Széchenyi István University

This study presents a novel multi-material topology optimization framework for geometrically nonlinear continuum structures exhibiting elasto-plastic behavior. The main contribution lies in the integration of the Bi-directional Evolutionary Structural Optimization (BESO) method with geometrically nonlinear elasto-plastic finite element analysis through a MATLAB-ABAQUS coupling, enabling simultaneous consideration of material yielding, large deformations, and optimal multi-material distribution within a unified optimization framework. The formulation is governed by the plastic-limit ultimate load multiplier, enabling direct control of structural collapse resistance during the optimization process. The nonlinear structural response is evaluated through incremental finite element analysis accounting for large deformations. An extended BESO strategy is adopted to distribute multiple material phases with distinct mechanical characteristics under prescribed volume constraints. A consistent interpolation scheme is incorporated to facilitate smooth transitions between candidate materials and ensure stable convergence. By integrating nonlinear analysis with evolutionary material redistribution, the proposed methodology generates stiffness-efficient topologies while enforcing plastic-limit admissibility, thereby ensuring that the optimized layouts satisfy the required collapse resistance under geometrically nonlinear elasto-plastic behavior. The effectiveness of the proposed framework is demonstrated through four benchmark problems, including one elastic case and three geometrically nonlinear elasto-plastic examples. Additional comparative studies with linear elastic and single-material formulations further verify the advantages of the proposed methodology, confirming its robustness, effectiveness, and capability to identify mechanically meaningful optimal layouts under realistic nonlinear loading conditions.

Although the link between perfectionism, performance, and feedback has been widely studied, findings remain inconclusive, partly due to the lack of objective assessment methods. Biological correlates, such as heart rate variability, may help clarify these relationships. This study investigates physiological responses to performance feedback during a coloring task and examines the association between perfectionism and task performance.Eighty-three participants (Mage = 20.74, SD = 3.41), including 64 females, completed the study, with perfectionism assessed using the Short Almost Perfect Scale (SAPS). Heart rate variability (HRV) was recorded to assess autonomic nervous system activity. Participants took part in an experimental intervention involving a 10-min mandala coloring task, during which they received positive, negative, and neutral performance feedback.Higher High Standards scores were significantly associated with fewer correctly colored pixels (τ = −0.161, p = .036) and reduced overall coverage (τ = −0.153, p = .046), and these associations remained significant after controlling for age and gender. Higher Discrepancy scores were also significantly associated with fewer correctly colored pixels (τ = −0.153, p = .046) and reduced overall coverage (τ = −0.150, p = .049), although these associations were no longer significant after controlling for age and gender. Neither perfectionism dimension was significantly associated with HRV responses to evaluative feedback. Exploratory analyses revealed associations between HRV responses to evaluative feedback and overall coloring performance.The results suggest that different dimensions of perfectionism are associated with task performance, while physiological responses to evaluative feedback are related to overall coloring performance.

This study investigates the evolving intersection of explainable artificial intelligence (XAI) and the financial sector. It explores how machine learning models’ transparency and interpretability shape decision-making processes in areas such as credit scoring, risk assessment, and market prediction. While conventional AI methods often function as opaque black boxes, XAI offers a solution to promote model accountability, fairness, and trust, which are critical factors in highly regulated and risk-sensitive financial environments. Drawing on 90 peer-reviewed articles retrieved from Scopus and Web of Science, this research applies both co-word analysis and BERTopic modeling to uncover major research themes and semantic structures within the relevant literature. The co-word network reveals distinct thematic clusters related to explainable credit risk models, interpretability in financial forecasting, and the integration of environmental, social, and governance (ESG) factors into AI-driven financial analysis. Meanwhile, topic modeling uncovers additional topics, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME)-based explainability techniques, human-AI collaboration in decision-making, and explainable deep learning for asset pricing and sovereign risk analysis. A temporal analysis of publication trends indicates increasing scholarly attention to transparency in AI in the wake of regulatory pressure and ethical considerations in finance. The findings point to a growing emphasis on embedding interpretability into AI models to support fairness, regulatory compliance, and better-informed financial judgments. This study contributes to both academic discourse and practical application by offering a detailed map of current XAI research in finance and identifying key directions for future inquiry and technological development.

Urban environments are increasingly vulnerable to climate change, with extreme weather events expected to become more frequent and severe. This paper addresses sustainable urban development and the importance of stormwater retention, integrating adaptation and mitigation strategies. It evaluates the publicly funded Hungarian “Green City” program’s water management, focusing on blue-green infrastructures. The 198 implemented projects in the program were assessed for green credentials, vegetation concepts, and rainwater retention using public databases of real municipal data and Google Earth spatial analyses rather than hypothetical scenarios. A lifetime climate change impact assessment with sensitivity analysis was conducted using two case studies from the “Green City” program, highlighting the benefits of prioritizing rainwater over tap water for irrigation. The study proposes a three-pillar—environmental as operational carbon footprint, economic as extended net present value (NPV), and social as accessibility and recreational benefit—evaluation method for urban blue-green developments. It found that many projects rely on tap water irrigation, thus resulting in higher lifetime carbon emissions. The financial assessment of carbon footprint within the extended NPV method emphasizes the need for improved green area irrigation strategies. By modernizing irrigation practices and implementing effective rainwater retention measures, blue-green infrastructures can significantly reduce carbon dioxide emissions while improving long-term economic performance and social benefits through improved usability. The research offers valuable insights into the role of blue-green infrastructures in urban development to combat climate change. The combined three-pillar framework integrating LCA to assess green projects is a transferable decision-support tool that can be adapted to locally available data, advocating the use of rainwater over tap water to achieve environmental, social, and economic benefits. Unlike earlier studies that used hypothetical scenarios, this research relies on the implemented projects of the “Green City” development program with their observed designs and available real data, thus providing a framework for urban blue-green implementations to integrate sustainable practices and effectively address the challenges posed by climate change.

Technostress has become a significant challenge in digital workplaces, potentially affecting employees’ well-being and productivity. This study investigates the presence of technostress in a railway transportation company, focusing on digital overload and knowledge hoarding. It also examines how organizational trust supports knowledge sharing and helps employees adapt to technological challenges in the workplace. A quantitative survey was conducted among railway employees who regularly use IT tools in their daily work. The study applied descriptive statistics, cross-tabulation analysis, correlation analysis, and cluster analysis. No regression modeling was performed, as the aim of the empirical analysis was exploratory and relationship-oriented rather than model estimation. The findings indicate that digital overload and knowledge hoarding represent the most prominent sources of technostress in the examined organization. Demographic factors show only a limited influence on technostress perceptions, while organizational trust plays an important role in supporting knowledge sharing and mitigating technostress. The results also reveal that employees place greater trust in human collaboration than in technological systems, although no significant distrust in technology was observed. The results suggest that organizations can reduce technostress by strengthening trust-based organizational cultures, promoting knowledge sharing, and implementing HR practices that support employees’ digital adaptation. Transparent communication, targeted training, and supportive leadership can contribute to improving employee well-being and managing technostress in digitally intensive workplaces. This study contributes to the literature on technostress by highlighting the interrelationship between technostress, organizational trust, and knowledge sharing in a railway industry context. The findings provide practical insights into how trust-based organizational cultures can support employees in coping with technological change and digital transformation.