Search Results (1,005)

Current research on disaster-adaptive resilience predominantly focuses on urban systems, with insufficient attention paid to the unique scale of traditional settlements and their formation mechanisms and pathways to systemic realization remain significantly understudied. There is also a lack of multi-dimensional coupling analysis and innovative methods tailored to the specific contexts of rural areas. To address this, this study innovatively introduces ant colony bionic intelligence, drawing on its characteristics of swarm intelligence, positive feedback, path optimization, and dynamic adaptation to reframe emergency decision-making logic in human societies. An evaluation model for disaster-adaptive resilience is constructed based on these four dimensions as the criterion layer. The weights of dimensions and indicators are determined using a combined AHP–entropy weight method, enabling a comprehensive assessment of settlement resilience. Taking Fenghuang Ancient Town as an empirical case, the research utilizes methods such as field surveys, questionnaire surveys, and GIS data analysis. The results indicate that (1) the overall resilience evaluation score of Fenghuang Ancient Town is 3.408 (based on a 5-point scale); (2) the path optimization dimension contributes the most to the overall resilience, with road redundancy design (C21) being the core driving factor; within the positive feedback mechanism dimension, soil and water conservation projects (C15) provide the fundamental guarantee for village safety; (3) based on these findings, hierarchical planning strategies encompassing infrastructure reinforcement, community capacity enhancement, and ecological risk management are proposed. This study verifies the applicability of the evaluation model based on ant colony bionic intelligence in assessing the disaster resilience of traditional settlements, revealing a new paradigm of “bio-intelligence-driven” resilience planning. It successfully translates ant colony behavioral principles into actionable planning and design guidelines and governance tools, providing a replicable method for resilience evaluation and enhancement for traditional settlements in ecological barrier areas such as the Qinling Mountains. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

This study presents a multi-factor coupled assessment of seismic disaster risk for approximately 635,000 individual building units in Ankang City, Shaanxi Province, China, utilizing a high-resolution dataset. The assessment methodology innovatively integrates the three core components of risk: seismic vulnerability V of load-bearing structures, site-specific seismic hazards R, and potential consequences C of damage, to formulate the Seismic Resilience Index I_SR = C·R·V. Crucially, the approach advances established risk assessment frameworks by enhancing the spatial resolution of the site influence coefficient R using a high-resolution national site classification map and detailed local geological data. The results reveal that the areas with the lowest I_SR values (indicating the lowest resilience and thus the highest risk) are predominantly concentrated in older residential districts of counties such as Ningshan, Hanyin, and Ziyang, where unreinforced masonry structures built prior to 1989 are widespread. The model assessment results align with expected structural performance characteristics, and the study concludes by offering quantified, priority-based recommendations for targeted structural intervention and seismic retrofitting in the identified highest-risk regions and building typologies. Full article

Background/Objectives: Women’s sexual quality of life is a multidimensional construct shaped by individual, psychological, relational, and health-related factors. This exploratory cross-sectional study aimed to identify selected determinants of sexual functioning in young women, with a particular focus on partner relationships and sexual dysfunction symptoms within the couple. Methods: Data from 199 female university students aged 18–30 years, recruited via Facebook, were analyzed. Participants completed the Female Sexual Function Index (FSFI) and an author-designed questionnaire assessing sociodemographic variables, relationship characteristics, and self-perceived sexual difficulties in themselves and their partners. Descriptive statistics, bivariate analyses, and multivariable linear regression models were used to examine factors associated with the FSFI total and domain scores. Results: In this self-selected, non-representative sample, over 75% of women reported at least one self-perceived sexual difficulty, while 35.2% obtained FSFI scores at or below the established cutoff, indicating an increased risk of female sexual dysfunction rather than a confirmed diagnosis. In multivariable analysis, higher intercourse frequency, greater overall sexual satisfaction in the last 12 months, and fewer self-reported dysfunction symptoms emerged as the strongest independent predictors of higher FSFI total scores. Women who perceived premature ejaculation in their male partners tended to report lower orgasm and satisfaction domain scores, although this perception was not independently associated with the FSFI total score after adjustment for individual and relationship factors. Conclusions: These findings underline the role of both individual and relational factors in young women’s sexual functioning and support a holistic, couple-centred perspective in sexual health assessment. Full article

Runoff forecasting is essential for flood control, disaster mitigation, and sustainable water resources management. However, runoff processes are highly nonlinear and uncertain due to multiple interacting meteorological and underlying surface factors. Current models can be divided into process-driven and data-driven types. The former offers clear physical interpretability but involves complex calibration and simplifications, while the latter captures nonlinear relationships effectively but lacks physical consistency. To integrate their strengths, this study constructs process-based models and data-driven models, and proposes two hybrid strategies: (1) incorporating intermediate variables from physical models, such as soil moisture and runoff yield, as additional features for data-driven models, and (2) embedding physics-based constraints and synthetic data into loss functions. Using the Songxi River Basin as a case study, results show that both hybrid strategies significantly outperform standalone models. SHapley Additive exPlanations (SHAP)-based interpretability analysis further reveals the contribution mechanisms of key physical variables. This study demonstrates that coupling physical processes with data-driven learning effectively enhances runoff forecasting accuracy and offers a promising paradigm to support sustainable watershed management, climate-resilient water regulation, and flood risk reduction. Full article

A storage-aware water-scarcity risk assessment framework coupling satellite remote sensing, geospatial datasets with the IPCC exposure-hazard-vulnerability (EHV) paradigm was designed to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades. To achieve this, a performance-weighted ensemble machine learning approach was employed to reconstruct long-term terrestrial water storage (TWS) from satellite observations, augmented with glacier-mass calibration to improve reliability in cryosphere-affected regions. Global water withdrawal dataset was generated by integrating remote sensing, geospatial dataset, and machine learning to mitigate the dependency of parameterized land surface hydrological models and enable consistent risk mapping. Satellite-derived results reveal obvious TWS declines in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions. EHV paradigm and big datasets further identified high-water scarcity risk in Asia and Africa, especially in agricultural regions. Water stress has intensified in Africa over the past two decades, while a decreasing trend is observed in parts of Asia. Vulnerability levels in Asia and Africa are approximately eight times higher than those in other global regions. Results reveal a strong connection between water stress and socioeconomic factors in Asia and Africa, reflecting global disparities in water resource availability. Full article

Heavy metal contamination in agricultural soils poses serious threats to food safety, ecosystem integrity, and public health. This study investigates the concentrations, ecological risks, and human health impacts of nine heavy metals Cr, Mn, Co, Ni, Cu, Zn, Pb, As, and V in homestead agricultural soils collected from two depths, surface (0–20 cm) and subsurface (21–50 cm), across industrial and non-industrial regions of Bangladesh, using inductively coupled plasma mass spectrometry (ICP-MS). Results revealed that surface soils from industrial areas exhibited the highest metal concentrations in order of Mn > Zn > Cr > Pb > V > Ni > Cu > As > Co. However, maximum As levels were detected in non-industrial areas, suggesting combined influences of local geology, intensive pesticide application, and prolonged irrigation with As-contaminated groundwater. Elevated concentrations in surface soils indicate recent contamination with limited downward migration. Multivariate statistical analyses indicated that industrial and urban activities are the major sources of contamination, whereas Mn remains primarily geogenic, controlled by natural soil forming processes. Contamination factor (CF) and pollution load index (PLI) analyses identified Pb and As as the principal pollutants, with hotspots in Nairadi, Majhipara (Savar), Gazipur sadar, and Chorkhai (Mymensingh). Ecological risk (ER) assessment highlighted As and Pb as the dominant environmental stressors, though overall risk remained low. Human health risk analysis showed that ingestion is the primary exposure pathway, with children being more susceptible than adults. Although the hazard index (HI) values were within the acceptable safety limits, the estimated carcinogenic risks for As and Cr exceeded the USEPA thresholds, indicating potential long term health concerns. Therefore, the cumulative carcinogenic risk (CCR) results demonstrate that As is the primary driver of lifetime carcinogenic risk in homestead soils, followed by Cr, while contributions from other metals are minimal. These findings emphasize the urgent need for continuous monitoring, improved industrial waste management, and targeted mitigation strategies to ensure safe food production, a cleaner environment, and better public health. Full article

Ground instability represents a major environmental hazard in the Loess Hilly region of Northwest China, threatening infrastructure and human safety. This study establishes an integrated information-theoretic framework for evaluating regional instability risk by coupling the information value model with analytic hierarchy process (AHP) weighting and subsequent hazard–exposure synthesis. Seven conditioning factors—geomorphic type, slope, aspect, lithology, distance to faults, river system, and NDVI—were analyzed to derive susceptibility, while rainfall, peak ground acceleration, and human engineering activity were incorporated as triggering elements of hazard. Exposure was quantified from population density and infrastructure exposure, and overall risk was defined as the product of hazard and exposure after normalization and calibration. Results indicate that hilly landforms, slopes of 10–20°, and NDVI values between 0.3 and 0.6 are the dominant controls on instability occurrence. Extreme-risk zones are concentrated in central Guyuan and northwest Shizuishan (0.16% of the study area), with high-risk zones covering 21.87%, moderate-risk zones covering 41.65%, and low-risk zones covering 6.32%. Model validation yields an AUC of 0.833 and a consistent increase in observed disaster-point density from low to extreme classes, confirming strong predictive reliability. These results demonstrate that the proposed calibrated framework provides a practical and transferable tool for ground-instability risk assessment and land-use planning in loess terrains. Full article

Drill pipe pullback gravel packing is a novel sand control method for marine natural gas hydrate reservoirs, enabling rapid and uniform filling by synchronizing fluid injection with pipe retraction. However, the complex liquid–solid two-phase flow mechanisms and parameter sensitivities in this dynamic process remain unclear. To address this gap, a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach is adopted in accordance with the trial production requirements in the South China Sea. This investigation systematically analyzes the relative contributions of injection rate (0.8–2.2 m³/min) and sand-carrying ratio (30–60%) to the packing effectiveness. Additionally, the effects of carrier fluid viscosity and drill pipe pullback speed are explored. Results show that injection rate and sand-carrying ratio positively affect performance, with sand-carrying ratio as the decisive factor, exhibiting an impact approximately 73 times greater than that of the injection rate. Optimal parameters in this study are injection rate of 2.2 m³/min and sand-carrying ratio of 60%, which yield the highest gravel volume fraction and stable bed height. Furthermore, it is also found that while increasing carrier fluid viscosity improves bed height, excessive viscosity hinders particle settling and compaction. Similarly, a trade-off exists for the pullback speed to balance packing density and pipe burial risks. These findings provide a theoretical basis for optimizing sand control operations in hydrate trial productions. Full article

Recognized as the world’s “Third Pole”, the Qinghai–Xizang Plateau poses significant challenges to human health due to its harsh environment. With improved transportation and a tourism boom industry bringing over 90 million low-altitude residents to the plateau annually, hypoxia has become a critical concern. This study analyzes oxygen content data (2017–2022) together with environmental variables including elevation, temperature, precipitation, and vegetation cover, using the GeoDetector method to identify key drivers of near-surface oxygen distribution. Within the framework of disaster system theory, we evaluated the risk of hypoxia among short-term residents. Results show that the near-surface oxygen distribution across the plateau is primarily regulated by climatic and topographic factors. Interactions among environmental variables markedly enhance the explanatory power for spatial variation in oxygen content, with the coupled effects of humidity, atmospheric pressure, elevation, and temperature being especially pronounced. A high hypoxia hazard prevails across the plateau, particularly in the high-altitude western, northern, and central regions. The spatial distribution of hypoxia risk is strongly shaped by human activities, with high-risk zones clustering in densely populated towns, transportation corridors, and regions of intensive tourism. This results in a distinctive coexistence of “high hazard–low exposure” and “low hazard–high exposure” patterns. These findings provide scientific insights for tourism planning, health protection, and risk management in plateau regions. Full article

Background and Clinical Significance: Tyrosine kinase inhibitors (TKIs) have transformed Philadelphia chromosome-positive chronic myeloid leukemia (Ph+ CML) into a largely manageable chronic disease. However, off-target toxicities are increasingly recognized; rarer complications such as bone marrow edema (BME) remain underreported. BME is a radiological syndrome characterized by excess intramedullary fluid on fat-suppressed T2/STIR magnetic resonance imaging sequences and may progress to irreversible osteochondral damage if unrecognized. We report a case series of TKI-associated BME and propose a practical diagnostic-therapeutic framework. Case Presentation: We describe three patients with Ph+ CML who developed acute, MRI-confirmed BME of the lower limb during TKI therapy. Case 1 developed unilateral then bilateral knee BME, temporally associated first with dasatinib and subsequently with imatinib; symptoms improved after TKI interruption, bisphosphonate therapy, and supportive measures, and did not recur after switching to bosutinib. Case 2 presented with proximal femoral BME during long-term imatinib; imatinib was stopped, intravenous neridronate administered, and bosutinib initiated with clinical recovery and later near-complete radiological resolution. Case 3 experienced multifocal foot and ankle BME during imatinib; symptoms resolved after drug discontinuation and bisphosphonate therapy, and disease control was re-established with bosutinib without recurrence of BME. All patients underwent molecular monitoring and mutational analysis to guide safe therapeutic switching. Discussion: Temporal association across cases and the differential kinase profiles of implicated drugs suggest PDGFR (and to a lesser extent, c-KIT) inhibition as a plausible mechanistic driver of TKI-associated BME. PDGFR-β blockade may impair pericyte-mediated microvascular integrity, increase interstitial fluid extravasation, and alter osteoblast/osteoclast coupling, promoting intramedullary edema. Management combining MRI confirmation, temporary TKI suspension, bone-directed therapy (bisphosphonates, vitamin D/calcium), symptomatic care, and, when required, therapeutic switching to a PDGFR-sparing agent (bosutinib) led to clinical recovery and preservation of leukemia control in our series. Conclusions: BME is an underrecognized, potentially disabling, TKI-related adverse event in CML. Prompt recognition with targeted MRI and a multidisciplinary, stepwise approach that includes temporary TKI adjustment, bone-directed therapy, and consideration of PDGFR-sparing alternatives can mitigate morbidity while maintaining disease control. Prospective studies are needed to define incidence, risk factors, optimal prevention, and management strategies. Full article

The modern power system is becoming increasingly complex, and the uncertainty in the operation of each link has intensified the possibility of risks emerging. Therefore, efficient risk prediction is of great significance for maintaining the reliable operation of the entire system. In this paper, to address the uncertainty and spatiotemporal coupling in local power grids with renewable integration, an integrated “state prediction–risk assessment–early warning” framework is proposed. A spatiotemporal graph neural network is used to predict node voltage, power, and phase angles under topological constraints, where physics-aware graph attention, disturbance-enhanced temporal modeling, and prediction-smoothing constraints are jointly incorporated to improve sensitivity to renewable fluctuations and ensure stable multi-step forecasting. Furthermore, voltage deviation, power fluctuation, and phase-angle variation are quantified to compute a composite risk index via normalized softmax weighting, with factor contributions enhancing interpretability. Test results on the IEEE 33-bus system under diverse disturbances show improved accuracy and stability over baselines, showing consistently lower MAE/RMSE than three baselines across all disturbance scenarios while pinpointing high-risk nodes and causes, highlighting good engineering potential. Full article

This study introduces a sensor-centric cybersecurity framework for railway infrastructure that extends Failure Mode and Effects Analysis (FMEA) from traditional reliability evaluation into the domain of cyber-induced failures affecting data integrity, availability and authenticity. The contribution lies in bridging regulatory obligations of the NIS2 Directive with field-layer monitoring by enabling risk indicators to evolve dynamically rather than remain static documentation artefacts. The approach is demonstrated using a scenario-based dataset collected from approximately 250 trackside, rolling-stock, environmental and power-monitoring sensors deployed over a 25 km operational segment, with representative anomalies generated through controlled spoofing, replay and injection conditions. Risk was evaluated using RPN scores derived from Severity–Occurrence–Detectability scales, while anomaly-detection performance was observed through detection-latency variation, changes in RPN distribution, and qualitative responsiveness of timestamp-based alerts. Instead of presenting a fixed benchmark, the results show how evidence from real sensor streams can recalibrate O and D factors in near-real-time and reduce undetected exposure windows, enabling measurable compliance documentation aligned with NIS2 Article 21. The findings confirm that coupling FMEA with streaming telemetry creates a verifiable risk-evaluation loop and supports a transition toward continuous, evidence-driven cybersecurity governance in railway systems. Full article

Conventional tunnel face stability models are constrained by idealized steady-state seepage assumptions, one-dimensional formulations for inherently three-dimensional flow, and the neglect of transient filter-cake effects. To address these limitations, this study focuses on blowout failure triggered by excess slurry pressure in slurry pressure balance shield tunneling. We establish a limit-analysis framework that couples slurry infiltration with transient seepage, developing a work rate-balance formulation and a three-dimensional rotational failure mechanism. This framework incorporates heterogeneous, time-dependent filter-cake pressure transfer and the spatiotemporal evolution of pore pressure—key factors overlooked in traditional models. Transient seepage simulations demonstrate that the spatiotemporal heterogeneity of the dynamic filter cake provides the fundamental pressure basis for blowout failure. A prominent hydraulic gradient within the potential core failure zone (Z/R ≤ 2.0, Y/R ≤ 2.0) drives failure initiation and propagation, with the vertical hydraulic gradient in the high-risk subregion (Z/R < 0.5) reaching values as high as 12. Results indicate that passive failure risk increases markedly when excess slurry pressure exceeds 200 kPa, accompanied by a sharp decline in the safety factor. Validation against the Heinenoord No. 2 Tunnel case confirms that the proposed three-dimensional model more accurately captures 3D seepage characteristics and critical failure pressures compared to traditional wedge–prism approaches. By overcoming steady-state and one-dimensional simplifications, this framework deepens the understanding of blowout evolution and provides theoretical guidance for the rational control of slurry pressure and improved tunnel-face stability assessment under complex transient conditions. Full article

Rice lodging is a major agricultural disaster that reduces yield and quality. Accurate lodging detection and causal analysis are essential for disaster mitigation and precision management. To overcome the limited coverage and low automation of conventional approaches, we propose MSR-LodfNet, an enhanced semantic-segmentation model driven by multi-scale remote-sensing imagery, enabling high-precision lodging mapping from regional to field scales. The study selected 13 state-owned farms in Jiansanjiang, Heilongjiang Province, and jointly used PlanetScope satellite images (3 m) and UAV images (0.2 m) to build an integrated workflow of “satellite macro-monitoring, UAV fine verification, and agronomic factor coupling analysis.” The model synergistically optimizes WFNet, DenseASPP multi-scale context enhancement, and Condensed Attention, markedly improving feature extraction and boundary recognition under multi-source imagery. Experimental results show that the model achieves mIoU 84.34% and mPA 93.31% on UAV images and mIoU 81.96% and mPA 90.63% on PlanetScope images, demonstrating excellent cross-scale adaptability and stability. Causal analysis shows that the high-EVI range is significantly positively correlated with lodging probability; its risk is about 6 times that of the low-EVI range, and the lodging probability of direct-seeded rice is about 2.56 times that of transplanted rice, indicating that it may be associated with a higher lodging risk. The results demonstrate that multi-scale remote sensing combined with agronomic parameters can effectively support the mechanism analysis of lodging disasters, providing a quantitative basis and technical reference for precision rice management and lodging-resistant breeding. Full article