Search Results (848)

Natural and human-induced disasters can cause significant loss of life and property, particularly at the building level, highlighting the need for effective early detection, real-time monitoring, and rapid post-disaster response. Current disaster management approaches largely rely on citizen reports and manual observations, which may lead to delays and inefficient resource allocation, especially in large-scale events. This study proposes a secure, modular, multi-layer disaster monitoring and decision-support architecture that integrates sensor-based building-edge monitoring units deployed at both the building and apartment levels with a central emergency monitoring system. The architecture comprises three main layers: edge sensing, secure cellular communication, and central decision-making. Building-edge monitoring units collect data related to structural motion and inclination indicators, fire, flooding, and gas leaks, perform preliminary processing, and transmit aggregated data securely to the central system. Communication security is ensured through a certificate-based authentication mechanism supported by a dedicated certificate authority, reducing the risk of unauthorized access and fraudulent data injection. The central system performs automated event detection and separately evaluates physical building condition and communication status, enabling prioritized response planning. To evaluate feasibility, a two-building prototype was implemented and tested through scenario-based experiments involving two independently operating building-edge monitoring units connected to the same central monitoring system. The prototype demonstrated concurrent secure data acquisition and central aggregation from two buildings; however, district- and regional-scale performance requires further validation through larger-scale controlled load tests and field deployments. Under laboratory conditions, the prototype demonstrated sensor-data acquisition, authenticated transmission, and centralized event classification. End-to-end latency and building-edge monitoring unit power consumption were also measured; however, the prototype was not validated under environmental conditions representative of real disasters. Overall, the findings suggest that sensor-based, secure, and centralized monitoring systems may complement traditional disaster management approaches. Full article

Accurate detection in the early stages of a fire is a crucial prerequisite for the efficient implementation of fire suppression and emergency rescue operations. Its accuracy and timeliness directly affect the control of disaster loss severity. Traditional fire detection methods mainly include three categories, which are manual inspection, sensor detection, and visual recognition. However, manual inspection is restricted by labor costs and time efficiency, making it difficult to achieve large-scale, high-frequency and real-time fire monitoring. Sensor detection is easily interfered by environmental factors such as temperature, humidity, and dust, leading to frequent false alarms and missed alarms. Visual recognition technology has shortcomings in aspects such as detailed feature perception, dynamic scene modeling, and reasoning robustness in complex environments, making it difficult to meet the requirements of high-precision detection. To address these issues, this study innovatively proposes a lightweight fire and smoke detection model based on semantic enhancement and frequency domain perception modeling, which is named the SemaFire you only look once (SemaFire-YOLO) model. The model constructs a large language and vision assistant (LLaVA) semantic guidance module, which uses a large language model to understand and guide the semantic features of images, thereby enhancing the saliency representation intensity of small and weak target regions. Then, a Haar wavelet-based downsampling module is adopted, which compresses spatial information while preserving high-frequency features such as flame edges and smoke textures, improving the accuracy of target recognition. Next, the convolution modulation mechanism is introduced to replace the traditional attention mechanism, enhancing the overall modeling efficiency and reducing computational overhead. Finally, a Dynamic Tanh normalization module is adopted to replace the batch normalization module in the traditional YOLO algorithm, strengthening the model’s representation stability and reasoning robustness under unstable input distributions. Experimental results show that the SemaFire-YOLO model achieves a mean average precision (mAP@0.5) of 64.30% on the fire image dataset, which is 0.8, 2.0, 0.6, and 3.8 percentage points higher than that of mainstream models such as YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n, respectively. It exhibits better boundary detection capability and practical deployment potential. Through visual analysis, the results indicate that the improved SemaFire-YOLO model achieves more accurate detection and higher confidence in actual complex scenarios, further verifying the model’s robustness and accuracy in complex scenarios such as low contrast and dynamic fire conditions. Full article

Efficient and resilient humanitarian logistics is critical for smart cities facing large-scale disasters, where infrastructure disruptions, uncertain demand, and time-critical deliveries complicate operational planning. This study proposes a multi-echelon vehicle-routing framework that integrates trucks, electric unmanned aerial vehicles (UAVs), and micromobility systems under uncertainty. Demand and travel-time variability are modeled through scenario-based representations, while delivery flexibility is captured through triangular fuzzy time windows defined by earliest-acceptable, preferred, and latest-tolerable delivery times. The problem is formulated as a four-objective optimization model that minimizes total cost, response time, CO₂ emissions, and fuzzy lateness. To solve the resulting highly constrained multi-objective problem, NSGA-II and NSGA-III are adapted with problem-specific repair operators, including capacity-splitting, range-feasibility correction, and fuzzy time-shift adjustment mechanisms, and embedded in a simulation-based evaluation framework. The proposed approach is validated using a geo-referenced earthquake scenario in Istanbul, constructed from open-source GIS, traffic, and demographic data. The computational results show that evolutionary methods generate feasible solutions within minutes, whereas exact optimization approaches fail to converge for realistic instances. Compared with NSGA-II, NSGA-III achieves superior performance, including a 9% higher hypervolume and improved robustness under stress-test scenarios. Furthermore, the hybrid truck–UAV–micromobility strategy reduces average cost by up to 30%, delivery time by 43%, and CO₂ emissions by 65% relative to a truck-only baseline, while eliminating fuzzy lateness. These findings demonstrate that evolutionary multi-objective optimization provides an effective and scalable decision-support framework for uncertainty-aware and sustainable humanitarian logistics in smart cities. Full article

Post-disaster debris detection is important for rapid damage assessment, emergency response, and recovery planning. However, debris objects in aerial imagery are often fragmented, irregularly shaped, partially occluded, and visually confused with shadows, vegetation, roofs, vehicles, and damaged structures. This study proposes YOLO-SDD, a YOLO-based Shape-Guided Debris Detector built on YOLOv8 for localized debris identification in high-resolution post-disaster aerial imagery. YOLO-SDD combines a high-resolution P2 detection pathway with a shape-guided feature refinement module that uses box-supervised pseudo-mask and pseudo-boundary cues to refine P2-level features before final debris detection. A multi-event aerial imagery dataset was constructed from NOAA Emergency Response Imagery using images collected after hurricanes and a tornado in the United States. The model was evaluated using an image-level split, an event-level holdout test, component-level ablation studies, COCO-style scale-specific evaluation, and multi-seed stability analysis. On the image-level test set, YOLO-SDD achieved a precision of 0.959, recall of 0.933, mAP@50 of 0.970, and mAP@50:95 of 0.755, remaining competitive with larger YOLO-family models at lower computational complexity. In the event-level holdout test, YOLO-SDD achieved an AP@50 of 0.80 and an F1 score of 0.79, outperforming the YOLOv8s baseline and the selected large YOLO-family comparison model. The scale-specific evaluation showed improved AP@50 and recall for small and medium debris groups, while failure cases remained associated with shadows, vegetation, low contrast, and highly fragmented debris. The results indicate that shape-guided P2 refinement can improve localized debris screening under the tested conditions, although broader datasets, workflow integration, and human-in-the-loop validation are still needed before operational deployment. Full article

This article compares urban planning affecting historic buildings in Berlin and Stockholm. It examines some cases of preservation, demolition and reconstruction of historic buildings: the Hansa Quarter and the Palace of the Republic in Berlin, and Sergels torg with the House of Culture and Vällingby in Stockholm. Today, while Berlin has opted for reconstruction in several cases, Stockholm is preserving the status quo achieved by the large-scale demolitions during the 1950s and 1960s. Different historic approaches in urban planning are subsumed under the categories “architecture as wellbeing” and “the automotive city.” The policy cycle serves as a framework for a qualitative content analysis of debates on urban planning in both city councils. The article tests the hypothesis whether war destructions present in Berlin, but not in Stockholm, can explain the lack of plans for reconstruction of historic buildings in Stockholm. The examination of historic developments and current legislation on German and Swedish cultural policy and the case studies of the above-named buildings yield the result that the hypothesis is proven wrong. Instead, possible explanations for the lack of will to reconstruct in Stockholm are Swedish legal tradition since the 19th century, which provides little and weak protection to historic buildings, and the “people’s home” ideology shaping the Swedish self-perception as a modern nation. International legislation on monument protection such as the ICOMOS-ICCROM Guidance on Post-Disaster and Post-Conflict Recovery and Reconstruction (2023), which becomes ever more encompassing, will perhaps introduce a future policy change. Full article

As one of the key vehicles to integrating culture and tourism in urban and rural development, cultural tourism-oriented characteristic towns are increasingly facing natural and social disaster risks caused by global climate variability, large-scale expansion of town areas, and intensified human engineering activities. In particular, characteristic towns that have rapidly developed through tourism based on historical and cultural heritage face challenges such as compact layouts of ancient architectural complexes, extensive outward expansion of newly developed areas, and inadequately planned emergency evacuation systems—making them ill-equipped to cope with increasingly uncertain disaster risks. In response to these issues, this study takes Zhangguying Town in Yueyang County, Hunan Province, as a case study. Through field investigations, interviews, and GIS-based hydrological simulations, the research systematically identifies the characteristics and influencing factors of disaster risks in the town. It also reveals the core dilemmas confronting current disaster prevention planning and proposes strategies such as enhancing chain disaster prevention measures, promoting micro-scale, site-specific disaster prevention retrofitting, and establishing a multi-scale disaster prevention system through “point-line” linkages. By reducing disaster risks, preserving cultural heritage, and optimizing emergency response capacities, this research effectively supports the sustainable development of cultural tourism-oriented characteristic towns from a disaster prevention perspective, enabling these towns to withstand natural hazards while sustaining their historical, cultural, and socio-economic functions. The findings provide a theoretical basis and methodological reference for comprehensive disaster prevention planning in similar cultural tourism-oriented characteristic towns. Full article

Remote sensing change detection (CD) aims to localize land-surface changes from bi-temporal imagery and plays an important role in applications such as urban monitoring, disaster assessment, and environmental analysis. In high-resolution scenarios, CD performance is often degraded by cross-temporal appearance inconsistency, large variations in target scale, and boundary ambiguity introduced during multi-level decoding. To address these challenges, we propose MIGA-Net, an end-to-end framework that jointly models spatio-temporal interaction, adaptive multi-scale context aggregation, and hierarchical boundary refinement. Specifically, the Spatio-Temporal Graph Interaction Module (ST-GIM) combines interactive attention and graph reasoning to suppress pseudo-changes caused by illumination or seasonal shifts; the Adaptive Gated Context Pyramid Module (AGCP) performs content-driven scale selection and regulates context injection through a gated residual mechanism to reduce noise amplification; and the Hierarchical Boundary-Aware Refinement Module (HBAR) integrates semantic channel filtering and explicit boundary attention for progressive contour recovery. Experiments on LEVIR-CD, WHU-CD, and SYSU-CD demonstrate that MIGA-Net achieves F1 scores of 91.84%, 92.52%, and 82.92%, and IoU scores of 84.91%, 86.08%, and 70.83%, respectively. The proposed method yields consistent improvements in both quantitative metrics and structural boundary quality, indicating its effectiveness for robust pseudo-change suppression and structurally faithful prediction in high-resolution remote sensing CD. Full article

In large-scale seismic scenarios, the operational continuity of evacuation shelters is frequently compromised by limited governmental capacity, necessitating the strategic integration of Disaster Relief Volunteers (DRVs). Traditional assessments often rely on coarse regional aggregates, overlooking the critical spatial mismatch between volunteer availability and localized demand. In this study, a high-resolution, spatially explicit framework was developed to evaluate urban operational resilience in New Taipei City. Utilizing the TERIA platform, a nocturnal magnitude 6.8 Shanjiao Fault rupture was simulated, identifying that approximately 460,000 individuals would be affected, with shelter demand reaching 300,000—double the current capacity. Service areas were delineated using ArcGIS Network Analyst (Dijkstra’s algorithm) based on an 800 m walking threshold, further refined by a secondary assignment rule to redistribute “shadow demand” from peripheral populations. Quantitative analysis of the newly introduced “DRV shortfall” metric reveals that 74% of shelters (148/200) face concurrent spatial and manpower saturation. Notably, 42 analysis units lack resident volunteers entirely, with the most severe shortfall magnitude reaching 361 DRVs at a single “high-risk convergence node”. These results uncover a profound deficiency in urban disaster resilience driven by significant spatial mismatch. This research contributes a three-fold advancement: (i) the high-resolution coupling of volunteer residential data with dynamic demand patterns; (ii) the formalization of the DRV shortfall as a standardized metric for resource adequacy; and (iii) the formulation of a strategic policy framework for multi-shelter activation sequencing, “Support Hub” designation, and resource synchronization in hyper-dense urban environments. Full article

Background: Adolescents exposed to large-scale natural disasters are at increased risk for psychological distress, yet individual psychological characteristics associated with stress and trauma responses remain insufficiently explored. Self-compassion has been suggested as a relevant factor in stress regulation and emotional adjustment. This study aimed to examine the associations between self-compassion, perceived stress, and trauma-related reactions in adolescents exposed to an earthquake. Methods: The sample consisted of 3362 adolescents (57.3% female) aged 14–17 years (mean age = 15.01 ± 0.97 years) who were exposed to the 2020 Elazığ–Sivrice earthquake. Participants completed the Perceived Stress Scale (PSS), the Self-Compassion Scale–Short Form (SCS-SF), and the Child Post-Traumatic Stress Reaction Index (CPTS-RI). Group comparisons and correlation analyses were conducted to examine relationships among the study variables and earthquake-related characteristics. Results: Adolescents residing in severely and moderately damaged areas reported significantly higher perceived stress and lower self-compassion compared to those in mildly damaged areas. Self-compassion was negatively correlated with trauma-related symptoms, whereas perceived stress showed a positive association with trauma reactions. Female adolescents reported higher perceived stress and trauma-related symptoms and lower self-compassion than males. Conclusions: The findings underscore the relevance of self-compassion in understanding adolescents’ psychological responses to earthquake-related stress and trauma and suggest that self-compassion may be considered in post-disaster psychological support efforts. Full article

With the gradual improvement of residential electricity reliability, the lower design strength of the distribution network makes it more prone to large-scale power outages in resisting natural disasters. Among them, the cold load start-up effect will significantly prolong the recovery time and affect reliability indicators. Based on this, this article proposes a reliability evaluation method for urban distribution networks based on a lightning search algorithm, which is used for optimal recovery planning and reliability calculation of urban power systems with highly concentrated load under constant temperature control. Firstly, a delay index model is used to establish a time-sharing power demand calculation model for cold load start-up events, and an optimal recovery model with the goal of minimizing recovery time and its corresponding constraints are proposed. Then, the cold load start-up event is incorporated into the Monte Carlo simulation platform for reliability assessment, and the lightning search algorithm is used to develop the optimal recovery plan. The recovery time and sequence are determined based on the duration of the power outage and the electricity demand at the time of recovery. Finally, the test distribution system was used to verify that the optimal recovery plan considering cold load start events does not violate the constraint conditions, and the stability and convergence robustness of the lightning search algorithm are stronger than the current mainstream algorithms. It can effectively improve the reliability of the distribution grid when considering cold load start events. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Landfill tire fires are complex environmental disasters generating toxic pollutants with severe health risks. This study quantified emission dynamics and toxicological consequences of a large-scale tire fire in an urban ecosystem. A comprehensive source-to-receptor approach was applied, integrating Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric dispersion modeling with comparison against air quality monitoring data. Soil samples collected from the fireground and surrounding urban allotment gardens were analyzed for tire-specific tracers (Zn) and 16 priority polycyclic aromatic hydrocarbons (PAHs). Human health risks were assessed using Incremental Lifetime Cancer Risk (ILCR), Toxic Equivalency Quotient (TEQ), and Mutagenic Equivalency Quotient (MEQ) metrics. Fire emissions were dominated by particulate matter (PM₁₀: 1.34 t) and PAHs (17.7 kg). Soil at the fire site showed severe contamination (Σ PAHs: 148.9 mg/kg), with benzo[a]pyrene as the primary carcinogen. The cumulative ILCR for children reached 9.7 × 10⁻⁴, exceeding the commonly used upper regulatory benchmark of 10⁻⁴. Dermal contact was identified as the dominant exposure pathway for pyrogenic PAHs. Elevated risk levels persisted at distal residential sites (ILCR: 10⁻⁵–10⁻⁴), indicating long-term environmental contamination Ecological risk quotients (RQ) exceeded unity for PAHs across all fire-impacted locations and for Zn and Cu in the immediate vicinity of the fire scene. These findings demonstrate that acute tire fire events can evolve into persistent terrestrial health hazards, highlighting the critical role of dermal exposure in PAH uptake and the need for long-term environmental monitoring and adaptive land-use management strategies to mitigate chronic health risks in urban populations. Full article

Min County experiences intense debris flow activity due to extreme weather and geological events. This study analyzes debris flow activity in Min County using GIS spatial analysis, time-series statistics, correlation analysis, periodic fitting, and field investigations across four event-based key periods (2002, 2012, 2013, and 2020). Long-term meteorological records (1951–2020) are introduced to support climatic trend analysis. Results indicate that stratigraphic lithology and fault tectonics control about 85–90% of the spatial distribution of debris flows, while extreme short-duration rainstorms trigger large-scale outbreaks and strong earthquakes further intensify activity. The high-occurrence cycle of debris flows (7–8 years) does not fully align with the annual wetness cycle (12 years). On a short time scale (years to decades), extreme earthquakes and rainstorms exert more significant impacts than normal precipitation patterns. This study preliminarily infers potential future peak periods of debris flows in Min County, with uncertainty from climate fluctuations and uncertain seismic events considered. The coupled mechanism of seismic weakening and rainfall triggering, together with lag-time characteristics, is revealed to support disaster prevention and mitigation. Full article

The Kubuqi Desert serves as a critical zone for both renewable energy development and ecological management in China. Large-scale photovoltaic (PV) deployment has fundamentally altered the regional underlying surface, impacting near-surface wind–sand dynamics. To elucidate these disturbance mechanisms, we selected three representative surfaces—a PV area, a resource base, and Qixing Lake—and conducted field observations from September to December 2023 using meteorological towers and wind erosion sensors. Results indicate that all surfaces significantly attenuated near-surface wind speeds by over 30% through modified flow field structures. A strong linear positive correlation existed between wind speed and friction velocity (R² ≈ 0.99). Notably, for the same friction velocity, the actual wind speed required to initiate sand movement was lowest in the PV zone (high k) and highest at Qixing Lake (low k), signifying enhanced surface stability due to PV infrastructure and moisture. Threshold analysis revealed distinct initiation speeds: >6.0 m·s⁻¹ in peripheral quicksand, >4.3 m·s⁻¹ in inter-panel zones, and >4.6 m·s⁻¹ beneath panels. The tilted PV panels accelerate airflow downward, generating cyclonic vortices that intensify sand particle impacts under and between panels. This study reveals the tri-dimensional mechanism of wind regulation–sand suppression–stability enhancement, providing theoretical support for mitigating wind–sand disasters while advancing green energy in desert regions. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article