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When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x 2 ) or are included in interaction terms (e.g., x · z ). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.

In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional variance expression obtained when the values of X and Z are fixed. A simulation study using randomly sampled predictors compared four approaches: (a) the Aiken and West ( 1991 ) test of simple slopes at fixed population values of Z, (b) the Aiken and West test at sample-estimated values of Z, (c) a 95% percentile bootstrap confidence interval approach, and (d) a fully Bayesian approach with diffuse priors. The results showed that approach (b) led to inflated Type 1 error rates and 95% confidence intervals with inadequate coverage rates, whereas other approaches maintained acceptable Type 1 error rates and adequate coverage of confidence intervals. Approach (c) had asymmetric rejection rates at small sample sizes. We used an empirical data set to illustrate these approaches.

For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.

The multiple regression model statistical technique is employed to analyze the relationship between the dependent variable and several independent variables. The multicollinearity problem is one of the issues affecting the multiple regression model, occurring in regard to the relationship among independent variables. The ordinal least square is the standard method to evaluate parameters in the regression model, but the multicollinearity problem affects the unstable estimator. Liu regression is proposed to approximate the Liu estimators based on the Liu parameter, to overcome multicollinearity. In this paper, we propose a modified Liu parameter to estimate the biasing parameter in scaling options, comparing the ordinal least square estimator with two modified Liu parameters and six standard Liu parameters. The performance of the modified Liu parameter is considered, generating independent variables from the multivariate normal distribution in the Toeplitz correlation pattern as the multicollinearity data, where the dependent variable is obtained from the independent variable multiplied by a coefficient of regression and the error from the normal distribution. The mean absolute percentage error is computed as an evaluation criterion of the estimation. For application, a real Hepatitis C patients dataset was used, in order to investigate the benefit of the modified Liu parameter. Through simulation and real dataset analysis, the results indicate that the modified Liu parameter outperformed the other Liu parameters and the ordinal least square estimator. It can be recommended to the user for estimating parameters via the modified Liu parameter when the independent variable exhibits the multicollinearity problem. [ABSTRACT FROM AUTHOR]

A well-known property of the multiple linear regression is that its plane goes through the point of the mean values of all variables, and this feature can be used to find the model's intercept. This work shows that a regression by n predictors also passes via n additional points of the specificweighted mean values. Thus, the regression is uniquely defined by all these n+1multidimensional points ofmeans, and approximation of observations by the theoretical model collapses to the interpolation function going through the knots of the weighted means. This property is obtained from the normal system of equations which serves for finding the linear regression parameters in the ordinary least squares approach. The derived features can be applied in nonlinearmodeling for adjusting the model parameters so that the fitted values would go through the same reference points of means, that can be useful in applied regression analysis. Numerical examples are discussed. The found properties reveal the essence of regression function as hyperplane going through special points of mean values, which makes regression models more transparent and useful for solving and interpretation in various applied statistical problems. [ABSTRACT FROM AUTHOR]

Ecuador is one of the world's leading banana exporters; however, low productivity resulting from inadequate agronomic management requires an analysis of banana production units. This study aimed to define the types of banana production units based on the different agronomic management practices adopted by producers in two Ecuadorian provinces. Data from the National Institute of Statistics and Censuses (INEC) for 2021 were used, with a sample of 319 production units. Principal component and cluster analyses were applied to identify the different types of production units, resulting in four types: high technology conventional (Cluster 1), balanced conventional (Cluster 2), intensive conventional (Cluster 3), and agroecological (Cluster 4). It is important to highlight that 58% of the production units are intensive conventional and use an average of 3.5 management practices, with 98% using fertilizers, 100% using fungicides and pesticides, and 45% using improved genotypes. In contrast, agroecological production is still incipient in Ecuador (4.7%). Regression analysis showed that waste is important in high-yield production units in the three clusters. In addition, Cluster 2 relied on regional factors, family labor, and irrigation efficiency, while in intensive conventional farms (Cluster 3), banana yield was related to fungicide application. Therefore, public policies should be customized according to cluster-specific characteristics to optimize agronomic management practices and facilitate their transfer among groups. [ABSTRACT FROM AUTHOR]

This study explores the intersection of risk-taking behavior and information security compliance within the context of a federal agency, aiming to illuminate the impact of individual behaviors on cybersecurity efforts. Amidst rising expenditures on information security by both corporations and governments, and an increasing trend of sophisticated cyber-attacks, this research underscores the critical role of the human factor in safeguarding digital assets. Utilizing a quantitative correlational methodology grounded in the Knowledge, Attitudes, and Behaviors (KAB) and Risk Propensity Model (RPM) frameworks, this study engaged 127 federal employees to examine the correlation between their propensity to take risks and their adherence to information security policies. Through descriptive statistics and multiple regression analysis, the study found that risk-taking propensity significantly influences information security compliance, contrary to age, which showed no significant effect. These findings suggest that enhancing organizational cybersecurity can be achieved by focusing on the individual risk profiles of employees, proposing the development of targeted strategies to address and mitigate risk-taking behaviors. This research contributes to the ongoing discourse on optimizing information systems for improved organizational performance and highlights the necessity of integrating psychological dimensions into cybersecurity management strategies. [ABSTRACT FROM AUTHOR]

A part's surface layer quality is dictated by several factors, including surface roughness, the microstructure of the metal surface layer, and the part's mechanical and physical state. The operating characteristics of machine components, such as wear resistance, vibration resistance, contact strength, connection strength, part strength under cyclic loads, etc., are influenced by surface layer conditions. The part's surface roughness is just one of the primary geometric attributes of a part's quality, which is its accuracy in terms of size and shape. The current work models the roughness surface of 3D selective laser melting of metal materials using genetic programming and multiple regression. It then explains how to measure surface roughness using this method. A novel approach to pattern recognition for analyzing the roughness of metal materials melted using a 3D selective laser is introduced. Fractal geometry determines the complexity of 3D selective laser melting of metal materials. [ABSTRACT FROM AUTHOR]

Tourists' trip experience and tourist satisfaction are frequently explored in tourism studies. The trip experience is an amalgamation of all the experiences that tourists encounter that underpins tourists' satisfaction. Our research examines the connection between trip experiences and overall tourist satisfaction among tourists in India. For this purpose, an on-site survey was conducted at three World heritage sites and seven hotels in New Delhi, soliciting 309 responses from tourists. The data from the sample was analyzed in two steps. First, exploratory factor analysis (EFA) was performed; afterward, multiple regression analysis was done to determine the relative effect of trip experience dimensions on overall satisfaction. The results indicated that five dimensions (trip excitement, hotel choice, tour guide, tour leader, and welcoming) significantly impacted overall tourist satisfaction. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Ciencias Sociales (13159518) is the property of Revista de Ciencias Sociales de la Universidad del Zulia Venezuela 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 traditional assessment of wind resource considers the ambient factors like temperature, pressure and humidity as a secondary parameter, and focus on the statistically evaluation of wind magnitude and direction. Nevertheless, the evaluation requires a deeper analysis of the above ambient factors, because they are thermodynamically linked, and the energy flux among them will affect the wind dynamics. This research aims to analyse the interactions of temperature, pressure, and humidity variables with the wind speed with an inverse approach, it means that wind will be controlled by the ambient variables. We conducted a statistical analysis using data from a meteorological station located in the University of the Coast in Barranquilla, Colombia. The methodology is based on a Design of Experiments-Analysis of Variance (DOE-ANOVA) with a 3² factorial design. The estimated effects from the DOEANOVA results were utilized to generate standardized effect equations, for evaluating the response of wind speed during changes of the studied ambient variables (factors). The results evidenced that the applied methodology provided information about the non-linear interactions of the analysed variables, and the standardized effect equations were tested against a liner regression model with satisfactory results. [ABSTRACT FROM AUTHOR]

The fight against the decline, especially in commercial terms, of small and medium-sized town centers lies at the core of the Action Cœur de Ville and Petites Villes de Demain policies. This article demonstrates that the commercial decline is not as pervasive as it may initially appear. By utilizing the open data from the Base Permanente des Equipements, the dynamics of a selected range of "city-center" businesses (such as bakeries, butchers, pharmacies, hair salons, clothing stores, furniture stores, and appliance stores) in small and medium-sized urban areas between 2009 and 2019 (747, including 545 multi-municipal areas) have been quantified. Some businesses, such as hair salons, show an increase and spatial diffusion, while others, like appliance stores, exhibit a decrease in numbers and territorial contraction within small and medium-sized towns. The dynamics of other types of businesses are more intricate and characterized by significant variability. A multiple regression model is subsequently implemented, encompassing four explanatory factors related to demographic dynamics, business competition dynamics, daily mobility, and city center vulnerabilities. The model demonstrates a high explanatory power for expanding businesses. However, it fails to explain the declining businesses dynamics or those exhibiting highly variable patterns from one city to another. [ABSTRACT FROM AUTHOR]

This paper presents a study on the dry turning of polyoxymethylene copolymer POM-C. The effect of five factors (cutting speed, feed rate, depth of cut, nose radius, and main cutting edge angle) on machinability is evaluated using four output parameters: surface roughness, tangential force, cutting power, and material removal rate. To do so, the study relies on three approaches: (i) Pareto statistical analysis, (ii) multiple linear regression modeling, and (iii) optimization using the genetic algorithm. To conduct the investigation, mathematical models are developed using response surface methodology based on the Taguchi -16 orthogonal array. The results indicate that feed rate, nose radius, and cutting edge angle significantly influence surface quality, while depth of cut, feed, and speed have a notable impact on other machinability parameters. The developed mathematical models have determination coefficients greater than or very close to 95%, making them very useful for the industry as they allow predicting response values based on the chosen cutting parameters. Finally, the optimization using the genetic algorithm proves to be promising and effective in determining the optimal cutting parameters to maximize productivity while improving surface quality. [ABSTRACT FROM AUTHOR]

Organizational research increasingly tests moderated relationships using multiple regression with interaction terms. Most research does so with little concern regarding curvilinear relationships. But methodologists have established that omitting quadratic terms of correlated primary variables may create false interaction positives (type 1 errors). If dependent variables are generated by the canonical process where fully specified regressions satisfy the Gauss-Markov assumptions, including quadratics solves the problem. But our empirical analysis of published organizational research suggests that dependent variables are often generated by processes where, even with quadratics included, regression analyses will remain Gauss-Markov non-compliant. In such cases, our linear algebraic analysis demonstrates that including quadratics—even those motivated by compelling theory—may exacerbate rather than mitigate the incidence of false interaction positives. The interaction coefficient may substantially change its magnitude and even flip sign once quadratics are included, and not necessarily for the better. We encourage researchers to present two full sets of results when testing moderating hypotheses—one with, and one without, quadratic terms. Researchers should then answer five questions developed here in order to determine the preferable set of results. [ABSTRACT FROM AUTHOR]

For complex pattern classification, the neural network(NN) has unique advantages. However, when encountering complex data, one single NN is usually difficult to achieve satisfactory classification accuracy. In this study, the design of sub-classifier involves the selection of two distinct types of NNs. RBF or Elman NNs have some defects, which restrict the operating efficiency of classification system.The genetic neural network(GNN) is constructed to adopt a novel optimization approach combining hybrid encoding and simultaneous evolution.While being ensemble learning, integrated classification system is modeled by multiple regression(MR), and the output of each sub-classifier is used to establish regression model. The ordinary least squares(OLS) theory is employed to optimize the regression equation, and subsequently determine the weights of each sub-classifier. A novel ensemble classification algorithm is established by means of two consecutive optimization procedures.The reliability of the new algorithm is being verified through the arrangement of three experiments.The experimental results demonstrate significant enhancements in the new algorithm's generalization ability, operational efficiency, and classification precision. [ABSTRACT FROM AUTHOR]

glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has been rapidly gained recognition and popularity among ecologists. However, the previous glmm.hp package was limited to work GLMMs derived exclusively from the lme4 and nlme packages. The latest glmm.hp package has extended its functions. It has integrated results obtained from the glmmTMB package, thus enabling it to handle zero-inflated generalized linear mixed models (ZIGLMMs) effectively. Furthermore, it has introduced the new functionalities of commonality analysis and hierarchical partitioning for multiple linear regression models by considering both unadjusted R 2 and adjusted R 2. This paper will serve as a demonstration for the applications of these new functionalities, making them more accessible to users. [ABSTRACT FROM AUTHOR]

Land value is one of the economic issues of cadastral systems which is the base of sustainable urban and regional planning. The current paper concerns the estimation of the land values according to many essential factors, which are adopted as ten variables generally. Among these ten parameters, the frontage of the parcel (width), the value of rent, the width of streets, and the level of services represent the most effective parameters that play the main role in process of land price estimation over the Erbil City. The current research introduces the nature of land values and their homogeneous distribution and evaluates the suggested algorithm of land price estimation as one of the efficient factors that affect the national economic situation. The data collection was done for 100 parcels in different locations within the Erbil city boundary, which is being selected to apply the linear multiple regression equation to find the coefficients of the effective factors and to define the correlation between them. The obtained results of the linear multiple regression equation show that the level of existing services and the value of the rent have the maximum effect among these four factors, and they have the maximum correlation with the land price, whereas the road’s width has the minimum correlation among them. The worked-out algorithm for land price estimation (which is a vital issue of the modern cadastral systems), is recommended to be applied by the institutions and organizations concerning the land prices and land taxes task. [ABSTRACT FROM AUTHOR]

To provide a scientific basis for the prevention and prediction of apple marssonina blotch, in this study, based on the long-term observation data (2012-2020) of apple marssonina blotch and apple phonological as well as the daily meteorological data of temperature, precipitation, humidity, and light in the main apple-producing areas located in Linyi county of Yuncheng, the correlations between the apple marssonina blotch development situation and the meteorological conditions were analyzed by using correlation analysis and multiple regression. The significantly correlated meteorological elements were selected to construct the prediction models of apple marssonina blotch in different development stages and occurrence degrees as well as disease index. The results showed that the average temperature in mid-late March and the precipitation in mid-April all had the most significant effects on the occurrence and development as well as the occurrence degree of apple marssonina blotch. High temperatures in March and heavy rain in April could lead to the onset and prevalence of the disease which started earlier and had a higher level of occurrence. The disease indexes of apple marssonina blotch were closely related to the average temperature and precipitation of the previous ten days. When the average temperature in the previous ten days was 23-28 ℃, and the moderate precipitation lasted for more than 3 days, the disease index of the next ten days increased rapidly and the disease spreaded rapidly leading to the serious occurrence of apple marssonina blotch. Selecting the meteorological factors significantly correlated to the different development periods of apple marssonina blotch, the prediction equations of disease early onset date and prevalence start date as well as the occurrence degree of apple marssonina blotch were all established respectively, and the detailed prediction equations for the disease index were also established. The fitting accuracy rate of models on occurrence degree and maximum disease index of historical regression test separately reached 100% and 88.9%, and the accuracy rate of models on early onset date and prevalence start date reached 66.7% and 55.6% respectively. Additionally, the forecast results of test report were consistent with the actuality in the 2021, which indicated that models had a good prediction effect on occurrence and development time as well as the severity of apple marssonina blotch. [ABSTRACT FROM AUTHOR]

A new method is presented to efficiently estimate daily groundwater level time series at unmonitored sites by linking groundwater dynamics to local hydrogeological system controls. The proposed approach is based on the concept of comparative regional analysis, an approach widely used in surface water hydrology, but uncommon in hydrogeology. Using physiographic and climatic site descriptors, the method utilizes regression analysis to estimate cumulative frequency distributions of groundwater levels (groundwater head duration curves, HDC) at unmonitored locations. The HDC is then used to construct a groundwater hydrograph using time series from distance‐weighted neighboring monitored (donor) locations. For estimating times series at unmonitored sites, in essence, spatio‐temporal interpolation, stepwise multiple linear regression (MLR), extreme gradient boosting (XGB), and nearest neighbors are compared. The methods were applied to 10‐year daily groundwater level time series at 157 sites in unconfined alluvial aquifers in Southern Germany. Models of HDCs were physically plausible and showed that physiographic and climatic controls on groundwater level fluctuations are nonlinear and dynamic, varying in significance from "wet" to "dry" aquifer conditions. XGB yielded a significantly higher predictive skill than nearest neighbor and MLR. However, donor site selection is of key importance. The study presents a novel approach for regionalization and infilling of groundwater level time series that also aids conceptual understanding of controls on groundwater dynamics, both central tasks for water resources managers. Key Points: Daily groundwater levels at unmonitored sites are estimated through transfer of head duration curves based on the similarity of site characteristics at monitored sitesNonlinearity of controls on groundwater levels favors using of Machine Learning (e.g., regression trees) over multiple linear regression for predictionThe dynamic nature of controls on groundwater levels can be disentangled, which is central for studies of recharge seasonality, droughts, and floods [ABSTRACT FROM AUTHOR]

Current study is devoted to the development of an ideotype of winter wheat variety for cultivation in the conditions of the South of Ukraine. The investigation is based on the results of regional ecological varietal testing, conducted in the Southern Steppe zone on the non-irrigated lands. Varietal traits, included in the study, embraced growing season duration, 1000 grains weight, plant height, and ear length. The results of the testing were further processed using statistical procedures of linear Pearson's correlation analysis and multiple regression analysis. As a result, the model of a winter wheat variety for the non-irrigated lands of the South of Ukraine was developed. The developed model is characterized by very high fitting quality (R² = 0.9476) and good prediction accuracy (MAPE = 23.27%). According to the model, the variety should be late ripening with moderate to high plant height to provide the highest grain yield. The trait of 1000 grains weight was found out to be unimportant. The main trait, providing for the grain yield increase, is growing season duration, which must be long enough. Further ecological varietal testing studies with inclusion of additional varietal traits, such as cold-resistance, drought-resistance, frost-resistance, tolerance to diseases, etc., are to be conducted to extend the ideotype of winter wheat. [ABSTRACT FROM AUTHOR]

Background: Finding trends in freight transportation activities enables businesses and policy makers to build an understanding of freight transportation patterns and their impact on logistics planning when making investments in a region's transportation infrastructure and intermodal freight transport system. To the best of our knowledge, there is limited literature and data-driven analysis about trends in transportation mode choices and the influencing factors in Atlantic Canada. Methods: In this study, a data-driven method has been used to analyze the Canadian Freight dataset to identify trends in transportation activities within Maritime, Canada. Freight transportation mode, product categories, distance, number/weight of shipments, and revenue were examined. Results: The results revealed that the top five product categories exported from Atlantic provinces to the rest of Canada, the US, and Mexico are miscellaneous items, food products, forest products, minerals, and other manufactured goods, where Truck for Hire is the most deployed mode of transportation. A multiple linear regression analysis indicated that the weight, distance, and number of shipments are positively and rather strongly correlated with revenue generation. Conclusions: This study provides a unique overview of Canadian Freight Analysis Framework (CFAF) data with a focus on maritime activities. [ABSTRACT FROM AUTHOR]

We developed a fatigue residual useful life (RUL) prediction model using the available time-series fatigue data of Ni-base alloy welds via a long short-term memory (LSTM) network. The effects of some LSTM network hyperparameters on model performance were investigated through sensitivity studies. The LSTM network model outperformed multiple regression models when the LSTM model hyperparameters were appropriately tuned. However, the additional gain was insignificant, considering that the LSTM network was much more complex than multiple regression models. The best performance of the LSTM network model was achieved when the number of hidden units, input window size, and batch size were small and the number of LSTM layers was large. [ABSTRACT FROM AUTHOR]

This study seeks to compare the impact of the Subindices of the Global competitiveness index on the global competitiveness index of India, Pakistan and China during the period of 2008 to 2017. The study uses multiple regression equations to examine the impact of sub-indices of GCI on the GCI of India, Pakistan and China. It uses comparative analysis to find out the result. The global competitiveness index ranks a country based on its competitiveness capacity. This index is released every year by the World Economic Forum to measure competitiveness among countries. The study first tests the normality of the data by using the Jacque-Bera test. The stationarity of the data is checked by using the unit root test. The study examines multicollinearity, autocorrelation, and heteroscedasticity in the regression model. The study uses a residuals test to check the validity of the regression model. The study applies BDS (Brock-Dechert-Scheinkman) independence test to examine the non-linear pattern of the residuals distribution. It is found that series are not independently identically distributed. And it is observed that variables are stationary at the first difference, and there is a significant impact of sub-indices of GCI ON the GCI of India, Pakistan and China. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic affected the industrial sector by decreasing productivity, including in private companies. Some private companies have laid off their employees to achieve company efficiency. This situation drives a threat that is arising insecurity, anxiousness, and decreasing engagement among employees. This research aims to understand the role of calling and Perceived Organizational Support (POS) toward work engagement. The research participants are 144 employees from private companies working for at least six months, and they Work From Home (WFH). This study hypothesizes that POS and calling predict work engagement in a positive direction. Multiple regression analysis was utilized in this study. Results showed increased calling and POS followed by increased work engagement. The implication of this study has two directions; the first is for the organization to improve perceived organizational support, and the other hand, the employee needs to internalize the calling more. [ABSTRACT FROM AUTHOR]

The water chemical effects of copper have been a focus in the study of water quality criteria (WQC). Currently, multiple regression models are commonly used to quantitatively describe the impact of environmental factors on Cu toxicity in WQC studies. However, the influence of species-specific effects may consequently lead to poor prediction results of the regression models in practical application. For this issue, a backpropagation neural network (BPNN) model optimized using a genetic algorithm was developed in this study. The results showed when pooled data of given taxonomic groups were used, the BPNN mixed models had higher Adj.R2 for five out of seven groups in the predicted toxicity values compared to the MNLR mixed models. When using species-specific models, the BPNN model still showed higher predictive performance. Further comparison of the two models for the species M. galloprovincialis revealed that, in addition to the good predictive performance of the BPNN models, the pre-set species codes of different species in the taxonomic group for the BPNN mixed model also reduced the impact of species-specific effects among species. Finally, the WQCs under different water quality parameter ranges were obtained using predicted toxicity values from mixed BPNN and MNLR models. The short-term WQC range for common water quality parameters (salinity: 25–30 ppt, DOC: 0.5–2.5 mg/L) obtained from the BPNN mixed model in natural marine environments was 1.6–4.41 μg/L, which aligns with guidance values provided by major global institutions, demonstrating the feasibility of applying the BPNN mixed model to WQC derivation. This study aims to provide valuable references for future research on WQC. [Display omitted] • Using backpropagation neural network (BPNN) to derive marine water quality criteria. • The BPNN model demonstrated flexibility and accuracy compared to the MNLR model. • The BPNN model has shown good potential in handling species-specific effects. • The WQC range of copper under natural marine environments conditions is 1.6–4.41 μg/L. [ABSTRACT FROM AUTHOR]

In the literature on emigration, the challenges of integration at the destination are largely discussed. How do those who become immigrants change or not and what is the impact of their inclusion in the arrival communities? It is less discussed what happens in the communities of emigration, in the country of origin. For the communities from which emigrants have gone abroad, research has been done especially on the causes of emigration. The consequences of emigration were seen especially at the level of the families leaving, on the children and elderly left behind. Community consequences as a result of emigration, are either neglected, treated through case studies or approached piecemeal, with a focus on ageing, etc. But what if we are interested in all the communities of origin, comparing those with many, few or no emigrants? This last question and its approach are of major interest in this article. The four hypotheses of the research relate the intensity of emigration from the locality and the option of emigration destinations (Italy, Spain, France, Germany, Great Britain) with different facets of development processes in the communities of origin, about seven years after emigration. The facets are given by the intensity of polarised poverty-development phenomena and their components (state of health, material capital in the community and socio-human capital). A whole series of phenomena are estimated and introduced into the analysis to contextualise the relations between emigration and poverty-development levels of origin communities (previous development, urban accessibility, residence environment, community involvement in temporary internal emigration processes, etc.). What is the impact of emigration on the well-being or levels of poverty development in origin communities? The answers and the statistical validations, to this question are, in my intentions, the original contribution of this article. [ABSTRACT FROM AUTHOR]

Basis pursuit (BP), basis pursuit deNoising (BPDN), and least absolute shrinkage and selection operator (LASSO) are popular methods for identifying important predictors in the high‐dimensional linear regression model Y=Xβ+ε. By definition, when ε=0, BP uniquely recovers β when Xβ=Xb and β≠b implies ‖b‖1>‖β‖1 (identifiability condition). Furthermore, LASSO can recover the sign of β only under a much stronger irrepresentability condition. Meanwhile, it is known that the model selection properties of LASSO can be improved by hard thresholding its estimates. This article supports these findings by proving that thresholded LASSO, thresholded BPDN, and thresholded BP recover the sign of β in both the noisy and noiseless cases if and only if β is identifiable and large enough. In particular, if X has iid Gaussian entries and the number of predictors grows linearly with the sample size, then these thresholded estimators can recover the sign of β when the signal sparsity is asymptotically below the Donoho–Tanner transition curve. This is in contrast to the regular LASSO, which asymptotically, recovers the sign of β only when the signal sparsity tends to 0. Numerical experiments show that the identifiability condition, unlike the irrepresentability condition, does not seem to be affected by the structure of the correlations in the X matrix. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation 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.)

A long-term analysis of temperature can be used to describe the main mechanisms that operate at the surface of the ocean. The average sea surface temperature (SST) contour plots for the Indian Ocean are produced based on the World Ocean Atlas Data Set (1994). SST, together with the independent variables wind (Wx, zonal component of pseudo-stress wind and Wy, meridional component of pseudo-stress wind), net-down-fresh-water-flow (NDFF), and Ekman pumping, are included in a multiple regression analysis to define the relative importance of each one of these variables in the physical processes at the surface of the Indian Ocean. The NDFF data set is based on COADS (Comprehensive Ocean-Atmosphere Data Set). The wind data is obtained from the Florida State University (FSU). The harmonic terms of the variables are calculated, which is considered to be stationary and expressed by a Fourier series as a cosine function. The harmonic terms are multiplied by the maximum amplitude of the variables and then added to their mean annual values. The isotherms are mainly meridional along the western boundary, but zonal in the southern Indian Ocean. The annual component is seen to have a maximum in July, Summer Monsoon (SW Monsoon) and a minimum in January, during the Winter Monsoon (NE Monsoon). The amplitude of the semiannual component is smaller, with two maxima in May and October and two minima in February and August. The small magnitude of these residuals errors is an indication that the temperature variability during this period and for this area can be explained reasonably well by the two harmonic terms. In the Arabian Sea, the final regression equations for SST variability show that it is mainly affected by the Wx, Ekman pumping and NDFF. For most of the areas of the Bay of Bengal, as well as for most of the locations in the southern tropical Indian Ocean, the entered independent variables can explain SST. Two components fit to observation can be used to predict SST together with the regression equations. Although harmonic analysis can be used to study SST variability, a multiple regression analysis is required to identify and quantify the variables related to areas of large annual and semiannual variability. Different techniques are therefore used together to provide more reliable results in SST configuration in the Indian Ocean. [ABSTRACT FROM AUTHOR]

For the first time, a low-cost and eco-friendly adsorbent prepared from pomegranate peel was used for the efficient removal of aniline blue (AB) dye from wastewater. After carbonization at 500 °C for 1 h, chemical activation using HCl was done. Textural characterization and adsorbent properties were analysed using FTIR, SEM along with EDX and pHZPC. The influence of various operating parameters such as initial solution pH, dosage time, adsorbent mass and initial dye concentration for AB removal was investigated. Kinetic studies were conducted at room temperature (30 °C) by varying guest molecules concentrations. Guest–host interaction was maximum (90.78%) at pH 4. Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherms were employed to design and optimize the adsorption data. The data obtained agreed well with the Freundlich isotherm and followed pseudo-second-order kinetics. The maximum AB uptake was predicted from Langmuir model (27.322 mg/g at 30 °C for 0.1 g pomegranate peel activated carbon). Thermodynamic studies performed in the temperature range of 30–50 °C indicated a decrease in randomness at the solid–liquid interface during physic-sorption, and the process was exothermic. Multiple regression (MR) and state-of-the-art artificial intelligence technique, namely epsilon-insensitive loss function-support vector regression (ε-SVR), were used for modelling. Prediction performance and analyses of the developed MR and ε-SVR models were done using statistical parameters such as AARE, R, RMSE, SD, MAE, Q2LOO and Q2Ext. Parity plots between the adsorption data and the predicted data demonstrated that the SVR-based model was a robust model with high accuracy and generalizability. [ABSTRACT FROM AUTHOR]

• The purpose of this study is to analyze the impact of building shape on safety accidents. • 227 cases of real safety accident data were collected from 33 residential building projects that were completed between September 2018 and May 2021. • The collected data was then used to carry out a correlation analysis and regression analysis between factors. Preventing various types of safety accidents on construction sites with safety control efforts centered around site engineers has limitations. To resolve this issue, efforts have been made for the last decade to identify safety risk elements that arise during construction and have them reflected in the Design for Safety (DfS). Taking two buildings with the same total floor area for example, the building with a more complex shape or higher floors is expected to have a higher probability of safety accidents in DfS terms. However, little research is available that has used safety accident data from real construction to analyze the impact of building shape on safety accidents. One of the reasons is that many construction companies do not disclose safety accident data. The purpose of this study is to analyze the impact of building shape on safety accidents. For this research, actual project data collection including safety accidents and analysis on their correlation with each risk element were performed in series. As a result, it was demonstrated that R2 was 0.947 for the correlation between shape factor and safety accidents and R2 was 0.881 for the correlation between the number of building floors and safety accidents, showing that safety accidents are closely related to both elements. It is recommended according to the research findings that designers should take into account of building shape in order to perform more effective DfS and developers and constructors should add more safety facilities and manpower depending on building shape for the same total floor area. [ABSTRACT FROM AUTHOR]

The study was conducted in Alappuzha, Thrissur, Palakkad and Idukki districts of Kerala, India during 2020-2021 to analyse the risk attitude of vegetable farmers. The study examined the personal and social characteristics of respondents and the association of these characteristics with risk attitude. Simple random sampling technique was used to select 270 vegetable farmers from 6 panchayats representing the Special Agricultural Zones for vegetables in Kerala. Collected data were analyzed using statistical methods like mean, Kruskal-Wallis test, correlation coefficient and multiple regressions. The study revealed that majority (74.07%) of the farmers belonged to a medium category of risk attitude. Majority (55.18%) of the farmers were in the age group of 35-55 years, 46.29% had gone up to middle school, 72.59% of the farmers had an area below 2.31 acres and 67.4% respondents were engaged in vegetable farming and allied works and 54.07% had economic water scarcity. It was noticed that majority of the respondents were in the low category in the case of vegetable farming experience (61.48%), annual income (55.56%), innovative proneness (56.67%), economic motivation (56.3%), extension participation (70%), social participation (94.44%) and high category with regard to credit orientation (53.7%). Correlation analysis revealed that variables area under vegetable cultivation, education, annual income, irrigation potential, extension participation, innovative proneness, economic motivation and social participation were positively and significantly corelated with risk attitude of respondents at 1% level and at 5% level. Regression analysis revealed that 60% of the variation in farmer risk attitude was explained by the independent variables. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new four-parameter generalized version of the Chen model called the exponentiated extended Chen distribution. Some results about the reliability characteristics of hazard rate function as well as some mathematical properties are provided. The maximum likelihood estimators and five approaches based on the concept of minimum spacing distance estimators are given for estimation of the model parameters and their performances in estimating of parameters are compared by means of Monte Carlo simulations. Also, a multiple regression model with the censored data based on proposed distribution is introduced. [ABSTRACT FROM AUTHOR]

An attempt to evaluate potential hazard for quality of manufactured construction elements and concrete in case of use of leachate as technological water was made in this paper. This may lead to negative impact on strength parameters of the product made in the analysed company. Raw industrial leachate must not be discharged into sewerage system due to increased concentrations of some mineral components; it must be initially purified. Selected elements of multiple regression module and principal components analysis were used to describe the above phenomena. The model we have developed uses selected significant parameters of pollutants contained in leachate from a process industrial waste heap. The model takes into account leachate conductivity and sulphate ions, in which increased lead compounds concentration was determined. The model was verified during its use. The variables used in the model explain the significant percentage of variability noted for the dependent variable. [ABSTRACT FROM AUTHOR]

Common hamsters (Cricetus cricetus) are hibernators that rely both on body fat reserves and food stores for the winter period. They face an ongoing population decline in most parts of their distribution and recently were classified as critically endangered. Knowledge on individual body fat proportions in this species is of particular interest for conservation, because it could contribute to better understand the high plasticity in overwintering strategies, overwinter mortality rates, individual variations in reproductive output, and give information on the animals' health state. To calculate body fat proportions, we validated a method that can be applied in the field without the use of anesthesia. To develop this method, we first analyzed the body fat in carcasses of common hamsters using Soxhlet extractions and measured four morphometric parameters (body mass, head length, tibia length, foot length). The morphometric measurements were then integrated in a linear regression model to predict body fat proportions based on the measured values. The morphometric variables yielded an explained variance (adjusted R 2) of 96.42% and body fat proportions were predicted with a mean absolute error of 1.27 ± 0.11% from measured values. We applied the model to predict body fat for available field data, which consistently produced reliable values. By measuring the four morphometric parameters and following the provided instructions, body fat proportions can be reliably and noninvasively estimated in captive or free-ranging common hamsters. Furthermore, the method could be applicable to other rodents after species-specific validation. [ABSTRACT FROM AUTHOR]

Objective: To detect factors of production which can explain the total value of agricultural production (agricultural GDP) and to carry out a descriptive analysis. Methodology: A multiple linear regression adjustment and a descriptive analysis of the variables were carried out, using the available information. Results: Five variables have a significant association with agricultural GDP. In the last 20 years, the agricultural sector has experienced an exponential economic growth, with a 7.7% annual growth rate (AAGR). About 60% of the total production value is provided only by 7 states. Agricultural exports have increased from 2015, as a result of the USA demands for Mexican fruits and vegetables. Implications: The information available about the state of agriculture in Mexico makes it possible to understand trends and take actions to increase the agricultural GDP. The analysis is limited by the lack of information about many variables. Conclusions: Sown areas have decreased and the factors of production analyzed maintain a positive average annual growth rate, which indicates a process of transition from extensive crops (such as grains and cereals) to intensively exploited crops (such as fruits and vegetables). An investment aimed to generate agricultural data is needed. [ABSTRACT FROM AUTHOR]

In order to address the problem that the direction‐of‐arrival (DOA) estimation is restricted by the number of array elements, this study comes up with a novel DOA estimation algorithm based on discrete‐Fourier‐transformation (DFT) and multiple regression, which focuses on the direct data domain from each array element rather than the traditional covariance domain of the whole array. In this algorithm, the authors regard each channel of the array signal model as an independent form of multiple regression and achieve the DOA by estimating the regression coefficient of the reconstructed source signal from each array element one by one iteratively. In addition, the DOAs estimated from the multiple array elements are averaged to improve the accuracy by exploiting the asymptotic normality. This algorithm is proposed, modelled, and developed theoretically at first. Then the capabilities that the authors' algorithm can estimate not only the very close DOAs since they distinguish the irrelevant sources by the DFT on temporal frequency but also the DOAs when the number of sources is larger than that of array elements are fully demonstrated by simulations. Furthermore, no requirement for the known number of sources brings the algorithm broader application foreground. [ABSTRACT FROM AUTHOR]

Entrepreneurship innovation is found many times in the literature, but empirical research on the growth and development of start-ups is underdeveloped. Many studies have pointed out that the rate of the launch of start-ups being high and start-ups not able to survive is highly correlated. This article is a modest attempt to bridge this gap by identifying the entrepreneurial factors affecting the performance of early-stage start-ups based in Uttarakhand. This is an exploratory study based on the primary data collected from a sample of 147 start-up founders. A self-administered questionnaire, including 18 Likert items for three independent variables was used and pilot-tested which subsequently came down to thirteen after deleting five items. A random sampling technique was applied to choose respondents from the Dehradun and Haridwar districts of Uttarakhand state. The data were analysed using descriptive statistics, exploratory factor analysis, Cronbach's alpha, with the help of SPSS software (version 23). Multiple regression analysis was used to analyse the impact of three extracted factors on the performance of start-ups. The study has revealed that the leadership skills, technical skills and marketing skills of entrepreneurs significantly contribute to the performance of start-ups in Uttarakhand state. [ABSTRACT FROM AUTHOR]

The California Bearing Ratio (CBR) value is an important variable in pavement design since it determines the strength of the subgrade soils. However, it should be noted that the CBR test is arduous and time-consuming. As a result, this work attempts to establish relationships between CBR and several soil index parameters such as liquid limit (LL), plastic limit (PL), optimum moisture content (OMC), and maximum dry density (MDD). Regression analysis and neural networks were used to develop three prediction models for correlating soaked CBR values with LL, PL, OMC, and MDD for soil samples taken from different locations in Guwahati, Assam, India. Because Assam is prone to flooding, and some rural roads are inundated for two to three days under water, a soaked CBR is considered. According to the results, ANN can more accurately predict soaked CBR values. [ABSTRACT FROM AUTHOR]

Regression equations are commonly used to predict residual lung volume (RV) during underwater weighing when measurement is not practical. However, the equations currently available were derived from on‐land measures of RV and may account for changes in lung capacity during submersion, thus leading to inaccuracies in assessment of percent body fat (%BF). The purpose of this study was to (1) develop a new equation (RVNEW) for the prediction of underwater RV, (2) cross‐validate RVNEW and compare it to existing RV equations, and (3) compare the effects of RVNEW and existing equations on underwater %BF. One‐hundred seventy‐five healthy adults were recruited to complete simultaneous hydrostatic weighing and RV measurements. The sample was randomly divided into development (n = 131) and cross‐validation (n = 44) cohorts. Regression analysis in the development cohort resulted in the following equation: underwater RV = −3·419 + 0·026 × height (cm) + 0·019 × age (y) (p < 0·001; R2 = 0·53; SEE = 0·26). In the cross‐validation cohort, Bland‐Altman analysis revealed that the new equation provided the best overall agreement with underwater RV (bias ± 1·96 SD, 0·07 ± 0·5 L), while existing equations produced significantly different values from measured RV and wider limits of agreement. When used to calculate %BF, the new RV equation produced the strongest agreement with underwater %BF (−0·5% ± 3·8%), although all equations produced strong correlations (all r > 0·95) and limits of agreement ≤4·7%. The results of this study suggest that RVNEW may be more appropriate for RV estimation during hydrostatic weighing than existing equations. However, its applicability to populations outside the current study needs to be examined. [ABSTRACT FROM AUTHOR]

Based on analysis of mechanical test the results a search is completed for the optimum content of the main alloying elements of maraging steels (MS) based on Fe–Cr–Ni–Mo that provides a combination of increased strength properties, impact strength, and static crack resistance. A multiple regression model that determines the dependence of the mechanical properties of steel on the alloying elements contained within them is proposed. It is shown that the optimization task is multi-factorial with conflicting criteria and can be effectively resolved using an "ideal point" method. [ABSTRACT FROM AUTHOR]

Here, we explore the question: What makes a photograph interesting? Answering this question deepens our understanding of human visual cognition and knowledge gained can be leveraged to reliably and widely disseminate information. Observers viewed images belonging to different categories, which covered a wide, representative spectrum of real-world scenes, in a self-paced manner and, at trial's end, rated each image's interestingness. Our studies revealed the following: landscapes were the most interesting of all categories tested, followed by scenes with people and cityscapes, followed still by aerial scenes, with indoor scenes of homes and offices being least interesting. Judgments of relative interestingness of pairs of images, setting a fixed viewing duration, or changing viewing history – all of the above manipulations failed to alter the hierarchy of image category interestingness, indicating that interestingness is an intrinsic property of an image unaffected by external manipulation or agent. Contrary to popular belief, low-level accounts based on computational image complexity, color, or viewing time failed to explain image interestingness: more interesting images were not viewed for longer and were not more complex or colorful. On the other hand, a single higher-order variable, namely image uprightness, significantly improved models of average interest. Observers' eye movements partially predicted overall average interest: a regression model with number of fixations, mean fixation duration, and a custom measure of novel fixations explained >40% of variance. Our research revealed a clear category-based hierarchy of image interestingness, which appears to be a different dimension altogether from memorability or awe and is as yet unexplained by the dual appraisal hypothesis. [ABSTRACT FROM AUTHOR]

Dewatering is a major process in a wide range of manufacturing industries. One of the most commonly used technologies for dewatering is the screw press. The optimized setting of the screw press increases the amount of recycled water for further production. Additionally, it reduces waste disposal costs by lowering the weight of discharged sludge. In this paper, we used the design of experiments to determine the parameters that affect the dryness of the final waste. Our study showed the significant parameters were the pump speed, the screw rotation speed, and the properties of inflowing sludge. Using the significant factors, a regression model was developed to predict the dryness of the final waste. Then, the regression model was used as the objective function of an optimization model to find the optimal settings of the screw press that maximizes the dryness of sludge. The optimum setting of the screw press increased the dryness of sludge by 2.3%, saving 387 metric tons of water annually. The financial effects of improving the dryness of the sludge leaving the screw press were calculated showing a reduction in disposal costs and reduction in water usage costs because the water extracted from the sludge is recycled. Further investigations showed that the amounts and types of chemicals used to pretreatment the inflowing sludge to the head box can affect the dryness of the sludge leaving the screw press. [ABSTRACT FROM AUTHOR]

[Objectives] This study was conducted to explore the relationship between morphological traits and body mass trait of Lutraria sieboldii. [Methods] 110 were randomly selected from 120 2n,i-instar L. sieboldii collected from the Tieshangang area of Beihai, Guangxi, and 132 were randomly selected from 150 shellfish at the instar of 0.6. Their morphological traits were measured: shell length (SL), shell height (SH), shell width (SW), anterior length (AL), posterior length (PL), nose length (NL) in closed shell state, and maximum open shell width (OS) between two shells in closed shell state, and the body mass trait BM was also measured. Statistical methods such as path analysis and multiple regression were used for data analysis, and the effects of these seven morphological traits on the body mass trait were studied, respectively. The correlation between the tested seven quantitative traits and one body mass trait was all positive, all reaching an extremely significant level (P <0.01). [Results] The body mass trait of the shellfish at the instar of 2 had the highest correlation coefficient with shell length (0. 922), that is, shell length had the greatest direct impact on the body mass trait; the path coefficient was 0.700; and the final multiple regression equation established was BM - -124.882 + 1.189SL + 1.551 SH+1.035SW + 0.119NL, and the total determination coefficient (ß2) on body mass was 0.849. The body mass trait of the shellfish at the instar of 0. 6 had the highest correlation coefficient with shell length (0. 859), that is, shell length had the greatest direct impact on the body mass trait; the path coefficient was 0.494; and the final multiple regression equation established was BM = -1.917 + 0.111SL +0.021 NL +0.078SW + 0.0320S, and the total determination coefficient (ß2) on body mass was 0. 828. The multivariate regression variance analysis showed that the regression between the morphological traits and body mass trait of the L. sieboldii at the instars of 2 and 0. 6 reached an extremely significant level (P <0. 01). [Conclusions] This study provides a scientific basis for the selection of broodstock in the selection and breeding of L. sieboldii. [ABSTRACT FROM AUTHOR]

Seismic design of combined piled raft foundation (CPRF) supported superstructure assumes fixed-base conditions but failures of pile group-supported structures during past seismic events unfolded the significance of seismic soil-structure interaction (SSSI). The current investigation is centred on unveiling the seismic behaviour of CPRF-supported superstructure embedded in sand incorporating the complex SSSI through a nonlinear three-dimensional finite element based numerical analysis. First, the efficacy of the developed model is verified. Thereafter dynamic analysis is performed considering past recorded seven different short, medium, and long-duration earthquakes and encompassing parametric variations of the superstructure and CPRF. Primarily, this study presents closed-form empirical relationships based on regression analysis to predict the maximum acceleration response of CPRF-supported multistorey buildings in sand. Further, the effect of complex SSSI on design responses, such as storey displacement and pile bending moment, is studied considering the impact of several controlling design parameters. The findings of the study suggest that acceleration and displacement response at various periods of superstructures are affected by the inclusion of soil flexibility. The peak acceleration is amplified as the waves pass through soil, piled raft, and superstructures. At last, the seismic design concepts obtained from this study are illustrated through a case study problem. [ABSTRACT FROM AUTHOR]

To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error. [ABSTRACT FROM AUTHOR]