mean-square errors of 0.3, 1.5, 4.2, and 9.8 ppm, respectively, using a computationally inexpensive electrostatically embedded two-body PBE0 fragment model. Oxygen chemical shieldings prove particularly sensitive to local many-body effects, and using a combined cluster/fragment model instead of the simple two-body fragment model decreases the root-mean-square errors to 7.6 ppm. These fragment-based model errors compare favorably with GIPAW PBE ones of 0.4, 2.2, 5.4, and 7.2 ppm for the same 1H, 13C, 15N, and 17O test sets. Using these benchmark calculations, a set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided and their robustness assessed using statistical cross-validation. We demonstrate the utility of these approaches and the reported scaling parameters on applications to 9-tertbutyl anthracene, several histidine co-crystals, benzoic acid and the C-nitrosoarene SnCl2(CH3)2(NODMA)2. PMID:27431490

Benchmark fragment-based (1)H, (13)C, (15)N and (17)O chemical shift predictions in molecular crystals.

PubMed

Hartman, Joshua D; Kudla, Ryan A; Day, Graeme M; Mueller, Leonard J; Beran, Gregory J O

2016-08-21

The performance of fragment-based ab initio(1)H, (13)C, (15)N and (17)O chemical shift predictions is assessed against experimental NMR chemical shift data in four benchmark sets of molecular crystals. Employing a variety of commonly used density functionals (PBE0, B3LYP, TPSSh, OPBE, PBE, TPSS), we explore the relative performance of cluster, two-body fragment, and combined cluster/fragment models. The hybrid density functionals (PBE0, B3LYP and TPSSh) generally out-perform their generalized gradient approximation (GGA)-based counterparts. (1)H, (13)C, (15)N, and (17)O isotropic chemical shifts can be predicted with root-mean-square errors of 0.3, 1.5, 4.2, and 9.8 ppm, respectively, using a computationally inexpensive electrostatically embedded two-body PBE0 fragment model. Oxygen chemical shieldings prove particularly sensitive to local many-body effects, and using a combined cluster/fragment model instead of the simple two-body fragment model decreases the root-mean-square errors to 7.6 ppm. These fragment-based model errors compare favorably with GIPAW PBE ones of 0.4, 2.2, 5.4, and 7.2 ppm for the same (1)H, (13)C, (15)N, and (17)O test sets. Using these benchmark calculations, a set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided and their robustness assessed using statistical cross-validation. We demonstrate the utility of these approaches and the reported scaling parameters on applications to 9-tert-butyl anthracene, several histidine co-crystals, benzoic acid and the C-nitrosoarene SnCl2(CH3)2(NODMA)2.

Atomic charge transfer-counter polarization effects determine infrared CH intensities of hydrocarbons: a quantum theory of atoms in molecules model.

PubMed

Silva, Arnaldo F; Richter, Wagner E; Meneses, Helen G C; Bruns, Roy E

2014-11-14

Atomic charge transfer-counter polarization effects determine most of the infrared fundamental CH intensities of simple hydrocarbons, methane, ethylene, ethane, propyne, cyclopropane and allene. The quantum theory of atoms in molecules/charge-charge flux-dipole flux model predicted the values of 30 CH intensities ranging from 0 to 123 km mol(-1) with a root mean square (rms) error of only 4.2 km mol(-1) without including a specific equilibrium atomic charge term. Sums of the contributions from terms involving charge flux and/or dipole flux averaged 20.3 km mol(-1), about ten times larger than the average charge contribution of 2.0 km mol(-1). The only notable exceptions are the CH stretching and bending intensities of acetylene and two of the propyne vibrations for hydrogens bound to sp hybridized carbon atoms. Calculations were carried out at four quantum levels, MP2/6-311++G(3d,3p), MP2/cc-pVTZ, QCISD/6-311++G(3d,3p) and QCISD/cc-pVTZ. The results calculated at the QCISD level are the most accurate among the four with root mean square errors of 4.7 and 5.0 km mol(-1) for the 6-311++G(3d,3p) and cc-pVTZ basis sets. These values are close to the estimated aggregate experimental error of the hydrocarbon intensities, 4.0 km mol(-1). The atomic charge transfer-counter polarization effect is much larger than the charge effect for the results of all four quantum levels. Charge transfer-counter polarization effects are expected to also be important in vibrations of more polar molecules for which equilibrium charge contributions can be large.

Modeling RP-1 fuel advanced distillation data using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and partial least squares analysis.

PubMed

Kehimkar, Benjamin; Parsons, Brendon A; Hoggard, Jamin C; Billingsley, Matthew C; Bruno, Thomas J; Synovec, Robert E

2015-01-01

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as elucidating the relationships between fuel compositions and their properties. Herein, we study multidimensional chromatographic data obtained by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC-TOFMS) to analyze RP-1 fuels. For GC × GC separations, RTX-Wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc.), providing a significant level of chemical compositional information. The GC × GC-TOFMS data were analyzed using partial least squares regression (PLS) chemometric analysis to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the ADC data. The PLS modeling correlates compositional information found in the GC × GC-TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The root-mean-square error of calibration (RMSEC) ranged from 0.1 to 0.5 °C, and was typically below ∼0.2 °C, for the PLS calibration of the ADC modeling with GC × GC-TOFMS data, indicating a good fit of the model to the calibration data. Likewise, the predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) ranging from 1.4 to 2.6 °C, and was typically below ∼2.0 °C, at each % distilled measurement point during the ADC analysis.

Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: sonomyography assessment for arm muscles.

PubMed

Shi, Jun; Zheng, Yong-Ping; Huang, Qing-Hua; Chen, Xin

2008-03-01

The aim of this study is to demonstrate the feasibility of using the continuous signals about the thickness and pennation angle changes of muscles detected in real-time from ultrasound images, named as sonomyography (SMG), to characterize muscles under isometric contraction, along with synchronized surface electromyography (EMG) and generated torque signals. The right biceps brachii muscles of seven normal young adult subjects were tested. We observed that exponential functions could well represent the relationships between the normalized EMG root-mean-square (RMS) and the torque, the RMS and the muscle deformation SMG, and the RMS and the pennation angle SMG for the data of the contraction phase, with exponent coefficients of 0.0341 +/- 0.0148 (Mean SD), 0.0619 +/- 0.0273, and 0.0266 +/- 0.0076, respectively. In addition, the preliminary results also demonstrated linear relationships between the normalized torque and the muscle deformation as well as the pennation angle with the ratios of 9 .79 +/- 3.01 and 2.02 +/- 0.53, respectively. The overall mean R2 for the regressions was approximately 0.9 and the overall mean relative root mean square error (RRMSE) smaller than 15%. The potential values of SMG together with EMG to provide a more comprehensive assessment for the muscle functions should be further investigated with more subjects and more muscle groups.

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-07-23

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal.

Assessing and calibrating the ATR-FTIR approach as a carbonate rock characterization tool

NASA Astrophysics Data System (ADS)

Henry, Delano G.; Watson, Jonathan S.; John, Cédric M.

2017-01-01

ATR-FTIR (attenuated total reflectance Fourier transform infrared) spectroscopy can be used as a rapid and economical tool for qualitative identification of carbonates, calcium sulphates, oxides and silicates, as well as quantitatively estimating the concentration of minerals. Over 200 powdered samples with known concentrations of two, three, four and five phase mixtures were made, then a suite of calibration curves were derived that can be used to quantify the minerals. The calibration curves in this study have an R2 that range from 0.93-0.99, a RMSE (root mean square error) of 1-5 wt.% and a maximum error of 3-10 wt.%. The calibration curves were used on 35 geological samples that have previously been studied using XRD (X-ray diffraction). The identification of the minerals using ATR-FTIR is comparable with XRD and the quantitative results have a RMSD (root mean square deviation) of 14% and 12% for calcite and dolomite respectively when compared to XRD results. ATR-FTIR is a rapid technique (identification and quantification takes < 5 min) that involves virtually no cost if the machine is available. It is a common tool in most analytical laboratories, but it also has the potential to be deployed on a rig for real-time data acquisition of the mineralogy of cores and rock chips at the surface as there is no need for special sample preparation, rapid data collection and easy analysis.

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

A theoretical framework to predict the most likely ion path in particle imaging.

PubMed

Collins-Fekete, Charles-Antoine; Volz, Lennart; Portillo, Stephen K N; Beaulieu, Luc; Seco, Joao

2017-03-07

In this work, a generic rigorous Bayesian formalism is introduced to predict the most likely path of any ion crossing a medium between two detection points. The path is predicted based on a combination of the particle scattering in the material and measurements of its initial and final position, direction and energy. The path estimate's precision is compared to the Monte Carlo simulated path. Every ion from hydrogen to carbon is simulated in two scenarios, (1) where the range is fixed and (2) where the initial velocity is fixed. In the scenario where the range is kept constant, the maximal root-mean-square error between the estimated path and the Monte Carlo path drops significantly between the proton path estimate (0.50 mm) and the helium path estimate (0.18 mm), but less so up to the carbon path estimate (0.09 mm). However, this scenario is identified as the configuration that maximizes the dose while minimizing the path resolution. In the scenario where the initial velocity is fixed, the maximal root-mean-square error between the estimated path and the Monte Carlo path drops significantly between the proton path estimate (0.29 mm) and the helium path estimate (0.09 mm) but increases for heavier ions up to carbon (0.12 mm). As a result, helium is found to be the particle with the most accurate path estimate for the lowest dose, potentially leading to tomographic images of higher spatial resolution.

Wetland Assessment Using Unmanned Aerial Vehicle (uav) Photogrammetry

NASA Astrophysics Data System (ADS)

Boon, M. A.; Greenfield, R.; Tesfamichael, S.

2016-06-01

The use of Unmanned Arial Vehicle (UAV) photogrammetry is a valuable tool to enhance our understanding of wetlands. Accurate planning derived from this technological advancement allows for more effective management and conservation of wetland areas. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool to enhance the assessment of wetland ecosystems. The UAV images were collected during a single flight within 2½ hours over a 100 ha area at the Kameelzynkraal farm, Gauteng Province, South Africa. An AKS Y-6 MKII multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. Twenty ground control points (GCPs) were surveyed using a Trimble GPS to achieve geometrical precision and georeferencing accuracy. Structure-from-Motion (SfM) computer vision techniques were used to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The geometric accuracy of the data based on the 20 GCP's were 0.018 m for the overall, 0.0025 m for the vertical root mean squared error (RMSE) and an over all root mean square reprojection error of 0.18 pixel. The UAV products were then edited and subsequently analysed, interpreted and key attributes extracted using a selection of tools/ software applications to enhance the wetland assessment. The results exceeded our expectations and provided a valuable and accurate enhancement to the wetland delineation, classification and health assessment which even with detailed field studies would have been difficult to achieve.

Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time

NASA Astrophysics Data System (ADS)

Martin, Stephanie L.-O.; Carek, Andrew M.; Kim, Chang-Sei; Ashouri, Hazar; Inan, Omer T.; Hahn, Jin-Oh; Mukkamala, Ramakrishna

2016-12-01

Pulse transit time (PTT) is being widely pursued for cuff-less blood pressure (BP) monitoring. Most efforts have employed the time delay between ECG and finger photoplethysmography (PPG) waveforms as a convenient surrogate of PTT. However, these conventional pulse arrival time (PAT) measurements include the pre-ejection period (PEP) and the time delay through small, muscular arteries and may thus be an unreliable marker of BP. We assessed a bathroom weighing scale-like system for convenient measurement of ballistocardiography and foot PPG waveforms - and thus PTT through larger, more elastic arteries - in terms of its ability to improve tracking of BP in individual subjects. We measured “scale PTT”, conventional PAT, and cuff BP in humans during interventions that increased BP but changed PEP and smooth muscle contraction differently. Scale PTT tracked the diastolic BP changes well, with correlation coefficient of -0.80 ± 0.02 (mean ± SE) and root-mean-squared-error of 7.6 ± 0.5 mmHg after a best-case calibration. Conventional PAT was significantly inferior in tracking these changes, with correlation coefficient of -0.60 ± 0.04 and root-mean-squared-error of 14.6 ± 1.5 mmHg (p < 0.05). Scale PTT also tracked the systolic BP changes better than conventional PAT but not to an acceptable level. With further development, scale PTT may permit reliable, convenient measurement of BP.

Four-dimensional data coupled to alternating weighted residue constraint quadrilinear decomposition model applied to environmental analysis: Determination of polycyclic aromatic hydrocarbons.

PubMed

Liu, Tingting; Zhang, Ling; Wang, Shutao; Cui, Yaoyao; Wang, Yutian; Liu, Lingfei; Yang, Zhe

2018-03-15

Qualitative and quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) was carried out by three-dimensional fluorescence spectroscopy combining with Alternating Weighted Residue Constraint Quadrilinear Decomposition (AWRCQLD). The experimental subjects were acenaphthene (ANA) and naphthalene (NAP). Firstly, in order to solve the redundant information of the three-dimensional fluorescence spectral data, the wavelet transform was used to compress data in preprocessing. Then, the four-dimensional data was constructed by using the excitation-emission fluorescence spectra of different concentration PAHs. The sample data was obtained from three solvents that are methanol, ethanol and Ultra-pure water. The four-dimensional spectral data was analyzed by AWRCQLD, then the recovery rate of PAHs was obtained from the three solvents and compared respectively. On one hand, the results showed that PAHs can be measured more accurately by the high-order data, and the recovery rate was higher. On the other hand, the results presented that AWRCQLD can better reflect the superiority of four-dimensional algorithm than the second-order calibration and other third-order calibration algorithms. The recovery rate of ANA was 96.5%~103.3% and the root mean square error of prediction was 0.04μgL -1 . The recovery rate of NAP was 96.7%~115.7% and the root mean square error of prediction was 0.06μgL -1 . Copyright © 2017 Elsevier B.V. All rights reserved.

Alternative approaches to predicting methane emissions from dairy cows.

PubMed

Mills, J A N; Kebreab, E; Yates, C M; Crompton, L A; Cammell, S B; Dhanoa, M S; Agnew, R E; France, J

2003-12-01

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited value where predictions are obtained for nutrient intakes and diet types outside those used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three nonlinear alternatives that were all of modified Mitscherlich (monomolecular) form. Of the linear models tested, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

Estimate of accuracy of determining the orientation of the star sensor system according to the experimental data

NASA Astrophysics Data System (ADS)

Avanesov, G. A.; Bessonov, R. V.; Kurkina, A. N.; Nikitin, A. V.; Sazonov, V. V.

2018-01-01

The BOKZ-M60 star sensor (Unit for Measuring Star Coordinates) is intended for determining the parameters of the orientation of the axes of the intrinsic coordinate system relative to the axes of the inertial system by observations of the regions of the stellar sky. It is convenient to characterize an error of the single determination of the orientation of the intrinsic coordinate system of the sensor by the vector of an infinitesimal turn of this system relative to its found position. Full-scale ground-based tests have shown that, for a resting sensor the root-mean-square values of the components of this vector along the axes of the intrinsic coordinate system lying in the plane of the sensor CCD matrix are less than 2″ and the component along the axis perpendicular to the matrix plane is characterized by the root-mean-square value of 15″. The joint processing of one-stage readings of several sensors installed on the same platform allows us to improve the indicated accuracy characteristics. In this paper, estimates of the accuracy of systems from BOKZ-M60 with two and four sensors performed from measurements carried out during the normal operation of these sensors on the Resurs-P satellite are given. Processing the measurements of the sensor system allowed us to increase the accuracy of determining the each of their orientations and to study random and systematic errors in these measurements.

Performance of MIDAS Over East African Longitude Sector: Case Study During 4-14 March 2012 Quiet to Disturbed Geomagnetic Conditions

NASA Astrophysics Data System (ADS)

Giday, Nigussie M.; Katamzi-Joseph, Zama T.

2018-02-01

This study investigates the performance of a tomographic algorithm, Multi-Instrument and Data Analysis System (MIDAS), during an extended period of 4-14 March 2012, containing moderate and strong geomagnetic storms conditions, over an understudied and data scarce Eastern African longitude sector. Nonetheless, a relatively better distribution of Global Navigation Satellite Systems stations exists along a narrow longitude sector between 30°E and 44°E and latitude range of 30°S and 36°N that spans the equatorial, middle-, and low-latitude ionosphere. Then results are compared with independent global models such as International Reference Ionosphere 2012 (IRI-2012) and global ionosphere map (GIM). MIDAS performance was better than that of the IRI-2012 and GIM models in terms of capturing the diurnal trends as well as the short temporal total electron content (TEC) structures, with least root-mean-square errors (RMSEs). Overall, MIDAS results showed better agreement with the observation vertical TEC (VTEC) with computed maximum correlation coefficient (r) of 0.99 and minimum root-mean-square error (RMSE) of 2.91 TEC unit (1 TECU = 1,016 el m-2 over all the test stations and the validation days. Conversely, for the IRI-2012 and GIM TEC estimates, the corresponding maximum computed r values were 0.93 and 0.99, respectively, while the minimum RMSEs were 13.03 TECU and 6.52 TECU, respectively, for all the test stations and the validation days.

MRI-based intelligence quotient (IQ) estimation with sparse learning.

PubMed

Wang, Liye; Wee, Chong-Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

2015-01-01

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time.

PubMed

Martin, Stephanie L-O; Carek, Andrew M; Kim, Chang-Sei; Ashouri, Hazar; Inan, Omer T; Hahn, Jin-Oh; Mukkamala, Ramakrishna

2016-12-15

Pulse transit time (PTT) is being widely pursued for cuff-less blood pressure (BP) monitoring. Most efforts have employed the time delay between ECG and finger photoplethysmography (PPG) waveforms as a convenient surrogate of PTT. However, these conventional pulse arrival time (PAT) measurements include the pre-ejection period (PEP) and the time delay through small, muscular arteries and may thus be an unreliable marker of BP. We assessed a bathroom weighing scale-like system for convenient measurement of ballistocardiography and foot PPG waveforms - and thus PTT through larger, more elastic arteries - in terms of its ability to improve tracking of BP in individual subjects. We measured "scale PTT", conventional PAT, and cuff BP in humans during interventions that increased BP but changed PEP and smooth muscle contraction differently. Scale PTT tracked the diastolic BP changes well, with correlation coefficient of -0.80 ± 0.02 (mean ± SE) and root-mean-squared-error of 7.6 ± 0.5 mmHg after a best-case calibration. Conventional PAT was significantly inferior in tracking these changes, with correlation coefficient of -0.60 ± 0.04 and root-mean-squared-error of 14.6 ± 1.5 mmHg (p < 0.05). Scale PTT also tracked the systolic BP changes better than conventional PAT but not to an acceptable level. With further development, scale PTT may permit reliable, convenient measurement of BP.

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

NASA Technical Reports Server (NTRS)

Forman, Barton A.; Reichle, Rolf Helmut

2014-01-01

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis.

PubMed

Lee, Byeong-Ju; Kim, Hye-Youn; Lim, Sa Rang; Huang, Linfang; Choi, Hyung-Kyoon

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

PubMed Central

Lim, Sa Rang; Huang, Linfang

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values. PMID:29049369

Estimating current and future streamflow characteristics at ungaged sites, central and eastern Montana, with application to evaluating effects of climate change on fish populations

USGS Publications Warehouse

Sando, Roy; Chase, Katherine J.

2017-03-23

A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.

Analytic Method for Computing Instrument Pointing Jitter

NASA Technical Reports Server (NTRS)

Bayard, David

2003-01-01

A new method of calculating the root-mean-square (rms) pointing jitter of a scientific instrument (e.g., a camera, radar antenna, or telescope) is introduced based on a state-space concept. In comparison with the prior method of calculating the rms pointing jitter, the present method involves significantly less computation. The rms pointing jitter of an instrument (the square root of the jitter variance shown in the figure) is an important physical quantity which impacts the design of the instrument, its actuators, controls, sensory components, and sensor- output-sampling circuitry. Using the Sirlin, San Martin, and Lucke definition of pointing jitter, the prior method of computing the rms pointing jitter involves a frequency-domain integral of a rational polynomial multiplied by a transcendental weighting function, necessitating the use of numerical-integration techniques. In practice, numerical integration complicates the problem of calculating the rms pointing error. In contrast, the state-space method provides exact analytic expressions that can be evaluated without numerical integration.

Remote sensing of ocean currents

NASA Technical Reports Server (NTRS)

Goldstein, R. M.; Zebker, H. A.; Barnett, T. P.

1989-01-01

A method of remotely measuring near-surface ocean currents with a synthetic aperture radar (SAR) is described. The apparatus consists of a single SAR transmitter and two receiving antennas. The phase difference between SAR image scenes obtained from the antennas forms an interferogram that is directly proportional to the surface current. The first field test of this technique against conventional measurements gives estimates of mean currents accurate to order 20 percent, that is, root-mean-square errors of 5 to 10 centimeters per second in mean flows of 27 to 56 centimeters per second. If the full potential of the method could be realized with spacecraft, then it might be possible to routinely monitor the surface currents of the world's oceans.

Evaluation of the CEAS model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The CEAS yield model is based upon multiple regression analysis at the CRD and state levels. For the historical time series, yield is regressed on a set of variables derived from monthly mean temperature and monthly precipitation. Technological trend is represented by piecewise linear and/or quadriatic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-79) demonstrated that biases are small and performance as indicated by the root mean square errors are acceptable for intended application, however, model response for individual years particularly unusual years, is not very reliable and shows some large errors. The model is objective, adequate, timely, simple and not costly. It considers scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

An agreement coefficient for image comparison

USGS Publications Warehouse

Ji, Lei; Gallo, Kevin

2006-01-01

Combination of datasets acquired from different sensor systems is necessary to construct a long time-series dataset for remotely sensed land-surface variables. Assessment of the agreement of the data derived from various sources is an important issue in understanding the data continuity through the time-series. Some traditional measures, including correlation coefficient, coefficient of determination, mean absolute error, and root mean square error, are not always optimal for evaluating the data agreement. For this reason, we developed a new agreement coefficient for comparing two different images. The agreement coefficient has the following properties: non-dimensional, bounded, symmetric, and distinguishable between systematic and unsystematic differences. The paper provides examples of agreement analyses for hypothetical data and actual remotely sensed data. The results demonstrate that the agreement coefficient does include the above properties, and therefore is a useful tool for image comparison.

Simulation of groundwater flow to assess future withdrawals associated with Base Realignment and Closure (BRAC) at Fort George G. Meade, Maryland

USGS Publications Warehouse

Raffensperger, Jeff P.; Fleming, Brandon J.; Banks, William S.L.; Horn, Marilee A.; Nardi, Mark R.; Andreasen, David C.

2010-01-01

Increased groundwater withdrawals from confined aquifers in the Maryland Coastal Plain to supply anticipated growth at Fort George G. Meade (Fort Meade) and surrounding areas resulting from the Department of Defense Base Realignment and Closure Program may have adverse effects in the outcrop or near-outcrop areas. Specifically, increased pumping from the Potomac Group aquifers (principally the Patuxent aquifer) could potentially reduce base flow in small streams below rates necessary for healthy biological functioning. Additionally, water levels may be lowered near, or possibly below, the top of the aquifer within the confined-unconfined transition zone near the outcrop area. A three-dimensional groundwater flow model was created to incorporate and analyze data on water withdrawals, streamflow, and hydraulic head in the region. The model is based on an earlier model developed to assess the effects of future withdrawals from well fields in Anne Arundel County, Maryland and surrounding areas, and includes some of the same features, including model extent, boundary conditions, and vertical discretization (layering). The resolution (horizontal grid discretization) of the earlier model limited its ability to simulate the effects of withdrawals on the outcrop and near-outcrop areas. The model developed for this study included a block-shaped higher-resolution local grid, referred to as the child model, centered on Fort Meade, which was coupled to the coarser-grid parent model using the shared node Local Grid Refinement capability of MODFLOW-LGR. A more detailed stream network was incorporated into the child model. In addition, for part of the transient simulation period, stress periods were reduced in length from 1 year to 3 months, to allow for simulation of the effects of seasonally varying withdrawals and recharge on the groundwater-flow system and simulated streamflow. This required revision of the database on withdrawals and estimation of seasonal variations in recharge represented in the earlier model. The calibrated model provides a tool for future forecasts of changes in the system under different management scenarios, and for simulating potential effects of withdrawals at Fort Meade and the surrounding area on water levels in the near-outcrop area and base flow in the outcrop area. Model error was assessed by comparing observed and simulated water levels from 62 wells (55 in the parent model and 7 in the child model). The root-mean-square error values for the parent and child model were 8.72 and 11.91 feet, respectively. Root-mean-square error values for the 55 parent model observation wells range from 0.95 to 30.31 feet; the range for the 7 child model observation wells is 5.00 to 24.17 feet. Many of the wells with higher root-mean-square error values occur at the perimeter of the child model and near large pumping centers, as well as updip in the confined aquifers. Root-mean-square error values decrease downdip and away from the large pumping centers. Both the parent and child models are sensitive to increasing withdrawal rates. The parent model is more sensitive than the child model to decreasing transmissivity of layers 3, 4, 5, and 6. The parent model is relatively insensitive to riverbed vertical conductance, however, the child model does exhibit some sensitivity to decreasing riverbed conductance. The overall water budget for the model included sources and sinks of water including recharge, surface-water bodies and rivers and streams, general-head boundaries, and withdrawals from permitted wells. Withdrawal from wells in 2005 was estimated to be equivalent to 8.5 percent of the total recharge rate.

Barley root hair growth and morphology in soil, sand, and water solution media and relationship with nickel toxicity.

PubMed

Lin, Yanqing; Allen, Herbert E; Di Toro, Dominic M

2016-08-01

Barley, Hordeum vulgare (Doyce), was grown in the 3 media of soil, hydroponic sand solution (sand), and hydroponic water solution (water) culture at the same environmental conditions for 4 d. Barley roots were scanned, and root morphology was analyzed. Plants grown in the 3 media had different root morphology and nickel (Ni) toxicity response. Root elongations and total root lengths followed the sequence soil > sand > water. Plants grown in water culture were more sensitive to Ni toxicity and had greater root hair length than those from soil and sand cultures, which increased root surface area. The unit root surface area as root surface area per centimeter of length of root followed the sequence water > sand > soil and was found to be related with root elongation. Including the unit root surface area, the difference in root elongation and 50% effective concentration were diminished, and percentage of root elongations can be improved with a root mean square error approximately 10% for plants grown in different media. Because the unit root surface area of plants in sand culture is closer to that in soil culture, the sand culture method, not water culture, is recommended for toxicity parameter estimation. Environ Toxicol Chem 2016;35:2125-2133. © 2016 SETAC. © 2016 SETAC.

Determination and prediction of digestible and metabolizable energy concentrations in byproduct feed ingredients fed to growing pigs

PubMed Central

Son, Ah Reum; Park, Chan Sol; Kim, Beob Gyun

2017-01-01

Objective An experiment was conducted to determine digestible energy (DE) and metabolizable energy (ME) of different byproduct feed ingredients fed to growing pigs, and to generate prediction equations for the DE and ME in feed ingredients. Methods Twelve barrows with an initial mean body weight of 31.8 kg were individually housed in metabolism crates that were equipped with a feeder and a nipple drinker. A 12×10 incomplete Latin square design was employed with 12 dietary treatments, 10 periods, and 12 animals. A basal diet was prepared to mainly contain the corn and soybean meal (SBM). Eleven additional diets were formulated to contain 30% of each test ingredient. All diets contained the same proportion of corn:SBM ratio at 4.14:1. The difference procedure was used to calculate the DE and ME in experimental ingredients. The in vitro dry matter disappearance for each test ingredient was determined. Results The DE and ME values in the SBM sources were greater (p<0.05) than those in other ingredients except high-protein distillers dried grains. However, DE and ME values in tapioca distillers dried grains (TDDG) were the lowest (p<0.05). The most suitable regression equations for the DE and ME concentrations (kcal/kg on the dry matter [DM] basis) in the test ingredients were: DE = 5,528–(156×ash)–(32.4×neutral detergent fiber [NDF]) with root mean square error = 232, R2 = 0.958, and p<0.001; ME = 5,243–(153 ash)–(30.7×NDF) with root mean square error = 277, R2 = 0.936, and p<0.001. All independent variables are in % on the DM basis. Conclusion The energy concentrations were greater in the SBM sources and were the least in the TDDG. The ash and NDF concentrations can be used to estimate the energy concentrations in the byproducts from oil-extraction and distillation processes. PMID:27857027

A finite element method based microwave heat transfer modeling of frozen multi-component foods

NASA Astrophysics Data System (ADS)

Pitchai, Krishnamoorthy

Microwave heating is fast and convenient, but is highly non-uniform. Non-uniform heating in microwave cooking affects not only food quality but also food safety. Most food industries develop microwavable food products based on "cook-and-look" approach. This approach is time-consuming, labor intensive and expensive and may not result in optimal food product design that assures food safety and quality. Design of microwavable food can be realized through a simulation model which describes the physical mechanisms of microwave heating in mathematical expressions. The objective of this study was to develop a microwave heat transfer model to predict spatial and temporal profiles of various heterogeneous foods such as multi-component meal (chicken nuggets and mashed potato), multi-component and multi-layered meal (lasagna), and multi-layered food with active packages (pizza) during microwave heating. A microwave heat transfer model was developed by solving electromagnetic and heat transfer equations using finite element method in commercially available COMSOL Multiphysics v4.4 software. The microwave heat transfer model included detailed geometry of the cavity, phase change, and rotation of the food on the turntable. The predicted spatial surface temperature patterns and temporal profiles were validated against the experimental temperature profiles obtained using a thermal imaging camera and fiber-optic sensors. The predicted spatial surface temperature profile of different multi-component foods was in good agreement with the corresponding experimental profiles in terms of hot and cold spot patterns. The root mean square error values of temporal profiles ranged from 5.8 °C to 26.2 °C in chicken nuggets as compared 4.3 °C to 4.7 °C in mashed potatoes. In frozen lasagna, root mean square error values at six locations ranged from 6.6 °C to 20.0 °C for 6 min of heating. A microwave heat transfer model was developed to include susceptor assisted microwave heating of a frozen pizza. The root mean square error values of transient temperature profiles of five locations ranged from 5.0 °C to 12.6 °C. A methodology was developed to incorporate electromagnetic frequency spectrum in the coupled electromagnetic and heat transfer model. Implementing the electromagnetic frequency spectrum in the simulation improved the accuracy of temperature field pattern and transient temperature profile as compared to mono-chromatic frequency of 2.45 GHz. The bulk moisture diffusion coefficient of cooked pasta was calculated as a function of temperature at a constant water activity using desorption isotherms.

Psychometric evaluation of the Finnish version of the impact on participation and autonomy questionnaire in persons with multiple sclerosis.

PubMed

Karhula, Maarit E; Salminen, Anna-Liisa; Hämäläinen, Päivi I; Ruutiainen, Juhani; Era, Pertti; Tolvanen, Asko

2017-11-01

The objective of this study was to evaluate the psychometric properties of the impact on participation and autonomy (IPA) questionnaire. The Finnish version of IPA (IPAFin) was translated into Finnish using the protocol for linguistic validation for patient-reported outcomes instruments. A total of 194 persons with multiple sclerosis (MS) (mean age 50 years SD 9, 72% female) with moderate to severe disability participated in this study. A confirmatory factor analysis (CFA) was used to confirm the four factor structure of the IPAFin. The work and educational opportunities domain was excluded from analysis, because it was only applicable to 51 persons. Internal consistency was investigated by calculating Cronbach's alpha. CFA confirmed the construct validity of the IPA (standardized root mean square residual (SRMR) = 0.06, comparative fit index (CFI) = 0.93, Tucker-Lewis index =0.93, root mean square error of approximation (RMSEA) = 0.06), indicating a good fit to the model. There was no difference in the models for females and males. Cronbach's alpha for the domains ranged between 0.80 and 0.91, indicating good homogeneity. The construct validity and reliability of the IPAFin is acceptable. IPAFin is a suitable measure of participation in persons with MS.

A structural equation model of perceived and internalized stigma, depression, and suicidal status among people living with HIV/AIDS.

PubMed

Zeng, Chengbo; Li, Linghua; Hong, Yan Alicia; Zhang, Hanxi; Babbitt, Andrew Walker; Liu, Cong; Li, Lixia; Qiao, Jiaying; Guo, Yan; Cai, Weiping

2018-01-15

Previous studies have shown positive association between HIV-related stigma and depression, suicidal ideation, and suicidal attempt among people living with HIV/AIDS (PLWH). But few studies have examined the mechanisms among HIV-related stigma, depression, and suicidal status (suicidal ideation and/or suicidal attempt) in PLWH. The current study examined the relationships among perceived and internalized stigma (PIS), depression, and suicidal status among PLWH in Guangzhou, China using structural equation modeling. Cross-sectional study by convenience sampling was conducted and 411 PLWH were recruited from the Number Eight People's Hospital from March to June, 2013 in Guangzhou, China. Participants were interviewed on their PIS, depressive symptoms, suicidal status, and socio-demographic characteristics. PLWH who had had suicidal ideation and suicidal attempts since HIV diagnosis were considered to be suicidal. Structural equation model was performed to examine the direct and indirect associations of PIS and suicidal status. Indicators to evaluate goodness of fit of the structural equation model included Chi-square Statistic, Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and Weighted Root Mean Square Residual (WRMR). More than one-third (38.4%) of the PLWH had depressive symptoms and 32.4% reported suicidal ideation and/or attempt since HIV diagnosis. The global model showed good model fit (Chi-square value = 34.42, CFI = 0.98, RMSEA = 0.03, WRMR = 0.73). Structural equation model revealed that direct pathway of PIS on suicidal status was significant (standardized pathway coefficient = 0.21), and indirect pathway of PIS on suicidal status via depression was also significant (standardized pathway coefficient = 0.24). There was a partial mediating effect of depression in the association between PIS and suicidal status. Our findings suggest that PIS is associated with increased depression and the likelihood of suicidal status. Depression is in turn positively associated with suicidal status and plays a mediating role between PIS and suicidal status. Therefore, to reduce suicidal ideation and attempt in PLWH, targeted interventions to reduce PIS and improve mental health status of PLWH are warranted.

Health Promotion Behavior of Chinese International Students in Korea Including Acculturation Factors: A Structural Equation Model.

PubMed

Kim, Sun Jung; Yoo, Il Young

2016-03-01

The purpose of this study was to explain the health promotion behavior of Chinese international students in Korea using a structural equation model including acculturation factors. A survey using self-administered questionnaires was employed. Data were collected from 272 Chinese students who have resided in Korea for longer than 6 months. The data were analyzed using structural equation modeling. The p value of final model is .31. The fitness parameters of the final model such as goodness of fit index, adjusted goodness of fit index, normed fit index, non-normed fit index, and comparative fit index were more than .95. Root mean square of residual and root mean square error of approximation also met the criteria. Self-esteem, perceived health status, acculturative stress and acculturation level had direct effects on health promotion behavior of the participants and the model explained 30.0% of variance. The Chinese students in Korea with higher self-esteem, perceived health status, acculturation level, and lower acculturative stress reported higher health promotion behavior. The findings can be applied to develop health promotion strategies for this population. Copyright © 2016. Published by Elsevier B.V.

1.5% root-mean-square flat-intensity laser beam formed using a binary-amplitude spatial light modulator.

PubMed

Liang, Jinyang; Kohn, Rudolph N; Becker, Michael F; Heinzen, Daniel J

2009-04-01

We demonstrate a digital micromirror device (DMD)-based optical system that converts a spatially noisy quasi-Gaussian to an eighth-order super-Lorentzian flat-top beam. We use an error-diffusion algorithm to design the binary pattern for the Texas Instruments DLP device. Following the DMD, a telescope with a pinhole low-pass filters the beam and scales it to the desired sized image. Experimental measurements show a 1% root-mean-square (RMS) flatness over a diameter of 0.28 mm in the center of the flat-top beam and better than 1.5% RMS flatness over its entire 1.43 mm diameter. The power conversion efficiency is 37%. We develop an alignment technique to ensure that the DMD pattern is correctly positioned on the incident beam. An interferometric measurement of the DMD surface flatness shows that phase uniformity is maintained in the output beam. Our approach is highly flexible and is able to produce not only flat-top beams with different parameters, but also any slowly varying target beam shape. It can be used to generate the homogeneous optical lattice required for Bose-Einstein condensate cold atom experiments.

A hybrid method for synthetic aperture ladar phase-error compensation

NASA Astrophysics Data System (ADS)

Hua, Zhili; Li, Hongping; Gu, Yongjian

2009-07-01

As a high resolution imaging sensor, synthetic aperture ladar data contain phase-error whose source include uncompensated platform motion and atmospheric turbulence distortion errors. Two previously devised methods, rank one phase-error estimation algorithm and iterative blind deconvolution are reexamined, of which a hybrid method that can recover both the images and PSF's without any a priori information on the PSF is built to speed up the convergence rate by the consideration in the choice of initialization. To be integrated into spotlight mode SAL imaging model respectively, three methods all can effectively reduce the phase-error distortion. For each approach, signal to noise ratio, root mean square error and CPU time are computed, from which we can see the convergence rate of the hybrid method can be improved because a more efficient initialization set of blind deconvolution. Moreover, by making a further discussion of the hybrid method, the weight distribution of ROPE and IBD is found to be an important factor that affects the final result of the whole compensation process.

Application of multispectral reflectance for early detection of tomato disease

NASA Astrophysics Data System (ADS)

Xu, Huirong; Zhu, Shengpan; Ying, Yibin; Jiang, Huanyu

2006-10-01

Automatic diagnosis of plant disease is important for plant management and environmental preservation in the future. The objective of this study is to use multispectral reflectance measurements to make an early discrimination between the healthy and infected plants by the strain of tobacco mosaic virus (TMV-U1) infection. There were reflectance changes in the visible (VIS) and near infrared spectroscopy (NIR) between the healthy and infected plants. Discriminant models were developed using discriminant partial least squares (DPLS) and Mahalanobis distance (MD). The DPLS models had a root mean square error of calibration (RMSEC) of 0.397 and correlation coefficient (r) of 0.59 and the MD model correctly classified 86.7% healthy plants and up to 91.7% infected plants.

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

PubMed

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

2015-05-01

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

Comparing Thermal Process Validation Methods for Salmonella Inactivation on Almond Kernels.

PubMed

Jeong, Sanghyup; Marks, Bradley P; James, Michael K

2017-01-01

Ongoing regulatory changes are increasing the need for reliable process validation methods for pathogen reduction processes involving low-moisture products; however, the reliability of various validation methods has not been evaluated. Therefore, the objective was to quantify accuracy and repeatability of four validation methods (two biologically based and two based on time-temperature models) for thermal pasteurization of almonds. Almond kernels were inoculated with Salmonella Enteritidis phage type 30 or Enterococcus faecium (NRRL B-2354) at ~10 8 CFU/g, equilibrated to 0.24, 0.45, 0.58, or 0.78 water activity (a w ), and then heated in a pilot-scale, moist-air impingement oven (dry bulb 121, 149, or 177°C; dew point <33.0, 69.4, 81.6, or 90.6°C; v air = 2.7 m/s) to a target lethality of ~4 log. Almond surface temperatures were measured in two ways, and those temperatures were used to calculate Salmonella inactivation using a traditional (D, z) model and a modified model accounting for process humidity. Among the process validation methods, both methods based on time-temperature models had better repeatability, with replication errors approximately half those of the surrogate ( E. faecium ). Additionally, the modified model yielded the lowest root mean squared error in predicting Salmonella inactivation (1.1 to 1.5 log CFU/g); in contrast, E. faecium yielded a root mean squared error of 1.2 to 1.6 log CFU/g, and the traditional model yielded an unacceptably high error (3.4 to 4.4 log CFU/g). Importantly, the surrogate and modified model both yielded lethality predictions that were statistically equivalent (α = 0.05) to actual Salmonella lethality. The results demonstrate the importance of methodology, a w , and process humidity when validating thermal pasteurization processes for low-moisture foods, which should help processors select and interpret validation methods to ensure product safety.

Calibration of a texture-based model of a ground-water flow system, western San Joaquin Valley, California

USGS Publications Warehouse

Phillips, Steven P.; Belitz, Kenneth

1991-01-01

The occurrence of selenium in agricultural drain water from the western San Joaquin Valley, California, has focused concern on the semiconfined ground-water flow system, which is underlain by the Corcoran Clay Member of the Tulare Formation. A two-step procedure is used to calibrate a preliminary model of the system for the purpose of determining the steady-state hydraulic properties. Horizontal and vertical hydraulic conductivities are modeled as functions of the percentage of coarse sediment, hydraulic conductivities of coarse-textured (Kcoarse) and fine-textured (Kfine) end members, and averaging methods used to calculate equivalent hydraulic conductivities. The vertical conductivity of the Corcoran (Kcorc) is an additional parameter to be evaluated. In the first step of the calibration procedure, the model is run by systematically varying the following variables: (1) Kcoarse/Kfine, (2) Kcoarse/Kcorc, and (3) choice of averaging methods in the horizontal and vertical directions. Root mean square error and bias values calculated from the model results are functions of these variables. These measures of error provide a means for evaluating model sensitivity and for selecting values of Kcoarse, Kfine, and Kcorc for use in the second step of the calibration procedure. In the second step, recharge rates are evaluated as functions of Kcoarse, Kcorc, and a combination of averaging methods. The associated Kfine values are selected so that the root mean square error is minimized on the basis of the results from the first step. The results of the two-step procedure indicate that the spatial distribution of hydraulic conductivity that best produces the measured hydraulic head distribution is created through the use of arithmetic averaging in the horizontal direction and either geometric or harmonic averaging in the vertical direction. The equivalent hydraulic conductivities resulting from either combination of averaging methods compare favorably to field- and laboratory-based values.

Bayesian estimation of the discrete coefficient of determination.

PubMed

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Superficial vessel reconstruction with a multiview camera system

PubMed Central

Marreiros, Filipe M. M.; Rossitti, Sandro; Karlsson, Per M.; Wang, Chunliang; Gustafsson, Torbjörn; Carleberg, Per; Smedby, Örjan

2016-01-01

Abstract. We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are ∼1  mm. PMID:26759814

Near infrared spectral linearisation in quantifying soluble solids content of intact carambola.

PubMed

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-04-12

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques.

Near Infrared Spectral Linearisation in Quantifying Soluble Solids Content of Intact Carambola

PubMed Central

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-01-01

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques. PMID:23584118

The ultimate quantum limits on the accuracy of measurements

NASA Technical Reports Server (NTRS)

Yuen, Horace P.

1992-01-01

A quantum generalization of rate-distortion theory from standard communication and information theory is developed for application to determining the ultimate performance limit of measurement systems in physics. For the estimation of a real or a phase parameter, it is shown that the root-mean-square error obtained in a measurement with a single-mode photon level N cannot do better than approximately N exp -1, while approximately exp(-N) may be obtained for multi-mode fields with the same photon level N. Possible ways to achieve the remarkable exponential performance are indicated.

Silicon photonic integrated circuit for fast and precise dual-comb distance metrology.

PubMed

Weimann, C; Lauermann, M; Hoeller, F; Freude, W; Koos, C

2017-11-27

We demonstrate an optical distance sensor integrated on a silicon photonic chip with a footprint of well below 1 mm 2 . The integrated system comprises a heterodyne receiver structure with tunable power splitting ratio and on-chip photodetectors. The functionality of the device is demonstrated in a synthetic-wavelength interferometry experiment using frequency combs as optical sources. We obtain accurate and fast distance measurements with an unambiguity range of 3.75 mm, a root-mean-square error of 3.4 µm and acquisition times of 14 µs.

High-accuracy Aspheric X-ray Mirror Metrology Using Software Configurable Optical Test System/deflectometry

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

Huang, Run; Su, Peng; Burge, James H.

The Software Configurable Optical Test System (SCOTS) uses deflectometry to measure surface slopes of general optical shapes without the need for additional null optics. Careful alignment of test geometry and calibration of inherent system error improve the accuracy of SCOTS to a level where it competes with interferometry. We report a SCOTS surface measurement of an off-axis superpolished elliptical x-ray mirror that achieves <1 nm<1 nm root-mean-square accuracy for the surface measurement with low-order term included.

i-LOVE: ISS-JEM lidar for observation of vegetation environment

NASA Astrophysics Data System (ADS)

Asai, Kazuhiro; Sawada, Haruo; Sugimoto, Nobuo; Mizutani, Kohei; Ishii, Shoken; Nishizawa, Tomoaki; Shimoda, Haruhisa; Honda, Yoshiaki; Kajiwara, Koji; Takao, Gen; Hirata, Yasumasa; Saigusa, Nobuko; Hayashi, Masatomo; Oguma, Hiroyuki; Saito, Hideki; Awaya, Yoshio; Endo, Takahiro; Imai, Tadashi; Murooka, Jumpei; Kobatashi, Takashi; Suzuki, Keiko; Sato, Ryota

2012-11-01

It is very important to watch the spatial distribution of vegetation biomass and changes in biomass over time, representing invaluable information to improve present assessments and future projections of the terrestrial carbon cycle. A space lidar is well known as a powerful remote sensing technology for measuring the canopy height accurately. This paper describes the ISS(International Space Station)-JEM(Japanese Experimental Module)-EF(Exposed Facility) borne vegetation lidar using a two dimensional array detector in order to reduce the root mean square error (RMSE) of tree height due to sloped surface.

Double Density Dual Tree Discrete Wavelet Transform implementation for Degraded Image Enhancement

NASA Astrophysics Data System (ADS)

Vimala, C.; Aruna Priya, P.

2018-04-01

Wavelet transform is a main tool for image processing applications in modern existence. A Double Density Dual Tree Discrete Wavelet Transform is used and investigated for image denoising. Images are considered for the analysis and the performance is compared with discrete wavelet transform and the Double Density DWT. Peak Signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. The proposed techniques give the better performance when comparing other two wavelet techniques.

Effects on Physiology and Performance of Wearing the Aviator NBC ensemble While Flying the UH-60 Helicopter Flight Simulator in a Controlled Heat Environment.

DTIC Science & Technology

1992-09-01

and collecting and processing data. They were at the front line in interacting with the subjects and maintaining morale. They did an excellent job. They...second for 16 parameter channels, and the data were processed to produce a single root mean square (RMS) error value for each channel appropriate to...represented in the final analysis. Physiological data The physiological data on the VAX were processed by sampling them at 5-minute intervals throughout the

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.

Two ultraviolet radiation datasets that cover China

NASA Astrophysics Data System (ADS)

Liu, Hui; Hu, Bo; Wang, Yuesi; Liu, Guangren; Tang, Liqin; Ji, Dongsheng; Bai, Yongfei; Bao, Weikai; Chen, Xin; Chen, Yunming; Ding, Weixin; Han, Xiaozeng; He, Fei; Huang, Hui; Huang, Zhenying; Li, Xinrong; Li, Yan; Liu, Wenzhao; Lin, Luxiang; Ouyang, Zhu; Qin, Boqiang; Shen, Weijun; Shen, Yanjun; Su, Hongxin; Song, Changchun; Sun, Bo; Sun, Song; Wang, Anzhi; Wang, Genxu; Wang, Huimin; Wang, Silong; Wang, Youshao; Wei, Wenxue; Xie, Ping; Xie, Zongqiang; Yan, Xiaoyuan; Zeng, Fanjiang; Zhang, Fawei; Zhang, Yangjian; Zhang, Yiping; Zhao, Chengyi; Zhao, Wenzhi; Zhao, Xueyong; Zhou, Guoyi; Zhu, Bo

2017-07-01

Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes.

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

Chemical library subset selection algorithms: a unified derivation using spatial statistics.

PubMed

Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F

2002-01-01

If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

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

Analysis of the quality of image data acquired by the LANDSAT-4 Thematic Mapper and Multispectral Scanners

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1984-01-01

The geometric quality of TM film and digital products is evaluated by making selective photomeasurements and by measuring the coordinates of known features on both the TM products and map products. These paired observations are related using a standard linear least squares regression approach. Using regression equations and coefficients developed from 225 (TM film product) and 20 (TM digital product) control points, map coordinates of test points are predicted. The residual error vectors and analysis of variance (ANOVA) were performed on the east and north residual using nine image segments (blocks) as treatments. Based on the root mean square error of the 223 (TM film product) and 22 (TM digital product) test points, users of TM data expect the planimetric accuracy of mapped points to be within 91 meters and within 117 meters for the film products, and to be within 12 meters and within 14 meters for the digital products.

Instrumental variables vs. grouping approach for reducing bias due to measurement error.

PubMed

Batistatou, Evridiki; McNamee, Roseanne

2008-01-01

Attenuation of the exposure-response relationship due to exposure measurement error is often encountered in epidemiology. Given that error cannot be totally eliminated, bias correction methods of analysis are needed. Many methods require more than one exposure measurement per person to be made, but the `group mean OLS method,' in which subjects are grouped into several a priori defined groups followed by ordinary least squares (OLS) regression on the group means, can be applied with one measurement. An alternative approach is to use an instrumental variable (IV) method in which both the single error-prone measure and an IV are used in IV analysis. In this paper we show that the `group mean OLS' estimator is equal to an IV estimator with the group mean used as IV, but that the variance estimators for the two methods are different. We derive a simple expression for the bias in the common estimator which is a simple function of group size, reliability and contrast of exposure between groups, and show that the bias can be very small when group size is large. We compare this method with a new proposal (group mean ranking method), also applicable with a single exposure measurement, in which the IV is the rank of the group means. When there are two independent exposure measurements per subject, we propose a new IV method (EVROS IV) and compare it with Carroll and Stefanski's (CS IV) proposal in which the second measure is used as an IV; the new IV estimator combines aspects of the `group mean' and `CS' strategies. All methods are evaluated in terms of bias, precision and root mean square error via simulations and a dataset from occupational epidemiology. The `group mean ranking method' does not offer much improvement over the `group mean method.' Compared with the `CS' method, the `EVROS' method is less affected by low reliability of exposure. We conclude that the group IV methods we propose may provide a useful way to handle mismeasured exposures in epidemiology with or without replicate measurements. Our finding may also have implications for the use of aggregate variables in epidemiology to control for unmeasured confounding.

Overview of the TOPEX/Poseidon Platform Harvest Verification Experiment

NASA Technical Reports Server (NTRS)

Morris, Charles S.; DiNardo, Steven J.; Christensen, Edward J.

1995-01-01

An overview is given of the in situ measurement system installed on Texaco's Platform Harvest for verification of the sea level measurement from the TOPEX/Poseidon satellite. The prelaunch error budget suggested that the total root mean square (RMS) error due to measurements made at this verification site would be less than 4 cm. The actual error budget for the verification site is within these original specifications. However, evaluation of the sea level data from three measurement systems at the platform has resulted in unexpectedly large differences between the systems. Comparison of the sea level measurements from the different tide gauge systems has led to a better understanding of the problems of measuring sea level in relatively deep ocean. As of May 1994, the Platform Harvest verification site has successfully supported 60 TOPEX/Poseidon overflights.

Visual Data Analysis for Satellites

NASA Technical Reports Server (NTRS)

Lau, Yee; Bhate, Sachin; Fitzpatrick, Patrick

2008-01-01

The Visual Data Analysis Package is a collection of programs and scripts that facilitate visual analysis of data available from NASA and NOAA satellites, as well as dropsonde, buoy, and conventional in-situ observations. The package features utilities for data extraction, data quality control, statistical analysis, and data visualization. The Hierarchical Data Format (HDF) satellite data extraction routines from NASA's Jet Propulsion Laboratory were customized for specific spatial coverage and file input/output. Statistical analysis includes the calculation of the relative error, the absolute error, and the root mean square error. Other capabilities include curve fitting through the data points to fill in missing data points between satellite passes or where clouds obscure satellite data. For data visualization, the software provides customizable Generic Mapping Tool (GMT) scripts to generate difference maps, scatter plots, line plots, vector plots, histograms, timeseries, and color fill images.

Scoring Methods in the International Land Benchmarking (ILAMB) Package

NASA Astrophysics Data System (ADS)

Collier, N.; Hoffman, F. M.; Keppel-Aleks, G.; Lawrence, D. M.; Mu, M.; Riley, W. J.; Randerson, J. T.

2017-12-01

The International Land Model Benchmarking (ILAMB) project is a model-data intercomparison and integration project designed to improve the performance of the land component of Earth system models. This effort is disseminated in the form of a python package which is openly developed (https://bitbucket.org/ncollier/ilamb). ILAMB is more than a workflow system that automates the generation of common scalars and plot comparisons to observational data. We aim to provide scientists and model developers with a tool to gain insight into model behavior. Thus, a salient feature of the ILAMB package is our synthesis methodology, which provides users with a high-level understanding of model performance. Within ILAMB, we calculate a non-dimensional score of a model's performance in a given dimension of the physics, chemistry, or biology with respect to an observational dataset. For example, we compare the Fluxnet-MTE Gross Primary Productivity (GPP) product against model output in the corresponding historical period. We compute common statistics such as the bias, root mean squared error, phase shift, and spatial distribution. We take these measures and find relative errors by normalizing the values, and then use the exponential to map this relative error to the unit interval. This allows for the scores to be combined into an overall score representing multiple aspects of model performance. In this presentation we give details of this process as well as a proposal for tuning the exponential mapping to make scores more cross comparable. However, as many models are calibrated using these scalar measures with respect to observational datasets, we also score the relationships among relevant variables in the model. For example, in the case of GPP, we also consider its relationship to precipitation, evapotranspiration, and temperature. We do this by creating a mean response curve and a two-dimensional distribution based on the observational data and model results. The response curves are then scored using a relative measure of the root mean squared error and the exponential as before. The distributions are scored using the so-called Hellinger distance, a statistical measure for how well one distribution is represented by another, and included in the model's overall score.

Contribution Of The SWOT Mission To Large-Scale Hydrological Modeling Using Data Assimilation

NASA Astrophysics Data System (ADS)

Emery, C. M.; Biancamaria, S.; Boone, A. A.; Ricci, S. M.; Rochoux, M. C.; Garambois, P. A.; Paris, A.; Calmant, S.

2016-12-01

The purpose of this work is to improve water fluxes estimation on the continental surfaces, at interanual and interseasonal scale (from few years to decennial time period). More specifically, it studies contribution of the incoming SWOT satellite mission to improve hydrology model at global scale, and using the land surface model ISBA-TRIP. This model corresponds to the continental component of the CNRM (French meteorological research center)'s climatic model. This study explores the potential of satellite data to correct either input parameters of the river routing scheme TRIP or its state variables. To do so, a data assimilation platform (using an Ensemble Kalman Filter, EnKF) has been implemented to assimilate SWOT virtual observations as well as discharges estimated from real nadir altimetry data. A series of twin experiments is used to test and validate the parameter estimation module of the platform. SWOT virtual-observations of water heights along SWOT tracks (with a 10 cm white noise model error) are assimilated to correct the river routing model parameters. To begin with, we chose to focus exclusively on the river manning coefficient, with the possibility to easily extend to other parameters such as the river widths. First results show that the platform is able to recover the "true" Manning distribution assimilating SWOT-like water heights. The error on the coefficients goes from 35 % before assimilation to 9 % after four SWOT orbit repeat period of 21 days. In the state estimation mode, daily assimilation cycles are realized to correct TRIP river water storage initial state by assimilating ENVISAT-based discharge. Those observations are derived from ENVISAT water elevation measures, using rating curves from the MGB-IPH hydrological model (calibrated over the Amazon using in situ gages discharge). Using such kind of observation allows going beyond idealized twin experiments and also to test contribution of a remotely-sensed discharge product, which could prefigure the SWOT discharge product. The results show that discharge after assimilation are globally improved : the root-mean-square error between the analysis discharge ensemble mean and in situ discharges is reduced by 30 %, compared to the root-mean-square error between the free run and in situ discharges.

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

USGS Publications Warehouse

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

2012-01-01

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

A Comparison of Energy Expenditure Estimation of Several Physical Activity Monitors

PubMed Central

Dannecker, Kathryn L.; Sazonova, Nadezhda A.; Melanson, Edward L.; Sazonov, Edward S.; Browning, Raymond C.

2013-01-01

Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multi-sensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE. PURPOSE To determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors. METHODS Nineteen healthy young adults (10 male, 9 female), completed a four-hour stay in a room calorimeter. Participants wore a footwear-based physical activity monitor, as well as Actical, Actigraph, IDEEA, DirectLife and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS Estimated EE using the shoe-based device was not significantly different than measured EE (476(20) vs. 478(18) kcal) (Mean (SE)), respectively, and had a root mean square error (RMSE) of (29.6 kcal (6.2%)). The IDEEA and DirectLlife estimates of EE were not significantly different than the measured EE but the Actigraph and Fitbit devices significantly underestimated EE. Root mean square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, Actigraph and Fitbit respectively. CONCLUSIONS The shoe based physical activity monitor provides a valid estimate of EE while the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity. PMID:23669877

Testing of Lagrange multiplier damped least-squares control algorithm for woofer-tweeter adaptive optics

PubMed Central

Zou, Weiyao; Burns, Stephen A.

2012-01-01

A Lagrange multiplier-based damped least-squares control algorithm for woofer-tweeter (W-T) dual deformable-mirror (DM) adaptive optics (AO) is tested with a breadboard system. We show that the algorithm can complementarily command the two DMs to correct wavefront aberrations within a single optimization process: the woofer DM correcting the high-stroke, low-order aberrations, and the tweeter DM correcting the low-stroke, high-order aberrations. The optimal damping factor for a DM is found to be the median of the eigenvalue spectrum of the influence matrix of that DM. Wavefront control accuracy is maximized with the optimized control parameters. For the breadboard system, the residual wavefront error can be controlled to the precision of 0.03 μm in root mean square. The W-T dual-DM AO has applications in both ophthalmology and astronomy. PMID:22441462

An improved partial least-squares regression method for Raman spectroscopy

NASA Astrophysics Data System (ADS)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

Nonlinear filtering properties of detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Kiyono, Ken; Tsujimoto, Yutaka

2016-11-01

Detrended fluctuation analysis (DFA) has been widely used for quantifying long-range correlation and fractal scaling behavior. In DFA, to avoid spurious detection of scaling behavior caused by a nonstationary trend embedded in the analyzed time series, a detrending procedure using piecewise least-squares fitting has been applied. However, it has been pointed out that the nonlinear filtering properties involved with detrending may induce instabilities in the scaling exponent estimation. To understand this issue, we investigate the adverse effects of the DFA detrending procedure on the statistical estimation. We show that the detrending procedure using piecewise least-squares fitting results in the nonuniformly weighted estimation of the root-mean-square deviation and that this property could induce an increase in the estimation error. In addition, for comparison purposes, we investigate the performance of a centered detrending moving average analysis with a linear detrending filter and sliding window DFA and show that these methods have better performance than the standard DFA.

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis.

PubMed

Peng, Jiangtao; Peng, Silong; Xie, Qiong; Wei, Jiping

2011-04-01

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7-19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33-68%). Copyright © 2011 Elsevier B.V. All rights reserved.

Testing of Lagrange multiplier damped least-squares control algorithm for woofer-tweeter adaptive optics.

PubMed

Zou, Weiyao; Burns, Stephen A

2012-03-20

A Lagrange multiplier-based damped least-squares control algorithm for woofer-tweeter (W-T) dual deformable-mirror (DM) adaptive optics (AO) is tested with a breadboard system. We show that the algorithm can complementarily command the two DMs to correct wavefront aberrations within a single optimization process: the woofer DM correcting the high-stroke, low-order aberrations, and the tweeter DM correcting the low-stroke, high-order aberrations. The optimal damping factor for a DM is found to be the median of the eigenvalue spectrum of the influence matrix of that DM. Wavefront control accuracy is maximized with the optimized control parameters. For the breadboard system, the residual wavefront error can be controlled to the precision of 0.03 μm in root mean square. The W-T dual-DM AO has applications in both ophthalmology and astronomy. © 2012 Optical Society of America

Online Soft Sensor of Humidity in PEM Fuel Cell Based on Dynamic Partial Least Squares

PubMed Central

Long, Rong; Chen, Qihong; Zhang, Liyan; Ma, Longhua; Quan, Shuhai

2013-01-01

Online monitoring humidity in the proton exchange membrane (PEM) fuel cell is an important issue in maintaining proper membrane humidity. The cost and size of existing sensors for monitoring humidity are prohibitive for online measurements. Online prediction of humidity using readily available measured data would be beneficial to water management. In this paper, a novel soft sensor method based on dynamic partial least squares (DPLS) regression is proposed and applied to humidity prediction in PEM fuel cell. In order to obtain data of humidity and test the feasibility of the proposed DPLS-based soft sensor a hardware-in-the-loop (HIL) test system is constructed. The time lag of the DPLS-based soft sensor is selected as 30 by comparing the root-mean-square error in different time lag. The performance of the proposed DPLS-based soft sensor is demonstrated by experimental results. PMID:24453923

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.

Heuristic-driven graph wavelet modeling of complex terrain

NASA Astrophysics Data System (ADS)

Cioacǎ, Teodor; Dumitrescu, Bogdan; Stupariu, Mihai-Sorin; Pǎtru-Stupariu, Ileana; Nǎpǎrus, Magdalena; Stoicescu, Ioana; Peringer, Alexander; Buttler, Alexandre; Golay, François

2015-03-01

We present a novel method for building a multi-resolution representation of large digital surface models. The surface points coincide with the nodes of a planar graph which can be processed using a critically sampled, invertible lifting scheme. To drive the lazy wavelet node partitioning, we employ an attribute aware cost function based on the generalized quadric error metric. The resulting algorithm can be applied to multivariate data by storing additional attributes at the graph's nodes. We discuss how the cost computation mechanism can be coupled with the lifting scheme and examine the results by evaluating the root mean square error. The algorithm is experimentally tested using two multivariate LiDAR sets representing terrain surface and vegetation structure with different sampling densities.

Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant— Picrorhiza kurrooa

NASA Astrophysics Data System (ADS)

Shivling, V. D.; Ghanshyam, C.; Kumar, Rakesh; Kumar, Sanjay; Sharma, Radhika; Kumar, Dinesh; Sharma, Atul; Sharma, Sudhir Kumar

2017-02-01

Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg-Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Smoothness of In vivo Spectral Baseline Determined by Mean Squared Error

PubMed Central

Zhang, Yan; Shen, Jun

2013-01-01

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

Estimating Inflows to Lake Okeechobee Using Climate Indices: A Machine Learning Modeling Approach

NASA Astrophysics Data System (ADS)

Kalra, A.; Ahmad, S.

2008-12-01

The operation of regional water management systems that include lakes and storage reservoirs for flood control and water supply can be significantly improved by using climate indices. This research is focused on forecasting Lag 1 annual inflow to Lake Okeechobee, located in South Florida, using annual oceanic- atmospheric indices of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO). Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM), belonging to the class of data driven models, are developed to forecast annual lake inflow using annual oceanic-atmospheric indices data from 1914 to 2003. The models were trained with 80 years of data and tested for 10 years of data. Based on Correlation Coefficient, Root Means Square Error, and Mean Absolute Error model predictions were in good agreement with measured inflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, revealed a strong signal for AMO and ENSO indices compared to PDO and NAO indices for one year lead-time inflow forecast. Inflow predictions from the SVM models were better when compared with the predictions obtained from feed forward back propagation Artificial Neural Network (ANN) models.

Comparison of estimators of standard deviation for hydrologic time series

USGS Publications Warehouse

Tasker, Gary D.; Gilroy, Edward J.

1982-01-01

Unbiasing factors as a function of serial correlation, ρ, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation σ of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. Three methods provided estimates of σ which were much less biased but had greater mean square errors than the usual estimate of σ: s = (1/(n - 1) ∑ (xi −x¯)2)½. The three methods may be briefly characterized as (1) a method using a maximum likelihood estimate of the unbiasing factor, (2) a method using an empirical Bayes estimate of the unbiasing factor, and (3) a robust nonparametric estimate of σ suggested by Quenouille. Because s tends to underestimate σ, its use as an estimate of a model parameter results in a tendency to underdesign. If underdesign losses are considered more serious than overdesign losses, then the choice of one of the less biased methods may be wise.

[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.

Missing value imputation in DNA microarrays based on conjugate gradient method.

PubMed

Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh

2012-02-01

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2015-01-01

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

14 CFR Appendix G to Part 25 - Continuous Gust Design Criteria

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Values of Ā (ratio of root-mean-square incremental load root-mean-square gust velocity) must be... gust velocity, ft./sec. Ω=reduced frequency, radians per foot. L=2,500 ft. (3) The limit loads must be... velocity Uσ: (i) At speed Vc: Uσ=85 fps true gust velocity in the interval 0 to 30,000 ft. altitude and is...

14 CFR Appendix G to Part 25 - Continuous Gust Design Criteria

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Values of Ā (ratio of root-mean-square incremental load root-mean-square gust velocity) must be... gust velocity, ft./sec. Ω=reduced frequency, radians per foot. L=2,500 ft. (3) The limit loads must be... velocity Uσ: (i) At speed Vc: Uσ=85 fps true gust velocity in the interval 0 to 30,000 ft. altitude and is...

14 CFR Appendix G to Part 25 - Continuous Gust Design Criteria

Code of Federal Regulations, 2012 CFR

2012-01-01

...) Values of Ā (ratio of root-mean-square incremental load root-mean-square gust velocity) must be... gust velocity, ft./sec. Ω=reduced frequency, radians per foot. L=2,500 ft. (3) The limit loads must be... velocity Uσ: (i) At speed Vc: Uσ=85 fps true gust velocity in the interval 0 to 30,000 ft. altitude and is...

Using First Differences to Reduce Inhomogeneity in Radiosonde Temperature Datasets.

NASA Astrophysics Data System (ADS)

Free, Melissa; Angell, James K.; Durre, Imke; Lanzante, John; Peterson, Thomas C.; Seidel, Dian J.

2004-11-01

The utility of a “first difference” method for producing temporally homogeneous large-scale mean time series is assessed. Starting with monthly averages, the method involves dropping data around the time of suspected discontinuities and then calculating differences in temperature from one year to the next, resulting in a time series of year-to-year differences for each month at each station. These first difference time series are then combined to form large-scale means, and mean temperature time series are constructed from the first difference series. When applied to radiosonde temperature data, the method introduces random errors that decrease with the number of station time series used to create the large-scale time series and increase with the number of temporal gaps in the station time series. Root-mean-square errors for annual means of datasets produced with this method using over 500 stations are estimated at no more than 0.03 K, with errors in trends less than 0.02 K decade-1 for 1960 97 at 500 mb. For a 50-station dataset, errors in trends in annual global means introduced by the first differencing procedure may be as large as 0.06 K decade-1 (for six breaks per series), which is greater than the standard error of the trend. Although the first difference method offers significant resource and labor advantages over methods that attempt to adjust the data, it introduces an error in large-scale mean time series that may be unacceptable in some cases.

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.

An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China.

PubMed

Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2014-06-01

In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.

Multi-muscle FES force control of the human arm for arbitrary goals.

PubMed

Schearer, Eric M; Liao, Yu-Wei; Perreault, Eric J; Tresch, Matthew C; Memberg, William D; Kirsch, Robert F; Lynch, Kevin M

2014-05-01

We present a method for controlling a neuroprosthesis for a paralyzed human arm using functional electrical stimulation (FES) and characterize the errors of the controller. The subject has surgically implanted electrodes for stimulating muscles in her shoulder and arm. Using input/output data, a model mapping muscle stimulations to isometric endpoint forces measured at the subject's hand was identified. We inverted the model of this redundant and coupled multiple-input multiple-output system by minimizing muscle activations and used this inverse for feedforward control. The magnitude of the total root mean square error over a grid in the volume of achievable isometric endpoint force targets was 11% of the total range of achievable forces. Major sources of error were random error due to trial-to-trial variability and model bias due to nonstationary system properties. Because the muscles working collectively are the actuators of the skeletal system, the quantification of errors in force control guides designs of motion controllers for multi-joint, multi-muscle FES systems that can achieve arbitrary goals.

The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, R. M.; Tai, K.-S.

2013-01-01

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.

PLS-LS-SVM based modeling of ATR-IR as a robust method in detection and qualification of alprazolam

NASA Astrophysics Data System (ADS)

Parhizkar, Elahehnaz; Ghazali, Mohammad; Ahmadi, Fatemeh; Sakhteman, Amirhossein

2017-02-01

According to the United States pharmacopeia (USP), Gold standard technique for Alprazolam determination in dosage forms is HPLC, an expensive and time-consuming method that is not easy to approach. In this study chemometrics assisted ATR-IR was introduced as an alternative method that produce similar results in fewer time and energy consumed manner. Fifty-eight samples containing different concentrations of commercial alprazolam were evaluated by HPLC and ATR-IR method. A preprocessing approach was applied to convert raw data obtained from ATR-IR spectra to normal matrix. Finally, a relationship between alprazolam concentrations achieved by HPLC and ATR-IR data was established using PLS-LS-SVM (partial least squares least squares support vector machines). Consequently, validity of the method was verified to yield a model with low error values (root mean square error of cross validation equal to 0.98). The model was able to predict about 99% of the samples according to R2 of prediction set. Response permutation test was also applied to affirm that the model was not assessed by chance correlations. At conclusion, ATR-IR can be a reliable method in manufacturing process in detection and qualification of alprazolam content.

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

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

PubMed

Hossain, Monowar; Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Horan, Ben; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

PubMed Central

Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M.; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations. PMID:29702645

A root-mean-square approach for predicting fatigue crack growth under random loading

NASA Technical Reports Server (NTRS)

Hudson, C. M.

1981-01-01

A method for predicting fatigue crack growth under random loading which employs the concept of Barsom (1976) is presented. In accordance with this method, the loading history for each specimen is analyzed to determine the root-mean-square maximum and minimum stresses, and the predictions are made by assuming the tests have been conducted under constant-amplitude loading at the root-mean-square maximum and minimum levels. The procedure requires a simple computer program and a desk-top computer. For the eleven predictions made, the ratios of the predicted lives to the test lives ranged from 2.13 to 0.82, which is a good result, considering that the normal scatter in the fatigue-crack-growth rates may range from a factor of two to four under identical loading conditions.

Cystatin C-Based Equation Does Not Accurately Estimate the Glomerular Filtration in Japanese Living Kidney Donors.

PubMed

Tsujimura, Kazuma; Ota, Morihito; Chinen, Kiyoshi; Adachi, Takayuki; Nagayama, Kiyomitsu; Oroku, Masato; Nishihira, Morikuni; Shiohira, Yoshiki; Iseki, Kunitoshi; Ishida, Hideki; Tanabe, Kazunari

2017-06-23

BACKGROUND Precise evaluation of a living donor's renal function is necessary to ensure adequate residual kidney function after donor nephrectomy. Our aim was to evaluate the feasibility of estimating glomerular filtration rate (GFR) using serum cystatin-C prior to kidney transplantation. MATERIAL AND METHODS Using the equations of the Japanese Society of Nephrology, we calculated the GFR using serum creatinine (eGFRcre) and cystatin C levels (eGFRcys) for 83 living kidney donors evaluated between March 2010 and March 2016. We compared eGFRcys and eGFRcre values against the creatinine clearance rate (CCr). RESULTS The study population included 27 males and 56 females. The mean eGFRcys, eGFRcre, and CCr were, 91.4±16.3 mL/min/1.73 m² (range, 59.9-128.9 mL/min/1.73 m²), 81.5±14.2 mL/min/1.73 m² (range, 55.4-117.5 mL/min/1.73 m²) and 108.4±21.6 mL/min/1.73 m² (range, 63.7-168.7 mL/min/1.73 m²), respectively. eGFRcys was significantly lower than CCr (p<0.001). The correlation coefficient between eGFRcys and CCr values was 0.466, and the mean difference between the two values was -17.0 (15.7%), with a root mean square error of 19.2. Thus, eGFRcre was significantly lower than CCr (p<0.001). The correlation coefficient between eGFRcre and CCr values was 0.445, and the mean difference between the two values was -26.9 (24.8%), with a root mean square error of 19.5. CONCLUSIONS Although eGFRcys provided a better estimation of GFR than eGFRcre, eGFRcys still did not provide an accurate measure of kidney function in Japanese living kidney donors.

Evaluation of an experimental LiDAR for surveying a shallow, braided, sand-bedded river

USGS Publications Warehouse

Kinzel, P.J.; Wright, C.W.; Nelson, J.M.; Burman, A.R.

2007-01-01

Reaches of a shallow (<1.0m), braided, sand-bedded river were surveyed in 2002 and 2005 with the National Aeronautics and Space Administration's Experimental Advanced Airborne Research LiDAR (EAARL) and concurrently with conventional survey-grade, real-time kinematic, global positioning system technology. The laser pulses transmitted by the EAARL instrument and the return backscatter waveforms from exposed sand and submerged sand targets in the river were completely digitized and stored for postflight processing. The vertical mapping accuracy of the EAARL was evaluated by comparing the ellipsoidal heights computed from ranging measurements made using an EAARL terrestrial algorithm to nearby (<0.5m apart) ground-truth ellipsoidal heights. After correcting for apparent systematic bias in the surveys, the root mean square error of these heights with the terrestrial algorithm in the 2002 survey was 0.11m for the 26 measurements taken on exposed sand and 0.18m for the 59 measurements taken on submerged sand. In the 2005 survey, the root mean square error was 0.18m for 92 measurements taken on exposed sand and 0.24m for 434 measurements on submerged sand. In submerged areas the waveforms were complicated by reflections from the surface, water column entrained turbidity, and potentially the riverbed. When applied to these waveforms, especially in depths greater than 0.4m, the terrestrial algorithm calculated the range above the riverbed. A bathymetric algorithm has been developed to approximate the position of the riverbed in these convolved waveforms and preliminary results are encouraging. ?? 2007 ASCE.

A device for testing the dynamic performance of in situ force plates.

PubMed

East, Rebecca H; Noble, Jonathan J; Arscott, Richard A; Shortland, Adam P

2017-09-01

Force plates are often incorporated into motion capture systems for the calculation of joint kinetic variables and other data. This project aimed to create a system that could be used to check the dynamic performance of force plate in situ. The proposed solution involved the design and development of an eccentrically loaded wheel mounted on a weighted frame. The frame was designed to hold a wheel mounted in two orthogonal positions. The wheel was placed on the force plate and spun. A VICON™ motion analysis system captured the positional data of the markers placed around the rim of the wheel which was used to create a simulated force profile, and the force profile was dependent on spin speed. The root mean square error between the simulated force profile and the force plate measurement was calculated. For nine trials conducted, the root mean square error between the two simultaneous measures of force was calculated. The difference between the force profiles in the x- and y-directions is approximately 2%. The difference in the z-direction was under 0.5%. The eccentrically loaded wheel produced a predictable centripetal force in the plane of the wheel which varied in direction as the wheel was spun and magnitude dependent on the spin speed. There are three important advantages to the eccentrically loaded wheel: (1) it does not rely on force measurements made from other devices, (2) the tests require only 15 min to complete per force plate and (3) the forces exerted on the plate are similar to those of paediatric gait.

MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

PubMed Central

Wang, Liye; Wee, Chong-Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

2015-01-01

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge. PMID:25822851

Over length quantification of the multiaxial mechanical properties of the ascending, descending and abdominal aorta using Digital Image Correlation.

PubMed

Peña, Juan A; Corral, Victoria; Martínez, Miguel A; Peña, Estefanía

2018-01-01

In this paper, we hypothesize that the biaxial mechanical properties of the aorta may be dependent on arterial location. To demonstrate any possible position-related difference, our study analyzed and compared the biaxial mechanical properties of the ascending thoracic aorta, descending thoracic aorta and infrarenal abdominal aorta stemming from the same porcine subjects, and reported values of constitutive parameters for well-known strain energy functions, showing how these mechanical properties are affected by location along the aorta. When comparing ascending thoracic aorta, descending thoracic aorta and infrarenal abdominal aorta, abdominal tissues were found to be stiffer and highly anisotropic. We found that the aorta changed from a more isotropic to a more anisotropic tissue and became progressively less compliant and stiffer with the distance to the heart. We observed substantial differences in the anisotropy parameter between aortic samples where abdominal samples were more anisotropic and nonlinear than the thoracic samples. The phenomenological model was not able to capture the passive biaxial properties of each specific porcine aorta over a wide range of biaxial deformations, showing the best prediction root mean square error ε=0.2621 for ascending thoracic samples and, especially, the worst for the infrarenal abdominal samples ε=0.3780. The micro-structured model with Bingham orientation density function was able to better predict biaxial deformations (ε=0.1372 for ascending thoracic aorta samples). The root mean square error of the micro-structural model and the micro-structured model with von Mises orientation density function were similar for all positions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time

PubMed Central

Martin, Stephanie L.-O.; Carek, Andrew M.; Kim, Chang-Sei; Ashouri, Hazar; Inan, Omer T.; Hahn, Jin-Oh; Mukkamala, Ramakrishna

2016-01-01

Pulse transit time (PTT) is being widely pursued for cuff-less blood pressure (BP) monitoring. Most efforts have employed the time delay between ECG and finger photoplethysmography (PPG) waveforms as a convenient surrogate of PTT. However, these conventional pulse arrival time (PAT) measurements include the pre-ejection period (PEP) and the time delay through small, muscular arteries and may thus be an unreliable marker of BP. We assessed a bathroom weighing scale-like system for convenient measurement of ballistocardiography and foot PPG waveforms – and thus PTT through larger, more elastic arteries – in terms of its ability to improve tracking of BP in individual subjects. We measured “scale PTT”, conventional PAT, and cuff BP in humans during interventions that increased BP but changed PEP and smooth muscle contraction differently. Scale PTT tracked the diastolic BP changes well, with correlation coefficient of −0.80 ± 0.02 (mean ± SE) and root-mean-squared-error of 7.6 ± 0.5 mmHg after a best-case calibration. Conventional PAT was significantly inferior in tracking these changes, with correlation coefficient of −0.60 ± 0.04 and root-mean-squared-error of 14.6 ± 1.5 mmHg (p < 0.05). Scale PTT also tracked the systolic BP changes better than conventional PAT but not to an acceptable level. With further development, scale PTT may permit reliable, convenient measurement of BP. PMID:27976741

[Modeling of sugar content based on NIRS during cider-making fermentation].

PubMed

Peng, Bang-Zhu; Yue, Tian-Li; Yuan, Ya-Hong; Gao, Zhen-Peng

2009-03-01

The sugar content and the matrix always are being changed during cider-making fermentation. In order to measure and monitor sugar content accurately and rapidly, it is necessary for the spectra to be sorted. Calibration models were established at different fermentation stages based on near infrared spectroscopy with artificial neural network. NIR spectral data were collected in the spectral region of 12 000-4 000 cm(-1) for the next analysis. After the different conditions for modeling sugar content were analyzed and discussed, the results indicated that the calibration models developed by the spectral data pretreatment of straight line subtraction(SLS) in the characteristic absorption spectra ranges of 7 502-6 472.1 cm(-1) at stage I and 6 102-5 446.2 cm(-1) at stage II were the best for sugar content. The result of comparison of different data pretreatment methods for establishing calibration model showed that the correlation coefficients of the models (R2) for stage I and II were 98.93% and 99.34% respectively and the root mean square errors of cross validation(RMSECV) for stage I and II were 4.42 and 1.21 g x L(-1) respectively. Then the models were tested and the results showed that the root mean square error of prediction (RMSEP) was 4.07 g x L(-1) and 1.13 g x L(-1) respectively. These demonstrated that the models the authors established are very well and can be applied to quick determination and monitoring of sugar content during cider-making fermentation.

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.

Determination of propranolol hydrochloride in pharmaceutical preparations using near infrared spectrometry with fiber optic probe and multivariate calibration methods.

PubMed

Marques Junior, Jucelino Medeiros; Muller, Aline Lima Hermes; Foletto, Edson Luiz; da Costa, Adilson Ben; Bizzi, Cezar Augusto; Irineu Muller, Edson

2015-01-01

A method for determination of propranolol hydrochloride in pharmaceutical preparation using near infrared spectrometry with fiber optic probe (FTNIR/PROBE) and combined with chemometric methods was developed. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). The treatments based on the mean centered data and multiplicative scatter correction (MSC) were selected for models construction. A root mean square error of prediction (RMSEP) of 8.2 mg g(-1) was achieved using siPLS (s2i20PLS) algorithm with spectra divided into 20 intervals and combination of 2 intervals (8501 to 8801 and 5201 to 5501 cm(-1)). Results obtained by the proposed method were compared with those using the pharmacopoeia reference method and significant difference was not observed. Therefore, proposed method allowed a fast, precise, and accurate determination of propranolol hydrochloride in pharmaceutical preparations. Furthermore, it is possible to carry out on-line analysis of this active principle in pharmaceutical formulations with use of fiber optic probe.

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

PubMed Central

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-01-01

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]′), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal. PMID:26213935

Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

PubMed Central

Li, Zhining; Zhang, Yingtang; Yin, Gang

2018-01-01

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust. PMID:29373544

Determination of total phenolic compounds in compost by infrared spectroscopy.

PubMed

Cascant, M M; Sisouane, M; Tahiri, S; Krati, M El; Cervera, M L; Garrigues, S; de la Guardia, M

2016-06-01

Middle and near infrared (MIR and NIR) were applied to determine the total phenolic compounds (TPC) content in compost samples based on models built by using partial least squares (PLS) regression. The multiplicative scatter correction, standard normal variate and first derivative were employed as spectra pretreatment, and the number of latent variable were optimized by leave-one-out cross-validation. The performance of PLS-ATR-MIR and PLS-DR-NIR models was evaluated according to root mean square error of cross validation and prediction (RMSECV and RMSEP), the coefficient of determination for prediction (Rpred(2)) and residual predictive deviation (RPD) being obtained for this latter values of 5.83 and 8.26 for MIR and NIR, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of Fourier transform near-infrared spectroscopy combined with high-performance liquid chromatography in rapid and simultaneous determination of essential components in crude Radix Scrophulariae.

PubMed

Li, Xiaomeng; Fang, Dansi; Cong, Xiaodong; Cao, Gang; Cai, Hao; Cai, Baochang

2012-12-01

A method is described using rapid and sensitive Fourier transform near-infrared spectroscopy combined with high-performance liquid chromatography-diode array detection for the simultaneous identification and determination of four bioactive compounds in crude Radix Scrophulariae samples. Partial least squares regression is selected as the analysis type and multiplicative scatter correction, second derivative, and Savitzky-Golay filter were adopted for the spectral pretreatment. The correlation coefficients (R) of the calibration models were above 0.96 and the root mean square error of predictions were under 0.028. The developed models were applied to unknown samples with satisfactory results. The established method was validated and can be applied to the intrinsic quality control of crude Radix Scrophulariae.

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Application of Partial Least Square (PLS) Analysis on Fluorescence Data of 8-Anilinonaphthalene-1-Sulfonic Acid, a Polarity Dye, for Monitoring Water Adulteration in Ethanol Fuel.

PubMed

Kumar, Keshav; Mishra, Ashok Kumar

2015-07-01

Fluorescence characteristic of 8-anilinonaphthalene-1-sulfonic acid (ANS) in ethanol-water mixture in combination with partial least square (PLS) analysis was used to propose a simple and sensitive analytical procedure for monitoring the adulteration of ethanol by water. The proposed analytical procedure was found to be capable of detecting even small adulteration level of ethanol by water. The robustness of the procedure is evident from the statistical parameters such as square of correlation coefficient (R(2)), root mean square of calibration (RMSEC) and root mean square of prediction (RMSEP) that were found to be well with in the acceptable limits.

Quantitative transmission Raman spectroscopy of pharmaceutical tablets and capsules.

PubMed

Johansson, Jonas; Sparén, Anders; Svensson, Olof; Folestad, Staffan; Claybourn, Mike

2007-11-01

Quantitative analysis of pharmaceutical formulations using the new approach of transmission Raman spectroscopy has been investigated. For comparison, measurements were also made in conventional backscatter mode. The experimental setup consisted of a Raman probe-based spectrometer with 785 nm excitation for measurements in backscatter mode. In transmission mode the same system was used to detect the Raman scattered light, while an external diode laser of the same type was used as excitation source. Quantitative partial least squares models were developed for both measurement modes. The results for tablets show that the prediction error for an independent test set was lower for the transmission measurements with a relative root mean square error of about 2.2% as compared with 2.9% for the backscatter mode. Furthermore, the models were simpler in the transmission case, for which only a single partial least squares (PLS) component was required to explain the variation. The main reason for the improvement using the transmission mode is a more representative sampling of the tablets compared with the backscatter mode. Capsules containing mixtures of pharmaceutical powders were also assessed by transmission only. The quantitative results for the capsules' contents were good, with a prediction error of 3.6% w/w for an independent test set. The advantage of transmission Raman over backscatter Raman spectroscopy has been demonstrated for quantitative analysis of pharmaceutical formulations, and the prospects for reliable, lean calibrations for pharmaceutical analysis is discussed.

Determination of ensemble-average pairwise root mean-square deviation from experimental B-factors.

PubMed

Kuzmanic, Antonija; Zagrovic, Bojan

2010-03-03

Root mean-square deviation (RMSD) after roto-translational least-squares fitting is a measure of global structural similarity of macromolecules used commonly. On the other hand, experimental x-ray B-factors are used frequently to study local structural heterogeneity and dynamics in macromolecules by providing direct information about root mean-square fluctuations (RMSF) that can also be calculated from molecular dynamics simulations. We provide a mathematical derivation showing that, given a set of conservative assumptions, a root mean-square ensemble-average of an all-against-all distribution of pairwise RMSD for a single molecular species, (1/2), is directly related to average B-factors () and (1/2). We show this relationship and explore its limits of validity on a heterogeneous ensemble of structures taken from molecular dynamics simulations of villin headpiece generated using distributed-computing techniques and the Folding@Home cluster. Our results provide a basis for quantifying global structural diversity of macromolecules in crystals directly from x-ray experiments, and we show this on a large set of structures taken from the Protein Data Bank. In particular, we show that the ensemble-average pairwise backbone RMSD for a microscopic ensemble underlying a typical protein x-ray structure is approximately 1.1 A, under the assumption that the principal contribution to experimental B-factors is conformational variability. 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.