Search

Uncovering the intricate relationships within the realm of ecosystem services (ESs) across various spatial and temporal dimensions, as well as their nonlinear relationships with natural–social factors, is a fundamental condition for regional ecosystem management. This study focuses on Honghe Prefecture, Yunnan Province, and it quantifies the supply of ESs at the grid and township scales, clarifies the interrelationships among ESs and influencing elements, and proposes cross-scale regional ecological management strategies. The findings indicate the following: (1) ESs exhibited spatial variability. In the last 20 years, the supply capacity of food production (FP) increased by about 46%, while other ESs showed a downward trend. (2) Synergistic effects among ESs primarily occurred between WY, habitat quality (HQ), carbon sequestration (CS), and soil conservation (SC), while trade-off effects mainly took place between FP and other ESs. (3) Significant and dramatic changes in the ecosystem service bundles were observed in the southern mountainous areas. At the grid scale, the overall area of the integrated ecological bundle declined by approximately 88%. However, the proportion of the HQ-CS key synergy bundle increased from 15.68% to 40.60%. Similar spatial patterns and trends were also observed at the township scale. (4) There was a notable reduction in the comprehensive supply of the ecosystem service index (ESI) in the southwest, in which human activities and climate drought factors played a major negative driving role, and some driving factors had threshold effects with the ESI. Existing research often ignores the nonlinear relationship between complex spatiotemporal dynamics and ecosystem services. Thus, this study constructed a comprehensive cognitive framework for regional ES status from the perspective of "supply–interaction–driving–threshold" for ESs, providing a more comprehensive understanding of regional ES management. [ABSTRACT FROM AUTHOR]

The main objective of this study is to contribute to the literature by forecasting green bond index with different machine learning models supported by artificial intelligence. The data from 1 June 2021 to 29 April 2024, collected from many sources, was separated into training and test sets, and standard preparation was conducted for each. The model's dependent variable is the Global S&P Green Bond Index, which monitors the performance of green bonds in global financial markets and serves as a comprehensive benchmark for the study. To evaluate and compare the performance of the trained machine learning models (Random Forest, Linear Regression, Rational Quadratic Gaussian Process Regression (GPR), XGBoost, MLP, and Linear SVM), RMSE, MSE, MAE, MAPE, and R² were used as evaluation metrics and the best performing model was Rational Quadratic GPR. The concluding segment of the SHAP analysis reveals the primary factors influencing the model's forecasts. It is evident that the model assigns considerable importance to macroeconomic indicators, including the DXY (US Dollar Index), XAU (Gold Spot Price), and MSCI (Morgan Stanley Capital International). This work is expected to enhance the literature, as studies directly comparable to this research are limited in this field.

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making.

Background: There is a growing number of patients suffering from sensitive skin secondary to acne, but its prevalence and influencing factors are not yet well‐understood. Objective: The aim of this study is to investigate the nonlinear relationship between air pollutants and secondary sensitive skin in acne patients. Methods: A cross‐sectional study comprising 4325 acne outpatients in China was carried out between September 2021 and December 2022, employing a simple random sampling approach. Air pollutants data was derived from the nearest air quality monitoring station corresponding to the subjects' residential locations. Furthermore, socio‐economic characteristics, biological attributes, and lifestyle data of patients were acquired via questionnaire surveys. The data were subsequently analyzed utilizing the XGBoost machine learning model. Results: A nonlinear relationship has been observed between secondary sensitive skin in acne patients and various factors, including particulate matter (PM2.5), inhalable particulate matter (PM10), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), the severity of depression, different levels of exercise intensity, acne grading, frequency of sunscreen application, gender, and age. Conclusion: The occurrence of secondary sensitive skin in acne patients be mitigated through the implementation of measures such as the control of air pollutant emissions, regulation of negative emotions, and improvement of personal lifestyle. [ABSTRACT FROM AUTHOR]

Definition: The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making. [ABSTRACT FROM AUTHOR]

Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subway station density of 0.5–0.8 stations per kilometer being optimal for stimulating both types of vitality. The minimum threshold for economic vitality in a given unit is a housing price exceeding 6000 RMB/m2; (4) the future vitality map suggests that urban planners should pay greater attention to non-central districts with high development potential. Moreover, spontaneous social interactions and consumer behaviors stimulated by various shops are critical components of urban vitality. In designing the physical environment and urban spatial forms, special attention should be given to enhancing the attractiveness of physical spaces and their capacity to accommodate social interaction. [ABSTRACT FROM AUTHOR]

IntroductionUniversity campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, and nearby communities. However, the mechanisms through which campus environments influence walking activities remain insufficiently understood. This study examines universities in Wuhan, China, using crowdsourced data and machine learning methods to analyze the nonlinear and interactive effects of campus built environments on exercise walking.MethodsThis study utilized crowdsourced exercise walking data and incorporated diverse campus characteristics to construct a multidimensional variable system. By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning framework was established to evaluate the importance of various factors, explore the nonlinear relationships between variables and walking activity, and analyze the interaction effects among these variables.ResultsThe findings underscore the significant impact of several key factors, including the proportion of sports land, proximity to water bodies, and Normalized Difference Vegetation Index NDVI, alongside the notable influence of six distinct campus area types. The analysis of nonlinear effects revealed distinct thresholds and patterns of influence that differ from other urban environments, with some variables exhibiting fluctuated or U-shaped effects. Additionally, strong interactions were identified among variable combinations, highlighting the synergistic impact of elements like sports facilities, green spaces, and waterfront areas when strategically integrated.ConclusionThis research contributes to the understanding of how campus built environments affect walking activities, offering targeted recommendations for campus planning and design. Recommendations include optimizing the spatial configuration of sports facilities, green spaces, and water bodies to maximize their synergistic impacts on walking activity. These insights can foster the development of inclusive, health-promoting, and sustainable campuses.

Understanding the associations between demographic groups' metro travel behaviors and the built environment is crucial for addressing automobile dependence and promoting transportation equity and reasonable urban construction. This study examines the nonlinear relationships and threshold effects of the built environment on the metro travel patterns of three groups (i.e., commuters, seniors, and students) by applying smart card data in Kunming, China. We select the optimal machine learning model—gradient boosting decision trees (GBDTs)—and consider various built environment attributes. Our findings indicate that: 1) built environment attributes universally have nonlinear and threshold effects on metro travel for all groups; 2) the collective contributions of density and diversity differ greatly across groups compared to other attributes; and 3) only a few built environment attributes have similar effect directions and degrees across all three groups, while most have unique effects on each group. The findings suggest metro station area planning strategies to promote metro use and transportation equity for different groups. • Analyze the relationship between the built environment and different metro travelers. • Apply machine learning methods to evaluate nonlinearity. • Built environment attributes universally have nonlinear effects on all groups' metro travel. • A few built environment variables have similar effect directions and degrees for all three groups. • The effects of most variables vary across groups. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Research in Economics, Politics & Finance / Ekonomi, Politika & Finans Arastirmalari Dergisi is the property of Journal of Research in Economics, Politics & Finance 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.)

Rapid urbanization is universally acknowledged to degrade ecosystem services, posing significant threats to human well-being. However, the effects of urban shrinkage, a global phenomenon and a counterpart to urbanization, on ecosystem services (ESs) remain unclear. This study focuses on China's Rust Belt during the period from 2000 to 2020, constructing a comprehensive analytical framework based on long-term remote sensing data to reveal the temporal and spatial patterns of ESs and their associations with cities experiencing varying degrees of shrinkage. It employs a random forest (RF) model and a Shapley additive explanation (SHAP) model to measure and visualize the significance and thresholds of socioeconomic factors influencing changes in ESs. Our findings highlight the following: (1) Since 2010, the three provinces of Northeast China (TPNC) have begun to shrink comprehensively, with the degree of shrinkage intensifying over time. Resource-based cities have all experienced contraction. (2) Regional urban shrinkage has been found to enhance the overall provision capacity of ESs, with the most significant improvements in cities undergoing continuous shrinkage. (3) The impact of the same socioeconomic drivers varies across cities with different levels of shrinkage; increasing green-space ratios and investing more in public welfare have been identified as effective measures to enhance ESs. (4) Threshold analysis indicates that the stability of the tertiary sector's proportion is critically important for enhancing ESs in cities undergoing intermittent shrinkage. An increase of 10% to 15% in this sector can allow continuously shrinking cities to balance urban development with ecological improvements. This research highlights the positive aspects of urban shrinkage, demonstrating its ability to enhance the provision capacity of ESs. It offers new insights into the protection and management of regional ecosystems and the urban transformation of the three eastern provinces. [ABSTRACT FROM AUTHOR]

Investigating urban vitality and comprehending the influence mechanisms of the built environment is essential for achieving sustainable urban growth and improving the quality of life for residents. Current research has rarely addressed the nonlinear relationships and synergistic effects between urban vitality and the built environment at the neighborhood scale. This oversight may overlook the influence of key neighborhoods and overestimate or underestimate the influence of different factors on urban vitality. Using Guangzhou's central urban area as a case study, this research develops a comprehensive urban vitality assessment system that includes economic, social, cultural, and ecological dimensions, utilizing multi-source data such as POI, Dazhong Dianping, Baidu heatmap, and NDVI. Additionally, the XGBoost-SHAP model is applied to uncover the nonlinear impacts of different built environment factors on neighborhood vitality. The findings reveal that: (1) urban vitality diminishes progressively from the center to the periphery; (2) proximity to Zhujiang New Town is the most critical factor for neighborhood vitality (with a contribution of 0.039), while functional diversity and public facility accessibility are also significant (with contributions ranging from 0.033 to 0.009); (3) built environment factors exert nonlinear influences on neighborhood vitality, notably with a threshold effect for subway station accessibility (feature value of 0.1); (4) there are notable synergistic effects among different built environment dimensions. For example, neighborhoods close to Zhujiang New Town (feature value below 0.12) with high POI density (feature value above 0.04) experience significant positive synergistic effects. These findings can inform targeted policy recommendations for precise urban planning. [ABSTRACT FROM AUTHOR]

Assisted succession could enable long‐term restoration where successional trajectories stall due to competition from invasive plants. Many invasives are shade‐intolerant; therefore, interventions reducing light availability should suppress invasion and re‐establish successional processes. However, given how ubiquitous nonlinearities are in ecology, restoration success also depends on identifying critical system thresholds, for example invader abundances below which regeneration of desired species is possible. We report the successful use of assisted succession to restore a swamp forest invaded by Phalaris arundinacea (reed canarygrass; hereafter Phalaris), initiated by a high‐density planting of woody species to outcompete Phalaris by reducing light availability.We established five pre‐planting treatments in a Phalaris near‐monoculture in Wisconsin, USA: herbicide‐only, herbicide+plough, herbicide+burn, mow+herbicide and control. In 2003 we planted 22 tree and shrub species at high densities, then in 2019 we censused the site to: (1) screen for long‐term differences among treatments, (2) evaluate long‐term effects of our interventions on community composition and (3) characterize the critical thresholds that enable invader suppression and restoration success.Vegetation responses and light availability across our four pre‐planting invader removal treatments did not differ. Late fall glyphosate application suppressed Phalaris long enough that a dense canopy of native woody species could establish and eventually out‐shade it. Overstory tree and shrub densities of 0.071/m2 suppressed Phalaris to 50% cover, but, due to nonlinearities, much higher densities were needed to reduce light availability and thus Phalaris cover enough to shift the system from being invader‐dominated. Compositional similarities between juvenile woody species and the overstory suggest a long‐term restoration success.Synthesis and applications. Invasive species management and the restoration of target plant communities can be aided by assisting successional trajectories that have stalled. We document a restoration strategy for forests invaded by shade‐intolerant invaders that is both effective and economical, as only a simple site preparation and single planting effort is required. Establishing a dense canopy of woody species in this way can break the feedbacks maintaining invader dominance and re‐introduce feedbacks enabling long‐term ecosystem recovery. We also illustrate the value of identifying critical thresholds influencing the abundance and impact of key invasive species. [ABSTRACT FROM AUTHOR]

The Karst region is a critical ecological barrier and functional zone in China. Understanding the spatiotemporal evolution of its ecosystem services and its relationship with human activities and climate change is of importance for achieving regional ecological protection and high-quality development. In this study, we used the InVEST model and CASA model to evaluate the spatiotemporal evolution pattern of ecosystem services in the study area from 2000 to 2020. The XGBoost–SHAP model was used to reveal the key indicators and thresholds of changes in major ecosystem services in the study area due to climate change and human activities. The results showed significant land use changes in the study area from 2000 to 2020, particularly the conversion of cropland to construction land, which was more intense in economically developed areas. The areas of forest and grassland increased initially but later decreased due to the impact of human activities and natural factors. Habitat quality (HQ) showed an overall declining trend, while soil retention (SR) and water yield (WY) services exhibited significant interannual variations due to climate change. The changes in rainfall had a particularly notable impact on these services; in years with excessive rainfall, soil erosion intensified, leading to a decline in SR services, whereas in years with moderate rainfall, SR and WY services improved. Carbon fixation (CF) services were enhanced with the expansion of forest areas. The XGBoost–SHAP model further revealed that the effects of rainfall and sunshine duration on ecosystem services were nonlinear, while population density and the proportion of construction land had a significant negative impact on habitat quality and soil retention. The expansion of construction land had the most significant negative impact on habitat quality, whereas the increase in forest land significantly improved carbon fixation and the soil retention capacity. By revealing the mechanisms of the impact of climate change and human activities on ecosystem services, we aimed to provide support for the promotion of ecological conservation and sustainable development strategies in the study area, as well as to provide an important reference for areas with geographic similarities to the study area. [ABSTRACT FROM AUTHOR]

Vegetation plays an essential role in terrestrial carbon balance and climate systems. Exploring and understanding relationships between vegetation dynamics and climate changes in Southwest China is of great significance for ecological environment conservation. Nonlinear relationships between vegetation and natural factors are extraordinarily complex in Southwest China with complicated topographic conditions and changeable climatic characteristics. Considering the complex nonlinear relationships, the Random Forest (RF) and an integration of Convolutional Neural Networks and Long Short-Term Memory network (CNN-LSTM) were used with multi-source data from 2000-2020. Performance of two models were compared with precision indicators, and influence of topographic and hydro-climatic factors on vegetation was quantified based on the optimal models. Results revealed that the Normalized Difference Vegetation Index had a significant negative correlation with elevation and a positive correlation with land surface temperature and evapotranspiration. According to precision indicators, the RF model (RF3) built with longitude, latitude, elevation, slope, temperature, precipitation, evapotranspiration and surface solar radiation as inputs outperformed other models. Relative importance of the eight natural factors was quantified based on the RF3, and results indicated that elevation, temperature and evapotranspiration were major factors that influenced vegetation growth. Responses of vegetation toward climatic variables exhibited significant seasonal change, and there were different decisive factors, which influenced vegetation growth in forests, grasslands and croplands. [ABSTRACT FROM AUTHOR]

Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subway station density of 0.5–0.8 stations per kilometer being optimal for stimulating both types of vitality. The minimum threshold for economic vitality in a given unit is a housing price exceeding 6000 RMB/m2; (4) the future vitality map suggests that urban planners should pay greater attention to non-central districts with high development potential. Moreover, spontaneous social interactions and consumer behaviors stimulated by various shops are critical components of urban vitality. In designing the physical environment and urban spatial forms, special attention should be given to enhancing the attractiveness of physical spaces and their capacity to accommodate social interaction.

This study uses generalized additive models to identify and analyze nonlinear relationships between accounting-based and market-based independent variables and how these affect bankruptcy predictions. Specifically, it examines the independent variables that Altman (J Financ 23:589–609, 1968; Predicting financial distress of companies. Revisiting the Z-score and ZETA® models. Working paper, 2000) and Campbell et al. (J Financ 63:2899–2939, 2008) used and analyzes what specific form these nonlinear relationships take. Drawing on comprehensive data on listed U.S. companies, we show empirically that the bankruptcy prediction is influenced by statistically and economically relevant nonlinear relationships. Our results indicate that taking into account these nonlinear relationships improves significantly several statistical validity measures. We also use a validity measure that is based on the profitability of the bankruptcy prediction models in the context of credit scoring. The findings demonstrate that taking into account nonlinear relationships can substantially increase the discriminatory power of bankruptcy prediction models. [ABSTRACT FROM AUTHOR]

Urban cold island effects have become increasingly relevant with accelerating climate change. However, the relationship between such effects and their causal variables remains unclear. In the present study, we analyzed the relationship between blue-green space variables and land surface temperature (LST) and park cooling intensity (PCI) in central Zhengzhou City using a random forest regression model. Cool urban areas corresponded to the location of blue-green spaces. The average temperatures of these spaces were 2 °C and 1 °C lower than those of the built-up areas and the full study region, respectively. Blue-green spaces also had a maximum temperature that was 8 °C lower than those of the built-up areas and the study region. The three primary variables determining LST were blue space proportion and area and vegetation cover, whereas the three variables determining PCI were blue-green space width, vegetation cover, and patch density. At a width of 140 m, blue-green spaces caused a PCI peak, which further improved at 310 m. The proportion of blue space had a stepwise effect on PCI. A vegetation coverage of 56% represented the lower threshold of LST and the higher threshold of PCI. These results reflect a nonlinear relationship between blue-green variables and urban cold islands. In conclusion, the study provides data that could inform the efficient use of blue-green spaces in urban construction and renewal.

Copyright of Journal of Technology Economics is the property of Chinese Society of Technology Economics 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.)

Physicians facing the COVID-19 pandemic are likely to experience acute and chronic, and often unpredictable, occupational stressors that can incur post-traumatic stress symptoms (PTSS), prevention of which is of utmost importance to enhance healthcare workforce efficiency. Unlike previous studies, in this paper we developed a generalized data-driven framework to generate insights into the complex, nonlinear associations of cognitive/occupational factors with physicians’ PTSS-risk. Data were collected from practicing physicians in the 18 states with the largest COVID-19 cases by deploying a cross-sectional, anonymous, web-based survey, following the second COVID-19 peak in the US. Analyses revealed that physicians directly treating COVID-19 patients (frontline) were at higher occupational risk of PTSS than those who didn’t (secondline). We implemented a suite of eight statistical learning algorithms to evaluate the associations between cognitive/occupational factors and PTSS in frontline physicians. We found that random forest outperformed all other models, in particular the traditionally-used logistic regression by 6.4% (F1-score) and 9.6% (accuracy) in goodness-of-fit performance, and 4.8% (F1-score) and 4.6% (accuracy) in predictive performance, indicating existence of complex interactions and nonlinearity in associations between the cognitive/occupational factors and PTSS-risk. Our results show that depression, burnout, negative coping, fears of contracting/transmitting COVID-19, perceived stigma, and insufficient resources to treat COVID-19 patients are positively associated with PTSS-risk, while higher resilience and support from employer/friends/family/significant others are negatively associated with PTSS-risk. Insights obtained from this study will help to bring new attention to frontline physicians, allowing for more informed prioritization of their care during future pandemics/epidemics.

Understanding how natural and anthropogenic factors affects the watershed runoff and sediment yield at event scale can provide useful insights into watershed hydrological processes to guide watershed management. This article investigates the nonlinear relationships between natural and anthropogenic factors and event runoff and sediment yield with a boosted regression tree (BRT) model for 38 watersheds within the Loess Plateau in China during 2006 to 2016. The BRT model captures the relative importance of each natural and anthropogenic factors to the variability in runoff and sediment yield. The results show that these relationships are complex and highly nonlinear. The event runoff was most related to NDVI in the grass land (NDVI_g), with a 21.3% contribution, followed by 3‐day antecedent precipitation index (AP3), 10‐day antecedent precipitation index (AP10), precipitation (P), and 7‐day antecedent precipitation index (AP7). For event sediment yield, the strongest factor is AP3, with contribution of 33.3%, followed by P, NDVI in the forest land (NDVI_f), AP7, and NDVI in the crop land (NDVI_c). The marginal effect curves produced by the BRT are often characterized by thresholds. For instance, NDVI_f has the greatest effect on event sediment yield reduction when NDVI_f = 0.42, suggesting that a very intense green coverage is not necessary to achieve maximal soil erosion control. Vegetation coverage and meteorological factors can explain 54.7% of event runoff variation and 55.6% of event sediment yield variation. Our study identify the nonlinear relationships between runoff and sediment yield with vegetation cover and meteorological factors and provide scientific support for the planning of subsequent reforestation projects in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Science walks on two legs. One leg consists of asking: How things are? This leads to observation, measurement, graphing, and statistical description. The other leg consists of asking: How things should be, on logical grounds? This leads to logical models that should become quantitatively predictive. Science largely consists of such models, tested with data. Developed science establishes not only connections among individual factors but also connections among these connections. As an illustration, I use laws about human activity I have found. But social sciences often take the lazy road of fitting raw data with a straight line or some fashionable format, unaware of the need to think and build models based on logic, as stressed by Karl Deutsch. As expounded in my Making Social Sciences More Scientific (2008) and Logical Models and Basic Numeracy in Social Sciences, www.psych.ut.ee/stk/Beginners_Logical_Models.pdf, I call for a major widening in social science methodology.

Though much evidence demonstrates the benefits of urban green spaces (UGS) for mental health, it remains uncertain if a greener living environment necessarily leads to better mental health. This study makes up this gap by exploring the potential non-linear effects of UGS provision (availability, accessibility, visibility, quality) and utilization (frequency, duration) on mental health, focusing on their nonlinear patterns and thresholds. Using geospatial and social survey data from Wuhan, China, and controlling for socioeconomic, built, and social environmental factors, we find that: (1) Both UGS provision and utilization have significant nonlinear effects on mental health. (2) Inverted-U-shaped relationships exist between mental health and both UGS availability (measured by Normalized Difference Vegetation Index, NDVI) and accessibility (distance to nearest park), with peak benefits observed at an NDVI of 0.25 or a distance of 0.24 km. A similar L-shaped relationship is observed for UGS visibility, suggesting that higher visibility does not necessarily translate to improved mental health. The positive health effects of UGS quality exist, yet offer marginal benefits. (3) UGS utilization plays a crucial mediating role, explaining up to 47 % of the relationship between visibility and mental health. This highlights the importance of active engagement with UGS for realizing mental health benefits, supporting the 'environmental provision→individual behavior→mental health' pathway. These findings provide urban planners with valuable dosage references for UGS allocation, emphasizing the need to consider both provision and factors promoting utilization to maximize mental health benefits within urban environments. • We explore the nonlinear pathway: environmental provision → individual behavior → mental health by focusing on UGS provision and utilization. • Urban green space provision shows non-linear links to mental health, with thresholds for optimal benefits. • KHB results reveal a nonlinear mediating role of UGS utilization. • Study offers practical dosage guidance for urban planners to design green spaces that improve mental health. [ABSTRACT FROM AUTHOR]

Graph Convolutional Networks have been widely used for node classification. Since the original data usually contains nonlinear relationships that are difficult to capture and includes noise that leads to the poor performance of the constructed graph representation, the paper proposes a novel Nonlinear Graph Learning-Convolutional Network (NGLCN) based on the kernel method and graph representation learning. Specifically, NGLCN first uses a kernel method to map the original data into kernel space, making the original linearly separable to capture the nonlinear relationship between the data, and then uses a feature selection based on structure information to remove the noisy and redundant feature and constructs a high-quality graph representation, and finally employs a common graph convolutional network to conduct node classification tasks. Experimental results on eight benchmark datasets show that NGLCN outperforms the state-of-the-art traditional graph convolutional networks. [ABSTRACT FROM AUTHOR]

AbstractThe ‎purpose of this study is to introduce a financial network based on non-‎linear relationships between ‎stocks to optimize the portfolio of ‎investors,identify the leaders of the Iranian stock market using ‎centrality ‎criteria‏ ‏and finally clustering non-linear financial network.In this study,the top ‎‎100 ‎companies listed on the stock exchange with the highest capital registered ‎in the 11-year period ‎‎(December 2009 to January 2020) were selected.The results show that ‎according to the degree ‎centrality, the stocks of Sepahan Cement,Omid Capital ‎Financing,and Omid ‎Investment,according to the criterion of closeness centrality‎‎,Ghadir investment ‎stocks, investment of National Development and Khuzestan Steel, ‎According ‎to the closeness centrality,Ghadir investment stocks,National Development ‎‎and Khuzestan Steel Investment Group, according to the betweenness ‎centrality,Ghadir ‎Investment stocks, Sepahan Cement and National ‎Development Investment and ‎according to the bottleneck centrality, the stocks ‎of Khuzestan Steel,Sepahan Cement ‎and International Building Development ‎have the most impact on the stock market and were ‎identified as market ‎leaders. To categorize the top stocks, the fast greedy algorithm was used, in ‎‎which the network was divided into 11 clusters, and each of these clusters ‎represents the largest ‎relationship between the shares of companies in the ‎financial network. ‎ Introduction:A stock portfolio is a collection of the best stocks in which each stock has a certain return and risk. What is very important in forming a portfolio with the least amount of risk is to find stocks that have the least amount of relationship with each other. In order to examine the relationship between the stocks of different companies and consequently the selection of the optimal stock portfolio, there are different methods and techniques that can be used. One of the best techniques for identifying and selecting the optimal portfolio of diversified stocks is to identify the relationship and correlation and then clustering between different stocks and grouping them based on the important factors that investors consider for investing. Using this technique, stock selection and the formation of an optimal portfolio of different groups is done, which in addition to being able to solve the problem of expected returns of investors, also the problems caused by the investment risk in the stock market can be solved. One of the most important problems in modern financial discussions is finding efficient methods for presenting and summarizing data produced by the stock exchange, and this information is displayed in thousands of forms, each of which separately represents the price movement of each stock. As the number of stocks increases, the analysis of these forms will become more complex According to recent research, the complex network method is highly recommended for visualizing and summarizing stock data and examining the relationship between stock prices. Using complex network analysis, a clear picture of the internal structure of the stock exchange can be provided Analyzing stock market statements, examining how they evolve over time, and describing patterns within the stock market are important and useful for developing and designing investment strategies. Therefore, the purpose of this study is to create and introduce a financial network based on stock relationships in companies listed on the Tehran Stock Exchange, which will be provided by a minimum spanning tree. This network will be examined by the centrality measures and among the stocks of companies, top stocks and stock market leaders will be examined according to different measures, and finally the top stocks clustered will help to investors in order to optimize the portfolio and maximize Investment profit. Method and Data:The present study is applicable in terms of purpose, quantitative in terms of implementation process, retrospective and post-event in terms of time. R software is used to analyze data. The daily data of 100 companies that had the most market capital in Tehran Stock Exchange were received in 243 working days from "Tehran Stock Exchange site" from 2009 to 2019. This data corresponds to 11 solar years that have been selected as a sample to make a spanning tree and compare companies based on them. The financial network was converted to logarithmic returns using adjusted closing price. The concepts of graph theory and prim algorithm were used to explore the relationships and distances between stocks to construct a minimum spanning tree. Findings:The Findings show that ‎according to the degree centrality, the stocks of Sepahan Cement Companies, Omid Capital ‎Financing, and Omid Investment Management, according to the criterion of closeness centrality ‎‎, Ghadir investment stocks, investment of National Development Group and Khuzestan Steel, ‎According to the closeness centrality, Ghadir investment stocks, National Development Group ‎and Khuzestan Steel Investment Group, according to the betweenness centrality, Ghadir ‎Investment Company stocks, Sepahan Cement and National Development Investment and ‎according to the bottleneck centrality, the stocks of Khuzestan Steel Company, Sepahan Cement ‎and International Building Development have the most impact on the stock market and were ‎identified as market leaders. To categorize the top stocks, the fast-greedy algorithm was used, in ‎which the network was divided into 11 clusters, and each of these clusters represents the largest ‎relationship between the shares of companies in the financial network. ‎ Conclusion and discussion: This study sought to investigate the nonlinear relationship between the most valuable stocks in the stock market. In addition to creating a network to identify relationships between stocks, market leaders were also identified who can influence the network based on various measures. Finally, to optimize the stock portfolio, all stocks in the network were clustered to reduce portfolio risk.

Many solar wind parameters correlate with one another, which complicates the causal‐effect studies of solar wind driving of the magnetosphere. Conditional mutual information (CMI) is used to untangle and isolate the effect of individual solar wind and magnetospheric drivers of the radiation belt electrons. The solar wind density (nsw) negatively correlates with electron phase space density (PSD; average energy ∼1.6 MeV) with time lag (τ) = 15 hr. The effect of nsw has been attributed to magnetopause shadowing or other loss mechanisms, but when the effect of solar wind velocity (Vsw) is removed, τ shifts to 7–11 hr, which is a more accurate time scale for this process. The peak correlation between Vsw and PSD shifts from τ = 30–50 to 44–56 hr, when the effect of nsw is removed. This suggests that the time scale for electron acceleration to 1–2 MeV is about 44–56 hr following Vsw enhancements. The effect of nsw is significant only at L* = 4.5–6 (L* > 6 is highly variable), whereas the effect of Vsw is significant only at L* = 3.5–6.5. The peak response of PSD to Vsw is the shortest and most significant at L* = 4.5–5.5. As time progresses, the peak response broadens and shifts to higher τ at higher and lower L*, consistent with local acceleration at L* = 4.5–5.5 followed by outward and inward diffusion. The outward radial diffusion time scale at L* = 5–6 is ∼40 hr per RE. Plain Language Summary: Many solar wind parameters correlate with one another, which complicates the causal‐effect studies of solar wind driving of the magnetosphere. We use conditional mutual information, which is part of information theory, to untangle and isolate the effect of individual solar wind and magnetospheric drivers of the radiation belt electrons. For example, the solar wind density negatively correlates with electron phase space density (PSD) (average energy ∼1.6 MeV) with the response time lag of 15 hr. This has been attributed to the electron loss process such as magnetopause shadowing. The time lag suggests the time scale for this process is 15 hr. However, when the effect of solar wind velocity is removed, the time lag is 7–11 hr, which is a more accurate time scale for this process. As another example, the time lag of the correlation between solar wind velocity and PSD shifts from 30 to 50 to 44–56 hr, when the effect of solar wind density is removed. This suggests that the time scale for electron acceleration to 1–2 MeV is about 44–56 hr following the solar wind velocity enhancements. We also show that the effects of solar wind velocity and density have dependence on radial distance. Key Points: The effect of nsw on radiation belt electrons is significant only at L* = 4.5–6 and not significant at L* < 4.5The effect of Vsw on radiation belt electrons is significant at L* = 3.5–6.5 and not significant at L* < 3.5The radiation belt electron response time lag to Vsw suggests local acceleration at L* = 4–5.5 followed by outward and inward diffusion [ABSTRACT FROM AUTHOR]

In the United States, several studies have looked at the association between automobile ownership and sociodemographic factors and built environment qualities, but few have looked at household travel characteristics. Their interactions and nonlinear linkages are frequently overlooked in existing studies. Utilizing the 2017 US National Household Travel Survey, the authors employed an extreme gradient boosting tree model to evaluate the nonlinear and interaction impacts of household travel characteristics and built environment factors on vehicle ownership in three states of the United States (California, Missouri, and Kansas) that are different in population size. To develop these models, three main XGBT parameters, including the number of trees, maximal depth, and minimum rows, were optimized using a grid search technique. In California, the predictability of vehicle ownership was driven by household travel characteristics (cumulative importance: 0.62). Predictions for vehicle ownership in Missouri and Kansas were dominantly influenced by sociodemographic factors (cumulative importance: 0.53 and 0.55, respectively). In all states, the authors found that the number of drivers in a household plays a vital role in the vehicle ownership decisions of households. Regarding the built environment attributes, deficiencies in cycling infrastructure were the most prominent attribute in predicting household vehicle ownership in California. This variable, however, has threshold connections with vehicle ownership, but the magnitude of these relationships is small. The outcomes imply that improving the condition of cycling infrastructure will help reduce the number of vehicles. In addition, incentives that encourage the households' drivers not to buy new vehicles are helpful. The outcomes of this study might aid policymakers in developing policies that encourage sustainable vehicle ownership in the United States. [ABSTRACT FROM AUTHOR]

This study empirically investigates factors predicting students' behavioral intentions towards the continuous use of mobile learning. Two baseline models namely the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) with the addition of habit as an exogenous construct were used for this purpose. The data were collected from 171 engineering undergraduates and analyzed based on structural equation modeling. The results suggest (1) Behavioral intention was positively and significantly influenced by mobile learning self-efficacy, attitude, and perceived usefulness; (2) Attitude was positively and significantly influenced by subjective norm, perceived usefulness, and mobile learning self-efficacy; (3) Mobile learning self-efficacy was only influenced by perceived ease of use and (4) Habit of using WhatsApp did not influence perceived usefulness nor perceived ease of use but had a positive and significant relationship with mobile learning self-efficacy. Nonlinear relationships were also observed between (1) Behavioral intention with perceived ease of use, perceived usefulness, and subjective norm (2) Habit with perceived usefulness and mobile learning self-efficacy. The nonlinear findings indicate that the relationships between these constructs, which were previously reported as linear, are prone to saturation and warrants further investigation. Our findings also stipulate a practical reference for higher educational institutions targeting to practice mobile learning for engineering undergraduates.

Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The latest global assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) warns that biodiversity loss can make ecosystems more vulnerable to the effects of climate change and other stressors. Economic growth has been identified as one of the key drivers of these losses, however, the impact pathway may depend on how society organizes economic activity and distributes its benefits. Here we use a global country-level dataset to show how the strength of national institutions and economic inequality in society can mediate the loss of biodiversity worldwide. We find that the interplay of institutions and inequality fully mediates the impact of economic growth on plant biodiversity, but only partially mediates the impact on animal biodiversity. Furthermore, in sustaining biodiversity, the effectiveness of institutions depends on inequality in society, such that biodiversity loss is ameliorated when institutions are strong and inequality low, but in regions with high inequality, institutions tend to lose their efficacy. The analysis also uncovers nonlinearities in inequality, institutions, and biodiversity interactions, which are important to investigate further and consider for policy purposes. [ABSTRACT FROM AUTHOR]

Covariance and correlation are two widespread tools in statistics and finance to measure how two entities vary together. Correlation measures the linear relationship between two variables and is not an adequate measure when the two exhibit nonlinear relationships. In this paper, we extend linear correlation to an α-grade monomial one; α values that maximize correlation indicate which type of nonlinear relationship data exhibit. Lagrange representation allows us to define a contro-correlation measure to represent how two entities are not related and a measure of relative variability. Finally, a simulation study and a real-world data application are performed to assess the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Estimating wave-breaking indexes such as wave height and water depth is essential to understanding the location and scale of the breaking wave. Therefore, numerous wave-flume laboratory experiments have been conducted to develop empirical wave-breaking formulas. However, the nonlinearity between the parameters has not been fully incorporated into the empirical equations. Thus, this study proposes a multilayer neural network utilizing the nonlinear activation function and backpropagation to extract nonlinear relationships. Existing laboratory experiment data for the monochromatic regular wave are used to train the proposed network. Specifically, the bottom slope, deep-water wave height and wave period are plugged in as the input values that simultaneously estimate the breaking-wave height and wave-breaking location. Typical empirical equations employ deep-water wave height and length as input variables to predict the breaking-wave height and water depth. A newly proposed model directly utilizes breaking-wave height and water depth without nondimensionalization. Thus, the applicability can be significantly improved. The estimated wave-breaking index is statistically verified using the bias, root-mean-square errors, and Pearson correlation coefficient. The performance of the proposed model is better than existing breaking-wave-index formulas as well as having robust applicability to laboratory experiment conditions, such as wave condition, bottom slope, and experimental scale.

Multivariate methods can improve wind speed prediction accuracy under specific conditions. In this work, Multiple Linear Regression (MLR) and Multiple Mass Probability (MMP) models are compared to the single correlation approach in selected tests. The novel MMP method can be trained using an unlimited number of inputs. Time series of outcome variables are then reconstructed using multiple probability functions. These functions consist of a main component, obtained from an input discrete event, and marginal components, obtained using a few nearest bins for the input variables; the use of the marginal components allows improving the algorithm's precision. A significant improvement of the wind speed prediction (by up to 52% for MMP) is achieved using four reference points. As rule of thumb, the target point is equidistant from the reference sites. In this condition, both multivariate methods perform better than the single correlation approach. This is also confirmed when estimating the Weibull parameters in a long-term period of ten years and the theoretical energy produced by two kinds of Wind Turbine Generators. Further applications of MMP concern wind power production forecast and, more in general, nonlinear relationships. In these cases, the direct use of MLR can lead to lack of accuracy. • Multiple Mass Probability improves wind speed prediction by up to 52%. • The best performances are achieved when the testing site is equidistant from four predictor points. • MMP, MLR, LR methods estimate the long-term wind speed characteristics better than the ones obtained from the short-term data. • Multivariate Mass Probability can handle nonlinear relationships. [ABSTRACT FROM AUTHOR]

Political skill, a social competence that enables individuals to achieve goals due to their understanding of and influence upon others at work, can play an important role in manager performance. We argue that the political skill–manager performance relationship varies as a nonlinear function of differing levels of enterprising job demands (i.e., working with and through people). A large number of occupations have some enterprising features, but, across occupations, management roles typically contain even greater enterprising expectations. However, relatively few studies have examined the enterprising work context (e.g., enterprising demands) of managers. Specifically, under conditions of high enterprising job demands, we argue and find that, as political skill increases, there is an associated exponential increase in enterprising performance, with growth beyond the mean of political skill resulting in outsized performance gains. Whereas, under conditions of low (relative to other managers) enterprising job demands, political skill will have an asymptotic relationship with enterprising job performance, such that the positive relationship becomes weaker as political skill grows, with increases on political skill beyond the mean resulting in minimal performance improvements. Our hypotheses are generally supported, and these findings have important implications for managers, as the performance gains in managerial roles were shown to be a joint function of manager political skill and enterprising job demands. [ABSTRACT FROM AUTHOR]

Aim: Ecological properties governed by threshold relationships can exhibit heightened sensitivity to climate, creating an inherent source of uncertainty when anticipating future change. We investigated the impact of threshold relationships on our ability to project ecological change outside the observational record (e.g., the 21st century), using the challenge of predicting late‐Holocene fire regimes in boreal forest and tundra ecosystems. Location : Boreal forest and tundra ecosystems of Alaska. Time period : 850–2100 CE. Major taxa studied : Not applicable. Methods: We informed a set of published statistical models, designed to predict the 30‐year probability of fire occurrence based on climatological normals, with downscaled global climate model data for 850–1850 CE. To evaluate model performance outside the observational record and the implications of threshold relationships, we compared modelled estimates with mean fire return intervals estimated from 29 published lake‐sediment palaeofire reconstructions. To place our results in the context of future change, we evaluate changes in the location of threshold to burning under 21st‐century climate projections. Results: Model–palaeodata comparisons highlight spatially varying accuracy across boreal forest and tundra regions, with variability strongly related to the summer temperature threshold to burning: sites closer to this threshold exhibited larger prediction errors than sites further away from this threshold. Modifying the modern (i.e., 1950–2009) fire–climate relationship also resulted in significant changes in modelled estimates. Under 21st‐century climate projections, increasing proportions of Alaskan tundra and boreal forest will approach and surpass the temperature threshold to burning, with > 50% exceeding this threshold by > 2 °C by 2070–2099. Main conclusions : Our results highlight a high sensitivity of statistical projections to changing threshold relationships and data uncertainty, implying that projections of future ecosystem change in threshold‐governed ecosystems will be accompanied by notable uncertainty. This work also suggests that ecological responses to climate change will exhibit high spatio‐temporal variability as different regions approach and surpass climatic thresholds over the 21st century. [ABSTRACT FROM AUTHOR]

China is the world's largest emitter of carbon dioxide. The logistics industry is the second largest source of CO 2 emissions, second only to the manufacturing industry. Therefore, developing effective environmental regulations to achieve low-carbon transformation in the logistics industry is an urgent task for government managers. Based on the 2005–2021 panel data of China's 30 provinces, this article adopts a data-driven nonparametric additive model to investigate the role of environmental regulations in CO 2 emission reduction in the logistics industry. The results display that overall, environmental regulations contribute to carbon reduction in the logistics industry (an inverted N-shaped effect). After endogeneity test and various robustness tests such as replacing the variable value of core variable, this conclusion still has good reliability. Further heterogeneity analysis indicates that the carbon reduction effect of mandatory environmental regulations fluctuates frequently (a W-shaped impact). However, the carbon reduction impact of incentive environmental regulations has gradually shifted from insignificant in the early stages to prominent in the later stages (an inverted U-shaped pattern). Regional heterogeneity analysis shows that the impact of environmental regulations on carbon reduction in the eastern, central, and western regions also has significant nonlinear characteristics. Finally, this article verifies that technological innovation and energy consumption structure are effective intermediate mechanisms for environmental regulations to affect emission reduction. The research findings provide practical inspiration for the government to formulate relevant environmental policies and promote the green development of the logistics industry. [ABSTRACT FROM AUTHOR]

There is a pressing need to study spatial heterogeneity of ridesourcing usage determinants to develop better-targeted transportation and land use policies. This study incorporates spatial information (i.e., the geographic coordinates of census tracts) into the machine learning model and leverages state-of-the-art explainable machine learning techniques to analyze census-tract-to-census-tract ridesourcing usage, identify the key factors that shape the usage, and explore their nonlinear associations across different spatial contexts. Specifically, we analyze the spatial heterogeneity of ridesourcing travel in Chicago based on three spatial contexts, including downtown, neighborhood and airport. The results reveal that built environment variables collectively contribute to the largest importance for the downtown and airport context, while socioeconomic and demographic variables are the strongest predictors for the neighborhood context. Travel cost, the number of commuters and transit supply variables have evident nonlinear associations with ridesourcing usage, and these associations show strong differences across these three spatial contexts. Moreover, incorporating geographic coordinates is shown to be useful in improving model's capability to capture spatial information and thus enhance its predictive performance. These findings provide transportation professionals with location-based insights to better plan and manage ridesourcing services in Chicago. [ABSTRACT FROM AUTHOR]

Designing effective built environment policies to reduce auto use is key to promoting sustainable transportation in suburban areas. However, most studies on the association between the built environment and auto use focus on the entire region rather than suburban areas. In addition, previous studies often ignore the possible nonlinear association between them. Applying Gradient Boosting Decision Trees to the data in the Twin Cities, USA, this study explores the nonlinear relationships between built environment attributes and driving distance in suburban areas and illustrates how the relationships differ from those in urban areas. The results show that suburban residents are less sensitive to the built environment than urban residents. More importantly, built environment policies that work in urban areas might be infertile in suburban areas. Although many studies have advocated population densification and mixed-use development for driving mitigation, this study suggests that these policies are ineffective in suburban areas. Instead, promoting job accessibility and densifying intersection density are promising to reduce auto use in suburban areas. Densifying transit stops has a small but nontrivial contribution to mitigating auto use. [ABSTRACT FROM AUTHOR]

Across 2 studies and 4 samples (Ns = 8,332, 2,136, 4,963, and 753, respectively), we tested whether the relation between conscientiousness and variables associated with important aspects of individuals' lives were curvilinear such that being high on conscientiousness was manifestly negative. Across multiple outcomes including measures of health, well-being, relationship satisfaction, job satisfaction, and organizational citizenship, we found no evidence for a systematic curvilinear relation between conscientiousness and these outcomes. Furthermore, heeding the call to use more sophisticated psychometric modeling of the conscientiousness spectrum, we used different types of scale construction and scoring methods (i.e., dominance and ideal point) and again found no evidence of curvilinear relationships between conscientiousness and the aforementioned variables. We discuss the potential reasons for the inconsistency with past research. [ABSTRACT FROM AUTHOR]

• Fills a gap in the use of DRL in futures trading. • Demonstrates the ability of DRL to gain profits from the nonlinear relationship between assets and outperform traditional linear methods in dealing with trading tasks. • Provides a quantitative investment strategy for reference. • For the whole futures market, the application of pairs trading strategy in China's soybean futures market is conducive to establish a more appropriate and effective price discovery mechanism. The pairs trading strategy involves selecting two highly correlated securities to profit from mean reversion. However, the traditional simple threshold method is subjective, random, and ignores nonlinear relationships. This paper proposes a new cointegration deep reinforcement learning (DRL) pairs trading model applied to Dalian Commodity Exchange futures to capture nonlinear relationships and gain profits. The CA-DRL model outperforms other models in terms of efficiency and performance. [ABSTRACT FROM AUTHOR]