Barry Turner’s 1978 Man-made Disasters and Charles Perrow’s 1984 Normal Accidents were seminal books but a detailed comparison has yet to be undertaken. Doing so is important to establish content and priority of key ideas underpinning contemporary safety science. Turner’s research found socio-technical and systemic patterns that meant that major organisational disasters could be foreseen and were preventable. Perrow’s macro-structuralist industry focus was on technologically deterministic but unpredictable and unpreventable ‘system’ accidents, particularly rare catastrophes. Andrew Hopkins and Nick Pidgeon respectively suggested that some prominent writers who wrote after Turner may not have been aware of, or didn’t properly acknowledge, Turner’s work. Normal Accidents didn’t cite Turner in 1984 or 1999. Using a methodology involving systematic reading and historical, biographical and thematic theory analysis, a detailed review of Turner’s and Perrow’s backgrounds and publications sheds new light on Turner’s priority and accomplishment, highlighting substantial similarities as well as clear differences. In a previously unpublished letter, Perrow confirmed reading Man-made Disasters while writing Normal Accidents. Turner became better known after a 1997 second edition but under-acknowledgment issues continued. Ethical citation and potential reasons for under-acknowledgment are discussed. It is concluded that Turner’s foundational importance for safety science should be better recognised.

Remote sensing data and techniques utilized for various purposes including natural disasters such as earthquake as well as flood. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds utilized in addition processed through Google Engine Platform. Due to the materials as well as the color of airplane body different from the area which is a plane crashed there, moreover, it should be the characteristics of the plane shapefile different in terms of albedo, temperature as well as vegetation index value. The research observed Landsat 8 data as well as methods utilized in this research, especially, NDVI, albedo in addition to band 4, capable to distinguish between the plane and its surrounding green area. Therefore, our result confirms during the research period, there was no plane on the location as well as MH370 not crashed in this site.

Debates on climate change challenges around the world are moving in favour of nature-based solutions (NBS) as potential mitigation and adaptation options. Mangroves and associated blue carbon ecosystems are nature-based solutions against sea level rise and other coastal hazards. As such, protecting the existing mangrove forests and restoration of the degraded areas is integral in this aspect. However, conventional mangrove restoration approaches in high-energy areas result in poor performance due to the removal of seedlings by waves. Here, we assessed the efficacy of using modified Riley Encasement Methods in the restoration of eroding intertidal areas of Gazi Bay, Kenya. Vegetation and soil baseline data were collected in 49 square plots of 100m2; established along belt transects perpendicular to the waterline. The following mangrove vegetation data was collected; species composition, tree height (m), and stem diameter (cm); from which the importance value index (IV), basal area and standing density (stems/ha) were derived. Sediment cores were made in the center of each square plot for carbon and grain size analysis. Mangrove (Rhizophora mucronata) planting adopted a randomized complete block design (RCBD) in which the planting area was divided into three blocks (A, B, C). Within each block, treatments (bamboo and different-sized PVC pipes) were randomly assigned locations. Results show that seedlings grown within PVC encasements had high survival and growth rates, surpassing bamboo and control groups. This implies that PVC pipes offer better protection from external factors. These findings are critical in highlighting potential enhancements for the effectiveness of PVC pipes in mangrove restoration. They also reinforce on the need to consider the incorporation of PVC encasements as an alternative mangrove restoration technique in high-energy locations.

The Mine Internet of Things (MIoT), as a key technology for reconstructing post-disaster communication networks, enables to realize the safety monitoring and controlling of the affected roadway. However, due to the challenging underground mine environment, the MIoT suffers from severe signal attenuation, vulnerable nodes, and limited energy, which result in low network reliability of the post-disaster MIoT. To improve the transmission reliability as well as to reduce energy consumption, a directional-area-forwarding-based energy-efficient opportunistic routing (DEOR) for the post-disaster MIoT is proposed. DEOR defines a forwarding zone (FZ) for each node to route packets toward the sink. The candidate forwarding set (CFS) is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. The nodes in CFS are prioritized based on the routing quality evaluation by taking the local attributes of nodes into consideration, such as the directional angle, transmission distance, and residual energy. DEOR adopts a recovery mechanism to address the issue of void nodes. The simulation results validate that the proposed DEOR outperforms ORR, OBRN and ECSOR in terms of energy consumption, average hop count, packet delivery rate, and network lifetime.

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

At 12:28 UTC (8:28 PM, local time) on 15 January 2023, a shallow, moderate earthquake with a magnitude (Mw) of 4.7 and a depth of one (1) kilometer shook the northern part of Leyte Island in the central part of the Philippines. The ground shaking was felt at PHIVOLCS Earthquake Intensity Scale (PEIS) VI, (very strong) equivalent to Modified Mercalli Intensity (MMI) VI in areas along the strike of the Leyte segment of the Philippine Fault. Even though this earthquake is classified as moderate in size, significant geologic, structural, and socio-economic impacts were documented. Most of the earthquake impacts that include surface rupture, liquefaction, and mass movements are limited near the epicentral area. The earthquake produced an 8-km-long surface rupture, the first documented rupture generated by a magnitude <6 along the Philippine Fault, and is unusual in other surface rupturing earthquakes worldwide. It injured 18 persons and damaged 434 residential buildings and 26 public infrastructures which amounts to about 500 thousand US dollars. Structural damages were caused by surface rupture and/or poor engineering construction practices. Analysis of the seismic waveform and recorded peak ground acceleration (PGA) suggests that the subsurface geology influenced the ground motion amplification in an area near the epicenter. Based on the hypocenter, focal mechanism solution, aftershock distribution, and actual field observation, the causative fault for this event is the northern end of the Philippine Fault – Leyte segment. Moderate in size, this earthquake emphasizes the need and importance of documenting moderate-sized earthquakes as a tool and guide for medium and long-term earthquake risk assessment and resiliency.

Nurses and paramedics play a pivotal role when Mass Casualty Incidents (MCI) occur, yet they often feel unprepared for such events. Implementation strategies for training activities, including Virtual Reality (VR) and Augmented Reality (AR) simulations, offer realistic and immersive learning experiences, enhancing skills and competencies for nursing students. The aim of this work was to investigate the adopted tools in studies on VR&AR simulations for training nursing and paramedic students in managing MCI. A scoping review was performed following the PRISMA-ScR statement, and the search strategy was conducted through five electronic databases from December 2022 to March 2023. Of 162 records identified, 27 full-text were screened and, six studies were included in this review. These studies involved students who were assigned to different training methods, including immersive VR simulation, written instruction, and traditional lecture. VR&AR and immersive simulation generally show promising evidences in enhancing practical skills and knowledge in MCI management. VR&AR showed to be promising in disaster education and preparedness training, offering different levels of immersiveness and engagement, encouraging active and experential learning. Further research is needed to determine their long-term effectiveness. The choice of training method should consider program goals, target population, and available resources.

This paper aims to provoke fundamental thinking and action around the value and importance of socially-equitable development to the economic advancement, resilience, and prosperity of communities, as we contend with the 21^st Century grand challenge of the changing climate and disasters. As local communities and the global community have experienced an increased frequency, intensity and duration of natural and man-made disasters over the past several decades, opportunities have also grown to identify and reap the benefits of socially-equitable economic development. Reflecting on the COVID-19 pandemic, we discuss the critical importance of socially-equitable economic development to the resilience and sustainability of communities and the infrastructure that supports them. To this end, we: (1) examine what constitutes socially-equitable economic development at different spatial scales of community; (2) explore whether socially-equitable development can occur at different scales of community; (3) explicate the importance of formally considering the inputs, processes, outputs and outcomes for socially-equitable development; (4) explain why the pursuit of equal distribution of the benefits and burdens of development is a necessary but not sufficient endeavor for socially-equitable economic development; (5) analyze the relationships between socially-equitable development, and resilient and sustainable infrastructure and communities; (6) explain why socially-equitable development should be a key component of infrastructure and community resilience strategies in the 21^st Century; and, (7) explain why socially-equitable development can ultimately be viewed as a long-term strategy for prosperity.

Here, we propose a prescriptive simulation model of a process operator’s decision-making assisted by artificial intelligence (AI) algorithm in a technical system control loop. We analyze situations fraught with a catastrophic threat that may cause unacceptable damage. Operators’ decision-making is interpreted in terms of a subjectively admissible probability of disaster and subjectively necessary reliability of its assessment. We distinguish four extreme decision-making strategies corresponding to different ratios between the above variables. An experiment simulating a process facility, an AI algorithm and operator's decision-making strategy was held. It showed that depending on the properties of a controlled process (the speed of hazard onset) and the AI algorithm characteristics (Type I and II error rate), each of such strategies or some intermediate strategy may prove to be more beneficial than others. The same approach is applicable to the identification and analysis of sustainability of strategies applied in real-life operating conditions, as well as to the development of a computer simulator to train operators to control hazardous technological processes using AI-generated advice.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Recently, the destructive impact of wildfires has proliferated; for instance, the August Complex wildfire in 2020 burned around 4% of California's landmass. This has caused increased economic damage and risk to human life. Additionally, climate change is anticipated to increase the severity of wildfires, making it imperative for accurate and efficient detection of wildfires. Machine Learning approaches allow for the automatic detection of wildfires, simultaneously prioritizing accuracy and efficiency, with minimal human intervention, thus decreasing the likelihood of increased economic damage and increasing firefighting responses. Convolutional Neural Networks (CNNs), while showing promise, are often limited by their inability to learn and capture deep spatial dependencies in satellite imagery tasks. In this paper, we propose FlameViT, a novel wildfire detection architecture based on Vision Transformers (ViT). Satellite images are more efficient to obtain and can cover wide areas prone to wildfires. We obtain a dataset of 40K+ satellite images from Canada's Open Government Portal, allowing FlameViT to be optimized to detect wildfires in satellite imagery. FlameViT uses Patch Extractor and Patch Embedding layers, followed by multiple Transformer Encoder layers with Multi-Head Self-Attention and feed-forward neural networks. FlameViT is hyperparameter-tuned and achieves a validation accuracy of 95%, outperforming various baselines in the wildfire detection task. FlameViT shows the power of Vision Transformers for wildfire detection tasks, and in conjunction with the use of satellite imagery, can provide an efficient and accurate way of detecting wildfires.

On April 17, 2019, a coach with tourists from Germany crashed in Madeira requiring repatriation by the German Air Force. The Advance Trauma Life Support (ATLS) concept was the central component of patient care. Data in Madeira were collected through a structured interview. The analysis of the Aeromedical Evacuation was based on intensive care transport records. In Germany all available were reviewed for data collection. Quality of life (HRQoL) was evaluated by the 12-item Short-Form Health Survey (SF-12). Twenty-eight prehospital patients were transported to the Level III Trauma Center in Funchal (Madeira). Five operative procedures were performed. Fifteen patients were eligible for Aeromedical Evacuation (AE). In the second hospital phase in Germany in total 82 radiological images and nine operations were performed. Hospital stay lasted 11 days (median, IQR 10–18). Median Follow-up (14 of 15 patients) was 16 months (IQR 16–21). 80% (8 out of 10) showed an increased risk for post-traumatic stress disorder (PTSD). Six key findings were identified in this study: divergent injury classification, impact of AE mission on health status, lack of communication, need of PTSD prophylaxis, patient identification, media coverage. Those findings may improve AE missions in the future e.g. required after armed conflicts.

Spatial Data Infrastructures (SDI) support the harvesting, curating, storage, and sharing of data along with providing access to development, analytic, and visualization tools that enable the building of innovative applications to address broad or specific challenges. SDIs can be especially powerful in bringing together data and tools supporting a particular theme – and this paper discusses and demonstrates the value of an SDI focused on Health. Many potential benefits of a Health SDI are proposed, and the case of supporting emergency response efforts is developed in detail. Leveraging a Health SDI, a Health Risk Index was created that provides emergency response personnel (both Emergency Operations Managers and Emergency Medical Responders) key insights into the unique health risks the impacted population faces due to the disaster. In order to establish the Health Risk Index, datasets from multiple national and global sources representing health data and social data that influences health outcomes – typically called social determinants of health – are harvested, merged, and republished to support further efforts at advancing the Health Risk Index. Visualizations of the Health Risk Index at the global, national, and sub-national levels down to the address level are presented along with demonstrations of its use.

This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindness’s meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.

Earth Observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: Atmosphere, Water, Agriculture, Land, Disaster Risk Reduction (DRR), Cultural Heritage, Energy, Marine Safety and Security (MSS) and Big Earth Data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and Land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high resolution and near real time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems and services.