Search Results (5,997)

Existing runway incursion risk approaches can estimate risk under specified conditions but have limited ability to represent how runway protection constraints weaken across operational stages. This study develops an integrated System-Theoretic Process Analysis (STPA)–Dynamic Bayesian Network (DBN) framework to examine how safety-constraint degradation propagates across predefined operational stages toward runway incursion risk. A Dynamic Causal Translation Logic (DCTL) provides principle-guided organization of STPA-derived artifacts into a traceable single-slice Bayesian Network (BN), which is subsequently extended across semantic stages through selected inter-slice transitions and sequential evidence updating. The model was parameterized using 66 manually coded runway incursion-related reports from the Aviation Safety Reporting System (ASRS). Intra-slice conditional probability tables (CPTs) were estimated and calibrated from these reports, while transition CPTs used expert-informed first-order Markov assumptions. The 2023 runway incursion event at John F. Kennedy International Airport (JFK) was used as a single case-informed demonstration of staged inference and does not constitute independent or cross-case validation. Posterior updating indicated weakened unobserved monitoring and verification constraints, higher unsafe control action likelihood, and sustained tendencies toward runway occupancy conflict and Category B severity. Scenario-based analysis showed that alternative safety-constraint states produced distinct local changes in downstream hazard and Category B posterior probabilities under their respective stage-specific conditioning settings. The framework supports tracing safety-constraint degradation and stage-specific runway safety management. Full article

As health services shift to digital delivery, older adults face practical online risks, including operational errors, difficulty interpreting clinical information, unclear privacy consent procedures, and telecommunications fraud. Existing empirical work mostly adopts questionnaire and interview designs to document older adults’ real-world digital usage, yet few studies systematically analyze policy documents as an external intervention system and assess how they address four core usage barriers: limited digital proficiency, constrained service access, weak sustained participation, and individualized online safety threats. This paper analyzes 1008 policy records extracted from the Peking University Legal Database covering 2007–2025, and develops a four-dimensional analytical framework split into B1 (Digital Capacity and Age-Friendly Support), B2 (Digital Health Management Support), B3 (Home- and Community-Based Care Support), and B4 (Institutional Care and Risk Governance). The analytical toolkit combines a Capability–Opportunity–Motivation–Behavior (COM-B) diagnostic model, an M1–M4 intervention-mechanism coding protocol, Latent Dirichlet Allocation (LDA) topic modeling, and two indices: the Digital Adaptation Policy Orientation Index (DAPOI) and the Behavioral Intervention Diversity Index (BIDI). Coding outputs reveal a pronounced structural imbalance in policy allocation for older adults’ digital health in China: community and institutional governance clauses (B3, B4) dominate the corpus, while person-centred support including one-on-one operational coaching, clinical data interpretation guidance and offline manual backup channels accounts for a marginal share of all coded entries (B1, B2). Age-friendly digital policies issued circa 2020 serve primarily short-term emergency adjustments instead of permanent institutional arrangements. Built on full-text policy coding outputs, this work defines policy-supported digital resilience as a holistic protective system formed by coordinated institutional clauses. Every result reported is sourced exclusively from supply-side policy records, which provides policy-level evidence for subsequent behavioral research on older adults’ digital risk management and digital welfare construction. Full article

Background/Objectives: Toileting-related falls may arise from interacting urinary symptoms, impaired mobility and balance, cognitive or psychological factors, environmental hazards, and caregiver limitations that are often managed separately. This study aims to evaluate the implementation, safety, and 6-month clinical outcomes of an integrated multidisciplinary prevention, treatment, and rehabilitation pathway for older adults and people with disabilities at risk of toileting-related falls. Methods: This multicenter, prospective, single-arm implementation study will enroll 300 adults aged ≥18 years across 15 centers. Participants will undergo standardized assessment of urinary function, gait and balance, physical function, psychological/cognitive factors, activities of daily living, fall history, and toileting-related environmental risks. Prespecified risk domain criteria will guide individualized urinary, rehabilitation, psychological/cognitive, caregiver, and environmental interventions, with reassessment and adjustment during follow-up. The primary outcome will be the 6-month cumulative incidence of at least one toileting-related fall. The secondary outcomes will include nighttime and total falls, urinary symptoms, nocturia, mobility, and functional independence. The implementation will be evaluated using RE-AIM and intervention fidelity measures. Expected Results: The study will characterize pathway reach, adoption, delivery, fidelity, safety, follow-up performance, data completeness, clinical event rates, and within-participant change. Conclusions: The findings may support a reproducible multidisciplinary care pathway and inform subsequent comparative evaluations of effectiveness, sustainability, component contributions, and resource use. Full article

Grain warehouses are critical nodes in food storage, circulation, and reserve systems, and their safe operation is relevant to occupational protection, reserve stability, and food-system resilience. However, unsafe warehouse behaviors are still mainly identified through manual patrols and fragmented inspections, making continuous observation, cross-site coordination, and early intervention difficult. To address this problem, this study proposes a grain warehouse safety governance framework with federated visual intelligence. The framework combines a real-time visual perception module, data-local federated learning, and a city-level digital supervision platform. Drawing on the Wenzhou grain reserve application scenario, the study defines twelve visually observable risk categories, including personal protective equipment (PPE) violations, and links artificial intelligence (AI) recognition with multi-source inspection, spatial mapping, warning generation, collaborative verification, work approval, closed-loop rectification, and large-model-assisted analysis. A four-client technical pilot compares isolated local, centralized, Federated Averaging (FedAvg), and Federated Proximal Optimization (FedProx) training across three selected recognition tasks. The task-specific results provide quantitative evidence from held-out warehouse-level validation and an intermittent-client stress test. The best held-out weighted F1-score (F1) values were 0.7712 for crossing over a conveyor, 0.5891 for passing under a conveyor, and 0.8481 for missing five-point safety harness. In the one-small-client-absent stress test, FedProx achieved weighted F1 scores of 0.5701, 0.4324, and 0.8064 for the same tasks, respectively. The framework translates visual detections into warning outputs, management indicators, and accountable safety workflows under data-governance constraints. It positions computer vision (CV) as an automated observation mechanism for responsible AI governance and embeds visual intelligence into a human-confirmed, data-local, and auditable safety-management workflow for sustainable food-storage infrastructure. Full article

Mining-induced subsidence remains a critical concern for mine safety, issues related to surface and underground infrastructure, mine planning and environmental risk. This paper presents a focused critical review that is specifically devoted to mining-induced subsidence prediction using 3D numerical modeling techniques, with its advancements, applications, challenges, and opportunities. It first traces the evolution from empirical, graphical, and influence function approaches to analytical approaches, which include numerical methods and probabilistic methods, and then to statistical and deep-learning-based models for subsidence predictions, highlighting their capabilities and limitations in complex geological conditions. These methods are included to establish the historical and comparative context, while the central focus of the review remains the development, application, calibration, validation, limitations and future use of 3D numerical modeling for mining-induced subsidence prediction. The review then concentrates on 3D numerical frameworks, including continuum (FDM, FEM, and BEM), discontinuum (DEM and DFN-based) and hybrid methods (FEM–DEM/BEM–FEM), covering their historical development, application in coal and metal mines, and strengths for simulating caving, time-dependent behavior and multi-seam conditions. Particular attention is given to geotechnical inputs (in situ tests, inverse analysis, and rock-mass classification); constitutive modeling, including creep and damage; and data assimilation, calibration and validation, using InSAR/MTInSAR, GNSS, and levelling data. The review synthesizes key challenges like geological heterogeneity, uncertainty regarding rock-mass parameters and discontinuities, scale effects, computational demands and human/operational factors and outlines opportunities where 3D numerical models, coupled with probabilistic tools and machine learning techniques which can enhance hazard prediction, infrastructure and water-management planning, regulatory compliance, and community land-use decisions, can be deployed. Overall, the paper concludes that InSAR-calibrated 3D numerical modeling, embedded in transparent calibration/validation workflows, currently provides the most robust foundation for safer and more sustainable mining operations. Full article

Background: Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for real-time disease monitoring in muscle-invasive bladder cancer (MIBC). This narrative review summarizes current clinical evidence regarding ctDNA across disease stages. Methods: We examine recent translational and clinical findings, incorporating key prospective data from practice-changing trials, as well as insights into minimal residual disease (MRD) detection, treatment escalation and de-escalation strategies, and systemic barriers to adoption. Results: Postoperative ctDNA positivity consistently identifies patients with molecular residual disease (MRD) who face a substantially higher risk of recurrence and mortality, frequently preceding radiographic relapse by several months. Prospective evidence now validates ctDNA as a predictive biomarker to guide adjuvant immunotherapy escalation, while sustained ctDNA negativity correlates with high long-term disease-free survival. Beyond plasma ctDNA, emerging multi-compartment liquid biopsies—integrating urinary tumor DNA (utDNA)—demonstrate enhanced sensitivity, particularly in bladder-sparing and local surveillance settings. Furthermore, integrating genomic ctDNA profiling with novel post-transcriptional layers like epitranscriptomics offers a functional framework to capture tumor adaptation under therapeutic pressure. However, clinical translation remains constrained by a lack of assay harmonization, variable analytical sensitivity, and the need for standardized intervention thresholds. Conclusions: Longitudinal liquid biopsies are rapidly shifting MIBC management from static, stage-based paradigms toward dynamic, molecularly informed precision oncology. While ctDNA-guided strategies show robust clinical utility, prospective interventional validation and technical standardization are required before widespread routine integration. Full article

Northeast Thailand’s Chi-Mun River Basin is among Southeast Asia’s most flood-prone regions, experiencing annual monsoon inundation and extreme flood events that threaten community sustainability. Despite substantial investment in flood management infrastructure, community responses to flood hazards vary considerably, with some areas demonstrating adaptive capacity while others exhibit maladaptive development patterns that increase vulnerability. This study assesses community resilience to flood hazards by integrating flood exposure, environmental conditions (Sentinel-2 spectral indices), and population dynamics across 1157 locations in the Upper Chi River Basin, Maha Sarakham Province. A two-stage analytical framework combining K-means clustering and Random Forest classification identified five resilience classes: Slow Recovery (36.3%), Vulnerable Decline (25.8%), Unknown (17.7%), High Resilience (14.2%), and Maladaptive Growth (6.1%). Results reveal that Maladaptive Growth and High Resilience were clearly distinguished by population volatility (254.3 vs. 65.4), population change (+585.8% vs. −18.7%), and elevation (152.7 m vs. 168.1 m). Population trend emerged as the strongest predictor (importance = 0.135), followed by MNDWI (0.114) and elevation (0.113), indicating that demographic dynamics and topographic characteristics are more influential than flood frequency alone in determining resilience class membership. The findings reveal that Maladaptive Growth areas exhibit extreme population growth with high volatility in lower-elevation areas, whereas High Resilience communities maintain environmental quality and demographic stability despite population decline. These findings inform targeted interventions for sustainable flood risk management and contribute to understanding maladaptation in flood-prone regions, supporting the achievement of Sustainable Development Goals 11, 13, and 15. Full article

Volatile non-ferrous metal prices create a sustainability challenge for electrical equipment manufacturers because input cost pressure can affect resource adjustment decisions and operational continuity. This study examines the association between metal price pressure and the asymmetric adjustment of selling and administrative (SG&A) expenses and assesses the moderating role of persistent hedging. Using 2353 firm-quarter observations from 94 Chinese listed electrical equipment manufacturers over 2016–2024, we estimated panel models of asymmetric SG&A cost adjustment. The results indicate that stronger metal price pressure is associated with greater downward adjustment of SG&A expenses when revenue declines, corresponding to weaker cost stickiness. This association becomes weaker as persistent hedging increases, a pattern consistent with lower sensitivity of internal resource adjustment to short-term input price pressure. Given the observational design and the disclosure-based Hedge measure, the results are interpreted as conditional associations. By connecting commodity price pressure, persistent hedging, and asymmetric cost adjustment, this study provides firm-level evidence on the relationship between upstream input risk, financial risk management, and organizational resource allocation. The findings highlight the importance of balancing short-term cost flexibility with organizational resource preservation for operational resilience and economic sustainability. Full article

Cropland abandonment is a critical challenge for food security and the sustainable use of land resources, especially in the hilly and mountainous regions of southern China. To characterize the spatiotemporal evolution and associated-factor patterns of cropland abandonment in these regions, this study uses Ji’an City, Jiangxi Province, as a case study and develops an integrated framework for abandonment identification and monitoring, predictive-association analysis, relative risk assessment, and zoning management. Using Landsat remote sensing imagery from 1990 to 2024, crop phenological features, and multisource topographic, soil, climatic, ecological, and socioeconomic data, we identified and evaluated long-term cropland abandonment. The results show that, after incorporating phenological features and temporal filtering, annual land-use classification evaluated with spatially separated testing samples achieved a mean overall accuracy of 95.27% and a mean Kappa coefficient of 0.931, and the overall accuracy of cropland abandonment identification reached 91.58%, with a Kappa coefficient of 0.83. Cropland abandonment in Ji’an City showed an overall accelerating trend, with the abandonment rate exceeding 15% during 2020–2021. Spatially, abandonment was more intensive in the west than in the east and more severe in mountainous areas than in plains. Paddy field abandonment was the dominant form of cropland abandonment across the city, whereas dryland abandonment was more limited in extent but showed stronger interannual fluctuations. The predictive-association analysis indicated that tree cover consistently showed high predictive importance for both paddy fields and drylands. Paddy field predictions were more strongly associated with topographic position, surface roughness, and soil conservation, whereas dryland predictions showed stronger associations with aridity and population density. From 1993 to 2023, the model-estimated relative abandonment risk increased, and high-risk areas expanded from deep mountainous regions to low-hill areas. By coupling relative risk levels with associated-factor patterns, we divided paddy fields and drylands into four management zones: core protection zones, dynamic warning zones, integrated improvement zones, and ecological fallow zones. Differentiated management strategies were then proposed for each zone. This study provides methodological support and decision-making evidence for cropland abandonment monitoring, cropland protection, and differentiated land management in the hilly and mountainous regions of southern China. Full article

Soil salinization constrains agricultural sustainability in coastal low plain regions, yet the mechanisms regulating salt spatial heterogeneity under different water management regimes remain unclear. This study investigated soil soluble ion distribution and influencing factors across 0–200 cm profiles (n = 78) under freshwater irrigation (FWI), brackish water irrigation (BWI), and non-irrigation (NOI) in the North China Plain. We hypothesized that irrigation affects soil salinization through dual pathways: direct ion inputs and indirect modification of soil physicochemical properties. Partial least squares structural equation modeling was used to quantify these effects. Results identified irrigation as an important correlate, exerting a significant direct negative effect on salt accumulation (−0.117) and an indirect effect mediated by soil physicochemical properties (0.218). The three regimes produced markedly distinct soluble ion composition and vertical salt distribution. FWI exhibited a bottom-accumulated distribution of soil salinity. BWI was characterized by significant Na⁺ and Cl⁻ enrichment in the 50–100 cm layer, displaying a mid-profile accumulation pattern despite temporary surface desalination. NOI exhibited the most severe soil sodification, alongside a surface-accumulated profile. These findings reveal a trade-off in coastal water management: brackish water can partially offset freshwater scarcity, but long-term sustainability requires depth-specific salinity monitoring to mitigate subsurface salt accumulation risks. Full article

The digital transformation of manufacturing enterprises necessitates more efficient engineering change management through data-driven decision support and process optimization. Although process mining has advanced considerably in recent years, most existing studies focus on descriptive or predictive process analysis, while prescriptive optimization integrated with sustainability considerations remains insufficiently explored. This study aims to develop and evaluate a prescriptive optimization model for Engineering Change Management based on process mining methods and Environmental, Social, and Governance considerations. The proposed model combines rule-based case routing, resource profiling, and risk-oriented alerts and is evaluated using a real-world event log from a manufacturing and engineering company. The methodological framework integrates process discovery using the Inductive Miner algorithm, conformance checking, performance and bottleneck analysis, deterministic rule-based case classification, resource profiling, and prescriptive analytics. The proposed model was evaluated through retrospective validation on a test dataset using both parametric and non-parametric statistical methods. The retrospective analysis estimated a 36.8% reduction in average active case processing time, from 6.31 to 3.98 h, while maintaining the modeled satisfaction level (p < 0.001). These results represent model-based estimates rather than effects observed after prospective implementation. The proposed approach extends existing process mining applications by combining prescriptive analytics with sustainability-oriented process governance. It provides a basis for integration into Product Lifecycle Management, Enterprise Resource Planning, and Engineering Change Management systems as a decision-support module, with the potential to improve operational efficiency and support sustainable manufacturing practices. Full article

Low-impact development (LID) and nonpoint-source pollution reduction facilities are increasingly expected to function as long-term components of urban infrastructure rather than as isolated environmental features. Their sustainability depends not only on technical design but also on institutional review, construction assurance, operation and maintenance, performance verification, information continuity, and adaptive policy learning. This study comparatively examines the content and structure of three major South Korean guidance documents issued in 2013, 2016, and 2020: the national LID Technology-Element Guideline, Seoul’s Guidance for the LID Prior-Consultation System, and the national Manual for the Installation and Management/Operation of Nonpoint-Source Pollution Reduction Facilities. A directed qualitative content analysis was conducted using seven literature-informed lifecycle-governance dimensions covering problem framing, planning and approval, facility selection and sizing, installation and construction control, operation and maintenance, monitoring and performance verification, and adaptive feedback. Documentary provisions were assessed using a four-level ordinal scale ranging from absent to strong coverage. The analysis indicates a functional expansion of governance provisions across the three documents, from LID principles and technology selection to pre-permit review, quantitative runoff-management requirements, installation guidance, operation and maintenance, monitoring, and performance testing. However, the documents collectively show discontinuities between approval and construction verification, construction and long-term operation, maintenance and compliance, monitoring and policy learning, and lifecycle information management. In response, this study proposes a six-stage lifecycle-governance framework linking planning, pre-permit verification, construction commissioning, asset registration, risk-based maintenance and monitoring, and adaptive policy learning. The framework is presented as an analytical and normative synthesis rather than an empirically validated governance model. Because the study is limited to documentary analysis, the findings do not demonstrate actual implementation effectiveness, regulatory compliance, maintenance quality, or environmental performance and should be validated through future empirical and comparative research. Full article

This study evaluates how digital transformation can reshape communication effectiveness in the context of cultural tourism through the restructuring of the technology value chain role. Through the support of technology, cultural values are reinforced and conveyed authentically and engagingly to enhance personal and social experiences, thereby improving communication effectiveness. Through multiple surveys with 393 participants, 380 responses were collected, ultimately yielding 368 valid responses in Da Nang, Vietnam, of which domestic tourists accounted for the majority at 54.6%, with the remainder being international tourists. Data analysis was performed using Microsoft Excel, SPSS, and AMOS, with hypotheses tested. The results show a positive but uneven impact of digital transformation on the quality of digital cultural content (greatest impact), digital engagement (second highest), and digital cultural experience. These results challenge some assumptions that role-playing experiences are the central strategy in creating digital value. It also shows that digital technology effectively supports the quality of media content while enhancing the inherent role of digital media in sharing, interaction, and personal immersion. Furthermore, perceived risks weaken the power of digital transformation on personal digital experiences but do not impact content quality. This result reflects the digital trust paradox, demonstrating both the effectiveness in increasing value for both customers and businesses, and the cautious attitude of tourists towards the potential risks from digital platforms. The distinct role of travel purpose in shaping travel behavior also reflects the contextual nature of the study. This research expands on implications beyond purely technology-accepting models by reflecting how technological infrastructure drives the creation of social value in terms of meaning and cultural tourism experiences. In practical terms, destination managers need to evaluate digital transformation as a strategic tool in creating human-centered content, fostering social–emotional connection and experiences to enhance sustainable destination communication effectiveness. Full article

Food-safety systems increasingly need to manage large detection files and externally generated analytical results across multiple organizations while linking these records to batch-level control actions. This study proposes a blockchain-based dual-track mechanism for trusted circulation of food-safety detection data and batch-level risk control, using aflatoxin B1 (AFB1) as the empirical case. High-dimensional files are stored in InterPlanetary File System (IPFS) and anchored on-chain by content identifiers (CIDs); three authorized oracles use two-of-three matching of detection values and evidence hashes before contract-based state determination. A stage–device–operation inverted index identifies associated batches, while signed second-track confirmations drive GREEN/YELLOW/RED state transitions. Experiments on a four-node Quorum Byzantine Fault Tolerance (QBFT) network used 57 independent HyperPistachio samples with 86.625-mebibyte (MiB) band-interleaved-by-line (BIL) files. Real-file access was successfully completed, single-oracle failures were tolerated when two consistent oracle reports remained, and associated-batch query latency increased only from 13.06 to 16.78 ms as fanout rose from 1 to 40. A 115.15 min sustained run maintained consistent states across all four nodes. The study manages externally supplied AFB1 results rather than evaluating analytical AFB1 detection accuracy, and its experimental validation is limited to the AFB1 case. Full article

Planetary health recognizes that human health, well-being, and sustainable development depend fundamentally on the integrity of natural ecosystems that regulate climate, maintain biodiversity, secure food and water resources, and protect populations from environmental risks. As environmental degradation increasingly threatens this life-support system, there is a growing need for economic indicators that explicitly recognize the value of natural capital alongside conventional measures of development. Gross Ecosystem Product (GEP) has emerged as comprehensive metric for quantifying the monetary value of ecosystem services and informing evidence-based policy. This study estimates India’s GEP for the period 2011–2012 to 2020–2021 using an ecosystem accounting framework aligned with the System of Environmental Economic Accounting (SEEA). The analysis integrates provisioning services (timber, non-timber forest products, fuelwood, and fisheries), regulating services (carbon sequestration), and cultural services (nature-based tourism) using market price, replacement cost, and benefit transfer methods based on secondary data from national and published sources. India’s total GEP is estimated at US$207.05 billion, highlighting the substantial but under-recognized contribution of ecosystems to the national economy and societal well-being. Regulating services dominate GDP, with carbon sequestration alone accounting for US$166.73 billion, followed by provisioning services, while cultural services contribute a relatively smaller share. These findings reveal a significant gap between ecosystem contributions and their representation in conventional metrics such as Gross Domestic Product (GDP), underscoring the limitations of growth-centric development frameworks. By integrating ecosystem values into national accounting, GEP provides a practical tool for advancing planetary health, strengthening climate resilience, conserving biodiversity, supporting ecosystem-based management, and guiding policies that align economic development with ecological sustainability and long-term human well-being. Full article