Thermo-oxidative (TO) preaging significantly influences the fatigue behavior of natural rubber (NR). However, existing artificial neural network (ANN) models are generally limited to isothermal aging conditions and cannot directly represent multi-temperature preaging spectra encountered in practical applications. This study develops a framework for extending ANN-based fatigue-life predictors trained exclusively using isothermal preaging data to multi-temperature aging histories. An optimized physics-informed neural network (PINN) is employed as the underlying fatigue-life predictor, incorporating a physics-based constraint to preserve the expected power-law behavior of the generated S-N curves. Two spectral-aging representations are investigated. The first converts the aging spectrum into an equivalent isothermal condition using weighted-mean-temperature or Arrheniusbased equivalent-time formulations. The second introduces an incremental-degradation representation in which the individual aging steps are evaluated sequentially while accounting for the preceding aging history. The optimized PINN achieves mean absolute percentage errors (MAPE) of 16.5% and 14.8% for unseen data within the training conditions and left-out isothermal aging conditions, respectively. For spectral preaging, the Arrhenius-based equivalent-time method substantially outperforms the weighted-mean-temperature approach. The proposed incremental-degradation representation provides the highest accuracy, with a MAPE of 6.13%, while preserving the sequential nature of the aging process and avoiding the need for a global reference temperature. Its predictions are also in close agreement with the experimental S-N curve and compare favorably with the analytical Neuhaus model, without requiring explicit determination of temperature and displacement dependent activation-energy parameters. The proposed methodology therefore provides a practical approach for extending isothermally trained ANN-based fatigue-life models to multi-temperature preaging histories.
Empowering females and enhancing their representation in leadership roles are critical components of sustainable development. This study uniquely explores how female directors with Non-Governmental Organization (NGO) backgrounds influence the relationship between Environmental, Social, and Governance (ESG) practices and firm performance in Malaysia, through the lens of feminist theory. Utilizing comprehensive data from 398 firm-year observations across various sectors from 2018 to 2021, the study employs advanced econometric techniques, including the Durbin-Watson test for autocorrelation, Propensity Score Matching (PSM) to mitigate selection bias, and Generalized Least Squares (GLS) to ensure robust estimation. The findings highlight that female directors with NGO experience significantly strengthen ESG integration within firms, particularly by enhancing the social dimension through improved stakeholder engagement, transparency, and ethical governance. This distinctive influence notably translates into enhanced corporate performance, closely aligning corporate strategies with broader sustainability goals. By linking feminist empowerment principles to corporate governance, female NGO directors effectively bridge civic engagement with strategic business decision-making processes. This study offers valuable insights for organizations and policymakers aiming to integrate gender diversity and NGO expertise into corporate governance frameworks, promoting sustainable, inclusive, and equitable business practices.
Samples of martensitic-hardened bearing steel 100Cr6 (AISI 52100) were exposed to hydrogen atmospheres at different gas pressures. The resulting hydrogen contents were measured, and a pressure-dependent hydrogen charging curve was established. Tensile tests on cylindrical specimens charged at various hydrogen pressures showed a pronounced reduction in strength at hydrogen contents as low as 0.75 × 10–6. Fractographic analysis revealed predominantly intergranular fracture with crack propagation along carbide-featured grain boundaries, indicating hydrogen enrichment at microstructural traps. Specimens, in which diffusible hydrogen was allowed to outgas prior to testing, largely recovered their initial strength, demonstrating that the observed embrittlement is mainly governed by diffusible hydrogen and is, to a significant extent, reversible. These findings provide insight into the pressure-dependent influence of hydrogen on the strength and reliability of bearing components operating in hydrogen environments.
Evaluating urban mobility transitions is essential to determine whether local transport interventions support broader sustainability goals. Cities increasingly implement initiatives to promote public transport, active mobility, and low-carbon transport systems. Still, assessing their impact on city-scale structural change remains challenging. Existing evaluation approaches often rely on project-level monitoring or fragmented indicators, which limits cross-city comparison and the assessment of long-term system transformation. This paper proposes a dual-track methodology to evaluate sustainable urban mobility interventions. The first track uses city-defined key performance indicators to capture local implementation processes, governance dynamics, and perceived outcomes. The second track relies on publicly available open data to assess city-scale changes in mobility indicators, including public transport accessibility, cycling infrastructure provision, and traffic-related air pollution. The methodology is applied to ten European cities using open data and satellite-based environmental indicators. Results indicate that while cities report progress at the project level, external indicators show limited short-term structural change in city-wide mobility systems. These findings highlight the value of open data as an independent evaluation layer that contextualises local results and supports transparent assessment of urban mobility transitions.
This study investigates the fatigue behavior of carbon black-filled natural rubber under combined effects of thermal preaging and testing temperature. A comprehensive experimental dataset comprising 410 fatigue tests under 36 distinct conditions is analyzed. Fatigue life is predicted as a function of displacement amplitude, preaging temperature and duration, and test temperature. Several modeling approaches are examined, including an analytical semi-empirical model, a conventional artificial neural network (ANN), an Assisted-ANN, and a Physics-Informed Neural Network (PINN). The ANN models are trained using carefully designed training, validation, and testing datasets to ensure objective performance assessment. Fatigue life is modeled in logarithmic space to improve numerical robustness and reduce sensitivity to data scatter. Model performance is evaluated using quantitative metrics such as mean absolute percentage error (MAPE) and mean absolute error (MAE), as well as qualitative assessment of the predicted S–N relationships. Results show that conventional ANNs significantly outperform the analytical model in terms of prediction accuracy but may produce physically inconsistent S–N curves when trained on sparse and highly scattered data. Incorporating physics-based guidance improves robustness. With the availability of sufficient training data, the Assisted-ANN achieves the lowest overall relative prediction errors with improved physical consistency, and minimal implementation and computational effort. The proposed PINN further enforces physical consistency by constraining the local log–log slope of the S–N relationship, rather than relying on explicit fatigue damage laws or baseline model predictions. As a result, the PINN approach provides the most physically consistent predictions and superior robustness under severe data scarcity conditions. Overall, the results demonstrate that hybrid physics-guided ANN approaches offer substantial advantages for fatigue life prediction of natural rubber under complex preaging and temperature effects.
