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This study evaluated the relationship between Zn in citrus leaves and some easily available soil properties using artificial neural network (ANN), gene expression programming (GEP), and stepwise regression methods. The data of 40 soil samples collected at the South of Kerman region (Iran) were used. Different combinations of input parameters, including soil texture, pH, organic carbon (OC), total neutralizing value (TNV), electrical conductivity (EC), and soil available P (Polsen), were considered. The coefficient of determination (R2), the normalized root means square error (NRMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NS) were used to evaluate the accuracy of the models. The results showed that the models with whole input parameters offered the lowest NRMSE and the highest R2. The ANN model with four input variables showed that 77% of the Zn variation in leaves could be elucidated by the explicit soil parameters, including clay, silt, OC, and Polsen. These results showed that the ANN model with six neurons in the hidden layer had the best performance in modeling Zn uptake. However, since the main goal of this research was to improve the models based on easily measurable variables, the GEP model with three input variables, including silt, OC, and Polsen, was found to be beneficial in estimating 71% of Zn-leaves variability. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructive and time‐consuming. This study constructs a prediction model for antioxidant compounds utilizing machine learning (ML) and multiple linear regression based on environmental, plant growth and agronomic fruit quality‐related parameters as well as antioxidant levels. These were studied in three farms at two‐week intervals during two years of cultivation.During the ML model screening, artificial neural network (ANN)‐boosted models displayed a moderate coefficient of determination (R2) at 0.68–0.78 and relative root mean square error (RRMSE) at 3.8–4.8% in polyphenols and total ASA levels, as well as a high R2 of 0.96 and low RRMSE at <3.0% in antioxidant activity. Additionally, we developed variable selection models regarding the antioxidant activity, and variables two and five (environmental parameters and leaf length, respectively) with high accuracy were selected. The linear regression analysis between the actual and predicted data of antioxidants in the ANN‐boosted models revealed high fitness with all parameters in almost all training, validation and test sets. Furthermore, environmental parameters are essential in developing such reliable models.We conclude that ANN‐boosted, stepwise and double‐Lasso regression models can predict antioxidant compounds with enhanced accuracy, and the relevant parameters can be easily acquired on‐site without the need for any specific equipment. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Methodological experts suggest that psychological and educational researchers should employ appropriate methods for data-driven model exploration, such as Bayesian Model Averaging and regularized regression, instead of conventional hypothesis-driven testing, if they want to explore the best prediction model. I intend to discuss practical considerations regarding data-driven methods for end-user researchers without sufficient expertise in quantitative methods. I tested three data-driven methods, i.e., Bayesian Model Averaging, LASSO as a form of regularized regression, and stepwise regression, with datasets in psychology and education. I compared their performance in terms of cross-validity indicating robustness against overfitting across different conditions. I employed functionalities widely available via R with default settings to provide information relevant to end users without advanced statistical knowledge. The results demonstrated that LASSO showed the best performance and Bayesian Model Averaging outperformed stepwise regression when there were many candidate predictors to explore. Based on these findings, I discussed appropriately using the data-driven model exploration methods across different situations from laypeople's perspectives. [ABSTRACT FROM AUTHOR]

We investigate an unexplored channel—loss given default (LGD)—through which accounting information can shape the design of debt contracts. Using a sample of defaulted bonds, we find that borrower accounting information available at contract initiation possesses significant power for predicting realized LGD at the subsequent default date. We then use this model to construct an accounting-based measure of expected LGD at the contracting date for a large sample of bond issuances. We find that this measure is positively associated with issuance date interest spread and covenant use, and document that these relations are not artifacts of an association between LGD and probability of default. We then show that accounting-based expected LGD has a stronger association with issuance date spread when the borrower's underlying accounting is more conservative and when the accounting-based LGD predictors are more persistent. Our results increase our understanding of both the informational role and contracting role of accounting information. [ABSTRACT FROM AUTHOR]

Individual modelling is a foundational approach to study the natural forest growth and in this paper, we develop a serial distance-depended individual tree model for some species in natural forest which would provide prediction and characteristics for natural species. The data used to develop individual model for natural mixed forests were collected from 712 remeasured 10-year periodic permanent sample plots of in Baihe Forest Bureau of Changbai Mountains, northeast China. Based on analyzing relationship between diameter increment of individual trees with tree size, competitive status, and site condition and finding out the major independent variables, the growth models for individual trees of 15 species in the natural mixed forests, that have good predicting precision, and easily applicable, were developed using stepwise regression method. The individual growth model developed in this study can reflect the tree increment of 15 species and be generally well suited for simulating tree and stand growth for natural mixed forests in Changbai Mountains. The research results for individual trees growth model of each species showed that main variable to influence on diameter increment of individual trees for natural mixed forests were tree size (D) and then competition index. The site condition was not related with diameter increment. The natural logarithm of DBH (lnD) and square diameter (D2) were included in the predicting models of diameter increment for all 15 species. The diameter increment was directly proportional to tree diameter for each species. For the competitive indexes in growth model, the relative diameter (RD), canopy closure (P), and the ratio of diameter of subject tree with maximum diameter (DDM) were related to diameter increment and the stand density measures were not significantly influenced on diameter increment. As canopy closure increase, tree increment decreases. The site conditions were performance less of factors in increment predictions in the model. [ABSTRACT FROM AUTHOR]

In order to obtain more accurate and reliable geotechnical parameters and improve the efficiency of geotechnical investigation, This paper takes the Quaternary Holocene lacustrine(Q4 l ) silty soil and Quaternary Upper Pleistocene eluvial(Q3el) silty soil in Huangshi City as the research objects, adopts the gray correlation analysis method to analyze the degree of association between various geotechnical parameters, establishes a stepwise regression model, and obtains empirical formulas for mechanical parameters. The results show that the mechanical parameters of the soil are related to its physical parameters, and the correlation between the mechanical parameters and the physical parameters of the two typical soils in Huangshi City is determined. The empirical formula between the mechanical parameters and the physical parameters obtained by the stepwise regression model has a good fitting effect. The research conclusions provide a reference for the rapid acquisition and calibration of relevant parameters of geotechnical engineering investigation. [ABSTRACT FROM AUTHOR]

Regression models (LEEMYR: Low Energy Electron MLT geosYnchronous orbit Regression) predict hourly 4.1–30 keV electron flux at geostationary orbit (GOES‐16) using solar wind, IMF, and geomagnetic index parameters. Multiplicative interaction and polynomial terms describe synergistic and nonlinear effects. We reduce predictors to an optimal set using stepwise regression, resulting in models with validation comparable to a neural network. Models predict 1, 3, 6, 12, and 24 hr into the future. Validation correlations are as high as 0.78 (4.1 and 11 keV, 1 hr prediction) and Heidke Skill scores (HSS) up to 0.66. A 3 hr ahead prediction is more practical, with slightly lower validation correlation (0.75) and HSS (0.61). The addition of location (MLT: magnetic local time) as a covariate, including multiplicative interaction terms, accounts for location‐dependent flux differences and variation of parameter influence, and allows prediction over the full orbit. Adding a substorm index (SME) provides minimal increase in validation correlation (0.81) showing that other parameters are good proxies for an unavailable real time substorm index. Prediction intervals on individual values provide more accurate assessments of model quality than confidence intervals on the mean values. An inverse N‐weighted least squares approach is impractical as it increases false positive warnings. Physical interpretations are not possible as spurious correlations due to common cycles are not removed. However, SME, Bz, Kp, and Dst are the highest correlates of electron flux, with solar wind velocity, density, and pressure, and IMF magnitude being less well correlated. Plain Language Summary: As high levels of electrons in the radiation belts can damage satellites, an accurate forecasting model is needed. Electron levels can be predicted from regression models using data from the solar wind, the interplanetary magnetic field, and indices measuring disturbances in Earth's magnetic field. Dependable predictions can be made using predictors from either an hour or 3 hr before electron changes occur. A 3 hr ahead prediction is more practical, giving time to respond to electron increases, and results in only slightly lower prediction ability. Key Points: Stepwise regression picks parsimonious predictive models of 4.1–30 keV geostationary electron fluxPredictor correlations with 4.1–30 keV flux are lower than those seen with higher energy electronsHowever, reasonably good predictions can be made at geosynchronous orbit over all MLT [ABSTRACT FROM AUTHOR]

Abstract Individual modelling is a foundational approach to study the natural forest growth and in this paper, we develop a serial distance-depended individual tree model for some species in natural forest which would provide prediction and characteristics for natural species. The data used to develop individual model for natural mixed forests were collected from 712 remeasured 10-year periodic permanent sample plots of in Baihe Forest Bureau of Changbai Mountains, northeast China. Based on analyzing relationship between diameter increment of individual trees with tree size, competitive status, and site condition and finding out the major independent variables, the growth models for individual trees of 15 species in the natural mixed forests, that have good predicting precision, and easily applicable, were developed using stepwise regression method. The individual growth model developed in this study can reflect the tree increment of 15 species and be generally well suited for simulating tree and stand growth for natural mixed forests in Changbai Mountains. The research results for individual trees growth model of each species showed that main variable to influence on diameter increment of individual trees for natural mixed forests were tree size (D) and then competition index. The site condition was not related with diameter increment. The natural logarithm of DBH (lnD) and square diameter (D2) were included in the predicting models of diameter increment for all 15 species. The diameter increment was directly proportional to tree diameter for each species. For the competitive indexes in growth model, the relative diameter (RD), canopy closure (P), and the ratio of diameter of subject tree with maximum diameter (DDM) were related to diameter increment and the stand density measures were not significantly influenced on diameter increment. As canopy closure increase, tree increment decreases. The site conditions were performance less of factors in increment predictions in the model.

The purpose of this study is to use an extended gravity model to examine the factors that influence the demand for tourism from origin nations to Vietnam. Data from eleven major marketplaces were gathered for the study between 2005 and 2020. The research employs stepwise regression techniques to choose variables and testing procedures to identify the most suitable model among Ordinary Least Squares (OLS), Random Effects Model (REM), Fixed Effects Model (FEM), and Feasible Generalized Least Squares (FGLS) models. The gravity model comprises variables associated with geographic features, tourism supply capacity, demand attributes, and interconnected variables. Surprisingly, geographical distance holds no statistical significance in the international tourist demand model based on the gravity model, a phenomenon bolstered by globalization. Conversely, the emerging investment factor in restaurants and hotels emerges as the most pivotal determinant influencing tourism demand. Hence, the tourism sector must devise nation-specific policies that target key influencing factors and actively appeal to potential source markets to attract visitors.

Identifying significant predictors of behavioral outcomes is of great interest in many psychological studies. Lasso regression, as an alternative to stepwise regression for variable selection, has started gaining traction among psychologists. Yet, further investigation is valuable to fully understand its performance across various psychological data conditions. Using a Monte Carlo simulation and an empirical demonstration, we compared Lasso regression to stepwise regression in typical psychological datasets varying in sample size, predictor size, sparsity, and signal-to-noise ratio. We found that: (1) Lasso regression was more accurate in within-sample selection and yielded more consistent out-of-sample prediction accuracy than stepwise regression; (2) Lasso with a harsher shrinkage parameter was more accurate, parsimonious, and robust to sampling variability than the prediction-optimizing Lasso. Finally, we concluded with cautious notes and recommendations in practice on the application of Lasso regression.

The grain moisture content of maize inbred lines at maturity is one of the most important indicators for mechanical harvesting of kernels. In this study, 116 maize inbred lines from a wide range of sources were used as research materials and 30 traits of grain moisture content were analyzed using multivariate statistical analysis. The results showed that all 30 traits had some correlations. Principal component analysis downscaled the 30 traits into 10 principal component factors, reflecting 77.674% of the information in the original traits. Cluster analysis categorized the 116 inbred lines into 5 major groups containing 26, 29, 31, 16 and 14 inbred lines. Based on the D value of the overall evaluation, discriminant analysis reclassified the maize inbred lines by principal component scores and 98 maize inbred lines were correctly discriminated with a probability of 84.48%, which can be regarded as a relatively reliable clustering result. The stepwise regression method was further used to screen seven traits: GMC2, GDR1, HMC3, NH, GDR2, CD and EL and to establish a comprehensive evaluation model for the grain moisture content of maize inbred lines. Among 116 maize inbred lines, 14, represented by H21 and MS71, had the lowest grain moisture content at maturity. [ABSTRACT FROM AUTHOR]

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study's findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R2), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R2 and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R2 values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R2 values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike. [ABSTRACT FROM AUTHOR]

Identifying significant predictors of behavioral outcomes is of great interest in many psychological studies. Lasso regression, as an alternative to stepwise regression for variable selection, has started gaining traction among psychologists. Yet, further investigation is valuable to fully understand its performance across various psychological data conditions. Using a Monte Carlo simulation and an empirical demonstration, we compared Lasso regression to stepwise regression in typical psychological datasets varying in sample size, predictor size, sparsity, and signal-to-noise ratio. We found that: (1) Lasso regression was more accurate in within-sample selection and yielded more consistent out-of-sample prediction accuracy than stepwise regression; (2) Lasso with a harsher shrinkage parameter was more accurate, parsimonious, and robust to sampling variability than the prediction-optimizing Lasso. Finally, we concluded with cautious notes and recommendations in practice on the application of Lasso regression. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• This study aims to develop a rational model of the intention to risky behaviour based on behavioural characteristics, demographic information, and the amount of exposure and comparing aggregate and disaggregate models of input variables. • In this study, the self-reported data of Tehran taxi drivers was collected in the form of a questionnaire based on the theory of planned behaviour, and then the amount of intention to commit violations was predicted and modeled using stepwise regression and decision tree regression. • The variables were compared and refined in two stages of exploratory factor analysis and stepwise regression before being entered into the tree regression model. • The results obtained from different modelling cases showed that the presence or absence of the exposure variable did not have much effect on the models. The inclusion of attitude, subjective norm, perceived behavioural control and descriptive norm increased the explained variance of intention to violate. The need to predict and understand drivers' intention to violate emphasizes the importance of developing a model based on the motives behind the action. This study aims to develop a model to predict a taxi driveŕs intention to speed in an urban area using variables including psychological factors, demographic information, and exposure. Aggregate (factor-based) and disaggregate (item-based) models of input variables will also be compared. In this study, the self-reported data on Tehran taxi drivers was collected in the form of a scenario-based questionnaire which is inspired by the Theory of Planned Behaviour (TPB). The intention to commit violations was predicted and modelled using stepwise regression and decision tree regression models. The results, based on the stepwise regression model showed a higher value of fitting score for an item-based linear model (R2 = 0.695), but the final model using the tree-based regression presented a better fit for the factored model (R2 = 0.746) considering the cross-validated model with the lowest deviation criteria (RMSD = 0.26). Furthermore, in this model attitude made the greatest contribution to the prediction of intention. The results obtained from the two different modelling approaches showed that exposure and age did not have much effect on the models. A main advantage of the current study is that the variables were compared and refined in two stages of exploratory factor analysis and stepwise regression before being entered into the tree regression model. In addition, the results of the factor-based model were compared with the item-based model. [ABSTRACT FROM AUTHOR]

This study was conducted to evaluate the relationship between leaf iron and some easilyavailable soil properties in citrus orchards in the southern region of Kerman province by artificial neural network modeling and stepwise regression. For this purpose, 40 orchards were selected from the study area and the physical and chemical properties of soil and iron in the plant leaves were measured. Using artificial neural network in different models with different data from soil properties as input and leaf iron as output, the ability of these models to predict leaf iron concentration was evaluated. The results showed artificial neural network with variables of organic carbon, pH, clay, phosphorus, TNV and electrical conductivity with explanation coefficient of 0.86 and 0.81 and root mean square error (RMSE) of 14.60 and 20.13 mg.kg-1 for data Training and testing were the best models in estimating leaf iron. Comparison of regression and neural network models in the test data showed that the neural network had a higher accuracy with an explanation coefficient of 0.81 than stepwise regression with an explanation coefficient of 0.2. The amount of RMSE in the neural network also improved and increased from 27.72 mg.kg-1 in the stepwise regression model to 20.13 mg.kg1 in the neural network. Artificial neural networks have been able to predict the iron in plant leaves based on the easily-available properties of the soil, so that by choosing organic carbon as the input of the first model to the best model by selecting organic carbon, pH, clay, phosphorus, TNV and electrical conductivity, model accuracy increased. [ABSTRACT FROM AUTHOR]

Abstract Ornamental foliage plants that have a dense appearance are highly valued. One way to achieve this is by using plant growth regulators as a tool for plant growth management. In a greenhouse with a mist irrigation system, a study was conducted on dwarf schefflera, an ornamental foliage plant, which was exposed to foliar application of gibberellic acid and benzyladenine hormones. The hormones were sprayed on dwarf schefflera leaves at 0, 100, and 200 mg/l concentrations, at 15-day intervals in three stages. The experiment was conducted as a factorial based on a completely randomized design, with four replicates. The combination of gibberellic acid and benzyladenine at 200 mg/l concentration had a significant effect on leaf number, leaf area, and plant height. The treatment also resulted in the highest content of photosynthetic pigments. Furthermore, the highest soluble carbohydrate to reducing sugars ratio was observed in treatments of 100 and 200 mg/l benzyladenine, and 200 mg/l gibberellic acid + benzyladenine. Stepwise regression analysis showed that root volume was the first variable to enter the model, explaining 44% of variations. The next variable was root fresh weight, and the two-variable model explained 63% of variations in leaf number. The greatest positive effect on leaf number was related to root fresh weight (0.43), which had a positive correlation with leaf number (0.47). The results showed that 200 mg/l concentration of gibberellic acid and benzyladenine significantly improved morphological growth, chlorophyll and carotenoid synthesis, and reducing sugar and soluble carbohydrate contents in dwarf schefflera.

Due to the complex conditions and strong heterogeneity of tight sandstone reservoirs, the reservoirs should be classified and the controlling factors of physical properties should be studied. Cast thin section observations, cathodoluminescence, scanning electron microscopy (SEM), X-ray diffraction (XRD), and high-pressure mercury injection (HPMI) were used to classify and optimize the reservoir. The Brooks-Corey model and stepwise regression were used to study the fractal dimension and main controlling factors of the physical properties of the high-quality reservoir. The results show that the reservoirs in the study area can be divided into four types, and the high-quality reservoir has the best physical properties and pore-throat characteristics. In the high-quality reservoir, the homogeneity of transitional pores was the best, followed by that of micropores, and the worst was mesopores. The porosity was controlled by depth and kaolinite. The model with standardized coefficients is y = 12.454 − 0.778 × (Depth) + 0.395 × (Kaolinite). The permeability was controlled by depth, illite/montmorillonite, and siliceous cement, and the model with standardized coefficients is y = 1.689 − 0.683 × (Depth) − 0.395 × (Illite/Montmorillonite) − 0.337 × (Siliceous Cement). The pore-throat evolutionary model shows that the early-middle diagenetic period was when the reservoir physical properties were at their best, and the kaolinite intercrystalline pores and residual intergranular pores were the most important. [ABSTRACT FROM AUTHOR]

A Master of Business Administration (MBA) degree is the most popular graduate degree available. It is valuable to those who work in a wide range of business management areas or to prospective entrepreneurs. Many universities have developed compressed 8-week semesters to better meet student needs. Critics question instructor course delivery and student learning. We examine the impact of compressed semesters on MBA student satisfaction and learning in both face-to-face and online courses. Five instructors were assigned courses to be taught in a 16-week semester, followed by the same courses in an 8-week semester. At least one section of the courses was taught in face-to-face and/or online formats. Student satisfaction was measured via transactional distances as well as by their willingness to recommend the course to a friend. A total of 602 responses were obtained, of which 402 were usable. Stepwise multiple regression was used to measure satisfaction. Logistic regression was used to determine which factors influenced students to recommend the course to a friend. The results indicated that there was no reason to believe that there was a significant difference in student satisfaction, nor in their willingness to recommend the course. We also did not find a difference in learning outcomes. [ABSTRACT FROM AUTHOR]

【Objective】Screening shade-tolerant soybean varieties (lines) suitable for maize-soybean intercropping planting mode in northern Jiangsu,and constructing an accurate and reasonable soybean shade tolerance evaluation system.【Methods】The natural shade environment created by maize-soybean intercropping in northern Jiangsu was used as the treatment,and the clear seed was used as the control. 12 agronomic traits such as plant height,pod height,and branch number of 24 soybean varieties (lines) were measured at maturity stage. Principal component analysis,membership function method,and multiple linear stepwise regression analysis were used to construct shade tolerance coefficients and evaluate the comprehensive shade tolerance of each soybean variety (lines) under intercropping mode.【Results】Index of shade tolerance coefficient of 12 agronomic traits were transformed into 4 independent comprehensive indexes,representing 74.771% of the original data information. The results of comprehensive shade tolerance evaluation value (D) and cluster analysis showed that 24 soybean varieties (lines) could be divided into 3 categories according to their adaptability under shade stress,including strong shade-tolerant (six varieties/lines),moderate shade-tolerant (nine varieties/lines) and weak shade-tolerant (nine varieties/lines) . The regression equation of shade tolerance was constructed by multiple linear stepwise regression analysis：D=-1.158+0.062X2+0.249X5+0.201X7+1.002X10+0.665X11 (R²=0.972 2),and the fitting accuracy was more than 89.14%. Five shade tolerance identification indexes of pod height,pod number per plant,seed number per plant,100-seed weight and yield were selected.【Conclusion】According to the shade tolerance ability,the 24 soybean varieties (lines) were divided into three categories：strong shade-tolerant,moderate shade-tolerant and weak shade tolerant. Under the maize-soybean intercropping model in northern Jiangsu,five traits,including pod height,number of pods per plant,number of grains per plant,100 grain weight,and yield,can be selected for comprehensive evaluation of soybean shade tolerance. Under the maize-soybean intercropping mode in northern Jiangsu,five traits,including pod height,pod number per plant,seed number per plant,100 seed weight,and yield,could be selected to comprehensively evaluate the shade tolerance ability of soybean. [ABSTRACT FROM AUTHOR]

Soybean frogeye leaf spot (FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance. [ABSTRACT FROM AUTHOR]

1- Introduction Water erosion is the result of interactions between various environmental factors such as topography, soil characteristics, climate characteristics, runoff and land use and management. Sediment production is highly dependent on runoff, so doubling the speed of runoff increases its leaching capacity and transportability up to five and six times. Knowing the effective factors in sediment production plays an important role in determining the amount of sedimentation of a watershed and understanding the phenomenon of erosion and its consequences, and it can be used in prioritizing the sub-basins of a watershed. The purpose of this research is to investigate the relationship between morphological characteristics and the amount of sediment production in the catchment area of the Gharasu River in Ardabil using principal component analysis and multivariate regression, in this regard, using 16 independent variables and 1 dependent variable in 13 sub-basins of the Gharasu River. 2- Methodology As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude. In this study, 1:100,000 scale geological maps, 1:25,000 scale topographic maps, and discharge and suspended sediment statistics and information of 13 hydrometric stations of Gharasu River sub-basin were used in the 50-year period from 1350 to 1399. In order to check the correlation between independent and dependent variables and to test the normality and normal distribution of data, Shapiro-Wilk and Kolmogorov-Smirnov tests were used in SPSS software. Also, in this research, the OLI Landsat 8 satellite images were used to extract the vegetation cover index (NDVI). For this purpose, step-by-step linear regression is used to determine the most effective variables and to determine the most appropriate statistical relationship between suspended sedimentation and the variables used, and principal component analysis is used to determine the most effective factors of suspended sediment production in the watershed. 3- Results In this study, the average annual suspended sediment of the basin was considered as dependent variable and other parameters as independent variables, and Pearson correlation method was used to check the correlation between independent variables and dependent variable. The variables of time of concentration, length of the main waterway, area, Slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basin than other variables. Based on the models obtained from sediment correlation analysis, the amount of sediment produced with the concentration time variable had a positive correlation and was significant at the 5% level. The results of the analysis of the principal components show that the four factors of area, length of the main waterway, concentration time and slenderness ratio of the basin have an eigenvalue greater than 1, and the number of extracted factors is 4 factors. The first factor (area) has been able to explain 37.230 percent of the variance of all research variables. This value is 24.735, 16.950 and 9.849 percent for the second factor (length of the main waterway), the third factor (concentration time) and the fourth factor (slenderness ratio) respectively. 4- Discussion & Conclusions The present research was conducted with the aim of investigating the relationship between geomorphic parameters, vegetation and erodible formations of selected sub-basins of Gharasu basin with the average annual sediment. For this purpose, the statistics and information of 17 variables (16 independent variables and 1 dependent variable) were obtained for 13 sub-basins of Gharasu basin from the regional water of Ardabil province. The relationship between geomorphic parameters and annual precipitation was determined using stepwise multivariate regression method. The research results indicate that geomorphic parameters have a high correlation with the amount of annual sediment production and can be used in sediment prediction. Meanwhile, the variables of concentration time, length of the main waterway, area, slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basins than other variables. Among these variables, the basin concentration time variable was used in the final step-by-step regression model and was selected as the sediment predictor variable. This variable alone can predict 98% of annual precipitation. In order to ensure the appropriateness of the data, the KMO coefficient was used to analyze the main components. The value of KMO = 0.78, as a result, the data will be suitable for factor analysis. The results showed that the four factors of area, main waterway length, concentration time and elongation factor could explain 88.754% of the variance of all research variables. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Sustainable Production is the property of University of Tabriz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Geoscience is the property of Natural Gas Geoscience and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the low thickness of cold-formed steel (CFS) sections, welding of traditional studs on them is not recommended; and there is a need to use shear connectors suitable for this type of sections. In this paper, the behavior of U-shaped shear connectors as connectors compatible with CFS composite beams has been investigated, to offer practical relationships for predicting the ultimate strength of this type of connectors. Accordingly, after the development of finite element (FE) models with the ability to predict the ultimate strength of U-shaped connectors, their performance against experimental results was validated. Then, an extensive parametric study, consisting of 216 numerical samples, was accomplished to provide a reliable database. As the main and most important of this study, two methods of artificial neural networks and stepwise regression were developed, and practical formulations were proposed to predict the ultimate strength of U-shaped shear connectors in CFS composite beams. Finally, in addition to evaluating the accuracy of the proposed formulas, a comparison was made between them and the relationships proposed by AISI, AS/NZS and EC3 codes for CFS screw connections. The relationships presented in this paper can be used by practical engineers in the design process of CFS composite beams. [ABSTRACT FROM AUTHOR]

The classification of the natural and anthropogenic destabilizing factors of a telecommunications network as a complex system is presented herein. This research shows that to evaluate the parameters of a telecommunications network in the presence of destabilizing factors, it is necessary to modify classical linear methods to reduce their sensitivity to the incompleteness of a priori information. Using generalized linear models of multiple regression, a combined method was developed for assessing and predicting the survivability of a telecommunications network under conditions of uncertainty regarding the influence of destabilizing factors. The method consists of accumulating current information about the parameters and state of the network, the statistical analysis and processing of information, and the extraction of sufficient sample statistics. The basis of the developed method was balancing multiple correlation–regression analysis with the number of regression equations and the observed results. Various methods of estimating the mathematical expectation and correlation matrix of the observed results under the conditions of random loss of part of the observed data (for example, removing incomplete sample elements, substituting the average, pairwise crossing out, and substituting the regression) were analyzed. It was established that a shift in the obtained estimates takes place under the conditions of a priori uncertainty of the statistics of the observed data. Given these circumstances, recommendations are given for the correct removal of sample elements and variables with missing values. It is shown that with significant unsteadiness of the parameters and state of the network under study and a noticeable imbalance in the number of regression equations and observed results, it is advisable to use stepwise regression methods. [ABSTRACT FROM AUTHOR]

A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms.

Statistical modelling of peripheral haemodynamics indicators, which is conducted based on determining anthropometric and somatotypic features of the organism, can be considered a new way of personalising instrumental diagnostic examination. Therefore, determining the suitable rheovasographic parameters of the shin in highly skilled volleyball players of a particular somatotype is relevant. The purpose of this study was to investigate the total influence of indicators of the external structure of the body on the value of indicators reflecting the specific features of blood circulation in the shin in volleyball players of mesomorphic somatotype. A comprehensive clinical and laboratory study of the state of health and physical development of 108 volleyball players aged 16-20 years was conducted. The method of tetrapolar rheography with the use of a diagnostic computer multifunctional apparatus was used to determine the rheovasographic parameters of the shin, amplitude, time, and indicators of the ratio of amplitude and time rheovasographic parameters were evaluated. An anthropometric study of total and partial body measurements was carried out. Somatotyping was performed using the Heath-Carter method. The study selected 28 volleyball players of mesomorphic somatotype. Statistical models were created using direct stepwise multivariate regression analysis. For all amplitude and time rheovasographic parameters of the hip in volleyball players of mesomorphic somatotype, statistical models with high accuracy of description of signs were built. The 10 models included 94 anthropometric dimensions. The key determinants of shin blood circulation parameters are body circumference (most often forearm, neck, and shin circumference), chest and pelvic diameters, thickness of skin and fat folds (most often on the shin, abdomen, flank), and width of the epiphyses of long tubular bones. It was found that the indicators of external body structure within 63.84- 99.99% determine the value of indicators of regional blood circulation on the shin in volleyball players of mesomorphic somatotype. Statistical modelling makes it possible to determine the proper values of individual rheovasographic indicators on the shin depending on the anthropometric parameters of individuals of a certain gender, age, somatotype, which is especially significant for athletes of a certain sport, since their cardiovascular system indicators are greatly influenced by the specifics of sports activity

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method's effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. [ABSTRACT FROM AUTHOR]

Background and Objectives: Cotton yield and most important traits in terms of economic and breeding values are polygenic traits, and direct selection for them has not been very successful. This study was conducted to identify the relationships between yield and its related traits and to determine the most important traits affecting cotton lint-yield. Materials and Methods: an experiment including nine genotypes with two commercial varieties of Fars province (Bakhtegan and Golestan) as controls, in a randomized complete blocks design with four replications, was carried out at Darab Agricultural Research Station during two years of 2017 and 2018. In this study, the traits of plant height, monopodial length, monopodial number, sympodial length, sympodial number, number of boll per plant, boll weight, earliness percentage and lint-yield were investigated. Results: The combined variance analysis results for the studied traits showed that the genotype had a significant effect on all traits except for the monopodial length and the sympodial number at the 1% probability level. The comparison of genotype means showed that the genotypes A-NB414 and A-NBK had the highest lint-yield of 5445 and 5154 kg/ha, respectively. The lint-yield of these two genotypes were significantly different from those of the other genotypes. The genotypes A-NB414 and A-NBK also had higher values for plant height, sympodial number, number of boll per plant, and earliness percentage than the other genotypes. The phenotype correlation analysis showed a significant positive correlation between lint-yield and number of boll per plant (r=0.80**), boll weight (r=0.36**), and plant height (r=0.60**) at the 1% probability level. The stepwise regression analysis results showed that the traits of number of boll per plant, boll weight, plant height, and sympodial length had the most significant effects on cotton lint-yield (R² =0.77). According to the results of the path analysis, the number of boll per plant had the most direct effect on lint-yield (P=0.78). The indirect effect of plant height on lint-yield was exerted through the number of boll per plant. The lint-yield of these two genotypes was significantly different from other genotypes. A-NB414 and A-NBK genotypes were superior to other genotypes in plant height, sympodial number, number of boll per plant, and earliness percentage. The phenotypic correlation showed a high and significant correlation at 1% level of probability respectively between the lint-yield and the number of boll per plant (r=0.80**), boll weight (r=0.36**) and plant height (r=0.60**). The results of step-by-step regression analysis showed that the traits of boll number, boll weight, plant height and sympodial length explain the most lint-yield changes (R2 =77.59). According to the results of the path analysis, the most direct effect on the lint-yield trait was related to the number of boll per plant (P=0.78). The most indirect effect of plant height was exerted on lint-yield through the number of boll per plant. Conclusion: Considering the very high correlation between the lint-yield and the number of boll per plant, it can be concluded that the number of boll per plant is the main factor causing genotype differences in lint-yield. Since boll weight, number of boll per plant and plant height have the largest direct effects on cotton lint-yield, these traits can be used as selection criteria. Additionally, since the number of boll per plant has the most direct and indirect effect on lint-yield, it can be used to increase lintyield in breeding programs. [ABSTRACT FROM AUTHOR]

Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K-1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn’s disease. [ABSTRACT FROM AUTHOR]

Background: Leader empowering behaviors have a positive effect on quality nursing care, nurses’ job satisfaction and organizational outcomes. Leaders empower nurses through motivating staff, enhancing the meaningfulness of work, facilitating nurses’ efforts to learn, grow and use their skills to participate in decision-making. They also exhibit confidence in employees’ abilities and performance, support them with information and resources, and increase their work autonomy. This study aimed to evaluate Jordanian nurse leaders’ empowering behaviors and shed light on the contributing factors as perceived by staff nurses. Methods: Descriptive, cross-sectional, correlational designs were used. A convenience sample of 414 registered nurses from 12 hospitals in Jordan was recruited to complete the leader empowering behaviors scale developed by Hui. Results: Jordanian staff nurses reflected moderate levels of leader empowering behaviors. Seven factors were found to affect Jordanian nurse leaders’ empowering behaviors as perceived by staff nurses: shift work, staff participation in continuing education programs, leadership style, hospital type, nurses’ age and experience, and daily census rate of the department. A multiple linear regression model debriefed three variables (continuing education programs, nurses’ experience, and shift work). These variables explained approximately 55% of the variance in leader empowering behaviors. Conclusion: This study deepens the understanding that nurses’ capabilities and experience are the most important factors in nurse leader empowering behaviors as perceived by Jordanian nurses. For better empowerment, nurse managers should encourage staff nurse participation in different continuing education programs to advance their skills and abilities, and enhance professional development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The reconstruction models of hourly 10‐m wind speeds were developed for each of 2384 stations over China using stepwise regression, random forest and XGBoost machine learning approaches based on hourly observed and ERA5 reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets. Furthermore, the procedures employed the past dynamically consistent states of the atmosphere simulated by ERA5 reanalysis techniques to reduce/remove wind‐observed data impacts of long‐term non‐meteorological condition changes over time (e.g., urbanization around weather stations), which provide homogenous hourly wind speed data sets from 1959 to 2021. The systematic errors of the models' simulations derived from three approaches are similarly small, with almost two orders of magnitude smaller than ERA5 original data sets. The systematic errors of reconstructed data sets derived from stepwise regression are similar to its simulation; however, the biases from two machine learning methods are even much greater than ERA5 original data sets. This result implies that machine learning methods are not suitable for such typical time‐series predictions using the previous‐hour wind speed as a predictor to reconstruct wind speed data for the next hour. Therefore, stepwise regression was selected to reconstruct hourly wind speed data sets, which have much better quality than ERA5 reanalysis data with the median correctness increased by >50% and the median rRMSE decreased by 25%–50%. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China. Plain Language Summary: The reliable wind speed data with great spatial and temporal coverages are essential meteorological information for more accurately evaluating and forecasting wind power characteristics and variabilities. The observed historical wind speed data sets have various limitations: insufficient spatial/temporal coverages, non‐meteorological condition changes over time (e.g., urbanization around weather stations, measurement instruments, station locations). The reanalysis products cannot accurately reproduce surface wind fields since the systematic biases are usually caused by the reanalysis data assimilation processing/modeling. To overcome the limitations of both data sets, the reconstruction models of hourly wind speeds were developed based on hourly observed and reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets and employed the past dynamically consistent states of the atmosphere simulated by reanalysis techniques to reduce/remove wind‐observed data impacts of non‐meteorological condition changes over time. Therefore, the reconstruction models are able to provide homogenous hourly wind speed data sets from 1959 to 2021 for available stations over China, which have much better quality than the reanalysis data. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China. Key Points: The reconstruction models of hourly wind speed data sets for available stations over China were developed using three different approachesThe hourly wind speed data from 1959 to 2021 for 2384 stations over China were reconstructed based on ERA5 reanalysis/observed data setsThe reconstructed historical homogeneous hourly wind speed data have advantages of being better quality than reanalysis data [ABSTRACT FROM AUTHOR]

The paper highlights the impact of the accumulation (preparatory) period on the changes in the level of motor skills in rugby league players (n=20) of the first league in the Slovak Republic. Motor skills were measured using fifteen tests (Star Run, Zig-Zag Run, Agility T-Run, 22 m hopping & bounding test, diagonal run, bench press, barbell squat, deadlift, solid ball throw, standing long jump, 1-min push-ups, pull-ups, 30 m run, 1-min situps, 1-min squats) during the months of September, November and December. Statistical significance of changes was monitored by t-test at 5% and 1% significance level. Stepwise regression made it possible to reveal the motor skills that determined performance in agility tests. The accumulation period enriched with developmental means of speed, speed-strength and strength abilities had a positive effect on the results of all motor tests (p < 0.01). The greatest correlation was observed between the lower limb explosive endurance (22 m hopping & bounding test, Beta*r = 0.663 - 0.834), straight line acceleration (30 m run, Beta*r = 0.793 - 0.855), and speed endurance (384 m diagonal run, Beta*r = 0.723) on one side and the Agility Star run, Zig-Zag Agility Run and Agility T-Run on the other. The obtained results showed the focus of the preparatory period, which aims to develop the motor skills determining the game performance of players and the team. It was demonstrated that speed, speed-strength and strength skills are significant determinants of agility performance in the game of rugby. Compared to published standards, the general motor performance of the studied sample of rugby players is rated as average to poor. [ABSTRACT FROM AUTHOR]

Introduction Pepper is a rich source of essential vitamins and minerals. Like tomatoes, pepper plays an important role in preventing heart diseases due to its high amount of antioxidants. Fruit yield is a complex trait that is not only controlled by several genes, but also greatly influenced by the environment. On the other hand, fruit yield is affected by a large number of other traits and their interaction. Therefore, it is very important for plant breeders to know the relationships between these traits and their interaction effects. The path coefficient analysis is a method that clarifies the relationships between traits and their direct and indirect effects on fruit yield. In this method, the correlation coefficient between two attributes is divided into components that measure direct and indirect effects. Considering the limited studies regarding the evaluation of relationships between fruit yield and other traits affecting fruit yield in pepper, this research was conducted with the aim of identifying these important relationships and evaluating their direct and indirect effects in Iranian pepper populations. Materials and Methods In order to carry out this research, the seeds of 30 Iranian pepper accessions were collected directly from the farmers. The experiment was conducted in the form of pot cultivation in the research greenhouse of the Faculty of Agriculture of Urmia University in a completely randomized design with five replications during 2015-2016. After the flowering stage, the desired traits were estimated. Variance analysis was estimated, after examining the basic hypotheses of variance analysis by SAS9.4, as well as the genotypic and phenotypic correlation between traits based on the restricted maximum likelihood (REML) procedure in the SAS9.4 software. Step-by-step regression analysis was used to determine the traits with the most variation justified the fruit yield. The Durbin-Watson test was performed to investigate the independence of experimental errors. Analysis of path coefficients was performed based on the results of stepwise regression and genotypic correlation of traits in the R V.4.0.5. Results and Discussion In order to understand the relationships between traits and use them in breeding programs, the phenotypic correlation was estimated. In this study based on the results of phenotypic correlation, leaf width and leaf length (0.651), single fruit weight and fruit circumference (0.784), fruit circumference and fruit diameter (0.625) and pulp weight and fruit diameter (0.610), showed positive and significant correlation. The purpose of estimating genotypic correlation coefficient is to determine relationships in conditions which in environmental factors are not involved. In the investigation of genotypic correlation, a positive, strong and significant relationship between fruit yield and pulp weight (0.907), fruit circumference (0.891), fruit diameter (0.697), single fruit weight (0.646) and around the plant (0.381) were observed. Given that most of these traits are factors contributing to fruit yield, the presence of such positive and significant genotypic correlation coefficients is reasonable. It can be inferred that pepper accessions with higher fruit characteristics, encompassing factors such as plant density and branching, are likely to exhibit higher fruit yields as well. It's important to note that correlation coefficients are mathematical tools used to measure the linear relationship between two variables. Their significance lies in their mathematical interpretation, and as such, they alone do not provide sufficient proof of a cause-and-effect relationship. Utilizing the results of stepwise regression, less impactful traits or those with minimal effects were eliminated from the model. As a result, seven key traits were identified as the most influential factors affecting fruit yield: pulp weight, plant density, fruit diameter, fruit count, plant height, total seed weight, and branch count.The first characteristic was pulp weight, which was included in the model and explained 78.8% of the fruit yield changes between genotypes. The second characteristic (around the plant) along with pulp weight explained 80.9% of the fruit yield variations. Fruit diameter, together with the previous two characteristics, explained 81.5% of fruit yield variations. In total, the traits included in the model for fruit yield justified 84.6% of the total changes in fruit yield in 30 pepper accessions. In order to better understanding and more accurately interpret of the results, as well as to know the direct and indirect effects and the effect of the traits that were entered into the model through stepwise regression, the path coefficient analysis method was used in this research. Fruit diameter (0.709) and pulp weight (0.289) respectively showed the most positive and direct effect on fruit yield. Fruit pulp weight through fruit diameter had the most positive indirect effect (0.595) on fruit yield. Around the plant showed an indirect positive effect on fruit yield through pulp weight (0.157), fruit diameter (0.392) and number of branches (0.080). Conclusion In the present study, the trait of fruit diameter had a positive, strong and significant genotypic correlation (0.697) with fruit yield, and it also showed a positive direct effect (0.709) on fruit yield, these two coefficients can be considered equal, Approximately. Therefore, direct selection based on fruit diameter proves to be a valuable strategy for enhancing fruit yield. The magnitude of residual effects serves as an indicator of the model's accuracy in path analysis. When this value is substantial, it may be advisable to incorporate additional causal variables into the model. In the current study, the residual effects value (0.213) affirms the model's optimal accuracy.This research highlights the effectiveness of employing stepwise multivariate regression and path coefficient analysis to gain a deeper understanding of the fundamental relationships between traits. It underscores that relying solely on correlation relationships is insufficient for comprehensively justifying the associations between these traits.

The estimation of vertical ground reaction forces (VGRFs) during running is necessary to understand running mechanisms. For this purpose, the use of force platforms is fundamental. However, to extend the study of VGRFs to real conditions, wearable accelerometers are a promising alternative to force platforms, whose use is often limited to the laboratory environment. The objective of this study was to develop a VGRF model using wearable accelerometers and a stepwise regression algorithm. Several models were developed and validated using the VGRFs and acceleration signals collected during 100 stances performed by one participant. The validated models were tested on eight participants. In a sensitivity study, the strongest correlations were observed at cut-off frequencies of ≤25 Hz and in models developed with 30 to 90 stances. After the validation phase, the 10 best models had, on average, low relative differences (≤10%) in the estimation of discrete VGRF parameters, i.e., the passive peak (εpp=6.26%), active peak (εap=2.22%), and loading rate (εlr=2.17%). The results indicate that the development of personalized models is more suitable for achieving the best estimates. The proposed methodology opens many perspectives for monitoring VGRFs under real conditions using a limited number of wearable sensors.

The effect of heat stress on yield, yield component and some agronomic traits of barley, was evaluated during the growing season of 2022-2023 using a randomized complete block design with three replications under normal and heat stress conditions. Eight barley cultivars including Nik, Mehr, Khatam, Reyhan, Nosrat, Fajr 30, Yoosef and Afzal were grown in Abarkouh, Yazd, central Iran. The results of combined analysis of variance revealed that cultivar effect was significant in all traits except 1000 grain weight and peduncle length. Heat stress × cultivar interaction was significant for spikes/m2, grains/spike, and grain yield. Heat stress led to a significant decrease in days from sowing to physiological maturity, plant height, peduncle and spike length, spikes/m2, grains/spike, and grain yield and a significant increase in days from sowing to tillering. Grain yield under both conditions had significant negative correlation with days from sowing to tillering. Grain yield had significant positive correlation with spikes/m2 under normal condition and with spikes/m2, and grains/spike under heat stress condition. Stepwise regression results showed that grains/spike and plant height could be used as criteria for increaseing grain yield under heat stress condition. Mean comparison of data for studied cultivars showed that Reyhan and Nosrat genotypes could be suggested for cultivation under both conditions.

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R2), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R2 and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R2 values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R2 values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

The grain moisture content of maize inbred lines at maturity is one of the most important indicators for mechanical harvesting of kernels. In this study, 116 maize inbred lines from a wide range of sources were used as research materials and 30 traits of grain moisture content were analyzed using multivariate statistical analysis. The results showed that all 30 traits had some correlations. Principal component analysis downscaled the 30 traits into 10 principal component factors, reflecting 77.674% of the information in the original traits. Cluster analysis categorized the 116 inbred lines into 5 major groups containing 26, 29, 31, 16 and 14 inbred lines. Based on the D value of the overall evaluation, discriminant analysis reclassified the maize inbred lines by principal component scores and 98 maize inbred lines were correctly discriminated with a probability of 84.48%, which can be regarded as a relatively reliable clustering result. The stepwise regression method was further used to screen seven traits: GMC2, GDR1, HMC3, NH, GDR2, CD and EL and to establish a comprehensive evaluation model for the grain moisture content of maize inbred lines. Among 116 maize inbred lines, 14, represented by H21 and MS71, had the lowest grain moisture content at maturity.