Search Results (467)

In risk assessment, deriving dermal absorption values is essential for evaluating plant protection products. Applicants submit study data, which authorities assess during the authorisation process. If no data are provided, default values from the European Food Safety Authority 2017 Guidance on dermal absorption (EFSA GD2017) apply. The German Federal Institute for Risk Assessment compiled an updated dermal absorption dataset of 356 more recent human in vitro studies evaluated under to the newest guidance. We applied the same empirical and modelling approaches used to derive default values for concentrates (commercially available product concentrations) and dilutions in different formulation type categories in EFSA GD2017 to the new dataset and compared the resulting values. We also assessed the impact of applying the alternative definition of ‘concentrate’ (>50 g/L) according to SCoPAFF. Default values obtained by analysing the new dataset were considerably lower than current default values, particularly for solids applied in dilutions. The alternative definition of ‘concentrate’ did not have a large impact on default values. Our results suggest that a revision of the default values based on newer studies evaluated under the most current guidance may be warranted. Full article

In this paper, we introduce a survey-based methodology to audit LLM-generated personas by simulating 200 US residents and collecting responses to socio-demographic questions in a zero-shot setting. We investigate whether LLMs default to standardized profiles, how these profiles differ across models, and how conditioning on specific attributes affects the resulting portrayals. Our findings reveal that LLMs often produce homogenized personas that underrepresent demographic diversity and that conditioning on attributes such as gender, ethnicity, or disability may trigger stereotypical shifts. These results highlight implicit biases in LLMs and underscore the need for systematic approaches to evaluate and mitigate fairness risks in model outputs. Full article

In the context of industrial transitions and tariff frictions, financial markets are experiencing frequent defaults, emphasizing the urgency of upgrading credit scoring methodologies. A novel credit risk identification model integrating generative adversarial networks (GAN) and the minimax probability machine (MPM) is proposed. GAN generates realistic augmented samples to alleviate class imbalance in the credit score dataset, while the MPM optimizes the classification hyperplane by reformulating probability constraints into second-order cone problems via the multivariate Chebyshev inequality. Numerical experiments conducted on the South German Credit dataset, which represents individual (consumer) credit risk, demonstrate that the proposed generative adversarial network’s minimax probability machine (GAN-MPM) model achieves 76.13%, 60.93%, 71.78%, and 72.03% for accuracy, F1-score, sensitivity, and AUC, respectively, significantly outperforming support vector machines, random forests, and XGBoost. Furthermore, SHAP analysis reveals that the installment rate in percentage of disposable income, housing type, duration in month, and status of existing checking accounts are the most influential features. These findings demonstrate the effectiveness and interpretability of the GAN-MPM model, offering a more accurate and reliable tool for credit risk management. Full article

Amid growing economic uncertainty and rising corporate default risks, effective legal reforms are essential for financial stability and sustainable business practices. This study examines the impact of China’s security interest system reform on corporate credit risk, highlighting its implications for corporate social responsibility (CSR) and sustainable development. Using a Difference-in-Differences approach and data from Chinese listed firms, the results show that the reform significantly reduces credit risk, particularly for asset-light, risk-averse, and growth-stage enterprises. Lower credit risk alleviates financing constraints, enabling firms to allocate more resources to Environmental, Social, and Governance (ESG) activities and long-term sustainability initiatives. The findings reveal that legal reforms in secured transactions not only serve as risk management tools but also function as institutional mechanisms that foster CSR engagement and contribute to building a sustainable economic cycle. This research fills a gap in linking legal system reform, credit risk mitigation, and CSR, offering practical insights for future policy design in sustainable finance and green innovation. Full article

The specification of inlet boundary conditions plays a critical role in computational fluid dynamics (CFD) simulations of pollutant dispersion from nuclear facilities, particularly in regions characterized by uneven terrain. Previous studies have often simplified such terrain by approximating it as a flat surface to reduce computational complexity. However, this approach fails to adequately capture the realistic atmospheric boundary layer dynamics inherent to uneven topographies. To address this limitation, this study conducted atmospheric dispersion tracer experiments specifically designed for nuclear facilities situated on non-uniform terrain. A novel inlet boundary condition, termed the Atmospheric Boundary Layer of Uneven Terrain (ABLUT), was developed by modifying the existing atmBoundaryLayer model in OpenFOAM. Numerical simulations were performed using both the default and the proposed ABLUT boundary conditions, incorporating different turbulence models and examining the influence of turbulent Schmidt numbers across a range of 0.3 to 1.3. The results demonstrate that the ABLUT boundary condition, particularly when coupled with a turbulent Schmidt number of 0.7 and the $SSTk-\omega$ turbulence model, yields the closest agreement with experimental tracer dispersion data. Notably, comparative analyses between the default and improved models revealed significant discrepancies in near-surface wind speed profiles, with deviations becoming increasingly pronounced at higher elevations. Numerical simulations were conducted to assess the ground-level distribution of Total Effective Dose Equivalent (TEDE) for four typical radionuclides (³$H$, ¹⁴$C$, ⁸⁵$Kr$ and ¹²⁹$I$) emitted from nuclear facilities under both higher and lower wind speed conditions. Results demonstrate that the TEDE maxima across all scenarios remain orders of magnitude below regulatory annual limits. These findings provide critical insights for enhancing the accuracy of wind field simulations in the vicinity of nuclear facilities located on uneven terrain, thereby contributing to improved risk assessment and environmental impact evaluations. Full article

Backgrounds/Objectives: Atrial fibrillation (AF) confers a fivefold greater risk of acute ischaemic stroke (AIS) relative to normal sinus rhythm. Among patients with AF-related AIS (AFAIS), recurrence is common: AFAIS rate is sixfold higher in secondary versus primary prevention patients. Guidelines recommend oral anticoagulation for primary and secondary prevention on the basis of CHA₂DS₂-VASc. However, guideline adherence is poor for secondary prevention. This is, in part, because the predictive value of CHA₂DS₂-VASc has not been ascertained with respect to recurrence: patients with and without previous stroke were not routinely differentiated in validation studies. We put forth a protocol to (1) validate, and (2) update CHA₂DS₂-VASc for secondary prevention, aiming to deliver a CPR that better captures 90-day recurrence risk for a given AFAIS patient. Overwhelmingly poor quality of reporting has been deplored among published clinical prediction rules (CPRs). Combined with the fact that machine learning (ML) and deep learning (DL) methods are rife with challenges, registered protocols are needed to make the CPR literature more validation-oriented, transparent, and systematic. This protocol aims to lead by example for prior planning of primary and secondary analyses to obtain incremental predictive value for existing CPRs. Methods: The Virtual International Stroke Trials Archive (VISTA), which has compiled data from 38 randomised controlled trials (RCTs) in AIS, was screened for patients that (1) had an AF diagnosis, and (2) were treated with vitamin K antagonists (VKAs) or without any antithrombotic medication. This yielded 2763 AFAIS patients. Patients without an AF diagnosis were also retained under the condition that they were treated with VKAs or without any antithrombotic medication, which yielded 7809 non-AF AIS patients. We will validate CHA₂DS₂-VASc for 90-day recurrence and secondary outcomes (7-day recurrence, 7- and 90-day haemorrhagic transformation, 90-day decline in functional status, and 90-day all-cause mortality) by examining discrimination, calibration, and clinical utility. To update CHA₂DS₂-VASc, logistic regression (LR), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP) models will be trained using nested cross-validation. The MLP model will employ transfer learning to leverage information from the non-AF AIS patient cohort. Results: Models will be assessed on a hold-out test set (25%) using area under the receiver operating characteristic curve (AUC), calibration curves, and F1 score. Shapley additive explanations (SHAP) will be used to interpret the models and construct the updated CPRs. Conclusions: The CPRs will be compared by means of discrimination, calibration, and clinical utility. In so doing, the CPRs will be evaluated against each other, CHA₂DS₂-VASc, and default strategies, with test tradeoff analysis performed to balance ease-of-use with clinical utility. Full article

Corporate defaults represent a critical risk factor for financial institutions and stakeholders. In today’s complex economic environment, precise and timely risk assessment has become an essential component of financial strategies. One promising strategy consists of analyzing and learning from financial patterns observed in distressed or bankrupt firms. However, this requires processing highly imbalanced datasets in which bankruptcy cases are substantially underrepresented relative to solvent firms. This imbalance, coupled with the data’s intrinsic complexity—such as overlapping features and nonlinear patterns, poses significant difficulties for traditional classifiers like Support Vector Machines (SVMs), which tend to favor the majority class. To overcome these challenges, we employ a Fuzzy Shadowed SVM, which allows for a more refined modeling of minority class instances. This method leverages granular computing paradigms to enhance predictive robustness. Empirical results based on real-world datasets show that our model significantly outperforms traditional machine learning approaches, particularly in recognizing minority-class instances. Full article

In this study, we aim to provide a comprehensive analysis of the risk management potential of sectoral Credit Default Swaps (CDSs) within financial portfolios. Our objectives are threefold: (i) to investigate the safe haven properties of sectoral CDSs; (ii) to assess their hedging effectiveness and evaluate the diversification benefits of incorporating sectoral CDSs into both conventional and Islamic stock market portfolios; and (iii) to compare these findings with those obtained from alternative assets such as the VSTOXX, gold, and Bitcoin indices. To achieve this, we estimate time-varying hedge ratios using a range of multivariate GARCH (MGARCH) models and subsequently compute hedging effectiveness metrics. Conditional correlations derived from the Asymmetric Dynamic Conditional Correlation (ADCC) model are employed in linear regression analyses to assess safe haven characteristics. This methodology is applied across different subperiods to capture the impact of the crypto currency bubble and the COVID-19 pandemic on hedging performance. Full article

Credit rating transitions and defaults are critical indicators of corporate creditworthiness, yet their accurate modeling remains a persistent challenge in risk management. Traditional models such as logistic regression (LR) and structural approaches (e.g., Merton’s model) offer transparency but often fail to capture nonlinear relationships, temporal dynamics, and firm heterogeneity. This study proposes a hybrid machine learning (ML) framework to explain and predict corporate rating transitions and defaults, addressing key limitations in existing literature. We benchmark four classification algorithms—LR, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM)—on a structured corporate credit dataset. Our approach integrates segment-specific modeling across rating bands, out-of-time validation to simulate real-world applicability, and SHapley Additive exPlanations (SHAP) values to ensure interpretability. The results demonstrate that ensemble methods, particularly XGBoost and RF, significantly outperform LR and SVM in predictive accuracy and early warning capability. Moreover, SHAP analysis reveals differentiated drivers of rating transitions across credit quality segments, highlighting the importance of tailored monitoring strategies. This research contributes to the literature by bridging predictive performance with interpretability in credit risk modeling and offers practical implications for regulators, rating agencies, and financial institutions seeking robust, transparent, and forward-looking credit assessment tools. Full article

This study investigates the nonlinear and regime-dependent relationship between climate policy uncertainty (CPU) and sovereign credit default swap (CDS) spreads across a panel of developed and emerging economies from February 2010 to March 2025. Utilizing the Quantile-on-Quantile Regression (QQR) and Multivariate QQR (MQQR) frameworks, we capture the heterogeneous effects of CPU under varying market states and assess the marginal role of global risk factors, including geopolitical risk (GPR), economic policy uncertainty (EPU), and market volatility (VIX). The findings indicate that in developed markets, CPU exerts a nonlinear impact that intensifies during periods of heightened sovereign risk, while in low-risk regimes, its effect is often muted or reversed. In contrast, emerging economies exhibit more volatile and state-contingent responses, with CPU exerting stronger effects in calm conditions but diminishing in explanatory power once global risks are taken into account. These dynamics highlight the importance of institutional credibility and financial integration in moderating CPU-driven credit risk. Full article

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection methods, including SHapley Additive exPlanations (SHAP) values and Mutual Information (MI), and resampling techniques such as Synthetic Minority Over-sampling TEchnique (SMOTE), SMOTE-Tomek, and SMOTE Edited Nearest Neighbor (SMOTE-ENN), are evaluated. The results show that combining SHAP-based feature selection with SMOTE-Tomek resampling and a Stacked Classifier consistently achieves superior predictive performance. These findings highlight the value of explainable AI in enhancing credit risk assessment for auto lending. This research also offers valuable insights for addressing other financial modeling challenges involving imbalanced datasets, supporting more informed and reliable decision-making. Full article

The prevailing view is that ESG performance contributes to corporate financial stability, particularly regarding long-term sustainability objectives. However, there is a notable lack of critical research exploring its short-term financial effects, especially within capital-intensive sectors experiencing green transformation. This study examines the theoretical gap by investigating whether increased ESG performance may unintentionally heighten the financial burden and default risk in the short run. To verify the stability of each variable in the series, we employed the short-panel unit root test on panel data from 234 Chinese energy industry companies covering the years 2015 to 2023. Including enterprise fixed effects as well as time fixed effects, we find that higher ESG ratings increase the possibility of default risk in the Chinese energy sector. This effect remains robust after controlling firm size, financial leverage, return on assets, return on equity, earnings per share, beta and firm age. In addition, we conduct robustness checks using alternative default risk measures, both endogeneity- and component-based, and the outcomes demonstrate that the impact is substantial and consistent. Consequently, we may draw the conclusion that raising the ESG rating has an adverse effect on reducing corporate default risk, which fills the knowledge gap regarding the influence of listed companies’ default risk on China’s energy sector. Moreover, it has been found that green innovation plays a strengthening role in the analysis of the interaction term between green innovation and ESG on default risk. This suggests that while green innovation is a strategic initiative aimed at long-term sustainability, it requires a significant amount of capital and resources in the short term, which may result in higher default risk in the beginning. Full article

Alexithymia is regarded as one of the risk factors for several prevalent mental disorders, and there is a growing need for convenient and objective methods to assess alexithymia. Therefore, this study proposes a method for constructing models to assess alexithymia using machine learning and electroencephalogram (EEG) signals. The explanatory variables for the models were functional connectivity calculated from resting-state EEG data, reflecting the default mode network (DMN). The functional connectivity was computed for each frequency band in brain regions estimated by source localization. The objective variable was defined as either low or high alexithymia severity. Explainable artificial intelligence (XAI) was used to analyze which features the models relied on for their assessments. The results indicated that the classification model suggested effective assessment depending on the threshold used to define low and high alexithymia. The maximum receiver operating characteristic area under the curve (ROC-AUC) score was 0.70. Furthermore, analysis of the classification model indicated that functional connectivity in the theta and gamma frequency bands, and specifically in the Left Hippocampus, was effective for alexithymia assessment. This study demonstrates the potential applicability of EEG signals and machine learning in alexithymia assessment. Full article

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, Distress Insurance Premium, and SRISK. In the time-series dimension, we use the Dynamic OLS model and find that financial variables, such as credit default swap spreads, equity correlation, and firm size, significantly affect the SRMs, but the COVID-19 variables do not appear to drive the SRMs. However, if we focus on the first wave of the COVID-19 pandemic in March 2020, we find a positive and significant COVID-19 effect, especially before the government interventions. In the cross-sectional dimension, we run fixed-effect and event-study regressions with clustered variance-covariance matrices. We find that market capitalization helps to reduce a firm’s contribution to the SRMs, while firm size significantly predicts the surge in a firm’s SRM contribution when the pandemic first hits the system. The policy implications include that proper market interventions can help to mitigate the negative pandemic effect, and policymakers should continue the current regulation of required capital holding and consider size when designating systemically important financial institutions. Full article

This study assesses the financial soundness of Sudanese commercial banks during escalating civil conflict by integrating Altman’s Z-score models with scenario-based stress testing. Using audited financial data from 2016 to 2022 (pre-war) and projections through to 2028, the analysis evaluates resilience under low- and high-intensity conflict scenarios. Altman’s Model 3 (for non-industrial firms) and Model 4 (for emerging markets) are applied to capture liquidity, retained earnings, profitability, and leverage dynamics. The findings reveal relative stability between 2017–2020 and in 2022, contrasted by significant vulnerability in 2016 and 2021 due to macroeconomic deterioration, sanctions, and political instability. Liquidity emerged as the most critical driver of Z-score performance, followed by earnings retention and profitability, while leverage showed a context-specific positive effect under Sudan’s Islamic finance framework. Stress testing indicates that even under low-intensity conflict, rising liquidity risk, capital erosion, and credit risk threaten sectoral stability by 2025. High-intensity conflict projections suggest systemic collapse by 2028, characterized by unsustainable liquidity depletion, near-zero capital adequacy, and widespread defaults. The results demonstrate a direct relationship between conflict duration and systemic fragility, affirming the predictive value of Altman’s models when combined with stress testing. Policy implications include the urgent need for enhanced risk-based supervision, Basel II/III implementation, crisis reserves, contingency planning, and coordinated regulatory interventions to safeguard the stability of the banking sector in fragile states. Full article