Search Results (379)

Mediterranean-type forest ecosystems are becoming increasingly vulnerable to intensifying drought, threatening the resilience of even highly adapted ecosystems such as the Northern Jarrah Forest in south-western Australia. This study quantifies multi-decadal dynamics of canopy water stress using a 36-year multispectral satellite archive (1988–2024) and the newly developed Infrared Canopy Dryness Index (ICDI). We combined this spatiotemporal dataset with a MaxEnt-based risk assessment framework to identify the biophysical drivers of drought-induced canopy loss and to delineate high-risk zones under accelerating climate-forcing changes. Our results demonstrate a systematic spatial expansion of canopy dryness, paralleling a deteriorating regional climatic water balance. Hotspot analysis revealed a transition from localized, peripheral stress to widespread, chronic drought conditions across the landscape. The modelling achieved high diagnostic accuracy (AUC = 0.952), significantly outperforming conventional assessment methods. Regolith depth was identified as the primary determinant of drought-induced canopy collapse, followed by ICDI, NDVI, and slope. Crucially, high-biomass stands exhibited disproportionately higher risk of collapse, revealing a density-dependent vulnerability that suggests productive forests are approaching critical hydraulic thresholds. Conversely, lower-stature forests to the east of the study area demonstrated greater stability, likely due to reduced evapotranspirative demand. These findings provide robust spatial evidence for transitioning from reactive monitoring to proactive forest management. We conclude that targeted interventions, such as ecological thinning and prescribed burning in identified high-risk zones, are imperative to protect the forest and preserve the structural integrity of Mediterranean ecosystems in a drying climate. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

The Iranian Ground Jay (Podoces pleskei) is the endemic bird species inhabiting the deserts and steppes of Iran, a region experiencing severe ecological disturbances like habitat loss and fragmentation of preferred habitat. Despite its remarkable adaptation to arid environments, Iranian Ground Jay exhibits strong habitat specialization, making it both ecologically resilient and vulnerable—an intriguing case for evaluating how the species responds to climate-driven habitat shifts. The present study aims to assess the current and future distribution of Iranian Ground Jay under climatic change using MaxEnt incorporating presence records and bioclimatic variables. We modeled the species’ potential distribution under two climate models (HadGEM3-GC31-LL and MIROC6) for 2070. Then, using the predicted habitats, we estimated the coverage of protected areas in Iran. Among climatic variables, we predicted that the annual precipitation (bio12), precipitation of driest quarter (bio17), and temperature seasonality (bio4) significantly influenced the distribution of Iranian Ground Jays. The highly suitable distributions of the species are concentrated in Eastern, Southeastern, and Central Iran. Our results indicated that a vast range of potential distribution is located outside protected areas, emphasizing the importance of conservation efforts. Our investigation shed lighted the consequences of global warming, where the highly suitable habitat is expected to shift under predicted climatic changes, resulting in a reduction in suitable habitat extent projected for the future. Based on these insights, it becomes imperative to reassess current conservation policy and devise an action plan specifically tailored for the Iranian Ground Jay, particularly emphasizing the protection of its core habitats within anthropogenically altered landscapes and non-protected regions. Full article

Climate resilience has evolved and transitioned from a concept focused on disaster risk to a strategic development paradigm. It has become a core area of focus for researchers, professionals, and policymakers due to the increasing frequency and severity of climate change hazards. The academic landscape persists in a fragmented state in spite of its significant prominence due to diverse conceptual frameworks, various definitions, and a lack of precise indicators to assess climate resilience across sectors. The crucial objective of this research is to conduct a comprehensive bibliometric analysis of the academic literature on climate resilience, measure the scientific influence, and identify gaps and opportunities. This bibliometric review was conducted using data from Web of Science, consisting of 1096 articles published between 2015 and 2025. Vosviewer represents the main software used to evaluate the network of leading authors, journals, international collaborations, and the dominant countries. Terms such as climate change, resilience, and indicators received particular attention, representing the main conceptual connections. This study reveals an overview of the field’s progression, themes, trends, and challenges. The results reveal a sustained increase in research output and a heterogeneous landscape organized around key domains, including urban resilience, ecosystem dynamics, agricultural systems, governance, climate impacts, and sustainability transitions. Resilience is assessed using diverse, context-specific indicators, with governance, vulnerability, and adaptive capacity frequently identified as core dimensions. However, measurement approaches remain inconsistent and lack standardization. Scientific production is concentrated in a limited number of countries, although international collaboration is gradually expanding. These findings underscore the multidimensional and evolving characteristics of climate resilience research, with no clear movement toward a unified measurement framework. Full article

The increasing penetration of distributed energy resources and the growing vulnerability of centralized power systems to natural hazards, terrorist attacks, acts of sabotage, technical incidents, and operational uncertainties have intensified the need for resilient and secure energy infrastructures. Microgrids have emerged as a promising solution to enhance energy security by enabling the localized generation, autonomous operation, and flexible integration of renewable energy sources. However, their effective deployment introduces complex risks related to technical, economic, and operational uncertainties. This paper presents a comprehensive framework for risk management in microgrids within modern power systems, aiming to improve the overall security and resilience of Romania’s power system. The study systematically identifies and evaluates the main risk scenarios affecting the power system: natural disasters, terrorist attacks, acts of sabotage, and technical incidents. In addition, to achieve an in-depth analysis, the paper also discusses the SWOT and PESTEL analyses of the Romanian power system, as well as its resilience. A multi-level risk assessment methodology is proposed, combining probabilistic analysis with severity (impact) analysis. The proposed approach is validated through case studies based on risk scenario assessments, demonstrating its effectiveness in improving microgrid performance under diverse disturbance conditions. The results highlight the critical role of proactive risk management in supporting energy security objectives, while ensuring stable and resilient operation of the Romanian power system. This research contributes to the development of adaptive and sustainable power systems, capable of addressing future challenges in an increasingly decentralized energy landscape, and can be adapted to any modern power system worldwide. Full article

This study presents a bibliometric and systematic review of 480 articles meeting the following inclusion criteria: English-language articles, reviews, or proceeding papers focusing on building topics with full text available, retrieved from the Web of Science Core Collection on 9 Jannary 2026 to map the intellectual landscape of smart-sustainable building (SSB) research. Employing the PRISMA framework combined with scientometric mapping (VOSviewer), thematic classification, and qualitative synthesis (no risk of bias assessment was performed as this was a bibliometric review), the analysis reveals exponential publication growth since 2022, identifying three dominant thematic clusters: digital enabling technologies (41.0%), energy systems (30.8%), and advanced building envelopes and materials (28.3%). Keyword analysis identifies “smart buildings,” “green buildings,” and “energy efficiency” as central conceptual anchors, while temporal trends indicate increasing attention to artificial intelligence, digital twins, and blockchain. Notably, 51.4% of articles address two or more themes simultaneously, confirming the field’s interdisciplinary character. Critical analysis reveals persistent fragmentation: sustainable building rating tools (e.g., BREEAM, LEED) and smart building evaluation methods (e.g., Smart Readiness Indicator). Seven challenges, including assessment fragmentation, high costs, and cybersecurity vulnerabilities, are identified as barriers to SSB adoption. Limitations include reliance on a single database (Web of Science) and subjective thematic classification. This review provides a roadmap for future research emphasizing integrated assessment frameworks and interdisciplinary collaboration. Registration: Not pre-registered. Funding: National Key R&D Program of China (2025YFF0521003). Full article

Airports, as Critical National Infrastructure (CNI), operate as tightly coupled socio-technical systems exposed to multifaceted threats, including cyber, physical, social, environmental, and Chemical, Biological and Radiological (CBR) threats. This study presents a structured review of the synthesis of conceptual frameworks, airport structural configurations, sensor networks, and multi-domain threat landscapes, as well as airport security and resilience analysis, while comparatively examining risk assessment approaches. The review shows that existing approaches are effective for threat identification and prioritisation but remain predominantly static, with limitations in scalability, data dependency, and real-time applicability. To address these limitations, Threat-Vulnerability-Risk Assessment (TVRA) is adopted as a structured, reusable approach to support metric allocation, redundancy design, and emergency capability development. It further serves as a bridge between traditional risk assessment and resilience-oriented system design by enabling the transformation of static risk scores into scenario-based inputs, thereby supporting stress-testing and lifecycle-based resilience planning across the prepare, act, and recover phases. However, its inherently static structure limits its ability to capture temporal dynamics and cascading interdependencies, highlighting the need to integrate it with dynamic modelling approaches. Full article

Artificial intelligence (AI) systems increasingly depend on complex, multi-stage supply chains that incorporate pre-trained models, third-party datasets, open-source libraries, and automated training pipelines. This dependency creates a rapidly expanding attack surface in which model poisoning, dependency compromise, and provenance manipulation can undermine system integrity long before deployment. Existing AI governance frameworks—including the NIST AI Risk Management Framework and NIST’s Secure Software Development Framework—acknowledge supply chain risks but do not define a verifiable model provenance structure or cryptographically durable integrity guarantees. Simultaneously, the transition to post-quantum cryptography (PQC) introduces new requirements for long-lived AI artifacts: classical digital signatures used to verify model lineage, dataset integrity, and pipeline attestation will become vulnerable to quantum-enabled forgery within the expected operational lifetime of many AI systems. This paper synthesizes evidence from policy, standards, and benchmark sources to characterize the emerging AI supply chain threat landscape and identify cryptographic dependencies that the PQC transition disrupts. We propose a formal Model Bill of Materials with PQC-safe extensions (MBOM-PQC), a unified signing and attestation pipeline integrating ML-DSA and hybrid signature modes, and a five-level Supply Chain Assurance Maturity Model (SCAMM) supporting repeatable organizational evaluation. Together, these contributions aim to provide a structured foundation for AI supply chain integrity, supporting verifiable model lineage, authenticity, and trustworthiness through the PQC transition and beyond. The framework is presented as a design-science contribution comprising three integrated artifacts and is extended with operational guidance for continuous-learning pipelines (§6.5), a formal scoring methodology for organizational assessment (§7.3.5), and a hardware-root-of-trust migration cost matrix (§8.3.6). Full article

This study examines the relationship between land use–land cover (LULC) changes and drought risk dynamics in southwestern Bangladesh, focusing on the Kushtia District, Chuadanga District, Jhenaidah District, and Jashore District. Multi-temporal Landsat data (1994–2018) were used to classify six LULC types, and future scenarios (2028–2050) were projected using a CA–Markov chain model. The Combined Drought Index (CDI) was integrated with LULC fractions through Pearson correlation and linear regression to assess drought variability. Results reveal significant landscape transformation, with settlement areas expanding sharply (≈18–27% in 1994 to 66–85% by 2050), while agricultural land, vegetation, and water bodies declined across all districts. Strong statistical associations were observed between CDI and settlement (negative relationship), as well as agricultural land, barren land, and char land (R² = 0.63–0.82, p < 0.05). Future projections indicate increasing drought vulnerability, particularly in Jashore District, where CDI may decrease from 0.67 (2028) to 0.35 (2050), suggesting a transition toward extreme drought conditions. The Jhenaidah District may shift toward mild drought conditions, while the Kushtia District and Chuadanga District show gradual declines in CDI values, remaining largely within the normal drought range. The findings highlight spatially varying linkages between land use dynamics and drought variability, underscoring the importance of sustainable land management strategies to mitigate potential future drought risks. Full article

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Land degradation caused by soil erosion is a major challenge in Mediterranean sloping agroecosystems, where extreme weather events and conventional land management practices accelerate soil loss and threaten long-term sustainability. This study evaluates and compares three complementary approaches to estimate soil erosion in an olive orchard in Messenia, Greece. Field-based runoff plots provided direct measurements of sediment yield, drone-based Light Detection and Ranging (LiDAR) surveys enabled soil surface change detection through the Difference of Digital Elevation Models (DoD) method, and the Revised Universal Soil Loss Equation (RUSLE) was applied to model erosion risk using site-specific parameters. Results indicate that field measurements and RUSLE estimates are broadly consistent, particularly when the model is calibrated with empirical data, offering reliable insights into soil loss dynamics. In contrast, the LiDAR-DoD analysis identified patterns of soil surface displacement, which reflected spatial variation in surface change across the olive orchard. Overall, the integration of field monitoring, remote sensing, and modeling highlights the strengths and limitations of each method and demonstrates the value of multi-method approaches for improving erosion assessment and supporting sustainable land management in vulnerable Mediterranean landscapes. Full article

The global transition toward renewable energy and decarbonization is intrinsically linked to the management of critical materials. Rare Earth Elements (REEs) are no exception, as they play a strategic role at the center of climate goals. Therefore, this review provides a comprehensive assessment of the REE landscape, explicitly addressing the proposed Demand-Sustainability-Risk Nexus (DSR-Nexus), which integrates technological demand, environmental sustainability, and geopolitical supply risks. A systematic review based on PRISMA methodology was conducted to analyze scientific contributions published between 2015 and 2026, revealing a significant research imbalance. By 2025, while 87% of works focus on resource availability, production, and recycling, only 1.4% address the global supply chain and its geopolitical implications. Key findings highlight that China’s dominance in mining, processing, and refining capacities, accounting for 69.5%, 92%, and 94%, respectively, creates structural vulnerabilities for future environmental goals. In contrast, emerging producers such as Malaysia and the United States are expected to contribute 9% and 8% of refining capacity, respectively. Furthermore, this review discusses environmental trade-offs, including high energy intensity, water consumption, and radioactive byproducts. It also examines mitigation strategies, such as recycling, urban mining, and material substitution. Ultimately, achieving a resilient energy transition requires expanding supply, strengthening circular strategies, and international cooperation. Full article

Background: High-grade serous ovarian carcinoma (HG-SOC) remains the most lethal gynecological malignancy, largely due to intrinsic or acquired resistance to platinum-based chemotherapy. Although large-scale sequencing studies have delineated the genomic landscape of HG-SOC, clinically actionable biomarkers predictive of platinum response and outcome are still lacking. This study aimed to identify genomic alterations associated with platinum sensitivity, resistance, or refractoriness, and to assess their prognostic relevance. Methods: Tumor DNA from 24 HG-SOC patients with optimal cytoreductive resection, classified as platinum-sensitive (n = 9), platinum-resistant (n = 8), or platinum-refractory (n = 7) underwent targeted next-generation sequencing of 409 cancer-associated genes. Somatic variants were filtered and classified for oncogenicity using established criteria incorporating predicted functional impact, REVEL scores, and population allele frequencies. Associations between mutational profiles, platinum response, and overall survival (OS) were evaluated using Kaplan–Meier and Cox regression analyses. Key findings were validated in the TCGA ovarian serous carcinoma (TCGA-OV) dataset using survival analyses. Results: A total of 1367 protein-altering somatic variants across 301 genes were identified. While TP53 mutations were ubiquitous, platinum-resistant and platinum-refractory tumors showed enrichment of pathogenic alterations affecting DNA repair, transcriptional regulation, epigenetic modification, and oncogenic signaling, including FANCA, ATF1, MAF, NCOA2, PIK3CA, and TET1. Mutations in these genes were associated with reduced overall survival in exploratory analyses (median 2.5–9 months vs. 27.5–45 months). Multivariate analysis identified FANCA and ATF1 as potential independent predictors in exploratory modeling. In the TCGA-OV cohort, patients harboring pathogenic variants in a multi-gene panel derived from this study (excluding BRCA1/2) exhibited significantly worse survival compared with both BRCA1/2-mutated cases and the overall cohort. Conclusions: This exploratory study identifies a set of genomic alterations converging on transcriptional and epigenetic regulation, DNA repair, and oncogenic signaling that are associated with platinum resistance and adverse prognosis in HG-SOC. Independent validation in TCGA supports the potential clinical relevance of this mutational signature. These findings warrant further validation in larger prospective cohorts and functional studies to clarify their role as biomarkers of aggressive disease and therapeutic vulnerability. Full article