Search

During the COVID 19 pandemic, most schools implemented flexible learning with the help of the quipper platform due to the suspension of face-to-face classes. The primary purpose of this study was to seek a variable that best predicts pupils' academic performance in science. The sample size of 102 participants was selected using proportionate stratified random sampling. The descriptive correlational and causal design was employed in this study. Based on the data gathered, Grades 4, 5, and 6 levels have a highly positive overall mean level of pupils' Attitude towards Quipper regarding references and assignments and uses, respectively. Respondents have a proficient level of computer skills in terms of General Windows, and advanced level on Microsoft Word, Internet and Email, and Excel and Spreadsheet. The level of the Students' Academic Performance in Science was described as very satisfactory. The Attitude towards Quipper in terms of references and assignments and Computer skills in MS Word has a positive significant relationship to pupils' academic performance in science. As to the Attitude in uses, Computer skills in MS Word and Computer skills in MS Excel/Spreadsheet, indicated that these variables are significant predictors with Attitude in uses being the best predictor. [ABSTRACT FROM AUTHOR]

This research sought to uncover the interconnected dynamics, barriers, and opportunities that existed within this context to inform strategies that would support teachers in their pivotal role in shaping the future of society. The elementary school teachers of the Naawan District participated in this survey as respondents. The survey questionnaire employed in this study underwent a comprehensive validation process. Notably, the largest age group falls within the range of 36 to 45 years. The overwhelming majority of teachers were females and a substantial portion was married. The dominance of Teacher I positions underscored the prevalence of entry-level roles, with the majority having earned Masteral Units. The survey findings shed light on the challenges teachers faced in adopting digital tools and integrating them into reading practices. These findings underscored the nuanced influence of socio-demographic factors on teachers' challenges in digital integration, suggesting the need for tailored support programs. The significant correlation underscored the impact of external factors, such as technological challenges, on teachers' ability to deliver effective instruction. The multiple regression analysis found that socio-economic factors did not significantly predict teachers' classroom teaching performance. However, facing challenges in adopting digital tools and integrating them into reading practices significantly impacted teaching performance. The action plan aimed to address the study's findings on the significant impact of challenges in digital integration on teaching performance. [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]

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]

Several techniques, such as hydromulching (HM) and addition of organic residues (such as biodegradable plastics, BP) to soil have been proposed for conservation of soil affected by deforestation and wildfire. However, there is the need to support the task of land managers for the adoption of the most effective soil conservation technique, considering that the impacts on soil properties and hydrology are different due to the different mechanisms (mainly based on root actions for hydromulching and on supply of organic matter for application of bioplastics residues). This study comparatively evaluates the hydrological and erosive effects of HM, addition of BP residues to soil, and lack of any treatments (control) at the plot scale and under simulated rainfall in deforested and burned forestlands of Northern Iran. These effects have been associated to changes in key properties of soil and root characteristics due to the treatments, using multivariate statistical analysis. Moreover, regression models have been setup to predict surface runoff and soil erosion for both treatments. HM was more effective (–65% of runoff and –61% in soil loss) than application of BP (–22% and –19%, respectively) in controlling the soil's hydrological and erosive response, the latter being extremely high in control plots (over 6 tons/ha). These reductions were closely associated to significant increases in organic matter and aggregate stability of soil, to a decrease in bulk density after the treatments, and to the grass root growth, which further improved soil hydrology after HM. The Principal Component Analysis provided a synthetic parameter measuring the soil response to rainfall and treatments. The cluster analysis discriminated the three soil conditions (HM, application of BP and control), according to the changes in soil properties and root growth in HM, in as many groups of soil samples. The multiple regression analysis provided two linear models that predict surface runoff and soil loss with a very high accuracy (r2 > 0.98) for a precipitation with given depth and intensity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature 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.)

Validation of groundwater models is relatively challenging due to the need to reserve scarce water level data for calibration targets. In addition, traditional statistical validation metrics are unintuitive for non-technical audiences and do not directly identify model behaviors that require further refinement. We developed a novel model validation method that analyzes rate change events at pump-and-treat wells and statistically compares the water level responses at nearby monitoring wells between the data and model. The method takes advantage of events that occur alongside ambient pumping, unlike parameter estimation techniques that require independent drawdown or recovery events. The ability of the model to match well connections that are evident (or not evident) in the observations is characterized statistically, leading to four decision scenarios: model matches the observed connection (1) or lack thereof (2), model exhibits a connection that is not observed (3), or model over- or understates the observed connection (4). The method is applied to an FEHM-based groundwater flow and transport model that is shown to match 84.5% of the well connections analyzed. The method provides novel perspectives on the influence of calibration targets on the flow field and suggests that although the overall effect of drawdown targets was to improve the model, the choice of target well pairs creates flow pathways that may be inconsequential during normal operational conditions. The model adequately matches the flow over short spatial scales (<800 m) and over-represents the flow over greater distances (300–1200 m), suggesting the need for "null" drawdown targets in subsequent rounds of calibration. [ABSTRACT FROM AUTHOR]

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

Theory suggests that a secure base can moderate the effects of racism on mental health outcomes among people of Color (Mikulincer & Shaver, 2022). Thus, the current study tested this hypothesis with a sample of 301 Asian American adolescents who completed a 25-minute online survey. Two hierarchical multiple regression models examined whether secure, anxious, and avoidant attachment with mothers and fathers moderated the link between racism and mental health. We found that when Asian American adolescents reported frequent experiences of racism, secure attachment with fathers was not enough to mitigate the effects of racism. Specifically, simple slopes indicated that at high and moderate levels of secure attachment with fathers, adolescents reported slightly better mental health with few racism experiences but indicated a slight decrease in mental health with frequent racism experiences. Findings highlight the importance of dismantling racial discrimination to support Asian American adolescents' mental health. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study explores the impact of hormonal fluctuations during pregnancy and lifestyle factors on stomatognathic system (SS) health. The aim is to determine how pregnancy-related hormonal changes and oral hygiene behaviors affect the onset of stomatognathic issues, such as gingival inflammation (GI) and dental erosion (DE). Materials and Methods: A prospective, observational study was conducted with 100 pregnant women, divided into two groups: Group A (60 women with significant stomatognathic alterations) and Group B (40 women without such alterations). Multiple regression analysis was used to evaluate the influence of hormonal levels, oral hygiene habits, and vomiting episodes on stomatognathic health. Results: Age and socioeconomic status showed no significant association with stomatognathic health (p > 0.05). In contrast, elevated levels of estrogen (p = 0.001) and progesterone (p = 0.003) were significantly linked to the severity of stomatognathic changes. Oral hygiene habits also had a statistically significant impact (p = 0.02), while vomiting frequency was not an important factor (p > 0.05). Conclusions: Hormonal changes during pregnancy, particularly increased estrogen and progesterone levels, are key predictors of stomatognathic health. These findings suggest that while oral hygiene is important, hormonal fluctuations play a dominant role in influencing stomatognathic system (SS) health during pregnancy. [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.)

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. [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]

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]

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

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 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 article 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. Supplementary materials for this article are available online.

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.

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.

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.

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 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]

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 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]

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.)

Wetland plant is an indispensable part of the wetland ecosystem. To study the relationship between wetland plants and the nutritional status of lake water, nine disc lakes in Lake Poyang, Ni Lake, Chang Lake, southern branch of gan river, Dalianzi Lake, Baisha Lake, Zhanbei Lake, Zhushi Lake, Changhuchi Lake and Dahuchi Lake, were selected as the research sites in this study. The dominant species of Sedge in Poyang Lake wetland was selected as the research object, and the water environmental factors in disc lakes were selected as the main environmental control factors. The contribution values of water environmental factors to the biomass and plant density of Sedge were determined by principal component analysis and multiple regression.The results showed that: (1) Dahu Lake and Zhushi Lake were in the mesotrophic state, and other disc lakes were in the light eutrophic state. The water quality of the disc-shaped lake in the western region of Poyang Lake was better than that in the southern and eastern regions. (2) Principal component analysis showed that NO3-N, PO4-P, NH4-N, TN, COD and TP were the key factors affecting the vegetation characteristics of Carex. (3) NH4 - N and pH were significantly correlated with the density of Sedge (P＜0.01). Nutrient status index of dish lake was significantly correlated with biomass on bolting grassland(P＜ 0.05). [ABSTRACT FROM AUTHOR]

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]

Introduction: Powerful and practical statistical packages have simplified the analysis and thus developed the application of data science in all research fields. Accordingly, regression has been applied to almost all aspects of the life sciences. However, misuse of this model has been reported in the past decades. This article aims to examine modeling with this important statistical method and introduce readers to the correct use of this method. Materials and methods: This review article uses real data, and the supplementary materials provide the method for performing the regression analysis in SAS and R statistical software and their related codes. Results: In the required assumptions of the regression model, the residuals of the model must be normally distributed, but performing the normality test for the actual values of the response variable or any of the explanatory variables is not mandatory. Therefore, researchers should not obsess more than necessary about the normal distribution of real data. On the other hand, almost all normality test methods, such as Kolmogorov-Smirnov, are designed for large numbers of data, typically more than a thousand samples. This suggests that using such methods to test the normality of model residuals estimated from a small number of data, mostly less than a hundred cases, would be inaccurate. Another issue regarding applying the regression model is related to the co-linearity of the explanatory variables. There are still signs of correlation in a data set where all variables are generated separately and randomly in a statistical package. This means that it is very hard to find a correlation coefficient equal to zero (r = 0) even between any pair of separate, random variables. Therefore, in all regression models, there are some kinds of correlation between explanatory variables, but the important issue here is that only high correlation causes severe problems in the model. For collinearity test it would be better to use specialized methods such as Variance Inflation Factor (VIF) or Principal Component Analysis (PCA). The linearity of the model is one other assumption of regression model. Data transformation might be helpful under the situation of non-linearity of the model. However, transformation changes the variables unit, altering the array direction in a geometric space. Researchers should be careful regarding the use of modeling a large number of data affects the probability values in variance analysis due to increasing the value of the degree of freedom of the model. Conclusion: As the number of data points increases, the degree of freedom of the error term increases rapidly. Therefore, the final error mean squared significantly reduces. In contrast, the scatter of data points around the regression line may be too wide. For this reason, using the coefficient of determination, usually called (R-Squared), is a suitable criterion for testing the model's fit. High coefficient values indicate a suitable model for the data set used. It should be noted that in a multiple regression model, the higher the number of explanatory variables used in the model, the higher the value of this coefficient increases. For such conditions, when the number of explanatory variables is large, another form of this coefficient, called the adjusted coefficient of determination (adjusted R²), has been introduced. The use of this coefficient in the approximations creates a limit on the number of variables used in the regression model. Accordingly, the number of variables in the model as explanatory variables should not exceed the number of samples (or the number of tens) in a set, and researchers should avoid using more variables than the number of samples. [ABSTRACT FROM AUTHOR]

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

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]

Purpose: The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud. Design/methodology/approach: To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment). Findings: The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa's banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3). Practical implications: This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa's financial institutions. It may also assist South Africa's financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa's financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability. Originality/value: Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses. [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]

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]