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With the development of earthquake disaster reduction efforts in China, the content of earthquake disaster reduction policies has become increasingly enriched and improved. Particularly, multiple provincial governments have proposed earthquake disaster reduction planning policies. It is important to explore whether these policies can affect disaster mitigation. Therefore, this paper summarizes the earthquake disaster reduction plans and factors influencing seismic resilience. Panel data from 24 provinces between 2012 and 2021 were collected, and a difference-in-differences approach was used to construct an econometric model to evaluate the policy effects and analyze the enhancement of seismic resilience. The results show that the implementation of earthquake disaster reduction policies has a positive impact on earthquake monitoring, evacuation, and emergency relief capabilities, and the estimated policy effects are statistically significant. Moreover, a series of tests were conducted. The conclusions are as follows: (1) Earthquake disaster reduction policies have a positive impact on the improvement of seismic resilience in provinces. (2) Provinces with a higher number of earthquakes experience more significant effects from earthquake disaster reduction policies. (3) Provinces with higher seismic peak ground acceleration values exhibit more pronounced improvements in seismic resilience. [ABSTRACT FROM AUTHOR]

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people's property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem's convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark. [ABSTRACT FROM AUTHOR]

The need for pedestrian target detection in uncrewed aerial vehicle (UAV) remote sensing images has become increasingly significant as the technology continues to evolve. UAVs equipped with high-resolution cameras can capture detailed imagery of various scenarios, making them ideal for monitoring and surveillance applications. Pedestrian detection is particularly crucial in scenarios such as traffic monitoring, security surveillance, and disaster response, where the safety and well-being of individuals are paramount. However, pedestrian detection in UAV remote sensing images poses several challenges. Firstly, the small size of pedestrians relative to the overall image, especially at higher altitudes, makes them difficult to detect. Secondly, the varying backgrounds and lighting conditions in remote sensing images can further complicate the task of detection. Traditional object detection methods often struggle to handle these complexities, resulting in decreased detection accuracy and increased false positives. Addressing the aforementioned concerns, this paper proposes a lightweight object detection model that integrates GhostNet and YOLOv5s. Building upon this foundation, we further introduce the SPD-Conv module to the model. With this addition, the aim is to preserve fine-grained features of the images during downsampling, thereby enhancing the model's capability to recognize small-scale objects. Furthermore, the coordinate attention module is introduced to further improve the model's recognition accuracy. In the proposed model, the number of parameters is successfully reduced to 4.77 M, compared with 7.01 M in YOLOv5s, representing a 32% reduction. The mean average precision (mAP) increased from 0.894 to 0.913, reflecting a 1.9% improvement. We have named the proposed model "GSC-YOLO". This study holds significant importance in advancing the lightweighting of UAV target detection models and addressing the challenges associated with complex scene object detection. [ABSTRACT FROM AUTHOR]

Central Asian countries, which include Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan, are known to be highly exposed to natural hazards, particularly earthquakes, floods, and landslides. With the aim of enhancing financial resilience and risk-based investment, planning to promote disaster and climate resilience in Central Asia, the European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), launched the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) regional programme. Within this framework, a consortium of national and international scientific institutions was established and tasked with developing a regionally consistent multi-hazard and multi-asset probabilistic risk assessment. The overall goal was to improve scientific understanding on local perils and to provide local stakeholders and governments with up-to-date tools to support risk management strategies. However, the development of a comprehensive risk model can only be done with the basis of an accurate hazard evaluation, the reliability of which depends significantly on the availability of local data and direct observations. This paper describes the preparation of the input datasets required for the implementation of a probabilistic earthquake model for the Central Asian countries. In particular, it discusses the preparation of a new regional earthquake catalogue harmonized between countries and homogenized in moment magnitude (Mw), as well as the preparation of a regional database of selected active faults with associated slip rate information to be used for the construction of the earthquake source model. The work was carried out in collaboration with experts from the local scientific community, whose contribution proved essential for the rational compilation of the two harmonized datasets. [ABSTRACT FROM AUTHOR]

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Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The outbreak of pandemic at the beginning of 2020 has been urban exclusive and brought the existing inequalities across class and different types of settlements into the forefront. A prolonged period of lockdown, promotion of social distancing, and quarantine of infected individuals, which had been recommended as preventive measures raises a question on the feasibility of these measures in Indian cities considering the challenges related to adequate housing and exclusive access to basic amenities. Against this background, this paper tries to understand the level of pandemic preparedness of urban households across different socio-economic groups, city-size, and settlement types and its changes in the last two decades based on an analysis of unit level data from the National Sample Survey (NSS). The paper has attempted to construct a 'Pandemic Preparedness Index' (PPI) to measure a household's ability to maintain isolation and social distancing. Results indicate that urban households have become more prepared for such pandemics over this period. However, the poor and slum households, particularly located in metropolitan cities still face multiple deprivations related to housing and basic amenities, which make them more vulnerable to such pandemics. This paper calls for urban household's pandemic and disaster preparedness with an increased policy focus on poor and slum households in metropolitan cities. [ABSTRACT FROM AUTHOR]

The seismic response detection and operational safety assessment of high-speed railway (HSR) bridges play a crucial role in ensuring HSR systems' operational safety and reliability. Smartphones have introduced intelligent inspection tools for structural health detection, becoming a new tool for intelligent structural inspection. Combining the public and smartphones is the key to public participation in structural health detection. This study utilizes smartphone-based structural seismic response inspection technology to investigate the framework of public participation in earthquake response inspection and assessment. This system comprises the Smart Bridge Brain (SBB), which integrates data from multiple sources and systems, an assigning mechanism for public participation inspection tasks, and smartphone-based HSR bridge structural seismic response inspection technology. At the same time, the Unreal Engine 5.0 software is used to create a mixed-reality virtual simulation experimental environment to validate the feasibility of this framework. The results indicate that the intelligent optimization of task allocation by the SBB successfully assigns detection tasks to each public participant. Public participants can promptly reach predefined damage structure detection targets and rapidly inspect bridge structural seismic response indicators using smartphones. In addition, this paper also conducts a comprehensive evaluation and analysis of the detection of the work efficiency index (WEI) within the system. Furthermore, optimization strategies for the efficient execution of detection tasks are proposed based on WEI variations influenced by different factors. The system framework is expected to enhance cluster-based HSR bridges' intelligent disaster prevention and mitigation capabilities. [ABSTRACT FROM AUTHOR]

The western region of Sichuan Province, located in the Hengduan Mountains, is one of China's most seismically active zones. Due to limited land resources, many villages in western Sichuan Province are situated in mountainous and valley areas, facing harsh climatic conditions, lagging economic development, and insufficient infrastructure. After experiencing seismic disasters, these villages often encounter challenges, such as cut-off mountain roads, difficulties in delivering relief supplies, and sharply a significant drop in nighttime temperatures due to the high-altitude climate. Consequently, in the case of the economic underdevelopment and limited resources, how to repurpose existing buildings for disaster relief has become a crucial issue for disaster prevention and mitigation in these villages. This paper takes Ganbao Tibetan Village, located in the alpine gorge regions of western Sichuan Province, as a case study. It evaluates the interior space utilization status of different types of existing public buildings during disasters, calculates the effective utilization rate of interior space disaster reduction conversion based on the spatial layout model, and extracts the typical interior space dimensions that are conducive to disaster relief and conversion of public buildings based on the interior space utilization characteristics of disaster relief. This study provides a reference for the design of public service buildings considering the requirements of disaster resilience transformation. [ABSTRACT FROM AUTHOR]

Recent destructive earthquakes in Turkey (2023) with devastating effects have reawakened the discussion about seismic activity in Brazil and the vulnerability of the Brazilian buildings. Despite the lower magnitudes, national records should not be disregarded, as earthquakes with a magnitude below five may still cause loss of life and property damage. The assessment of existing RC buildings allows us to understand the performance of a structure during an earthquake and the adoption of mitigating measures. This paper preliminarily investigates the seismic vulnerability of the Metropolitan Cathedral in Brasilia, which was recognized as a World Heritage Site by ONU in 1987. The Rapid Visual Screening (RVS) procedure has been adopted for qualitative vulnerability assessment through the guidelines of the Japan Building Disaster Prevention Association at the procedure updated in accordance with the Brazilian reality. This research examined the structure under horizontal acceleration defined by national seismic code, and various scenarios are assumed as time aging, structural irregularities, and the level of importance of the structure. In this study, the Monte Carlo simulation is carried out to evaluate the level of safety. A performed analysis has shown that the examined building possesses the seismic capacity required against the expected earthquake motions. Even with reliability analysis, the structure achieves an acceptable level of safety. The results highlight the importance of a probabilistic approach to make reliable decisions on the conservation and rehabilitation of architectural heritage buildings. [ABSTRACT FROM AUTHOR]

In recent years, internet of underwater things (IoUT) has become a hot topic of research and attracted many researchers to use them for applications such as underwater environment monitoring and exploration, monitoring geological and biological changes, defense, disaster monitoring and prevention. Smart underwater objects in IoUT are generally equipped with high definition cameras for capturing underwater images. Storage and transmission of high-quality images captured by these smart objects is a challenging task due to huge volumes of data generated. Compressive sensing (CS) based data sampling and acquisition strategy provides promising ways to compress image at low computational cost and power. However, the samples are chosen randomly compromising reconstructed image's visual quality. To select signal samples with more importance, Adaptive Block Compressive Sensing (ABCS) can be utilized. In this research paper, we implement two-modified fast haar wavelet transform (MFHWT) coefficient thresholding based ABCS strategies for compression of underwater surveillance images in IoUT. The novelty lies in the fact that the proposed strategies choose only 5–20% of samples/measurements to achieve a rise in PSNR of about 3-5dB with respect to conventional techniques. The results show that the proposed strategies have NCC and SSIM values closer to 1 and NAE values closer to 0. Also, around 70% memory space can be saved. [ABSTRACT FROM AUTHOR]

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably. [ABSTRACT FROM AUTHOR]

Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications. [ABSTRACT FROM AUTHOR]

This paper presents insights from a community-led initiative in South Australia aimed at enhancing disaster resilience in culturally and linguistically diverse (CALD) migrant and refugee communities. Anchored in the principles of community cohesion, empowerment and informed action, the project addresses socio-cultural dynamics and communication barriers. A series of workshops and forums, tailored to account for cultural sensitivity, showed strengthened community collaboration, skills development and increased awareness of the psychosocial effects of disasters, prompting proactive strategies that consider the unique vulnerabilities and strengths of CALD communities. Participant testimonials noted increased community spirit and practical application of acquired knowledge, including understanding local hazard risk profiles and trust-building with emergency services organisations. The findings demonstrate the importance of mitigating access challenges, integrating diverse community perspectives into resiliencebuilding activities and ensuring the inclusivity of management policies. Highlighting the significance of strengths-based community-driven approaches in emergency and disaster management, particularly for marginalised groups, this initiative found that engaging communities as active contributors enables more resilient and self-efficacious populations that effectively bridge cultural and linguistic gaps in preparedness. [ABSTRACT FROM AUTHOR]

This paper collates and critically reviews the literature on men, masculinities and disasters in Australia and New Zealand for the decade from 2013 to 2023. It explores the relevant literature through 6 themes of the masculinist nature of emergency services and emergency services management, the influences of masculinity in preparation for disasters, the impact of disasters on men and the role of masculinity in men's post-disaster recovery, masculinity and men's violence against women in the aftermath of disasters, the challenge to binary assumptions about gender and the implications for masculinity and diverse sexual and gender identities and how masculinism frames disaster prevention and policies addressing global warming and climate catastrophes. The origins of the research into men, masculinity and disasters pre-dates the time period of this review and arises from the early gender and disaster literature that focused on the vulnerabilities of women and girls in the aftermath of disasters. This followed an era where gendered inequalities in disasters were ignored. This review focuses on contemporary analyses confined to Australian and New Zealand authors and disaster locations. [ABSTRACT FROM AUTHOR]

The effects of emergencies and disasters pose greater challenges for people within culturally and linguistically diverse (CALD) communities. Several barriers can prevent people in CALD communities from adequately preparing for such events and this contributes to an increased vulnerability. Queensland experiences heightened natural hazard risks, therefore it is crucial to ensure that preparedness information is accessible and relevant to all communities, including CALD communities. This paper describes a qualitative study that examined the emergency and disaster preparedness information needs of people in CALD communities in Queensland. The aim was to identify better ways of delivering preparedness initiatives through tailored engagement approaches. Three focus groups were conducted with 16 CALD community leaders from the Gold Coast, Logan and Ipswich local government areas. The data gathered showed that CALD communities in these areas possessed low levels of awareness of emergencies and disasters and low levels of preparedness for such events. This study highlights the need for tailored and strengthsbased engagement approaches. Disseminating information in suitable formats through preferred communication channels and partnering with trusted sources, including community leaders and places of worship, were found to be effective ways to engaging CALD communities in disaster preparedness. [ABSTRACT FROM AUTHOR]

With the continuous development of the oblique photography technique, it has been used more and more widely in the field of geological disasters. It can quickly obtain the three-dimensional (3D) real scene model of dangerous mountainous areas under the premise of ensuring the safety of personnel while restoring the real geographic information as much as possible. However, geological disaster areas are often accompanied by many adverse factors such as cliffs and dense vegetation. Based on this, the paper introduced the flight line design of oblique photogrammetry, analyzed the multi-platform data fusion processing, studied the multi-period data dynamic evaluation technology and proposed the application methods of data acquisition, early warning, disaster assessment and decision management suitable for geological disaster identification through the analysis of actual cases, which will help geologists to plan and control geological work more scientifically and rationally, improve work efficiency and reduce the potential personnel safety hazards in the process of geological survey, to offer technical support to the application of oblique photogrammetry in geological disaster identification and decision making and provide the scientific basis for personal and property safety protection and later-stage geological disaster management in disaster areas. [ABSTRACT FROM AUTHOR]

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability. [ABSTRACT FROM AUTHOR]

The early identification of potential landslide hazards has always been a hot and difficult issue in the field of international landslide research. In recent years, many scholars have conducted extensive and beneficial explorations in this field, making significant contributions to the effective prevention of landslide disasters. However, until now, there are very few review documents on summarizing such valuable experience in the system, which makes it difficult to meet the ever-increasing demand of researchers in scientific documents. To address the gap, this paper systematically reviews 843 documents collected by the two data platforms of Web of Science (WOS) and Scopus from 1971 to 2023 by using the bibliometric analysis software. This paper first figures out the internal relationship between documents by analysing their spatial and temporal distribution characteristics, and then emphatically analyses the application, advantages and disadvantages of different early identification methods based on the influencing factors of landslide disaster formation and multi-source data acquisition links. And finally, this paper discusses the challenges and development trends in this field from four aspects of cooperative analysis, multi-source data, topic analysis and research trends, and puts forward some suggestions. This research can help researchers to use various early identification methods reasonably and provide summary and integration services of scientific document achievements for efficient research in this field. [ABSTRACT FROM AUTHOR]

Seismic vulnerability assessment is one of the most impactful engineering challenges faced by modern societies. Thus, authorities require a reliable tool that has the potential to rank given structures according to their seismic vulnerability. Various countries and organizations over the past decades have developed Rapid Visual Screening (RVS) tools aiming to efficiently estimate vulnerability indices. In general, RVS tools employ a set of structural features and their associated weights to obtain a vulnerability index, which can be used for ranking. In this paper, Machine Learning (ML) models are implemented within this framework. The proposed formulation is used to train binary classifiers in conjunction with ad hoc rules, employing the features of various Codes (e.g., the Federal Emergency Management Agency, New Zealand, and Canada). The efficiency of this modeling effort is evaluated for each Code separately and it is clearly demonstrated that ML-based models are capable of outperforming currently established engineering practices. Furthermore, in the spirit of the aforementioned Codes, a linearization of the fully trained ML model is proposed. ML feature attribution techniques, namely SHapley Additive exPlanations (SHAP) are employed to introduce weights similar to engineering practices. The promising results motivate the potential applicability of this methodology towards the recalibration of the RVS procedures for various types of cases. [ABSTRACT FROM AUTHOR]

Refugees often face structural inequities, a disproportionate share of environmental challenges, and receive inadequate risk information. In most cases, the host country, United Nations, and other humanitarian organizations provide critical weather and climate information and disaster risk management support. This is equally true for the Rohingya refugee in Bangladesh, who fled their home country Myanmar as a response to political violence. To provide critical humanitarian services to the Rohingya population, the in‐country United Nations agencies formed the Inter‐Sector Coordination Group (ISCG). This paper investigates the effectiveness of the ISCG's strategies for communicating monsoon risks in the Rohingya context. Particularly, it measures ISCG communication effectiveness by both the increased formalization of ISCG communication and the number of Rohingya refugees affected during the monsoon seasons in the years 2021–2022. The paper highlights the importance of the ISCG's existence in this humanitarian context as an effective coordinating institution. The ISCG provides impoartant environmental risk information to partner organizations that benefit the Rohingya's overall physical well‐being. Even though the geographical focus of this paper is on the Rohingya populations in Cox's Bazar, the discussions and findings have important policy implications in other parts of the world facing similar social, environmental, political, and humanitarian challenges. [ABSTRACT FROM AUTHOR]

Emergency management bears the important responsibility of preventing and resolving security risks and responding to disasters and accidents in time. At the same time, it shoulders the important mission of protecting people's lives and property and maintaining social stability. However, there is little research on the forecast of the huge gap and demand for talent. To strengthen the emergency management ability and modernization, improve the ability of emergency management personnel training and solve the shortage of emergency management personnel at this stage, this paper studied the positioning of emergency management personnel in China and the demand for various emergency management personnel. By means of field interviews, questionnaire surveys, participatory research and expert meetings, this research analyzed the characteristics of emergency management talents in China, defined its classification and forecasted its demand. The results showed that emergency management talents in China could be divided into four categories: administrative management talents, practical talents, expert talents and support talents. According to the data calculation results of five representative provinces, Guangdong Province (area with relatively developed economy), Beijing (administrative centre and economically developed area), Sichuan Province (natural disaster‐prone area), Shanxi Province (developed area of energy industry such as coal) and Jiangsu Province (industrially developed areas), it is concluded that the total demand for emergency management talents in China at this stage is about 794,900, and the gap is about 411,200. At present, China's emergency management talents are in short supply. The research results of this paper can provide sufficient data support for the training of emergency management talents. [ABSTRACT FROM AUTHOR]

This paper examines the form, function and impact of previous Olympic security arrangements and their intersection with planning practice. Drawing from prior and ongoing empirical research investigating the security practices at summer Olympic Games, the paper argues that wider shifts towards 'total' security models comprising continually reproduced security motifs can be observed that are increasingly standardized, mobile, globalized and planned-in. For most Olympic organizers, preparations now necessarily include attempts to equate spectacle with safety and to 'design-out' terrorism by relying on highly militarized tactics and expensive and detailed contingency planning. Such securitizing practices have intensified in form and scale since 9/11, with such intensification set to continue at the XXXIII Olympiad in Paris, where a vast security infrastructure is being embedded into the large-scale and long-term master-plans for the central city. This represents a high point in spatial planning practice through embracing principles of security-by-design where Games-time security infrastructure, whilst providing effective protection, becomes a less visible but permanent, physical legacy that can also contribute to local programmes of regeneration, climate resilience and crime prevention. The paper concludes by reflecting upon what the continual evolution of security infrastructure means for the balancing of planned-in security and spectacle at future Olympiads. [ABSTRACT FROM AUTHOR]

Extreme precipitation events (EPEs) have garnered considerable social concerns due to their hazardous and destructive nature. To identify the possible causes of EPEs in China, this paper presents an in-depth investigation of how EPEs coincide with atmospheric conditions, i.e., atmospheric instability, moisture availability and moisture convergence, as well as driving variables, i.e., vertical velocity, relative humidity and air temperature. Specifically, the classic coincidence probability is devised to explicitly relate 72-h EPEs to convective available potential energy (CAPE), precipitable water (PW), vertical velocity at 700 hPa (verV), relative humidity at 700 hPa (RH), average air temperature between 850 and 500 hPa (Tavg) as well as air temperature difference between 850 and 500 hPa (Tdiff). The results show that at the annual timescale, EPEs in Southeast and Southwest China are dominantly controlled by verV, in North and Central China by PW and in Northwest China by CAPE roughly. At the seasonal timescale, the spatial distributions of coincidence probability values and "competition" among atmospheric conditions and driving variables exhibit similar patterns as observed throughout the entire year except for December–January–February. Moreover, the diagnostic plots generated for three case study regions in China provide valuable insights into the temporal evolution of precipitation events, cumulative distribution function curves of influential factors and dominant controlling factors of EPEs. This paper contributes to understandings of the dominant controlling factors of EPEs for the whole of China. The spatial patterns of EPEs and their related atmospheric conditions and driving variables yield useful information for rainstorm forecasting and disaster risk management. Plain Language Summary: Atmospheric conditions, i.e., atmospheric instability, moisture availability and wind convergence, and driving variables, i.e., vertical velocity, relative humidity and air temperature, play important parts in the occurrence of extreme precipitation events (EPEs). In this paper, the 72-h EPEs in China are explicitly related to atmospheric conditions and driving variables by using the classic coincidence probability. Specifically, the relationships are measured by the ratio of the time when precipitation and the given factor both reaching at extreme to the whole time in EPEs. Assuming that the factor exhibiting the highest coincidence probability influences EPEs the most, it is observed that at the annual timescale, vertical velocity at 700 hPa has the greatest impact on EPEs in Southeast and Southwest China as well as part of Northeast China, precipitable water in North and Central China and convective available potential energy in Northwest China. Also, it is found that the results at the seasonal timescale are consistent with that throughout the entire year except for winter. Overall, the results of this paper can help us to understand the possible causes of EPEs and serve in rainstorm forecasting and risk management at grid cell level for the whole of China. [ABSTRACT FROM AUTHOR]

Despite the great development and advancement of technology over time, the problem of disaster and crisis management and dealing with it remains a major and great challenge. Early detection of natural disasters, strict laws against man-made disasters, and even the enforcement of the safety requirements for industrial disasters could not stop the occurrence of disasters and crises that leave devastation, general disability, suffering, and deprivation, in addition to injuries, wounded, victims, and even missing and dead human beings. Therefore, technologies, algorithms, and modern methods such as mechanical, electronic, robots, image, and signal processing, artificial intelligence, wireless communication, and so on must be harnessed to deal with disasters after their occurrence as well as limit their effects. Because preserving the lives of people and helping them is greatly important, this research has been prepared to review the work and techniques of researchers. The reviewed research dealt with the early detection of disasters and managing them in the fastest time and with high efficiency, including detecting and locating victims and also relieving survivors to reduce the psychological, physical, and economic impact of these disasters. Also, the paper presented the development of using some technology as a robot in this field. This paper can be a base for other researchers and rescue workers to improve and enhance their operations or mission of managing disasters or crises. [ABSTRACT FROM AUTHOR]

Ministry of Emergency Management of the People's Republic of China was established on the basis of the original Work Safety Supervision and Management Department. In addition to assuming the responsibilities of the two committees and four departments, it also administers the four major industries such as non-coal mines, metallurgical industry and trade, hazardous chemicals, fireworks, and firecrackers. The comprehensive coordination quality of the emergency management personnel, and professional quality are constantly improved. This paper conducts a study on the Professionalization of China's Emergency Management Team, and provides a research basis for further developing the Emergency Management. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a digital model to run an evacuation simulation that reflects the road network blockage caused by the landslide and river flooding damage in Marumori-machi, Miyagi Prefecture, which was severely damaged by Typhoon No. 19 in 2019. In particular, we propose an evacuation agent simulation model that can be extended in the future to scenarios related to disaster response decisions, education, and awareness on the part of residents and can reproduce the evacuation agent situation of a real disaster. The method adjusts a set of parameters of vehicles and pedestrian agents to reproduce the evacuation situation. Then, using the parameter set, we perform the agent simulations under different scenarios varying the time of disaster occurrence and evacuation. Finally, we evaluate the changes in the number of people who have completed their evacuation, the number of victims, etc. The results of the scenarios with different disaster occurrence times showed that the final evacuation rate situation improved by at least 1% (about 170 people who completed evacuation) during daylight time compared to nighttime. The relationship between sunset time and the start of evacuation was qualitatively and quantitatively demonstrated to be supported. It was also confirmed that the evacuation situation did not change much with the time of the evacuation announcement. These results show trends limited to the present study's disasters and scenarios and do not necessarily provide generalized findings for disaster response. However, the results indicate that applying the proposed methodology to a greater number of disasters and scenario conditions could lead to better analysis and optimization of disaster response. Interviews with government disaster management officials in the target areas suggest that confirming the effectiveness of disaster response while visualizing the distribution of disaster risk in the areas from a bird's eye view, as in this study, could enhance existing response plans. This approach may also present information comprehensibly for staff and residents who did not experience the disaster firsthand, simulating the experience for better understanding. [ABSTRACT FROM AUTHOR]

This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions. [ABSTRACT FROM AUTHOR]

Autonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security. [ABSTRACT FROM AUTHOR]

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Wireless sensors are used for smart building monitoring, biomedical applications, intelligent home appliances, urgent disaster management and precision agriculture production. These devices today operate with batteries that are very difficult to change. The major problem hindering the widespread deployment of wireless sensor networks is the need to constantly replace them because their batteries expires. We can see that it is possible to charge them using energy harvested from solar, vibration, thermal or ambient radio frequency sources. we can implement sustainable health surveillance systems, agriculture, mining, environmental protection and national infrastructure, which will improve the quality of life, save money and achieve ecological goals. We realized that it would be wise to specify a field of application for internet of things in order to be able to clarify the identification of the problem of energy consumption and its relation with renewable energies. In addition, one of the most interesting applications for the socio-economic fields in the world in recent years is Smart Farming. In this paper, we reviewed a state of the art on the use of renewable energy harvesting in precision agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Risk, Hazards & Crisis in Public Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Risk, Hazards & Crisis in Public Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology. [ABSTRACT FROM AUTHOR]

Subway tunnel flooding is one of the typical scenarios in urban waterlogging. When the emergency decision-making is untimely and inaccurate, it can lead to serious casualties. However, it is expected that through some responsive monitoring methods, an intelligent system can be constructed to achieve the real-time perception of the underground flood situation and to predict the process and intensity of the disaster. This study is based on a scale model experiment of flooding in straight subway tunnels. Firstly, it classified the basic patterns of the flood process. Some fitting formulas were respectively established for water depth and kinematic parameters. A calculation method for quickly predicting the flood inundation process was constructed. Secondly, this paper proposed a mode for the monitoring and early warning system. Based on the flood patterns and characteristic variables in subway tunnel flooding, the system can quickly determine the flow rate and entry position of flood and deduce the flood trend in the future at a relatively low cost. The system workflow was divided into three phases, and the mechanism for each phase of the system was sorted out. It could help to provide scientific and reliable information for emergency decision-making and gain time for the emergency response. [ABSTRACT FROM AUTHOR]

Accurate precipitation prediction is very important for meteorological disaster prevention, water resources management, and agricultural decision making. To improve the accuracy of precipitation prediction, a hybrid model based on variational mode decomposition (VMD), crested porcupine optimization algorithm (CPO), and long short-term memory model (LSTM) is proposed in this paper. The model first uses VMD to decompose the precipitation time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of precipitation data. Then, the CPO algorithm is used to optimize LSTM adaptive parameters to improve the global search ability and robustness of the model. Finally, the decomposed precipitation component is input into the LSTM network to learn the spatiotemporal dependence relationship and improve the ability of long-term prediction. The experimental results show that compared with the traditional LSTM model, CPO-LSTM model, and VMD-LSTM model, the hybrid model achieves better performance in many evaluation indices and effectively improves the accuracy of precipitation prediction. The application of the model can provide an effective tool for the fields of meteorology and water resources management, as well as provide new ideas for related research. [ABSTRACT FROM AUTHOR]

The paper presents Giorgio Agamben's philosophical-social stance in analyzing the particular situation of the pandemic. The latter is considered in terms of the power associated with biomedical expertise (biopower). The predisposition of the disciplines of knowledge to transcend their epistemic limitations, along with the interest of power in preserving life at all costs in the conditions of a perennial state of emergency, presents the human being as stripped of sociality and emotionality. Agamben's skepticism of measures and medical expertise during the pandemic is founded on a philosophical postulate: Human life cannot be protected at all costs, without stripping it of its very essence. As a consequence, the social inscribed in the condition of a perennial state of emergency, transcend the ethical and human limits of the human condition. Most of these scenarios are examined using gestures, figures, and social practices such as extreme social isolation, quarantine, and the prohibition of funeral ceremonies. Many of these social practices lost their meaning and function during the pandemic, suspending the individual and paralyzing the social body. Agamben's critical theory, from this perspective, preserves the dimensions of sociality, affection, dignity, and individuality as inviolable and defining elements of a human being. [ABSTRACT FROM AUTHOR]

The flood and heavy rain monitoring and alert system using Arduino is a crucial tool for enhancing disaster management in flood-prone areas. By utilizing low-cost Arduino microcontrollers and sensors, the system provides real-time data on water levels and rainfall intensity, enabling timely alerts to be sent to residents and authorities. Its simplicity and affordability make it an ideal solution for developing countries and rural communities where traditional monitoring systems may be lacking. The proposed work represents a significant step towards improving early warning systems for floods and heavy rains, ultimately saving lives and reducing property damage in residential areas during flood-affected times. [ABSTRACT FROM AUTHOR]

Cyber attacks have a significant business impact, with the potential to escalate into crises if poorly managed. A recurring pattern is strategic dilemmas that cannot be resolved satisfactorily. Some dilemmas are more pronounced, others less so, and therefore often catch decision-makers unprepared, leaving only bad options for decision-making. Something that all dilemmas have in common is that the associated decisions can have a lasting impact on relationships with stakeholders. This paper introduces four recurring dilemmas; shows the typical considerations; lists options for mitigating these dilemmas; and describes the basic requirements for implementing mitigations. The dilemmas and options, in turn, are rooted in the organisation-specific design of: cyber security incident management and response; IT service continuity and disaster recovery management; business continuity management; and crisis management and communication. [ABSTRACT FROM AUTHOR]

There have been a large number of masscasualty incidents in recent years, including climate change-related disasters, mass shootings, terrorist attacks, transportation accidents and a global pandemic. Communities, families and friends have suffered grief and loss, while nations continue to bear the scars of trauma. Disasters caused by acts with criminality, although necessarily managed by the police for the investigative aspect, must be planned for, and responded to with victim-centred practices by the police, local government and other relevant community stakeholders for the duration of the response and recovery. Inconsistency and confusion over terminology and language in emergency management can lead to a lack of understanding about which stakeholders or agencies should be engaged in, and responsible for different aspects of the planning, preparedness, mitigation and response to a community disaster — regardless of what type of disaster it is, and irrespective of the disaster being caused by a person or persons with ill-intent. This paper discusses how a wholeof- community and victim-centred approach to criminal act disaster response should be applied to support those persons most adversely affected by the incident. It also promotes the application of victim-centred practices to ensure that the needs of victims are regarded ethically, and with compassion following any disaster caused by an act of criminality. The term 'mass victimisation incident' will be introduced and applied through a case study. [ABSTRACT FROM AUTHOR]

Malan loess is a wind-deposited sediment that is loose and porous and has high water permeability. It has experienced many dry–wet cycles during tens of thousands of years, resulting in unique engineering properties. The unique properties of loess are closely related to its texture and microstructure formed during dry–wet cycles. This paper investigates the influence of dry–wet cycles on the microstructure of Malan loess and its formation mechanism. The results show the following. (1) In an open environment, the ion content of the loess samples decreased after the dry–wet cycles, indicating that the cementation of the loess particles had weakened and the structural strength of the loess has decreased due to the loss of cementation materials as the cycle number increased. (2) The ion content of the soil samples did not change significantly after dry–wet cycles in a closed environment. However, after several cycles, the clay and colloid particles migrated and were adsorbed around the skeleton particles, and soluble salt ions were enriched in the form of cementation, with a slight increase in structural strength. The proposed dry–wet cycle model in this paper showed that the loess structure property was weaker in an open environment and stronger in a closed environment, revealing the formation and evolution of loess structure at the microscale. The results of this study provide very important theoretical and practical engineering significance for deep understanding of the mechanical and hydraulic properties of loess and the prediction and prevention of geological disasters. [ABSTRACT FROM AUTHOR]

Bio-mediated geotechnology refers to the technology that utilizes various types of microbial processes to improve the hydro-mechanical behavior of soil and rock, aiming for the prevention and mitigation of geoengineering problems. This paper provides a detailed and comprehensive review of bio-mediated geotechnologies, aiming to enhance understanding in this field. The review encompasses mechanisms, influencing factors, engineering properties, applications, as well as challenges and prospects associated with bio-mediated geotechnologies. Three typical bio-mediated geotechnologies are examined: biomineralization, biofilm, and biogas, with a specific focus on microbially induced calcium carbonate precipitation (MICP). Key factors affecting MICP efficacy, including bacteria species, bacteria concentration, temperature, pH, cementation solution, soil properties, and treatment strategies, are elaborated upon. The paper highlights the significant improvements in mechanical strength, permeability reduction, and erosion resistance achieved through MICP treatment. Furthermore, a wide range of applications for MICP in geotechnical and environmental domains are reviewed, spanning from foundation treatment to geological disaster prevention. Despite its promise, MICP faces challenges such as environmental impact mitigation, uniform distribution, and large-scale application. The paper concludes by discussing future research directions, emphasizing interdisciplinary collaborations and innovative approaches to address these challenges and fully realize the potential of MICP in geotechnical engineering applications. [ABSTRACT FROM AUTHOR]

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020–2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes"). [ABSTRACT FROM AUTHOR]

To address the issue of poor accuracy in typhoon wind data, this paper presents a methodology for the calibration of typhoon wind data while conducting an analysis and evaluation of the associated uncertainties. The approach commences with the introduction of two wind field data calculation models. The first model facilitates the correction of buoy attitude, thereby transforming inaccurate buoy wind speed data into real-speed data. In parallel, the second model enables the conversion of buoy-observed true velocity into neutral stable stratified wind parameters, leveraging satellite-derived data for precise calculations. Subsequently, the paper undertakes the task of spatio-temporal alignment between buoy data and satellite observations. Ultimately, a comprehensive comparative analysis is conducted by juxtaposing the ERA5 database with data collected from a moored buoy equipped with the R.M. YOUNG wind monitor. A new simulation method for satellite wind speed data inversion is proposed, and the experimental results demonstrate the effectiveness of the proposed calibration method in enhancing the accuracy of typhoon wind field data. In particular, the maximum wind speeds recorded were 20.15 m s−1 and 13.22 m s−1 during Typhoon "Jangmi (202005)" and "Maysak (202009)," respectively. Furthermore, the mean square errors (MSE) for our method were measured at 0.31 m s−1 and 0.28 m s−1, outperforming the satellite-derived indicators. The expanded uncertainty of measurement results for the two typhoons was calculated at 0.39 m s−1 and 0.34 m s−1, closely aligning with the MSE values. These computational models present a valuable means of enhancing the precision and reducing uncertainty in satellite-derived data. The findings presented in this paper hold great promise for applications in typhoon forecasting, investigations of air-sea interactions, and disaster prevention and mitigation efforts. [ABSTRACT FROM AUTHOR]

Drought poses a continual threat to both lives and livelihoods in the Global South. Although the impact on food security from drought could be reduced through early release of funds, the humanitarian sector typically reacts to crises rather than anticipates them. A significant challenge lies in devising a drought monitoring and forecasting system that can function across environmentally and economically diverse regions. This is particularly evident in Pakistan, which encompasses environments ranging from fertile riverbeds to arid deserts. This paper details the development, implementation, and operation of an anticipatory drought Disaster Risk Financing (DRF) programme for the provinces of Punjab, Sindh, and Baluchistan in Pakistan. Key to the DRF development are a new yield model for the primary crop in the target season (winter wheat), and a novel forecasting system for four seasonal drought indicators - namely winter wheat yield, precipitation, normalised difference vegetation index (NDVI) and vegetation health index (VHI). Formal evaluations demonstrate that the forecasts are skillful up to 2 months in advance of the end of the season - enabling anticipatory release of funds. The work presented here is applicable beyond Pakistan. Indeed, the model and the methodologies are sufficiently broad and adaptable to be utilised in arid and semi-arid regions across the Global South. [ABSTRACT FROM AUTHOR]