Enhancing Emergency Response Research Articles

Comparative Analysis of Machine Learning Models for Real-Time Disaster Tweet Classification: Enhancing Emergency Response with Social Media Analytics

Comparative Analysis of Machine Learning Models for Real-Time Disaster Tweet Classification: Enhancing Emergency Response with Social Media Analytics

Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility.

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity.

Counseling Experiences of the Safe Healthcare Environment Advisory Program at Chiayi Hospital, Ministry of Health and Welfare of Taiwan

<p>近年國內多起醫療機構火災事件導致人員傷亡，顯示醫療機構環境安全之重要性。民眾對於醫療環境安全愈加重視，醫院應更加重視防火安全，並從病人安全及消防安全著手。嘉義醫院為建構安全的醫療環境品質，特參加2023年醫療機構環境安全諮詢計畫「防火安全模組」輔導計畫，獲取外部專家意見，建構更安全的醫療就醫環境。本次透過全員參與，全面性、系統性的盤點各項硬體設備與緊急應變計畫，瞭解自身的風險脆弱點，提升火災發生時緊急應變處理能力。借助國內外專家學者經驗，現場審視與指導，依照醫院特性或高潛在風險因子，在有限資源下，討論改善的優先順序與相關替代方案。希望藉由本文分享參加輔導計畫之過程與經驗，提供醫療機構共參考，共同提升醫院環境安全。</p> <p> </p><p>In recent years, numerous fires in healthcare institutions in Taiwan have resulted in casualties, highlighting the importance of environmental safety in medical facilities. With growing public concern over the safety of healthcare environments, hospitals must prioritize fire safety, focusing on both patient and fire safety measures. Chiayi Hospital staff participated in the 2023 Fire Safety Module counseling project of the Safe Healthcare Environment Advisory Program to establish a safe medical environment. This program incorporated guidance from external patient safety and fire safety experts to construct safe treatment environments. A thorough inventory of equipment and emergency response plans was conducted, involving the collaborative participation of all staff members to identify institutional vulnerabilities and enhance emergency response capabilities in the event of a fire. On-site inspections and guidance leveraged the counsel of domestic and international experts and scholars to address risk factors unique to the hospital, discussing solutions and areas of improvement to prioritize in accordance with the hospital’s limited resources at hand. This paper outlines Chiayi Hospital’s experiences in the Safe Healthcare Environment Advisory Program to benefit other hospitals and enhance hospital environmental safety throughout Taiwan.</p> <p> </p>

Impact of Basic Life Support Training Program among Saudi Primary School Teachers in Riyadh

Introduction: Basic Life Support (BLS) training plays a crucial role in equipping individuals to provide immediate emergency care until medical help is available. In the school setting, where injuries are common among students, teachers serve as primary caregivers and first responders. This study aimed to assess the impact of BLS training on the resuscitation knowledge and skills of primary school teachers in Riyadh, Saudi Arabia.Methods: A randomized interventional trial was conducted with 48 primary school teachers, aged 23 to 50 years. After providing a concise BLS training program, participants completed a pre-and post-test questionnaire assessing their understanding of resuscitation techniques. Descriptive statistics and chi-square tests were used for data analysis.Results: Before the BLS training, the participants demonstrated limited knowledge and skills in resuscitation techniques. For instance, only 20.8% correctly identified the ideal compression depth, and 33.3% were unsure about the ideal steps for Basic Life Support. However, after the training, a significant improvement was observed in participants' knowledge and skills, with a higher percentage answering correctly in the post-training questionnaire (e.g., 87.5% correctly identified the recommended compression rate, and 75% accurately identified the ideal steps for Basic Life Support). Conclusion: This study highlights the positive impact of BLS training on the resuscitation knowledge and skills of primary school teachers in Riyadh. Primary school teachers can enhance emergency response capabilities and contribute to student safety, by providing teachers with the necessary knowledge and practical skills. These findings emphasize the importance of implementing targeted training programs in schools to improve resuscitation education for teachers.

Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach

Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach

Optimization of new fire department location using an improved GIS algorithm for firefighters travel time estimation

PurposeThe rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.Design/methodology/approachThis paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.FindingsThe results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.Practical implicationsThe results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.Originality/valueThe proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

Methods for Addressing Pavement Defects Based on Digital Twin Technology--A Case Study of Snow and Water Accumulation on Road Surface

This paper explores the utilization of digital twin technology for the management of urban road infrastructure challenges, specifically addressing issues related to snow and water accumulation. The study emphasizes the constraints associated with traditional road maintenance methods and proposes the implementation of a digital twin city framework to augment real-time monitoring and enhance emergency response efficiency. Through the integration of real-time data from sensors, traffic, and weather sources, the digital twin model replicates road conditions and forecasts potential issues, thereby enabling early warning systems. Furthermore, the research delves into the application of conductive asphalt concrete and superhydrophobic materials to mitigate road-related challenges. The results indicate that digital twin technology significantly enhances economic and operational efficiency in urban road infrastructure management when compared to conventional approaches.

Early detection of critical urban events using mobile phone network data.

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the early detection of critical urban events, including fires, large accidents, stampedes, terrorist attacks, and sports and leisure gatherings, especially if these events significantly impact mobile phone network activity in the affected areas. This paper presents empirical evidence that advanced NSD can detect anomalies in mobile traffic service consumption, attributable to critical urban events, with fine spatial (a spatial resolution of a few decameters) and temporal (minutes) resolutions. We introduce two methodologies for real-time anomaly detection from multivariate time series extracted from large-scale NSD, utilizing a range of algorithms adapted from the state-of-the-art in unsupervised machine learning techniques for anomaly detection. Our research includes a comprehensive quantitative evaluation of these algorithms on a large-scale dataset of NSD service consumption for the Paris region. The evaluation uses an original dataset of documented critical or unusual urban events. This dataset has been built as a ground truth basis for assessing the algorithms' performance. The obtained results demonstrate that our framework can detect unusual events almost instantaneously and locate the affected areas with high precision, largely outperforming random classifiers. This efficiency and effectiveness underline the potential of NSD-based anomaly detection in significantly enhancing emergency response strategies and urban planning. By offering a proactive approach to managing urban safety and resilience, our findings highlight the transformative potential of leveraging NSD for anomaly detection in urban environments.

Research on Collaborative Mechanism for Emergency Medical Supplies Reserve and Supply Based on Scenario Analysis

Ensuring an adequate reserve, timely supply, and rational distribution of emergency medical supplies in the aftermath of public health emergencies is a critical factor in enhancing emergency response capabilities. To address this issue, this paper first establishes a scenario analysis model for emergency medical supply reserve and supply systems based on convolutional neural networks, using case attribute similarity. Subsequently, by analyzing the information sharing mechanism of reserve and supply entities, a multi-agent emergency medical supply reserve and supply system framework is constructed. Different scenarios such as material demand, entity responsibility, and emergency objectives are considered both vertically and horizontally to achieve resource balance and establish collaborative mechanisms for human, material, financial, and information resources. Taking the emergency medical supply reserve and supply during the COVID-19 pandemic as a case study, simulations are conducted using the Simpy platform. The results demonstrate that 1）scenario analysis models can ensure precise provisioning of emergency medical supplies; 2) the utilization of multi-agent technology promotes intelligent management of emergency medical supplies; 3) collaborative operational mechanisms reduce the risk of disruptions in the emergency medical supply chain. Finally, targeted measures are proposed for the assurance of emergency medical supply reserves and distribution to mitigate the impact of unforeseen events on societal stability.

Situational deductions and emergency decisions in response to food quality and safety emergencies in food production enterprises

Food quality and safety emergencies pose significant challenges to public health and safety, requiring effective emergency decision-making strategies. However, much scholarly attention has focused on the characteristics, evolutionary mechanism, and operational mechanism of emergencies, but those debates typically overlook the particularity of food production enterprises and the complexity, urgency, and harmfulness of such emergencies. Considering these, we explore the occurrence, development, evolutionary mechanism, and development process of food quality and safety emergencies from the perspective of food production enterprises. Then, taking Coca-Cola's chlorine pollution emergency as an example, we establish a situational deduction model using the Bayesian network. Subsequently, an emergency decision-making system is constructed, and emergency decision schemes are provided. Our findings show that food quality and safety emergencies occur when the entropy value exceeds the system threshold. The implementation of emergency decision-making is a process of energy exchange with the external environment to form a new dissipative structure. Scenario analysis is a cyclic process influenced by the emergency evolution, external environment, and emergency decision-making. Effective emergency decision-making requires a comprehensive analysis of the event situation, rational resource mobilization, and the use of professional knowledge and experience. This study provides a novel approach to understanding and managing food quality and safety emergencies in food production enterprises, emphasizing the need to consider the particularity and complexity of such emergencies when developing decision-making strategies. Our situational deduction model and emergency decision-making system provide a practical tool for enhancing emergency response capabilities, ensuring public health and safety, and maintaining brand image.

Enhancing Disaster Triage Competencies through Simulation-Based Training: An Interventional Study among Undergraduate Nursing Students

This pre–post interventional study explores the effectiveness of simulation-based training in enhancing disaster nursing skills among nursing students at Taif University, Saudi Arabia. The training, which uses a realistic train accident simulation and involves a response team of healthcare professionals, aims to improve knowledge and performance in crisis management and triage during mass casualty incidents. The study’s necessity stems from the critical role nurses play in disaster response, requiring a comprehensive understanding of challenges, collaboration among stakeholders, and improved capabilities. A random sample of 101 nursing students voluntarily participated in the study, with the necessary approvals obtained. We measured their emergency management skills and knowledge using a detailed questionnaire (27 items) and conducted pretest and posttest evaluations. Data analysis was performed using SPSS. The results indicate the training’s effectiveness, as a significant portion of participants achieved high performance levels in the posttest, contrasting with a higher percentage of fail-level grades in the pretest. These findings underscore the potential to improve disaster management protocols and nursing professionals’ preparedness in Saudi Arabia. The study emphasizes the importance of comprehensive education in disaster nursing in enhancing emergency response and patient outcomes.

Research on Quantitative Assessment and Dynamic Reasoning Method for Emergency Response Capability in Prefabricated Construction Safety

In response to the common issues of lacking a comprehensive quantitative assessment system and insufficient dynamic understanding of emergency response capability in prefabricated construction safety, this study proposes a research methodology based on decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCM) to promote the construction of emergency response capacity. Firstly, a quantitative evaluation indicator system comprising 4 core categories of organizational management, personnel quality, technical measures, and emergency resources, along with 16 main categories, is established using grounded theory and three levels of coding approach. Subsequently, through a combination of expert surveys and quantitative analysis, DEMATEL is employed to unveil the causal relationships and key indicators of the evaluation criteria. Next, the DEMATEL and FCM models are integrated to conduct predictive and diagnostic reasoning analysis based on key indicators. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and methodology. The results demonstrate that indicators related to organizational management and personnel quality belong to the cause group, while technical measures and emergency resources fall into the effect group. The “completeness of emergency plans” exhibits the most significant influence on other indicators and is also the most influenced indicator by others. Predictive reasoning analysis reveals that well-controlled “emergency organizational structure and procedures” are crucial for enhancing emergency response capacity. Diagnostic reasoning analysis indicates that the improvement of emergency response capability should focus on enhancing the “completeness of emergency plans”. The synergistic effect between “emergency organizational structure and procedures” and “completeness of emergency plans” contributes to the enhancement of emergency response capability in prefabricated construction safety. The study holds both theoretical and practical significance for advancing safety management in prefabricated construction. Considering the dynamic coupling of multiple factors will be the primary direction of research in the field of safety management in the future.

SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning

The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.

Enhancing Emergency Response through Artificial Intelligence in Emergency Medical Services Dispatching; a Letter to Editor.

Enhancing Emergency Response through Artificial Intelligence in Emergency Medical Services Dispatching; a Letter to Editor.

Educational Program Development on Electrical Disaster Prevention for Different Students to Enhance the Abilities to Emergency Response

Since the majority of fires caused by the disaster was due to electric-related fires, it is possible to support the early restoration of a community and the functions of the electrical equipment of each household at the time of restoration if the individual residents from those households turn off the circuit breakers before evacuation. This paper presented the development of a new electrical disaster prevention education program for junior high school students to address the impact of electrical equipment and to enhance emergency response abilities during disasters. Moreover, this study focused on developing abilities to restore the entire community at an early stage, with attention to differences among students, i.e., junior high school and elementary school students. After incorporating the experiment into our electrical disaster prevention education program, it was observed that the psychological structure for disaster prevention of emergency response abilities of students decreased from four to three axes, fewer axes means that features of educational effectiveness can be extracted more easily.

Optimal decision of a disaster relief network equilibrium model

<abstract><p>Frequent natural disasters challenge relief network efficiency. This paper introduces a stochastic relief network with limited path capacity, develops an equilibrium model based on cumulative prospect theory, and formulates it as a stochastic variational inequality problem to enhance emergency response and resource allocation efficiency. Using the NCP function, Lagrange function, and random variables, the model dynamically monitors disasters, enabling rational resource allocation for quick decision-making. Compared to traditional methods, our model significantly improves resource scheduling and reduces disaster response costs. Through a random network example, we validate the model's effectiveness in aiding intelligent decision-making for relief plans and resource allocation optimization.</p></abstract>

Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

During the charging process of electric vehicles (EV), the circuit inside the charger plug is connected in series, the charger input voltage does not match the rated input voltage, the temperature caused by the severe heating of the charging time is too high for too long, and other factors are very likely to trigger a fire in the vehicle charging pile. In this paper, an improved You Only Look Once v4 (YOLOv4) real-time target detection algorithm based on machine vision is proposed to monitor the site based on existing monitoring equipment, transmit live video information in real-time, expand the monitoring range, and significantly reduce the cost of use. During the experiment, the improved neural network model was trained by a homemade fire video image dataset, and a K-means clustering algorithm iwasintroduced to recalculate the anchor frame size for the specific object of flame; the existing dataset was used to perform multiple divisions by using a tenfold cross-validation algorithm, thus avoiding the selection of chance hyperparameters and models that do not have generalization ability because of special divisions. The experimental results show that the improved algorithm is fast and accurate in detecting large-size flames in real-time and small-size flames at the beginning of a fire, with a detection speed of 43 fps/s, mAP value of 91.53%, and F1 value of 0.91. Compared with YOLOv3 and YOLOv4 models, the improved model is sensitive to detecting different sizes of flames. It can suppress false alarms well in a variety of complex lighting environments. The prediction frame size fits the area where the target is located, the detection accuracy remains stable, and the comprehensive performance of the network model is significantly improved to meet the demand of real-time monitoring. It is significant for developing the EV industry and enhancing emergency response capability.

A Fundamental Study on Electrical Disaster Prevention Education to Enhance Emergency Response during Floods

In recent years, increasing attention has focused on cutting off the power supply to electrical appliances off before a disaster occurs. If the residents turn off the breakers of each household before evacuation, it is possible to support the early restoration of the local community as well as the function of the electrical equipment of each household at the time of restoration. In this paper, we have developed a new electrical disaster prevention education program for children on the impact of electrical equipment in the event of floods and grasped the enhancing emergency response during floods. We are developing abilities to restore the entire community at an early stage, in addition to the perspectives of prevention and mitigation against flood damage.

Combination of flipped learning format and virtual simulation to enhance emergency response ability for newly registered nurses: a quasi-experimental design

ABSTRACT 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. Unfortunately, traditional learning has not been good enough in cultivating nursing skills and fostering self-directed learning which plays a key role in the learning process. Therefore, we attempted to integrate virtual simulation into the flipped learning format to optimize the training of newly registered nurses, and investigate its effectiveness. This study employs a pre- and post-test quasi-experimental design. The control and experimental groups each consisted of 43 newly registered nurses recruited in 2019 and 2020, respectively. The control group was trained using the traditional learning while the experimental group used the flipped learning format combined with virtual simulation for a 4–week emergency response training course. The data were collected through The Assessment Questionnaire of Clinical First-aid Capability of Nurses in the Non-Emergency Department and the Rating Scale of Self-directed Learning Competence for Nurses. Multivariate analysis of covariance was used for statistical analysis and the results show that the flipped learning combined with virtual simulation is more suitable and effective in improving nurses’ emergency response abilities and self-directed learning compared to traditional learning.

Identifying and addressing drinking water challenges in well-reliant communities after natural disasters: lessons from a Louisiana flood

BACKGROUND AND AIM: The 2016 Louisiana flood caused extensive structural damage to private drinking water wells, which were submerged under several feet of contaminated surface water for days. Well-reliant communities, which make up 13% of Louisiana’s population, are solely responsible for ensuring the safety of their water. The aims of this study are to evaluate private well user preparedness and recovery, and develop recommendations for flood-prone well-reliant communities. METHODS: A convenience sample of flood-impacted well owners was surveyed and offered free well water testing ten weeks after flood waters subsided (n=106). Surveys collected information to characterize knowledge gaps, risk perceptions, flood impacts, resource accessibility, and well maintenance barriers; while water tests evaluated total coliform and E. coli. RESULTS:Surveyed well owners needed information on well testing labs (90%), water contamination (77%), and water treatment (78%). Of respondents with flooded wells (n=75), one-third resided in low flood risk zones, indicating a need for improved risk determinations. After the flood, over half (57%) of flood-impacted well users (n=75) continued consuming well water after the flood; yet, 26% had water which tested positive for total coliforms. 69% of well users who resumed well water consumption (n=43) disinfected their water, but testing indicated that microbes can continue to regrow or re-enter wells. CONCLUSIONS:Results suggest that well owners lack the knowledge, means, resources, or motivation to protect well water and treat water adequately after floods. In addition, disinfection methods may be either insufficient, will not work due to well system failures, or are conducted incorrectly. Recommendations are made to enhance emergency response communications, and to improve well water surveillance. As the likelihood that more frequent and severe flooding events will increase, officials need to update and enhance technical resources, and provide well user training to facilitate well user preparedness, self-reliance and resilience. KEYWORDS: Flood, private well, well water, disaster preparedness, natural disaster