Search Results (3,374)

Against the backdrop of high uncertainty in the economic environment, corporate resilience is not only a key guarantee for enterprises to build their own competitive advantages, but also a vital cornerstone for China’s economy to achieve high-quality development. Sampling from China’s A-share listed companies ranging from 2009 to 2023, this paper employs the Difference-in-Differences (DID) model to examine the association between the Fair Competition Review System (FCRS) and corporate resilience. The empirical results reveal that the application of the FCRS can significantly improve corporate resilience. A mechanism analysis indicates that the FCRS enhances corporate resilience by alleviating credit misallocation and reducing agency costs. Further analysis suggests that the positive impact of the FCRS on corporate resilience is more remarkable in samples with low market competition intensity, high rule-of-law levels, and eastern and central regions. The findings not only provide a practicable path for the deep combination of competition policy and corporate resilience, but also offer useful implications for promoting corporate sustainable development. Full article

Background/Objectives: Globally, rheumatoid arthritis (RA) patients struggle to meet the expectation of being active in their daily lives. The burden on these individuals is twofold, including physical limitations and emotional stress, which make looking after themselves a major challenge. Supporting self-management requires more than just offering strategies; it requires fitness. Whether we are proposing new daily habits or creative outlets like ceramic workshops, it is essential to tap into the patient’s perspective to understand exactly what kind of support will resonate with them. This study explored how ceramic workshops can help people with RA to build resilience and improve their well-being. Methods: Using a phenomenological study design, we interviewed 16 patients with RA in Hong Kong who engaged in ceramic workshops. These participants were selected through purposive sampling, and their insights were gathered via semi-structured interviews. We applied Colaizzi’s seven-step method to analyze the findings. Results: Four key themes emerged: (i) embodied manageability; (ii) clear comprehension of body limits; (iii) the meaningfulness of creating art; and (iv) supporting resilience. Conclusions: This study reveals that effective self-management support should prioritize patient-driven needs, particularly peer interaction and high-demand creative pursuits such as ceramic workshops. Full article

The integration of Building Information Modeling [BIM] and Digital Twins [DT] has emerged as a central driver of digital transformation in the architecture, engineering, and construction sector. Yet, its systemic impact remains constrained by conceptual fragmentation and uneven institutional adoption. This study synthesizes contemporary BIM–DT scalability and each to identify dominant technological and application dimensions, examine the governance conditions shaping scalability, and develop an analytical framework that advances understanding beyond technology-centered syntheses. A two-stage analytical design was employed, combining bibliometric keyword co-occurrence analysis of 1295 Scopus-indexed records with systematic qualitative synthesis of 56 peer-reviewed journal articles published between 2020 and 2025, following PRISMA guidelines. Six interrelated analytical dimensions characterize the current BIM–DT research landscape: BIM–DT integration advancements and applications; interoperability and visualization; safety enhancement; energy efficiency; data-driven decision making; and stakeholder collaboration. Across these dimensions, a persistent misalignment emerges between technological capability and organizational readiness, with deficiencies in standards, governance, and sociotechnical coordination constituting the principal barriers to large-scale deployment. The findings reframe BIM–DT convergence not as a discrete technological upgrade but as the emergence of a coordinated socio-technical information ecosystem spanning the full building lifecycle. By foregrounding governance conditions, data stewardship, and institutional coordination, this study extends understanding of how digital twins expand BIM from design coordination to operational governance and establishes a foundation for more systematic implementation of intelligent, resilient, and sustainable built-environment systems. Full article

Urbanisation reshapes Land Cover and Land Use (LCLU) by driving deforestation, wetland loss, and the conversion of natural and agricultural areas into built environments. However, integrated analyses of LCLU change in response to climate variability in topographically complex, rapidly urbanising African cities remain limited. Therefore, this study examined 2000–2024 LCLU changes in hilly Gasabo District (Kigali, Rwanda) using 30 m Landsat imagery and a Random Trees classifier (92.7% accuracy, 70/30 train-test split), with 2032 projections via a population-driven hybrid trend model. Population estimates/projections 320,516 in 2002 to 967,512 in 2024, 1.41 million by 2032, were derived from Rwanda’s census data and exponential growth modelling (calibrated to 5.05% annual growth). Rapid population growth has driven a 539% expansion of Built-up Areas, accompanied by notable declines in cropland and Forest. Local climate trends (Meteo Rwanda stations) aligned with global datasets (ERA5-Land and CHIRPS): rainfall fluctuation and temperature rose, with strong correlations between population-driven Built-up Areas expansion. From 2024 to 2032, LCLU projections indicate that Built-up Areas will continue to expand by 29.5%. Cropland was forecast to decline to 15.9%, while Forest loss slowed to 5.7%. MLR analysis revealed strong correlations between population-driven expansion of Built-up Areas, cropland/forest loss, warming, and rainfall fluctuations in Gasabo. An ARDL model was applied to address multicollinearity among LCLU predictors, which limited the interpretation of individual coefficients, and confirmed the core MLR correlation trends, with statistically significant (p < 0.05) coefficients. The results highlight the need for data-driven spatial planning in Gasabo (stricter zoning, high-rise buildings, targeted reforestation, climate-resilient green infrastructure) to mitigate population and urbanisation-driven environmental degradation. Full article

The COVID-19 pandemic had a global impact on students transitioning into higher education. During quarantine measures, students often turned to social media for connectedness and peer support in an adverse time. The aim of this research was to explore the perceived impact of social media use during COVID-19 on the psychological resilience of students transitioning into higher education. Data were collected via a qualitative online survey completed by 51 students across the United Kingdom. Following a reflexive thematic analysis of the survey data, three themes were generated: (1) A challenging transition: restricted visits, remote learning, and seeking connection. (2) Facing adversity: becoming resilient in a transitional period. (3) A valued resource: social media as a facilitator of resilience. The findings suggest that social media helped students build virtual connections to overcome feelings of isolation during this transition. Several participants perceived their psychological resilience to have increased through developing strategies to regulate their emotional and mental well-being. Nonetheless, some participants believed that their psychological resilience either decreased or remained untouched. Furthermore, it was explicitly argued that social media played a facilitating role in enhancing participants’ perceived psychological resilience through operating as a ‘coping mechanism’, which fostered a sense of community and togetherness amongst like-minded students. Full article

Beyond their classical role as “cellular powerhouses”, mitochondria are increasingly recognized as dynamic and interconnected networks whose architecture, quality control, and intercellular communication influence cellular and organismal homeostasis. Mitochondrial dynamics—including fusion–fission balance, mitophagy–biogenesis coupling, intracellular organization, and intercellular transfer via tunneling nanotubes, extracellular vesicles, or transient cell fusion—contribute to tissue adaptation and functional decline during aging. Focusing on cardiac muscle, skeletal muscle, and the nervous system, this narrative review synthesizes current evidence describing how aging disrupts mitochondrial network integrity through altered dynamics, impaired organelle positioning and transport, reduced mitophagy, mtDNA instability, and compromised metabolic coupling between cells. These alterations propagate across tissues, limiting energetic flexibility, stress resilience, and regenerative capacity. Building on these mechanisms, we discuss a systems-level perspective in which aging is associated with progressive loss of mitochondrial network coherence rather than solely cumulative molecular damage. Within this framework, mitochondrial connectivity functions as an integrative descriptor of cellular resilience: well-organized networks counteract metabolic perturbations, whereas functionally decoupled networks amplify stress and promote maladaptive aging trajectories. Emerging evidence indicates that physiological and pharmacological interventions, including endurance exercise, caloric restriction or mimetics, fusion-supporting pathways, and mitophagy-enhancing strategies, can partially restore network organization even later in life. Molecular, cellular, and tissue-level insights are integrated to highlight mitochondrial network dynamics as both a mechanistic contributor to aging and a potentially modifiable target for future preventive and therapeutic interventions. Full article

The electric vehicle (EV) battery industry has become a strategic pillar of the low-carbon transition, with far-reaching implications for industrial competitiveness and sustainability. This paper compares the policy mixes governing EV batteries in the EU and China and examines how different approaches shape technological innovation, industrial development, and export performance. A qualitative comparative case study is conducted, combining content analysis of core policy and regulatory documents with descriptive indicators on EV deployment, patenting activity, manufacturing capacity, and international trade. The analysis identifies two distinct but partly complementary policy models. The EU relies on innovation-driven and regulation-based instruments, coupling large research and development programs with stringent sustainability and circular-economy requirements; this model is associated with stronger performance in regulatory upgrading, collaborative innovation, and sustainability-oriented governance. China emphasizes demand expansion, large-scale fiscal support, and long-term industrial planning, which has accelerated capacity build-up, cost reductions, supply-chain integration, and manufacturing-based export competitiveness. The findings show that these contrasting policy mixes generate different technological trajectories and value-chain configurations, while both contribute to strengthening strategic competitiveness in the EV battery sector. More broadly, the study demonstrates that policy effectiveness depends less on any single instrument than on the coherence of the overall policy mix. It concludes that effective EV battery strategies should combine strong innovation incentives with mechanisms that support industrial scaling, supply-chain resilience, and high environmental standards. Full article

As natural and anthropogenic hazards intensify, improving the performance of reinforced-concrete (RC) infrastructure within a resilience-oriented assessment framework while limiting environmental burdens has become an important challenge for sustainable construction. In this context, this study proposes an OpenBIM-based digital-twin methodology to compare two equivalent RC structural scenarios: a conventional solution and an alternative incorporating unprocessed waste sheep wool fibres into cementitious materials. Using an IFC-based model of a high-rise building, the workflow enables automated extraction of structural quantities and a consistent building-scale assessment of material use, environmental impacts, and circularity indicators. Laboratory evidence from the literature is translated into element-level performance criteria through a dual-factor selection strategy based on key structural properties and secondary indicators related to cracking and post-cracking behaviour. The results show that the wool-fibre alternative enables the incorporation of a relevant amount of waste wool into the structure while causing only negligible increases in embodied energy and carbon emissions relative to the conventional RC scenario. The selected formulations also maintain or improve the governing mechanical and serviceability-related factors, indicating potential benefits in crack control, toughness, and repairability. Overall, this methodology provides a reproducible pathway for linking laboratory-scale material innovation with building-scale digital assessment, supporting more sustainable and performance-aware decision-making in RC construction. Full article

Despite being a biodiversity hotspot, the Republic of Seychelles faces a critical challenge with an estimated 90% of its food imported. This dependency exposes the country to global supply disruptions and climate-related risks, while pressure on protected ecosystems continues to rise. In response, the Erasmus+ Capacity Building Higher Education GreenTraINT project (Green Training INTernational Program for agriculture, livestock farming, and conservation), co-funded by the European Union (2024–2026), aims to strengthen local expertise in sustainable agriculture, livestock farming, and biodiversity conservation. Through a transnational partnership involving European and Seychellois universities and institutions, GreenTraINT is co-designing innovative higher education modules tailored to the island’s priorities in agriculture, livestock, and biodiversity conservation. This paper focuses on a detailed needs analysis conducted in early 2025 across a diverse group of 84 stakeholders, including students, educators, NGOs, and professionals. The findings reveal a strong demand for applied training in sustainable food systems and biodiversity conservation, blended teaching methods, and programs that bridge theory with hands-on skills. Inspired by other Erasmus+ projects such as NETCHEM and SPARKLE, GreenTraINT adopts a multi-stakeholder, needs-driven approach that aligns international academic expertise with local development goals. As a key milestone, a Summer School in 2026 will pilot the newly developed modules. In the long term, GreenTraINT seeks to leave a lasting legacy by integrating its curriculum into national education pathways, thereby contributing to food security and environmental resilience. With less than four years remaining to achieve the 2030 Agenda targets, the project positions higher education reform as a strategic accelerator for SDG implementation in small island developing states (SIDS). By linking curriculum innovation to measurable sustainability priorities, GreenTraINT helps narrow the SDG implementation gap in vulnerable island contexts. The project offers a model for international collaboration in higher education for sustainability in SIDS. Full article

This study develops a Life-Cycle Sustainability Framework for circular business models in the context of post-war economic reconstruction and sustainable value chain transformation. Ukraine is used as the main case study due to its post-war reconstruction context and the need for resource-efficient economic recovery strategies. Under conditions of disrupted supply systems, resource constraints, and structural economic change, circular economy principles are conceptualized as strategic mechanisms for enhancing resilience, resource efficiency, and long-term competitiveness rather than solely as environmental policy instruments. Building on a structured hierarchy of circular business models aligned with product life-cycle stages, the framework emphasizes value retention through functional and usage extension beyond material recovery. The framework includes a hierarchical classification of 12 circular business models and a sustainability evaluation approach based on four criteria (K1–K4), which allow for the comparative assessment of circular business models and their combinations across life-cycle stages. Using secondary statistical data and policy review as analytical inputs, the study identifies sectors with high potential for circular transformation and sustainable investment, including agriculture, energy, industry, construction, and logistics. The results indicate that circular business models applied at early life-cycle stages, such as reuse, repair, and remanufacturing, provide the highest potential for reducing resource intensity and improving long-term economic sustainability, while recycling and energy recovery play a supporting role. These findings highlight how life-cycle-oriented circular strategies can support sustainable reconstruction pathways, strengthen international cooperation, and inform policy and managerial decision-making in transitional economic contexts. Full article

Hospitals in earthquake-prone regions must evacuate heterogeneous occupants rapidly while preserving operational continuity under disrupted conditions. However, many hospital-evacuation studies still rely on static routing assumptions or narrowly defined behavioral rules, which limits their value for building-level resilience planning. This paper develops a comparative hospital-campus evacuation framework that combines GIS-based geodesic routing, heterogeneous agent-based modeling, and reinforcement-learning-based decision policies. Puge County People’s Hospital in Sichuan, China, is used as the case study. Six algorithms are evaluated: three rule-based baselines—Shortest Path (SP), Random Walk (RW), and the Social Force Model (SFM)—together with a training-free density-aware heuristic, Density-Aware Gradient Routing (DAGR), and two reinforcement-learning approaches, Density-Aware Q-Learning (DAQL) and SARSA. Experiments cover three population scales ($N\in \{50,100,200\}$), normal daytime conditions, staffing-variation scenarios, and a blocked-exit disruption scenario, with 30 independent runs for each main condition. The results show that the rule-based and training-free methods remain the most reliable under full multi-agent evaluation: the SFM and RW achieve the highest completion ratios (approximately 100% and 93.5%, respectively), while DAGR provides the strongest balance between completion and evacuation efficiency among the non-trained methods. In contrast, the trained RL agents perform substantially worse in direct multi-agent deployment with DAQL reaching approximately 37% completion and SARSA approximately 17%, highlighting a train–evaluation distribution shift associated with independent Q-learning. The ablation analysis further shows that collision avoidance is the most critical reward component, whereas density-avoidance shaping can unintentionally induce collective deadlock when all agents execute the learned policy simultaneously. Among the enhanced variants, DAQL_RoleAware yields the best overall improvement, increasing the completion ratio to approximately 52% and reducing the 90th-percentile evacuation time to approximately 363 s. Overall, this paper clarifies both the promise and the present limitations of density-aware reinforcement learning for hospital evacuation while providing a more building-centred and reproducible basis for future coordination-aware evacuation design and emergency-planning research. Full article

The rising demand for cooling in hot semi-arid cities like Jaipur is putting increasing pressure on energy infrastructure and urban resilience. This study investigates the potential of Jaali, a traditional perforated screen used in Indian architecture, as a passive strategy to reduce energy demand in a contemporary office building through data-driven optimisation and computational analysis. Using detailed energy simulations in DesignBuilder, this research explores how variations in orientation, cavity depth, perforation ratio and screen thickness affect cooling performance during the summer months through a systematic parametric study generating 84 simulation configurations. The model is based on a 12-storey office building designed according to local energy codes. The results show that the optimal configuration differs by orientation. On the south façade, the optimal combination is a 100 mm Jaali with 20% perforation and a 1.5 m cavity, which delivers the best performance. The west façade performs best with a thicker 150 mm screen, the same 20% perforation ratio, and a 1.0 m cavity depth. On the east façade, the strongest performance is achieved with a 150 mm Jaali, 50% perforation, and a 1.5 m cavity, with cooling demand reduction of up to 8.71%. These findings demonstrate that traditional design elements, when optimised for modern use, can offer measurable energy savings through predictive modelling frameworks. More importantly, their widespread adoption could support urban cooling strategies, reduce peak electricity loads and contribute to sustainable development across rapidly growing cities in hot climates. The comprehensive dataset generated provides a foundation for future AI-enhanced building energy optimisation applications. Full article

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region. Full article

Tor is an anonymization network that enables access to hidden services and protects user identity through layered encryption. While its core technology offers strong privacy, users can still be exposed through indirect attack methods or configuration mistakes. This research not only explores de-anonymization techniques but also provides a practical guide for constructing a fully functional experimental Tor environment using virtual machines. The custom-built testbed allows for safe simulation of attacks without impacting the public Tor network. Within this environment, three key information-gathering approaches were evaluated: (1) malware-based reverse shells that establish external communication, (2) malicious PDF and Office files used to trigger outbound connections, and (3) analysis of service misconfigurations that may reveal the IP address of hidden services. The results confirm that although the Tor network itself is resilient, user behavior, improper configurations, and insecure content handling can lead to significant privacy risks. By combining practical environment setup with real-world attack scenarios, this paper serves both as a reference for building experimental Tor networks and as a security-oriented analysis of known de-anonymization vectors. The findings emphasize the critical need for user awareness and precise configuration in privacy-focused technologies. Full article