Nonlinear Effects Research Articles

Effect of compressive stress on piezoelectric coefficients: A multiscale modeling approach

The effect of compressive stress on linearized coefficients for ferroelectric ceramics has been extensively reported experimentally. However, there is no proper model for this effect which could be pertinent to designers of force sensors. This paper introduces a model based on a ferroelectric multiscale model in order to predict the effect of compressive stress on the piezoelectric constitutive laws. The comparison with experimental data shows an excellent qualitative agreement and the use of this model gives new insights on this nonlinear and hysteretic effect.

Entrance opportunity vs. academic performance: unpacking the nonlinear capitalization effects of multidimensional school qualities on housing sales and rental prices

It is widely documented that school quality has a capitalization effect on housing prices. However, existing literature has commonly reported a linear relationship between housing sales prices and the academic performance of nearby schools. This study challenges this prevalent understanding by examining how the entrance opportunity as an integral part of school quality affects both housing sales prices and housing rental prices, in response to the newly adopted point-based school enrolment policy in China whereby tenants are allowed to access local public schools but with fewer opportunities compared to homeowners. Employing machine learning algorithms, this study identifies which key school indicators and what ranges of these indicators have significant impacts on different housing sales and rental price segments in Shenzhen, China. Our results reveal that housing rental prices, particularly in the lower-priced segment, are more determined by the entrance opportunity than the academic performance, while the latter is the most important factor influencing housing sales prices. The nonlinear capitalization effect suggests that tenants and homebuyers are willing to pay more for school quality increases only within specific ranges, namely, from median to a relatively high level of school quality. These findings contribute to a more nuanced and updated understanding of the education capitalization effect and thus inform more inclusive and equitable urban planning and governance.

Identifying predictors of multi-year cannabis vaping in U.S. Young adults using machine learning

IntroductionIncreasing number of current cannabis users report using a vaporized form of cannabis and young adults are most likely to vape cannabis. However, the number of studies on cannabis vaping is limited, and predictors of cannabis vaping among U.S. young adults remain unclear. Previous studies on cannabis vaping have known limitations, as they (1) relied heavily on regression-based approaches that often fail to examine complex and non-linear interactive effects, (2) focused on examining cannabis vaping initiation but not on its use over multiple years, and (3) failed to account for recreational cannabis legalization (RCL) status. MethodsThis study was a secondary analysis of the restricted use files of the Population Assessment of Tobacco and Health Study, Waves 4–6 (December 2016-November 2021). A two-stage machine learning approach, which included Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Tree (CART), was used to identify predictors of multi-year cannabis vaping while accounting for state-level RCL status among a representative sample of U.S. young adults. ResultsStratified CART created a five-terminal-node prediction model for states with RCL (split by cannabis use, cigarette use, bullying behavior, and ethnicity) and a different five-terminal-node prediction model for states without RCL (split by cannabis use, heroin use, nicotine vaping, and hookah use). ConclusionsCharacteristics predicting multi-year cannabis vaping appear to differ from those of cannabis vaping initiation. Results also highlight the importance of accounting for RCL status because predictors of cannabis vaping may differ for individuals living in states with and without RCL.

On examining the predictive capabilities of two variants of the PINN in validating localized wave solutions in the generalized nonlinear Schrödinger equation

We introduce a novel neural network structure called strongly constrained theory-guided neural network (SCTgNN), to investigate the behaviour of the localized solutions of the generalized nonlinear Schrödinger (NLS) equation. This equation comprises four physically significant nonlinear evolution equations, namely, the NLS, Hirota, Lakshmanan–Porsezian–Daniel and fifth-order NLS equations. The generalized NLS equation demonstrates nonlinear effects up to quintic order, indicating rich and complex dynamics in various fields of physics. By combining concepts from the physics-informed neural network and theory-guided neural network (TgNN) models, the SCTgNN aims to enhance our understanding of complex phenomena, particularly within nonlinear systems that defy conventional patterns. To begin, we employ the TgNN method to predict the behaviour of localized waves, including solitons, rogue waves and breathers, within the generalized NLS equation. We then use the SCTgNN to predict the aforementioned localized solutions and calculate the mean square errors in both the SCTgNN and TgNN in predicting these three localized solutions. Our findings reveal that both models excel in understanding complex behaviour and provide predictions across a wide variety of situations.

Dynamic Modelling and Performance Analysis of Deployable Telescopic Tubular Mast

AbstractThis work presents the longitudinal and transverse coupling vibrations of a deployable Telescopic Tubular Mast (TTM), a multi-stepped structure integrated into a spacecraft system, while considering the rigid-flexible coupling phenomenon. The model is derived using the principle of virtual work and discretized via the variable separation method. The von Kármán strain is employed to incorporate geometric nonlinear effects. Semi-analytical results for the shape functions and natural frequencies of the quasi-static multi-stepped boom are obtained using the extended transfer matrix method (ETMM). These natural frequencies are validated against results from Nastran, confirming the ETMM's accuracy. In addition, the model accounts for the continuously changing natural frequencies and shape functions during the deployment phase. Finally, the dynamic phenomena of the longitudinal and transverse displacements are analyzed at various deploying states, including locking and restart behaviors. The influence of the structural damping on the vibration evolution is also contained in the numerical analysis.

Advancing the complex adaptive systems approach to enterprise risk management with quantified risk networks (QRNs)

Business enterprises are complex adaptive systems (CAS) subject to fragility caused by the non-linear effects of uncertain risk events. The regional bank collapses in the United States are a case in point. Recent papers have highlighted a shortcoming of the traditional risk management process, which focuses on compliance but considers neither the interdependence between complex risks nor the mechanism of their non-linear impact on the organization. A new perspective on Enterprise Risk Management (ERM) has instead called for a shift of mindset from mitigating risk to building resilience by honing a CAS view to contextualize, assess, and manage complex risks. But the building blocks needed for a CAS representation of ERM have not yet been systematically developed. Specifically, we are missing a typological inventory of risks and a mapping of risks onto the general structure of an enterprise. In this paper, we build an industry-agnostic inventory of plausible risk factors using information extraction on large scale text data. Additionally, we develop an understanding of risk-function and function-function interdependencies through a survey of top business managers. The result is a novel complex network view of enterprise risk called a Quantified Risk Network (QRN) that displays small-world properties and highlights internal company functions central to non-linear risk propagation mechanisms within the enterprise. The QRN draws attention to vulnerabilities in the enterprise structure such as risk-function connections measured by Edge Betweenness centrality. The generic QRN developed herein is a proof of concept and we advocate that enterprises build their own company-specific QRNs to identify highly connected and central functions in their company structure that could lead to cascading failure when specific risks arise. QRNs can contribute to the objective of building enterprise resilience.

Openness influences business model innovation: the mediating role of digital capability

PurposeExploring open value, cultivating digital capability (DC) and driving business model innovation (BMI) have become an inevitable choice for enterprises to meet market demand and adapt to environmental changes. However, as one of the situational variables of BMI, the positive or negative influence of openness has not been proved and the path mechanism between DC and BMI is not clear. Based on the dynamic capability theory, this paper takes manufacturing enterprises as an example to explore the internal impact mechanisms of organizational openness on BMI. It extends the analysis by introducing DC as a mediating variable and introducing manufacturing enterprise type (high-tech and non-high-tech) as a moderating variable.Design/methodology/approachA questionnaire survey was conducted using data collected in China, data from 355 manufacturing enterprises were collected to carry out empirical research. Participants were mainly middle and senior managers with a comprehensive grasp of their firms’ information. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the reliability and validity of the sample data, and negative binomial regression analysis was used to test hypothesis relationships.FindingsThe authors find an inverted U-shaped relationship between openness and BMI, and explain that excessive openness may lead to low resource utilization, organizational inertia, cooperation distrust, which will have a negative impact on BMI. DC includes digital resource capability (DRC), digital management capability (DMC) and digital collaboration capability (DCC), which promote BMI and play a mediating role between openness and BMI. Enterprise type has a moderating effect on the relationship between DC and BMI.Research limitations/implicationsThe results of this paper summarize the opportunities and threats of open innovation, help enterprises fully understand the double-edged sword impact of openness, guide manufacturing enterprises to be sensitive to openness and achieve sustainable innovation. By analyzing the path of DRC, DMC and DCC to BMI, managers can improve their understanding of digital-driven value creation process and improve the competitive advantage of enterprises.Originality/valueThis paper presents the relationships among openness, DC and BMI. We find the non-linear effects of openness on DC and BMI, bridging the inconsistent view of positive or negative relationship between openness and organizational change in previous studies. The introduction of DC extends the theory of dynamic capability in the digital age, and opens the “black box” from opening to BMI from the process perspective of DRC, DMC and DCC. From the perspective of enterprise type, this paper provides different choices of capability upgrading and strategic innovation based on openness for high-tech and non-high-tech manufacturing enterprises.

Kerr nonlinearity effect on the stability of Wannier-Stark states in active optical systems

The paper provides an analytical and numerical investigation of the dynamics of a one-dimensional chain of coupled optical resonators with conservative cubic nonlinearity and the gain saturated by nonlinear losses. The linear dependency of the resonator eigenfrequencies on their indexes makes it possible to use Wannier-Stark states as lasing modes. Numerical simulations have shown that the dependency of the resonant frequencies on the light intensity strongly affects the stability of Wannier-Stark states. To explain the observed destabilization of monochromatic lasing based on Wannier-Stark states a simple perturbation theory has been developed and compared with the data obtained in the numerical simulations.

Corporate governance and financial distress in China a multi-dimensional nonlinear study based on machine learning

Promoting governance efficiency is crucial for preventing corporate financial distress. However, previous research has been constrained by limited dimensions of governance predictors and insufficient linear estimations to predict financial distress. To address this issue, this study gathers 37 corporate governance indicators of Chinese publicly listed firms from 2009 to 2022 in the dimensions of ownership structure, board features and executive traits. The LightGBM machine learning approach is then used to compare the predicting power of these individual indicators, as well as the predicting power of the dimensions. The SHAP (SHapley Additive exPlanations) method is further adopted to conduct an in-depth interpretability analysis upon the established prediction. Our approach identifies the nonlinear effects of important corporate governance indicators on financial distress and prioritizes those indicators based on their impact levels. Our results show that the most influential indicator is institutional ownership, followed by managerial ownership and executive compensation disparity. In addition, the dimension of ownership structure has the highest predicting power among the three. Overall, our study provides new insights into how firms can optimize their corporate governance mechanisms to prevent financial distress.

Measurement of the Static Nonlinear Third‐Order Elastic Moduli of Rocks: Problems and Applicability

AbstractThe third‐order elastic (TOE) model has been used to describe the widely observed nonlinear mechanical behaviors of earth materials. In addition to linear elastic constants (λ, μ), three nonlinear elastic moduli (A, B, C) are required for isotropic rocks. Contrary to previous research on dynamic TOE moduli, this study followed the protocol to measure strain and stress under uniaxial and hydrostatic compressive tests statically, which were later used to invert for the full set of TOE moduli for four standard rock types with differing pore structures of a porous oolitic limestone, quartz‐rich sandstones, and a dense crystalline basalt. The applicability of the TOE model to characterize nonlinearity depends on the fulfillment of path‐independence and small‐strain assumptions. Using the measured static TOE moduli, the finite element model demonstrates that the stress in the vicinity of the wellbore is more amplified than the stress in the linear elastic case, which leads to a wider zone of rock failure around the wellbore. Due to the long‐standing discrepancy between static and dynamic moduli, the rarely reported full set of static TOE moduli is necessary and will benefit future research in understanding the effect of rock nonlinearity on geophysical and geomechanical applications, such as long‐term safe storage of CO2 and generating process of geohazards.

Dietary inflammatory index and all-cause mortality in adults with COPD: a prospective cohort study from the NHANES 1999-2018.

Chronic inflammation is closely linked to Chronic Obstructive Pulmonary Disease (COPD); however, the impact of the Dietaryq Inflammatory Index (DII) on mortality among COPD patients remains uncertain. To assess the correlation between the DII and all-cause mortality in COPD patients using data from the National Health and Nutrition Examination Survey (NHANES). We conducted a retrospective cohort study on 1,820 COPD patients from the NHANES dataset (1999-2018). The influence of DII on mortality was evaluated using multivariate Cox regression, smoothing spline fitting, and threshold effect analysis. Additionally, Kaplan-Meier survival analysis was performed to compare survival curves among different DII groups. Subgroup analyses and E-values identified sensitive cohorts and assessed unmeasured confounding. Over an average follow-up of 91 months, multivariate Cox regression models revealed a significant positive correlation between DII scores and mortality risk, with each unit increase in DII associated with a 10% higher risk of death (HR: 1.10, 95% CI: 1.03-1.16; P = 0.002). Among the DII tertiles, individuals in the second tertile (T2: 1.23-2.94) experienced a 67% increase in mortality risk compared to those in the lowest tertile (T1: -5.28-1.23) (HR: 1.67, 95% CI: 1.26-2.21; p < 0.001). The third tertile (T3) did not show a statistically significant increase in mortality risk (HR: 1.30, 95% CI: 0.98-1.72; p=0.074). A restricted cubic spline analysis indicated a significant nonlinear association between DII and all-cause mortality (p = 0.021). Threshold effect analysis further revealed that below a DII of 2.19, there was a significant increase in all-cause mortality risk (HR = 1.19, 95% CI: 1.07-1.33; p = 0.002), while at or above this threshold, the risk increase was not statistically significant (HR=0.89, 95% CI: 0.68-1.15; p = 0.380). Kaplan-Meier analysis revealed significant differences in survival curves among DII tertiles (p < 0.001), with the lowest DII tertile showing the highest survival probability. Both subgroup and sensitivity analyses confirmed the robustness of these findings. DII is positively correlated with mortality risk in COPD patients, showing nonlinear characteristics and threshold effects, underscoring its prognostic value.

A Brief Look Into Nonlinear Optical Properties Of MXenes (M2X) Ti2C, Nb2C, And V2C Through Four Wave Mixing Generation In 2 µm Fiber Laser

Generation of four wave mixing (FWM) effect in MXenes with chemical ratio of M2X, particularly Ti2C, Nb2C, and V2C were reported in this work. The setup was operated at 2 µm wavelength region with tapered fiber as the material host. The experiment managed to show conversion efficiencies of −43.93 dB, −42.60 dB, and −41.20 dB and idlers’ SNR of 7.73 dB, 9.10 dB, and 9.80 dB for Ti2C, Nb2C, and V2C, respectively. The relation of nonlinear optical properties such as nonlinear absorption coefficient and nonlinear refractive index to FWM generation were explored. The result of the investigation successfully showed the relationship of third order nonlinear optical properties and FWM effect in the specified MXenes with comparisons.

Pharmacokinetic and pharmacodynamic data from the NEOLEV1 and NEOLEV2 studies

ObjectivesTo confirm that levetiracetam (LEV) demonstrates predictable pharmacokinetics(PK) at higher doses and to study the pharmacodynamics(PD) of LEV.DesignPharmacokinetic data from the NEOLEV1 and NEOLEV2 trials were analysed using a non-linear...

Efficient second-harmonic generation in a lithium niobate metasurface governed by high-Q magnetic toroidal dipole resonances.

Lithium niobate (LN) is an excellent nonlinear optical material due to its large nonlinear coefficient, low loss, and broad optical transparency window. So, it is widely used in the generation of nonlinear harmonics. Magnetic toroidal dipole (MTD) resonance is a special optical resonance mode, which can effectively localize the light field inside the device, thus enhancing the nonlinear effects of the materials. In this work, we numerically study the second-harmonic generation (SHG) effect of the LN metasurface based on the MTD mode with a high quality factor (Q-factor). The designed LN nanorod dimer metasurface supports high Q-factor MTD guided mode resonances (GMRs), which are excited by varying the center spacing of the two nanorods, and the Q-factor can be controlled by the offset distance. The excited MTD can effectively confine the electric field within the device, which enables the LN metasurface SHG conversion efficiency to reach 1.15 × 10-2. In addition, by adjusting the structural parameters, it is possible to effectively modulate the wavelength and conversion efficiency of the SHG. Our results provide a new route for high-quality nonlinear light sources.

Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)

We propose a new method for the simultaneous selection and estimation of multivariate sparse additive models with correlated errors. Our method called Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe) simultaneously selects among null, linear, and smooth non-linear effects for each predictor while incorporating joint estimation of the sparse residual structure among responses, with the motivation that accounting for inter-response correlation structure can lead to improved accuracy in variable selection and estimation efficiency. CoMPAdRe is constructed in a computationally efficient way that allows the selection and estimation of linear and non-linear covariates to be conducted in parallel across responses. Compared to single-response approaches that marginally select linear and non-linear covariate effects, we demonstrate in simulation studies that the joint multivariate modeling leads to gains in both estimation efficiency and selection accuracy, of greater magnitude in settings where signal is moderate relative to the level of noise. We apply our approach to protein-mRNA expression levels from multiple breast cancer pathways obtained from The Cancer Proteome Atlas and characterize both mRNA-protein associations and protein-protein subnetworks for each pathway. We find non-linear mRNA-protein associations for the Core Reactive, EMT, PIK-AKT, and RTK pathways. Supplementary Materials are available online.

Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces

Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.

The Nonlinear Effects of Digital Finance on Corporate ESG Performance: Evidence from China

Digital finance enhances corporate ESG performance and is essential for achieving sustainable development; however, its consistent effectiveness in improving ESG outcomes remains contested. Using panel data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China from 2011 to 2021, this study empirically examines nonlinear effects, transmission mechanisms, and moderating factors. The results indicate a U-shaped relationship between digital finance and ESG performance, with a positive impact becoming apparent when digital finance exceeds the threshold of 3.81. Mechanism tests reveal that green technological innovation and public environmental attention are crucial transmission channels for the nonlinear effects. Furthermore, financial regulation levels and environmental uncertainty negatively moderate this relationship, while corporate digital transformation has a positive moderating effect. Further analysis shows that the U-shaped relationship is more pronounced in areas with lesser financial advancement and higher levels of environmental regulation, as well as in non-high-tech industries, non-manufacturing sectors, smaller firms, and companies without political connections. This study provides empirical evidence and policy insights to support the promotion of financial services that better facilitate corporate sustainability.

Nonlinear impact of climate risks on renewable energy stocks in China: A moderating effects study

This paper assesses the physical climate risks renewable energy stocks face in China. Employing a functional coefficient model, the study investigates the nonlinear impacts and moderating effects of climate risk on the returns and volatility of these stocks. The key findings are as follows: (1) The physical climate risk levels for renewable energy stocks exhibit a normal distribution pattern. (2) Physical climate risk has a U-shaped nonlinear effect on both stock returns and volatility, where higher levels of risk enhance returns but also increase volatility. (3) Climate transition risk has a U-shaped influence on stock returns and an inverted U-shaped influence on stock volatility. Specifically, high climate transition risk not only elevates returns but also stabilizes the renewable energy stock market. Moreover, heightened climate transition risk amplifies the negative effects of oil price volatility on stock returns and increases overall stock volatility.

Performance evaluation of housing price index prediction model using DNN and SVR

This study used the base interest rate, mortgage interest rate, deposit interest rate, consumer price index, exchange rate, and composite stock price index as variables to predict the housing price index. The variables were normalized using the min-max normalization method. Machine learning methods, Deep Neural Networks (DNN) and Support Vector Regression (SVR), were employed to construct a prediction model, and the fit of the model was evaluated using the Root Mean Square Error (RMSE). The analysis results of the DNN model indicated that when applying the softplus activation function, the RMSE was 0.1298, resulting in the smallest prediction error, and the correlation coefficient between the predicted and actual values was highest at 0.9342. The analysis results of the SVR model using the Gaussian RBF kernel showed that the RMSE was smallest at 0.2781 when the parameters were set to C=100 and Gamma=0.1. Therefore, the DNN model was found to be more fit for predicting the housing price index. Additionally, it was confirmed that the consumer price index had a non-linear effect with a positive relationship, and the base interest rate had a linear effect with a negative relationship.

Financial inclusion through digitalization and economic growth in Asia-Pacific countries

Digital technology for financial inclusion tends to bring more benefits to people at the bottom of the economic pyramid across the Asia-Pacific region; therefore, financial inclusion is crucial for sustainable development and economic growth in this region. Digital financial services, based on technology-driven innovation, contribute to enhanced financial inclusion at affordable prices, which potentially reaches many new people who are unbanked or underbanked. This paper examines the impact of digital financial inclusion (DFI) on economic growth in 30 Asia-Pacific countries based on panel data for 2014, 2017, and 2021. A three-stage principal component analysis is used to develop the DFI index for the Asia-Pacific region, capturing both the digital and traditional aspects. We use fixed effect regression to examine the linear effect of DFI on economic growth, and results show a significant positive impact, contributing to the Theory of Finance and Growth, System Theory, and Diffusion Theory of Innovation. Given the different roles of DFI in different countries and the wide digital divide across the region, this paper employs a panel threshold regression model to identify any non-monotonic influence of DFI on economic growth. The results show a significant threshold effect of DFI, indicating the non-linear effect of financial inclusion on economic growth. The paper brings evidence-based policy implications to boost DFI to achieve inclusive growth.