Dominant role of soil iron and organic matters in arsenic transfer from soil to plant in a mine area in Hunan Province, Central China.

The transfer of arsenic (As) from soil to plant could be significantly influenced by soil parameters through regulating soil As bioavailability. To distinguish the bioavailable As provided by soil and the As uptaken by plants, herein two different soil bioavailable were defined, namely potential soil bioavailable As (evaluated through the bioavailable fraction of As) and actual soil bioavailable As (assessed through plant bioaccumulation factor, BF, and BFavailable). To identify the dominant soil parameters for the two soil bioavailable As forms, soil and plant samples were collected from a former As mine site. The results showed that the potential bioavailable As only accounted for 1.77 to 11.43% in the sampled soils, while the BF and BFavailable in the sampled vegetables ranged from 0.00 to 1.01 and 0.01 to 17.87, respectively. Despite a similar proportion of As in the residual fraction, soil with higher pH and organic matter (OM) content and lower iron (Fe) content showed a higher potential soil bioavailable As. Correlation analysis indicated a relationship between the soil pH and potential soil bioavailable As (r = 0.543, p < 0.01) and between the soil Fe and actual soil bioavailable As (r = - 0.644, p < 0.05, r = - 0.594, p < 0.05). Stepwise multiple linear regression (SMLR) analysis was employed to identify the dominant soil parameters and showed that soil pH and phosphorus (P) content could be used to predict the potential soil bioavailable As (R2 = 0.69, p < 0.001). On the other hand, soil Fe and OM could be used to predict the actual soil bioavailable As (R2 = 0.18-0.86, p < 0.001-0.015, in different vegetables). These results suggest that different soil parameters affect potential and actual soil bioavailable As. Hence, soil Fe and OM are the most important parameters controlling As transfer from soil to plant in the investigated area.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management.

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China.

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.

[Evaluation of Shallow Groundwater Quality and Optimization of Monitoring Indicators in Nanchang].

In the process of groundwater environmental monitoring, while ensuring the representativeness of groundwater quality evaluation, the number of monitoring indicators should be optimized as much as possible, which is of great significance to groundwater environmental management. Based on monitoring data of shallow groundwater in Nanchang in 2014 and 2019, the chemical characteristics of water and the changes in water quality were analyzed via statistical analysis, a Piper three-line diagram, and the entropy-weighted water quality index (EWQI). Furthermore, a key indicator optimization method based on water quality evaluation was constructed by coupling stepwise multiple linear regression analysis. The feasibility of this method was also evaluated. The results showed that the water chemistry type of groundwater in 2014 and 2019 was mainly HCO3-Ca, and the five abnormal indicators pH value, NO3-, I-, Fe, and Mn were the main influencing factors of water quality change. The water quality in 2019 was generally higher than that in 2014, which was considered as overall "moderate," and the average EWQI values of the two years were 53.72 and 82.34, respectively. The optimal model EWQImin-4 constructed based on the key indicator optimization method could better represent the actual EWQI; the key indicators included Mn, NO3-, TH, Fe, pH value, and I-; and the determination coefficient (R2) and percentage error (PE) values were 0.865 and 10.61%, respectively. Therefore, the optimization method of groundwater monitoring indicators based on entropy-weighted water quality evaluation could be used as an important reference for optimizing monitoring indicators and provide a method for regional groundwater environmental management.

Prediction of mustard yield using different machine learning techniques: a case study of Rajasthan, India.

Mustard is the second most important edible oilseed after groundnut for India. Adverse weather drastically reduces the mustard yield. Weather variables affect the crop differently during different stages of development. Weather influence on crop yield depends not only on the magnitude of weather variables but also on weather distribution pattern over the crop growing period. Hence, developing models using weather variables for accurate and timely crop yield prediction is foremost important for crop management and planning decisions regarding storage, import, export, etc. Machine learning plays a significant role as it has a decision support tool for crop yield prediction. The models for mustard yield prediction was developed using long-term weather data during the crop growing period along with mustard yield data. Techniques used for developing the model were variable selection using stepwise multiple linear regression (SMLR) and artificial neural network (SMLR-ANN), variable selection using SMLR and support vector machine (SMLR-SVM), variable selection using SMLR and random forest (SMLR-RF), variable extraction using principal component analysis (PCA) and ANN (PCA-ANN), variable extraction using PCA and SVM (PCA-SVM), and variable extraction using PCA and RF (PCA-RF). Optimal combinations of the developed models were done for improving the accuracy of mustard yield prediction. Results showed that, on the basis of model accuracy parameters nRMSE, RMSE, and RPD, the PCA-SVM model performed best among all the six models developed for mustard yield prediction of study areas. Performance of mustard yield prediction done by optimum combinations of the models was better than the individual model.

The rhizospheric transformation and bioavailability of mercury in pepper plants are influenced by selected Chinese soil types.

Understanding and prediction of mercury (Hg) phytoavailability in vegetable-soil systems is essential for controlling food chain contamination and safe vegetable production as Hg-contaminated soils pose a serious threat to human health. In this study, four typical Chinese soils (Heilongjiang, Chongqing, Yunnan, and Jilin) with varied physicochemical properties were spiked with HgCl2 to grow sweet pepper (Capsicum annuum L.) in a pot experiment under greenhouse condition. The chemical fractionation revealed a significant decrease in exchangeable Hg, while an increase in organically bound Hg in the rhizosphere soil (RS) compared to bulk soil (BS). This observation strongly highlights the vital role of organic matter on the rhizospheric Hg transformation irrespective of contamination levels and soil properties. Stepwise multiple linear regression (SMLR) analysis between Hg concentration in plants, Hg fractions in RS and BS, and soil properties showed that Hg in plant parts was significantly influenced by soil total Hg (THg) (R2 = 0.90), soil clay (R2 = 0.99), amorphous manganese oxides (amorphous Mn) (R2 = 0.97), amorphous iron oxides (amorphous Fe) (R2 = 0.70), and available Hg (R2 = 0.97) in BS. Nevertheless, in the case of RS, Hg accumulation in plants was affected by soil THg (R2 = 0.99), amorphous Mn (R2 = 0.97), amorphous Fe oxides (R2 = 0.66), soil pH, and organically bound Hg fraction (R2 = 0.96). Among all the evaluated soils (n = 04), metal (mercury) concentration in terms of plant uptake was reported highest in the Jilin soil. Based on SMLR analysis, the results suggested that the phytoavailability of Hg was mainly determined by THg and metal oxides regardless of the rhizospheric effect. These findings facilitate the estimation of Hg phytoavailability and ecological risk that may exist from Hg-contaminated areas where pepper is the dominant vegetable.

Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors.

Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure-activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were Ntrain = 62, R2 = 0.727, RMS = 0.494, F = 159.537, Q2LOO = 0.727, and Q2pred = 0.725 for the training set; and Ntest = 17, R2 = 0.721, RMS = 0.508, F = 38.773, and q2ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: Ntrain = 62, R2 = 0.956, RMS = 0.199, F = 1279.919, Q2LOO = 0.955, and Q2pred = 0.855 for the training set; and Ntest = 17, R2 = 0.880, RMS = 0.367, F = 110.980, and q2ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.

Associations between the size of individual plantar intrinsic and extrinsic foot muscles and toe flexor strength.

BACKGROUND: The size of the plantar intrinsic and extrinsic foot muscles has been shown to be associated with toe flexor strength (TFS). Previous studies adopted the size of limited plantar intrinsic foot muscles or a compartment containing several muscles as an independent variable for TFS. Among the plantar intrinsic and extrinsic foot muscles, therefore, it is unclear which muscle(s) primarily contributes to TFS production. The present study aimed to clarify this subject. METHODS: In 17 young adult men, a series of anatomical cross-sectional area of individual plantar intrinsic and extrinsic foot muscles was obtained along the foot length and the lower leg length, respectively, using magnetic resonance imaging. Maximal anatomical cross-sectional area (ACSAmax) and muscle volume (MV) for each constituent muscle of the plantar intrinsic foot muscles (flexor hallucis brevis; flexor digitorum brevis, FDB; abductor hallucis; adductor hallucis oblique head, ADDH-OH; adductor hallucis transverse head, ADDH-TH; abductor digiti minimi; quadratus plantae) and extrinsic foot muscles (flexor hallucis longus; flexor digitorum longus) were measured. TFS was measured with a toe grip dynamometry. RESULTS: TFS was significantly associated with the ACSAmax for each of the ADDH-OH (r = 0.674, p = 0.003), ADDH-TH (r = 0.523, p = 0.031), and FDB (r = 0.492, p = 0.045), and the MV of the ADDH-OH (r = 0.582, p = 0.014). As for the ADDH-OH, the correlation coefficient with TFS was not statistically different between ACSAmax and MV (p = 0.189). Stepwise multiple linear regression analysis indicated that ACSAmax and MV of the ADDH-OH alone explained 42 and 29%, respectively, of the variance in TFS. CONCLUSION: The ADDH-OH is the primary contributor to TFS production among the plantar intrinsic and extrinsic foot muscles as the result of the stepwise multiple linear regression analysis.

Watershed runoff modeling through a multi-time scale approach by multivariate empirical mode decomposition (MEMD).

Accurate runoff modeling has an important role in water resource management. Attributable to the effects of climate variability and vegetation dynamics, runoff time series is nonstationary, resulting in the difficulty of runoff modeling. Detecting the temporal features of runoff and its potential influencing factors can help to increase the modeling accuracy. Selecting the Yihe watershed in the rocky mountainous area of northern China as a case study, multivariate empirical mode decomposition (MEMD) was adopted to analyze the time scales of the monthly runoff and its influencing factors, i.e., precipitation (P), normalized difference vegetation index (NDVI), temperature (T), relative humidity (RH), and potential evapotranspiration (PE). Using the MEMD technique, the original monthly runoff and its influencing factors were decomposed into six orthogonal and bandlimited functions, i.e., intrinsic mode functions (IMF1-6) and one residue, respectively. Each IMF is a counterpart of the simple harmonic function and represents a simple but general oscillatory mode in the original time series data. The results of the IMF contribution rate showed that the annual cycle had the most important role in runoff, P, NDVI, T and PE change. The contribution of quarterly oscillation was the largest contribution for the month RH variability. The monotonic residue showed that the predominant trends of runoff, P, NDVI, T, RH, and PE were decreasing from 2006 to 2015. Stepwise multiple linear regression (SMLR) was chosen to simulate the runoff IMFs and residue. The modeling results using the IMFs and residue of the potential influencing factors as input variables (R2 ranges from 0.53 to 1.0) were better than those using the original time series of influencing factors as input variables (R2 ranges from 0.17 to 0.6). By summing all the modeled IMFs and residues, the monthly runoff model was obtained, which increased the R2 value by 24.2% compared with the SMLR model using the original time series of influencing factors as input. The results indicated that MEMD was efficient for improving the accuracy of nonstationary runoff modeling.

Accurate assessment of body weights using morphometric measurements in Landlly pigs.

The aim of the study was to evaluate 16 novel morphometric traits of Landlly piglets at weaning (6 weeks) and post weaning (8 weeks) stage and to predict corresponding body weight from the measurements. A total of 279 Landlly piglets (n = 279, 75% Landrace + 25% Ghurrah crosses) were enrolled in this study. Body length, heart girth, paunch girth, height at wither, height at back, rump width, thigh circumference, neck circumference, and body depth had high correlation coefficients (0.8-0.97) with body weight at both the stages. Stepwise regression showed that body length and heart girth contributed most in prediction of both body weights while height at wither for body weight at 6 weeks and neck circumference for body weight at 8 weeks was the next highest contributing trait. Akaike's information criterion, Bayesian information criteria, adjusted R2, concordance correlation coefficient, bias correction factor, modeling efficiency, and coefficient of model determination were used to determine the most appropriate model for the prediction of body weight. Model containing body length and heart girth was fitted best to data for prediction of body weight at both weaning and post weaning stage with adjusted R2 values of 0.94 and 0.96, respectively. Hence, 2 different models were proposed for accurately predicting body weight in Landlly pigs at 6 and 8 weeks.

Enhancement of sewage sludge dewaterability by fungal conditioning with Penicillium simplicissimum NJ12: from bench- to pilot-scale consecutive multi-batch investigations.

Bench- and pilot-scale successive multi-batch trials were conducted to investigate the performance and sustainability of fungal conditioning with Penicillium simplicissimum NJ12 for improving sludge dewatering. The dominant factors affecting the sludge dewaterability improvement by P. simplicissimum NJ12 were also identified. Fungal treatment with P. simplicissimum NJ12 at a volume fraction of 5% of the inoculum greatly improved the sludge dewaterability. This improvement was characterized by sharp decreases in the specific resistance to filtration from 1.97 × 1013 to 3.52 × 1011 m/kg and capillary suction time from 32 to 12 s within 3 days. Stepwise multiple linear regression analysis showed that a marked decrease (58.8%) in the protein content in slime extracellular polymeric substances and an increase in the zeta potential of the sludge (from -35 to -10 mV) were the most important factors that improved the dewaterability of sludge after fungal treatment. Consecutive processes of fungal treatment could be realized by recirculating the fungal-treated sludge with a recycling rate of 1:2 (Vbiotreated sludge/Vtotal sludge). The treatment effectiveness was maintained only over three successive cycles, but replenishment with fresh P. simplicissimum NJ12 would be provided periodically at set batch intervals. These findings demonstrate the possibility of P. simplicissimum NJ12-assisted fungal treatment for enhancing sludge dewatering.

Multiphase distribution and migration characteristics of heavy metals in typical sandy intertidal zones: insights from solid-liquid partitioning.

With the increase of development and utilization of coastal tidal flats, the desertification of intertidal zone is becoming more and more serious, which will inevitably lead to changes in the distribution and migration of heavy metals. This study reported the multiphase distribution and solid-liquid partitioning of Cr, Ni, Cu, Zn, Pb and Cd in typical sandy intertidal zones and predicted the migration of heavy metals with stepwise multiple linear regression. The distribution of heavy metals in surface water was comparable with that in pore water, while the content of heavy metals in suspended solids was obviously greater than that in sediments. Compared to non-sandy sediments, the bioavailability state of heavy metals extracted from sandy sediments by diethylene triamine penta-acetic acid was much smaller. The mean partitioning coefficient values (Kd) ranged from 21.56 to 166.18, which were 10-40 times lower than those of organic-rich sediments and 100-750 times lower than those of mineral soils. The dynamics in solid clay, SOC and ORP greatly affected the variations of Kd values. Clay had a significant positive correlation with bioavailability but did not have a significant correlation with logKd, indicating that the adsorption capacity of heavy metals in the intertidal zone is not the only factor controlling heavy metal migration. Stepwise multiple linear regression analysis confirmed that the prediction equations of heavy metals are composed of multiple physicochemical factors. All predicted and tested values were of the same order of magnitude, with R2 values ranging from 0.8223 to 0.9775. Although our data focus on a single species of sandy intertidal zone, characterizing the Kd value and its relationship with site-specific factors provides different tools for assessing the probability of heavy metal contamination and migration in sandy intertidal zones.

Evaluation of water quality in the South-to-North Water Diversion Project of China using the water quality index (WQI) method.

The world's longest trans-basin water diversion project, the Middle-Route (MR) of the South-to-North Water Diversion Project of China (SNWDPC), has officially been in operation for over 5 years since December 2014. Its water quality status has always attracted special attention because it is related to the health and safety of more than 58 million people and the integrity of an ecosystem covering more than 155,000 km2. This study presented and analysed the spatio-temporal variations and trends of 16 water quality parameters, including pH, water temperature (WT), dissolved oxygen (DO), permanganate index (PI), five-day biochemical oxygen demand (BOD5), fecal coliform (F. coli), total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), sulphate (SO42-), fluoride (F-), mercury (Hg), arsenic (As), selenium (Se), copper (Cu), and zinc (Zn), which were determined monthly from samples collected at 27 water quality monitoring stations in the MR of the SNWDPC from March 2016 to February 2019. The water quality index (WQI) was used to evaluate the seasonal and spatial water quality changes during the monitoring period, and a new WQImin model consisting of five crucial parameters, i.e., TP, F. coli, Hg, WT, and DO, was built by using stepwise multiple linear regression analysis. The results demonstrated that the water quality status of the MR of the SNWDPC has been steadily maintained at an "excellent" level during the monitoring period, with an overall average WQI value of 90.39 and twelve seasonal mean WQI values ranging from 87.67 to 91.82. The proposed WQImin model that uses the selected five key parameters and the weights of those parameters has exhibited excellent performance in the water quality assessment of the project, with the coefficient of determination (R2), Root Mean Square Error (RMSE), and Percentage Error (PE) values of 0.901, 2.21, 1.93%, respectively, showing that the proposed WQImin model is a useful and efficient tool to evaluate and manage the water quality. For the management department, the risk sources near certain stations with abnormally high values should be carefully inspected and strictly managed to maintain excellent water quality. The potential risks of algae proliferation in this project should be of concern in future research.

Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes.

The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.

Satisfaction survey among primary health care outpatients in the backward region: an empirical study from rural Western China.

BACKGROUND: With a growing health demand, patient satisfaction analysis is essential for evaluating the accessibility and performance of medical services. Previous studies had explored the Chinese outpatient satisfaction and influencing factors in developed areas and tertiary hospitals. Considering the lower education level, less income, and heavier economic burden, it was necessary to conduct a region-specific questionnaire survey for the outpatient's satisfaction in rural Western China. OBJECTIVE: To analyze the satisfaction of primary outpatient service in rural Western China, and explore the factors affecting outpatients' satisfaction. METHODS: Questionnaire composed of nine 5-Likert items was applied to survey outpatient satisfaction among randomly selected samples in 11 provinces of Western China. Exploratory factor analysis (EFA) was conducted to study the factor structure of questionnaire. Stepwise multiple linear regression analysis was performed to study the influencing factors. RESULTS: A total of 2,754 outpatients completed the questionnaire, the response rate was 88.7%. Respondents were most satisfied with medical staff service attitude (3.71±0.83) and least satisfied with medical cost (2.97±0.83). A 3-factor solution was adopted in EFA to explain the overall satisfaction. Factors identified were "Service attitude", "Facility and professional skills", and "Patients' cost". And, the questionnaire was proved to have good reliability and acceptable internal consistency. The stepwise multiple linear regression analysis results presented that factors, including sample hospital type (P<0.05), age (P<0.001), education level (P<0.05), occupation (P<0.01), monthly income (P<0.05), and chronic disease conditions (P<0.01) were significantly associated with the dimensional or overall satisfaction. CONCLUSION: The primary health care outpatient satisfaction in rural Western China is lower than developed areas and tertiary hospitals. Care providers in backward regions should pay more attention to patients' demographic characteristics and health status, to meet outpatients' actual demand. Efficient hospital management methods, modern technology, and staff training are needed to improve the service quality and care efficiency.

Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India.

Rice is generally grown under completely flooded condition and providing food for more than half of the world's population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22-0.98 and 24.02-607.29 kg ha-1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35-981.89 kg ha-1 and 0.98-36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.

Optimization of the Ion Source-Mass Spectrometry Parameters in Non-Steroidal Anti-Inflammatory and Analgesic Pharmaceuticals Analysis by a Design of Experiments Approach.

The flow rates of drying and nebulizing gas, heat block and desolvation line temperatures and interface voltage are potential electrospray ionization parameters as they may enhance sensitivity of the mass spectrometer. The conditions that give higher sensitivity of 13 pharmaceuticals were explored. First, Plackett-Burman design was implemented to screen significant factors, and it was concluded that interface voltage and nebulizing gas flow were the only factors that influence the intensity signal for all pharmaceuticals. This fractionated factorial design was projected to set a full 2(2) factorial design with center points. The lack-of-fit test proved to be significant. Then, a central composite face-centered design was conducted. Finally, a stepwise multiple linear regression and subsequently an optimization problem solving were carried out. Two main drug clusters were found concerning the signal intensities of all runs of the augmented factorial design. p-Aminophenol, salicylic acid, and nimesulide constitute one cluster as a result of showing much higher sensitivity than the remaining drugs. The other cluster is more homogeneous with some sub-clusters comprising one pharmaceutical and its respective metabolite. It was observed that instrumental signal increased when both significant factors increased with maximum signal occurring when both codified factors are set at level +1. It was also found that, for most of the pharmaceuticals, interface voltage influences the intensity of the instrument more than the nebulizing gas flowrate. The only exceptions refer to nimesulide where the relative importance of the factors is reversed and still salicylic acid where both factors equally influence the instrumental signal. Graphical Abstract ᅟ.

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

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

Quantitative structure-biodegradability relationships for biokinetic parameter of polycyclic aromatic hydrocarbons.

Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper, stepwise multiple linear regression analysis method was applied to establish quantitative structure biodegradability relationship (QSBR) between the chemical structure and a novel biodegradation activity index (qmax) of 20 polycyclic aromatic hydrocarbons (PAHs). The frequency B3LYP/6-311+G(2df,p) calculations showed no imaginary values, implying that all the structures are minima on the potential energy surface. After eliminating the parameters which had low related coefficient with qmax, the major descriptors influencing the biodegradation activity were screened to be Freq, D, MR, EHOMO and ToIE. The evaluation of the developed QSBR mode, using a leave-one-out cross-validation procedure, showed that the relationships are significant and the model had good robustness and predictive ability. The results would be helpful for understanding the mechanisms governing biodegradation at the molecular level.

Cariogram Model in Assessment of Dental Caries among Mentally Challenged and Visually Impaired Individuals of Udaipur, India.

OBJECTIVES: To assess the caries risk and contribution of diet, bacteria, circumstances and susceptibility sectors among special groups in comparison to the normal group of Udaipur using the Cariogram model. MATERIALS AND METHODS: A Cariogram model was used to identify risk factors among 160 subjects (40 mentally challenged, 60 visually impaired and 60 normal healthy individuals) aged 7-36 years. Statistical analysis was done using Chi-square/ Fischer's Exact followed by Marascuilo procedure and Stepwise multiple linear regression. RESULTS: Compared to the normal group (74%), mentally challenged (33%) and visually impaired (41%) groups showed less chances of avoiding future caries. Group (R= 0.660) was found to be strongest predictor for caries risk. Susceptibility sector contributed 61% for caries risk in all the groups. CONCLUSION: The caries risk was high among special groups compared to normal group. Susceptibility sector illustrated the highest contribution for caries risk in all groups.