Search Results (56)

Accurate detection of slope cracks plays a crucial role in early landslide disaster warning; however, traditional approaches often struggle to identify fine and irregular cracks. This study introduces a novel deep learning model, Crack-Net, which leverages a multi-modal feature fusion mechanism and is developed using transfer learning. To resolve the blurred representation of small-scale cracks, a nonlinear frequency-domain mapping module is employed to decouple amplitude and phase information, while a cross-domain attention mechanism facilitates adaptive feature fusion. In addition, a deep feature fusion module integrating deformable convolution and a dual attention mechanism is embedded within the encoder–decoder architecture to enhance multi-scale feature interactions and preserve crack topology. The model is pre-trained on the CrackVision12K dataset and fine-tuned on a custom dataset of slope cracks, effectively addressing performance degradation in small-sample scenarios. Experimental results show that Crack-Net achieves an average accuracy of 92.1%, outperforming existing models such as DeepLabV3 and CrackFormer by 9.4% and 5.4%, respectively. Furthermore, the use of transfer learning improves the average precision by 1.6%, highlighting the model’s strong generalization capability and practical effectiveness in real-world slope crack detection. Full article

Background/Objectives: Non-Communicable Diseases (NCDs) place an excessive strain on health systems in disaster-affected settings and may lead to a parallel public health emergency lasting months or years after a disaster. Although NCDs are increasingly recognized as a major challenge in disasters and humanitarian emergencies, a dedicated and standardized response plan is missing, as well as a shortage of evidence-based guidelines for NCD management in theses contexts. Over the years, Emergency Medical Teams (EMTs) have traditionally been deployed to manage acute conditions such as trauma and infectious diseases that quickly impact health systems. However, greater attention is needed to address acute exacerbation of NCDs and to ensure continuity of care for people with chronic health needs in disasters and emergencies. Methods: We conducted a scoping review exploring the EMTs’ management of chronic NCDs during disasters and humanitarian emergencies, in order to identify the strategies adopted, the challenges faced, and the recommendations provided to address this health problem. The online databases PubMed, Scopus, and EBSCO were searched to identify relevant papers. Results: After screening the papers against the eligibility criteria, 17 publications were retrieved. Five different areas of intervention concerning EMTs and NCDs management were identified: (i) EMTs pre-departure preparation, operational time, and length of stay; (ii) EMTs staff composition and training; (iii) EMTs logistics; (iv) EMTs integration with local health services; (v) EMTs clinical data record. Conclusions: The findings emerging from this study showed that NCDs significantly impact disaster response in different settings, underlining the need to implement a range of EMTs activities to guarantee assistance for chronic health needs. In view of strengthening the ability of health systems to cope with the NCDs’ burden, the EMTs’ initiatives should be considered as a bridge between the support provided during the acute phase of an emergency and the continuation of care ensured by the system in its early recovery phase. Full article

Engineered loess-filled gullies, which are widely distributed across China’s Loess Plateau, face significant stability challenges under extreme rainfall conditions. To elucidate the regulatory mechanisms of antecedent rainfall on the erosion and failure processes of such gullies, this study conducted large-scale flume experiments to reveal their phased erosion mechanisms and hydromechanical responses under different antecedent rainfall durations (10, 20, and 30 min). The results indicate that the erosion process features three prominent phases: initial splash erosion, structural reorganization during the intermission period, and runoff-induced gully erosion. Our critical advancement is the identification of antecedent rainfall duration as the primary “pre-regulation” factor: short-duration (10–20 min) rainfall predominantly induces surface crack networks during the intermission, whereas long-duration (30 min) rainfall directly triggers substantial holistic collapse. These differentiated structural weakening pathways are governed by the duration of antecedent rainfall and fundamentally control the initiation thresholds, progression rates, and channel morphology of subsequent runoff erosion. The long-duration group demonstrated accelerated erosion rates and greater erosion amounts. Concurrent monitoring demonstrated that transient pulse-like increases in pore-water pressure were strongly coupled with localized instability and gully wall failures, verifying the hydromechanical coupling mechanism during the failure process. These results quantitatively demonstrate the critical modulatory role of antecedent rainfall duration in determining erosion patterns in engineered disturbed loess, transcending the prior understanding that emphasized only the contributions of rainfall intensity or runoff. They offer a direct mechanistic basis for explaining the spatiotemporal heterogeneity of erosion and failure observed in field investigations of the engineered fills. The results directly contribute to risk assessments for land reclamation projects on the Loess Plateau, underscoring the importance of incorporating antecedent rainfall history into stability analyses and drainage designs. This study provides essential scientific evidence for advancing the precision of disaster prediction models and enhancing the efficacy of mitigation strategies. Full article

(1) Background: The COVID-19 pandemic caused widespread upheaval, presenting unique challenges for pregnant and postpartum women, who were already in a particularly vulnerable phase. As the COVID-19 pandemic and its public health response unfolded, it became crucial for clinicians and researchers to explore postpartum depression within the context of a global crisis. (2) Methods: We used data from two cross-sectional surveys of postnatal women conducted in our tertiary academic public hospital during the SARS-CoV-2 pandemic and the post-pandemic period, based on the retrospective assessments of two samples of mothers, each including 860 postpartum women. Our research has been conducted with the scope of evaluating postpartum depression disorder during and after the COVID-19 pandemic by using comparable data across time. (3) Results: The prevalence of postpartum depression was significantly higher among women who gave birth during the COVID-19 pandemic (major postpartum depressive disorder: 54.19%, minor depressive disorder: 15.58%), compared to pre-pandemic rates (10% in developed countries and 21–26% in developing countries) and post-pandemic rates (major depressive disorder 10.12%, minor depressive disorder 10.93%). The results of our research indicate that the COVID-19 pandemic had a major negative impact on perinatal mental health and, moreover, might have sped up an existing trend of the increasing prevalence of postpartum depression, despite the fact that the risk factors for postpartum depression disease remained consistent before, during, and after the pandemic. (4) Conclusions: Strengthening support systems during periods of heightened risk, such as during a pandemic, is crucial; therefore, policymakers and health planners should prioritize the mental health of this vulnerable group during global health crises or natural disasters, ensuring the implementation of effective mental health screenings, identification, enhanced support, follow-up, and reassurance measures to better address the challenges faced by susceptible postpartum women in future similar situations. Full article

The escalating frequency and complexity of natural disasters highlight the urgent need for deeper insights into how individuals and communities perceive and respond to risk information. Yet, conventional research methods—such as surveys, laboratory experiments, and field observations—often struggle with limited sample sizes, external validity concerns, and difficulties in controlling for confounding variables. These constraints hinder our ability to develop comprehensive models that capture the dynamic, context-sensitive nature of disaster decision-making. To address these challenges, we present a novel multi-stage simulation framework that integrates Large Language Model (LLM)-driven social–cognitive agents with well-established theoretical perspectives from psychology, sociology, and decision science. This framework enables the simulation of three critical phases—information perception, cognitive processing, and decision-making—providing a granular analysis of how demographic attributes, situational factors, and social influences interact to shape behavior under uncertain and evolving disaster conditions. A case study focusing on pre-disaster preventive measures demonstrates its effectiveness. By aligning agent demographics with real-world survey data across 5864 simulated scenarios, we reveal nuanced behavioral patterns closely mirroring human responses, underscoring the potential to overcome longstanding methodological limitations and offer improved ecological validity and flexibility to explore diverse disaster environments and policy interventions. While acknowledging the current constraints, such as the need for enhanced emotional modeling and multimodal inputs, our framework lays a foundation for more nuanced, empirically grounded analyses of risk perception and response patterns. By seamlessly blending theory, advanced LLM capabilities, and empirical alignment strategies, this research not only advances the state of computational social simulation but also provides valuable guidance for developing more context-sensitive and targeted disaster management strategies. Full article

Recent major disasters, including the COVID-19 pandemic and floods in Europe, highlight the unpredictability of crises and the necessity for systemic preparedness at all levels of crisis management, including pre-hospital emergency medical services. Paramedics observed, under these challenging conditions (but not exclusively during them), cases of insufficient knowledge and skills in providing pre-hospital medical care, which lead to inadequate or prolonged decision making in delivering assistance. For this reason, the authors aimed to determine the extent to which such situations occur and to examine their causes, focusing on potential gaps and shortcomings in the education of paramedics. This study examines the impact of educational systems on the professional preparedness of paramedics in V4 European countries (the Slovak Republic, the Czech Republic, Hungary, and Poland) during pre-hospital care through a questionnaire survey. A survey involving over 1600 respondents revealed significant disparities in perceived knowledge and skill gaps, with Poland demonstrating the highest deficiencies (78%) and the Slovak Republic the lowest (57%). Key factors influencing these gaps included the frequency of external educational and training activities, years of experience, and expertise in managing critical conditions. The findings underscore the importance of innovative technologies, such as simulations and virtual reality, in enhancing paramedic training, along with integrating digital solutions across all phases of disaster management. Recommendations focus on strengthening system resilience, fostering interdisciplinary approaches, and improving system sustainability and adaptability. By using the results obtained and leveraging technological advances, the study aims to contribute to more effective crisis preparedness, population protection, and sustainable development goals (SDGs), namely (3, 4, 9, and 11), especially in the field of crisis management. This reinforces the broader role of SDGs in building more robust, future-ready disaster management frameworks. Full article

The climate crisis, combined with the COVID-19 lockdown measures, exacerbated pre-existing psychological conditions among young people experiencing climate anxiety due to a lack of information and a diffuse sense of powerlessness. The current study aimed to find correlations between the health status of a community, the environmental determinants among youths, and how these affect their vision of climate change and their mental health. An exploratory survey was conducted among people aged between 18 and 33 years old from three continents, with a focus on emotional states related to natural disasters occurring in their regions. The online survey consisted of six questions. The pilot phase results showed that more females from India experienced stressful situations related to climate change, and that respondents aged between 18 and 20 years old were more informed about eco-anxiety. Given its growing frequency among young adults, further studies should be conducted to address the problem and create alternatives and coping mechanisms such as climate action. Full article

Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches. Full article

Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA. Full article

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics. Full article

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study’s methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations. Full article

The failure of rock in cold regions due to repeated freeze–thaw (F-T) cycles and periodic load-induced fatigue damage presents a significant challenge. This study investigates the evolution of the multi-scale structure of fractured granite under combined freeze–thaw (F-T) cycles and periodic loading and develops a constitutive damage model. The results indicate that after F-T cycles, network cracks develop around pre-existing cracks, accompanied by block-like spalling. After applying the fatigue load, the nuclear magnetic resonance (NMR) T₂ spectrum shifts to the right, significantly increasing the amplitude of the third peak. The freeze–thaw process induces a “liquid–solid” phase transition, weakening the original pore structure of the rocks and leading to meso-damage accumulation. The pores in fractured granite progressively enlarge and interconnect, reducing the rock’s load-bearing capacity and fatigue resistance. The combined effects of F-T cycles and periodic loading induce particle movement and alter fracture modes within the rock, subsequently affecting its macro-damage characteristics. The theoretical curves of the constitutive model align with the experimental data. The findings can serve as a theoretical reference for preventing and controlling engineering disasters in fractured rock masses in cold regions. Full article

Memorial facilities are one of the crucial places where citizens conduct activities facilitating disaster risk reduction (DRR). However, previous studies have primarily focused on the post-construction phase of official memorial facilities, neglecting the citizen activities collaborating with the official memorial construction process before and during the construction process. This research gap is important considering the urgency of disaster-affected regions to recover from spatial, social, and psychological voids while working towards the goal of DRR, including the efforts of citizens in the official efforts. This study addresses this gap by examining the case of the official memorial park in Ishinomaki, Tohoku region, following the Great East Japan Earthquake. Here, local citizens actively participated in activities before, during, and after park construction, engaging with official efforts. Data were gathered from various online sources to capture activity, space, and management information. Employing a mixed methods research approach, we conducted both quantitative analysis, counting labels of structural coding, and qualitative description of original texts. Our findings reveal that fostering mutual respect built on communication and collaborative tree-planting activities were crucial for maintaining the pre-existing citizen activities and collaborative construction during the official construction period. Additionally, the implementation of a collaborative regulation system was vital for integrating and managing spontaneous citizen activities to achieve the park’s intended objectives post-opening. In conclusion, we highlighted a framework elucidating the mechanisms through which these processes contribute to DRR across key phases of disaster risk management: preparedness, prevention, response, and recovery (PPRR). These insights are important in guiding efforts to engage citizens in DRR initiatives through recovery and reconstruction facilitated by memorial facilities. Full article

Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time. Full article

Wildfires and their long-term impacts on the environment have become a major concern in the last few decades, in which climate change and enhanced anthropogenic activities have gradually led to increasingly frequent events of such hazards or disasters. Geological materials appear to become more vulnerable to hazards including erosion, floods, landslides and debris flows. In the present study, the well-known 2017 wildfire and subsequent 2018 debris flows in the Montecito area of California are examined. It is found that the post-wildfire debris flows were initiated from erosion and entrainment processes and triggered by intense rainfall. The significant debris deposition in four major creeks in this area is investigated. Numerical modeling of the post-wildfire debris flows is performed by employing a multi-phase mass flow model to simulate the growth in the debris flows and eventual debris deposition. The debris-flow-affected areas estimated from the numerical simulations fairly represent those observed in the field. Overall, the simulated debris deposits are within 7% error of those estimated based on field observations. A similar simulation of the pre-wildfire scenario indicates that the debris would be much less significant. The present study shows that proper numerical simulations can be a promising tool for estimating post-wildfire erosion and the debris-affected areas for hazard assessment and mitigation. Full article