Nonlinear Effects Research Articles

Analytical study of nonlinear consolidation effect on contaminant transport in an aquitard coupling diffusion and adsorption

Analytical study of nonlinear consolidation effect on contaminant transport in an aquitard coupling diffusion and adsorption

Examining the nonlinear and interactive effects of built environment characteristics on travel satisfaction

Examining the nonlinear and interactive effects of built environment characteristics on travel satisfaction

Push-pull mechanisms in China's intercity population migration: Nonlinearity and asymmetry

Previous research has often assumed a predefined, typically linear, relationship between migration and city characteristics. However, few studies have explored how these factors asymmetrically influence migration as push factors at the origin and pull factors at the destination. This study utilizes mobility data from the return-to-home phase of Chunyun in 2020 and employs the Random Forest (RF) model to construct a nonlinear explanatory framework for the push-pull effects and their asymmetry in intercity population migration. The findings reveal three key insights: Firstly, there is a notable asymmetry between push and pull effects in the predictive importance of feature variables. Pull effects at the destination are more prominent for economic and employment factors, whereas push effects at the origin are more influential for factors related to the living environment and gender differences. Secondly, we identify the phase transition points and threshold intervals of coupling effects based on the variations in the shapes, gradients, and thresholds of the nonlinear push-pull effects. In addition, we examine the disparities in the context of push and pull dynamics for the interaction effects of multiple variables. Thirdly, heterogeneity in demographic subsamples and different urban scenarios is observed, with within-group differences exhibiting various patterns of change. These findings provide valuable insights for policymakers to develop targeted population policies that account for the complex interplay of push and pull factors.

Commentary: Without Values, Complexity is Reduced to Mathematics.

This commentary on Sturmberg and Mercuri's paper 'Every Problem is Embedded in a Greater Whole' [1] argues that those authors have approached complexity from a largely mathematical perspective, drawing on the work of Sumpter. Whilst such an approach allows us to challenge the simple linear causality assumed in randomised controlled trials, it is itself limited. Mathematical complexity can explain nonlinearity and network effects but it cannot explain human values. It overlooks, for example, how science itself is historically and culturally shaped and how values-driven misunderstandings and conflicts are inevitable when people with different world views come together to try to solve a problem. This paper argues that the mathematical version of complexity thinking is necessary but not sufficient in medical research, and that we need to enhance such thinking further with attention to human values.

A nonlinear sensitivity matrix for improving the imaging quality in electrical resistance tomography

Electrical Resistance Tomography (ERT) can image the conductivity distribution in a detection field by boundary measurements. The ERT imaging refers to a typical nonlinear inverse problem, and currently it is represented by a linear sensitivity matrix. However, the existing linearized sensitivity matrix cannot effectively represent the nonlinear effect in the imaging process, and leads to low-quality ERT image. This paper combines an integral equation on ERT with the Geselowitz-Lehr formula, and applies a Gaussian kernel function to derive a nonlinear sensitivity matrix. The new matrix can effectively reflect both linear and nonlinear characteristics in ERT, and has a clear theoretical basis and rationality. Essentially, the limitation of the existing linear sensitivity matrix is greatly addressed. Compared to four typical ERT imaging algorithms with the linear sensitivity matrix, the use of nonlinear sensitivity matrix can averagely decrease the relative error to 0.16, while can increase the correlation coefficient to 0.88 along with various noise levels. Hence, the proposed nonlinear sensitivity matrix can effectively improve ERT imaging quality.

The asymmetric effects of digital inclusive finance’s breadth and depth on the digital transformation of agribusinesses

Drawing on data from agribusinesses spanning 2000 to 2022, we uncover the non-linear effects of digital inclusive finance breadth and depth on their digital transformation. First, a U-shaped relationship exists between the breadth and depth of digital inclusive finance and the digital transformation of agribusinesses. Second, cash flow moderates the U-shaped relationship between the breadth of digital inclusive finance and digital transformation. This moderation flattens the U-shape, potentially inverting it beyond a certain point. Notably, cash flow does not moderate the relationship between the depth of digital inclusive finance and digital transformation. Third, the U-shaped relationship between the breadth and depth of digital inclusive finance and agribusinesses’ digital transformation is significant only in the eastern and central regions and is observed exclusively before the onset of the COVID-19 pandemic.

Revealing multiscale and nonlinear effects of urban green spaces on heat islands in high-density cities: Insights from MSPA and machine learning

Revealing multiscale and nonlinear effects of urban green spaces on heat islands in high-density cities: Insights from MSPA and machine learning

Decreases in pH from effluent had a devastating but reversible impact on the coastal plankton communities

An event of releasing untreated effluent caused serious decreases in surface seawater pH from 8.1 to lower than 7.5 in seven years and increased back to prior levels after 15 years. It gives us a rare natural experiment to examine the impacts of decreases in pH on the marine plankton communities (phytoplanktons, zooplanktons, shrimp larvae, crab larvae, fish eggs, and larvae) in the natural environment. Observed decreases in pH had a nonlinear effect ubiquitous on all plankton groups, leading to a reduction of approximately 50 % in their density and abundance compared to the level at pH 8.1. Non-linear responses of planktons implied the existence of specific groups more robust to decreases in pH. As pH bounced back to normal levels, the density and abundance of the plankton communities also recovered, further indicating that the negative impacts of decreases in pH on the marine plankton communities were reversible.

The non-linear effects of built environment on bus ridership of vulnerable people

Understanding public transit patterns of vulnerable people, and their influencing factors is crucial for equitable transportation planning. However, few studies have explored how the impact of built environment factors and according threshold effects vary among different population groups. This paper bridges this research gap through an empirical study carried out in the City of Wuhu, a prefectural-level Chinese city. Bus ridership of three disadvantaged groups (the elderly, disabled and the low-income) and the mainstream population was obtained using smart card transaction records and bus trajectory GPS data. The non-linear effects of built environment on bus ridership of different groups were then examined and compared using Gradient Boosting Regression Trees (GBRT). The results suggested that both the importance and threshold effects of built environment variables vary among four population groups. The effect ranges of built environment variables were further identified to shed light on land-use policy-making for promoting equitable public transit.

Simulation and prediction of PM2.5 concentrations and analysis of driving factors using interpretable tree-based models in Shanghai, China.

PM2.5 is a critical air pollutant, and understanding its drivers is essential for regional air quality control. This study employed meteorological and pollutant variables to predict PM2.5 concentrations in Shanghai using interpretable tree-based models. The random forest (RF) model performed best, achieving MAE, RMSE, MBE, and R2 values of 3.279, 4.609, 1.254, and 0.971, respectively, improving accuracy by 42.1%-85.5% compared to AdaBoost. Shapley additive explanations (SHAP) analysis identified CO, SO2, and O3 as the most influential factors. Partial dependence plots (PDPs) showed SO2 had the strongest impact below 40 μg/m³, while NO2 exhibited a linear positive correlation with PM2.5 up to 60 μg/m³. Atmospheric pressure and rainfall were negatively correlated with PM2.5, with notable reductions in concentrations under high-pressure conditions and rainfall levels between 0 and 20 mm. Temperature and relative humidity showed complex relationships, with sharp increases in PM2.5 at temperatures between -5°C and 15°C and SHAP values declining for humidity above 90%. Wind speed exhibited a non-linear effect, with minimal influence at higher velocities. The combined effects of different pollutants can be intensified significantly at higher levels. These findings offer valuable guidance for urban air quality management and pollution mitigation strategies.

Unraveling spatiotemporal dynamics of ridesharing potential: Nonlinear effects of the built environment

Unraveling spatiotemporal dynamics of ridesharing potential: Nonlinear effects of the built environment

TRADE OPENNESS AND MANUFACTURING GROWTH OF ASIAN ECONOMIES: INVESTIGATING LINEAR AND NON-LINEAR EFFECT

This study investigates the linear and non-linear effects of trade openness on manufacturing growth in 35 selected Asian economies from 2004 to 2022. Using linear and quadratic regression specifications derived from the production function framework of growth, panel data fixed effects models, two-stage least square (2SLS) method, and two-step difference and system generalized method of moments (GMM) estimation techniques, the study uncovers significant findings. The results from the linear static and dynamic specifications indicate a positive and significant impact of trade openness on manufacturing growth. Moreover, factors such as tariff rate, financial development, control of corruption, foreign direct investment, and foreign exchange reserve also play a significant role in influencing manufacturing growth in the Asian economies. The study further reveals that the relationship between trade openness and manufacturing growth is non-linear. While additional trade openness initially has a positive effect on manufacturing value-added growth, beyond a certain threshold, the marginal impact of trade openness becomes negative. This study makes valuable contributions to the existing literature by providing evidence on the linear effect of trade openness on manufacturing growth and by being the first to analyze the optimality or non-linearity of trade openness specifically in the Asian region. The findings suggest that higher levels of trade openness offer opportunities for knowledge sharing, technological advancements, innovation, increased productivity, access to new capital goods, and attraction of foreign investment in the manufacturing sector. However, the study emphasizes the importance of specialization in producing high-quality goods and developing expertise to achieve sustained growth in the manufacturing sector.

Nonlinear influences of climatic, vegetative, geographic and soil factors on soil water use efficiency of global karst landscapes: Insights from explainable machine learning.

Soil Water Use Efficiency (SWUE) represents a vital metric for assessing the relationship between carbon acquisition and soil moisture (SM) depletion in terrestrial ecosystems. However, the elucidation of time-lagged and cumulative effects, nonlinear influences, and indirect contributions of explanatory variables, including climate and vegetation characteristics, on SWUE in global karst landscapes remains limited. In this study, we analyzed the time-lagged and cumulative effects of climatic and biological factors on SWUE in global karst landscapes using the Autoregressive Distributed Lag Model. By comparing nine machine learning models, we further revealed the nonlinear effects, as well as the direct and indirect contributions of climatic, geographic, soil, and biological explanatory variables on SWUE across varying aridity, using the Random Forest Model, SHapley Additive exPlanations, Generalized Additive Model, and Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings suggested that precipitation and wind speed exert the most substantial time-lagged and cumulative impacts on SWUE in global karst landscapes, respectively. The Random Forest model outperforms eight other machine learning models, including CatBoost, LightGBM, and XGBoost, in accurately simulating SWUE. In global karst landscapes, SWUE was significantly affected by the positive contributions of evapotranspiration, leaf area index, and temperature, as well as the negative impacts of latitude and longitude. These influences exhibited varying degrees of nonlinearity across the aridity gradient. Using PLS-SEM based on the 'geo-climatic-soil-biological' cascade effect, it was found that gross primary production directly and significantly influences karst SWUE under both drought-prone and water-abundant conditions, significantly exceeding the impact of SM. Geographic, climatic, and biological factors indirectly influenced karst SWUE by affecting gross primary production. The impact of soil type, soil carbon and nitrogen content, and rootable depth on SWUE was minimal. This study enhances our understanding of carbon sinks and the water‑carbon cycle, providing valuable insights into resource use efficiency within karst environments.

Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data

Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data

Expectancy-disconfirmation and consumer satisfaction: A meta-analysis

Abstract Expectancy-disconfirmation has been the dominant paradigm to explain the formation of consumer satisfaction for over 40 years. Within this paradigm, it is possible for expectations to have opposing effects on consumer satisfaction depending on the underlying psychological processes presupposed. In general, assimilation processes predict positive effects, while contrast processes predict negative effects. A comprehensive assessment of the empirical evidence for these positions is missing. Hence, we provide a meta-analysis of expectancy-disconfirmation research, using 150 records (N = 58,597), to test the direct effects of perceived performance and performance expectations on consumer satisfaction, while also including disconfirmation as a mediator in each path (using meta-analytical path analysis). We found evidence for an overall positive relationship between expectations and consumer satisfaction (r = .29 [0.24, 0.34]) and no evidence supporting contrast effects. Moderator analyses revealed that the positive correlation between performance expectations and consumer satisfaction was significantly stronger for predictive (vs. normative) expectations, for services (vs. goods), and for cross-sectional (vs. longitudinal and experimental) studies. Furthermore, we found an unexpected downward publication bias, which suggests that the true correlation between disconfirmation and consumer satisfaction is higher than the (already high) estimate we found. We discuss how future research can empirically scrutinize popular practitioner views and promote the development of causal explanations, account for non-linear effects, and elucidate the anomalous publication bias found here.

Directional Selection and Evolution of Polygenic Traits in Eastern Eurasia: Insights from Ancient DNA.

This study explores directional selection on physical and psychosocial phenotypes in Eastern Eurasian populations, utilizing a dataset of 1245 ancient genomes. By analyzing polygenic scores (PGS) for traits including height, educational attainment (EA), IQ, autism, schizophrenia, and others, we observed significant temporal trends spanning the Holocene era. The results suggest positive selection for cognitive-related traits such as IQ, EA and autism spectrum disorder (ASD), alongside negative selection for anxiety and depression. The results for height were mixed and showed nonlinear relationships with Years Before Present (BP). These trends were partially mediated by genetic components linked to distinct ancestral populations. Regression models incorporating admixture, geography, and temporal variables were used to account for biases in population composition over time. Latitude showed a positive effect on ASD PGS, EA and height, while it had a negative effect on skin pigmentation scores. Additionally, latitude exhibited significant nonlinear effects on multiple phenotypes. The observed patterns highlight the influence of climate-mediated selection pressures on trait evolution. Spline regression revealed that several polygenic scores had nonlinear relationships with years BP. The findings provide evidence for complex evolutionary dynamics, with distinct selective pressures shaping phenotypic diversity across different timescales and environments.

Application of mean maximum Young's modulus value as a new parameter for differential diagnosis of prostate diseases.

Transrectal shear wave elastography (T-SWE) can be used non-invasively to diagnose prostate cancer (PCa) and benign prostatic hyperplasia (BPH). The prostate tissue can be viewed as an ellipsoidal sphere with viscoelastic characterization. Linear elastic model has been used to characterize soft tissues, and the simplification of partial characterization provides incomplete information. This retrospective study included 144 suspected prostate disease patients who had completed T-SWE from our hospital. The mean maximum Young's modulus value (m-Emax) of the maximum transverse section of prostate was obtained by calculating the mean of 12 measured maximum Young's modulus value (Emax) in the four quadrants. M-Emax was statistically correlated with and good discriminability for PCa and BPH. There was a nonlinear dose-response relationship between m-Emax and PCa risk, as well as between m-Emax and BPH risk. The relationship between m-Emax and prostate disease was consistent with the nonlinear effect showed by tissues as an elastic model in normal physiological activity areas. When stiffness increasing, the stress-strain relationship was approximates linear. M-Emax can be used as an innovative parameter of Young's modulus value, which represents the stiffness of prostate tissue in T-SWE, and has a good effect in the differential diagnosis of prostate diseases.

Estimation of Fire Counts and Fire Radiative Power Using Satellite Optical and Microwave Vegetation Indices With Random Forest Method

AbstractThe satellite microwave emissivity difference vegetation index (EDVI) has been used in previous studies to estimate FCs and FRP using traditional multivariate linear regression models. However, the nonlinear effects and contributions of numerous factors that affect forest fires cannot be disentangled by this model. Using the random forest (RF) model, this study utilized multiple EDVIs and the optical normalized difference vegetation index (NDVI) as key fuel properties to resolve the physical driving mechanisms of forest fires and to estimate the daily FCs and FRP over East Asia. The results showed that the estimated FCs and FRP were in good agreement with satellite observations, with a spatial R of 0.59 for FCs and 0.63 for FRP and a temporal R of 0.80 for FCs and 0.81 for FRP. The integration of EDVIs and NDVI into the RF model was found to improve model performance and generate overall lower systematic errors than the model without vegetation variables. Model performance was better than that in previous studies using multivariate linear regression models. In addition, EDVIs showed greater importance than NDVI. This was largely due to their daily temporal resolution that allowed EDVIs to capture forest fire dynamics in time. The combination of the RF model with satellite microwave and optical observations shows good performance and has great potential for FC and FRP estimations in global fire danger assessment.

Hybrid silica-azopolymer optical microfiber coupler for high-performance in-line all-optical switching

All-optical switches (AOSs) with the unique functionality of light controlling by light are the essential components of advanced photonics. However, it is still a grand challenge to propose materials with appropriate nonlinear effects and structures with high-performance all-optical switching. This study proposes an AOS with a hybrid silica-azopolymer optical microfiber coupler configuration. This structure is characterized by in-line switching functionality, extremely high extinction ratio of 22 dB at λ = 1550 nm, low-insertion loss (<1 dB), and ease of fabrication process. It is shown that, because of the photo-induced birefringence in the azopolymer microfiber, the functionality of the introduced switch depends on the polarizations of the controlling and propagating light signals. Moreover, using full-wave electromagnetic simulations, the performance of the hybrid AOS is explained and clarified.

How to sustain a competent and committed aging workforce: The age-differential effects of development human resource practices

PurposeThis paper aims to reveal the nonlinear moderating effect of age on the inverted U-shaped relationship between perceived development human resource practices (DHRPs) and work-related learning with affective commitment being the mediator.Design/methodology/approachRegression models combined with the bootstrap method and multigroup analysis, and two-wave survey data from 780 Chinese employees were used to test hypotheses.FindingsThe results revealed that perceived DHRPs related to affective commitment in an inverted U pattern and affective commitment further related to work-related learning positively, which resulted in an inverted U-shaped relationship between perceived DHRPs and work-related learning. Age moderated the nature of the relationships between perceived DHRPs and both the outcomes, such that they were related negatively among younger employees but inverted U-shaped among older employees.Originality/valueThis study reveals inverted U-shaped rather than linear relationships between perceived DHRPs and employee outcomes and shows that age moderates the nature instead of the strength of the relationships.