Search Results (126)

The increasing importance of climate change, natural resource constraints, and sustainability-related risks has created a growing need for quantitative measures that connect environmental performance with economic and financial decision-making. Existing environmental and ESG indicators provide valuable assessments of sustainability conditions, but they are generally designed as ranking or reporting measures rather than dynamic financial indices suitable for risk modeling, portfolio analysis, and long-term economic evaluation. This paper proposes a sovereign-level environmental wealth framework that translates environmental performance into financially interpretable time-series indices. Using fourteen World Development Indicators (WDIs) covering environmental conditions and environmentally relevant economic characteristics for ten major economies, we construct Dollar Environmental Financial Indices (DEFIs). The proposed framework combines standardized environmental information with economic capacity, represented by GDP per capita, to measure the economic value associated with national environmental performance. The resulting indices are not intended to represent directly traded financial securities, but rather synthetic environmental wealth benchmarks that allow sustainability-related risks to be analyzed using established tools from financial economics. We further construct a Global Dollar Environmental Financial Index (GDEFI), which represents the common global component of environmental performance and serves as a benchmark for evaluating systematic environmental exposure across countries. Using robust regression, dynamic econometric models, and volatility analysis, we estimate environmental beta coefficients and examine how country-level environmental conditions respond to global environmental movements. Estimated environmental betas range from $-0.708$ (Australia) to $1.617$ (China), while the explanatory power of the global benchmark reaches an adjusted $R^2$ of $0.886$ for the United Kingdom, demonstrating substantial cross-country heterogeneity in environmental exposure, risk dynamics, and resilience. To demonstrate the usefulness of the proposed framework, we apply risk-adjusted performance measures, including Jensen’s alpha, Sharpe ratio, Sortino ratio, and the Rachev ratio, together with mean–variance and CVaR-based portfolio optimization methods. These analyses show that environmental exposures exhibit distinct risk–return and tail-risk characteristics, highlighting potential diversification benefits in sustainability-oriented decision frameworks. Maximum-likelihood factor analysis further indicates that a three-factor structure provides an adequate representation of the common variation in environmental performance across countries. Finally, we provide a conceptual illustration of how environmental index-based derivatives could support future sustainability risk management. While DEFIs are not currently tradable assets, the proposed framework establishes a bridge between environmental measurement and financial modeling by allowing environmental risk to be evaluated through the analytical tools traditionally applied to financial markets. Full article

Bridge health assessment and deterioration prediction are essential for traffic safety and maintenance planning. However, conventional bridge evaluation still relies heavily on expert judgment and heuristic rules, limiting objectivity and long-term forecasting capability. This study proposes a physics-constrained multi-task learning–semi-Markov framework for bridge condition assessment, multi-year deterioration forecasting, and risk-aware maintenance prioritization. Guided by the Highway Bridge Technical Condition Rating Code, the proposed model jointly predicts health grade, defect type, and severity from inspection item/defect entry records, improving assessment robustness through cross-task information sharing. The predicted health states are then incorporated into a physics-constrained semi-Markov model to forecast bridge deterioration over a three-year horizon, and the current and predicted states are further used for maintenance prioritization. Experiments on real bridge inspection data show that the proposed model achieves test accuracies of 95.20%, 99.67%, and 99.72% for health grade, defect type, and severity, respectively, with a Cohen’s kappa of 0.872, while the proposed semi-Markov model achieves a three-year prediction accuracy of 95.4% and a weighted accuracy of 99.1%. Comparative and ablation studies further demonstrate the effectiveness of the proposed framework for bridge lifecycle management. Full article

The clinical and pathogenetic intersection of Familial Mediterranean Fever (FMF) and axial spondyloarthritis (axSpA) represents a compelling frontier in modern rheumatology, challenging the traditional binary classification of inflammatory diseases. FMF is a monogenic autoinflammatory disorder caused by mutations in the MEFV gene, characterized by dysregulation of the pyrin inflammasome and surges in interleukin-1β (IL-1β), while axSpA is an immune-mediated condition linked to the HLA-B27 antigen and the IL-23/IL-17 axis. Following a structured search of PubMed/MEDLINE and Scopus, this review integrates PRISMA-informed screening of epidemiological data with a comprehensive narrative synthesis of the molecular pathogenesis and clinical management of this association. The analysis demonstrates a substantially elevated prevalence of spondyloarthritis among FMF patients compared to the general population. It explores the molecular “bridge” where innate immune activation provides the requisite cytokine milieu for the expansion of Th17 cells that drive spinal inflammation. Clinical evidence defines a distinct FMF-associated spondyloarthritis phenotype, characterized by a balanced sex distribution, early onset, and high risk of destructive hip involvement and AA amyloidosis, particularly in M694V carriers. Management strategies focus on dual biologic blockade in refractory cases, targeting both the upstream IL-1 pathway and downstream TNF or IL-17 effectors. This report identifies critical knowledge gaps, emphasizing the need for large-scale clinical trials to optimize outcomes for this complex patient population. Full article

With the continuous expansion of government investment in transportation infrastructure, transportation investment has increased, and bridge engineering has flourished. However, safety accidents frequently occur during the construction stage, so the safety situation of bridge construction units remains unfavorable. Bridge construction is a high-risk occupational activity because operations are performed under complex site conditions, variable geological and hydrological environments, long construction periods, and frequent interaction among workers, machinery, materials, technologies, and surrounding environments. Existing bridge construction safety assessment methods have improved hazard identification and risk prioritization; however, many still have difficulty representing uncertainty, hesitation, and subjective judgment in expert-based occupational safety evaluation. To address this problem, this study proposes an integrated Pythagorean fuzzy TOPSIS decision-support framework for occupational safety risk prioritization in bridge construction projects. A safety risk indicator system is established from five dimensions: human, management, material, technical, and environmental factors. Expert judgments are expressed using linguistic variables and converted into Pythagorean fuzzy numbers. The Pythagorean fuzzy weighted average operator is used to aggregate multi-expert evaluation information, and criterion importance is determined from expert-based Pythagorean fuzzy assessments. The model is applied to a bridge reconstruction project, followed by sensitivity analysis and comparison with several existing methods. The results indicate that low work quality, non-standard construction, construction site environment, lack of safety awareness, and insufficient construction technology are the most critical occupational safety risk indicators. The proposed method provides a practical decision-support tool for identifying priority safety risks under fuzzy and uncertain evaluation conditions. Full article

The increasing availability of digital documentation in cultural heritage has amplified the need for interoperable systems capable of integrating heterogeneous data and supporting risk-informed conservation strategies. In the field of Disaster Risk Management (DRM), the application of structured methodologies—such as the ICCROM-CCI ABC Method—is often hindered by fragmented data sources, inconsistent terminology, and limited interoperability across institutions. This study presents a semantic workflow for the harmonization, enrichment, and integration of cultural heritage risk assessment data within a CIDOC Conceptual Reference Model (CIDOC-CRM)-compliant environment. The proposed system is structured as an Extract–Transform–Load (ETL) pipeline that converts heterogeneous assessment records into interoperable semantic knowledge graphs. The workflow combines controlled vocabularies, project-specific thesauri for risk agents and heritage typologies, and formal ontology mapping implemented through the Mapping Memory Manager (3M) and executed with the X3ML engine. The resulting data are deployed within a ResearchSpace environment, enabling semantic querying, cross-dataset exploration, and integration with external knowledge infrastructures. The workflow was applied to a dataset comprising 295 cultural heritage sites in the municipality of Ravenna (Italy). The transformation process generated a CIDOC-CRM-compliant knowledge graph containing 134,611 RDF triples and 18,954 entities, integrating information on cultural assets, risk scenarios, actors, documentary resources, and quantitative risk assessments. Through the adoption of persistent identifiers and semantic mappings, the workflow also supports interoperability with external cultural heritage resources, including ArCo and GeoNames, facilitating the contextualization and enrichment of local risk assessment data. By transforming fragmented assessment records into structured and interoperable knowledge, the proposed workflow contributes to bridging semantic and information gaps in cultural heritage risk management. The study demonstrates the feasibility of integrating risk assessment data within an ontology-based semantic infrastructure and highlights its potential to support data integration, semantic interoperability, knowledge reuse, and future decision-support applications for preventive conservation and territorial risk management. Full article

The sustainable management of chemical fumigants in intensive horticulture represents one of the most complex challenges in European agricultural policy, requiring the integration of agronomic knowledge, regulatory frameworks, economic viability, and stakeholder perspectives. This study proposes and tests a multi-phase consultation methodology designed to bridge the gap between individual expert knowledge and collective, evidence-based consensus, moving from qualitative field-based elicitation to structured multidisciplinary engagement and incorporating both scientific data and practical experience. A total of 72 experts were involved across two phases. In phase 1, in-depth face-to-face interviews (n = 18) captured field-level knowledge on integrated pest management strategies, risk perception, and decision-making criteria, including the economic sustainability of production systems, a dimension prioritized in the European Commission’s Vision for Agriculture and Food. Phase 2 consisted of a one-day multistakeholder event (n = 54)—bringing together researchers, regulators, industry representatives, and farmers—to confront qualitative findings with experimental data on operator safety, groundwater protection, and consumer residues. This deliberate transition from individual perception to informed, shared consensus represents the methodological core of the approach and its most distinctive contribution. The phase 1 results showed that the majority of experts considered chemical fumigants currently indispensable, while recognizing complementary strategies—particularly solarization and natural substances—as valuable supporting tools. The phase 2 experimental data confirmed operator exposure below regulatory thresholds, no groundwater contamination under professional application conditions, and the absence of detectable residues in treated crops. The results demonstrate that this structured consultation can generate actionable knowledge for integrated nematode and soil-borne disease management, with a methodology replicable across other complex regulatory and agronomic contexts within the European framework. Full article

Flood-induced scour remains one of the leading causes of bridge failure, yet the chain linking flood hazard to bridge decisions is still commonly treated as a sequence of disconnected tasks. This review examines that chain using uncertainty as a unifying interpretive framework, synthesizing the recent literature on non-stationary flood hazard assessment, bridge-scale hydraulics, scour processes and predictive models, scour monitoring, monitoring-informed forecasting, structural vulnerability, and risk-informed decision-making. The review synthesizes the state of the art across all these stages of the chain, highlighting how the dominant uncertainty changes along it: climate and hydrologic variability upstream; model-form, sediment, and parameter uncertainty in scour prediction; measurement noise and inverse-inference uncertainty in monitoring; and threshold and consequence uncertainty in closure, retrofit, and network-level decisions. Although major advances have been achieved in probabilistic modelling, machine learning, hybrid physics-informed methods, and multimodal sensing, most published frameworks still transfer deterministic outputs from one stage to the next. As a result, uncertainty is rarely propagated consistently to the decision level. The main value of this review lies in making the chain’s weak interfaces explicit, in showing how uncertainty propagation can serve as a unifying framework across otherwise disconnected literatures, and in identifying which methodological directions are most promising for connecting prediction, monitoring, and decision support into a coherent end-to-end probabilistic chain supporting climate-resilient bridge management. Full article

The extensive development of high-speed railway (HSR) networks often necessitates construction activities adjacent to operational lines. However, existing studies have mostly focused on the substructure construction phase, lacking systematic consideration of cumulative effects throughout the construction process. This study proposes an integrated framework for risk-informed monitoring throughout the full construction process. The framework integrates the Analytic Hierarchy Process (AHP), triangular fuzzy numbers, and fuzzy comprehensive evaluation to construct a quantitative risk assessment model, decomposing the construction process into hierarchical risk factors and quantifying the weights of each factor. Furthermore, a non-contact real-time monitoring system based on Digital Image Correlation (DIC) is designed and deployed, enabling high-frequency, high-precision three-dimensional pier deformation measurement. Applied to a new bridge crossing the Beijing–Shanghai HSR, the risk model identified pile cap and pier construction as the highest-risk stage (weight: 0.311). The DIC system, validated against total station measurements (relative error < 5%), recorded cumulative pier deformations across 31 construction stages, all remaining within the ±1.2 mm early warning threshold, thereby validating the proposed risk assessment model. The integrated AHP-Fuzzy and DIC framework provides a robust paradigm for proactive risk management, confirming that risk-informed monitoring ensures construction impacts on existing HSR infrastructure remain within safe limits. Full article

The management of European mountain landscapes is increasingly threatened by rural abandonment and escalating environmental risks. This study investigates an innovative Stewardship–Renewable Energy Communities model for the Central Apennines, exploring how post-seismic public reconstruction can serve as a financial engine for territorial maintenance. Utilizing Open Data Sisma administrative records and Photovoltaic Geographical Information System irradiation metrics, this research assesses the solar potential of 18 municipalities within the Sibillini seismic crater. To ensure a reliable baseline, a Building Suitability Coefficient was introduced as a conservative proxy for the public reconstruction sector. Results indicate that the implementation of a distributed network of 6.5 MWp across 325 public nodes, with a specific yield of 1390 kWh/kWp on the entire area, could generate 9 GWh/year. This translates to approximately EUR 1.08 million in annual revenue from energy incentives and sharing. This economic surplus provides a Stewardship Capacity sufficient to fund the active maintenance of 789.77 hectares per year through Nature-Based Solutions, based on a regional rate of 1200 EUR/ha. The novelty of this study lies in bridging post-disaster energy policy with landscape resilience, demonstrating that distributed rooftop solar portfolios represent a non-invasive, self-funding mechanism. By leveraging the reconstructed public stock, mountain territories can transition from passive neglect to active, energy-backed stewardship, offering a reproducible template for high-value cultural landscapes. Full article

Driven by China’s “dual carbon” (carbon peak and carbon neutrality) goals and the national strategy of new-type urbanization, prefabricated construction has emerged as a pivotal pathway toward industrialized and sustainable development in the construction sector—leveraging its distinctive advantages in construction efficiency, cost optimization, environmental performance, and design adaptability. Nevertheless, the inherently sequential and interdependent nature of the full construction process—encompassing off-site component manufacturing, logistics transportation, and on-site assembly—introduces pronounced cross-stage risk transmission mechanisms, with prefabricated components serving as critical risk carriers. Such transmission dynamics significantly impede the scalable and safe deployment of prefabricated construction. To date, scholarly efforts on construction safety in prefabricated buildings have predominantly addressed isolated, stage-specific risks, falling short in quantitatively modeling the coupled propagation of risks across stages, accommodating epistemic uncertainties and latent (i.e., unknown or unobserved) risks, and informing targeted, evidence-based mitigation strategies. To bridge this gap, this study develops a rigorous quantitative framework for assessing cross-stage risk transmission in prefabricated construction safety. Specifically, it aims to (i) uncover the structural patterns and driving mechanisms underlying inter-stage risk propagation; (ii) reduce the likelihood of safety incidents throughout the construction life cycle; and (iii) deliver actionable theoretical insights and methodological guidance for practitioners and policymakers. Methodologically, we first conduct a systematic identification of safety-critical risk factors and establish a hierarchical risk indicator system comprising three first-level dimensions and twenty second-level indicators. Second, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, causal relationships among risk factors are clarified, while incorporating the Leaky Noisy-or Gate (LNOG) extended model to account for unknown risks. Risk data are processed using triangular fuzzy functions, and a Bayesian network (BN) topology diagram is constructed via the GeNIe 5.0 platform, forming a DEMATEL-LNOG-BN-based model for assessing cross-phase risk transmission. Finally, applying the model to an actual project—”a prefabricated construction project in Shanghai”—the study conducts a cross-phase risk transmission analysis. Through forward probability inference, backward causality tracing, sensitivity analysis, and pathway decomposition, sensitivity comparisons are performed under different LNOG unknown risk parameters. Results are compared with those from the traditional DEMATEL-BN model to validate the stability and consistency of high-sensitivity risk factor identification, comprehensively verifying the applicability and predictive reliability of the proposed DEMATEL-LNOG-BN model. The study quantitatively reveals the progressive diffusion and amplification mechanisms of risks across the production–transportation–assembly process, providing scientific support and practical reference for precise safety risk prevention, critical node control, and the optimization of management systems in prefabricated construction sites. Full article

As bridge infrastructure globally transitions from theoretical monitoring toward intelligent digital management, Machine Learning (ML) has emerged as a transformative tool for data-driven lifecycle decision-making. This study presents a systematic and critical review of ML applications across the entire bridge lifecycle, integrating a PRISMA-based scientometric analysis (2020–2025) with a rigorous technical synthesis of 3 major domains. The research reveals a clear hierarchy in deployment readiness; while Design & Optimization and Seismic Fragility Assessment have achieved “High” readiness by leveraging deep learning surrogates to achieve up to a 50-fold computational speedup over traditional simulations, Vibration-Based Damage Identification remains at a “Low–Medium” level due to environmental noise sensitivity and low Signal-to-Noise Ratios (SNR). Technical findings indicate that vision-based models (e.g., ViT, YOLOv8) show strong and promising performance for surface defect detection in controlled or semi-controlled settings, though broader field deployment remains constrained by lighting variability, dataset diversity, and validation at scale. In deterioration modeling and Remaining Useful Life (RUL) prediction, temporal architectures (e.g., LSTM) effectively capture non-linear trends, though operational risks such as “model drift” and “domain shift” in simulation-dependent models necessitate periodic retraining. This review identifies critical bottlenecks, including the “small data” paradox and the “black-box” dilemma. The work concludes by outlining a strategic roadmap centered on Physics-Informed Neural Networks (PINNs), Federated Learning for cross-agency collaboration, and Explainable AI (XAI) to foster professional trust in safety-critical infrastructure management. Full article

Effective emergency relief allocation in dynamic post-disaster environments depends critically on accurate and timely demand information. From a sustainability perspective, improving allocation accuracy is essential for using scarce rescue resources efficiently and supporting resilient disaster response. However, existing demand forecasting approaches frequently exhibit systematic bias, leading to resource misallocation and diminished rescue outcomes. Although deploying on-site assessment teams can partially mitigate this limitation, a unified framework that systematically embeds field assessment feedback into operational allocation processes remains lacking. To bridge this gap, this study proposes a multi-agent joint assessment-allocation model that facilitates coordinated operations between demand assessment and resource distribution activities. The sequential decision-making process is formulated as a Markov Decision Process (MDP), and deep reinforcement learning is employed to coordinate the actions of assessment and allocation teams, enabling allocation policies to be continuously refined through real-time field feedback. By improving the match between actual demand and material supply, the proposed model aims to support more resource-efficient disaster response under demand uncertainty. An empirical case study based on the 2025 Dingri County earthquake in Tibet is conducted to validate the proposed framework. Results demonstrate that integrating assessment feedback substantially improves resource allocation performance: in multi-site rescue scenarios, the framework increases the number of rescued individuals, reduces mission completion time, and enhances overall demand satisfaction. Further sensitivity analysis reveals that a moderate increase in team size strengthens cross-site coordination, whereas excessive team deployment yields diminishing returns and may generate operational redundancy. These findings suggest that sustainable emergency management depends not only on the availability of relief resources, but also on the efficient coordination of real-time information acquisition and material allocation. The proposed framework offers a generalizable approach for integrating real-time information acquisition with dynamic relief allocation. It improves the efficient utilization of scarce rescue resources, reduces avoidable operational redundancy, and strengthens the resilience of emergency response systems, thereby contributing to sustainable disaster risk reduction. Full article

Cancer-associated thrombosis (CAT) is a major cause of morbidity and mortality in patients with cancer. The management of special situations—including recurrent venous thromboembolism (VTE), thrombosis at unusual sites, and central venous catheter-associated thrombosis (CVC-AT)—remains particularly challenging because of the limited availability of high-quality evidence. This narrative review synthesizes recommendations from major international and Spanish clinical practice guidelines and expert consensus documents, including those from SEOM, ESMO, ASCO, NCCN, ITAC and SEMI, to provide a structured framework for the management of these complex scenarios. Our analysis identified substantial heterogeneity across guidelines, particularly regarding anticoagulant selection, dosing strategies, and treatment duration. Although some convergence exists in the management of CVC-AT, important discrepancies and evidence gaps persist in areas such as splanchnic vein thrombosis, hepatic impairment, central nervous system involvement, and recurrent VTE despite treatment. In many cases, recommendations are based primarily on expert opinion rather than robust trial data, and several clinical scenarios are addressed by only a limited number of guidelines. These findings underscore the need for more standardized management strategies and prospective clinical studies to better inform decision-making in daily practice. Overall, this review highlights the growing importance of individualized anticoagulant management aimed at balancing thrombotic and bleeding risks in high-risk oncology patients, thereby helping to bridge the gap between expert consensus and evidence-based precision anticoagulation. Full article

Background: Early childhood caries (ECC) is one of the most common chronic diseases in children and remains unevenly distributed across populations. It is associated with pain, impaired function, and long-term health consequences. Although advances have been made in understanding its aetiology and prevention, important gaps in evidence limit progress in prevention, early detection, and equitable care. Objective: To examine current evidence on ECC and identify key research gaps across biological, behavioral, social, and health system domains. Methods: This narrative review draws on peer-reviewed literature addressing ECC epidemiology, pathogenesis, risk factors, diagnosis, management, and service delivery. The literature was examined to identify areas where evidence is limited, inconsistent, or insufficient to inform clinical practice and public health policy. Results: Research on ECC remains uneven across levels. Longitudinal evidence linking microbiome dynamics, host susceptibility, and lesion progression is limited, restricting causal understanding. Genetic and epigenetic contributions are incompletely defined, particularly in diverse populations. Although socioeconomic gradients are well established, integrative models connecting structural determinants with biological mechanisms are scarce. Emerging diagnostic tools, including biomarkers and artificial intelligence, lack robust evidence demonstrating improved clinical or behavioral outcomes. Implementation research addressing scalability, cost-effectiveness, and equity impact is underdeveloped, especially in low-resource settings. Long-term systemic and developmental consequences of ECC remain insufficiently characterized. Conclusions: Addressing ECC requires integrated and equity-oriented research frameworks that bridge biological, social, diagnostic, and implementation domains. Clarifying these gaps is essential to inform coherent prevention strategies and reduce persistent disparities in child oral health. Full article

Background: Bone and soft tissue sarcomas, while rare, making up less than 2% of adult cancers with an incidence below 5 per 100,000 annually, present a significant challenge due to their varied and often obscure pathology. Additionally, the absence of global sarcoma awareness contributes to delayed interventions, necessitating more-aggressive treatments and increasing mortality risks. Conversely, cancers such as breast and colon have seen improved outcomes through effective screening and early-management strategies. Methods: In this cross-sectional study, out of the total number of participants approached, using a preset questionnaire, by trained medical students to participate in this study, 626 met the inclusion criteria. The questionnaire started with an informed consent process followed by a set of questions regarding sociodemographic characteristics and lifestyle. Subsequently, the questionnaire delved into their understanding and awareness of bone and soft tissue sarcomas, focusing on risk factors, recognizable signs and symptoms, and tendencies regarding health-seeking behavior. Results: In this study with 626 participants, demographic insights showed a young cohort, with 43.5% between 21 and 30 years, and a male predominance of 60.1%. Risk factor awareness was moderate; genetics and smoking were recognized as primary risks for sarcomas. Participants showed limited awareness of sarcoma signs, symptoms, and management, with a substantial percentage unsure about the most at-risk age group, gender differences in risk, and recognizability of symptoms. Barriers to seeking medical care included a passive attitude towards healthcare, fear, and accessibility issues. Most participants had limited knowledge of sarcomas, with 58% unaware of risk factors and 72.3% of signs and symptoms. Conclusions: This study emphasizes the necessity for targeted interventions to bridge the knowledge gap and promote early detection practices, which could significantly impact the prognosis of sarcoma patients. Full article