Search Results (69)

In both scientific and industrial fields, there has been a notable increase in attention toward High-Altitude Pseudo-Satellites (HAPSs) in recent years. This surge is driven by their distinct advantages over traditional satellites and Remotely Piloted Aircraft Systems (RPASs). These benefits are particularly evident in critical areas such as intelligent transportation systems, surveillance, remote sensing, traffic and environmental monitoring, emergency communications, disaster relief efforts, and the facilitation of large-scale temporary events. This review provides an overview of key aspects related to the propellers and propulsion systems of HAPSs. To date, propellers remain the most efficient means of propulsion for high-altitude applications. However, due to the unique operational conditions at stratospheric altitudes, propeller design necessitates specific approaches that differ from those applied in conventional applications. After a brief overview of the propulsion systems proposed in the literature or employed by HAPSs, focusing on both the technical challenges and advancements in this emerging field, this review integrates theoretical foundations, historical design approaches, and the latest multi-fidelity optimization techniques to provide a comprehensive comparison of propeller design methods for HAPSs. It identifies key trends, including the growing use of CFD-based simulations methodologies, which contribute to notable performance improvements. Additionally, the review includes a critical assessment of experimental methods for performance evaluation. These developments have enabled the design of propellers with efficiencies exceeding 85%, offering valuable insights for the next generation of high-endurance, high-altitude platforms. Full article

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

While previous studies have explored the use of digital technologies in cultural heritage site reconstruction, limited attention has been given to systems that simultaneously support cultural restoration and psychological healing. This study investigates how multimodal, deep learning–assisted digital technologies can aid displaced populations by enabling both digital reconstruction and trauma relief within virtual environments. A demonstrative virtual reconstruction workflow was developed using the Great Mosque of Aleppo in Damascus as a case study. High-precision three-dimensional models were generated using Neural Radiance Fields, while Stable Diffusion was applied for texture style transfer and localized structural refinement. To enhance immersion, Vector Quantized Variational Autoencoder–based audio reconstruction was used to embed personalized ambient soundscapes into the virtual space. To evaluate the system’s effectiveness, interviews, tests, and surveys were conducted with 20 refugees aged 18–50 years, using the Impact of Event Scale-Revised and the System Usability Scale as assessment tools. The results showed that the proposed approach improved the quality of digital heritage reconstruction and contributed to psychological well-being, offering a novel framework for integrating cultural memory and emotional support in post-disaster contexts. This research provides theoretical and practical insights for future efforts in combining cultural preservation and psychosocial recovery. Full article

Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics and supporting disaster recovery and reconstruction processes. In this study, a novel Large-Scale Flood Risk Assessment Model (LS-FRAM) is proposed, incorporating the dimensions of hazard, exposure, vulnerability, and coping capacity. Multi-source heterogeneous data are utilized for evaluating the flood risks. Soil erosion modeling is incorporated into the assessment framework to better understand the interactions between flood intensity and land surface degradation. An index system comprising 12 secondary indicators is constructed and screened using Pearson correlation analysis to minimize redundancy. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to determine the weights of the primary-level indicators, while the entropy weight method, Fuzzy Analytic Hierarchy Process (FAHP), and an integrated weighting approach are combined to calculate the weights of the secondary-level indicators. This model addresses the complexity of large-scale flood risk assessment and management by incorporating multiple perspectives and leveraging diverse data sources. The experimental results demonstrate that the flood risk assessment model, utilizing multi-source data, achieves an overall accuracy of 88.49%. Specifically, the proportions of areas classified as high and very high flood risk are 54.11% in Henan, 31.74% in Shaanxi, and 18.2% in Shanxi. These results provide valuable scientific support for enhancing flood control, disaster relief capabilities, and risk management in the middle and lower reaches of the Yellow River. Furthermore, they can furnish the necessary data support for post-disaster reconstruction efforts in impacted areas. Full article

The 6 February 2023 Kahramanmaraş earthquakes were devastating events that caused widespread destruction. This mixed-methods study examined post-traumatic growth (PTG) in volunteers who participated in the relief efforts. A total of 169 volunteers participated in the quantitative phase, completing a standardized PTG measure. In-depth interviews were conducted with 14 volunteers during the qualitative phase. The study found that gender had a significant effect on total PTG scores. Additionally, gender, earthquake experience, and volunteer organization were significant factors in the “change in self-concept” sub-dimension. Gender was the only significant factor in the “change in philosophy of life” sub-dimension. Qualitative analysis revealed that participants experienced trauma symptoms after the earthquake but also reported positive changes in self-concept and life philosophy. This study suggests that disasters can lead to PTG, despite the presence of trauma symptoms. Further research is needed to explore PTG in different disaster response groups. Full article

This study focuses on Vehicle-Integrated Photovoltaic (VIPV) strategy adopted as an energy supply vector in disaster scenarios. As a matter of fact, energy supply may be a very critical issue in a disaster context, when grid networks may be damaged. Emergency vehicles, including ambulances and trucks, as well as mobile units such as containers and operating rooms, can be equipped with photovoltaic modules and can serve as mobile emergency energy sources, supporting both vehicle operations and disaster relief efforts. A methodology was developed to estimate energy production under unpredictable disaster conditions, by adapting existing VIPV simulation approaches. Obtained results show that VIPV strategy, even under minimal daily energy generation, can be a useful aid for disaster resilience and emergency prompt response. Ambulance performance, analyzed for worst-case scenarios (e.g., December), shows that they can power medical devices for 1 to 15 h daily. Additionally, the ambulance can generate up to 2 $\mathrm{M}\mathrm{W}\mathrm{h}$ annually, reducing CO₂ emissions by up to 0.5 tons. In optimal configurations, mobile operating rooms can generate up to 120 times the daily energy demand for medical devices. Full article

The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced pre-trained large language models, which offer superior generative and multimodal understanding capabilities. Using a question-answering approach, we extract textual descriptions and category prediction probabilities for disaster scenarios, which are then used as input to our proposed Mamba Multimodal Disaster Recognition Network (Mamba-MDRNet). This model integrates a large pre-trained model with the Mamba mechanism, enabling the selection of the most reliable modality information as a robust basis for scene classification. Extensive experiments demonstrate consistent performance improvements across various visual models with heterogeneous architectures. Notably, integrating EfficientNet within Mamba-MDRNet yielded 97.82% accuracy for natural scene classification, surpassing the performance of the CNN (91.75%), ViT (94.50%), and ResNet18 (97.25%). These results highlight the potential of multimodal models combining large models and the Mamba mechanism for disaster type prediction. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article

As a critical component of lifeline engineering, bridges play a vital role in post-earthquake rescue and disaster relief efforts. The rapid repair of earthquake-damaged piers is essential to ensure the uninterrupted functionality of lifeline systems. This paper presents a novel method for the rapid repair of earthquake-damaged pier columns using steel sleeves, based on a multi-level fortification approach, integrating numerical simulation, structural design, and experimental research. In alignment with the multi-level fortification requirements, the structural form of the outer steel sleeves was designed, key influencing factors were analyzed, and a design scheme for the outer steel sleeve was proposed. Furthermore, a quasi-static test was conducted to evaluate the seismic performance of the pier columns before and after repair. The results indicate that the maximum horizontal load the pier can withstand after repair is approximately 40% higher than that before the damage. When the pier’s bearing capacity reaches its maximum value, the horizontal displacement increases from 29.15 mm to 95.65 mm, indicating a significant improvement in the seismic performance of the repaired pier. Failure initiates with the buckling of the brace, followed by the buckling of the steel sleeves, demonstrating a multi-stage failure mode. This mode satisfies the requirements of multi-level fortification, with enhanced ductility achieved while maintaining the pier column’s bearing capacity, thereby enhancing the protection of the foundation. Full article

A fresh paradigm for classifying current studies on flood management systems is proposed in this review. The literature has examined methods for managing different flood management activities from a variety of fields, such as machine learning, image processing, data analysis, and remote sensing. Prediction, detection, mapping, evacuation, and relief efforts are all part of flood management. This can be improved by adopting state-of-the-art tools and technology. Preventing floods and ensuring a prompt response after floods is crucial to ensuring the lowest number of fatalities as well as minimizing environmental and financial damages. The following noteworthy research questions are addressed by the framework: (1) What are the main methods used in flood control? (2) Which stages of flood management are the majority of research currently in existence focused on? (3) Which systems are being suggested to address issues with flood control? (4) In the literature, what are the research gaps regarding the use of technology for flood management? To classify the many technologies that have been studied, a framework for classification has been provided for flood management. It was found that there were few hybrid models for flood control that combined machine learning and image processing. Furthermore, it was discovered that there was little use of machine learning-based techniques in the aftermath of a disaster. To provide efficient and comprehensive disaster management, future efforts must concentrate on integrating image processing methods, machine learning technologies, and the understanding of disaster management across all phases. The study has proposed the use of Generative Artificial Intelligence. Full article

An earthquake and tsunami on 1 January 2024, caused extensive damage across Ishikawa Prefecture, Japan. This study systematically examined the recovery process. It aimed to analyze the initial stages of recovery and highlight the lessons learned from these efforts. By collecting data from Ishikawa’s post-disaster records, including government reports, reconstruction plans, local news, and observational records, we compiled the first “Early Reconstruction Timeline for Ishikawa”. This timeline divided the recovery process into four phases: disaster occurrence and emergency response (January), reconstruction preparation (February–April), the beginning of early reconstruction (May–September), and the beginning of mid-reconstruction to the present (October–Present). Analysis of this timeline revealed several significant findings. First, Ishikawa’s reconstruction efforts were structured into three key phases: ‘Post-Disaster Relief and Emergency Response’, ‘Post-Disaster Reconstruction Preparation Period’, and ‘Infrastructure Reconstruction and Emergency Housing Construction Period’. This phased approach highlights an efficient and organized recovery process, distinguishing Ishikawa from other disaster-affected regions. Additionally, the housing reconstruction model showcased an innovative balance between emergency housing as a public resource and the specific needs of displaced residents. These findings not only establish a timeline-based framework for Ishikawa’s reconstruction but also provide practical insights for guiding early post-disaster recovery efforts in various disaster-affected contexts worldwide. Full article

Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of “Dongting Lake breach” in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining. Full article

Natural disasters can severely disrupt conventional communication systems, hampering relief efforts. High-altitude tethered balloon base stations (HATBBSs) are a promising solution to communication disruptions, providing wide communication coverage in disaster-stricken areas. However, a single HATBBS is insufficient for large disaster zones, and limited resources may restrict the number and energy capacity of available base stations. To address these challenges, this study proposes a cluster deployment of tethered balloons to form flying ad hoc networks (FANETs) as a backbone for post-disaster communications. A meta-heuristic-based multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize the placement of balloons and power control to maximize target coverage and system energy efficiency. Comparative analysis with a stochastic algorithm (SA) demonstrates that MOPSO converges faster, with significant advantages in determining optimal balloon placement. The simulation results show that MOPSO effectively improves network throughput while reducing average delay and packet loss rate. Full article

Throughout history, humanity has grappled with infectious diseases that pose serious risks to health and life. The COVID-19 pandemic has profoundly impacted society, prompting significant reflection on preparedness and response strategies. In the future, humans may face unexpected disasters or crises, making it essential to learn from the COVID-19 experience, especially in ensuring adequate emergency supplies and mobilizing resources effectively in times of need. Efficient emergency medical management is crucial during sudden outbreaks, and the preparation and allocation of medical supplies are vital to safeguarding lives, health, and safety. However, the unpredictable nature of epidemics, coupled with population dynamics, means that infection rates and supply needs within affected areas are uncertain. By studying the factors and mechanisms influencing emergency supply demand during such events, materials can be distributed more efficiently to minimize harm. This study enhances the existing dynamics model of infectious disease outbreaks by establishing a demand forecasting model for emergency supplies, using Hubei Province in China as a case example. This model predicts the demand for items such as masks, respirators, and food in affected regions. Experimental results confirm the model’s effectiveness and reliability, providing support for the development of comprehensive emergency material management systems. Ultimately, this study offers a framework for emergency supply distribution and a valuable guideline for relief efforts. Full article

Following an earthquake, abnormal travel demand causes traffic congestion and poses significant problems for relief efforts. Research on post-earthquake travel demand is essential for disaster management. An effective disaster management strategy ensures achieving sustainable development goals. This study focused on this critical period and analyzed post-earthquake trip decisions. The city of Elazığ, a region not at risk of tsunami, was used as a case study. A 6.8 magnitude earthquake hit Elazığ in January 2020. After the earthquake, data from 2739 individuals were collected by a household survey conducted face-to-face. The data were segregated into two categories, depending on the earthquake’s intensity. The study used a binary logit model to examine the potential factors of trip decisions after an earthquake. The results showed that 75% of participants made at least one trip within 24 h after the earthquake. It was observed that household, building-and disaster-related attributes influence earthquake survivors’ trip decisions. The initial location at the time of the earthquake was the most significant factor affecting trip decisions. It was also found that individuals who experienced the earthquake outside their homes in both datasets were more likely to make a trip. Additionally, the dataset with higher earthquake intensity had more significant variables affecting the trip decision. Full article