Sum Of Squared Errors Research Articles

Effect of ultrasonic pretreatment on textural properties and sensory attributes of cooked faba beans

Drying process extends the shelf-life of fresh faba beans and makes them available all year round. Dried and cooked faba beans are used to make a variety of traditional food products. Ultrasonic pretreatment, as a modern food processing technology, can shorten the drying time of fresh legumes and improve the quality and sensory properties of products. So, the present study aimed to analyze the impact of the ultrasonic treatment process (0, 5, 10, and 15 min, 40 kHz, and 150 W) on the mass transfer rate, drying time, and effective moisture diffusivity (Deff) of fresh faba beans. Also, the effect of ultrasonic treatment on textural properties and sensory attributes of cooked faba beans was studied. By using the ultrasonic process, the rate of water extraction from fresh faba beans, and thus their dehydration rate, can be increased. With increasing the duration of ultrasonic pretreatment from 0 to 15 min, the drying time of fresh faba beans decreased from 250 min to 150 min (p < 0.05). The Deff was calculated by Fick’s second law, and it significantly increased from 0.70 × 10−9 m2 s−1 to 1.05 × 10−9 m2 s−1 when the sonication duration was extended from 0 to 15 min (p < 0.05). The Page model best fitted the drying kinetic of fresh faba beans with a coefficient of determination (r) > 0.9968, and the sum of squared error (SSE) and root mean squared error (RMSE) were also closer to zero compared to other models. The rehydration ratio of dried faba beans (after cooking) significantly increased from 308.4 % to 327.1 % with the extension of processing time from 0 to 15 min (p < 0.05). The maximum and minimum crust hardness and texture firmness values were for the untreated and sonicated samples for 15 min, respectively. The sonication increased the sensory acceptance of the cooked faba beans and the highest appearance, odor, texture, flavor, and overall acceptance were for the 10 min sonicated faba beans.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

Enhancing parameter identification for proton exchange membrane fuel cell using modified manta ray foraging optimization

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.

A multi-model evaluation of Enhanced Tunicate Swarm Optimization for parameter identification

Parameter identification for a proton exchange membrane fuel cell (PEMFC) entails employing optimisation techniques to discover the best unknown parameter values required to generate an accurate fuel cell performance prediction model. This technique, known as parameter identification, is important since manufacturers' datasheets do not usually disclose these values. To address this, the manuscript examines five optimisation strategies, including the suggested algorithm, Enhanced Tunicate Swarm Optimizer (ETSO), for predicting these parameters in PEMFCs. Each technique uses the six unknown parameters as decision variables, aiming to reduce the sum squared error (SSE) between anticipated and observed cell voltages. The data reveal that the suggested strategy outperforms existing approaches and cutting-edge optimizers. The two models are used to assess the dependability and performance of the PEMFC. The results are also compared to the non-parametric tests, and it is found that the suggested method outperforms the other algorithms in both suggested models.

Predictive Resilience Modeling Using Statistical Regression Methods

Resilience describes the capacity of systems to react to, withstand, adjust to, and recover from disruptive events. Despite numerous metrics proposed to quantify resilience, few studies predict these metrics or the restoration time to nominal performance levels, and these studies often focus on a single domain. This paper introduces three methods to model system performance and resilience metrics, which are applicable to various engineering and social science domains. These models utilize reliability engineering techniques, including bathtub-shaped functions, mixture distributions, and regression analysis incorporating event intensity covariates. Historical U.S. job loss data during recessions are used to evaluate these approaches’ predictive accuracy. This study computes goodness-of-fit measures, confidence intervals, and resilience metrics. The results show that bathtub-shaped functions and mixture distributions accurately predict curves possessing V, U, L, and J shapes but struggle with W and K shapes involving multiple disruptions or sudden performance drops. In contrast, covariate-based models effectively track all curve types, including complex W and K shapes, like the successive shocks in the 1980 U.S. recession and the sharp decline in the 2020 U.S. recession. These models achieve a high predictive accuracy for future performance and resilience metrics, evidenced by the low sum of square errors and high adjusted coefficients of determination.

Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules

AbstractParameterprediction for PV solar cells plays a crucial role in controlling andoptimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model wascarried out using the Improved Grey Wolf Optimization (IGWO) algorithm, whichbuilds upon the Grey Wolf Optimization (GWO) algorithm. The parameters requiredfor the four‐diode PV model were optimized based on a predefined objectivefunction. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimizationresults. The evaluation of the optimization results revealed that the SumSquare Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstratethat the proposed IGWO algorithm outperforms the other algorithms used in thestudy, based on the minimized SSE values. This study emphasizes the importanceof parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. Thealgorithm's superior performance, as demonstrated through extensive testing andcomparison with existing algorithms, validates its efficacy in accuratelypredicting the parameters for the PV solar cell model.

Equilibrium and kinetic modeling studies for copper and cadmium removal from aqueous solutions by ground nut husk

This study investigates the equilibrium and kinetics of copper and cadmium adsorption from aqueous solutions using groundnut husk. Characterization of the groundnut husk reveals various functional groups and a porous morphology. Isothermal studies were conducted, and different models were evaluated to fit the data. The Langmuir model exhibits the best fit for copper adsorption, while the Temkin and Dubinin–Radushkevich (DR) isotherm models show superior fitting for cadmium adsorption. Results suggest that physisorption significantly influences both cases, with mean sorption adsorption energy lower than 8000 J/mole. Error analysis reveals that the Freundlich and Temkin models demonstrate maximum adsorption capacity, with lower Chi-square (ꭓ2), Sum of Square Errors (SSE), and SAE compared to Langmuir and DR models. Additionally, intra-particle diffusion was analyzed using the Weber and Morris equation. Evaluation of model parameters indicates that cadmium demonstrates better nonlinear fitting of data compared to copper. FTIR and SEM analysis confirm the involvement of various functional groups in adsorption and reveal changes in surface distribution post-adsorption due to copper and cadmium presence.

Parameter estimation of PEM fuel cells using metaheuristic algorithms

Developing an accurate model for precise parameter estimation is crucial for the design of polymer electrolyte membrane fuel cells (PEMFC). While many studies can meet the evaluation criteria for parameter estimation, there is a significant physical inconsistency when applying the Generalized Steady State of Electrochemical Model (GSSEM) prediction in the optimization framework. This study aims to address this inconsistency by adding necessary constraints to the optimization model. It employs the Artificial Hummingbird Algorithm (AHA) and Lévy flight improvement to find unknown parameters by minimizing the sum of squared errors (SSE). The proposed method has been rigorously tested with seven PEMFC cases, demonstrating its robustness and superior accuracy compared to alternative approaches. Notably, the cathode charge transfer coefficient exhibits zero relative percent difference (RPD) between the two sources, confirming its physical consistency. Furthermore, the proposed constrained optimization model lays the groundwork for achieving consistent parameter estimation results for PEMFC when utilizing GSSEM.

A New Constitutive Model Based on Taylor Series and Partial Derivatives for Predicting High-Temperature Flow Behavior of a Nickel-Based Superalloy.

Ni-based superalloys are widely used in aerospace applications. However, traditional constitutive equations often lack the necessary accuracy to predict their high-temperature behavior. A novel constitutive model, utilizing Taylor series expansions and partial derivatives, is proposed to predict the high-temperature flow behavior of a nickel-based superalloy. Hot compression tests were conducted at various strain rates (0.01 s-1, 0.1 s-1, 1 s-1, and 10 s-1) and temperatures (850 °C to 1200 °C) to gather comprehensive experimental data. The performance of the new model was evaluated against classical models, specifically the Arrhenius and Hensel-Spittel (HS) models, using metrics such as the correlation coefficient (R), root mean square error (RMSE), sum of squared errors (SSE), and sum of absolute errors (SAE). The key findings reveal that the new model achieves superior prediction accuracy with an R value of 0.9948 and significantly lower RMSE (22.5), SSE (16,356), and SAE (5561 MPa) compared to the Arrhenius and HS models. Additionally, the stability of the first-order partial derivative of logarithmic stress with respect to temperature (∂lnσ/∂T) indicates that the logarithmic stress-temperature relationship can be approximated by a linear function with minimal curvature, which is effectively described by a second-degree polynomial. Furthermore, the relationship between logarithmic stress and logarithmic strain rate (∂lnσ/∂lnε˙) is more precisely captured using a third-degree polynomial. The accuracy of the new model provides an analytical basis for finite element simulation software. This helps better control and optimize processes, thus improving manufacturing efficiency and product quality. This study enables the optimization of high-temperature forming processes for current superalloy products, especially in aerospace engineering and materials science. It also provides a reference for future research on constitutive models and high-temperature material behavior in various industrial applications.

Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

Artificial intelligence (AI) requires complex neurocomputing algorithms (NCAs) for robustness in predictive modeling. Since 1990, a plethora of NCAs have evolved, challenging selection of the most intelligent and error-tolerant model for predicting fuel consumption rate (ØFuel) in tillage. This study adopted 12 recent NCAs in modeling 72 neurocomputing architectures and evaluated their metacognitive robustness in predicting ØFuel using 80 tillage datasets. These included Levenberg-Marquardt (trainlm), Quasi-newton (trainbfg), Powell-Beale conjugate gradient (traincgb), Scaled conjugate gradient (trainscg), Fletcher-reeves conjugate gradient (traincgf), Polak-ribiére conjugate gradient (traincgp), Onestep secant (trainoss), Bayesian regularization (trainbr), Resilient backpropagation (trainrp), Gradient descent (traingd), Learning rate gradient descent (traingdx) and Gradient descent momentum (traingdm). Tillage variables were sequentially subjected to NCA architectures learning on logsig, Purelin, and tansig neuro-activation functions, targeting 1000 epochs. Neuro-cognitive performance was evaluated through broad multi-criteria of regressed neuron input–output functions using heuristic and accuracy metrics, i.e., training time (t), number of hidden layers (NL), and neurons (Nn), optimal epoch (Eopt), Coefficient of correlation (R) and determination (R2), Mean square error (MSE), Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE), Sum square error (SSE), Prediction scatter (Tscatter), Coefficient of variation (CV) and Prediction accuracy (PA). Prediction reliability and robustness were evaluated using a20-index (a20), Willmott’s index of agreement (IOA), Index of scatter (IOS), Variance accounted for (VAF), Performance index (PI), Total score rank (ξtotal), Sensitivity index (SAij) and Anderson-Darling (AD) test. Results indicated that varying NL and Nn did not correspond with t, Eopt and PA but only occurred in single-layered trainbr and double-layered trainlm. Although PA increased at reduced convergence error limits, relationships among Eopt, t, and PA were unclear. Single-layered trainbr (14-20-1) outperformed all NCAs with R (0.9993), R2 (0.9986), MSE (6.34e-06), RMSE (0.0025), MAE (0.003), MAPE (1.4e-06), SSE (8e-04), R2All (0.9998) Tvalue (1.0), CV (3.232 %) and PA (99.81 %). However, trainlm (14-20-10-1) most accurately modeled double-layered architectures i.e. R (0.9932), R2 (0.9864), MSE (7.4e-05), RMSE (0.009), MAE (0.004), MAPE (0.003), SSE (0.003), R2All (0.9929), Tvalue (0.9998), CV (3.212 %) and PA (97.8 %). Nonetheless, trainbr (14-20-1) achieved the best reliability metrics of a20 (100 %), VAF (99.98 %), IOA (1.00), PI (1.9971), IOS (0.0115), ξtotal (284) and AD-test (2.6852). Sensitivity analysis revealed that tractive force had the most significant influence on ØFuel while tire inflation pressure had the least. Learning on transig neuro-activation function in single-layered neural network, metacognitive robustness of trainbr is superiorly merited for predicting ØFuel during tillage.

Comparison of four rheological models for estimating viscosity and rheological parameters of microwave treated Basil seed gum

Dispersion of Basil seed gum has high viscosity and exhibits shear-thinning behavior. This study aimed to analyze the influence of microwave treatment (MT) at various time intervals (0, 1, 2, and 3 min) on the viscosity and rheological behavior of Basil seed gum dispersion (0.5%, w/v). The finding of this study revealed that the apparent viscosity of Basil seed gum dispersion (non-treated dispersion) reduced from 0.330 Pa.s to 0.068 Pa.s as the shear rate (SR) increased from 12.2 s−1 to 171.2 s−1. Additionally, the apparent viscosity of the Basil seed gum dispersion reduced from 0.173 Pa.s to 0.100 Pa.s as the MT time increased from 0 to 3 min (SR = 61 s−1). The rheological properties of gum dispersion were successfully modeled using Power law (PL), Bingham, Herschel–Bulkley (HB), and Casson models, and the PL model was the best one for describing the behavior of Basil seed gum dispersion. The PL model showed an excellent performance with the maximum r-value (mean r-value = 0.942) and the minimum sum of squared error (SSE) values (mean SSE value = 5.265) and root mean square error (RMSE) values (mean RMSE value = 0.624) for all gum dispersion. MT had a considerable effect on the changes in the consistency coefficient (k-value) and flow behavior index (n-value) of Basil seed gum dispersion (p < 0.05). The k-value of Basil seed gum dispersion decreased significantly from 3.149 Pa.sn to 1.153 Pa.sn (p < 0.05) with increasing MT time from 0 to 3 min. The n-value of Basil seed gum dispersion increased significantly from 0.25 to 0.42 (p < 0.05) as the MT time increased. The Bingham plastic viscosity of Basil seed gum dispersion increased significantly from 0.029 Pa.s to 0.039 Pa.s (p < 0.05) while the duration of MT increased. The Casson yield stress of Basil seed gum dispersion notably reduced from 5.010 Pa to 2.165 Pa (p < 0.05) with increasing MT time from 0 to 3 min.

Investigating the effect of using aluminum oxide nanofluid and twisted tapes with different twisted pitches on the heat transfer efficiency and using perceptron artificial neural networks in data prediction

This study investigates the effect of aluminum oxide nanofluid with 1% and 2% volume fractions (VFs) in four different tubes with identical bending and the uniform, low-high-low, low-high, and high-low-high longitudinal states with twist ratios of 0, 2 and 4. These tubes are affected by a constant thermal flux of 10000 W/m2 and Reynolds numbers of 2000-10000. For the numerical solution of these equations, the finite difference method on the basis of the control volume and the SIMPLEC algorithm and the K-ε-RNG method were applied for the turbulent flow modeling. The determined Nusselt number from the numerical methods were evaluated applying the perceptron neural network. The most optimal increase in the Nusselt number was in the twisted tube with twist ratio of 4 in the Reynolds number of 10,000 and VF of 1% and 2% nanofluid with increasing trend of length from low to high. The most optimal increase in Nusselt number compared to base fluid was about 25%. The sum of squared error and the relative error data obtained from the hyperbolic tangent function for the training and test data were 0.080-1.331 and 0.113-0.675, and from the sigmoid function for the training and test data were 0.093-0.392 and 0.097-0.147.

Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications

Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe3O4-Ag/EG, MWCNT-alumina/water-EG, Fe3O4-Ag/water-EG, and MWCNT-SiO2/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R2), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R2 values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe3O4-Ag/water-EG resulted in an R2 value as low as 0.72, to determine the nanofluids’ effective viscosity.

Exploring AI approaches for predicting groundwater levels in coastal agro-climatic zones: a case study in Cuttack District, Odisha

Groundwater level (GWL) prediction across various time scales is essential for efficient management and governance of water resources especially in regions characterized by arid and semi-arid climates, and it holds great significance. Within certain coastal regions, agro-climatic zones give rise to challenges like water scarcity in summer and waterlogging during the rainy season, resulting in reduced GWL during scarcity periods and saltwater intrusion that contaminates groundwater. This study emphasizes on application of diverse AI methodologies, encompassing Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Wavelet Transform-based ANN (W-ANN), ANFIS (W-ANFIS), SVR (W-SVR), and LSTM (W-LSTM) models for quantitative assessment of groundwater in Odisha's Cuttack District, aiming to comprehend GWL fluctuations across the region. The investigation leverages historical groundwater data from monitoring wells, incorporating monthly datasets of rainfall, temperature, relative humidity, and GWLs. Through comparative assessment using statistical methods namely Pearson’s R (R), co-efficient of determination (R2), Root Mean Squared Error (RMSE), and Sum of Squared Error (SSE), the most precise and robust AI approach for groundwater estimation in the area is identified. The W-LSTM (R2-0.78196, RMSE- 0.09254, R-0.88428 and SSE-2.66357) and W-ANFIS (R2-0.74068, RMSE-0.08229, R-0.86063 and SSE-2.10596) hybrid algorithms consistently achieved the most accurate predictions for GWLs followed by W-SVR, W-ANN hybrid models and LSTM and ANN for all stations. Overall, this study demonstrated promising outcomes, offering a dependable foundation for water resources planners to guide future investigations into groundwater resources.

An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

SillyPutty: Improved clustering by optimizing the silhouette width.

Clustering is an important task in biomedical science, and it is widely believed that different data sets are best clustered using different algorithms. When choosing between clustering algorithms on the same data set, reseachers typically rely on global measures of quality, such as the mean silhouette width, and overlook the fine details of clustering. However, the silhouette width actually computes scores that describe how well each individual element is clustered. Inspired by this observation, we developed a novel clustering method, called SillyPutty. Unlike existing methods, SillyPutty uses the silhouette width for individual elements as a tool to optimize the mean silhouette width. This shift in perspective allows for a more granular evaluation of clustering quality, potentially addressing limitations in current methodologies. To test the SillyPutty algorithm, we first simulated a series of data sets using the Umpire R package and then used real-workd data from The Cancer Genome Atlas. Using these data sets, we compared SillyPutty to several existing algorithms using multiple metrics (Silhouette Width, Adjusted Rand Index, Entropy, Normalized Within-group Sum of Square errors, and Perfect Classification Count). Our findings revealed that SillyPutty is a valid standalone clustering method, comparable in accuracy to the best existing methods. We also found that the combination of hierarchical clustering followed by SillyPutty has the best overall performance in terms of both accuracy and speed. Availability: The SillyPutty R package can be downloaded from the Comprehensive R Archive Network (CRAN).

Forecasting and validating fat mass ratio models through anthropometric measurements and health-related factors among people with HIV: a cross-sectional investigation.

There is a limited research on predictive models of fat mass ratio (FMR) in people living with human immunodeficiency virus (HIV) (PWH). This study aimed to develop models considering anthropometric and health-related factors to predict and validate FMR in PWH regardless of sex. One hundred and six Brazilian PWH (46.4±9.8 years) were evaluated for body composition using dual-energy X-ray absorptiometry (DXA), body circumference (BC), and skinfold thicknesses (SKs). FMR predictive models were developed using stepwise linear regression, and their agreement with DXA was assessed using Bland-Altman plots. Cross-validation was performed using the predicted residual error sum of squares (PRESS) method. Six FMR estimation models were developed for PWH, with adjusted R2 ranging from 0.43 to 0.72, standard error of the estimate (SEE) from 0.16% to 0.22%, and 95% confidence interval (CI) from 1.03 to 1.15. Model 6, including thigh SK, waist BC, therapy duration, subscapular SK, education years, and abdominal SK, exhibited the highest determination power (R2 adjusted 0.72, SEE 0.16%, and 95% CI: 1.06-1.15). The agreement between DXA-based FMR and predictive models showed minimal bias (-0.03 to +0.04) and narrower limits of agreement, particularly for the top-performing model (-0.33 to +0.30). Model 6 exhibited a high adjusted Q2PRESS (0.70) and low SPRESS (0.17). Our predictive models advance the study of body composition in PWH by consolidating the use of anthropometry for diagnosing and monitoring lipodystrophy regardless of sex.

Comparing the allometric model to machine learning algorithms for aboveground biomass estimation in tropical forests

Estimating aboveground biomass (AGB) pools is critical for understanding the structural characteristics of forest ecosystems, as the quantitative analysis of biomass contributes to a better understanding of carbon sequestration and in studying the productivity within forest ecosystems. A more precise estimation of AGB is necessary for the estimation of the amount of carbon stored in an ecosystem that is needed for the valuation of the ecosystem services. Though regression models have been used previously for both tree-level and stand-level biomass estimations, supervised machine learning models have rarely been put to test in field-based estimations of AGB. This study was done in the tropical forests of Northeast India (two reserve forests in Tripura) to compare the allometric performance of weighted nonlinear regression model (NLM) to random forest regression (RFR), extreme gradient boost regression (XGB), and support vector machine regression (SVR) in estimating tree-level AGB. Due to their ease of measurement in field conditions and ensuring practical use, tree diameter at breast height (DBH) and tree height (H) were used as the independent variables to train the models. 506 trees representing 21 local tree species were inventoried. Then, the four models (NLM, RFR, XGB, and SVM) were trained separately on the data and compared to find an optimal method to estimate AGB using modeling efficiency of fit (MEfit) and Root Mean Squared Error (RMSE). The models were then validated using the predicted residual error sum of squares (PRESS) statistics. The best fit model in our study was using RFR, with a high MEfit (0.95–0.99), low RMSE (63.10–132.39 kg), and outperforming all other methods in the PRESS statistics, with the highest modeling efficiency of prediction (0.94–0.99) and lowest mean of absolute PRESS residuals (34.99–78.23). The results from Northeast India presented here provide preliminary evidence that RFR could be a reliable method for determining the field-level carbon stock and understanding the biomass dynamics for evergreen and moist deciduous tropical forests. The model accuracy may be constrained by a limited number of variables chosen to suit practical use in the field, and incorporating additional tree-level or stand-level variables such as crown diameter and stand basal area could enhance the robustness and generalizability of AGB estimation.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical s of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Enhancing microbial safety and quality of milk with ultrasonication: Kinetics modeling of pathogenic Bacteria and milk characteristics

This study investigated the non-thermal inactivation kinetics of S. aureus NCDC109, E. coli EMC17, and Salmonella Typhimurium SMC25 in milk through ultrasonication, while concurrently evaluating its impact on milk's physicochemical properties. Milk samples post-inoculation, underwent treatment with varying ultrasonic amplitudes (40–70%) over different time intervals (0–30 min). The highest logarithmic reduction of 6.12, 6.19, and 5.68 for S. aureus NCDC109, E. coli EMC17, and S. Typhimurium SMC25, respectively, was attained with 70% amplitude for 30 min. Weibull model exhibited superior fit with lower sum of squared errors (SSE), mean square error (MSE), root mean square error (RMSE), Akaike information criterion (AIC) values as compared to the linear model. The Weibull model accurately predicted D-values for all strains and treatments, with proper accuracy factor (Af) and bias factor (Bf)values. The 5-log reduction time for E. coli EMC17 decreased from 46.05 to 20.01 min with amplitude increasing from 40% to 70%. Ultrasonication preserved milk's physicochemical properties, encompassing particle size, zeta potential, color, pH and viscosity. This study highlights the potential of ultrasonication in the dairy sector to combat bacterial contamination in milk and provides a valuable tool for predicting and validating responses using kinetic models for pathogens in milk.