Search

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]

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]

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.)

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 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]

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.)

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.

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.

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]

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]

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]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences 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.)

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]

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]

Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R2 greater than 0.6, with validation accuracy R2 values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm−2, indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making. [ABSTRACT FROM AUTHOR]

Introduction: The Stock Exchange is an economic indicator of sustainability in the global market over an extended period. The Indian economy has observed a downfall in foreign currency in quarter 2 of 2022, as per the reports of the International Monitory Fund. The central banks of many countries have been facing crises because of a piercing decline in their reserves, which is additionally affecting their sustainable performance. The Indian economy is one of the most potentially sound economies emerging as a global leader, and this study is an attempt to understand the economy's vulnerability to foreign factors. Purpose: The research explores the impact of the US Dollar, EURO and Japanese Yen on Bombay Stock Exchange and the National Stock Exchange Index. Methodology: Four variables have been considered for the conduct of the study: Sensex, Nifty, inflation and foreign exchange. Sensex and Nifty have been taken as dependent variables, while foreign exchange and inflation have been taken as independent variables. The regression analysis has been performed using Microsoft Excel: The variables used for the study are monthly values from January 2011 to December 2020. The specific period is selected to avoid the impact of COVID-19 on the stock market, avoiding biases in the results. Findings: All the variables are affecting the performance of each other up to a certain level. Practical Implication: The research chapter will help the investor understand the relationship between many variables and their impact on the stock market, which will assist them in gaining higher profits. [ABSTRACT FROM AUTHOR]

The liquid water transport and rapid drying behavior in fabrics usually referred to as moisture absorption and rapid drying means that when the human body generates a lot of sweat during intense exercise sweat can be quickly absorbed and transmitted to the outer surface of fabrics to quickly volatilize so that the body can maintain a dry and comfortable function. The behavior directly affects the human wearing comfort and health status. According to the current development trend of functional fabrics moisture absorption and rapid drying has become a basic requirement for underwear sports medical and clothing in other fields and has bright prospects for development. Combined with the exploration of related fields by researchers it can be seen that the liquid water transport and rapid drying in fabrics has become a research hotspot which makes the law of moisture absorption and rapid drying properties increasingly clear and the characterization methods are increasingly accurate and diversified. However as the liquid water transport and rapid drying in fabrics is affected by many factors it is necessary to strengthen the guidance of the research results for the actual product development. To keep the human body in a comfortable state when people sweat during exercise the study took polyester woven fabrics as the research object to characterize the liquid water transport and rapid drying behavior in fabrics. Firstly the drip diffusion test and water evaporation rate test of 10 kinds of fabrics with different specifications were carried out with the help of image processing technology and the data of water transport process were accurately obtained. Then the indexes of liquid water transport and rapid drying in fabrics were put forward such as the drip diffusion time maximum transmission time maximum transmission area maximum transmission span in the warp and weft direction and evaporation rate and the moisture absorption and rapid drying performance of fabrics was analyzed. Finally the factors influencing the moisture absorption and rapid drying performance were analyzed by multiple regression method. The results show that liquid water transport and rapid drying in fabrics undergo four stages the initial stage the weight loss increasing stage the stable stage and the weight loss decreasing stage. The appearance of the four stages is related to structural factors such as fabric structure fabric thickness yarn linear density yarn arrangement density and mass per square meter of fabrics. The liquid water transport in fabrics is first affected by the fabric thickness which will weaken the surface spreading of liquid water and is then affected by the yarn linear density which affects the evaporation of liquid water spreading by affecting the surface pores of fabrics. The warp density and weft density are influenced by matching with the thread density of the yarn. This study reveals the transport behavior of liquid water by observing the change of transport domain in fabrics thus proposing the corresponding evaluation index system and analyzing complex relationship between liquid water transport and fabric structure by multiple regression method. The method of characterizing liquid water transport and rapid drying behavior in this paper provides a new solution for clothing comfort and has important guiding significance for the research and development of high-quality sportswear fabrics. [ABSTRACT FROM AUTHOR]

As the depth of drilling increases, the prevention and control of reservoir damage caused by drilling fluids has become increasingly prominent, and reservoir protection has become a key factor in the full release of gas field production capacity. As the well depth deepens, the various properties of the deepwater drilling fluid change, causing the degree of reservoir damage to intensify and the direction of reservoir protection performance optimization becomes unclear. Therefore, this paper combines Pearson correlation analysis and grey relational analysis to perform attribution analysis of controllable factors of drilling fluids in reservoir damage, identify the main controlling factors, and establish a gas well productivity model. The results show that the solid particle size, surface tension, mineralization degree, and high-temperature and high-pressure fluid loss of drilling fluids are the main controlling factors causing reservoir damage. According to the attribution analysis results, an optimization method using a composite of different particle size distributions of calcium carbonate as a weighting agent is proposed, which increases the permeability recovery of drilling fluids by 12.1 to 19.68%. The applicability of the model is verified by collecting field parameters from wells Y8 and Y9, and the results show that the accuracy of the model established in this paper is over 94%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Parents play a vital role in developing their children's emotional intelligence, which is critical for equipping youth as future leaders with the necessary competencies and resilience to contribute to national development. Thus, this study examined the relationship between parental involvement and emotional intelligence of senior high school students across Sevilla District, Division of Bohol, in the school year 2019 - 2020. A descriptive-correlational research design was employed using standardized tools. Results revealed that the parent-respondents were slightly involved in their children's education regarding home-based involvement, school-based involvement, and home-school conferencing. The student-respondents exhibited a "Low" level of Emotional Intelligence in general; however, they obtained a high rating regarding utilizing their emotions. Other components were rated "Low." Parental involvement did not correlate with the student's emotional intelligence; however, Multiple Regression analysis showed that it predicted the students' ability to manage others' emotions and utilize their own emotions. Recommendations were proposed to address the results and findings. [ABSTRACT FROM AUTHOR]

This study has demonstrated practical ways to confront employee relations and conflict resolution within workspaces. Empirical research and data analysis are the focal themes of this research as it looks into the role of employee relations and dispute resolution in the enhancement of overall organizational performance. Such results highlight the significance of a pre-emptive attitude towards conflict management and the use of theoretical frameworks to solve the existing problems top-down when it comes to improving the atmosphere in the work environment. Managing employee relations and resolving conflicts within the workplace requires a proactive and structured approach to maintaining a harmonious work environment. Key techniques include fostering open communication through regular meetings and feedback sessions and ensuring transparency and fairness in all dealings. Training managers in conflict resolution skills and emotional intelligence is crucial, as these competencies help in identifying and addressing issues before they escalate. Implementing mediation and negotiation practices as formal mechanisms can also be effective. Additionally, establishing clear policies and procedures that outline acceptable behavior and the steps for resolving disputes is essential. Ultimately, promoting a culture of respect and inclusivity aids in minimizing conflicts and enhancing employee relations. [ABSTRACT FROM AUTHOR]

Purpose. This paper highlights the importance of taking into account the evaluation of the strength properties of limestone rock in the Ben Azzouz quarry. The purpose is to achieve optimum blasting quality based on the information on petro-physical and mechanical characteristics of the rock. Methodology. Models have been developed to estimate physico-mechanical properties of limestone rock. The models are based on the results of many laboratory tests by petro-physical and mechanical methods. Statistical analysis was performed on simple and multiple regression equations. Findings. Linear regression models have a higher estimated success rate, as expected. The best model for estimating the compressive strength of the rock (UCS, Uniaxial Compression Strength) based on simple regression is the model containing P-Velocity as an independent variable with a coefficient of determination R² of 0.81 and P-value = 0.000000003. Originality. To benefit from the enormous reserves in the quarry of Ben Azouz, knowing that there is no evaluation of the physico-mechanical characteristics of the rock, a set of the tests in the rock mechanics laboratory of polytechnic faculty of Mons in Belgium was carried out and limestone rock strength was estimated. Practical value. to Solid understanding of the physical and mechanical characteristics of the rock mass and the mechanism of blasting the rock is an essential step that must be taken gradually according to the development of mining works with the aim of minimizing the disadvantages in blasting and obtaining an optimal effectiveness. [ABSTRACT FROM AUTHOR]

Mental stress and associated heart disorders are some of the considerable causes of death in India and globally, as reported by the World Health Organization (WHO). The long-term presence of mental stress and hypertension in lifestyle can lead to significant disorders, including cardiac arrhythmias. Some researchers in the past have proposed methods to assess mental health by considering various physiological signals and artificial intelligence methods. Most studies have used complex algorithms and a combination of features extracted from bio-signals such as electroencephalogram (EEG), electrocardiogram (ECG), etc. This work aims to develop a simple and efficient method to classify mental stress from Heart Rate Variability (HRV) features of ECG signal. The proposed method for stress assessment is implemented using 1-min segments of real-time ECG signals from the Stress Recognition in Automobile Drivers (SRAD) database. We derived the HRV signal from the de-noised ECG signal and extracted time-domain statistical features. Correlation analysis is applied to statistical HRV features to establish the relationship among different metrics of HRV. The main contribution is to develop a Multiple Linear Regression (MLR) model to predict Heart Rate (HR) using statistically correlated HRV features. Another contribution is the design of a multinomial logistic classifier to classify the mental stress level into three classes: low, medium, and high stress. Simulation results demonstrate that time-domain HRV features are statistically correlated, and with a suitable choice of parameters, the proposed method can achieve a classification accuracy of 90.32%. The findings suggest that using fewer relevant features of ECG, the proposed method provides improved classification performance comparable to the state-of-the-art methods. Because of the non-invasive and simple approach, the proposed technique is suitable for accurately assessing mental stress in real-time environments. The presented approach strongly promotes the development of remote and automatic methods of mental stress assessment for real-world applications using wearable sensors and the Internet of Things. [ABSTRACT FROM AUTHOR]

This study investigates the meteorological variables exerting the most significant influence on the growth and productivity of four tomato hybrids cultivated for industrial processing in the Brazilian Savannah of Goiás State. Data on morphological and meteorological variables were collected in five locations (Hidrolândia, Itaberaí, Palmeiras de Goiás, Piracanjuba, and Silvânia) during the year 2020. Morphological characteristics assessed included plant height, number of branches, and leaf area index (LAI). Meteorological variables considered were average air temperature, global solar irradiance, thermal sum, and soil water availability. Pearson correlations, canonical correlations, and multivariate Stepwise regression were employed to analyze the relationships between morphological and meteorological variables. The study revealed that soil water availability significantly impacts tomato crop development, indicating the need for improved irrigation management. Adequate irrigation management contributed to an 86.1% increase in plant height and a 79.7% boost in leaf area index, while average air temperature influenced a 34% increase in the number of branches. In conclusion, proper irrigation management is crucial for enhancing tomato crop growth and development and optimizing water use.

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.

ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.

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.

In a realistic scenario, the dataset has missing values encountered during the data collection. To effectively build the prediction model, the missingness of the attributes that impact crop growth needs to be appropriately handled in the crop dataset. The study aims to impute missing data in the Wheat crop yield Dataset, consisting of climatic parameters and historical data of 370 districts of Major Wheat Producer states of India. This study plays a vital role in crop estimation or forecasting of production at regular intervals. The imputation techniques that replace missing data have been categorized into Statistical and Machine Learning based Methods. We explored the performance of popular Techniques such as Arithmetic Average Replacement, Median Imputation, Linear Interpolation, Average Imputation by Nearby Districts, K-Nearest Neighbour, Miss Forest, Regression, and MICE. We have also evaluated these methods on the UCI machine learning repository's Bias and Steel energy consumption datasets. These imputed results were fed to the multiple regression prediction models to evaluate the efficiency of the imputation approaches qualitatively. The results conclude that the Arithmetic Average Replacement method provides good results among the statistical methods (R2 = 0.83; RMSE = 0.47; MAE = 0.372; MSE = 0.229), whereas in Machine Learning based methods, Miss Forest Random Forest-based method, and MICE performed well (R2 = 0.80; MAE = 0.3825; MSE = 0.249; RMSE = 0.499) to impute the missing data. We hope our results help the researchers to select the appropriate pre-processing strategies and improve the data quality. [ABSTRACT FROM AUTHOR]