Impacts of mining disasters on the ambulatory care of the Brazilian national health system: the cases of Mariana and Brumadinho/Brazil.

BACKGROUND: Disasters are events that bring with them effects that contribute to the disruption of the normality of a population and thus highlight the vulnerabilities of the health system. In Mariana and Brumadinho, the collapse of the dam of ore tailings brought with it several impacts that were felt in the short term and will be felt in the medium and long term. And that by being intensely intertwined with issues of economic and productive nature, has as its meaning an uninterrupted result of its activities. METHODS: Through the DATASUS database, two specific variables were chosen to perform the analysis: the approved amount and the approved value. For this research, a methodological device, the segmented regression line, was used to observe the influences that the disasters that occurred in Mariana and Brumadinho had on the ambulatory health systems. RESULTS: The results of the segmented regression line show that, with Mariana, the amount approved continued to grow throughout the period, which shows that there was no change because of the disaster. There was a reduction in spending. In Brumadinho, regarding the amount approved, there was an upward trend in the disaster's month, which did not change immediately afterwards, and regarding expenditure, the growth pattern was maintained in all three periods. Corroborating this data, the relative and absolute base elements show an increase in the amount approved and in the number of services provided at various posts compared with Minas Gerais. CONCLUSIONS: Based on the findings, it was possible to understand that although disasters exert an influence that may have some effect on the health system, the lack of significance sometimes cannot be interpreted as a lack of impact on the disaster. The segmented regression line outlines some effects that are not conclusive but indicative of a numerical interpretation and a trend interpretation.

Sparse sensing to dense assessment: Incorporating spatial autocorrelation for assessing flood impacts.

A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor-driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood-impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood-impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor-uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor-driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy.

Mass online training of health care workers during COVID-19: approach, impact, and outcomes for over 10,000 health care providers.

OBJECTIVES: COVID-19 revealed major shortfalls in healthcare workers (HCWs) trained in acute and critical care worldwide, especially in low-resource settings. We aimed to assess mass online courses' efficacy in preparing HCWs to manage COVID-19 patients and to determine whether rapidly deployed e-learning can enhance their knowledge and confidence during a pandemic. STUDY DESIGN: Retrospective cohort study. METHODS: This international retrospective cohort study, led by a large Academic Medical Centre (AMC), was conducted via YouTube and the AMC's online learning platform. From 2020 to 2021, multidisciplinary experts developed and deployed six online training courses based on the latest evidence-based management guidelines. Participants were selected through a voluntary sample following an electronic campaign. Training outcomes were assessed using pre-and post-test questionnaires, evaluation forms, and post-training assessment surveys. Kirkpatrick's Model guided training evaluation to measure self-reported knowledge, clinical skills, and confidence improvement. We also captured the number and type of COVID-19 patients managed by HCWs after the trainings. RESULTS: Every 22.8 reach/impression and every 1.2 engagements led to a course registration. The 10,425 registrants (56.8% female, 43.1% male) represented 584 medical facilities across 154 cities. The largest segments of participants were students/interns (20.6%) and medical officers (13.4%). Of the 2169 registered participants in courses with tests, 66.9% completed post-tests. Test scores from all courses increased from the initial baseline to subsequent improvement post-course. Participants completing post-training assessment surveys reported that the online courses improved their knowledge and clinical skills (83.5%) and confidence (89.4%). Respondents managed over 19,720 COVID-19 patients after attending the courses, with 47.7% patients being moderately/severely ill. CONCLUSIONS: Participants' confidence in handling COVID-19 patients is increased by rapidly deploying mass training to a substantial target population through digital tools. The findings present a virtual education and assessment model that can be leveraged for future global public health issues, and estimates for future electronic campaigns to target.

The significance of local government in disaster management for international migrants: the case of Minoh City, Osaka Prefecture.

Disaster research predominantly focuses on citizens, not on migrants. This tilted spotlight needs to be readjusted, since many advanced countries around the world have become immigration countries, and safeguarding the lives of migrants at times of disaster has become an important and immediate policy issue. Hence, this research concentrates on disaster management to protect the lives of migrants in a disaster-prone and de facto immigration country. The particular country and event in question are Japan and the northern Osaka earthquake of June 2018. More than 100 migrants who lived near the earthquake's epicentre rushed to an evacuation shelter managed by the local municipal government of Minoh City, Osaka Prefecture. While non-governmental organisations attract more attention, this paper centres on a local government and demonstrates the key role that it can play in both bridging and building networks across different communities, and thus in safeguarding the lives of migrants at times of disaster.

Local knowledge as the basis of disaster management and humanitarian assistance.

Recent policy discourse on the localisation of disaster management and humanitarian assistance lacks attention to the culture, history, and traditions of the Global South. This special issue of Disasters argues that it is imperative to recognise the dynamic, interactive, contested, and negotiated nature of local knowledge. Such local knowledge saves lives by enabling responders to situate ad hoc, one-off events such as disasters in the broader and deeper context of community relationships, thereby providing more appropriate and more effective aid. Through the cases of China, Japan, Indonesia, and the Philippines, this special issue examines such dynamic local knowledge using an analytical framework consisting of three manifestations of local knowledge, namely: social capital; contextual historical memories; and adaptation to new ideas. These three manifestations show the ways in which local knowledge creates local capacity, via which local, national, and international disaster respondents can centre their response coordination, and in turn, demonstrate how local capacity reformulates local knowledge.

Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters.

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems.

The disaster literacy level of society, opinions on disaster management services, and related factors: A case from a province in Turkiye.

OBJECTIVE: This study aimed to investigate individuals' disaster literacy levels, their thoughts on disaster management services, and related factors. METHOD: The descriptive research was conducted with a total of 480 individuals between the ages of 18 and 60 living in a province in the first-degree earthquake zone in Turkey. The data were collected with the Disaster Literacy Scale and the Disaster Management Services Evaluation Form. RESULTS: In this study, it was found that more than half of the participants had inadequate/moderate disaster literacy. The total disaster literacy score and all subscale scores were found to be higher in individuals who had an earthquake kit at home, had an emergency plan, and had received disaster-related training (p < .05). The majority of the participants found the services provided for disaster management inadequate or very inadequate. CONCLUSIONS: In this study, it has been found that a significant portion of the society lacks the desired level of disaster literacy, and the services provided for disasters are deemed insufficient therefore, there is a need for multi-disciplinary national prevention programs to enhance disaster literacy and preparedness in the community, as well as urgently supporting policies to improve disaster-related services. Additionally, due to their comprehensive knowledge of the general structure of society, public health nurses need to play an active role in disaster preparedness and increasing disaster literacy levels.

Global insights from local actions: a case study on enhancing flood disaster management efficiency in China's Greater Bay Area.

This study examines the flood disaster management network within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events.

Exploring the role of large language models in radiation emergency response.

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, popularized by OpenAI's GPT-3, have demonstrated the emergent capabilities of AI in comprehending and producing text resembling human language, which has helped them transform several industries. But its role has yet to be explored in the nuclear industry, specifically in managing radiation emergencies. The present work explores LLMs' contextual awareness, natural language interaction, and their capacity to comprehend diverse queries in a radiation emergency response setting. In this study we identify different user types and their specific LLM use-cases in radiation emergencies. Their possible interactions with ChatGPT, a popular LLM, has also been simulated and preliminary results are presented. Drawing on the insights gained from this exercise and to address concerns of reliability and misinformation, this study advocates for expert guided and domain-specific LLMs trained on radiation safety protocols and historical data. This study aims to guide radiation emergency management practitioners and decision-makers in effectively incorporating LLMs into their decision support framework.

Understanding the impact of natural disasters on children within fist hours and days after an event: A phenomenological study through the experience of nurses.

BACKGROUND: Children are a particularly vulnerable group in natural disasters such as earthquakes, and although they represent a difficult group to research in such situations, there appears to be a lack of literature investigating children's experiences in the immediate post-earthquake period. Experienced nurses can shed light on children's experiences. AIM: This study was conducted to examine the experiences of nurses caring for children affected by the earthquake in Kahramanmaras, Turkey. METHODS: This phenomenological study was conducted with nurses who provided care to children during the Kahramanmaras earthquake between May and August 2023. The study sample consisted of 14 nurses selected using a purposive sampling technique. Data were collected through in-depth interviews by using the "Introductory Information Form" and "Semi-structured Interview Form" developed by the researchers. The interviews were subsequently analyzed using Colaizzi's method. RESULTS: The results revealed five themes under two main themes. Under the main theme of nurse, there are subthemes of chaos, helplessness-incompetence, while under the main theme of child, there are subthemes of unresponsiveness, intertwined needs, empathy, and care. The study also highlights the chaotic environment in the immediate aftermath of the earthquake, characterized by a high number of pediatric casualties and psychological trauma, and the nurses' feelings of helplessness as a result of being separated from their families. CONCLUSION: This study found that nurses caring for children in the earthquake had difficulties in managing children's hospital processes and they demonstrated an empathic approach by trying to communicate with the children in their care. The nurses stated that children should be supported psychologically as well as having their basic needs such as water, food, and shelter met. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Health policymakers should be aware that in the early stages of major disasters such as earthquakes, children are not only physically injured but also have basic needs such as water, shelter, and food, and are emotionally affected. It is recommended that disaster management plans be developed to address all children's needs and nurses be provided with psychological support and training to improve their knowledge and skills.

Disaster management competence, disaster preparedness belief, and disaster preparedness relationship: Nurses after the 2023 Turkey earthquake.

AIM: To examine the relationship between nurses' competency levels in disaster nursing management, their disaster preparedness and disaster preparedness beliefs. BACKGROUND: Nurses' competency in disaster nursing management makes disaster response easier. Factors that may affect this reason should be examined. METHODS: The study was conducted between April and July 2023 with nurses working in a province in the first-degree earthquake zone in Turkey. The sample of the study consisted of a total of 207 nurses who were selected from primary, secondary, and tertiary care institutions by using the stratified sampling method. Data were collected with a personal information form, the Competencies for Disaster Nursing Management Questionnaire, the Disaster Preparedness Scale, and the General Disaster Preparedness Beliefs Scale. Mann-Whitney U and Kruskal-Wallis tests, Spearman regression coefficient, and multiple linear regression analysis were used to analyze the data. RESULTS: There was a positive correlation between nurses' competency levels regarding duties and responsibilities and their disaster preparedness levels. Having disaster-related training also positively affected the level of competence in duties and responsibilities. Belief in disaster preparedness and having "partial" knowledge about the disaster had no relationship with competencies. DISCUSSION: According to the International Council of Nurses and current studies on disaster nursing, "competence areas" are among the priority study subjects. CONCLUSION: According to the study results, disaster preparedness and having disaster-related education are effective factors in nurses' competencies related to duties and responsibilities. IMPLICATIONS FOR NURSING POLICY: The results of this study may increase awareness in the healthcare system about nurses' disaster management competency levels and the factors that may affect this. It is recommended that disaster preparedness and disaster-related education factors should be taken into account in the initiatives and policies to be planned to improve the duty and responsibility competence of nurses.

Disaster preparedness perceptions and psychological first-aid competencies of psychiatric nurses.

AIM: This study aimed to determine the disaster preparedness perceptions and psychological first-aid competencies of psychiatric nurses. BACKGROUND: Nurses must recognize the risks before a disaster occurs and have an action plan for providing effective physical and psychological care to patients and other affected individuals during and after the disaster. It is especially important for psychiatric nurses caring for a high-risk group, such as psychiatric patients, to be prepared for disasters and be able to support patients by recognizing the psychological reactions that may occur after a disaster and managing their care accordingly. METHODS: For this cross-sectional and correlational study, data on 122 nurses were collected using a personal information form, the preparedness perception scale of disaster in nurses, and the psychological first-aid application self-efficacy scale. RESULTS: The mean score for the participants' perception of overall disaster preparedness was 76.70 ± 8.30, the mean score for the preparation stage was 27.02 ± 2.82, the mean score for the intervention stage was 30.40 ± 5.15, the mean score for the post-disaster stage was 19.27 ± 2.88, and the mean score for psychological first-aid application self-efficacy was 140.28 ± 19.17. CONCLUSION: The findings showed that psychiatric nurses were above the medium level in terms of disaster preparedness perceptions and psychological first-aid application self-efficacy. IMPLICATIONS FOR NURSING AND HEALTH POLICY: It is important for nurses caring for high-risk groups, such as psychiatric patients, to be aware of disaster preparedness, to recognize psychological reactions that may be observed after a disaster, and to be aware of psychological first-aid practices that can be used to intervene in a disaster.

Social vulnerability, impacts and adaptations strategies in the face of natural hazards: insight from riverine islands of Bangladesh.

BACKGROUND: Bangladesh is one of the countries at risk of natural disasters due to climate change. In particular, inhabitants of its riverine islands (char) confront ongoing climatic events that heighten their vulnerability. This study aims to assess social vulnerability, impacts, and adaptation strategies to climate change in the riverine island areas of Bangladesh. METHODS: A mixed-method approach incorporating qualitative and quantitative procedures was used on data collected from 180 households of riverine islands in Gaibandha, Bangladesh. The social vulnerability of riverine island communities was assessed based on their adaptation capacity, sensitivity, and exposure to climatic stressors. RESULTS: The findings show that char dwellers' vulnerability, impacts, and adaptation capability to climate change vary significantly depending on their proximity to the mainland. Social vulnerability factors such as geographical location, fragile and low-grade housing conditions, illiteracy and displacement, climate-sensitive occupation and low-income level, and so on caused to the in-height vulnerability level of these particular areas. This study also displays that climate change and its associated hazards cause severe life and livelihood concerns for almost all households. In this case, the riverine dwellers employed several adaptation strategies to enhance their way of life to the disaster brought on changing climate. However, low education facilities, deficiency of useful information on climate change, poor infrastructure, and shortage of money are still the supreme hindrance to the sustainability of adaptation. CONCLUSION: The findings underscore the importance of evaluating the susceptibility of local areas to climate change and emphasize the need for tailored local initiatives and policies to reduce vulnerability and enhance adaptability in communities residing in char households.

User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review.

BACKGROUND: Health-related hazards have a detrimental impact on society. The health emergency and disaster management system (Health EDMS), such as a contact-tracing application, is used to respond to and cope with health-related hazards. User compliance with Health EDMS warnings is key to its success. However, it was reported that user compliance with such a system remains low. OBJECTIVE: Through a systematic literature review, this study aims to identify the theories and corresponding factors that explain user compliance with the warning message provided by Health EDMS. METHODS: The systematic literature review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. The search was performed using the online databases Scopus, ScienceDirect, ProQuest, IEEE, and PubMed, for English journal papers published between January 2000 and February 2022. RESULTS: A total of 14 papers were selected for the review based on our inclusion and exclusion criteria. Previous research adopted 6 theories when examining user compliance, and central to the research was Health EDMS. To better understand Health EDMS, based on the literature reviewed, we mapped the activities and features of Health EDMS with the key stakeholders involved. We identified features that require involvement from individual users, which are surveillance and monitoring features and medical care and logistic assistance features. We then proposed a framework showing the individual, technological, and social influencing factors of the use of these features, which in turn affects compliance with the warning message from Health EDMS. CONCLUSIONS: Research on the Health EDMS topic increased rapidly in 2021 due to the COVID-19 pandemic. An in-depth understanding of Health EDMS and user compliance before designing the system is essential for governments and developers to increase the effectiveness of Health EDMS. Through a systematic literature review, this study proposed a research framework and identified research gaps for future research on this topic.

Improving fire risk communication between authorities and micro-entrepreneurs: A mental models study of Ghanaian central market fires.

This study conceptualizes how fire management authorities can empower nonexpert public to participate in fire risk communication processes and increase their own responsibilities for managing fire preventive, protective and recovery processes effectively. Drawing narratives from 10 disaster management experts working at government institutions and nine micro-entrepreneurs operating self-sustaining businesses in different merchandized lines in Ghana, we analyzed the data thematically and explored new insights on mental models to generate a two-way fire risk communication model. The findings suggest that fire management authorities planned fire disasters at the strategic level, collaborated with multiple stakeholders, disseminated information through many risk communication methods, and utilized their capabilities to manage fire at the various stages of fire risk communication, but the outcomes were poor. The micro-entrepreneurs sought to improve fire management outcomes through attitude change, law enforcement actions, strengthened security and better public trust building. The study has implications for policymakers, governments, and risk communication authorities of developing countries to strengthen their fire disaster policies to minimize commercial fire incidents and address the damaging effects of fire on people's livelihoods, businesses, properties, and environments. Our proposed two-way fire risk communication model is a new theoretical lens for experts and the nonexpert public to assess each other's beliefs about risk information and manage fire risk communication effectively at all stages.

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review.

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.

Early Fire Detection Using Long Short-Term Memory-Based Instance Segmentation and Internet of Things for Disaster Management.

Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. As the human population and global warming continue to rise, fires have emerged as a significant worldwide issue. These factors may contribute to the greenhouse effect and climatic changes, among other detrimental consequences. It is still challenging to implement a well-performing and optimized approach, which is sufficiently accurate, and has tractable complexity and a low false alarm rate. A small fire and the identification of a fire from a long distance are also challenges in previously proposed techniques. In this study, we propose a novel hybrid model, called IS-CNN-LSTM, based on convolutional neural networks (CNN) to detect and analyze fire intensity. A total of 21 convolutional layers, 24 rectified linear unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of the fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25% classification accuracy, 0.09% false positive rate (FPR), 0.65% false negative rate (FNR), and a prediction time of 0.08 s validates the proposed system.

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues.

With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big "sensed" data leads to an effective and efficient management of disaster situations so as to prevent human and economic losses. The advancement of built-in sensing technologies in smart mobile devices enables crowdsourcing of sensed data, which is known as mobile crowdsensing (MCS). This systematic literature review investigates the use of mobile crowdsensing in disaster management on the basis of the built-in sensor types in smart mobile devices, disaster management categories, and the disaster management cycle phases (i.e., mitigation, preparedness, response, and recovery activities). Additionally, this work seeks to unveil the frameworks or models that can potentially guide disaster management authorities towards integrating crowd-sensed data with their existing decision-support systems. The vast majority of the existing studies are conceptual as they highlight a challenge in experimental testing of the disaster management solutions in real-life settings, and there is little emphasis on the use cases of crowdsensing through smartphone sensors in disaster incidents. In light of a thorough review, we provide and discuss future directions and open issues for mobile crowdsensing-aided disaster management.

AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG.

An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences.

Fast Deployment of a UWB-Based IPS for Emergency Response Operations.

A wide range of applications from multiple sectors already use ultra-wideband (UWB) technology to locate and track assets precisely. This is not the case, however, for first responder localization during emergency response (ER) operations, which are highly conditioned by procedural and environmental constraints. After analyzing these limitations and reviewing the current state-of-the-art solutions, this work presents a UWB-based indoor positioning system (IPS) that relies on the global navigation satellite system real-time kinematic (GNSS-RTK) technology to quickly, accurately, and safely deploy its required infrastructure on site. A set of tests conducted on a two-story building prove the suitability of such a system, providing an average accuracy of less than 1 meter for static targets and the ability to faithfully reproduce the path followed by a mobile target inside the building. The obtained results strengthen the presented approach and pave the way for more sophisticated UWB-based IPSs that would include unmanned aerial vehicles (UAVs) and/or mobile robots to speed up network deployment even more while offering additional ER services.