Largest Coefficient Of Determination Research Articles

Development of a portable device for Ag+ sensing using CdTe QDs as fluorescence probe via an electron transfer process

Ag+ as one of the most commonly seen toxic heavy metal ions is involved in numerous vital biological processes, which would cause fatal damages and environmental contamination when Ag+ is excessive. In the present work, CdTe quantum dots (QDs) with green, orange, and red emission capped by mercaptoacetic acid (TGA) were synthesized at one time by controlling the synthesis time and utilized for Ag+ detection. Both fluorescence spectral red-shift and intensity decrease could be used for Ag+ discrimination. Fluorescence lifetime, Zeta potential, and XRD, etc. were carried out to analyze the detection mechanism. Results displayed that surface passivation and electron transfer due to binding effects of Ag+ to Te atom on traps of QDs could be relied on to explain the sensing mechanism. Additionally, in accordance with PCA analysis, Ag+ could be also be successfully differentiated from Hg2+ and the other metal ions. Importantly, a home-made portable device based on a 32 bit embed Micro Control Unit (MCU) system was first proposed for Ag+ detection. The power supply system adopt the mini-sized lithium cell instead of the power supply system, which ensure its practical applicability. The relative position of light source and detector is set at 90° to minimize the interference. According to the detection results, the linear detection range using the device was from 5 nM to 200 nM (with a larger coefficient of determination, R2), and the detection limit was calculated to be about 5 nM, which indicated that this proposed method and device could fulfil the practical application requirements.

Quantitative mapping of soil salinity using the DUALEM‐21S instrument and EM inversion software

AbstractTo generate baseline data for the purpose of monitoring the efficacy of remediation of a degraded landscape, we demonstrate a method for 3‐dimensional mapping of electrical conductivity of saturated soil paste extract (ECe) across a study field in central Haryana, India. This is achieved by establishing a linear relationship between calculated true electrical conductivity (σ) and laboratory measured ECe at various depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m). We estimate σ by inverting DUALEM‐21S apparent electrical conductivity (ECa) data using a quasi‐3‐dimensional inversion algorithm (EM4Soil‐V302). The best linear relationship (ECe = −11.814 + 0.043 × σ) was achieved using full solution (FS), S1 inversion algorithm, and a damping factor (λ) of 0.6 that had a large coefficient of determination (R2 = 0.84). A cross‐validation technique was used to validate the model, and given the high accuracy (RMSE = 8.31 dS m−1), small bias (mean error = −0.0628 dS m−1), large R2 = 0.82, and Lin's concordance (0.93), between measured and predicted ECe, we were well able to predict the ECe distribution at all the four depths. However, the predictions made in the topsoil (0–0.3 m) at a few locations were poor due to limited data availability in areas where ECa changed rapidly. In this regard, improvements in prediction can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies over short spatial scales. Also, equivalent results can be achieved using smaller combinations of ECa data (i.e., DAULEM‐1S, DUALEM‐2S), although with some loss in precision, bias, and concordance.

Estimates of diet selection in cattle grazing cornstalk residues by measurement of chemical composition and near infrared reflectance spectroscopy of diet samples collected by ruminal evacuation.

Six ruminally cannulated cows (570 ± 73 kg) fed corn residues were placed in a 6 × 6 Latin square to evaluate predictions of diet composition from ruminally collected diet samples. After complete ruminal evacuation, cows were fed 1-kg meals (dry matter [DM]-basis) containing different combinations of cornstalk and leaf and husk (LH) residues in ratios of 0:100, 20:80, 40:60, 60:40, 80:20, and 100:0. Diet samples from each meal were collected by removal of ruminal contents after 1-h and were either unrinsed, hand-rinsed or machine-rinsed to evaluate effects of endogenous compounds on predictions of diet composition. Diet samples were analyzed for neutral (NDF) and acid (ADF) detergent fiber, acid detergent insoluble ash (ADIA), acid detergent lignin (ADL), crude protein (CP), and near infrared reflectance spectroscopy (NIRS) to calculate diet composition. Rinsing type increased NDF and ADF content and decreased ADIA and CP content of diet samples (P < 0.01). Rinsing tended to increase (P < 0.06) ADL content of diet samples. Differences in concentration between cornstalk and LH residues within each chemical component were standardized by calculating a coefficient of variation (CV). Accuracy and precision of estimates of diet composition were analyzed by regressing predicted diet composition and known diet composition. Predictions of diet composition were improved by increasing differences in concentration of chemical components between cornstalk and LH residues up to a CV of 22.6 ± 5.4%. Predictions of diet composition from unrinsed ADIA and machine-rinsed NIRS had the greatest accuracy (slope = 0.98 and 0.95, respectively) and large coefficients of determination (r2 = 0.86 and 0.74, respectively). Subsequently, a field study (Exp. 2) was performed to evaluate predictions of diet composition in cattle (646 ± 89 kg) grazing corn residue. Five cows were placed in 1 of 10 paddocks and allowed to graze continuously or to strip-graze corn residues. Predictions of diet composition from ADIA, ADL, and NIRS did not differ (P = 0.99), and estimates of cornstalk intake tended to be greater (P = 0.09) in strip-grazed compared to continuously grazed cows. These data indicate that diet composition can be predicted by chemical components or NIRS by ruminal collection of diet samples among cattle grazing corn residues.

Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass

Many monitoring programmes of species abundance and biomass increasingly face financial pressures. Occupancy is often easier and cheaper to measure than abundance or biomass. We, therefore, explored whether measuring occupancy is a viable alternative to measuring abundance and biomass. Abundance- or biomass-occupancy relationships were studied for sixteen macrozoobenthos species collected across the entire Dutch Wadden Sea in eight consecutive summers. Because the form and strength of these relationships are scale-dependent, the analysis was completed at different spatiotemporal scales. Large differences in intercept and slope of abundance- or biomass-occupancy relationships were found. Abundance, not biomass, was generally positively correlated with occupancy. Only at the largest scale, seven species showed reasonably strong abundance-occupancy relationships with large coefficients of determination and small differences in observed and predicted values (RMSE). Otherwise, and at all the other scales, intraspecific abundance and biomass relationships were poor. Our results showed that there is no generic relationship between a species’ abundance or biomass and its occupancy. We discuss how ecological differences between species could cause such large variation in these relationships. Future technologies might allow estimating a species’ abundance or biomass directly from eDNA sampling data, but for now, we need to rely on traditional sampling technology.

Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China

Outbreaks of hand-foot-mouth disease (HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average (ARIMA) model for time series analysis was designed in this study. Eighty-four-month (from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as (1,0,1)(0,1,1)12, with the largest coefficient of determination (R 2=0.743) and lowest normalized BIC (BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations (P Box-Ljung (Q)=0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

Evaluation of the nitrogen content during the new-shoot-growing stage in apple leaves using two-dimensional correlation spectroscopy.

The new-shoot-growing stage is an important period of apple tree nutrition distribution. The objective of this study is to provide technical support for apple tree nutrition diagnosis by constructing quantitative evaluation models between the apple leaf nitrogen content during the new-shoot-growing stage and characteristic spectral parameters. The correlation coefficients between the original spectral data and the nitrogen content were calculated. Then, the sensitive bands of the nitrogen content were selected using the theory of two-dimensional (2D) correlation spectroscopy. Finally, partial least squares regression (PLSR) and support vector machine (SVM) evaluation models were established using 2 parameters: Rx (maximum spectral reflectivity in the waveband) and Sx (total spectral reflectivity in the waveband). The results showed that the sensitive bands in the 2D correlation synchronous and asynchronous spectrograms were 537–560 nm and 708–719 nm. The PLSR model can be used to estimate the nitrogen content. Compared with PLSR, SVM provided better modeling and testing results, with a larger coefficient of determination (R2) and a smaller root-mean-square error (RMSE). The SVM model based on Sx was a good backup method. The calibration R2 of the model was 0.821, its RMSE was 0.710 g·kg-1, the validation R2 was 0.768, and its RMSE was 1.019 g·kg-1. The SVM model based on 2D correlation spectroscopy can be used to quantitatively estimate the nitrogen content in apple leaves.

Artificial Neural Network (ANN) for Modelling Adsorption of Lead (Pb (II)) from Aqueous Solution

This study investigated the efficiency of rice husk carbon (RHC) for lead (Pb (II)) adsorption. The developed RHC was characterized by CHNS analyser, FTIR and FESEM. BET surface area, micropore area, micropore volume and average pore diameter of RHC were 58.54 m2/g, 14.53 m2/g, 0.007209 mL/g, and 45.46 A, respectively. Batch adsorption experiments were conducted to assess the effect of initial pH, contact time, initial Pb (II) concentration and RHC dose on Pb (II) removal. A contact time of 120 min and a pH value of 5 were found as optimum for Pb (II) adsorption; maximum adsorption occurred at 8 g/L of RHC dose. Artificial neural network (ANN) was applied to model Pb (II) adsorption. For prediction of Pb (II) adsorption from aqueous solution by RHC, the smallest mean square error (MSE) and the largest coefficient of determination (R2) values were, respectively, obtained as 6.0053 and 0.988567 with Levenberg–Marquardt algorithm (LMA). Hence, it was selected as the best training algorithm. Traincgf and traincgp functions followed this function with a MSE of 6.1496 and 6.2967, respectively. Adsorption of Pb (II) by RHC followed pseudo-second-order kinetics. The experimental data were described well by both Langmuir and Freundlich isotherm models. Thermodynamics study revealed that Pb (II) adsorption by RHC was spontaneous and endothermic, and the system randomness increased during the whole process. Pb (II) adsorption capacity of RHC was compared with different adsorbents. As evidenced by its high adsorption capacity, RHC can be used as an effective adsorbent for Pb (II) removal from aqueous solutions and wastewaters.

Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region

Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model’s efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month−1), root mean square error (1.14 BCM month−1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region.

Mapping cation exchange capacity using a Veris-3100 instrument and invVERIS modelling software

The cation exchange capacity (CEC) is one of the most important soil properties as it influences soil's ability to hold essential nutrients. It also acts as an index of structural resilience. In this study, we demonstrate a method for 3-dimensional mapping of CEC across a study field in south-west Spain. We do this by establishing a linear regression (LR) between the calculated true electrical conductivity (σ - mS/m) and measured CEC (cmol(+)/kg) at various depths. We estimate σ by inverting Veris-3100 data (ECa - mS/m) collected along 47 parallel transects spaced 12m apart. We invert the ECa data acquired from both shallow (0–0.3m) and deep (0–0.9m) array configurations, using a quasi-three-dimensional inversion algorithm (invVeris V1.1). The CEC data was acquired at 40 locations and from the topsoil (0–0.3m), subsurface (0.3–0.6m) and subsoil (0.6–0.9m). The best LR between σ and CEC was achieved using S2 inversion algorithm using a damping factor (λ)=18. The LR (CEC=1.77+0.33×σ) had a large coefficient of determination (R2=0.89). To determine the predictive capability of the LR, we validated the model using a cross-validation. Given the high accuracy (root-mean-square-error [RMSE]=1.69 cmol(+)/kg), small bias (mean-error [ME]=−0.00cmol(+)/kg) and large coefficient of determination (R2=0.88) and Lin's concordance (0.94), between measured and predicted CEC and at various depths, we conclude we were well able to predict the CEC distribution in topsoil and the subsurface. However, the predictions made in the subsoil were poor due to limited data availability in areas where ECa changed rapidly from small to large values. In this regard, improvements in prediction accuracy can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies over short spatial scales.

Surface velocity divergence model of air/water interfacial gas transfer in open-channel flows

Air/water interfacial gas transfer through a free surface plays a significant role in preserving and restoring water quality in creeks and rivers. However, direct measurements of the gas transfer velocity and reaeration coefficient are still difficult, and therefore a reliable prediction model needs to be developed. Varying systematically the bulk-mean velocity and water depth, laboratory flume experiments were conducted and we measured surface velocities and dissolved oxygen (DO) concentrations in open-channel flows to reveal the relationship between DO transfer velocity and surface divergence (SD). Horizontal particle image velocimetry measurements provide the time-variations of surface velocity divergence. Positive and negative regions of surface velocity divergence are transferred downstream in time, as occurs in boil phenomenon on natural river free-surfaces. The result implies that interfacial gas transfer is related to bottom-situated turbulence motion and vertical mass transfer. The original SD model focuses mainly on small-scale viscous motion, and this model strongly depends on the water depth. Therefore, we modify the SD model theoretically to accommodate the effects of the water depth on gas transfer, introducing a non-dimensional parameter that includes contributions of depth-scale large-vortex motion, such as secondary currents, to surface renewal events related to DO transport. The modified SD model proved effective and reasonable without any dependence on the bulk mean velocity and water depth, and has a larger coefficient of determination than the original SD model. Furthermore, modeling of friction velocity with the Reynolds number improves the practicality of a new formula that is expected to be used in studies of natural rivers.

Multicollinearity in Path Analysis: A Simple Method to Reduce Its Effects

The multicollinearity in path analysis was investigated in different scenarios. A biometrical approach identified the multicollinearity‐generating traits. Data derived from averages overestimated the correlation coefficients. The use of all sampled observations increased the accuracy in path analysis. A simple sample tracking method that reduces multicollinearity is proposed. Some data arrangement methods often used may mask correlation coefficients among explanatory traits, increasing multicollinearity in multiple regression analysis. This study was performed to determine if the harmful effects of multicollinearity might be reduced in the estimation of the X′X correlation matrix among explanatory traits. For this, data on 45 treatments (15 maize [Zea mays L.] hybrids sown in three places) were used. Three path analysis methods (traditional, with k inclusion, and traditional with trait exclusion) were tested in two scenarios: with X′X matrix estimated with all sampled observations (ASO, n = 900) and with the X′X matrix estimated with the average values of each plot (AVP, n = 180). The condition number (CN) was reduced from 3395 to 2004 when the matrix was estimated with all observations. On average, the factors that inflate the variance of regression coefficients were increased by 61% in the AVP scenario. The addition of the k coefficient reduced the CN to 85.40 and 51.17 for the ASO and AVP scenarios, respectively. Exclusion of multicollinearity‐generating traits was more effective in the ASO than the AVP scenario, resulting in CNs of 29.62 and 63.66, respectively. The largest coefficient of determination (0.977) and the smallest noise (0.150) were obtained in the ASO scenario after the exclusion of the multicollinearity‐generating traits. The use of all sampled observations does not mask the individual variances and reduces the magnitude of the correlations among explanatory traits in 90% of cases, improving the accuracy of biological studies involving path analysis.

A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system

Ammonia (NH3) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human’s vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with “Gbell” membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R2) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship.

SOURSOP LIQUOR PROCESSING: INFLUENCE OF THE PROCESS VARIABLES ON THE PHYSICAL AND CHEMICAL CHARACTERISTICS

ABSTRACT: Soursop is a well-appreciated tropical fruit, both in natura and in the form of juices and nectars. Despite its wide acceptance, there is lack of its use in the preparation of other products, such as alcoholic beverages. The objective of this study was to prepare soursop liquors by varying the concentrations of pulp and sucrose syrup with different contents of total soluble solids and to evaluate the effects of these on the physical and chemical characteristics of the beverages. In the production process, the pulp (300, 400 and 500 g) was macerated with distilled alcohol for 20 days, after which it was filtered, added to the syrup (50, 60 and 70 °Brix), bottled in polyethylene bottles and subjected to an accelerated aging. All drinks resulted in alcohol levels (15.25 to 16.69% v/v) and total sugars (12.63 to 17.97%) in accordance with the standards required by law. The experiments made with the lowest concentration of pulp showed the highest yields (84.17 and 85.25%). First-order models with interactions and 95% confidence intervals were obtained. The pulp consisted of the most significant factor, influencing the largest number of responses (yield, alcohol content, dry residue, titratable acidity, titratable acidity/soluble solids ratio and luminosity). The larger coefficient of determination (R2) values were found in the models adjusted to the data of yield, dry residue, total soluble solids, titratable acidity and total sugars, which were significant and predictive, showing values greater than 0.97.

Retrieval of the diffuse attenuation coefficient from GOCI images using the 2SeaColor model: A case study in the Yangtze Estuary

Abstract The 2SeaColor model (Salama & Verhoef, 2015) was proposed to analytically retrieve the diffuse attenuation coefficient (Kd) from remote sensing reflectance (Rrs), but its parameterization was based on approximations and subjected to large uncertainties. In this study, we present an improvement on the parameterization equations in the inverse scheme of the 2Seacolor model. The improved model is then validated with three in-situ datasets and compared with the Zhang model (Zhang & Fell, 2007) and the Lee model (Lee, Darecki et al., 2005). Validation with radiometric data shows that the 2SeaColor model provides the best estimates of Kd for the full range of observations, with the largest determination coefficient (R2 = 0.935) and the smallest root mean squared error (RMSE = 0.078 m− 1). For clear waters, where Kd(490) 0.2 m− 1, the 2SeaColor model is found to be more accurate, with an RMSE of 0.186 m− 1, compared to RMSEs of 0.279 m− 1 and 0.388 m− 1 for the Zhang model and the Lee model, respectively. The 2SeaColor model is finally applied to the GOCI (Geostationary Ocean Color Imager) level 2 product (L2P) to produce Kd maps over the Yangtze Estuary, resulting in a reasonable distribution and expected range of Kd, as for example Kd(490) was varying from 0.04 to 9.82 m− 1 for the image acquired at 02:16 UTC, on March 8th 2013. The analytical 2SeaColor model is able to provide consistently stable and fairly accurate Kd estimates in both clear and turbid waters without the need of tuning empirical coefficients from field measurements, and thus has great potential for estimating Kd over optically complex waters.

Soft Sensor Development for Distillation Columns Using Fuzzy C-Means and the Recursive Finite Newton Algorithm with Support Vector Regression (RFN-SVR)

Soft sensors are computer programs and inferential estimators with an auxiliary role in predicting the variables that are impossible or difficult to obtain by available process measurements. This paper presents a soft sensor design using the Fuzzy C-Means clustering combined with the Recursive Finite Newton algorithm to train a nonlinear support vector regression (FCM-RFN-SVR) machine. In the proposed approach, samples are split into some partitions; then, by means of RFN-SVR, a local model for each partition is set. The soft sensor presented in this paper is designed to be implemented on a distillation column so that it can predict the bottom product benzene concentration. Compared to typical support vector regression (SVR), the results demonstrate that the proposed method is more powerful in improving the generalization ability of the soft sensor (smaller mean squared error (MSE) and larger coefficient of determination (R2) for the testing data).

Non-Invasive Estimation Of Left Atrial Dominant Frequency In Atrial Fibrillation From Different Electrode Sites: Insight From Body Surface Potential Mapping.

The dominant driving sources of atrial fibrillation are often found in the left atrium, but the expression of left atrial activation on the body surface is poorly understood. Using body surface potential mapping and simultaneous invasive measurements of left atrial activation our aim was to describe the expression of the left atrial dominant fibrillation frequency across the body surface. 20 patients in atrial fibrillation were studied. The spatial distributions of the dominant atrial fibrillation frequency across anterior and posterior sites on the body surface were quantified. Their relationship with invasive left atrial dominant fibrillation frequency was assessed by linear regression analysis, and the coefficient of determination was calculated for each body surface site. The correlation between intracardiac and body surface dominant frequency was significantly higher with posterior compared with anterior sites (coefficient of determination 67±8% vs 48±2%, p<0.001). The site with largest coefficient of determination was 79.6% (p<0.001) and was a posterior site. In comparison with the site closest to lead V1 it had a coefficient of determination of 23.0% (p=0.033), and with the posterior body surface site closest to lead V9 had a coefficient of determination of 70.3% (p<0.001). Left atrial dominant fibrillation frequency was more closely represented at posterior body surface sites.

The Oswestry Risk Index

The revised Tokuhashi, Tomita and modified Bauer scores are commonly used to make difficult decisions in the management of patients presenting with spinal metastases. A prospective cohort study of 199 consecutive patients presenting with spinal metastases, treated with either surgery and/or radiotherapy, was used to compare the three systems. Cox regression, Nagelkerke's R(2) and Harrell's concordance were used to compare the systems and find their best predictive items. The three systems were equally good in terms of overall prognostic performance. Their most predictive items were used to develop the Oswestry Spinal Risk Index (OSRI), which has a similar concordance, but a larger coefficient of determination than any of these three scores. A bootstrap procedure was used to internally validate this score and determine its prediction optimism. The OSRI is a simple summation of two elements: primary tumour pathology (PTP) and general condition (GC): OSRI = PTP + (2 - GC). This simple score can predict life expectancy accurately in patients presenting with spinal metastases. It will be helpful in making difficult clinical decisions without the delay of extensive investigations.

A new metric for quantifying performance impairment on the psychomotor vigilance test

We have developed a new psychomotor vigilance test (PVT) metric for quantifying the effects of sleep loss on performance impairment. The new metric quantifies performance impairment by estimating the probability density of response times (RTs) in a PVT session, and then considering deviations of the density relative to that of a baseline-session density. Results from a controlled laboratory study involving 12 healthy adults subjected to 85 h of extended wakefulness, followed by 12 h of recovery sleep, revealed that the group performance variability based on the new metric remained relatively uniform throughout wakefulness. In contrast, the variability of PVT lapses, mean RT, median RT and (to a lesser extent) mean speed showed strong time-of-day effects, with the PVT lapse variability changing with time of day depending on the selected threshold. Our analysis suggests that the new metric captures more effectively the homeostatic and circadian process underlying sleep regulation than the other metrics, both directly in terms of larger effect sizes (4-61% larger) and indirectly through improved fits to the two-process model (9-67% larger coefficient of determination). Although the trend of the mean speed results followed those of the new metric, we found that mean speed yields significantly smaller (∼50%) intersubject performance variance than the other metrics. Based on these findings, and that the new metric considers performance changes based on the entire set of responses relative to a baseline, we conclude that it provides a number of potential advantages over the traditional PVT metrics.

Identifying effects of local and nonlocal factors of soil water storage using cyclical correlation analysis

AbstractThere are various factors governing the spatial and temporal variability of soil water storage including soil properties, topography and vegetation. Some factors act locally, whereas others act nonlocally, which means that a factor measured at one location has effect on soil water storage at another location. The objective of this study was to examine the effects of local and nonlocal controls of soil water storage in a hummocky landscape using cyclical correlation analysis. Soil water storage, soil properties and terrain indices were measured along a 128‐point transect of 576 m long from the semiarid, hummocky, prairie pothole region of North America. There are large coefficients of determination (r2) between soil water storage and sand content (r2 = 0.32–0.53), organic carbon content (r2 = 0.22–0.56), depth to carbonate layer (r2 = 0.13–0.63), wetness index (r2 = 0.25–0.45) and other variables at the measurement scale at different times, indicating strong local effects from these variables. The correlation coefficients were also calculated by physically shifting the spatial series of soil water storage with respect to that of controlling factors. The shifting improves the correlation between the spatial series, and the length of shifting indicated the difference in the response of soil water to its controlling factors. For example, the value of r2 increased more than eightfold (r2 = 0.47–0.64) after shifting the spatial series of soil water storage by 54 m, almost equal to the average length of existing slope, compared with the very weak correlation (r2 = 0.02–0.08) at the measurement scale. This indicated the nonlocal effect from the relative elevation. The identification of nonlocal effects from factors improves the prediction of soil water storage. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China

Agriculture has been identified as the major contributor of non-point source pollution of Hainan water resources. In this study, we coupled the Xinanjiang model and SWAT to a new model, EcoHAT, to assess the non-point source pollution in Hainan. The study site is located around the Songtao reservoir, Hainan Island, China, which is regarding as the most important water source system for Hainan. EcoHAT, including algorithms for the hydrological cycle, nutrient cycle, and plant growth cycle, simulated the non-point source pollution for the watershed in calculated grid cell units based on remote sensing data. Remote sensing data were used to interpret the spatial land surface information and derive the model parameters. Besides the remote sensing data, other essential databases such as the meteorological databases, soil chemical and physical databases, and plant nutrients databases were also used in this study. The EcoHAT model was calibrated and validated with 5 years of monitored water quantity and quality data in the Songtao reservoir watershed. The study results indicated that the EcoHAT model has simulated the hydrologic pollutant adequately. After the calibration and validation, the parameters were applied to simulate the nutrient and sediment transport in the Songtao watershed during 2003–2007. In the end, the effects of several specific scenarios of changes in the land covers or management practices on the local watershed nutrients transport were also simulated. The results revealed that: (1) the model has predicted the runoff volume within a range of acceptable accuracy which was reflected by a large coefficient of determination; (2) regression analysis between the observed and simulated values resulted in high values of coefficient of determination ( R 2) during the calibration and validation period. The high values of Nash–Sutcliffe simulation efficiency were achieved with a close agreement between the observed and simulated pollutants concentrations in the runoff. It also indicated that the model simulated the NO 3–N, NH 4 +–N, and P concentrations in the runoff for the Songtao watershed with considerable accuracy; (3) the sediment loads, TN, and TP, experienced temporal and spatial variations, with strong correlations existing between the parameters and the land use as well as the precipitation; (4) the scenario analysis showed that with 40% fertilization reduction, 7.51% and 7.76% reduction on TN and TP loads respectively could be reached. Besides, the conservation measures are more effective in the study area to reduce the sediment loads than in other areas.