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Improving the efficacy of emergency responses with digital means is receiving increasing attention. Currently, several innovative information technologies and systems are being developed to raise the situation awareness of first responders like firefighters. Among them, emergency response information systems (ERIS) appear to provide a particularly promising platform, which helps to gather, analyze, and share relevant information during emergencies. However, the conditions under which firefighters accept or reject such systems remain unclear. Existing theories explain the acceptance of information technologies only on a general level that does not consider the specific usage constraints existing in the firefighter domain. To fill this literature gap, we propose a detailed, domain-specific acceptance model with factors that explain the acceptance of ERIS by firefighters. It combines findings of the user satisfaction and the technology acceptance literature and was developed based on the input of 82 domain experts. An evaluation of the acceptance model in a survey with 212 firefighters from Germany indicates that it is effective in predicting a firefighter's intention to use an ERIS. The identified acceptance factors provide guidance for the design and evaluation of ERIS, enabling the so far mostly theoretical benefits of ERIS to be transferred into practical applications more effectively.

The high-speed train control system is essential to the safety and efficiency of train operation. With the rapid increase of high-speed railway (HSR) operating mileage and development of information technology, the disposal flow and methods in emergency response are still based on dispatchers and drivers' experience within the ``layered'' architecture of current system. There is a certain gap between current processing methods and effective resolution, which may even cause the spread of delay along with the railway networks. Therefore, we propose an integration system of operation control and online rescheduling to improve the recovery ability of HSR carrying capacity. We first describe the framework, information flow, and disposal process of the current system and analyze the shortcomings in handling emergencies. Then, the basic concept, system structure, and framework of the integration system are introduced. Finally, taking temporary speed restriction caused by strong wind as an example, we also analyze the principle of why and how the integration system can promote the recovery ability of HSR carrying capacity.

Natural and man-made disasters are becoming more prevalent and increasing in danger as climate change continues unabated and resources get scarce. Whether it be flood or terrorism, disasters displace people and it remains difficult to reach those in need when the disaster does strike. For years, mobile phone networks have been integral in responding to emergencies, but they rely on costly infrastructure that is prone to outages or outright attack. We propose a long-range, low-power mesh network infrastructure based on a new, still-developing networking protocol and embedded software implementation that seeks to fill the need of disaster communications telemetry gathering for a fraction of the cost of mobile phone networking infrastructure such as 4G LTE or 5G. While the protocol is simple, it was found to have an inadequate implementation at this stage. Error rates were high and bugs were found in implementation that led to an abnormal amount of time spent processing corrupted data, though power consumption was encouraging in spite of these conditions. We examine the strengths and weaknesses of this new protocol and suggest improvements to harden the implementation.

The ability to efficiently plan and execute search missions in challenging and complex environments during natural and man-made disasters is imperative. In many emergency situations, precise navigation between obstacles and time-efficient searching around 3D structures is essential for finding survivors. In this work we propose an integrated assessment and search planning approach which allows an autonomous UAV (unmanned aerial vehicle) agent to plan and execute collision-free search trajectories in 3D environments. More specifically, the proposed search-planning framework aims to integrate and automate the first two phases (i.e., the assessment phase and the search phase) of a traditional search-and-rescue (SAR) mission. In the first stage, termed assessment-planning we aim to find a high-level assessment plan which the UAV agent can execute in order to visit a set of points of interest. The generated plan of this stage guides the UAV to fly over the objects of interest thus providing a first assessment of the situation at hand. In the second stage, termed search-planning, the UAV trajectory is further fine-tuned to allow the UAV to search in 3D (i.e., across all faces) the objects of interest for survivors. The performance of the proposed approach is demonstrated through extensive simulation analysis., Comment: 2021 International Conference on Unmanned Aircraft Systems (ICUAS)

Unmanned Aerial Vehicles (UAVs) have been used for accurate orthophoto generation based on advanced Global Navigation Satellite System (GNSS) techniques. In recent years, the UAV systems have become an effective tool for fast monitoring of damages caused by disasters such as the earthquake hazards. The conventional orthophoto generation based on ground control points takes too much time during emergency situations. In the study, different methodologies for the processing of the acquired GNSS Positioning data for direct georeferencing of UAVs were investigated in terms of various orbit products. Evaluating the fitness for emergency response applications, the ground control points (GCPs) also used for validation of the generated orthophoto without using GCPs and based on Precise Point Positioning (PPP) approach. In this study, Ultra-Rapid, Rapid and Final PPP methods based on GNSS observations were used for direct geo-referencing. Thirteen GCPs were located at the study area for the validation of the orthophoto accuracy generated by direct geo-referencing.

Newly registered nurses’ emergency response capability falls short of their employers’ expectations. Therefore, they need to develop this ability to cope with various changes in the clinic. Unfortu...

For improving the emergency capacities during road transportation accidents of dangerous goods, this paper divides the accident emergency procedure into four subsystems: accident information processing, personnel rescue and evacuation, vehicle evacuation, and rescue materials delivery. On this basis, the causal mechanism and the loop diagrams of the emergency process are established by using the System Dynamics Method and Vensim software. First, six factors including transportation distance, the degree of mechanical modernization, delay time of personnel control, delay time of traffic information release, command level coefficient, and delay time of department arrival are selected as parameters to test the model’s sensitivity analysis. Then, the influences of the abovementioned factors on the observed value, such as the demand gap of on-site materials, number of people in safe area, number of vehicles in safe area, amount of disposal information, and the dynamic evolution behavior of the system, are analyzed. The results show that the transportation distance is shortened by half, and the time to fill the demand gap is reduced by 39%. The level of mechanical modernization is doubled, the peak inventory of materials in transit will increase by 9.2%, and the time to reach the peak will be shortened by 6.8%. If the delay time of personnel control is shortened by 480 s, the number of people to be evacuated in accident area will be reduced by 56. The delay time of traffic information release is shortened by 480 s, the number of vehicles to be evacuated is reduced by 74, and the time when the vehicle stops entering accident area is 1646 s in advance. The command level coefficient increases by 9.5%, and the speed of action execution increases by 9.6%.

Currently, the national epidemic prevention and control is still facing the challenges of secondary risk stacking, coordinated optimization of multi-party governance resources, and effective conversion of normal and abnormal conditions. Therefore, systematic thinking should be established in the normalized precision prevention and control and local emergency response, prevention and control should be implemented in accordance with the law, and source governance should be strengthened so as to overcome the risk challenges and improve governance efficiency.

Emergency response decision-making for maritime accidents needs to consider the possible consequences and scenarios of an accident to develop an effective emergency response strategy to reduce the ...

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.

Complete emergency measures are one of the important guarantees for effective emergency response. An evaluation method based on User-Generated Content (UGC) data is proposed to assess the completeness of emergency measures. To implement this method, UGC data comprising emergency needs from the users and emergency plans comprising emergency measures for specific emergency were firstly collected using a crawler program. A feature template was then established to identify relationships between different features, and a Conditional Random Field (CRF) model was used to extract emergency measures from the emergency plan and emergency needs from UGC data. The Siamese network was applied thereafter to compute the similarity between the emergency measures and emergency needs. The missing emergency measures were obtained based on the similarities, and a quantitative method to calculate the completeness was defined. Finally, using province A as a case study, the emergency measure completeness was evaluated and the emergency measures that need to be strengthened and improved were identified.

There are many unknowns regarding the characteristics and dynamics of human–AI teams, including a lack of understanding of how certain human–human teaming concepts may or may not apply to human–AI teams and how this composition affects team performance. This article outlines an experimental research study that investigates essential aspects of human–AI teaming such as team performance, team situation awareness, and perceived team cognition in various mixed composition teams (human-only, human–human–AI, human–AI–AI, and AI-only) through a simulated emergency response management scenario. Results indicate dichotomous outcomes regarding perceived team cognition and performance metrics, as perceived team cognition was not predictive of performance. Performance metrics like team situational awareness and team score showed that teams composed of all human participants performed at a lower level than mixed human–AI teams, with the AI-only teams attaining the highest performance. Perceived team cognition was highest in human-only teams, with mixed composition teams reporting perceived team cognition 58% below the all-human teams. These results inform future mixed teams of the potential performance gains in utilizing mixed teams’ over human-only teams in certain applications, while also highlighting mixed teams’ adverse effects on perceived team cognition.

Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV’s travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly.

This paper introduces a semi-automated system that facilitates emergency response vehicle (ERV) movement through a transportation link by providing instructions to downstream non-ERVs. The proposed system adapts to information from non-ERVs that are nearby and downstream of the ERV. As the ERV passes stopped non-ERVs, new non-ERVs are considered. The proposed system sequentially executes integer linear programs (ILPs) on transportation link segments with information transferred between optimizations to ensure ERV movement continuity. This paper extends a previously developed mathematical program that was limited to a single short segment. The new approach limits runtime overhead without sacrificing effectiveness and is more suitable to dynamic systems. It also accommodates partial market penetration of connected vehicles using a heuristic reservation approach, making the proposed system beneficial in the short-term future. The proposed system can also assign the ERV to a specific lateral position at the end of the link, a useful capability when next entering an intersection. Experiments were conducted to develop recommendations to reduce computation times without compromising efficiency. When compared with the current practice of moving to the nearest edge, the system reduces ERV travel time an average of 3.26 s per 0.1 mi and decreases vehicle interactions.

With the development of our society, the diversity of the university environment has been increased. The complexity of the diversified university environment also greatly increases the frequency of campus crisis incidents. Therefore, how to evaluate the emergency response ability of college students and how to take effective response measures have become problems that need urgent attentions. In this study, the evaluation of college students’ emergency response capability based on the questionnaire-TOPSIS innovative algorithm is conducted. Firstly, the questionnaire method is used to analyze the current situation of college students’ ability of emergency response, and then a comprehensive evaluation is made using the TOPSIS innovative algorithm. The results of the ranking of emergency response ability are as follows: excellent > good > undergraduate > graduate > inferior. Secondly, the reasons for the lack of ability of emergency response to students based on the evaluation results are studied intensively. Finally, the development countermeasures for college students’ emergency response ability training are put forward.

During the last few years, the technical and scientific advances in the Geomatics research field have led to the validation of new mapping and surveying strategies, without neglecting already consolidated practices. The use of remote sensing data for damage assessment in post-disaster scenarios underlined, in several contexts and situations, the importance of the Geomatics applied techniques for disaster management operations, and nowadays their reliability and suitability in environmental emergencies is globally recognized. In this paper, the authors present their experiences in the framework of the 2016 earthquake in Central Italy and the 2019 Cyclone Idai in Mozambique. Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid production of cartography to be used in emergency response operations. The performed analyses have been presented, and the strengths and weaknesses of the employed methods and techniques have been outlined. In conclusion and based on the authors' experience, some operational suggestions and best practices are provided and future research perspectives within the same research topic are introduced.

Emergency Rescue and Response Vessels (ERRV) is an integral component of any offshore emergency evacuation programme intended to recover and rescue personnel who have escaped or evacuated a facilit...

Evacuation Time Estimate (ETE) can provide decision-makers with a likelihood to implement evacuation of a population with radiation exposure risk by a nuclear power plant. Thus, the ETE is essential for developing an emergency response preparedness. However, studies on ETE have not been conducted adequately in Korea to date. In this study, different cohorts were selected based on assumptions. Existing local data were collected to construct a multi-model network by TSIS-CORSIM code. Furthermore, several links were aggregated to make simple calculations, and post-processing was conducted for dealing with the stochastic property of TSIS-CORSIM. The average speed of each cohort was calculated by the link aggregation and post-processing, and the evacuation time was estimated. As a result, the average cohort-based evacuation time was estimated as 2.4–6.8 h, and the average clearance time from ten simulations in 26 km was calculated as 27.3 h. Through this study, uncertainty factors to ETE results, such as classifying cohorts, degree of model complexity, traffic volume outside of the network, were identified. Various studies related to these factors will be needed to improve ETE’s methodology and obtain the reliability of ETE results.

Background Campus emergency response teams need continued education in order to retain critical skills and knowledge. Multiple studies have been conducted on the usefulness of virtual simulation games (VSGs); however, there is a gap in the literature for use in campus emergency response teams. Method A mixed-methods usability study was designed to review three VSGs designed as an online training tool for Campus Emergency Response Teams (CERT) and First Response team members. Quantitative data collection included a participant demographic survey and the Virtual Simulation Technology Acceptance Survey VSTAS. Qualitative data were obtained from individual interviews. Results Findings suggest strong support for the ease of use and usefulness of these VSGs. Internet bandwidth should be considered when implementing virtual simulation training methods. A lack of understanding of CERT member-specific scope of practice related to capillary glucose monitoring, which can inform future training practices. Conclusions This study demonstrates the VSGs are a useful educational tool. Further testing is needed to assess effectiveness in relation to learning outcomes and knowledge retention.

Pertolongan kecelakaan penerbangan dan pemadam kebakaran (PKP-PK) merupakan unit bagian dari penanggulangan keadaan darurat di bandar udara yang memiliki standar teknis dan operasi yang telah diatur oleh regulator. Mengingat pentingnya peran PKP-PK di suatu bandar udara, maka maka sudah sepantasnya PKP-PK memiliki Personel yang kompeten di bidangnya. Hal tersebut dapat diperoleh melalui Pendidikan dan pelatihan yang diselenggarakan oleh Lembaga Pendidikan Penerbangan. Salah satu program pelatihan Basic PKP-PK oleh Poltekbang Palembang secara keseluruhan berjalan dengan baik dan lancar, dengan rata-rata nilai 83,46 (kualifikasi: baik sekali) dapat menjadi indikator bahwa tujuan pembelajaran telah tercapai.

The concept of intelligent logistics has become mature. However, most of the researches on risk management and emergency response for intelligent logistics system only focus on some specific systems, and ignore the discussions on the overall system. Thus, in order to establish emergency response model for intelligent logistics system, the accident management process of intelligent logistics system was analyzed, the accident management model of the system based on the resilience theory was designed, and finally a model of emergency response for intelligent logistics system from the perspective of dynamic accident management was developed. Results show that this model reflects the dynamic process of intelligent logistics system subjected to random accidental impacts, the in-depth learning of the intelligent logistics system for incident handling and the enhancement of the system’s risk resistance through feedback data on incident handling are reflected in the model. Conclusion obtained from this study provides a theoretical reference for the optimizing of researches on mechanism of emergency response.

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response. Taking into account that the nature of accidents may change over time due to several reasons, the system enables users to build an updated version of the prediction model based on the historical and newly reported accidents. A dataset of accidents that occurred over a 6-year period (2008-2013) has been used for demonstration purposes throughout this paper. The accuracy of the prediction model was 65%. The Area Under the Curve (AUC) showed that the generated classifier can reasonably predict the severity of road traffic injuries. Importantly, using the cost-sensitive learning technique, the predictor overcame the problem of imbalanced severity distributions which are inherent in traffic accident datasets.

Domino effects are typically high-impact low-probability (HILP) accidents, which pose a serious threat to chemical process industries. Previous researches on domino effects in chemical industries focus more on static analysis at the spatial scale. From the perspective of the spatial and temporal characteristics of the accident, this study proposed a model to analyze dynamic evolution process of domino effects by using matrix calculation coupled with Monte Carlo simulation, and the dynamic propagation of pool fire accidents is considered as the evolution of domino effects. The algorithm of the model for dynamic domino probabilities considering the synergistic effects of multiple escalation vectors from different units can be used to analyze the complex scenarios of domino effects with high-level and multiple primary accident units. Moreover, the model can be applied in chemical areas with a large number of installations due to the greatly improved calculation efficiency. The proposed model is tested and validated using earlier studied dynamic Bayesian network method, and the application of the model is demonstrated on a complex multi-unit system. The results show that domino effects have strong temporal correlation, and the scenario with multiple primary accident units is much more serious than that with only one primary accident unit, which provide important support for the implementation of emergency response. The study highlights that the proposed model serves as an important tool to evaluate strategies for prevention and control of domino effects.

Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption, transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility, location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, data-driven optimization approach, and machine learning-based algorithms.

Major multi-reservoir cascades represent a primary mechanism for dealing with hydrologic variability and extremes within institutionally complex river basins worldwide. These coordinated management processes fundamentally reshape water balance dynamics. Yet, multi-reservoir coordination processes have been largely ignored in the increasingly sophisticated representations of reservoir operations within large-scale hydrological models. The aim of this paper is twofold, namely (i) to provide evidence that the common modeling practice of parameterizing each reservoir in a cascade independently from the others is a significant approximation and (ii) to demonstrate potential unintended consequences of this independence approximation when simulating the dynamics of hydrological extremes in complex reservoir cascades. We explore these questions using the Water Balance Model, which features detailed representations of the human infrastructure coupled to the natural processes that shape water balance dynamics. It is applied to the Upper Snake River basin in the western US and its heavily regulated multi-reservoir cascade. We employ a time-varying sensitivity analysis that utilizes the method of Morris factor screening to explicitly track how the dominant release rule parameters evolve both along the cascade and in time according to seasonal high- and low-flow events. This enables us to address aim (i) by demonstrating how the progressive and cumulative dominance of upstream releases significantly dampens the ability of downstream reservoir rules' parameters to influence flow conditions. We address aim (ii) by comparing simulation results with observed reservoir operations during critical low-flow and high-flow events in the basin. Our time-varying parameter sensitivity analysis with the method of Morris clarifies how independent single-reservoir parameterizations and their tacit assumption of independence leads to reservoir release behaviors that generate artificial water shortages and flooding, whereas the observed coordinated cascade operations avoided these outcomes for the same events. To further explore the role of (non-)coordination in the large deviations from the observed operations, we use an offline multi-reservoir water balance model in which adding basic coordination mechanisms drawn from the observed emergency operations is sufficient to correct the deficiencies of the independently parameterized reservoir rules from the hydrological model. These results demonstrate the importance of understanding the state–space context in which reservoir releases occur and where operational coordination plays a crucial role in avoiding or mitigating water-related extremes. Understanding how major infrastructure is coordinated and controlled in major river basins is essential for properly assessing future flood and drought hazards in a changing world.

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.

Emergency response (ER) planners have developed plans either under "all-hazards" approach, focusing on a full spectrum of emergencies or under a specific scenario—in which planning underlines aligned actions to respond to a particular situation. Either of them represents the so-called Work-As-Imagined (WAI) operation. However, the growing complexity, the scope of emerging situation and the level of uncertainty, create unpredicted challenges for ER operation, which represent another variety of work named Work-As-Done (WAD). These challenges require different degrees of adaptation to avoid the cascading impacts of an event into an accident, or even a disaster. Drawing upon the traditional Functional Resonance Analysis (FRAM), we provide a novel FRAM representation, which reflects adaptive capacities on functional inter-relationships, and their evolution over time in different scenarios. Rather than using time as an aspect of the FRAM hexagon in its traditional sense, we propose an explicit time-dependent analysis. We outline how to make the chimera of time response feasible in ER operations and how to represent respective sources of success. Based on our FRAM approach, we conduct an incident analysis referred to an event that happened in Gjøa in 2017, in Norway at the North Sea, to understand adaptation in the four different ER phases, that is mobilizing, alert/warning, combat and normalization.

Emergency response plays an important role in preventing or delaying the domino effect of chemical accidents. Requirements for emergency response to a major accident may exceed the capacity of the emergency department within the region of responsibility for the accident, in which case multi-department emergency response is necessary. In this work, modeling problems related to the arrival of emergency teams at different times in multi-department emergency response are discussed. These problems will dynamically influence the time to failure of adjacent facilities in the event of a fire. A timed colored hybrid Petri-net (TCHPN) approach is proposed to solve these problems and analyze multi-department emergency response processes. An example of responding to a tank fire illustrates the proposed approach. An emergency response process is simulated, and probabilities of fire escalation prevention under different fire levels are analyzed. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

In this paper, an emergency decision-making method, based on case-based reasoning and cloud model, is proposed to solve the risk decision-making problem in emergency response. Case-based reasoning,...

Dam break is an accident that may heavily threat downstream residents' life and property safety, especially in China. As revealed by accident investigation statistics, both flawed organizational behavior and inadequate downstream resident risk awareness have affected the safety risk of reservoir dams. Multiple information transferring mode and dynamic processes perform with the characteristics of social-technical systems. Based on the system dynamics approach, this study proposed a risk causation model aiming for factor interactions involving organizational, human, and technical system levels. The derived simulation model represented the historical risk evolution process of Gouhou reservoir in China and the rationality of the proposed model was verified. To further improve the efficiency of the organizational response and monitor real-time dam safety, a software tool called Dam Emergency Response Aids (DERA) was constructed to evaluate the potential safety benefits of risk control measures, and to overcome the defects of static emergency plans. By integrating relevant professional modules and data, the mobile application (APP) has been applied on the Jinniu Mountain reservoir dam in Nanjing of China and helped to maintain its excellent safety operation until now. It shows that the risk dynamics model proposed can improve the abilities of dam operating management organization for more effective responses under emergency circumstances. HIGHLIGHTS This research helps to remind the safety responsible organizations to focus their investment on publicizing and downstream resident training, which will increase their awareness of risks, enhance public understanding of the evacuation process and risk pre-judgment criteria, and maintain trusts in organization decision-making and community cohesion.

Developing a system for emergency response and rescue to find injured people within images has been an interest to many researchers. The key challenge is to define a proper feature to describe the human body's appearance. Various features are often extracted from low-level data, such as texture and colour (Zhang et al., Sensors. 19(5):1005, 2020). One of the strong features is the Sector Ring Histogram of Oriented Gradients (SRHOG) that has been successfully applied to human detection tasks. Despite good accuracy in finding humans, an SRHOG detection method produces a large amount of false-negative labels. Locating an injured body after a disaster in drone images using an imaging camera remained a challenge. This study presents a new extension to SRHOG, so-called AdSRHOG, to reduce the number of false labels. In our approach, the gradient filters used by SRHOG defined adaptively depending on the corresponding pixel location The proposed feature was used the Support Vector Machine (SVM) algorithm to detect humans on drone images. The experiments showed a significant improvement of up to 54.3% in reducing the false labels. It was also found that the overall accuracy of the human detection process had a notable improvement of 13.1% over a traditional SRHOG detection technique.

Amid the ongoing battle against COVID-19, the scientific community has high hope in wastewaterbased epidemiology (WBE). It was not only proposed as a complement to capacity-plagued clinical testing, but also an early warning tool that may enable timely intervention measures. In this study, we developed a wastewater SARS-CoV-2 RNA load model based on the fecal shedding profile of infected individuals. The epidemic data of COVID-19 in the Tokyo metropolitan area were used to perform a simulation to analyze the capability of WBE in providing early warning. The simulation result suggests that under the current settings, WBE is not a feasible approach as the detection limit is too high to provide a warning signal in the early stage of the epidemic. However, it also indicates that if the methodology can be reasonably improved by new experimental practices, optimized sampling strategy, and refined model, the concentration of viral RNA in Tokyo wastewater would exceed the detection limit as early as in April 2020, when Tokyo was being hit by the first wave of COVID-19 outbreak. This early detection may have great social benefit if the detection can be used to facilitate the decision-making process and form epidemic emergency response. © 2021. All Rights Reserved.

The current regulations provide a procedure for notification of the occurrence of railway emergencies, but do not define a procedure for responding to such situations. This can be explained by the fact that such situations are very diverse in nature and scale, as well as in the way they are handled. However, the more urgent is the need for a certain classification, typification of ways to respond to them. In connection with the above, the article shows that in the event of occurrence of WD AS, the head of the operational headquarters in complex conditions of lack of complete and sufficient information on cause-and-effect relations between components of such a situation should take a certain number of individual, collegial, information, organizational, operational decisions aimed at coordination, coordination and management of subordinate control points and response units, which may exceed his ability to make such decisions and / or influence their validity.

This paper investigates a patrol problem based on air-ground cooperation between multiple UAVs and police vehicles. Facing the uncertainty of patrol environment and patrol resources, the model guarantees the deterrence and emergency response capability of the patrol mission by optimizing the allocation strategy of patrol points and patrol routes. Relying on genetic algorithms, we encode patrol points and UAV launch/recovery points together to enhance the local search ability and convergence of the algorithm. Based on the real case of the D police station in Beijing, we explore the interactions among patrol elements and the impact on patrol tasks in different patrol environments. The results show that the Patrol missions formulated by Air-Ground Cooperative Patrol Optimization Model can be used to develop patrol tasks with better environmental adaptability. By analyzing the relationship between multiple groups of patrol elements, controlling the number of UAVs in future missions can improve the security of the area. And raise the ratio of hovering time in medium-risk areas to low-risk areas can improve the efficiency of patrols.

Bystanders are an integral part of an emergency scene. Their presence on site makes them the immediate potential responders, who can provide life-saving assistance as well as various rescue actions. Research has shown that bystanders are willing to and are capable of helping, especially (but not limited to) when a leader emerges, as the help is rooted in a collective action. In this article, we suggest a new framework for understanding the effectiveness of bystanders help, based on the evolving social process between bystanders and first responders. As we show, the transition from no help, at the very first moment right after the emergency has occurred, to an effective coordinated response where first responders and bystanders act together, goes through engagement of bystanders. Further, first responders should acknowledge the social organization of bystanders, utilize and leverage it in order to optimize their efforts. This framework has important insights for policy makers when managing emergency and disaster situations with limited resources.

Introduction. Fires of voltage-carrying electrical units are most dangerous and difficult to put out. Personnel of fire emergency response units are at high risk of electric shock when extinguishing such fires. Electrical units have recently obtained a new are of application, that is, electric cars. Electric car design entails the use of high-capacity batteries and high voltage electrical equipment, which pose a higher fire hazard. The objective of this article is to analyze potential assets to be protected and assess existing electrical fire extinguishing methods to substantiate principal areas of research into design of safe fire extinguishing systems designated for electrical units installed on motor vehicles.Main (Analytical) part. The fire extinguishing of the items, that have electrical units to be extinguished without any electricity outage, must be performed by the fire departments that have appropriately trained and equipped personnel. However, in the event of an electric car fire, it is impossible to forecast which fire department will be nearby and how well it will be equipped. The design features of electric vehicles show that potential sources of the fire hazard include lithium batteries and electrical circuit elements. These factors explain the need to consider the problem of extinguishing self-contained electrical fires inside motor vehicles from the standpoint of electrical safety. It is suggested to identify the fire extinguishing method in furtherance of the analysis of a set of principal fire extinguishing methods with regard for the design of efficient firefighting appliances that ensure safe extinguishing of electric car fires.Conclusions. It is expedient to use the pulse application of a fire-extinguishing agent that prevents current leakage while extinguishing various types of electrical appliances, including those installed on motor vehicles. The effects of exothermic reactions, involving lithium-containing elements, were not analyzed when solving this problem. This research will serve as the basis for conceptual technological solutions designated for fire emergency response units responsible for extinguishing electric car fires.

Because advances in broadband capabilities will soon allow first responders to access and use many forms of data when responding to emergencies, it is becoming critically important to design heads-up displays to present first responders with information in a manner that does not induce extraneous mental workload or cause undue interaction errors. Virtual reality offers a unique medium for envisioning and testing user interface concepts in a realistic and controlled environment. In this paper, we describe a virtual reality-based emergency response scenario that was designed to support user experience research for evaluating the efficacy of intelligent user interfaces for firefighters. We describe the results of a usability test that captured firefighters’ feedback and reactions to the VR scenario and the prototype intelligent user interface that presented them with task critical information through the VR headset. The paper concludes with lessons learned from our development process and a discussion of plans for future research.

Purpose The main goal of the physical education (PE) environment is that each individual trained should achieve self-fulfillment with the large group of students involved with their own efforts. Deep learning is applying transferrable knowledge in new situations to help the students master in tough circumstances. In PE training, injuries occur when working together as a team. Safety measures are taken immediately as an emergency response to reduce the potential risk in students by providing first aid. To provide safety measures for the injured student immediately, the environment is monitored in real-time using a GPS. Design/methodology/approach Theory of Humanities Education (ToHE) infers that it has less collection of theories and a wide range of applications than the state-of-the-art systems. ToHE allows students to think creatively and play a vital role in one’s health which is a critical aspect in PE. The ToHE theory focuses on two main concepts, i.e. by using a methodological approach to analyse and deep learning to solve the problem. PE motivates college students to follow a healthy and active lifestyle. Findings The proposed system is deployed in real time for monitoring the student’s performance and provides an emergency response with an accuracy rate of 90%. Originality/value The deep learning offers solutions to the injuries by using the deep convolutional neural network to provide interpretability of the consequence by training it with various injuries that occur in the playground and inappropriate use of sports equipment. A case study provided in this paper outlines an emergency response scenario to an injured student in sports training.

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.

The installation of closed-circuit television monitors (CCTV) has been rapidly increasing ever since the September 11 attacks and has become one of the most widely used types of sensors for the tasks that require instantaneous and long-term monitoring. With the distinguished characteristics of direct visual inspection of the subject of interests, the availability of CCTVs offers the EOC commanders a quick way to validate and access the reported disaster incidents during emergency response. However, the heterogeneity of CCTV systems and the lack of appropriate descriptions precludes the optimized use of CCTV and causes immense difficulties in effectively coordinating or appraising the use of CCTV systems. By arguing standardized metadata plays the most crucial role in quickly and precisely finding the needed CCTV during emergency response, regardless of its specifications, suppliers, or locations, we propose an integrated operational framework based on the CCTV metadata specifically designed for emergency response in this paper. This framework standardizes the metadata to unify the descriptions for heterogeneous CCTV systems and fulfill the requirements for searching and selecting CCTV. Instead of the 2D point-based location or sector-based CCTV FOV coverage, we also propose to extend the FOV from 2D to 3D to precisely describe the area and even the features that can be monitored by the CCTV. This not only improves the precision and efficiency of selecting CCTV but also increase the commander’s ability to make quick and accurate responses to disasters. The advantages of integrating heterogeneous CCTV systems can tremendously improve the possibility of monitoring and updating the real-time status caused by hazards.

Mobile emergency generators (MEGs) can effec-tively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are re-quired to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple sce-narios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effec-tiveness and superiority of the proposed three-stage MEG plan-ning model over the traditional two-stage model.

The emergency responses required during large-scale crises or disasters are extremely knowledge-intensive processes and are usually characterized by a high degree of unpredictability and unrepeatability. An emergency response is mission- and time-critical, unstructured, very dynamic, and it is very difficult to predefine or even to anticipate all possible response scenarios. Therefore, designing and implementing a software system to support such a response system is highly complicated and challenging. This research aims to investigate and discuss how Adaptive Case Management (ACM) can be leveraged in the design and implementation of a case-based emergency response system. In particular, this research considers the best practices of the National Incident Management System (NIMS), which is an essential part of the National Response Framework (NRF) developed in the United States. As a proof-of-concept, a prototype demonstration has been carried out on a leading commercial ACM platform. In addition, a walkthrough scenario is discussed to elaborate how ACM can support emergency response activities in real settings using the Incident Command System (ICS) organizational structure. The key benefit of this research is to guide the development and implementation of cased-based emergency response systems with a flexible and agile approach.

Early perception of anomaly traffic patterns, both spatially and temporally, is of importance for emergency response in the smart cities. To capture the spatiotemporal correlations among traffic flows for city dynamics modeling in correspondence with normal states, we conduct sparse representation on taxi activity over spatially partitioned cells in a city. We can perceive the deviation from the normal evolution of traffic flows and find the traffic anomalies. This method roots in the ideal of global traffic flow network detection. Therefore, it is more informative than local statistics since traffic flows evolve in a mutually interacting manner to spread out all over the city. The experimental results confirm its predictive power in detecting spatiotemporal traffic anomalies.

To determine whether occupants of collisions involving at least one vehicle with an available Automatic Collision Notification (ACN) system have quicker times from collision to 1) Emergency Medical Services (EMS) notification and 2) arrival to a medical center.Using data from the 2017 Crash Investigation Sampling System, vehicles were categorized as whether ACN was available using data from the CISS's dataset of crash avoidance system availability (in which ACN is included though notably not a crash avoidance system). A Cox proportional hazards model-overall and stratified by urbanization-was used to compare the time from collision to both EMS notification and EMS arrival to a medical center.A total of 2,034 collisions (weight n: 2,775,512) involving 4235 occupants (weighted n: 4,987,669) were included. An estimated 259,021 (9.3%) and 546,223 occupants (11.0%) were in a collision in which one vehicle had ACN equipped. The median time to EMS notification was longer for collisions in which no involved vehicles had ACN available (median 4, IQR 2-9 minutes) than ACN-exposed collisions (median 2, IQR 1-5 minutes). There was a marginally significant higher hazard (i.e., instantaneous risk) of EMS notification for collisions with at least one vehicle having ACN available (HR 1.77, 95% CI 0.99-3.15). Likewise, there was a higher instantaneous risk of medical center arrival for occupants (HR 1.80, 95% CI 1.41-2.30) involved in collisions in which at least one vehicle had ACN available. ACN was associated with quicker EMS notification only in urban areas (HR 3.06, 95% CI 1.57-5.97) and associated with a greater reduction in time to medical facility in less urban areas (median 36 vs 45 minutes, HR 2.12, 95% CI 1.22-3.63).This is the first study to directly compare EMS response-related times between collisions involving vehicles with and without ACN available. The current data corroborate prior literature reporting quicker EMS notification times among collisions involving ACN-equipped vehicles. This is the first study to find that ACN is also associated to quicker times to medical center arrival, particularly for collisions occurring in less urban areas. Future research examining whether these decreased times are associated with better clinical outcomes are needed in order to fully assess ACN's ability to prevent trauma-related mortality and morbidity.