Emergency Response Management Research Articles

The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review

The Industrial Internet of Things and Industry 4.0 are now two highly sought-after areas of research and development, attracting significant interest from both academic and industrial sectors. The two ideas, Industry 4.0 and IIoT, share significant similarities, with Industry 4.0 being seen as the use of IIoT specifically in the automation and manufacturing sectors. Within the framework of the present Industry 4.0 paradigm, many growth pathways have emerged, collectively leading to notable enhancements in terms of efficiency, flexibility, communication, adaptability, customization, and modularity in the industrial sector. The Industry 4.0 is rapidly evolving within the framework of the Industrial Internet of Things (IIoT), and the authors are recognizing the necessity for a comprehensive and in-depth overview of the many research areas that are currently expanding. The area will remain intriguing in the foreseeable future due to its significant potential for enhancing the existing industrial technologies. An exhaustive evaluation of the current systems in the automotive sector, emergency response, and chain management on IIoT has been conducted, revealing that IIoT has been widely adopted across several technological domains. Industry 4.0 is the term used to describe the present automation and data sharing trend in businesses. Presently, there is a dearth of agreement about the assessment of an organization's readiness for Industry 4.0. Industry 4.0 encompasses a diverse array of digital technologies that profoundly influence industrial enterprises. The literature on Industry 4.0 has had significant exponential growth during the previous decade. The results of our research confirm the idea of Industry 4.0 as a concept that goes beyond the Smart Manufacturing sector, hence opening up possibilities for collaboration with other interconnected disciplines.

A novel risk analysis method for hazardous cargo operations at port integrating the HFLC model and DEMATEL method

The development of maritime chemical and industrial trade has led to the large-scale hazardous cargo operations at port, making it essential to conduct risk analysis to analyze the risk factors affecting port safety and prevent the occurrence of accident. However, the complex interrelationships among risk factors in hazardous cargo operations and the uncertainties in the risk analysis process pose significant challenges to the effectiveness of risk analysis results. To this end, this paper proposes an integrated risk analysis method for hazardous cargo operations at port based on the hesitant fuzzy linguistic cloud (HFLC) model and the DEMATEL method. Firstly, 20 risks related to hazardous cargo operations are determined based on accident analysis and previous literatures. The HFLC model is then applied to characterise the linguistic judgements with uncertainty provided by the experts on the different risk factors. Next, the DEMATEL method is extended to the HFLC environment and utilized to calculate the centrality degree and causality degree of different risk factors in the form of HFLC. Finally, ten factors for cause group and effect group are identified separately, and the critical influence relationships between the factors are determined. This study can assist port managers in mitigating the risks of hazardous cargo operations at port, and can also serve as a crucial basis for the formulation of management decision-making and emergency response strategy.

Humanitarian Logistics and Emergency Response in Humanitarian Organizations in Kenya.

Disasters were unpredictable and unavoidable, necessitating adequate disaster response plans. Humanitarian logistics, which gained prominence after the 2004 Asian tsunami, played an important part in crisis prevention. Despite weak links in relief chain management, issues remained. Humanitarian logistics had a huge impact on disaster relief efforts, as studies revealed a variety of challenges, including a lack of sufficient SCM software and poor facilities. Despite its importance, few studies have explored the impact of disaster assistance on humanitarian organizations, particularly in Kenya. The study sought to address this gap by evaluating the impact of humanitarian logistics on emergency response in Nairobi, Kenya. Humanitarian groups, policymakers, and NGOs profited from the research, which informed strategic planning and policy creation to strengthen disaster response operations. The Resource Dependency Theory (RDT) proposed that organizations rely on external resources for survival and growth. It underlined the importance of forming strategic alliances to secure resources. In humanitarian settings, logistics were critical in procuring and allocating resources for successful emergency response. The research methodology used a cross-sectional descriptive design to investigate the association between humanitarian logistics management techniques and emergency response in Kenyan humanitarian organizations. Semi-structured questionnaires were used to obtain data from 64 Nairobi-based firms. Data analysis required the use of both descriptive and inferential statistical techniques, such as regression and correlation analysis. The study found a significant positive association (β = 0.732, p < 0.001) between logistics management practices and emergency response, indicating that improving logistics leads to better response outcomes. The study recommended prioritizing logistical development and resource allocation to improve emergency response capabilities.

Thresholds for estuarine compound flooding using a combined hydrodynamic–statistical modelling approach

Abstract. Estuarine compound flooding can happen when extreme sea level and river discharges occur concurrently, or in close succession, inundating low-lying coastal regions. Such events are hard to predict and amplify the hazard. Recent UK storms, including Storm Desmond (2015) and Ciara (2020), have highlighted the vulnerability of mountainous Atlantic-facing catchments to the impacts of compound flooding including risk to life and short- and long-term socio-economic damages. To improve prediction and early warning of compound flooding, combined sea and river thresholds need to be established. In this study, observational data and numerical modelling were used to reconstruct the historic flood record of an estuary particularly vulnerable to compound flooding (Conwy, North Wales). The record was used to develop a method for identifying combined sea level and river discharge thresholds for flooding using idealised simulations and joint-probability analyses. The results show how flooding extent responds to increasing total water level and river discharge, with notable amplification in flood extent due to the compounding drivers in some circumstances, and sensitivity (∼ 7 %) due to a 3 h time lag between the drivers. The influence of storm surge magnitude (as a component of total water level) on the flooding extent was only important for scenarios with minor flooding. There was variability as to when and where compound flooding occurred; it was most likely under moderate sea and river conditions (e.g. 60th–70th and 30th–50th percentiles) and only in the middle-estuary zone. For such cases, joint-probability analysis is important for establishing compound flood risk behaviour. Elsewhere in the estuary, either the sea state (lower estuary) or river flow (upper estuary) dominated the hazard, and single-value probability analysis is sufficient. These methods can be applied to estuaries worldwide to identify site-specific thresholds for flooding to support emergency response and long-term coastal management plans.

The Wheel Is Already Invented: Planning for the Next Crisis

The development and implementation of pandemic response protocols for museum workers and visitors evolved over many months following the recognition of the SARS-CoV-2 virus as a major public health threat and crisis trigger. Initial response to the crisis in many museums was cessation of all activity and interaction with the public and with coworkers. Museum functions were suspended. Museums closed their doors to visitors and staff. Access to exhibits and collections was disrupted. Ensuring the safety of objects on display was difficult. The air we breathed, the objects we touched, the spaces we were in were all suspect as potential transmitters of the virus. As research was conducted on virus transmission, research results were incorporated into the development of reopening protocols for museums and museum collections allied with a university museum consortium. Case studies of the compilation, distribution, implementation, and reflection of pandemic response protocols within consortium member museums provide insight into including these crisis protocols for emergency response and collections management policies going forward.

The effects of dynamic multi-hazard risk assessment on evacuation strategies in chemical accidents

With the agglomeration and complication of the chemical industry, the multi-hazard and dynamic characteristics have become new challenges for chemical safety management and loss prevention research. As an important topic of safety research in chemical industries, the evacuation strategy also needs to be improved according to the new characteristics of chemical accidents. Here we propose a method for evacuation strategy development in chemical accidents based on dynamic and multi-hazard risk assessment. This method adopts the cumulative individual risk as the evaluation indicator for the optimal evacuation strategy. This method can dynamically consider the multi-hazard characteristics and domino effects of chemical accidents during the evacuation. A multi-hazard chemical accident that happened in Xiangshui, China in 2019 is taken as a case study to show the effects of risk assessment results on evacuation strategy development. The case shows that a reasonable time period for shelter-in-place after the occurrence of the primary accident can reduce the cumulative individual risk in the evacuation process. It is expected that this paper can provide guidance and reference for loss prevention in chemical industries, and can also provide useful tools for the management of emergency response.

A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments

High-spatial-resolution urban buildings play a crucial role in urban planning, emergency response, and disaster management. However, challenges such as missing building contours due to occlusion problems (occlusion between buildings of different heights and buildings obscured by trees), uneven contour extraction due to mixing of building edges with other feature elements (roads, vehicles, and trees), and slow training speed in high-resolution image data hinder efficient and accurate building extraction. To address these issues, we propose a semantic segmentation model composed of a lightweight backbone, coordinate attention module, and pooling fusion module, which achieves lightweight building extraction and adaptive recovery of spatial contours. Comparative experiments were conducted on datasets featuring typical urban building instances in China and the Mapchallenge dataset, comparing our method with several classical and mainstream semantic segmentation algorithms. The results demonstrate the effectiveness of our approach, achieving excellent mean intersection over union (mIoU) and frames per second (FPS) scores on both datasets (China dataset: 85.11% and 110.67 FPS; Mapchallenge dataset: 90.27% and 117.68 FPS). Quantitative evaluations indicate that our model not only significantly improves computational speed but also ensures high accuracy in the extraction of urban buildings from high-resolution imagery. Specifically, on a typical urban building dataset from China, our model shows an accuracy improvement of 0.64% and a speed increase of 70.03 FPS compared to the baseline model. On the Mapchallenge dataset, our model achieves an accuracy improvement of 0.54% and a speed increase of 42.39 FPS compared to the baseline model. Our research indicates that lightweight networks show significant potential in urban building extraction tasks. In the future, the segmentation accuracy and prediction speed can be further balanced on the basis of adjusting the deep learning model or introducing remote sensing indices, which can be applied to research scenarios such as greenfield extraction or multi-class target extraction.

Deep Learning Approach to Improve Spatial Resolution of GOES-17 Wildfire Boundaries Using VIIRS Satellite Data

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

Joint User Association and Power Control in UAV Network: A Graph Theoretic Approach

Unmanned aerial vehicles (UAVs) have recently been widely employed as effective wireless platforms for aiding users in various situations, particularly in hard-to-reach scenarios like post-disaster relief efforts. This study employs multiple UAVs to cover users in overlapping locations, necessitating the optimization of UAV-user association to maximize the spectral and energy efficiency of the UAV network. Hence, a connected bipartite graph is formed between UAVs and users using graph theory to accomplish this goal. Then, a maximum weighted matching-based maximum flow (MwMaxFlow) optimization approach is proposed to achieve the maximum data rate given users’ demands and the UAVs’ maximum capacities. Additionally, power control is applied using the M-matrix theory to optimize users’ transmit powers and improve their energy efficiency. The proposed strategy is evaluated and compared with other benchmark schemes through numerical simulations. The simulation outcomes indicate that the proposed approach balances spectral efficiency and energy consumption, rendering it suitable for various UAV wireless applications, including emergency response, surveillance, and post-disaster management.

Emergency response, and community impact after February 6, 2023 Kahramanmaraş Pazarcık and Elbistan Earthquakes: reconnaissance findings and observations on affected region in Türkiye

AbstractTürkiye has a long history of devastating earthquakes, and on February 6, 2023, the region experienced two major earthquakes with magnitudes of 7.7 and 7.6, striking Pazarcık and Elbistan, Kahramanmaraş, respectively, on the East Anatolian Fault Zone. These earthquakes resulted in significant loss of life and property, impacting multiple cities across 11 cities, and leaving a lasting impact on the country. The 2023 Kahramanmaraş Earthquakes rank among the deadliest and most damaging earthquakes in Türkiye, alongside the historical significance of the 1939 Erzincan Earthquake and the 1999 Marmara Earthquake. Despite reforms following the 1999 Marmara Earthquake in disaster policy and preparedness, the scale of damage from the February 6 earthquakes has been shocking, necessitating further insights and lessons for future earthquake management. This paper presents the outcomes of immediate response efforts organized after the 2023 Kahramanmaraş earthquakes to elucidate emergency response activities and their impacts on communities, considering the substantial size and severity of the damages. The study focuses on evaluating the emergency response provided within the first 24 h, 3 days, and 2 weeks after the earthquakes, aiming to promptly identify the nature and effectiveness of these responses, as well as the conditions that hindered their efficacy. By shedding light on the specific experiences and challenges faced during these crucial timeframes, the research aims to offer valuable insights and lessons learned. These findings contribute to improved preparedness strategies and more efficient emergency response measures needed in responding to future disaster scenarios. Ultimately, this study provides a useful resource for all stakeholders involved in emergency response and disaster management, offering valuable guidance to enhance resilience and preparedness in the face of seismic hazards.

Brief communication: The Lahaina Fire disaster – how models can be used to understand and predict wildfires

Abstract. Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The results are in good agreement with observations recorded during the event. Extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making and emergency response management during wildfire events.

Deep Hybrid Attention Framework for Road Crash Emergency Response Management

Deep Hybrid Attention Framework for Road Crash Emergency Response Management

Pelatihan pencegahan dan penanggulangan bahaya kebakaran di fire station Bandar Udara Internasional Soekarno-Hatta

The importance of awareness of fire risks in the community, especially at airports, encourages the need for effective prevention and countermeasures. Community service by lecturers of the D.III PKP Study Program through this training aims to provide knowledge and skills to the community of airport service users, especially workers in tenants, regarding how to prevent and overcome fires. Involving the Tri Darma Perguruan Tinggi concept, this training not only touches on aspects of fire prevention but also discusses fire phenomena, risk analysis, emergency response, and fire management. It is hoped that training participants will benefit them through increased knowledge and understanding that can increase awareness of fire risks so that the community around Soekarno-Hatta International Airport can be better prepared and trained in dealing with potential fire hazards.

Flood Impact Assessment in Koton Karfe Using Sentinel-1 Synthetic Aperture Radar (SAR) Data

Flood has proven to be an incessant menace in Nigeria more threatening to riverine areas. The most recent flood ensued in 2022 as a result of heavy rainfall and the release of water from Lagdo Dam in Cameroon which became very devastating in many areas notably the Koton Karfe area in Kogi State, causing business shutdowns and the loss of lives and properties. In this work, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was used for flood inundation mapping, and the accompanying damages were investigated using Landsat derived Land cover maps of Koton Karfe during the 2022 devastating flood. Overall, the results obtained in this study show that the regions that felt the impact of the flood the most were the southern and western areas, which must have experienced such an impact due to their proximity to the rivers Niger and Benue and also the water coming from the upper stream part of Cameroon. Further findings revealed that during the flood period on October 13, 2022, the total inundated area in Koton Karfe was estimated to be 198.255 sq. km. In terms of damage assessment, the urban areas had reduced from 220.902 sq. km in May 2022 to 87.473 sq. km in November 2022. This shows that over 133 sq. km of the urban settlement have been lost, indicating that lives must have been lost, properties too, and humans must have been displaced. This research will assist in the space of flood emergency response and disaster management.

Vulnerability Assessment of Industrial Sites to Interface Fires and Wildfires

In the framework of climate change, the hazard caused by wildfires approaching the anthropic settlements is raising an increasing concern. Fatalities and relevant damage to properties were recently caused by wildfires affecting the Wildland-Urban and Wildland-Industrial Interfaces. Industrial sites storing large quantities of hazardous materials are vulnerable to interface fires, which have the potential to trigger specific cascading events such as Natech scenarios followed by domino effects. The present study aims at providing a methodology for the quantitative assessment of the vulnerability of industrial sites exposed to wildfires. The approach provides a novel framework for the identification and quantification of all the chains of failures that may occur due to wildfires or interface fires approaching industrial sites. The methodology accounts for the thermal radiation from fires in both primary Natech scenarios and cascading scenarios triggered by domino effects. The dynamic features of interface fires and the synergistic effects of multiple fires are also taken into account. The results of a case study demonstrated the importance of considering the dynamic behavior of wildfire, which strongly affects the vulnerability of industrial structures. The results also evidence the importance of emergency management and first response on the overall vulnerability figures.

Reclassifying historical disasters: From single to multi-hazards

Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This study presents an approach for identifying multi-hazard events during the past 123 years (1900–2023) using the EM-DAT global disaster database. Leveraging the ‘associated hazard’ information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19 % of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59 % of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels.

A flow-directed minimal path sets method for success path planning and performance analysis

Emergency operation plans are indispensable elements for effective process safety management especially when unanticipated events occur under extreme situations. In the paper, a synthesis framework is proposed for the integration success path planning and performance analysis. Within the synthesis framework, success path planning is implemented through flow-directed signal tracing, renaming and reconstruction from a complete collection of Minimal Path Sets (MPSs) that are obtained using graph traversal search on GO-FLOW model diagram. The performance of success paths is then evaluated and prioritized according to the task complexity and probability calculation of MPSs for optimum action plans identification. Finally, an Auxiliary Feed Water System of Pressurized Water Reactor (PWR-AFWS) is taken as an example system to demonstrate the flow-directed MPSs search method for success path planning and performance analysis. It is concluded that the synthesis framework is capable of providing procedural guidance for emergency response and safety management with optimal success path planning under extreme situations.

Intelligent accident detection system by emergency response and disaster management using vehicular fog computing

Traffic has increased significantly over the past few years as a result of an increase in the number of vehicles using the common routes. It has been noted that manually operating signals has grown to be difficult. The difficulties encountered during an accident are the backed-up ambulances during emergencies caused by vehicle congestion and poor weather conditions like fog and haze. Considering this, the study focuses on the outcomes of an intelligent accident detection system using Vehicular Fog Computing (VFC). Automatic identification of crash spots and free flow of ambulances on roadways at peak hours of traffic. VFC has recently gained popularity as a useful tool for assisting vehicles in computing and storing service demands. Using the built-in sensors on a smartphone to monitor vehicular collisions and report them to the closest accessible first responder, as well as providing real-time location monitoring for paramedics and emergency victims would greatly improve the odds of recovery for emergency victims while saving time and money. This computing model guarantees the optimization of traffic congestion and energy consumption in the accident and foggy environment. This method also relies on delivering medical records to the closest hospital before the ambulance arrives, so pre-treatment can begin in the hospital.

Assessing and validating the specialized competency framework for pharmacists in sales and marketing (SCF-PSM): a cross-sectional analysis in Lebanon

ObjectivesCompetencies refer to the knowledge, skills, attitudes, and behaviors individuals develop through education, training, and experience. These competencies can be formulated into a framework to support practitioner development for effective and sustained performance. In the absence of a national framework for pharmacy education and practice in Lebanon, the Order of Pharmacists of Lebanon (OPL, the official association of pharmacists in Lebanon) pioneered the development of a pharmacy competency framework in 2017. This study aimed to validate and assess the specialized competency framework for pharmacists in sales and marketing (SCF-PSM) after updating the framework previously published by the OPL. The secondary objective was to assess, in a pilot survey, the personal characteristics associated with these competencies.MethodsAfter validating the content of the specialized competency framework, a survey involving Lebanese pharmacists was performed through a 15-min online questionnaire distributed over social media platforms, groups of pharmacists, and individual pharmacists’ contact numbers.Key findingsPharmaceutical knowledge, communication, emergency response, and operation management during emergencies were satisfactory (more than 80/100). Other activities during emergencies, such as patient care and population health interventions and evaluation, research, and dissemination of research outcomes, received a moderate score (75–78/100), similar to legal practice (78/100), teamwork (76/100), and management skills (75/100). The lowest reported confidence was related to professional communication skills (other than communication per se), mainly negotiation, data processing skills, information technology, self-management, and ethical practice (< 75/100). This study reported deficiencies between what is acquired during undergraduate, postgraduate, and continuing education on the one hand and the competency framework suggested by the OPL on the other hand, showing a mismatch between the competencies of working pharmacists acquired during education and the market needs.ConclusionsThis study validated a competency framework for pharmacists in sales and marketing and explored the current gaps in self-reported competencies. It also identified areas necessitating reinforcement to optimize professional practice and underscored the need for improvement in undergraduate, postgraduate, and continuing professional education.

Unleashing the power of emergency response: controlling natural disasters by addressing environmental risk.

The primary focus of the analysis is to investigate the impact of emergency response management and environmental risk on natural disasters by controlling the variables of national income and financial development. To investigate the model empirically, we have employed the quantile autoregressive distributed lag model that estimates the short- and long-run estimates across various quantiles. The long-run estimates of emergency response management are negative and significant only at higher quantiles, i.e., from 60 to 95th quantiles. In the short run, emergency response management's estimated coefficients are negative and significant from 70 to 95th quantiles. Environmental risk shows a significant positive correlation with natural disasters across quantiles, while national income and financial development decrease natural disasters in the long run. Furthermore, we observed the asymmetric impact of emergency response management on natural disasters in both the short and long run.