Emergency Management Measures Research Articles

Use of Digital Technologies for the Conduct of Work in the Government System Based on the Demans of the Current Times

This article deals with the issues of ensuring the safety of the population in the residences of citizens. It is planned to implement an electronic platform that allows for quick collection and analysis of information on the technical and social condition of residential areas. These strategies focus on digital transformation in the national economy, industry, and society. In Uzbekistan, significant steps have been taken to implement modern information and communication technologies (ICT) in public administration, education, healthcare, and agriculture. One notable challenge addressed in this study is the management of carbon monoxide poisoning, fire hazards, and explosions during the heating season. The research highlights the limitations of manual data collection, including the risk of data loss, difficulty in locating information, and inefficiencies in processing critical safety data. The program processes data in less than a minute, facilitating more efficient emergency management and preventive measures. This system exemplifies the role of digital technology in enhancing public safety and optimizing government operations.

Recruitment of European sea bass (Dicentrarchus labrax) in northerly UK estuaries indicates a mismatch between spawning and fisheries closure periods.

European sea bass (Dicentrarchus labrax) is a species of high commercial and recreational value, but it exhibits highly variable recruitment rates and has been subject to recent declines. Emergency management measures put in place to protect spawning stocks include the annual closure of commercial and recreational fisheries over a 2-month, February-March, window. Whether this protection measure is having the desired outcome for this data-poor species remains unclear. Otolith microstructural analyses (counts and widths of daily growth rings and check marks indicative of settlement) were used to estimate (1) spawn timing, (2) pelagic larval duration and settlement timing, (3) growth rate and condition, and (4) the otolith-fish size relationship for juvenile European sea bass caught from two estuaries in Wales (Dwyryd, Y Foryd), located at the northern edge of the species range. We observed a significant mismatch between the timing of fisheries closures and the spawning, with 99.2% of recruits having been spawned after the fishery had reopened (back-calculated median spawn date = May 5 ± 17 days SD), suggesting that the closure may be too early to adequately protect this population. Further, we present the first empirically derived estimates of pelagic larval duration for sea bass recruits settling in UK habitats, which showed a strong negative relationship with spawn date. Finally, we found significant differences in fish condition between the two estuaries, suggesting local variation in habitat quality. The results suggest that the timing of current fisheries closures may not be adequately protecting the spawners supplying these northernmost estuaries, which are likely to become increasingly important as sea bass distributions shift northward in our climate future.

Emergency management and risk reduction measures during the Vulcano (Aeolian Islands) unrest 2021–2023

Emergency management and risk reduction measures during the Vulcano (Aeolian Islands) unrest 2021–2023

Overall Resilient Evaluation of Surrounding Rock of In-Service High-Speed Railway Tunnel Based on Information Fusion-Improved Fuzzy Matter-Element

Once the high-speed railway tunnel is put into use, its resilience will determine the possibility of permanent safety of the tunnel due to the closure of the structural space of the high-speed railway tunnel in service. Resilience theory is introduced into a risk analysis of operating high-speed rail tunnels to improve the ability to respond to risks in operating high-speed rail tunnels and to relieve the aging phenomenon caused by changes in the tunnel with time. First, an evaluation framework for the safety resilience of existing high-speed railway tunnels is constructed. Starting from the attributes of resilience such as resistance, adaptability, and resilience, and considering the characteristics of high-speed railway tunnels, protective measures, emergency management measures, and other factors, we fit the risk factors and probability of accident type of the high-speed railway tunnel and establish a tunnel safety resilience evaluation index system with 10 indexes. Secondly, the method of information fusion is used to combine subjective weighting and objective weighting. Then, the comprehensive weight of the evaluation index is obtained based on the principle of minimum discriminant information. Thirdly, the system resilience evaluation model based on the TOPSIS improved fuzzy matter-element is constructed to determine the classification criteria of resilience. On this basis, based on the temporal and spatial variability of the ductile tunnel, the concepts of ductile transition and ductile attenuation are introduced and the tunnel toughness optimization model is established to suppress the attenuation situation, enhance the transition ability, and then improve the system resilience level. On this basis, an optimal lifting scheme is obtained. Finally, taking Ai-Min tunnel of Ha-Mu high-speed railway as the engineering background, the flexibility of the resilience system is calculated, and the resilience grade (3) of the rock system surrounding the tunnel is obtained. Combined with the numerical model, improvement measures for specific tunnel facilities are proposed. The results show that the Ai-Min tunnel system has a general ability to resist external intrusion and prevent disasters, and the resilience level is general. It should focus on improving the resilience level of the transition index. The resilience evaluation results of the evaluation model are consistent with the actual situation of the project.

Impact of the healthcare reorganization of the Local Health Authority services in Rieti (Italy) during the SARS-CoV-2 pandemic.

The need to contain the spread of the SARS-CoV-2 pandemic has forced national and local organizations to define and implement targeted emergency response and management measures. As the knowledge about the infection grew, a wider range of organizational measures were deployed. This study involves the SARS-CoV-2 infected people managed by the Local Health Authority of Rieti (Italy). Diagnostic test waiting times and hospital admission rates in the Province of Rieti are investigated as the pandemic evolved. Trends were analyzed in relation to the tempora spreading of SARS-CoV-2, to the organizational actions taken by the Local Health Authority of Rieti, and to the deployment of actions across the territory. A municipalities classification of the province of Rieti was conducted after a cluster analysis based on the diagnostic test waiting times and the hospital admission rates. Our findings show a declining trend, thus indicating a possible positive effect of the measures taken to contain the pandemic. The cluster analysis of the municipalities of the Province of Rieti makes evident an inhomogeneous geographical distribution of examined parameters (diagnostic test waiting times and the hospital admission rates), demonstrating the capability of Local Health Authority of Rieti to reach even the most disadvantaged areas and implying that the differences are due to the demographical variabilities. Despite some limitations, this study outlines the importance of management measures in response of the pandemic. These measures should adapt to social, cultural and geographical nature of the territory involved. The findings of the present study will contribute to the update of further pandemic preparedness plans of the Local Health Authorities.

Discussion on emergency management of food safety from the perspective of foodborne diseases caused by mycotoxins

Mycotoxins exists in foods such as grains, vegetables, fruits, milk, nuts, and meat, which can cause foodborne illnesses seriously harmful to human health. However, people have not paid enough attention to foodborne mycotoxin poisoning. This paper reviews the food in some common mycotoxin cause foodborne disease, the potential danger of mycotoxin contaminated food to human body and other issues related to food science were revealed from the clinical point of view, also concerned with the immunomodulatory effects of mycotoxin and the antitumor properties of patulin, the applicable conditions of several risk assessment models are briefly compared the emergency management measures of food safety problems caused by mycotoxin were also discussed from the perspective of prevention.

Deduction of sudden rainstorm scenarios: integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory

Event scenarios serve as the basis for emergency decision making after sudden disasters, and the accuracy of scenario deduction directly determines the effectiveness of emergency management implementation. On July 20, 2021, an exceptionally heavy rainstorm disaster occurred in Zhengzhou, Henan Province, China, causing serious urban waterlogging, river floods, flash floods and landslides and resulting in major casualties and property losses:14.79 million people affected, 398 people killed or missing (380 people in Zhengzhou) and a direct economic loss of 120.06 billion RMB. In order to investigate the complex evolution process of this disaster, a dynamic Bayesian network, evidence theory and emotion update mechanism are integrated to develop an efficient and effective scenario deduction model, with an emphasis on combining subjective and objective factors. In this model, more attention is given to subjective factors such as decision makers' emotions. The elements of scenario deduction are classified into the situation status, meteorological factor, emergency activities, decision makers' emotions and emergency goals, the coupling relationship between the elements are comprehensively analyzed, and the influence of these elements on the evolution mechanism of the rainstorm disaster is investigated, so as to facilitate targeted emergency management measures for the rescue operations. The empirical results show that the proposed dynamic Bayesian network can effectively simulate the dynamic change process of scenario deduction, the improved Dempster–Shafer evidence theory can reduce the subjectivity of the model in dealing with the uncertainty of the evolution process, and the emotion update mechanism can adequately quantify and decrease the influence caused by the emotional changes of decision makers. The model may better replicate actual events, and it may apply to the scenario deduction of other disasters, making an impact on the study of sudden catastrophes.

Parameters Sensitivity Analysis of COVID-19 Based on the SCEIR Prediction Model

At the end of 2019, COVID-19 outbreaks occurred one after another in countries worldwide. Managing the outbreak efficiently and stably is an essential public health issue facing countries worldwide. In this paper, based on the SEIR model, we propose a SCEIR model that incorporates close contacts (C) and self-protectors (P). Firstly, the epidemic data of China, the USA, and Italy are predicted and compared with the actual data. Secondly, sensitivity analysis of each parameter in the SCEIR model was conducted using Anylogic. The study shows that the SCEIR model established in this paper has a certain validity. The infection rate in contact with E (𝛽) etc., has positive effects on the basic regeneration number (R0); the self-isolation rate (φ) etc., has a negative effect on the basic regeneration number (R0). Emergency management measures are proposed according to the influencing factors corresponding to the model parameters. These can provide theoretical guidance for developing effective epidemic prevention and control measures in areas where the epidemic has not yet been controlled. It also provides some reference for formulating prevention and control policies for similar epidemics that may occur in the future.

Scenario inference model of urban metro system cascading failure under extreme rainfall conditions

Metro systems have become high-vulnerability entities due to the increasing frequency and severity of urban flooding. Flood events may cause cascading failure to metro systems; therefore, exploring the cascading failure risk of the metro system is a prerequisite for urban flooding prevention and risk management. This study presented a Rank-Order Centroid (ROC) based CIA-ISM (Cross-Impact Analysis, and Interpretive Structural Modeling) method to accurately assess the reliability of emergency management in metro systems under extreme rainfall conditions. We applied this approach to a metro flooding case in Zhengzhou on July 20, 2021. The reliability results show that efficient rescue and timely shutdown notification are the most critical causal events in the cascading failure scenarios. The events of system vulnerability that have the most significant impact on casualties, property losses, and social panic are, respectively, timely notification of the shutdown, humanitarian aid, and public opinion guidance. In forecast scenarios with emergency management measures in effect, the probability of casualties, property losses, and social panic decrease by 96.3%, 58.58%, and 64.28%, respectively. Moreover, a comparison with Bayesian Network (BN) model verified the effectiveness of the ROC-based CIA-ISM approach. Based on the study, we suggest the metro companies release a timely notification of the shutdown. This study can provide scientific data for decision-makers to reasonably develop emergency strategies, significantly reducing flood losses and promoting cities’ sustainable development.

Analysis on the registration and review system of emergency medical devices in China and abroad in the context of COVID-19

Objective
 In the context of COVID-19, the domestic and foreign demand for emergency medical devices, such as medical masks and protective suits, is surging, and it is urgent to complete the registration and review of emergency medical devices with high efficiency and quality, which requires a mature and perfect registration and review system as the support. This paper aims to compare and analyze the domestic and foreign registration and review system of emergency medical device, summarize the good experience, and provide feasible suggestions for improving China's emergency medical device registration and review system.
 Method
 USA, Canada, Japan and the European Union were selected to make a comparative analysis with China from the aspects of legal system and emergency registration and review procedure by literature research, comparative analysis and other theoretical methods.
 Results
 The legal system and review mechanism of emergency medical device registration in China have been relatively perfect, but the safety and risk balance mechanism and the comprehensiveness of emergency management measures need to be further improved.
 Conclusion
 On the basis of maintaining its own institutional advantages, China should learn from foreign experience to further optimize the registration and review system of emergency medical devices, so as to improve the ability of response and implementation of China in public health emergencies.

Mapping the wildland-urban interface in California using remote sensing data

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

Research on Structural Similarity Design Emergency Exercise’s Scenario

In order to better apply the “situational response” model in the field of fluidized mining emergency management, it is the first step and the most critical problem to construct a reasonable scenario for fluidized mining emergency drills and reasonably put forward emergency management measures. Therefore, the structural similarity method is adopted in this paper to design emergency exercise scenarios. Firstly, a model of hierarchical structured scenarios is proposed, namely, modules of “Event-Environment-State of scenario-Disposal of task-Emergency action- Resources subject.” Secondly, a scenario chain is designed, and a prediction method of the event development trend under the current scenario is proposed. Thirdly, the calculation method of scenario similarity and the proposed emergency response scheme method under the current situation after similarity comparison are proposed. Finally, the structural similarity analysis method is used to verify the application of “scenario construction” in oil and gas pipeline accidents, and better analysis results are obtained. Through this research, the application of “scenario design” in fluidized mining emergency management has been expanded and enriched, and technical support for “scenario design” of fluidized mining assisted decisions is provided.

Push-Pull-Mooring Analysis of Massive Open Online Courses and College Students During the COVID-19 Pandemic.

The partial least squares structural equation modeling (PLS-SEM) provides researchers with an analysis tool for prediction theory. As the coronavirus disease 2019 (COVID-19) brings risks to teaching and learning, students have been forced to switch from classroom learning to online learning and most subjects have chosen massive open online courses (MOOCs) for online learning in China. This study examines whether MOOCs can replace traditional classroom education and explores the factors that influence the intentions of switching of the students from offline to online. We sequenced the PLS-SEM analysis of data with 397 students from a university in Zhejiang province of China, testing the model parameters, and discussing the push-pull-mooring (PPM) theory. Our data demonstrate that security risk is a push factor, switching costs are a mooring factor, and perceived usefulness and task–technology fit are pull factors that pull students from traditional, offline learning to MOOCs. In addition, the PPM model of the analysis results provides a more specific understanding of the importance–performance analysis of each factor. Our findings suggest that to constantly improve the switching intention to address unexpected challenges in the future, teachers should establish an effective emergency management measures, including curriculum design, to be consistent with their needs.

The application framework of big data technology in the COVID-19 epidemic emergency management in local government\u2014a case study of Hainan Province, China

BackgroundAs COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China’s SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks.MethodsThis study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan’s application of big data technology in its COVID-19 epidemic emergency management.ResultsHainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose.ConclusionsThis study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

Snow Avalanche Assessment in Mass Movement-Prone Areas: Results from Climate Extremization in Relationship with Environmental Risk Reduction in the Prati di Tivo Area (Gran Sasso Massif, Central Italy)

Mass movements processes (i.e., landslides and snow avalanches) play an important role in landscape evolution and largely affect high mountain environments worldwide and in Italy. The increase in temperatures, the irregularity of intense weather events, and several heavy snowfall events increased mass movements’ occurrence, especially in mountain regions with a high impact on settlements, infrastructures, and well-developed tourist facilities. In detail, the Prati di Tivo area, located on the northern slope of the Gran Sasso Massif (Central Italy), has been widely affected by mass movement phenomena. Following some recent damaging snow avalanches, a risk mitigation protocol has been activated to develop mitigation activities and land use policies. The main goal was to perform a multidisciplinary analysis of detailed climatic and geomorphological analysis, integrated with Geographic Information System (GIS) processing, to advance snow avalanche hazard assessment methodologies in mass movement-prone areas. Furthermore, this work could represent an operative tool for any geomorphological hazard studies in high mountainous environments, readily available to interested stakeholders. It could also provide a scientific basis for implementing sustainable territorial planning, emergency management, and loss-reduction measures.

Earthquake-induced landslides susceptibility evaluation: A case study from the Abruzzo region (Central Italy)

Landslides are widespread natural phenomena that play an important role in landscape evolution and are responsible for several casualties and damages. Slope instability is linked to the combination of geological, geomorphological, and climatic factors with various triggering mechanisms; among these, seismic shaking can induce relevant changes in the landscape, leading to coseismic and post-seismic phenomena such as landslide events. The Abruzzo Region (Central Italy) is severely affected by Earthquake-Induced Landslides (EILs), linked to the geomorphological dynamics and the severe seismicity of the area. The distribution, mechanisms, and typology of landslides are strictly related to the different physiographic and geological-structural settings. This paper focuses on the realisation of an EILs susceptibility map, following a heuristic approach combined with a statistical analysis, integrated using GIS technology. This approach led to the identification of nine instability factors. These factors were analysed for the construction of thematic maps. Hence, each factor was assigned proper expert-based ranks and weights based on the critical evaluation of literature data as well as on available landslide inventories and combined in a preliminary map wherein high/low numerical values correspond to a high/low propensity of the slope to fail; furthermore, a statistical analysis of these values was performed to derive suitable susceptibility classes. Results presented herein highlight the robustness of the approach; remarkably, the applied methodology is suitable even in areas where a detailed landslide catalogue is lacking, when the same classification and weighting of available parameters is performed. The statistical analyses and the adoption of an absolute scale ranging from minimum to maximum potential values, finally, ensures the comparability of results among different study areas. Finally, this work represents a scientific and multidisciplinary tool for better defining situations that could lead to hazards (such as landslides) following an earthquake to develop sustainable territorial planning, emergency management, and loss-reduction measures.

Analysis of Power Plant Operation Safety and Emergency Management Measures

Analysis of Power Plant Operation Safety and Emergency Management Measures

Emergency Management in a Dental Clinic During the Coronavirus Disease 2019 (COVID-19) Epidemic in Beijing

BackgroundIn mid-March 2020, the World Health Organization declared that COVID-19 was to be characterised as a pandemic. The purpose of this article is to recommend emergency management procedures for dental clinics during this public health emergency.Materials and MethodsWe have implemented a series of emergency management measures to prevent cross-infection in our dental clinic during the COVID-19 pandemic, including personnel scheduling, division of the clinic into functional areas, limitation or delay of non-emergency patients, staff protection and infection controls, clinical environmental disinfection, and the use of online consultation services, among others.ResultsDue to public health policy and dental emergency management, the number of dental visitors to our clinic dropped sharply, and no COVID-19 suspected cases or high-risk patients received treatment. There have been no reports of infection of dental staff or patients during dental treatment in China to date.ConclusionThese public health policies and dental emergency management measures were effective in controlling cross-infection of COVID-19 in the dental clinic.Practical ImplicationsWe share control measures for COVID-19, and hope that they will be helpful for dental professionals worldwide to continue to provide dental care in a safe and orderly manner.

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study.

BackgroundThe COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely.ObjectiveOur research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province.MethodsA graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses.ResultsAn integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts.ConclusionsDuring the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

COVID-19, Storms, and Floods: Impacts of Tropical Storm Cristobal in the Western Sector of the Yucatan Peninsula, Mexico

The presence of extreme hydrometeorological threats has co-occurred with the COVID-19 pandemic, increasing the potential risk of a disaster scenario occurring. The hurricane “Cristobal”, which impacted Mexico’s tropical regions, presented a high risk of contagion and death caused by the combined effects of violent winds, floods, and evacuations. This work aims to determine whether the presence of concurrent events during the pandemic caused an increase in confirmed positive COVID-19 cases in the Yucatan Peninsula’s western sector. To achieve this, a numerical analysis and identification of the tropical storm’s extreme characteristics were conducted. Next, a combined analysis of the territorial system subject to flooding and the rainfall level reported during the emergency period was conducted at the municipal level. The third phase consisted of analyzing the confirmed positive cases of COVID-19 at the municipal level on four strategic dates (before, during, observation, and end of the emergency period). Finally, a content analysis of the emergency bulletins, action guides, and disaster declarations was carried out to identify the measures and adaptations implemented during the pandemic. It is recognized that emergency management measures were implemented for municipalities with more than 30 confirmed positive cases of COVID-19 and where the shelter capacity was reduced. Protocols for caring for people were followed, these being one of the leading adaptive methods. From the analyzed data, it can be pointed out that there is no direct evidence for an increase in positive COVID-19 cases in 10 of the municipalities. However, in the case of the municipality of Escarcega, there was a sudden increase in cases from June 8, which continues to grow. Therefore, it is necessary to deepen the study of multiple events to recognize the actions that can prevent catastrophes in these times of crisis.