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Moisture, fats and fatty acids of 14 pelagic and demersal fishes were measured by conventional chemical analysis to relate these with the proton relaxation using Low Frequency Nuclear Magnetic Resonance (LF-NMR). Artificial intelligence was used to assess the predictability of composition using six relaxation parameters of LF-NMR. Multiple linear regression showed significant prediction for moisture (W) (P < 0.00001), total fat (F) (P < 0.0001), ω-6 fatty acid (O6) (P < 0.001), saturated fats (SF), fatty acids (FA), mono-unsaturated fatty acids (MU) and ω-3 fatty acid (O3) (P < 0.01). However, the highest regression coefficient was observed for water (R2: 0.490) and the lowest was observed for SF (R2: 0.224). The low regression coefficients indicated strong non-linear relationships exited between LF-NMR parameters and composition. However, decision tree showed higher regression coefficients for all compositions considered in this study (R2:0.780–0.694). In addition, it provided simple decision rules for the prediction of composition. General Regression Neural Network provided the highest prediction capability (R2:0.847–1.000 for training and 0.506–0.924 for validation). [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental 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.)

Using the controlled planting body-large-scale root chamber experimental device (hereinafter referred to as root chamber) as the research platform, the characteristics of soil respiration changes under different types of microplastic treatment were analyzed by continuously planting two crops each year for three years. A multiple regression equation was employed to model the temporal variations in soil CO2 concentration under varying conditions, including microplastic accumulation and molecular weight, with the results visually presented. The result showed that the regression models effectively described the patterns of soil CO2 concentration changes, with all models showing R² exceeding 0.75, indicating good simulation performance. A significant analysis of variance on the pre-and post-treatment data yielded a P-value of 0.007 3. Compared to the control group's CO2 concentration increase rate (ω=0.728), the presence of microplastics significantly enhanced the soil CO2 concentration increase rate (ω=0.762). The introduction of microplastics altered the microclimate of the soil environment, significantly promoting CO2 emissions from the soil. [ABSTRACT FROM AUTHOR]

With the strengthening of social environmental regulations, consumers' green products purchasing intention is also increasing significantly. Simultaneously, green marketing activities have developed into a vital factor affecting consumers' green products purchasing intention. The first research aim of this paper is to reveal the important relationship between environmental regulation and consumers' green products purchasing intention. Furthermore, another research aim is to reveal the role of green marketing in environmental regulation and consumers' green products purchasing intention. To achieve the above purpose, we primarily employ regression analysis, threshold effect analysis, spatial spillover effect analysis, and heterogeneity tests. We come up with some conclusions. First of all, environmental regulations could enhance consumers' green product purchasing intention. With the reinforcement of environmental regulations, consumers' green product purchasing intention could be effectively elevated. Secondly, green marketing is a positive mediator of the environmental regulations that affect the consumers' green product purchasing intention. By enhancing their green marketing capabilities, businesses could also strengthen consumers' green product purchasing intention. Lastly, in different regions of China, the impact of environmental regulations and green marketing on consumers' green product purchasing intention varies. In the Eastern region, the influence of environmental regulations on consumers' green product purchasing intention is most prominent. However, green marketing exerts the greatest impact on the consumers' green product purchasing intention in the Western region. In addition, this paper provides significant insights for the managers in making management decisions. This is beneficial for enhancing consumers' green product purchasing intention. [ABSTRACT FROM AUTHOR]

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]

The intellectually gifted have been found to have higher levels of many prosocial dispositions, but there is limited evidence of increased prosocial behavior. The present research used existing and original datasets to examine relations between intellectual giftedness and prosocial behavior and dispositions. In Study 1, those identified as intellectually gifted engaged in more political and helping behaviors than those not identified as intellectually gifted. In Study 2, gifted-identified undergraduate students reported more prosocial behavior (e.g., volunteering and giving) than nonidentified students; however, there were no clear differences in dispositions (e.g., personality and motivation) between gifted-identified and nonidentified students. Implications of these consistent linkages between intellectual giftedness and engaging in prosocial behavior for future research and practical applications are discussed. [ABSTRACT FROM AUTHOR]

Predicting the key plume evolution features of groundwater contamination are crucial for assessing uncertainty in contamination control and remediation, while implementing detailed complex numerical models for a large number of scenario simulations is time-consuming and sometimes even impossible. This work develops surrogate models with an effective and practicable pathway for predicting the key plume evolution features, such as the distance of maximum plume spreading, of groundwater contamination with natural attenuation. The representative various scenarios of the input parameter combinations were effectively generated by the orthogonal experiment method and the corresponding numerical simulations were performed by the reliable Groundwater Modeling System. The PSO-SVM surrogate models were first developed, and the accuracy was gradually enhanced from 0.5 to 0.9 under a multi-objective condition by effectively increasing the sample data size from 54 sets to 78 sets and decreasing the input variables from 25 of all the considered parameters to a smaller number of the key controlling factors. The statistical surrogate models were also constructed with the fitting degree all above 0.85. The achieved findings provide effective generic surrogate models along with a scientific basis and investigation approach reference for the environmental risk management and remediation of groundwater contamination, particularly with limited data. [ABSTRACT FROM AUTHOR]

This paper selects the agricultural mechanization data of 14 prefectures (cities) in Xinjiang from 2012 to 2021 to evaluate the high quality development level of agricultural mechanization. Moreover, a multivariate regression econometric model is constructed to empirically analyze the influence level and degree of urban-rural income ratio on Xinjiang’s high-quality agricultural mechanization development. The results show that the rural-urban income ratio has a positive effect on the development level of Xinjiang’s high-quality agricultural mechanization, and the economic and social condition variables also show a certain lag effect. [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 paper considers an extension of Hodrick–Prescott (HP) filter. It is a hybrid of HP filter and multiple regression. We refer to the filter as "HPX filter". It is well known that HP filter has a unique global minimizer and the solution can be represented in matrix notation explicitly. Does HPX filter also have a unique global minimizer? Is it accomplished without any additional assumptions? Can the solution be expressed in matrix notation explicitly? In this paper, we answer these questions. In addition, this paper (i) provides an alternative perspective on the filter by representing it as a generalized ridge regression and (ii) gives an extension of it, which is a hybrid of Whittaker–Henderson method of graduation and multiple regression. [ABSTRACT FROM AUTHOR]

The personality configuration of mentors and mentees is important in understanding mentoring outcomes. While the best mentors appear to have higher degrees of agreeableness and conscientiousness, entrepreneurs generally score lower on agreeableness and have higher degrees of narcissism, a personality trait that could be detrimental to mentoring. We investigated the interaction of narcissism with two traits from the Big Five Inventory, namely agreeableness and conscientiousness, to see how this interaction influenced learning from the relationship of mentee entrepreneurs. Our findings suggest that mentee narcissism negatively influences learning, and mentor agreeableness mitigates the negative effects on mentee learning. These findings show certain beneficial personality configurations in entrepreneurial mentoring and provide elements to consider in managerial practice when pairing mentors and mentees in this context. [ABSTRACT FROM AUTHOR]

Cloud-based e-learning is a technology used to enhance teaching and learning in universities. However, its adoption in technical universities is low, and research into the factors influencing teacher adoption is limited. To address this situation, a study was conducted at Ghanaian technical universities to examine the determinants of teachers' adoption of cloud-based e-learning. The study involved 1258 respondents, the majority of whom were male (853, or 67.8%), aged between 30 and 40 (47%), and 79.1% had a master's degree. The results showed that individuals with a master's degree had a higher level of knowledge of cloud-based e-learning compared to those with a bachelor's, doctorate, or another master's degree in technology. The study identified seven factors influencing the use of cloud-based e-learning, including pedagogical innovation, e-infrastructure readiness, cloud-based e-learning security, university location, the usefulness of cloud-based e-learning, and provider support. Interestingly, the cost of cloud-based e-learning had no significant impact. [ABSTRACT FROM AUTHOR]

Word reading fluency is crucial for early L2 development. Moreover, the practice of digital reading has become increasingly common for both children and adults. Therefore, the current study investigated factors that explain digital word reading fluency in English (L2) among Chinese children from Hong Kong. Eighty-six children (age: M = 9.78, SD = 1.42) participated in a digital silent word reading test using a mobile phone, a computer, or a tablet. This is a 10-minute timed test of English word reading. Overall, children's digital word reading fluency was highly correlated with print word reading fluency, even when measured a year apart. A hierarchical regression model revealed that socio-economic status (β =.333), grade (β =.455), and English reading motivation (β =.375) were positively and uniquely associated with performance in digital reading. These predictors explained 48.6% of the total variance in task performance. Two additional variables, i.e., the type of reading device and extraneous cognitive load, were included as well. Digital word reading fluency was significantly poorer when done using a phone as compared to a computer (β = -.187). No significant difference was found between reading on a tablet and a computer. Extraneous cognitive load (β = -.255) negatively and uniquely explained digital word reading fluency as well. Overall, the model explained 58.8% of the total variance. The present study represents the first attempt to highlight a comprehensive set of predictors of digital word reading fluency. [ABSTRACT FROM AUTHOR]

This study examines the relationship between a firm's tax planning and several observable characteristics of its top management team. We obtain empirical evidence based on multiple regressions of a sample of listed companies in China from 2013 to 2019. We find that a top management team's education level positively relates to tax planning. While tenure, age, and expert experience are negatively related to tax planning. These results are robust when further tests are conducted. These findings provide further evidence of the upper echelon theory and research in the field of taxation area. Practically, the study provides useful insights for board committees to appoint top management team members pursuing healthy performance. Furtherly, this study is valuable to investors, creditors, analysts, and auditors, as it serves as a reminder that top management team characteristics need to be considered when making decisions. [ABSTRACT FROM AUTHOR]

Abstract In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.

This article introduces four case studies to integrate healthcare analytics topics into classroom learning. Each case study employs distinct analytical methods, including optimization with Excel Solver, multiple linear regression, Monte Carlo simulation, and time series forecasting models, providing diverse practical applications in healthcare analytics. The cases offer students hands-on experience in practical healthcare challenges, enhancing their analytical and decision-making skills. For each case, we provide a detailed background, an in-depth data description, and comprehensive teaching notes. These elements are structured to facilitate understanding and teaching analytics concepts. The paper also summarizes student feedback collected from various courses where these case studies were implemented. This feedback consistently indicates that the cases significantly contributed to the student’s perceived learning, particularly in understanding and applying healthcare analytics in concrete scenarios. These case studies bridge the gap between theoretical knowledge and practical application and serve as a valuable resource for instructors seeking to enrich their healthcare analytics curriculum.

Objective Soil-air partitioning is a critical process influencing the environmental fate of volatile organic pollutants and a significant contributor to the risk of respiratory exposure. Methods Four typical soils-loess, red soil, black soil and sand were used to identify the soil factors affecting the partitioning of trichloroethylene. The partitioning behaviour of trichloroethylene at the soil-atmosphere interface was quantitatively investigated by single-factor controlled batch experiments, and the quantitative relationship between the trichloroethylene soil gas partition coefficient and soil factors was determined. Results The results showed significant differences in the soil-gas partition coefficients of the four typical soils (black soil>red soil>sandy soil>loess soil). The main influencing factors for the partition coefficient of black soil were primarily particle size, water content, and organic matter content. Whereas, for the other three soils, the main factors were particle size and water content. Conclusion The relationships between the soil gas partition coefficient of TCE and soil factors in black soil can be quantitatively expressed as follows: KSA=-0.744X1-0.224X2+0.704X3; sand: KSA=-0.724X1-0.222X2; loess: KSA=-0.291X1-0.268X2; and red soil: KSA=-0.589X1-0.338X2 (X1: water content; X2: particle size; X3: organic matter content). These research aids in a deeper understanding of the distribution behaviour of trichloroethylene at the soil-atmosphere interface in typical soils in China and the influence of soil factors and provide a theoretical basis for the quantification of multifactor coupling effects and health risk assessment in the process of soil gas partitioning.

The financial failures of Oando Plc, Cadbury Nigeria Plc, Enron and World Com have raised interest in audit committees, which review audited and unaudited financial accounts to ensure accuracy and prevent management from engaging in unethical or abusive accounting practices. Consequently, this research analyses how audit committee characteristics affect corporate performance. The study focused on Nigeria's publicly traded companies spanning from 2019 to 2021. Furthermore, 98 companies' public annual reports served as the data source for this investigation. Consequently, the research included a total of 294 firm-year observations, and the data was evaluated using Multiple Regression analysis. The findings revealed a significant positive effect of the independence of audit committees and frequent audit committee meetings on firm performance. However, the study's findings indicate an insignificant positive correlation between the size of the audit committee and the firm performance of Nigerian listed companies. The study's findings contribute to the current body of research by offering fresh perspectives on the function of the audit committee in corporate governance and its impact on financial performance, as measured by Tobin's Q, as well as the return on equity and return on assets. [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 Few population-based studies including younger adults have examined the potential of olfactory function tests to capture the degree of atrophy in memory-associated brain regions, which cannot be adequately explained by cognitive function tests screening for cognitive impairment. This population-based study investigated associations between high-resolution olfactory test data with few odours and grey matter volumes (GMVs) of the left and right hippocampi, amygdala, parahippocampi, and olfactory cortex, while accounting for differences in cognitive decline, in 1444 participants (aged 31–91 years). Regression analyses included intracranial volume (ICV)-normalised GMVs of eight memory-related regions as objective variables and age, sex, education duration, smoking history, olfaction test score, and the Montreal Cognitive Assessment-Japanese version (MoCA-J) score as explanatory variables. Significant relationships were found between olfactory test scores and ICV-normalised GMVs of the left and right hippocampi and left amygdala (p = 0.020, 0.024, and 0.028, respectively), adjusting for the MoCA-J score. The olfactory test score was significantly related to the right amygdalar GMV (p = 0.020) in older adults (age ≥ 65 years). These associations remained significant after applying Benjamini–Hochberg multiple testing correction (false discovery rate

The genesis and prediction of high-porosity and low-permeability sandstone reservoirs are hot spots in oil and gas geology research worldwide. High-porosity and low-permeability sandstone reservoirs are developed in the Cretaceous Bashkirchik Formation of the Luntai Uplift in the northern Tarim Basin, China. In this article, we conducted a systematic study on the geological origin and logging identification of high-porosity and low-permeability tight sandstone based on core observation, thin section, logging index response, and mathematical discrimination methods. The results show that the K1bs sandstone segment in the study area generally contains calcium carbonate, which mainly comes from carbonate rock debris and calcite cement. Calcite cement mainly fills the pores between primary particles, and it is the main factor leading to the densification of the reservoir. The geological origin of the formation of low-permeability layer is mainly due to the early cementation of carbonate, and the development mode of the low-permeability layer is “high content of calcium debris → severe calcium cementation → poor petrophysical properties → formation of low-permeability layer.” The low-permeability layer has the characteristics of high gamma and high resistivity, and the multi-parameter discriminant method established based on the Fisher criterion has a good identification effect for the low-permeability layer. The low-permeability layer has a small thickness, poor stability and continuity, and strong longitudinal heterogeneity, thus it can form a low-permeability baffle inside the reservoir, which greatly reduces the oil and gas migration capacity.

Purpose: Kinematically aligned-total knee arthroplasty (KA-TKA) has recently attracted significant interest because it delivers better clinical outcomes than mechanically aligned (MA)-TKA. However, the optimal preoperative factors that maximize the effectiveness of KA-TKA remain controversial. Therefore, we aimed to determine the preoperative factors that are appropriate for and might benefit from modified KA-TKA, based on patient-reported outcome measures (PROMs). Methods: The relationships between postoperative satisfaction and other variables in 84 patients with varus-type osteoarthritis of the knee who underwent primary modified KA-TKAs were investigated using stepwise multiple regression analysis. Patients were assigned to groups that were satisfied or dissatisfied with their outcomes after KA-TKA according to the 2011 Knee Society Scores. The cutoff for preoperative advanced activities scores (AASs) for excellent satisfaction were determined by analyzing receiver operating characteristic (ROC) curves. The variables correlated with excellent satisfaction were identified using logistic regression models. Results: A preoperative variable relevant to patient satisfaction was AAS (β ​= ​0.465). The results of the ROC curve analysis revealed eight optimal cut-offs for preoperative AASs that predicted excellent satisfaction, with a sensitivity and specificity of 0.700 and 0.625, respectively. Logistic regression analysis revealed that AASs significantly contributed to excellent patient satisfaction (odds ratio, 1.181; p ​= ​0.001). Conclusions: Individuals who were highly active preoperatively were good candidates for KA-TKA. This modified kinematically aligned surgical technique can satisfy the demands of highly active patients.

Correlation coefficients and linear regression values computed from group averages can differ from correlation coefficients and linear regression values computed using individual scores. This observation known as the ecological fallacy often assumes that all the individual scores are available from a population. In many situations, one must use a sample from the larger population. In such cases, the computed correlation coefficient and linear regression values will depend on the sample that is chosen and the underlying sampling distribution. The sampling distribution of correlation coefficients and linear regression values for group averages will be identical to the sampling distribution for individuals for normally distributed variables for random samples drawn from infinitely large continuous distributions. However, data that is acquired in practice is often acquired when sampling without replacement from a finite population. Our objective is to demonstrate through Monte Carlo simulations that the sampling distributions for correlation and linear regression will also be similar for individuals and group averages when sampling without replacement from normally distributed variables. These simulations suggest that when a random sample from a population is selected, the correlation coefficients and linear regression values computed from individual scores will not be more accurate in estimating the entire population values compared to samples when group averages are used as long as the sample size is the same. [ABSTRACT FROM AUTHOR]

With growing concerns about the danger of global climate change and worldwide demand for energy, the interest in the investigation and construction of renewable energy technologies has increased. Fixed platforms are a type of support structure for wind turbines composed of different types of tubular joints. These structures are under different kinds of cyclic loadings in ocean environmental conditions, which must be designed and reinforced against fatigue. In the present paper, the relationships between the parameters in DKT-joints reinforced with FRP under axial loads are investigated using several models, under 16 axial loading cases, with different nondimensional parameters and different FRP materials, and orientations were generated in ANSYS (total 5184) and analyzed. The four loading conditions that cause the maximum stress concentration factors were selected. After analyzing the 1296 reinforced models, relevant data were extracted, and possible samples were created. The extracted data were used in a multivariate data analysis of maximum stress concentration factors. The Pearson correlation coefficient is utilized to study the relationship between parameters and subsequently to make predictions. To reduce the number of variables and to group the data points into clusters based on certain similarities, hierarchical and non-hierarchical classifications are used, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Social Research / Turkiye Sosyal Arastirmalar Dergisi is the property of Turkish Journal of Social Research 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.)

Researchers are often interested in comparing predictors, a practice commonly done via informal comparisons of standardized regression slopes. However, formal interval-based approaches offer advantages over informal comparison. Specifically, this article examines a delta-method-based confidence interval for the difference between two standardized regression coefficients, building upon previous work on confidence intervals for single coefficients. Using Monte Carlo simulation studies, the proposed approach is evaluated at finite sample sizes with respect to coverage rate, interval width, Type I error rate, and statistical power under a variety of conditions, and is shown to outperform an alternative approach that uses the standard covariance matrix found in regression textbooks. Additional simulations evaluate current software implementations, small sample performance, and multiple comparison procedures for simultaneously testing multiple differences of interest. Guidance on sample size planning for narrow confidence intervals, an R function to conduct the proposed method, and two empirical demonstrations are provided. The goal is to offer researchers a different tool in their toolbox for when comparisons among standardized coefficients are desired, as a supplement to, rather than a replacement for, other potentially useful analyses. [ABSTRACT FROM AUTHOR]

In this exploratory, survey-based study (n = 606), we examined whether Muslim perceptions about giving have changed due to the increased government scrutiny of charitable donations. We also examined age-based differences in preferences for cash versus non-cash donations, sending cash abroad versus giving within the U.S., and the likelihood of Muslims donating to causes that benefit non-Muslims. Our empirical models controlled for demographic differences such as gender, marital status, income, education, race etc. Our statistical results suggest that in the U.S. (1) some Muslims feel that charitable giving to Muslim causes has decreased as a result of monitoring of such giving by the government; (2) older Muslims tend to prefer documented means of giving as opposed to cash donations; (3) Muslims who prefer to donate in cash within the U.S. tend to be similar in age to those who prefer to send money abroad; and (4) age has no bearing on U.S. Muslims' likelihood of donating Zakat to causes that benefit non-Muslims. Implications are discussed. [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.)

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]

Soil moisture monitoring is a significant aspect of environmental and agricultural studies, and Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a promising technology for this purpose. Traditionally, GNSS-IR is mainly employed for bare soil experiments, and the effectiveness of bare soil retrieval algorithm will be reduced due to the influence of vegetation and meteorological, etc. To address the limitations of the bare-soil retrieval algorithm, a multi-feature soil moisture retrieval approach was proposed. This approach integrated multiple factors including GNSS signals, cumulative precipitation, effective reflection height, and Normalized Microwave Reflection Index (NMRI), and then multi-layer perceptron (MLP) was employed to build retrieval models. In this study, measurements from the Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China were used for experiments and validation, and the geographical environment of stations are various. The experimental results demonstrated several key findings: (1) The delay phase is not sensitive to the variations in soil moisture before and after precipitation, but by integrating the cumulative precipitation data, the accuracy of the model could be improved. (2) The introduction of NMRI and reflection height can help remove the influence of vegetation and penetration depth. (3) Compared between three retrieval models (i.e., unary linear regression, multiple linear regression, and MLP), the decrease in the mean absolute error (MAE) of MLP is up to 96% most and the mean coefficient of determination (R2) is all above 0.98. Meanwhile, this study proved that the proposed method could fully utilize satellite reflection signals from all directions and better reflect the fluctuation of soil moisture. [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]

Increased industrialization and aging infrastructure have resulted in leaks of hazardous chemicals, such as CO. Leak modeling is crucial to developing emergency response strategies. Therefore, we simulated the time to criticality (TTC), which is the time to reach the threshold limit for occupational exposure, of a CO leak. The basis of the study is a fire dynamics simulator, a computational fluid dynamics model that was used to investigate the movement of CO in various scenarios, including using different building layouts and areas, temperatures, and leak diameters. Multiple regression analysis was performed to obtain regression equations for the TTC as a function of the independent variables. Ultimately, we found that the type of dispersion varies with respect to the temperature-dependent density of CO, and, among the independent variables, the leak diameter had the strongest effect on the TTC. The regression equations with logarithmic conversion were validated and found to have higher accuracy than those without logarithmic conversion. The findings provide useful information for developing emergency response plans regarding leak size in the case of hazardous chemical leakage. However, empirical studies of different gas types and leakage scenarios are required. [ABSTRACT FROM AUTHOR]

The relationship between ESG (environmental, social, and governance) and financial performance has been studied before. Contradictory results about the direction of this association are available, and there is a lack of work that considers other factors affecting that link. This article evaluated the relationship between these variables and corporate reputation among 13 Colombian companies in the COLCAP index. We assessed the relationship using ANOVA (analysis of variance), single, and multiple regression methods. The data was considered from 2018 to 2023. We find insufficient statistical evidence to reject the idea that ESG has a nonlinear relationship with financial performance. Moreover, we determined a positive relationship between ESG and reputation. These outcomes show that the effect of ESG efforts on business performance needs to be assessed by incorporating other dimensions, such as corporate reputation, and not only limiting the interpretation of financial outputs for decision-making purposes. [ABSTRACT FROM AUTHOR]

Within the context of China's commitment to carbon reduction goals, particularly in urban areas, addressing carbon emissions stemming from residents' travel activities assumes paramount significance. Drawing upon established theoretical frameworks, this study advances several hypotheses delineating the determinants of low-carbon behaviors among urban residents. It analyzes panel data from 30 provincial capitals in China using a time–individual dual fixed effects model. This study empirically scrutinizes the posited theoretical model, aiming to elucidate the factors shaping urban residents' low-carbon behavioral patterns and provide a decision-making basis for low-carbon construction and management of urban space. The findings underscore several notable associations. The disposable income, population density, and urban built-up areas exhibit significant positive correlations with carbon emissions among residents. Conversely, the urban gross domestic product (GDP) displays a significant negative correlation with carbon emissions. Furthermore, a positive correlation is discerned between the expanse of green spaces and the per capita carbon emissions intensity, while the availability of subway systems exhibits a negative correlation with both the per capita public green space area and the carbon emissions intensity. Notably, the configuration intensity of urban bus systems manifests an inverted U-shaped relationship with residents' carbon emissions intensity. Specifically, within a certain threshold, an escalation in the bus equipment intensity coincides with heightened carbon emission intensity; however, beyond this threshold, a notable reduction in the per capita carbon emissions intensity ensues. Additionally, a U-shaped relationship is observed between the number of urban parks and residents' carbon emissions intensity, indicating that an increase in parks may not necessarily contribute to carbon reduction efforts. Moreover, a discernible synergy is observed among various factors influencing carbon reduction efforts. These factors encompass residents' education levels and disposable incomes, the presence of subway and regular public transportation systems, urban land utilization scales, economic development levels, green space provisions, public transportation infrastructure, population densities, and land equilibrium. This interplay underscores the interconnectedness and interdependence of diverse variables in shaping strategies for mitigating carbon emissions within urban contexts. [ABSTRACT FROM AUTHOR]

Extreme weather events and global climate change have exacerbated the problem of evaporation rates. Thus, accurately predicting soil moisture evaporation rates affecting soil cracking becomes crucial. However, less is known about how novel feature engineering techniques and machine- learning predictions may account for estimating the soil moisture evaporation rate. This research focuses on predicting the evaporation rate of soil using machine learning (ML) models. The dataset comprised twenty-one ground-based parameters, including temperature, humidity, and soil-related features, used as features to predict evaporation potential. To tackle the high number of features and potential uncorrelated features, a novel guided backpropagation-based feature selection technique was developed to rank the most relevant features. The top-10 features, highly correlated with evaporation rate, were selected for ML model input, alongside the top-5 and all features. Several ML models, including multiple regression (MR), K-nearest neighbor (KNN), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg), random forest (RF), and a novel K-Nearest Oracles (KNORA) ensemble, were constructed for the purpose of forecasting the evaporation rate. The average error of these models was assessed using the root mean squared error (RMSE). Experimental results showed that the KNORA ensemble model performed the best, achieving a 7.54 mg/h RMSE in testing with the top-10 features. MLP was followed closely by a 25.1 mg/h RMSE in the same testing. An empirical model using all features showed a higher RMSE of 1319.1 mg/h, indicating the superiority of the ML models for accurate evaporation rate predictions. We highlight the implications of our results for climate-induced soil cracking in the real world. [ABSTRACT FROM AUTHOR]

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Financial education plays a significant role in poverty reduction and sustainable economic growth. It also increases individuals' financial well-being. Unfortunately, global financial literacy is low, and in developing countries, it is far below the global average. Nepal is a developing country with low financial literacy. Therefore, this study aimed to analyze the impact of financial literacy training on financial literacy levels and financial decisions among 399 randomly selected rural households in Bhojpur District, a rural area in Nepal. Multiple linear and logit regression models were used to analyze the data. The results revealed that financial literacy training increased the financial literacy level of training participants compared with that of non-participants. Education, financial literacy training, and migration of family members (personal migration history and plans to migrate) were other significant predictors. Respondents' financial and economic behavior was influenced by their financial literacy level, sex, age, education, family size, whether the respondent lives with parental family, and whether the respondent has migrated family members. The study suggested well-organized and inclusive financial literacy training program interventions to improve rural individuals' financial and economic decisions. [ABSTRACT FROM AUTHOR]

Freshwater mussel Parreysia corrugata is an ecologically important species endemic to south-east Asia, which has been suffering from a dramatic decline in recent years. The present study investigated the population dynamics and reproductive strategy of P. corrugata in a changing climate of Bangladesh waters. Mussels were sampled monthly from January to December 2021 from the Padma River near the Rajshahi city of Bangladesh. A total of 809 specimens were collected with a sizes ranging from 36.07 to 101.41 mm shell length and 7.92–87.54 g weight, respectively. The mussels displayed a growth pattern characterized by negative allometry in body weight-shell length relationship. The asymptotic length (L∞) was 106.05 mm, the growth coefficient (K) was 0.370 per year, and the growth performance index (Φ′) was 3.58. The recruitment of P. corrugata was occurred throughout the year, with a peak during the month of February. The annual total mortality (Z) was 1.15, and natural mortality (M) and fishing mortality (F) were 0.61 and 0.54 per year, respectively. The exploitation level was E = 0.47 which indicates a state of under exploitation. Sex was identifiable throughout the study period and the sex ratio of the mussels did not differ significantly from the 1:1 (M: F) ratio ((χ2 = 15.80, P > 0.05). In terms of gonadal development, maturation, and spawning activity, both sexes had similar trends. Histology identified five gonad development stages for both male and female mussels: early developing, late developing, ripe, spawning, and spent. Monthly GSI data showed significant seasonal fluctuations with the highest GSI in April and it was sharply declined in May indicating the peak spawning of the mussels, which was in consistent with condition index and gonadal histology. All the physicochemical parameters showed significant variation except for pH. The most important factors explaining mussel body condition (b value) and reproductive strategies (GSI and CI) were water temperature, dissolved oxygen and water level. Although no known major threats of this species have been documented from Bangladesh, conservation measures could be adopted to protect the natural habitats of P. corrugata from adverse physicochemical and hydrological factors.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models. HIGHLIGHTS New methods for estimating instantaneous peak flows from mean daily flows.; Data completion by filling in missing values of instantaneous peak flows.; Reliable estimation of design flood magnitudes.; Machine learning inspired fusion modeling to synthesize outputs of multiple methods.; Fulfilled a critical need of the climate change impact analysis and adaptation community.;

Abstract In the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs.

Although Python has the statsmodels library that can be used to perform different statistical analyses, including multiple regression, it has not yet implemented multivariate regression as one of its methods. An investigator interested in conducting a multivariate regression is thus limited to running a series of univariate regression models, which do not take into account the collinearity among variables in the models. Thus, the purpose of this tutorial is to demonstrate how to perform multivariate regression in Python using custom user-defined classes, and linear hypothesis testing using statsmodels.

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 L16 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.

AbstractDestructive leadership behaviors and their effects on the organization have received little attention in terms of research & theory development. As a result, the purpose of this article is to investigate the prevalence and determinants of destructive leadership behavior from the quantitative data collected through questionnaire. A total of 947 sample employees were included in this study stratified sampling technique. Personal behavior, ineffective decision, management incompetency, and political behavior have a positive significance difference at p = 0.01 after the data has been analyzed through factor analysis and regression analysis. However, at the 1% significance level, the difference in political behavior and negotiation problem is negative. On the other hand, poor communication has a negative relationship with destructive leadership behavior, but it is statistically insignificant at a 5% alpha level. According to the findings, ∼65% of public-sector employees in the Awi zone are susceptible to destructive leadership. In this case, the researchers recommended that organizations intentionally and consistently need to promote an environment in which employees feel free to speak up about leaders’ behavior that they believe it violates not only their own but also the organization’s values.