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As society becomes more complex, the importance of quick and effective emergency response systems gets more emphasized. This paper introduces a way to reduce the time it takes for emergency staff assembly to be handled during radioactive accidents, which is one of significant factors to manage early stage situations. Using a type of computer modeling called agent-based modeling (ABM) on the NetLogo software, we examine how emergency responses are carried out, focusing on how emergency staff assembly workers are sent out and how they work together in a simulated setting. By integrating traffic and path-finding models, along with real-world GIS (Geographic Information System) data, our methodology allows for more realistic evaluation of response times under various scenarios, including harsh weather conditions (e.g., Heavy snow) and infrastructural disruptions (e.g., Earthquake). The findings reveal significant variability in emergency staff assembly times, underscored by the impact of environmental factors. This research not only contributes to the understanding of emergency response mechanisms but also provides a practical framework for improving the efficacy of disaster management systems, thereby enhancing public safety and resilience against future radiological events. [ABSTRACT FROM AUTHOR]

The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. It is worth noting that in real-world scenarios, sudden disaster events may differ from the training data, which may require the multimodal network to predict them as unknown classes instead of misclassifying them to known ones. Previous studies have primarily focused on model accuracy in a closed environment and may not be able to directly detect unknown classes. Thus, we propose a novel multimodal model for categorizing social media related to disasters in an open-world environment. Our methodology entails utilizing pre-trained unimodal models as encoders for each modality and performing information fusion with a cross-attention module to obtain the joint representation. For open-world detection, we use a multitask classifier that encompasses both a closed-world and an open-world classifier. The closed-world classifier is trained on the original data to classify known classes, whereas the open-world classifier is used to determine whether the input belongs to a known class. Furthermore, we propose a sample generation strategy that models the distribution of unknown samples using known data, which allows the open-world classifier to identify unknown samples. Our experiments were conducted on two public datasets, namely CrisisMMD and MHII. According to the experimental results, the proposed method outperforms other baselines and approaches in crisis information classification. [ABSTRACT FROM AUTHOR]

IT has made significant progress in various fields over the past few years, with many industries transitioning from paper-based to electronic media. However, sharing electronic medical records remains a long-term challenge, particularly when patients are in emergency situations, making it difficult to access and control their medical information. Previous studies have proposed permissioned blockchains with limited participants or mechanisms that allow emergency medical information sharing to pre-designated participants. However, permissioned blockchains require prior participation by medical institutions, and limiting sharing entities restricts the number of potential partners. This means that sharing medical information with local emergency doctors becomes impossible if a patient is unconscious and far away from home, such as when traveling abroad. To tackle this challenge, we propose an emergency access control system for a global electronic medical information system that can be shared using a public blockchain, allowing anyone to participate. Our proposed system assumes that the patient wears a pendant with tamper-proof and biometric authentication capabilities. In the event of unconsciousness, emergency doctors can perform biometrics on behalf of the patient, allowing the family doctor to share health records with the emergency doctor through a secure channel that uses the Diffie-Hellman (DH) key exchange protocol. The pendant's biometric authentication function prevents unauthorized use if it is stolen, and we have tested the blockchain's fee for using the public blockchain, demonstrating that the proposed system is practical. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of technology has paved the way for innovative solutions aimed at enhancing personal safety and security. Among these, wearable Internet of Things (IoT) devices have emerged as a significant development, particularly in safeguarding vulnerable groups such as women and children. This paper introduces a smart security solution that leverages wearable IoT systems to provide real-time monitoring and protection. The increasing incidence of crimes against women and children highlights the urgent need for effective safety measures. Traditional security approaches often fall short in offering immediate assistance or preventive measures. However, wearable IoT devices, equipped with sensors and connectivity features, offer a proactive approach to security. These devices can monitor various physiological and environmental parameters, detect potential threats, and trigger timely alerts to guardians or authorities Our proposed smart security solution integrates advanced IoT technologies with user-friendly wearable devices designed specifically for women and children. This system encompasses several critical components, including GPS tracking, real-time communication, health monitoring, and emergency alert mechanisms. By harnessing the power of IoT, this solution aims to provide continuous protection, enhance situational awareness, and facilitate rapid response in case of emergencies. In this paper, we will explore the design, functionality, and potential impact of wearable IoT devices in improving the safety and security of women and children. We will also discuss the challenges and considerations in implementing such systems, including privacy concerns, data security, and the need for reliable connectivity. Through this comprehensive examination, we aim to demonstrate the viability and importance of IoT-based wearable technology in fostering a safer environment for vulnerable populations. [ABSTRACT FROM AUTHOR]

Emergency response decision making is crucial in managing critical incidents; however, several studies have demonstrated the negative effects of decision inertia. Understanding the manifestation and impact of decision inertia, as well as utilising extended reality (XR) technology with 360-degree immersion, should enhance decision making in high-stress environments and improve emergency response efforts. This study investigated decision inertia, using 109 participants, in an XR 360-degree environment and its impact on decision-making outcomes. The findings revealed that participants often opted for a sub-optimal outcome, and decision inertia scores varied across these outcomes. Linear regression analysis demonstrated that decision inertia scores significantly predicted decision outcomes, with higher decision inertia scores associated with sub-optimal decision-making. Participants prior moral decision-making did influence subsequent immersive reality decision outcomes and demonstrated a Bayesian updating effect. The Structured Tabular Thematic Analysis highlighted the importance of information validity, decision confidence, and scenario fidelity in decision-making within the immersive environment. The study provides insights into decision inertia in immersive virtual reality critical incidents and offers practical solutions for improving decision-making processes in emergency response contexts. [ABSTRACT FROM AUTHOR]

Explosion in the military zone emergency response plan includes the process of preparedness, risk reduction, and normalization of the emergency. A bomb blast causes human and financial losses to people. Therefore, to prepare and reduce the risk, it is necessary to simulate a bomb blast in a military zone. For this purpose, in this study, the risk of a bomb blast containing ethane, methane, and propane fuel was simulated by the Baker-Strehlow-Tang explosion method in the form of 3 scenarios. According to this method, at a distance of 6, 4.5, and 5 km from the place of ethane, methane, and propane bombs explosion, respectively, the amount of impulse caused by the explosion is reduced by 50%. According to these results, it is clear that the overpressure explosion of ethane, methane, and propane bombs in the military zone is not directly related to the distance from the site of the explosion. The pulse from the explosion of ethane, methane, and propane bombs occurs in one second and will be the same at all distances. [ABSTRACT FROM AUTHOR]

In the context of emergency response applications, real-time situational awareness is vital. Unmanned aerial vehicles (UAVs) with imagers have emerged as crucial tools for providing timely information in such scenarios. Convolutional neural networks (CNN) are effective in image processing. However, the deployment of CNN models in UAVs faces significant challenges. The CNN models involve large number of parameters and energy-costly floating-point computations beyond the memory and power available on-board the UAVs. To address these challenges, we propose a co-design optimization approach for deploying the EmergencyNet CNN model on resource-constrained UAVs. Our strategy includes channel-wise pruning to reduce the size and optimize the network architecture. Additionally, we apply additive powers-of-two (APoT) quantization to further compress the model and enhance computational efficiency. Using channel-wise network pruning we derive TinyEmergencyNet that is only 155KB in memory size and 50% smaller than EmergencyNet. This proposed approach is evaluated on Aerial Image Disaster Event Recognition (AIDER) dataset. We have achieved an F1-score of 93.6% with 4-bit APoT quantization that closely approaches the full precision (32-bit) accuracy of 94%. Furthermore, hardware-friendly bit-shifting operations as a result of APoT quantization present an added advantage in hardware accelerator implementations. This work pioneers the joint application of channel-wise pruning and non-uniform APoT quantization on EmergencyNet, presenting a suitable solution tailored for UAV-based emergency response applications. [ABSTRACT FROM AUTHOR]

Introduction: The recommended treatment for exertional heat stroke is immediate, whole-body immersion in < 10 °C water until rectal temperature (Tre) reaches ≤ 38.6 °C. However, real-time Tre assessment is not always feasible or available in field settings or emergency situations. We defined and validated immersion durations for water temperatures of 2–26 °C for treating exertional heat stroke. Methods: We compiled data for 54 men and 18 women from 7 previous laboratory studies and derived immersion durations for reaching 38.6 °C Tre. The resulting immersion durations were validated against the durations of cold-water immersion used to treat 162 (98 men; 64 women) exertional heat stroke cases at the Falmouth Road Race between 1984 and 2011. Results: Age, height, weight, body surface area, body fat, fat mass, lean body mass, and peak oxygen uptake were weakly associated with the cooling time to a safe Tre of 38.6 °C during immersions to 2–26 °C water (R2 range: 0.00–0.16). Using a specificity criterion of 0.9, receiver operating characteristics curve analysis showed that exertional heat stroke patients must be immersed for 11–12 min when water temperature is ≤ 9 °C, and for 18–19 min when water temperature is 10–26 °C (Cohen's Kappa: 0.32–0.75, p < 0.001; diagnostic odds ratio: 8.63–103.27). Conclusion: The reported immersion durations are effective for > 90% of exertional heat stroke patients with pre-immersion Tre of 39.5–42.8 °C. When available, real-time Tre monitoring is the standard of care to accurately diagnose and treat exertional heat stroke, avoiding adverse health outcomes associated with under- or over-cooling, and for implementing cool-first transport second exertional heat stroke policies. [ABSTRACT FROM AUTHOR]

Planning and decision making in emergency response systems face new challenges due to climate changes and the increased risk of multiple or compound natural hazards. This is especially the case in areas with inexperience of such events. The aim of this paper is to identify important activities concerning planning and decision-making during responses to natural hazards, and their potential need for decision support. The knowledge base of the study consists of interviews with 12 representatives from the Swedish emergency response system, supplemented by documents covering policies, operations, and responsibility. Thematic coding was applied to the interview data for identification of important planning and decision activities. Needs of decision support were identified by applying activity theory to the identified activities. We found needs of decision support connected to eight identified key activities concerning consequence analysis, national reinforcements, and resource management. The results illuminate a lack of technology to support response activities during both single and multiple natural hazards. The findings can inform policy makers of emergency response of where to concentrate the development of tools for collaborative preparedness and response work to cope with future challenges from natural hazards. [ABSTRACT FROM AUTHOR]

Chinese civilian rescue teams (CRTs) emergency response to major disasters in China has effectively complemented national and regional government emergency management systems. However, there has been little research into the CRT's functions or their joint disaster relief procedures. This study employed in-depth interviews and social network analysis to investigate the CRTs' functions and joint disaster relief response procedures during the 2021 Henan flood disaster emergency. It was found that the CRTs had a five-stage emergency response procedure and varied emergency response roles, with the main emergency support functions being search and rescue people and rescue supply transfers and distribution. During joint relief operations, government departments, the coordination center, public institutions, and foundations interact with CRTs in terms of information, personnel and resources; the coordination center was the main information bridge between the CRTs, the local government, and the other relief actors. As the CRTs were bottom-up participants, when they arrived at the disaster site, they were incorporated into the government-led, top-down command-based disaster relief system. Two CRT joint rescue operations were identified: joint rescue alliances in the same region and same brand joint rescue alliances. The results provide information for possible future policies on CRT regulation and development and the integration of CRTs into multi-organizational disaster and emergency response plans. [ABSTRACT FROM AUTHOR]

Landslides often occur in V-shaped valleys exhibiting strong river incisions and large elevation differences between the riverbed and surrounding mountains. This study expounded upon the topographical and flow conditions of typical landslide dam sites through field investigations and data collection and established a small-scale physical model for experimental research. The formation conditions of landslide dams, an important part of the long-term management of such events, were studied. Simultaneously, the experiments verified a series of engineering measures proposed based on calculus theory and implementation principles according to their experimental disposal effects to mitigate short-term flood risks. Results showed that the dam formation is mainly related to topographic characteristics, upstream flow, and dam compositions. Specifically, steep V-shaped valleys should be considered when establishing physical models as they provide great conditions for landslide mass blocking the river completely. In addition, for earth dams, the effects of the position and direction of excavated diversion channels were compared; it showed that when there are multiple passes on the dam crest, priority should be given to lateral excavation of channels from the pass to restrict the lateral development process. The dam-break flow direction should better be orthogonal rather than be parallel to the river bend, which can reduce peak flow discharge by natural energy dissipation. For earth and rockfill dams with wide particle distributions, piles and flexible nets were instead utilized to coarsen the dam breach and reduce peak flow discharge. Finally, applicable values and research significance were summarized based on the obtained results. This research enriches studies of mountain disaster management and provides guidance for on-site emergency response. [ABSTRACT FROM AUTHOR]

In this rapid review, we critically scrutinize the disaster management infrastructure in Saudi Arabia, illuminating pivotal issues of interoperability, global cooperation, established procedures, community readiness, and the integration of cutting-edge technologies. Our exploration uncovers a significant convergence with international benchmarks, while pinpointing areas primed for enhancement. We recognize that continual commitments to infrastructural progression and technology adoption are indispensable. Moreover, we underscore the value of robust community involvement and cross-border collaborations as key factors in bolstering disaster response capabilities. Importantly, we spotlight the transformative influence of emerging technologies, such as artificial intelligence and the Internet of Things, in elevating the effectiveness of disaster management strategies. Our review champions in all-encompassing approach to disaster management, which entails harnessing innovative technologies, nurturing resilient communities, and promoting comprehensive disaster management strategies, encapsulating planning, preparedness, response, and recovery. As a result of our analysis, we provide actionable recommendations to advance Saudi Arabia's disaster management framework. Our insights are timely and crucial, considering the escalating global focus on disaster response in the face of increasing disaster and humanitarian events. [ABSTRACT FROM AUTHOR]

Recent disasters occurring as natural events, industrial incidents, and public health crises, impose unprecedented challenges to humans and communities. Increasing uncertainty and complexity of disasters require multidisciplinary research efforts due to various elements involved in a disaster management system (DMS). Despite the growing body of knowledge regarding disasters, an Ergonomics approach toward the DMS is still limited. To address such a research gap, the current paper presents a Disaster Ergonomics framework that incorporates three essential system elements: demands from a disaster, strategies of responders and response organizations to satisfy the demands, and disaster tools and technologies to bridge the demands and the strategies. Especially, the framework is built upon three pillars of the DMS: resource management, command and coordination, and information and communication. As a form of simple validation, an analysis of 2017 Hurricane Harvey was conducted in line with the framework. Recommended future research topics and directions for full validation of the Disaster Ergonomics framework are also discussed. [ABSTRACT FROM AUTHOR]

On December 29, 2020, nine months after the March Mw 5.4 Zagreb earthquake and amidst the COVID-19 lockdown, a devastating Mw 6.4 earthquake struck near the town of Petrinja, about 50 km SE from the country's capital Zagreb. It was preceded by the Mw 4.9 foreshock from the day before. The main shock claimed 7 fatalities and caused widespread damage. Historical centers of nearby cities with invaluable heritage buildings were significantly affected as were the many residential buildings, built mainly of unreinforced masonry. Damage was observed as far as 60 km from the epicenter. This paper summarizes the seismological aspects of the Mw 6.4 Petrinja earthquake, the emergency response and the main impacts to people and buildings. The description and findings are based on the field observations and a series of post-earthquake activities led by the team of the Faculty of Civil Engineering, University of Zagreb. Typical damage to buildings and usability data are presented with examples based on 50,000 inspection results. By far the most affected were the unreinforced masonry buildings, followed by confined masonry, whereas reinforced concrete buildings were the least affected. The total direct and indirect losses are estimated to 4.8 billion EUR. The provided information represents a useful basis and impetus for improving emergency action and long-term disaster reduction plans in other regions with similar building exposure and seismotectonic settings. [ABSTRACT FROM AUTHOR]

Rapid detection and quantification of gross alpha/beta-emitting radionuclides by liquid scintillation counting (LSC) is vital in guiding response to a nuclear or radiological incidents. Liquid scintillation counters use signal pulse shape to discriminate alpha and beta events in samples but require precise optimization to minimize the spillover, or misclassification, of those events. In this study, samples at varying activity levels were analyzed by LSC to determine the effect of activity level, emitter type, and sample matrix on spillover. Analysis proved a matrix effect and a direct correlation of activity level on spillover percentage for both alpha and beta emitting-nuclides. [ABSTRACT FROM AUTHOR]

This paper presents an equilibrium-based modeling framework for emergency response (ER) workload balancing for robust operations in metropolitan areas. The problem is formulated as a non-linear mathematical program (NLP), which determines the optimal maximum workload for each ER station such that the weighted sum of the area-wide expected response time and its variation is minimized. The concept of Marginal Cost of Uncertainty (MCU) is introduced to measure the impact of a station's workload increase on the robustness of the area-wide service performance. The solution of the NLP is proved to be equivalent to a state of equilibrium in which all stations have a minimum MCU. An iterative solution methodology is developed, which adopts a modified version of the Frank-Wolfe decomposition algorithm for convex optimization. The workload is iteratively balanced among adjacent stations until the state of equilibrium is achieved. At equilibrium, no station can reduce its MCU value by unilaterally shifting a part of its workload to any other station(s) in the area. The developed framework is applied to determine the optimal workload balancing strategy for 58 fire stations serving the City of Dallas. The framework is shown to enhance the robustness of the ER service performance especially in situations with imbalanced workloads. [ABSTRACT FROM AUTHOR]

The traditional "forecast-response" paradigm is facing significant practical challenges. For example, when different scenarios require different reaction mechanisms, the applicability of this method is weak since this paradigm describes the crisis itself from a macroperspective, neglecting analyzing emergency response measures from a microperspective. Hence, in this paper, the research paradigm will be shifting to "scenario-response" paradigm, which analyzes the impact of corresponding measures on events from a microscopic perspective. In 2021, an expected and torrential rainfall in Zhengzhou, Henan, China, causing caused 398 deaths and an estimated direct economic loss of 120.6 billion RMB. Accordingly, an empirical analysis was conducted for this heavy rain event to examine the intricate evolution of emergency response utilizing a constructed scenario Bayesian network. This network was constructed by fusing the knowledge meta-theory, scenario evolution and Dempster's rule of combination along with 362 relevant historical representative events, and it has the capability to identify the development of the various emergency events and fuse the assessments of different experts. The effects of each measure on the probability of response outcomes were analyzed in an event-driven Bayesian network. The counterfactual outcomes of the interventions were also explored through causal inference to determine the priority measures in the emergency response process. The similarity between each target scenario and each source scenario reached more than 0.7, among which the highest similarity reached 0.78. The evolutionary accuracy of the incident response process was examined by comparing the scenario similarity. The method proposed in this study can help as a theoretical basis for implementing the "scenario response" paradigm. [ABSTRACT FROM AUTHOR]

Due to its harmful nature, any incident associated with hazardous material (hazmat) may cause tremendous impacts on the surrounding people and the environment. Focusing on the incident involving this specific type of good, we develop a reliable and robust emergency logistics network that considers both demand uncertainty and possible unavailability of particular links. A time-based risk measure is carefully designed upon the traditional risk assessment to reflect the stakeholder's sensitivity to risk over response time. The disruption and uncertainty are modeled as two sets of scenarios which are integrated into a bi-objective robust model to evaluate the trade-offs between risk and cost. The effectiveness of the emergency response can be assured by expenditures that add extra capacities to certain links or establish additional facilities that aid recovery from incidents. We apply our model and approach to a real-world network in Guangdong China. Analytical results reveal the necessity of embedding consideration of uncertainty and unreliability into emergency network design problems; outline the importance of hedging against unpredictability by system redundancies; and indicate the impact of stakeholder's orientation towards cost and risk on the location, allocation, and routing decisions in hazmat emergency response. [ABSTRACT FROM AUTHOR]

Copyright of Zentralblatt fuer Arbeitsmedizin, Arbeitsschutz und Ergonomie is the property of Springer Nature 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.)

This study examined how sociodemographics, risk perception, and prior experience affected typhoons' disaster preparedness and emergency response. A total of 286 valid questionnaires were obtained from a typhoon-prone district in Hong Kong. The results indicated that sociodemographics, risk perception, and prior experience explained significant amounts of variances in protective behaviors. Among these variables, risk perception was found to be the most powerful predictor and it mediated the effects of prior experience on disaster preparedness and emergency response toward typhoons. Furthermore, disaster preparedness initiated emergency response toward typhoons, indicating the spillover of protective behaviors. The paper contributes to the knowledge body by providing a better understanding of the process and mechanism of the performance of protective behaviors. Furthermore, this study may provide insights into improving typhoon management in Hong Kong as well as other coastal cities in Asia. [ABSTRACT FROM AUTHOR]

The measurement of uranium (U) isotope ratios in urine provides valuable information about the source of U exposure in humans and can be vitally important in a radiological emergency. This method provides rapid and accurate results for 235U/238U at 235U concentrations as low as 0.42 ng/L, which is equivalent to ~ 200 ng/L of total U for a depleted U (DU) at a 235U/238U ratio of ~ 0.002. The results are within 6% of Certified Reference Materials target values and agree with Department of Defense Armed Forces Institute of Pathology inter-laboratory comparison target values with a bias range of -6.9–7.6%. [ABSTRACT FROM AUTHOR]

Emergency service personnel experience high levels of psychological distress, with increasing evidence of associations with shame and trauma. Additionally, adolescence is a critical time in social and cognitive development, in which shame plays an important role. In Australia, adolescent volunteer surf lifesavers (SLS) are particularly vulnerable due to exposure to potentially traumatic experiences (PTEs) such as rescues and resuscitation of human lives. The aim of this study was to investigate the association between direct or indirect PTEs, and the relationship PTSS and shame may have in adolescent surf lifesavers. This cross-sectional study surveyed patrolling adolescent SLS, aged 13–17 years, recruited via internal communications and social media groups. Complete responses from patrolling adolescents (n = 118; 59% female; mean age 15.4 years) were used to determine exposure to PTEs across global, direct, and within SLS trauma domains. Associations between demographics, PTEs, post-traumatic stress symptoms (PTSS), with shame as a moderator were assessed for each trauma domain. PTEs and PTSS were positively associated across trauma domains. Shame was identified as a significant predictor of PTSS and as an important moderator of PTSS for experiences within SLSA, but not global or direct trauma. By exploring links between PTEs, PTSS, and shame, these findings contribute to the development of strategies and interventions for adolescents during stressful times. Responsiveness to adolescents via feedback and genuine, reassuring relationships that acknowledge the complexity of coping with stressful situations, may be potentially effective approaches to support coping with experiences of shame following PTE’s in adolescent surf lifesavers. [ABSTRACT FROM AUTHOR]

The Samos Island (Aegean Sea) Earthquake occurred on 30 October 2020. It produced a tsunami that impacted coastal communities, ground shaking that was locally amplified in some areas and that led to collapse of structures with 118 fatalities in both Greece and Turkey, and wide-ranging geotechnical effects including rockfalls, landsliding, and liquefaction. As a result of the global COVID-19 pandemic, the reconnaissance of this event did not involve the deployment of international teams, as would be typical for an event of this size. Instead, following initial deployments of separate Greek and Turkish teams, the reconnaissance and documentation efforts were managed in a coordinated manner with the assistance of international partners. This coordination ultimately produced a multi-agency joint report published on the 2-month anniversary of the earthquake, and this special issue. This paper provides an overview of the reconnaissance activities undertaken to document the effects of this important event and summarizes key lessons spanning topic areas from seismology to emergency response. [ABSTRACT FROM AUTHOR]

Copyright of Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz is the property of Springer Nature 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.)

Most have experienced the impact of vehicular accidents, whether it was in terms of increased commute time, delays in receiving goods, higher insurance premiums, elevated costs of services, or simply absorbing the daily tragedies on the evening news. While accidents are common, the complexity and dynamics of transportation systems can make it challenging to infer where and when incidents may occur, a critical component in planning for where to position resources for emergency response. The use of response resources is critical given that more efficient emergency responses to accidents can decrease the vulnerability of socio-economic systems to perturbations in the transportation system and contribute to greater resilience. To explore the resilience of transportation systems to disruptions due to vehicular accidents, a location modeling approach is described for identifying the origins of optimal responses (and associated response time) over time based upon the location of known accidents and response protocols. The characteristics of the modeled response can then be compared with those of the observed response to gain insights as to how resilience may change over time for different portions of the transportation system. The change in the location of the optimal sites over time or drift, can also be assessed to better understand how changes in the spatial distribution of accidents can affect the nature of the response and system resiliency. The developed approach is applied to investigate the dynamics of accident response and network resiliency over a three year period using vehicular crash information from a comprehensive statewide database. [ABSTRACT FROM AUTHOR]

Alleviating human suffering in disasters is one of the main objectives of humanitarian logistics. The lack of emergency rescue materials is the root cause of this suffering and must be considered when making emergency supply distribution decision. As large-scale disasters often cause varying degrees of damage to different influenced areas, which will cause differences in both human suffering and the demand for emergency supply in influenced areas. This paper considers a novel emergency supply distribution scenario in humanitarian logistics, which takes into account these differences. In the scenario, besides the economic goals such as minimizing costs, the humanitarian goal of alleviating the suffering of survivors is treated as one of the main bases of emergency supply distribution decision making. We first apply Markov Decision Process to establish the formulation of the emergency supply distribution problem. Then, to acquire the optimal resource allocation policy that can reduce the economic cost while decreasing the suffering of survivors, a Deep Q-Network-based approach for emergency supply distribution in Humanitarian Logistics (DHL) is developed. Numerical results demonstrate DHL has better performance and lower time complexity to solve the problem by comparing with other baselines. [ABSTRACT FROM AUTHOR]

Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments. [ABSTRACT FROM AUTHOR]

Atmospheric tank fire incidents demand a considerable amount of logistics and planning support and serious response operation. Tank fire incidents challenge response teams and usually are difficult to manage. Some organizations have several atmospheric tanks, and they need to select the worse scenarios to pre-plan. Therefore, it is necessary to develop some tools to select the most difficult tanks fires scenarios. First, the factors affecting the firefighting operation of atmospheric tanks were identified through reviewing available scientific sources, and then, these factors were divided into four groups: tank-related, response team-related, tank peripheral, and logistic factors. Then, the selected factors were finalized with the opinion of experts and weighed by Chang's Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method. The results of the study showed that among the influential factors in tank fire extinguishing management, "pre-incident planning (PIP)" (weighing 0.14551), "use of incident command system structure in response" (weighing 0.14549), "distance to adjacent tanks and facilities (domino potential)" (weighing 0.14451), "knowledge and experience" (weighing 0.14371), and "bund wall standard and integrity" (0.1443) have the most impact, respectively. [ABSTRACT FROM AUTHOR]

Information sharing systems are a critical component of emergency response—especially in campus attack situations that unfold very rapidly. The design of effective information sharing systems is often difficult, however, due to a lack of data on these assault events. This work takes an agent-based approach to simulate three campus emergency information sharing system design alternatives in the context of a college campus knife attack, and incorporates data from on-campus student surveys and parameter tuning experiments. Alternatives are evaluated according to: (1) improved student attack response outcomes; and (2) effective institutional response to the attack. The results confirm that increased awareness supports rapid emergency reporting, but an important gap exists between students' awareness and their ability to respond effectively, which depends on a number of campus-specific factors. A strong positive impact is seen from safe and efficient information sharing with authorities. This impact depends largely on reporting system implementation qualities, as opposed to campus-specific factors. On a campus in China, WeChat was used as a basis for messaging models. The simulation results show a 9% drop in casualties and a 22% faster police response time from a text-based reporting system using "base" WeChat features instead of traditional phone reporting. Our results also project a 30% drop in casualties and 52% faster police response time using a system designed around a WeChat Mini Program or stand-alone campus emergency reporting app. These outcomes suggest a number of recommendations for improving outdated campus emergency information-sharing systems and response strategies. [ABSTRACT FROM AUTHOR]

During an emergency response to a sudden fire, the video-driven techniques for detection and real-time analysis can significantly affect the scientificity and effectiveness of disposal, thus changing the process of fire rescue. In this paper, an information inversion model for video-driven fire based on object-oriented segmentation has been proposed and programmed with Python. Firstly, a video fire detection algorithm based on deep learning was adopted. Secondly, fire regions were segmented using the SEEDS superpixel-based method, from which the flame size with time was further extracted. Then the heat release rate was inversed over time. Finally, the fire development was analyzed based on the characteristics of flame images. The model was applied to three different types of fire, a diesel pool fire, a propane fire, and a full-scale wood crib fire. The results indicated that the model could achieve real-time information such as flame height, projection area, and volume, and the spatial–temporal evolution laws could be further estimated quickly. Committed to intelligent analysis of fire environment based on surveillance cameras, the model can provide technical assistance for fire-fighting and on-site emergency rescue. [ABSTRACT FROM AUTHOR]

Due to the catastrophic consequence of possible incidents caused by the storage and transportation of hazardous materials, effectively mitigating such risks is of vital importance. This paper proposes a gradual-coverage game describing the situation of locating emergency facilities, to which the Shapley value can be used to evaluate the candidate locations. To be specific, the rescue risk and utility are assessed in terms of the response time to construct a gradual-coverage game. Then, we characterize the Shapley value by proposing two new axioms. Furthermore, to ensure that the analytical model can be practically applied to real-world situations, an algorithm for locating emergency facilities is designed based on the properties of the Shapley value, which is then tested for efficiency. [ABSTRACT FROM AUTHOR]

In the early days of the Covid-19 pandemic, China implemented the most stringent and serious emergency response. To understand the effect of such an emergency response strategy on the economic system, this study proposed a simultaneous overall estimation method using intracity travel intensity data. The overall effect is represented by the difference between intracity travel intensity with and without the emergency response. Using historical data and time series analysis, we compared intracity travel intensity post China's implementation of the emergency response with predicted intracity travel intensity without such a response. The loss rates, defined by the proportion of intracity travel intensity loss, were calculated for 360 cities within 33 provincial-level regions in China based on data availability. We found that 30 days after the emergency response, 21% of the cities saw over 80% recovery and 10% of the cities showed more than 90% recovery; 45 days after the emergency response, more than 83% of the 360 cities witnessed 80% recovery. The correlation between gross domestic production loss rate and travel intensity loss rate was studied quantitatively to demonstrate the representativeness of the intracity travel intensity loss rate. This indicator was also used to analyze the spatial and temporal patterns of the effects on the economy. The results of this study can help us understand the economic effects caused by the early Covid-19 emergency response and the method can be a reference for fast and real-time economic loss estimation to support emergency response decision making under pandemic conditions. [ABSTRACT FROM AUTHOR]

In the aftermath of natural disasters, emergency medical teams (EMTs) are dispatched to help local rescue efforts. While some impact evaluations of EMTs are available, few comprehensive evaluations of the implementation of EMTs in natural disasters, have been published to date. As a result, the evidence base to inform global guidelines and best practices, is remarkably thin. This paper aims to provide a better understanding of the role of EMTs, by summarizing recent reports and case studies. Specifically, this summary aims to identify key improvement areas, as well as obstacles and opportunities for improvement. After a search of the literature, 40 publications met the inclusion criteria, and were included in this summary of the literature. The effective functioning of EMTs is codependent on interactions between different actors, including national governments, international organizations, NGOs, local government agencies, community stakeholders and the private sector. Five key improvement areas were identified: (1) coordination and integration of EMTs and other actors; (2) systematic classification and registration of EMTs; (3) national stewardship; (4) community engagement; (5) research and data collection. As the prevalence of natural disasters rise, effective disaster response will be an increasingly important component of global health in the coming decades. To optimize EMT efficiency, there needs to be increased recognition of the different actors involved, increased cooperation amongst EMTs under the coordination of international rosters, and increased research efforts to evaluate challenges to and opportunities for improved disaster response. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to test and validate a new hospital Mass Casualty Incident (MCI) training using MACSIM® (MAss Casualty SIMulation) system adapted to the specifications and MCI plan of a single hospital. Methods: The original MCI training format called MACSIM-PEMAF (Piano di Emergenza per il Massiccio Afflusso di Feriti, i.e., hospital disaster plan for massive influx of casualties) was developed for the Italian Society for Trauma and Emergency Surgery (SICUT) in 2016. It uses MACSIM®, a simulation tool for the training and assessment of healthcare professionals in MCI management. Between 2016 and 2018 the course was held several times at a university hospital in the Milan metropolitan area. The MACSIM® tool was used to reproduce different MCI scenarios with actual hospital resources. During the simulations, participants acted in their usual professional functions, testing both the local MCI plan as well as the individuals' knowledge and skills. Course effectiveness was validated by a pre- and post-curse self-assessment questionnaire. Results: MACSIM-PEMAF was tested over 7 courses, with a total of 258 participants. Pre- and post-course questionnaires showed a significant improvement for hospital staff in self-reported perceptions of knowledge and skills in MCI management. In total, on a 1–10 scale, all the staff increased their competencies from a value of 4.4 ± 2.5 to 7.5 ± 1.9 (p < 0.001). Conclusion: MACSIM-PEMAF demonstrated efficacy in fulfilling the requirements of Italian law for PEMAF implementation, testing local resources and resilience, as well as increasing the self-reported perception of the hospital staff ability to respond to a MCI. [ABSTRACT FROM AUTHOR]

Earthquakes as a natural hazard have caused substantial economic losses and human life loss in many countries. Taiwan, which is located on the western Circum-Pacific seismic belt, encountered this problem in the form of the Meishan, Hsinchu-Taichung, and Chi-Chi earthquakes a few years ago. In this paper, the researchers propose a novel robot-event integrated system capable of doing the automated inspection and emergency response due to a significant earthquake. As the household's earthquake warning receiving device picks up an alert, its built-in wireless communications system will send a signal to the robot. The robot will then commence the inspection of the indoor area via real-time image recognition and tracking. Upon detecting fallen people, it will approach them, regulating their movements via the robot operating system monitoring interface. The robot is designed to operate in a house that remains standing with acceptable damage in which the furniture might be falling and injure the occupants after an earthquake hit. The indoor experiment was conducted to verify the robot system and operation with a designed condition such as fallen and non-fallen people as the detected object. The precision of the robot arm was tested in the case of the delivery of supplies to the fallen people while waiting for the rescuers to arrive. The tests indicated that the proposed smart robot has prospective implementation in real-world applications with more research and development. The smart robot integrated with an earthquake early warning system is a promising approach to the temporary care of people affected by earthquakes. [ABSTRACT FROM AUTHOR]

This article aims to provide a quantitative study of immediate food supplies based on a three-stage analysis. Firstly, a numerical autoregressive integrative moving average (ARIMA) model is estimated based on the data of 14 key commodities in Sendai City of Japan during the 2011 Tohoku Earthquake. Though the temporal patterns of key food commodity groups are qualitatively similar, the results show that they tend to follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. Second, 3 of the 14 items are found to be significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements at an early stage. Finally, a network simulation of response operations performed in temporary shelters based on four specific policies shows that the exploration of the characteristics of critical emergency supplies is the key that strategically enables both effectiveness and efficiency when responding to a disaster situation. [ABSTRACT FROM AUTHOR]

Online teaching within disciplines such as Engineering require experiential learning that equip future graduates with highly intellectual and professional skills to meet the demands of employers and the industry. The outbreak of COVID-19 however, has shifted the academic community into new landscapes that require educators and students to adapt and manage their expectations. Although literature reports on research attempts to study the implications of Covid-19 on the Higher Education curricular, little has been reported on its impact on Engineering Education. This paper therefore uses the theory of Emergency Management Life Cycle (mitigation, preparedness, response, and recover) as a lens to examine the challenges faced by students and academics and coping mechanism during the COVID period. This study adopts a mixed method approach using a case study from the College of Engineering at a Higher Education Institution in the UAE due to the sudden migration to online teaching amid COVID-19. Data is collected through interviews and surveys with both students and instructors on challenges, strategies and online delivery good practices that enhanced students' learning experience. The results show that, Technology Supported Learning tools are capable of enhancing students' experiential learning and associated competencies, however there were a number of pedagogical, technological and psychological challenges that faced students and instructors as a result of the sudden migration online, which are likely to play a role in the impediment of the students' learning cycle, due to the lack of preparedness in response to the state of emergency created by Covid-19. Despite these challenges, the study found that instructors with effective communication skills and teaching style, competent use of technology, flexible, friendly and supportive attitude towards teaching, played a positive role in mitigating for the lack of preparedness in response to sudden migration online. The study also reveals that by overcoming some of the technical challenges such as slow internet connection and interruptions, lessons learnt from the sudden migration to online delivery amid COVID-19, will help create new opportunities for the use of blended learning approaches to meet the needs of the on-going COVID and future online deliveries. [ABSTRACT FROM AUTHOR]

Purpose Of Review: The COVID-19 pandemic has impacted lives globally, posing unique challenges to mental health services exposing vulnerability and limitations within these systems. During the course of the pandemic, telecommunications technologies (e-mental health care) have served a critical role in psychiatric care. It is important to understand current lessons learned in e-mental health care and implications for global mental health systems for both emerging from the pandemic and after the pandemic has ended.Recent Findings: There are significant regulatory, policy, and evaluation challenges for global e-mental health impacting patients, clinicians, health systems, and decision-makers. These include complex regulatory issues, difficulties of providing care across boundaries, and keeping pace with the implementation of new technologies in behavioral health. The collaborative development of global standards along with policies, appropriate regulations, and developing new models of research and development opens the possibility of improved access to care across national boundaries. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic had a tremendous effect on people's mental health. Healthcare workers were on the front lines in response to this crisis; therefore, they were among the most affected by the pandemic. The study aims to assess the stress perceived by healthcare workers and possible factors contributing to it, hoping that more efforts will be exerted to support the well-being of healthcare workers during public health emergencies. A cross-sectional study using an online survey was conducted. Data were collected from 118 healthcare professionals working with COVID-19 patients during the peak of the first wave of the COVID-19 pandemic. Results: Most of the respondents (75.2%) scored much higher stress levels than average, and 19.5% had slightly higher levels of stress. Most respondents were afraid of infecting their family and close ones (77.1 %), and about half of the respondents were afraid of getting infected (47.5%). Regression analysis revealed that the only significant independent variable predicting developing higher stress levels among the participants was assigning them to tasks outside their specialty. Conclusions: Healthcare workers are at high risk of developing mental health problems during public health emergencies. Their well-being is essential for the quality of services they provide. More efforts are needed to ensure the well-being of healthcare workers and to prepare them for such emergencies. Preparing healthcare workers before redeployment through training and providing PPEs will help to reduce the negative impact of the COVID-19 pandemic on their physical and mental health. [ABSTRACT FROM AUTHOR]

The major key attributes of decision-making during emergency to de-escalate disaster, reduce fatality and prevent asset loss are time and the efficiency of the process. Decision-makers faced the challenge of accessing adequate and precise information during emergency cases due to the time limitation, inadequate data on and about the disasters and thus decision-making process becomes complex and complicated. A well-advanced and developed mathematical tool is required to respond adequately in the presence of these challenges. The current study investigates the effects of post-flood management plans in Iran through sustainable development features in the possible early time. A new hybrid emergency decision-making approach integrating the best–worst method (BWM), Z numbers and zero‐sum game is proposed to ensure much more effective responses in realistic cases. The importance weights of criteria are computed using the BWM, the payoff assessments of decision-makers are collected employing the Z numbers, and finally, the zero‐sum game method is utilized to rank the alternative of emergency solutions. The proposed hybrid approach assists the decision-makers to deal decisively with the ambiguity associated with the data for assessing and evaluating the emergency circumstances. To show the efficiency of the proposed approach, a real-life example of the Golestan flood of 2019 is presented. More so, a comparison analysis is performed to assess the practicability and feasibility of the proposed hybrid approach. The result indicates that the proposed methodology has considerable merits compared with the existing tools and can adequately deal with these shortages. In this case, the aircraft emergency delivery system of the relief supplies is obtained as the best solution to the problem. [ABSTRACT FROM AUTHOR]

A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with lack of medical experts and state-of-the-art clinical equipment, especially in developing countries. The machine intelligence aided medical diagnosis systems can provide rapid, dependable, autonomous, and low-cost solutions for medical diagnosis in emergency conditions. In this paper, a privacy-preserving computer-aided diagnosis (CAD) framework, called Decentralized deep Emergency response Intelligence (D-EI), which provides secure machine learning based medical diagnosis on the cloud is proposed. The proposed framework provides a blockchain based decentralized machine learning solution to aid the health providers with medical diagnosis in emergency conditions. The D-EI uses blockchain smart contracts to train the CAD machine learning models using all the data on the medical cloud while preserving the privacy of patients' records. Using the proposed framework, the data of each patient helps to increase the overall accuracy of the CAD model by balancing the diagnosis datasets with minority classes and special cases. As a case study, the D-EI is demonstrated as a solution for COVID-19 diagnosis. The D-EI framework can help in pandemic management by providing rapid and accurate diagnosis in overwhelming medical workload conditions. [ABSTRACT FROM AUTHOR]

On 22 March 2014, a massive, catastrophic landslide occurred near Oso, Washington, USA, sweeping more than 1 km across the adjacent valley flats and killing 43 people. For the following 5 weeks, hundreds of workers engaged in an exhaustive search, rescue, and recovery effort directly in the landslide runout path. These workers could not avoid the risks posed by additional large-scale slope collapses. In an effort to ensure worker safety, multiple agencies cooperated to swiftly deploy a monitoring and alerting system consisting of sensors, automated data processing and web-based display, along with defined communication protocols and clear calls to action for emergency management and search personnel. Guided by the principle that an accelerating landslide poses a greater threat than a steadily moving or stationary mass, the system was designed to detect ground motion and vibration using complementary monitoring techniques. Near real-time information was provided by continuous GPS, seismometers/geophones, and extensometers. This information was augmented by repeat-assessment techniques such as terrestrial and aerial laser scanning and time-lapse photography. Fortunately, no major additional landsliding occurred. However, we did detect small headscarp failures as well as slow movement of the remaining landslide mass with the monitoring system. This was an exceptional response situation and the lessons learned are applicable to other landslide disaster crises. They underscore the need for cogent landslide expertise and ready-to-deploy monitoring equipment, the value of using redundant monitoring techniques with distinct goals, the benefit of clearly defined communication protocols, and the importance of continued research into forecasting landslide behavior to allow timely warning. [ABSTRACT FROM AUTHOR]

This paper highlights the principal features of the Mw5.4 Zagreb earthquake. Located within the city limits at a depth of 10 km, the earthquake generated a peak ground acceleration of more than 0.2 g and a maximum spectral acceleration of about 0.6 g at 0.1 s in the historic downtown area. The situation was particularly challenging since the event occurred amid a partial Covid-19 lockdown at temperatures close to 0 °C, emphasizing the extensive and complex vulnerability of the local communities and individuals. 27 people were reported severely injured, one of which later died. The surprisingly high economic costs, needed to achieve a full reconstruction of damaged buildings and infrastructure in the affected area, are currently evaluated at more than 10B euros. Description of the organization of the emergency response in the first days and the observed damage to buildings is given with typical examples. The focus is on the performance of older masonry residential and cultural heritage buildings in the historic downtown, their inspection and evaluation of damage to structural and non-structural components. This information provides the basis for understanding of the negative impacts and clarifies the overall context identifying the enablers and barriers to the still ongoing recovery process. It also helps to increase the awareness of the seismic vulnerability of European cities with similar construction practices. [ABSTRACT FROM AUTHOR]

Federal Regulation has suggested that all passengers in the airplane should finish the evacuation within 90 s. This research applied a Discrete Event Simulation (DES) model to investigate passengers' evacuation paths and decision-making processes for aircraft evacuation under emergency. ARENA® 14 was used on an Airbus A380 cabin configuration with capacity of 538 as the baseline model. Total evacuation time and the congestion conditions of each gate were used as a measure for the evacuation process. The baseline model was validated using a real world A380 certification evacuation drill. An experiment was conducted based on the modification of the baseline model to investigate the effect of different path planning strategies on total evacuation time. Results suggested that a flight attendant directed approach yielded significantly more efficiency than passenger random exit choosing and switching exits. Practical implications and future study were given at the end of the paper to consider a more realistic and dynamics simulation approach to address this problem. [ABSTRACT FROM AUTHOR]

As modern cities expand and develop, the resultant increase in population density gives rise to the need for smart solutions to cope with the demands applied to the infrastructure of the city. In this paper, we investigate the shortcomings of traffic policing and emergency response handling systems; propose an intelligent, autonomous UAV-enabled solution; and describe the system in a simulated environment. Several scenarios of traffic monitoring and policing system are considered in the simulation: traffic light violations and accident detection, mobile speeding traps and automated notification, congestion detection and traffic rerouting, flagged stolen vehicles/pending arrest warrants and vehicle tracking using UAVs, and autonomous emergency response handling systems. Furthermore, smart city infrastructure enable intelligent handling of emergencies by providing traffic light prioritization for ground emergency response units to reduce delay for patient care, automated physical bollard on routes with congested points due to accidents or hazards, first responder support UAV units—medical supplies UAV, fire fighting UAV to combat or control small fires, and numerous other benefits. Lastly, we present the results of the simulated system and discuss our findings. [ABSTRACT FROM AUTHOR]

In the case of a radiological or nuclear incident, valuable information could be obtained in a timely manner by using liquid scintillation counting (LSC) technique through fast screening of urine samples from potentially contaminated persons. This work describes the optimization of LSC parameters on PerkinElmer (PE) Tri-Carb and Quantulus GCT series instruments to develop a rapid method for screening urine in an emergency response situation. [ABSTRACT FROM AUTHOR]

With the increasing occurrence of disasters, how to respond to disasters has attracted a lot of interest. However, a systematic study of emergency response in disasters (ER) has been ignored. Based on the bibliometric analysis and visualization of 3678 journal articles (1970–2019) related to ER from the Web of Science, the current research situation in the field of ER has been studied. The important research questions and trends in the field of ER have been identified. In this study, the VOSviewer software is used to visually analyze publications about ER. Specifically, the most influential journals and authors, highly cited publications, authoritative research institutions, and co-citation are identified in this study. Moreover, the Ucinet software is applied to analyze the social network of 45 high-frequency keywords and the Citespace software is used to study the keyword citation bursts. The results show that the keywords "optimization" and "demand" are the most popular research interests. Besides, four main research topics on ER are summarized, which are emergency supplies and facility-related research, emergency preparedness and security-related research, emergency decision system-related research, and emergency medical rescue and cure-related research. Finally, this study puts forward the research work that should be paid attention to in the future according to different research topics, which can help researchers to understand the ER research progress systematically and focus on emerging research questions. [ABSTRACT FROM AUTHOR]

Summary: Objective: To summarize the successful experience of timely crisis management, correct measures, and successful display of the hospital image in the First Affiliated Hospital of Zhejiang University (FAHZU), to improve the ability of emergency response. Methods: The FAHZU, as the earliest designated hospital, accomplished the transformation from general hospital to infectious disease hospital under the guiding ideology of centralized patients, centralized experts, centralized resources, and centralized treatment with measures to transfer the Zhijiang campus hospitalized patients quickly, complete the space layout, create diagnosis and treatment space, streamline logistics, and transform logistics facilities within 48 h. As of 5 March, the hospital had admitted 104 patients. Results: Of the severe cases in Zhejiang province 95% underwent centralized treatment with the goal of zero deaths for severely ill patients, zero misdiagnoses for infected patients, and zero infections for medical staff, and this served as a reference for large medical institutions regarding how to manage such a public health emergency. Conclusion: The successful cases of FAHZU provided a valuable experience for large medical institutions on how to address public health emergencies and how to carry out diagnosis and treatment and streamline the layout and related facilities in emergency reconstruction. [ABSTRACT FROM AUTHOR]