Strong Explanatory Power Research Articles

Evolution of the spatiotemporal pattern of China's grain production in the past 20 years and its driving mechanism.

Food security is a goal and means of global sustainable development, and an important component of China's national security. Based on grain production data from 2000 to 2020, 31 provinces (cities, autonomous regions) in China were used as research units to analyze the spatiotemporal differences and driving forces of grain production in China using a combination of local correlation index, center of gravity transfer model, and geographic detector. The results as follows, ① During the research period, China's total grain production showed a decrease followed by an increase, and the yield per unit area of grain showed an increasing trend. Corn has become the "largest staple food" in China; ② During the research period, the focus of China's grain production continued to shift northward, with Heilongjiang, Henan and Shandong provinces becoming the main grain production areas, with Henan being the province where China's grain production center was located. Among the factors affecting grain yield, the effective irrigation area (0.971) has the strongest explanatory power. Finally, countermeasures and suggestions were proposed from five aspects, stabilizing grain production, reducing grain inventory pressure, implementing regional grain security responsibilities, improving grain circulation efficiency, promoting high-quality grain engineering construction, adjusting grain production structure, strictly implementing farmland protection responsibilities, scientifically applying pesticides, fertilizers and other production materials, emphasizing agricultural infrastructure construction and stabilizing the number of agricultural labor force.

Torque modulation mechanism of the knee joint during balance recovery

Exploring the torque modulation mechanisms of human joints is critical for analyzing the human balance control system and developing natural human-machine interactions for balance support. However, the knee joint is often overlooked in biomechanical models because of its limited range of motion during balance recovery. This poses a challenge in establishing mathematical models for the knee joint's torque modulation mechanisms using computer simulations based on the inverted pendulum model. This study aims to provide a simplified linear feedback model inspired by sensorimotor transformation theory to reveal the torque modulation mechanism of the knee joint. The model was validated using data from experiments involving support-surface translation perturbations. The goodness-of-fit metrics of the model, including R2 values and root mean square errors (RMSE), demonstrated strong explanatory power (R2 ranged from 0.77 to 0.90) and low error (RMSE ranging from 0.035 to 0.072) across different perturbation magnitudes and directions. Through pooling samples across various perturbation conditions and conducting multiple fits, this model revealed that knee torque is modulated using a direction-specific strategy with adaptable feedback gains. These results suggest that the proposed simplified linear model can be used to develop assistive systems and retrieve insights on balance recovery mechanisms.

Application of Panel Data Regression on Factors Influencing Poverty Levels in West Papua Province in 2018-2022

West Papua has consistently been one of the regions with the highest poverty rates in Indonesia, ranking second in 2022. This study investigates the factors influencing the poverty rate in West Papua from 2018 to 2022 using panel data analysis, which combines both cross-sectional and time-series data. The variables examined include the open unemployment rate, economic growth rate, and human development index. A Random Effect Model (REM) was selected as the most appropriate model for analyzing the data, given its ability to account for individual-specific effects while assuming that these effects are uncorrelated with the independent variables. The model produced a goodness of fit of 0.74, suggesting a strong explanatory power. The results indicate that the economic growth rate and human development index significantly influence the poverty rate, both showing a negative correlation with poverty levels. However, the open unemployment rate was found to be insignificant. These findings suggest that focusing on economic growth and improving human development outcomes could be key to reducing poverty in West Papua. Policy implications and recommendations for further research are also discussed.

Investigating the Factors that Influence the Implementation of Virtual Reality in Science Learning

Advanced virtual reality (VR) holds substantial promise in education and can be seamlessly integrated into classroom instruction, fostering rapid advancements in science education. This research aims to investigate the factors that influence the implementation of VR and the correlation between the technology acceptance model that can lead to its integration in the science educational setting. To address these objectives, structural equation modeling (SEM) is conducted to represent a hypothesis about the causal relationships among factors influencing preservice teachers to implement VR in the classroom, following their engagement in simulated science explorations related to the concept of weightlessness. The results reveal that the proposed model has strong explanatory power in predicting the intention to use VR in the classroom (R2=64.7%). This intention is influenced by perceived usefulness, perceived enjoyment, and the absence of cybersickness. Notably, our findings also indicate that Usage Attitude (UA), which pertains to participants’ positive or negative response towards technological usage, does not mediate the relationship between beliefs (PU, PEU, and PE) and Behavioral Intention (IU), prompting further exploration of the concepts of cognitive and affective attitude. Additionally, the findings from the preservice teachers’ responses support the notion that VR is valuable in educational contexts, enabling immersive experiences, authentic learning, and enhancing the learning journey, along with the concept of learning by doing.

Analysis of Dynamic Evolution and Driving Factors of Low-Carbon Utilization Efficiency of Cultivated Land in China

In order to cope with global climate warming, measurement of the low-carbon utilization efficiency (LCUE) of cultivated land, considering carbon sink and carbon emission effects, is proposed. To address this, based on the data of 30 provinces in China, this study conducts a LCUE evaluation system by the MinDS-U-M productivity index model in order to analyze the spatiotemporal patterns and driving factors of LCUE with the geographic detector model and GTWR model. The results show the following: (1) Over the past 20 years, the average LCUE value exhibits a slow increasing trend from 2001 to 2021, which ranges from 0.9864 to 1.0272. Provinces with mid-level LCUE ranging from 1.0000 to 1.0990 account for the highest proportion in each period. (2) The annual growth rate of LCUE in the central region is the highest, where the promotion of green technology and farmland protection policies have played important roles. (3) According to the Geodetector analysis, urbanization rate (UR), irrigation index (IR), grain output value (GOV), precipitation (PR), arable land area (ALA), and environmental pollution control (EPC) are important drivers of the spatial difference of LCUE. (4) The GTWR model shows that the positive effects of ALA and SRT have always been concentrated in the main grain-producing areas over time. UR and PR have strong explanatory power for the space/time differentiation of LCUE, especially in eastern coastal regions. IR has an increasing effect on LCUE in the Western region, and the positive effect of EPC on the LCUE is concentrated in the central region. In order to coordinate regional LCUE contradictions, it is suggested to be wary of land resource damage caused by economic development, warn about the impacts of climate change, and strengthen the supervision of land remediation projects in order to achieve sustainable land management.

Socio-Economic Indicators and their Impact on Sustainable Economic Development: An In-depth Analysis of Egypt

This research paper investigates the sophisticated relationship between socio-economic indicators and sustainable economic development in Egypt, employing a Linear Regression Model to provide an accurate analysis. Focusing on key variables such as Population, Life Expectancy at Birth, and Labor Force, the study seeks to discern their impact on Gross Domestic Product (GDP). With R Square of 0.9443, the model exhibits strong explanatory power. Noteworthy coefficients include a positive relationship between Population and GDP (β = $9,302.27), a negative relationship between Life Expectancy at Birth and GDP (β = -$45.77 Bln), and a positive relationship between Labor Force and GDP (β = $10,097.49). These findings contribute subtle insights to the literature and offer policymakers valuable information for fostering sustainable economic development in Egypt.

Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

Clarifying the spatio-temporal patterns of ecosystem services trade-off/synergy relationships (ESTSs) and their attribution in the Yellow River Basin is crucial to constructing ecological civilization in China. This study first analyzed the spatio-temporal change of ecosystem services (ESs) including the water yield, soil conservation, carbon sequestration, and habitat quality in the Yellow River Basin during 2000–2020 based on the InVEST and RUSLE models. Then, the spatial autocorrelation methods were used to quantify the spatio-temporal differentiation of ESTSs, and the Geo-detector method was employed to identify the contributions of driving factors associated with the natural, social-economic, and regional policy aspects of the ESTSs. Finally, the random forest and analysis of variance methods were used to validate the reasonability of major driving factors obtained by the Geo-detector. The main findings include: (1) In 2000–2020, water yield, soil conservation, and habitat quality increased, and carbon sequestration decreased. The ESs had a spatial pattern of high in the east and low in the west. (2) Overall, there were synergistic relationships between the four Ess. In the spatial distribution of ESTSs, the expansion of the synergy zone and trade-off zone occupied the majority. The synergy zones tended to be concentrated in the northwest and southeast of the study area. In contrast, the trade-off zones were more scattered than the synergy zone, mainly focused on the east-central and southwestern parts of the Yellow River Basin. (3) Geo-detector and random forest both showed that natural factors had a strong explanatory power on ESTSs, in which NDVI is a key driver. Both the results of Geo-detector and the analysis of variance showed that the interactions between natural factors exerted the most significant influence on ESTSs, followed by the interaction between natural factors and socio-economic factors.

Unveiling the Coupling Coordination and Interaction Mechanism between the Local Heat Island Effect and Urban Resilience in China

Climate change and urbanization have led to the increasing prominence of urban heat islands (UHIs) today, posing a huge challenge to cities. Urban resilience (UR) refers to the ability of a city or region to adapt to changes and risks. However, the influence between the heat island effect and regional urban resilience is not well understood. In this study, we proposed a methodological framework for unveiling the coupling coordination and interaction mechanism between UHIs and UR. This study first explored UHIs in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and described the creation of a multidimensional index system that evaluates urban resilience across social, economic, ecological, and engineering dimensions. Furthermore, this study unveiled the coupling coordination effect of UHIs and UR through the coupling coordination degree model, and the influence mechanism between the drivers of UHIs and the change in UR was detected using a geographic probe. The results showed that the UHI region forms a ring-shaped belt around the entrance to the Pearl River Delta. The UHIs of the GBA show a significant trend of expansion and escalation over time. The UR of the GBA shows a spatial distribution pattern of high resilience among regional central cities and low resilience among peripheral cities, with significantly uneven development in sub-resilience dimensions. The UHIs and UR of the GBA showed a certain coupling and coordination effect, improving from barely synergistic to a primary coordination state. Among the drivers of UHIs, population density, precipitation, average nighttime light brightness, and ground-average CO2 emissions have strong explanatory power for the spatial variation in UR. The interaction between two factors has a stronger influence on UR than individual factors. The purpose of this paper is to initially reveal the influence mechanism between UHIs and UR and to provide a theoretical basis for further exploring the path of sustainable urban development.

Spatial-temporal Evolution and Driving Factors Analysis of Ecological Environment Quality in Daihai Basin based on AWRSEI

Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced.

Refining ESG Disclosure's Role in Corporate Economic, Environmental, and Social Sustainability Performance

This research examines the complexities of corporate sustainability, exploring the interconnections between environmental, social, and governance (ESG) disclosure, corporate governance frameworks, investor engagement in ESG practices, and sustainability performance improvements. Regression analysis were employed to analyze data collected from 121 participants across various professions in India. The findings indicate that ESG disclosures have an impact on the Sustainability Performance Transformation Index (SPTI), suggesting that disclosures alone may not necessarily lead to improved sustainability. Additionally, SPTI was found to be correlated with company management practices and investor engagement in ESG issues. The model demonstrates strong explanatory power (R2 = 0.979), underscoring the importance of adopting multidisciplinary methodologies for achieving lasting transformation. The conclusions drawn from this study offer insights that businesses, investors, and policymakers can leverage to strike a balance between long-term sustainability objectives and economic development.

Analysis of the Effectiveness of Zakat Collection, Management, and Distribution in Improving Social Welfare in Indonesia

This research explores the effectiveness of Zakat collection, management, and distribution in improving social welfare in Indonesia, employing a sample of 250 participants and utilizing Structural Equation Modeling with Partial Least Squares (SEM-PLS). The measurement model analysis demonstrates robust loading factors, reliability, and convergent validity, while the discriminant validity analysis confirms the distinctiveness of the latent constructs. In the structural model, positive and statistically significant relationships are found between the effectiveness of Zakat distribution, collection, management, and the improvement of social welfare. The R-square and adjusted R-square values of 0.602 and 0.592, respectively, indicate a strong explanatory power of the model. These findings underscore the transformative potential of Zakat in addressing socio-economic disparities and contribute valuable insights for policymakers and practitioners in the field of social development.

Spatial-Temporal Evolution and Prediction of Carbon Storage in Jiuquan City Ecosystem Based on PLUS-InVEST Model

Based on the background of carbon peaking and carbon neutrality goal strategies, it is important to explore the impact of land use change on carbon storage and the drivers of spatial variation in carbon storage in the Northwest Arid Zone, which is vital to improve the carbon sink increment of the regional ecosystem and promote the regional carbon breakeven. The arid region of northwest China is an extremely fragile natural ecology, and with the rapid advancement of new urbanization, the rate of land use change has accelerated significantly, which has a certain impact on the carbon storage and fixation capacity of ecosystems. The PLUS-InVEST model was used to simulate the spatial and temporal evolution characteristics of carbon storage under natural development, intensive development, water resource constraint, and ecological protection scenarios in Jiuquan City in 2035, and the parameter optimal geographic detector model was used to analyze the spatial divergence drivers of carbon storage. The results showed that:① the area of cultivated land, watershed, and construction land in Jiuquan City showed a significant increasing trend from 1990 to 2020, whereas the area of the remaining land use types showed a decreasing trend. ② The carbon storage in Jiuquan City increased from 7 722 808.1 t to 7 784 371 t from 1990 to 2020, and the conversion of grassland into unused land was the main cause of the loss of regional carbon storage, accounting for 85% of the total loss. ③ All four development scenarios in 2035 showed an increasing trend of carbon storage, among which the ecological protection scenario had the most significant increase, with an increment of 76 989.29 t. ④ The degree of land use, population density, GDP density, and NDVI were the main driving factors of the spatial variation in carbon storage in Jiuquan City, among which the degree of land use had the strongest explanatory power (q value of 0.849), and the interaction of natural and anthropogenic factors enhanced the explanatory power of each factor on the spatial variation in carbon storage. The results of the study can provide a scientific basis and decision basis for the integrated ecosystem management and territorial space optimization in Jiuquan City.

An Empirical Study of A-share Stocks in the New Energy Sector --Based on the Fama-French Three-factor Model and the CAPM Model

In recent years, the new energy industry has been developing rapidly, also, the new energy industry has drawn much consideration in corporate security choices. This paper selects 20 stocks with better performance in the new energy industry, constructs three explanatory variables based on the Fama-French three-factor model and the CAPM model, namely, the market factor, the size factor, and the book-to-market ratio factor, and carries out empirical analyses on the daily returns of the selected stock portfolios. It is concluded that all three explanatory variables have better explanatory power for the sample data, with the market factor having a stronger explanatory power than the rest of the factors, and the Fama-French three-factor model having a better explanatory power than the CAPM model.

How food insecurity affects children's behavior problems in early childhood: The nutrition and family stress pathways.

This study examines how household food insecurity shapes young children's behavior problems in Singapore. The analysis is based on two waves of data collected before and during COVID-19 from a nationally representative sample of 2,601 children in the Singapore Longitudinal Early Development Study (SG-LEADS, Mage = 4.5 at wave 1, Mage = 6 at wave 2). Results based on propensity score matching, fixed effects analysis and lagged-variable models show a positive association between household food insecurity and children's behavior problems both concurrently and over a two-year period. Two mediating pathways of this association are identified-children's dietary intake and family stress. Children in food-insecure households tend to consume fewer vegetables and more sugar-sweetened beverages and carbohydrates, which is associated with elevated behavior problems. Parents in food-insecure households exhibit greater emotional distress, diminished parental warmth, and increased punitive parenting practices, also contributing to their children's behavior problems. The family stress pathway has a stronger explanatory power than the nutrition pathway on children's behavior problems. This study reveals that food insecurity is a risk factor for children's behavior problems in early childhood which can lead to later developmental vulnerabilities for children in financially deprived families.

The Impact of Digital Transformation on New Product Development with the Mediating Role of Organizational Intelligence

Objective: This article aims to investigate the impact of digital transformation on new product development (NPD) with the mediating role of organizational intelligence. Methods and Materials: The research employs a descriptive-correlational design, utilizing questionnaires and field studies to collect data from 100 experts in the rubber manufacturing industry, specifically from Barez and Artavil Tire companies. Confirmatory factor analysis (CFA) was used to assess the validity of the questionnaire, with structural equation modeling (SEM) conducted using Smart PLS software to test the hypotheses. The reliability and validity of the constructs were evaluated through composite reliability (CR), average variance extracted (AVE), and discriminant validity measures. Findings: The study found that digital transformation significantly affects NPD performance (path coefficient = 0.586, t-value = 9.849), and organizational intelligence strongly mediates this relationship (path coefficient = 0.817, t-value = 12.838). Additionally, digital transformation directly enhances organizational intelligence (path coefficient = 0.296, t-value = 2.865). The R² value for NPD was 0.717, and for organizational intelligence, it was 0.688, indicating strong explanatory power. Conclusion: Digital transformation significantly impacts NPD success, particularly when supported by high levels of organizational intelligence. Companies that invest in digital infrastructure and knowledge management systems are better equipped to handle the complexities of NPD, leading to more innovative and competitive products. Organizational intelligence serves as a key enabler in leveraging digital tools for successful product development.

Time Series Forecasting and Projection of Diabetes Prevalence in India from 2023 to 2035: A Cross-sectional Study

Introduction: Diabetes is a significant public health problem in India, with an estimated 74 million Indian adults suffering from the condition as of 2021, and the prevalence is continuing to grow. Aim: To investigate the prevalence and forecasted trends of Diabetes Mellitus (DM) in India through time series analysis and forecasting models. Materials and Methods: A cross-sectional study was conducted in the Department of Statistics at Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India, from January 2024 to March 2024. The prevalence of diabetes in India was forecasted from 2023 to 2035 using existing diabetes prevalence data from 2009 to 2021, with the help of Gretl software and the Autoregressive Intregated Moving Average (ARIMA) model. Results: Authors observed a consistent upward trajectory in DM prevalence, with rates steadily increasing from 7.1% in 2009 to 9.6% in 2021. Time series analysis reveals non stationarity in the data, necessitating the use of ARIMA models for forecasting. Among the models considered, ARIMA (2,1,2) emerges as the best fit, demonstrating strong explanatory power with an R-squared value of 0.80. Forecasting projections indicate a continued rise in DM prevalence, with rates projected to increase from 10.35% in 2023 to 13.46% by 2035, which translates to an increase from 97.5 million in 2023 to 139 million in 2035. Conclusion: These findings underscore the urgent need for comprehensive public health interventions to address the escalating burden of diabetes in India, emphasising the importance of prevention, early detection, and effective management strategies.

The Regionally Heterogeneous Impact of China’s Environmental Regulation on the Transformation and Upgrading of Its Industrial Structure

Approaches to promoting the transformation and upgrading of China’s industrial structure represent an important issue in high-quality economic development. The upgrading of industrial structures is beneficial not only for environmental protection but also for sustainable economic development. This article first tests the correlation between environmental regulation and the transformation and upgrading of industrial structure through an impulse response function and then uses provincial panel data to analyze the regionally heterogeneous impact of environmental regulation on industrial structure upgrading. The research results indicate that: (1) the presented impulse response graph indicates that environmental regulation shocks have a significant impact on the rationalization and upgrading of industrial structure, and subsequent variance decomposition indicates that environmental regulation has a strong explanatory power on the transformation and upgrading of industrial structure; (2) overall, strengthening the level of environmental regulation is not conducive to the rationalization of industrial structure (RIS), but a strong level of environmental regulation can significantly promote the upgrading of industrial structure (UIS); and (3) at the regional level, there is significant regional heterogeneity in the impact of environmental regulation on the transformation and upgrading of industrial structure in different regions. Environmental regulation can promote UIS in China’s eastern and western regions, but it is not conducive to RIS. Environmental regulation can promote RIS in China’s central region, but it has a negative impact on UIS. The conclusion of this article provides inspiration for sustainable economic development.

When Does S(he) Plan to Retire? Antecedents of Retirement Age In Family Businesses

The family CEOs' intention to retire seems to be critical to trigger the succession process. For a more comprehensive understanding of the leadership succession in family businesses, it is important to study the antecedents of the family CEO's intended retirement age. Using a global database, we examine the incumbents' retirement age from their own perspective as the determinant of retirement timing. We apply the theory of planned behavior to explore factors affecting retirement age with a forward perspective, using the intended age of retirement as an antecedent for effective retirement age. Our results suggest that individual factors of the intended retirement age have stronger explanatory power in leader intended retirement age compared to the family business factors.

Innovation for sustainability: Harnessing the power of efficient natural resource markets for a greener economic recovery

Amidst China's wealth of resources, not all are harnessed effectively. With a surging economy and population, enhancing ecological capacity through more efficient resource use is imperative. This study examines panel data from 2012 to 2020 to probe how wealth inequality influences 30 Chinese regions' resource efficiency. Rigorous analysis yielded strong explanatory power. Notably, disparities in green innovation capabilities and natural resource abundance emerge as primary drivers of differentiation. Findings indicate that a nation's wealth gap directly impacts its ecological innovation capacity and sustainable resource provision. Significant income disparities correlate with reduced resource efficiency, underscoring the role of economic inequality. Addressing this issue, strategies like industry clustering and eco-friendly tax policies can mitigate economic disparities' negative influence on resource efficiency. Furthermore, this study spotlights disruptive technological advancements, challenging the traditional inverse link between economic inequality and resource efficiency, and offering promising avenues for a greener future.

Forecasting stock volatility using pseudo-out-of-sample information

This paper proposes a novel forecast combination method and investigates the impact of technical indicators on volatility prediction in the Chinese stock market. Firstly, our analysis reveals that technical indicators based on good and bad volatility have a stronger explanatory power on stock volatility, exhibiting an evident asymmetric effect. In addition, we introduce two new categories of technical indicators based on price skewness risk and kurtosis risk, which exhibit more robust and significant predictive power on volatility than existing indicators. Finally, we propose a new forecast combination method that employs adjusted out-of-sample R2 as a weight, which outperforms a series of existing forecasting models. These findings are robustly confirmed in multiple robust tests, demonstrating the efficacy of our proposed approach.