Dynamic Effects Research Articles

Mandatory ESG disclosure and trade credit: international evidence

ABSTRACT This study documents that worldwide mandatory ESG disclosure significantly increases firms’ use of trade credit. The baseline results hold across staggered differences-in-differences design, alternative samples test, dynamic effect model, and heterogeneity treatment analyses. Enhanced financial reporting quality and accounting conservatism are channels through which ESG disclosure affects trade credit. The baseline effect is stronger for disclosure initiated by stock exchanges, firms in civil law markets, firms in non-climate-vulnerable industries, and firms with lower market shares. These findings underscore the pivotal role of ESG disclosures in fostering trust and facilitating trade credit provision.

Capturing the extent of climate's dynamic effects on runoff and nutrient yields across short- and long-term scales

Capturing the extent of climate's dynamic effects on runoff and nutrient yields across short- and long-term scales

Effects of debt dynamics and financial literacy on consumer market behavior

The consumption upgrading strategy and the dual circulation model position consumption as a critical driver of China's economic growth. Although the development of inclusive finance has somewhat promoted short-term consumption, challenges such as low financial literacy and rising debt burden threaten the sustainability of consumption. This study utilizes data from the 2013–2019 China Household Finance Survey, employing fixed-effect models to examine the impact of household debt on consumption. We also quantify financial literacy's role in moderating debt's influence on consumer behavior.

Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event Prediction

Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event Prediction

Exploration of urban-rural disparities in institutional delivery in Bangladesh: Assessing the effect of antenatal care dynamics

Exploration of urban-rural disparities in institutional delivery in Bangladesh: Assessing the effect of antenatal care dynamics

Lateral dynamic response of partially embedded piles in the sea considering wave loads and wave-induced seabed dynamic response load distribution effects

Lateral dynamic response of partially embedded piles in the sea considering wave loads and wave-induced seabed dynamic response load distribution effects

Dynamic roles of tumor-infiltrating B lymphocytes in cancer immunotherapy.

The amazing diversity of B cells within the tumor microenvironment is the basis for the diverse development of B cell-based immunotherapies. Here, we focus on elucidating the mechanisms of tumor intervention mediated by four tumor-infiltrating B lymphocytes. Naive B cells present the initial antigen, germinal center B cell subsets enhance antibody affinity, and immunoglobulin subtypes exert multiple immune effects, while regulatory B cells establish immune tolerance. Together they reflect the complexity of the changing dynamics of cancer immunity. Additionally, we have investigated the dynamic effects of tumor-infiltrating B lymphocytes in immunotherapy and their relationship to prognosis, providing new insights into potential treatment strategies for patients.

Diversified dynamic effects and their order origins in Boolean functions

Interaction relationships are fundamental to the behavior of complex systems. This study, grounded in Boolean discrete systems, conducts a comprehensive investigation of ordered Boolean functions, elucidating their stabilization capacities, sources of ordering, and significance in gene networks. Our findings confirm that, irrespective of their original configuration or mode, ordered Boolean functions enhance system stability and pattern formation in diverse manners, rescuing systems from chaotic states. This enhancement in stability is evident and intricately connected to mapping tables of functions, reflected through diverse order metrics such as sensitivity, effective edge, and intrinsic order. An analysis of current gene network data reveals that the prevalence of ordered Boolean functions is tightly associated with gene regulation modes. The prominence of canalized and threshold functions signifies distinct genetic regulation behaviors, whereas the abundance of clique functions suggests unexplored mechanisms underlying gene regulation.

Exploring currency interdependence in West Africa: a time-varying parameter vector autoregression analysis

PurposeConsidering the impact of significant economic and political events, this study investigates the return spillovers and connectedness among eight West African currencies from March 31, 2010, to March 28, 2024. It aims to enhance understanding of the interdependencies within the West African foreign exchange market, providing insights into the region’s risk management and diversification opportunities.Design/methodology/approachUsing the time-varying parameter vector autoregression (TVP-VAR) method, this study analyzes daily exchange rate returns to capture the dynamic spillover effects and connectedness among the selected currencies. This approach identifies key transmitters and receivers of return shocks, reflecting the evolving interactions among the currencies over time.FindingsThe results show that the Sierra Leonean Leone, Cape Verdean Escudo, and West African CFA Franc are significant net transmitters of return shocks. At the same time, the Ghana Cedi, Nigerian Naira, Gambian Dalasi, Guinean Franc, and Liberian Dollar are net receivers, with the Gambian Dalasi being the most affected. These findings suggest relatively low regional spillover connectedness, offering favorable diversification opportunities.Originality/valueThis study provides a comprehensive analysis of the interconnectedness of West African currencies, contributing to the limited literature on this region. The findings have practical implications for investors and policymakers in managing foreign exchange risks and designing interventions to stabilize the market.

The behavior dynamic of venture capitalists in hospitality and tourism equity financing: a study based on leader–follower partnerships

ABSTRACT This study aims to explore how the two identity statuses affect the role choice dynamic of venture capitalists in syndication and solo investment behavior dynamic in hospitality and tourism equity financing. Drawing from the identity status theory, this study uses a stochastic actor-oriented model and four waves of panel network data from investment events of 197 active VCs in 2012–2021 based on the distinct roles in the leader-follower relationship to test the dynamic effects of primary status and complementary status. The results obtained through SIENA method suggest that identity lock-in is evident in VCs syndication network of financing in hospitality and tourism industry. Additionally, VCs with higher primary status are likely to pursue independent investments, but VCs with higher complementary status are likely to deepen their reliance on collaborative investments. The findings provide theoretical and practical implications for the behavior dynamic of VCs in hospitality and tourism equity financing.

Interface Trap Dynamics and Thermal Effects in Novel Junctionless Dual Gate Inverted-U-Shaped FinFETs for Sub-5 nm Node: Device to Circuit Level Implementation

Interface Trap Dynamics and Thermal Effects in Novel Junctionless Dual Gate Inverted-U-Shaped FinFETs for Sub-5 nm Node: Device to Circuit Level Implementation

The impact of green finance and FinTech mechanisms on financial stability: evidence from advanced and emerging economies

PurposeThis study aims to examine the dynamic effect of FinTech on financial stability, with the moderating role of green finance (GF), its dimensions and mechanisms in the context of the spillover effects of the COVID-19 shock. This study used balanced panel data from 148 countries, including 76 developed and 72 emerging nations, from 2005 to 2022.Design/methodology/approachThe research utilized the dynamic two-step system (GMM), and robustness was performed with the bootstrapped panel quantile regression.FindingsThe findings reveal that FinTech significantly affects financial stability across the entire sample. The overall composite of GF boosts financial stability by improving financial soundness. The GF dimensions, such as environmental, resource and financial, positively influence FS, while the GF economic dimension hurts FS. The moderating role and all interaction terms of GF dimensions with FinTech contribute positively and significantly to FS. While the interaction term GF resources with FinTech negatively impacts FS, indicating that countries should utilize resources more efficiently. Additionally, the COVID-19 spillover effect negatively influences FS across all samples. In advanced countries, FinTech and green finance positively affect FS. In emerging countries, green finance (except for the resource dimension) and FinTech interactions enhance financial stability, (except for the environmental dimension), leading to environmental hazards from their highly intensive industrial carbon policies.Practical implicationsThe findings suggest that policymakers should prioritize promoting the adoption of initiatives related to FinTech and green finance by integrating sustainable transition finance policy frameworks to maintain stability and foster low-carbon economies for a sustainable future.Social implicationsImproved financial stability has more significant social effects, such as better investment instruments, confidence and economic growth. Policymakers can leverage these findings to establish resilient financial ecosystems, fostering sustainable economic development and decreasing the risk of financial crises.Originality/valueThis study offers novel insights into how FinTech and multi-dimensional green finance effect financial stability in advanced and emerging nations. It provides unique insights into context-specific dynamics and enhances the literature on financial stability.

Synthetically Relevant Post-Transition State Bifurcation Leading to Diradical and Zwitterionic Intermediates: Controlling Nonstatistical Kinetic Selectivity through Solvent Effects.

A post-transition state surface intersection (PTSSI) between radical and zwitterionic states that causes a bifurcation in the reaction pathway was discovered through density functional theory calculations on potential energy surfaces and ab initio molecular dynamics simulations of cycloadditions between a bicyclobutane and a triazolinedione (BCB-TAD). It was predicted that changes to the solvent polarity would enable control over the dynamic selectivity in this system; indeed, experimental evidence supported this prediction. This work not only provides new insights into an unusual type of post-transition state bifurcation, but also demonstrates how the nonstatistical dynamic effects that control selectivity for such reactions can be manipulated rationally to increase the yields of synthetically useful reactions.

Improving Nanoparticle Size Estimation from Scanning Transmission Electron Micrographs with a Multislice Surrogate Model.

The computational cost of simulating scanning transmission electron microscopy (STEM) images limits the curation of large enough data sets to train accurate and robust machine learning networks for deep feature extraction from atomically resolved STEM images. For nanoparticle size estimation in particular, a diverse data set is essential due to the large variations in size, shape, crystallinity, orientation, and dynamical diffraction effects in experimental data. To address this, we train a 3D convolutional neural network to predict STEM images from voxelized atomic models, achieving a 100x speed-up compared to traditional multislice simulations while maintaining high image quality. We then generate a data set of 100.000 synthetic multislice images and investigate the performance of different size-estimator architectures as a function of training set size. A ResNet18-based model trained on 4000 real and 100.000 synthetic images is found to perform the best, reducing the median size-estimation error from 9.89% without synthetic data to 5.26%.

Weight Effects on Vertical Transverse Vibration of a Beam with a Nonlinear Energy Sink

Reductions in the vibration of a continuum system via a nonlinear energy sink have been widely investigated. It is usually assumed that weight effects can be ignored if the vibration is measured from the static equilibrium configuration. The present investigation reveals the dynamic effects of weight on the vertical transverse vibrations of a Euler–Bernoulli beam coupled with a nonlinear energy sink. The governing equations considering and neglecting weights were derived. The equations were discretized with some numerical support. The discretized equations were analytically solved via the harmonic balance method. The harmonic balance solutions were compared with the numerical solution via the Runge–Kutta method. Finite element simulations were performed via ANSYS software (version number:2.2.1). Free and forced vibrations, predicted by equations considering or neglecting the weights, were compared with the finite element solutions. For the forced vibrations, the amplitude–frequency responses determined by the harmonic balance method agree well with those calculated by the Runge–Kutta method. The free and forced vibration responses predicted by the equations considering the weights are closer to those computed by the finite element method than the responses predicted by the equation neglecting the weights. The assumption that weights can be balanced by static deflections leads to errors in the analysis of the vertical transverse vibrations of a Euler–Bernoulli beam with a nonlinear energy sink.

Dynamic flow control through active matter programming language.

Cells use 'active' energy-consuming motor and filament protein networks to control micrometre-scale transport and fluid flows. Biological active materials could be used in dynamically programmable devices that achieve spatial and temporal resolution that exceeds current microfluidic technologies. However, reconstituted motor-microtubule systems generate chaotic flows and cannot be directly harnessed for engineering applications. Here we develop a light-controlled programming strategy for biological active matter to construct micrometre-scale fluid flow fields for transport, separation and mixing. We circumvent nonlinear dynamic effects within the active fluids by limiting hydrodynamic interactions between contracting motor-filament networks patterned with light. Using a predictive model, we design and apply flow fields to accomplish canonical microfluidic tasks such as transporting and separating cell clusters, probing the extensional rheology of polymers and giant lipid vesicles and generating mixing flows at low Reynolds numbers. Our findings provide a framework for programming dynamic flows and demonstrate the potential of active matter systems as an engineering technology.

Climate change dominates over urbanization in tropical cyclone rainfall patterns

The role of climate change and urbanization has typically been considered in isolation of each other and it remains unclear which one has a more dominant effect in terms of changes in rainfall from tropical cyclones. Here, we employ a convection-permitting numerical model to quantify the relative effects of urbanization and climate change on rainfall from ten typhoons across a highly urbanized area in China. We show that climate change is largely responsible for the changes in the spatial pattern and amounts of rainfall from tropical cyclones; on the other hand, urbanization consistently contributes to the redistribution of rainfall, and its positive effects are comparable to those of climate change in the urban core. Although the role of the thermodynamic component in altering tropical-cyclone rainfall increases under a warming climate, these rainfall changes are still largely driven by dynamic effects, especially when considering the role of urbanization.

Determinants of Employee Retention in Nepalese Financial Institutions

The issue of employee retention is crucial in Nepalese financial institutions. In this regard, this study aims to analyze the effect of compensation, career development, work-life balance, work environment, team dynamics, and leadership and management on employee retention. The casual comparative design was applied and primary data was collected through a questionnaire survey on a sample of 308 employees working in Nepalese financial institutions. Descriptive statistics, correlation analysis, and regression analysis were the statistical tools used. The empirical findings of regression analysis indicated mixed effects of the determinants used to predict employee retention. The result has shown that compensation, career development, and team dynamics have a positive and significant impact on employee retention. In contrast, work environment, work-life balance, and leadership and management have no significant effect on employee retention. The study has revealed some areas important to retain skilled and experienced employees and achieve competitive benefits by Nepalese financial institutions.

Nonadiabatic Quantum Dynamics of Molecules Scattering from Metal Surfaces.

Nonadiabatic coupling between electrons and molecular motion at metal surfaces leads to energy dissipation and dynamic steering effects during chemical surface dynamics. We present a theoretical approach to the scattering of molecules from metal surfaces that incorporates all nonadiabatic and quantum nuclear effects due to the coupling of the molecular degrees of freedom to the electrons in the metal. This is achieved with the hierarchical equations of motion (HEOM) approach, combined with a matrix product state representation in twin space. The method is applied to the scattering of nitric oxide from Au(111), for which strongly nonadiabatic energy loss during scattering has been experimentally observed, thus presenting a significant theoretical challenge. Since the HEOM approach treats the molecule-surface coupling exactly, it captures the interplay between nonadiabatic and quantum nuclear effects. Finally, the data obtained by the HEOM approach are used as a rigorous benchmark to assess various mixed quantum-classical methods, from which we derive insights into the mechanisms of energy dissipation and the suitable working regimes of each method.

Rationalizing protein-ligand interactions via the effective fragment potential method and structural data from classical molecular dynamics.

The Effective Fragment Potential (EFP) method, a polarizable quantum mechanics-based force field for describing non-covalent interactions, is utilized to calculate protein-ligand interactions in seven inactive cyclin-dependent kinase 2-ligand complexes, employing structural data from molecular dynamics simulations to assess dynamic and solvent effects. Our results reveal high correlations between experimental binding affinities and EFP interaction energies across all the structural data considered. Using representative structures found by clustering analysis and excluding water molecules yields the highest correlation (R2 of 0.95). In addition, the EFP pairwise interaction energy decomposition analysis identifies critical interactions between the ligands and protein residues and provides insight into their nature. Overall, this study indicates the potential applications of the EFP method in structure-based drug design.