Unpredictable Natural Disasters Research Articles

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

In the face of pandemic-induced emergencies and unpredictable natural disasters, industries are compelled to implement rescue plans to mitigate unexpected risks. In this context, Overall Equipment Effectiveness (OEE) is considered as a key metric, followed by sustainability efforts to manage unforeseen risks, encompassing social, environmental, and economic aspects. OEE is considered as a lean tool to determine the efficiency of equipment or processes on par with the world class OEE standard, i.e., 85%. Performance, Availability and Quality as three main drivers of OEE. This research study explores the implementation of OEE in conjunction with sustainability principles in an auto sector manufacturing firm, aiming to enhance operational efficiency and sustainability practices. The research involves a 12-week initial session from April to June 2022, followed by an analysis of July to September 2022, resulting in an impressive OEE value of 48%. Notable improvements in Availability (89.75%), Performance (72.68%), and Quality (73.82%) contribute significantly. The analysis reveals enhancements in scrap rework (17%), training (16%), maintenance (13%), material availability (12%), and production capability (11%). Achievements include improvements in green profile (25%), health and safety (20%), and energy efficiency (25%), along with reductions in carbon dioxide emissions (21%), waste management (17%), and scrap (15%). This research underscores the commitment of the case study industry to sustainable development and economic growth, showcasing significant enhancements in product quality and efficiency. The integration of sustainability principles into OEE initiatives is pivotal for modern industrial optimization. The study results highlight the profound significance of this synergistic relationship, particularly within the blending section, driving substantial positive outcomes in manufacturing processes and operational excellence. The implementation of sustainability efforts not only mitigates risks and fosters growth for automotive manufacturers but also yields environmental benefits. Based on findings of this study, a roadmap for automotive manufacturers is devised to achieve robust OEE while concurrently reaping economic and environmental rewards by employing sustainability principles.

Surgical and critical care management of earthquake musculoskeletal injuries and crush syndrome: A collective review

Earthquakes are unpredictable natural disasters causing massive injuries. We aim to review the surgical management of earthquake musculoskeletal injuries and the critical care of crush syndrome. We searched the English literature in PubMed without time restriction to select relevant papers. Retrieved articles were critically appraised and summarized. Open wounds should be cleaned, debrided, receive antibiotics, receive tetanus toxoid unless vaccinated in the last 5 years, and re-debrided as needed. The lower limb affected 48.5% (21.9%–81.4%) of body regions/patients. Fractures occurred in 31.1% (11.3%–78%) of body regions/patients. The most common surgery was open reduction and internal fixation done in 21% (0%–76.6%), followed by plaster of Paris in 18.2% (2.3%–48.8%), and external fixation in 6.6% (1%–13%) of operations/patients. Open fractures should be treated with external fixation. Internal fixation should not be done until the wound becomes clean and the fractured bones are properly covered with skin, skin graft, or flap. Fasciotomies were done in 15% (2.8%–27.2%), while amputations were done in 3.7% (0.4%–11.5%) of body regions/patients. Principles of treating crush syndrome include: (1) administering proper intravenous fluids to maintain adequate urine output, (2) monitoring and managing hyperkalemia, and (3) considering renal replacement therapy in case of volume overload, severe hyperkalemia, severe acidemia, or severe uremia. Low-quality studies addressed indications for fasciotomy, amputation, and hyperbaric oxygen therapy. Prospective data collection on future medical management of earthquake injuries should be part of future disaster preparedness. We hope that this review will carry the essential knowledge needed for properly managing earthquake musculoskeletal injuries and crush syndrome in hospitalized patients.

What data are needed to detect wildfire effects on coastal ecosystems? A case study during the Thomas Fire

Wildfires are growing in frequency and severity worldwide, with anthropogenic climate change predicted to worsen the effects of wildfires in the future. While most wildfire impacts occur on land, coastal fires can also affect the ocean via smoke production and ash deposition. The impacts of wildfires on marine ecology and biogeochemistry have been studied infrequently, as it is difficult to conduct fieldwork rapidly and safely during unpredictable natural disasters. Increasingly, remote sensing measurements are used to study the impacts of wildfires on marine ecosystems through optical proxies. Given the optical impacts of smoke and in-water ash, these measurements may be limited in their scope and accuracy. Here, we evaluate the potential and limitations of remote sensing data collected from MODIS-Aqua to describe the effects of wildfires on optics and phytoplankton observations. Using samples collected in the Santa Barbara Channel (California, USA) during the Thomas Fire in December 2017, we found that MODIS-Aqua data were unsuited for interpreting ecosystem effects during a wildfire. Our results identified a persistent overestimation of chlorophyll-a concentration from MODIS-Aqua compared to in situ measurements. Optical models applied to in situ radiometry data overestimated the absorption by colored dissolved organic matter (CDOM) during the wildfire. Satellites will remain an important tool to measure the impacts of wildfires on marine ecosystems, but this analysis demonstrates the importance of in situ sampling to quantify the impacts of wildfires on ocean ecology and biogeochemistry due to the difficulty of interpreting remote sensing data during these events.

The Use of Fear Appeal to Promote Safe Behaviors Among People in the Event of an Earthquake

Earthquake, an unpredictable natural disaster, poses a significant threat to public health, causing physical, human, social, cultural, and economic damages [1]. Iran is among the world’s top 10 earthquake-prone countries—remembering earthquakes cause the greatest human casualties. Preparedness and safe behavior before and during earthquakes are crucially important to reduce the damage of earthquakes and prevent life-threatening incidents and property damage [2]. Each year, unexpected events affect 200 million people, resulting in economic losses of up to 3% of the affected countries’ GDP (gross domestic product). By observing safe behaviors during earthquakes, it is possible to reduce risks and prevent casualties and damages [3].

Modeling Multi-Objective Optimization with Updating Information on Humanitarian Response to Flood Disasters

Unpredictable natural disasters brought by extreme climate change compound difficulties and cause a variety of systemic risks. It is thus critical to provide possibilistic scheduling schemes that simultaneously involve emergency evacuation and relief allocation. But the existing literature seldom takes emergency evacuation and relief supplies as a joint consideration, nor do they explore the impact of an unpredictable flood disaster on the scheduling scheme. A multi-stage stochastic programming model with updating information is constructed in this study, which considers the uncertainty of supply and demand, road network, and multiple types of emergency reliefs and vehicles. In addition, a fuzzy algorithm based on the objective weighting of two-dimensional Euclidean distance is introduced, through moderating an effect analysis of the fuzzy number. Computational results show that humanitarian equity for allocating medical supplies in the fourth period under the medium and heavy flood is about 100%, which has the same as the value of daily and medical supplies within the first and third period in the heavy scenarios. Based on verifying the applicability and rationality of the model and method, the result also presents that the severity of the flood and the fairness of resources is not a simple cause-and-effect relationship, and the consideration of survivor is not the only factor for humanitarian rescue with multi-period. Specifically, paying more attention to a trade-off analysis between the survival probability, the timeliness, and the fairness of humanitarian service is essential. The work provides a reasonable scheme for updating information and responding to sudden natural disasters flexibly and efficiently.

Straddling the Fields of Disaster Preparedness and Disaster Epidemiology: Lessons Learned from Planning a Study for Implementation Within Weeks of an Unpredictable Natural Disaster

Introduction:Studies on the impact of disasters on communities often occur months to years after the event. Pre- and peri-event details collected from participants may be imprecise or even unobtainable as memory is affected by time. More so, delays in data collection can introduce recall bias when participants with adverse outcomes provide differential responses about exposure. In 2019, the US Centers for Disease Control (CDC) and RTI International designed a study to mitigate these issues by surveying within weeks of a natural disaster to examine associations of preparedness to peri-event exposures, emergency services, and health.Method:Given the unpredictability of natural disasters, a significant challenge for the team was to plan a rigorous study design applicable to several types of severe weather events. This presentation will review our forethought, planning, and resulting strategy, including important considerations related to IRB and OMB applications with unspecified disaster/location details. We will share decision-making on sampling, instrumentation, communication materials, and multi-mode data collection procedures. The impact of delays due to COVID-19 and waiting to select a disaster that met a prior disaster inclusion/exclusion criteria will also be presented.Results:Results are forthcoming. We will present details on RTI’s 2022 survey implementation in the Fort Myers area of Florida within weeks of Hurricane Ian landfall including information on our final sampling strategy, field period, and outcome rates among key community groups and exposures.Conclusion:Conclusions will be presented. Pragmatic lessons learned related to timeline, labor, and other resources will be used to compare our strategy to rapid needs assessment methodology as well as more typical self-report surveys with later post-disaster data collection periods. Researchers working in emergency preparedness/response and disaster epidemiology will have gained a solid understanding of the advantages and disadvantages to planning studies for the immediate aftermath of undefined disasters.

The transformation education concept in the digital ecosystem of a territorial production cluster

A new approach to the formation of distributed digital educational ecosystems in territorial production clusters is proposed. This can not only eliminate the shortcomings of the traditional education system, but also create a promising platform for a new modern educational technology. The basis for building such an ecosystem are modern achievements in the field of control systems, digitalization and artificial intelligence. Currently, the education market is seriously influenced by scientific results in the field of artificial intelligence and the rapid development of digital technologies. As practice has shown, it is necessary to take into account quite unpredictable natural disasters and pandemics, as well as the complex geopolitical and economic situation in the world. In these conditions, one of the few serious opportunities for universities to maintain and strengthen their positions in the education market, which will change rapidly in the near future, is the transformation of processes within the framework of new technological trends based on digital platforms of industrial cluster ecosystems. The digital twin can be used both for real-time decision-making and for forecasting and planning the process for the entire training period. The university and the enterprises of the territorial cluster that provide part of the educational services under this approach are integrated into a single mechanism for solving the tasks of flexible individual training in accordance with the needs of the cluster in highly qualified personnel. Within the framework of the proposed approach, it is possible to build a hybrid educational university environment integrated with the real objects of the territory’s economy, which in this case becomes the most important component of the digital educational ecosystem. The proposed concept makes it possible to predict and plan the training of the right specialists, since the entire scheme of the educational process in this case is closely connected with the enterprises of the real sector for the entire period of training. This becomes possible due to the fact that training takes place according to flexible, updated programs that reflect the constantly changing requirements of enterprises to the competencies of employees.

Modern analytical and bioanalytical technologies and concepts for smart and precision farming.

Unpredictable natural disasters, disease outbreaks, climate change, pollution, and war constantly threaten food crop production. Smart and precision farming encourages using information or data obtained by using advanced technology (sensors, AI, and IoT) to improve decision-making in agriculture and achieve high productivity. For instance, weather prediction, nutrient information, pollutant assessment, and pathogen determination can be made with the help of new analytical and bioanalytical methods, demonstrating the potential for societal impact such as environmental, agricultural, and food science. As a rising technology, biosensors can be a potential tool to promote smart and precision farming in developing and underdeveloped countries. This review emphasizes the role of on-field, in vivo, and wearable biosensors in smart and precision farming, especially those biosensing systems that have proven with suitably complex and analytically challenging samples. The development of various agricultural biosensors in the past five years that fulfill market requirements such as portability, low cost, long-term stability, user-friendliness, rapidity, and on-site monitoring will be reviewed. The challenges and prospects for developing IoT and AI-integrated biosensors to increase crop yield and advance sustainable agriculture will be discussed. Using biosensors in smart and precision farming would ensure food security and revenue for farming communities.

The Relationship of Environmental Migration and Human Trafficking Concerning Natural Hazards at the Affected Regions of Africa

It is beyond dispute that the effects of climate change can be experienced more frequently at all parts of the of the ecosystem. The current change of our environment including soil erosion and the decrease of water supply contributes significantly to unpredictable natural disasters, that results increased number of children victims by human traffickers at the devastated areas, which mostly affected some parts of Sub-Saharan Africa. As the overwhelming natural hazards destroy the education system and other social services, human traffickers may take their victims for mainly sexual exploitation. Determination the trauma of the minor trafficked persons that has developed as a result of the exploitation and introducing Sendai Framework as a treatment suggestions. Furthermore as a theoretical backgound, introducing the significances of some natural hazards and human trafficking. Mixed method, quantitative content analysis and comparative document analysis as a desk review by the used documents were applyed. The study proved that its hypotheses that there is a strong and undisputed relationship between human trafficking and environmental migration at some regions of Africa intended to natural hazards. The Sendai Framework, ratified in 2015 can produce solution to this global challenge that affects millions of children. The highlights of the framework clearly summarizes the opportunities to reduce vulnerability and risk concerning the orphaned by natural disasters and who can easily become exploited persons by human traffickers.

Study on numerical seismic performance evaluation of 3D building frame with smart self-centering BRBs system

Structures are generally designed to withstand the applied loads safely. However, in order to secure the stability of structures from earthquakes, which are unpredictable natural disasters, seismic damage reduction technology is required, and various technologies are being developed so far. Recently, to resist seismic loads, new materials are used to reinforce structural members, or technologies to control vibration by applying seismic devices are being developed. In particular, Shape Memory Alloy (SMA), also known as a smart alloy, has a unique re-centering capacity that exhibits a flag-shaped hysteresis curve, so when applied in a structural system, it can provide safer structural performance with higher serviceability. In this study, to prove the structural stability of the SMA buckling restrained brace (BRB) system to which the seismic isolation is applied, a study was conducted to evaluate the seismic performance according to the structural material, type, and device. Response of SMA BRB and base isolation system has been calibrated by following quasi-static and dynamic loading test and substantially matches empirical results. A prototype steel frame has been designed by following standard code which has a single-story basement assumed to be surround by well graded sandy soil. In addition, two different column system has been considered namely as, Hollow Steel Section (HSS) and Concrete Filled Steel Tubes (CFST). For the nonlinear dynamic analysis, two sets of 11 bidirectional artificial earthquakes were considered, and the interstory drift ratio (IDR) and residual interstory drift ratio (RIDR) were considered as main performance evaluation parameters. In moderate ground motion, the performance of the SMA BRB was found to be higher compared to the steel BRB and base isolation system due to its superior self-centering capacity. However, in strong ground motions, periodic damage degrades the performance of the SMA BRB, which was found to be nearly identical to the steel BRB, but still superior to the base isolation system. This performance evaluation study will help to understand design aspects of seismic protection systems for similar types of building frames.

An empirical study of the effect of a flooding event caused by extreme rainfall on preventive behaviors against COVID-19.

Since the outbreak of COVID-19, wearing masks, vaccinations, and maintaining a safe distance has become social behaviors advocated by the government and widely adopted by the public. At the same time, unpredictable natural disaster risks brought by extreme climate change compound difficulties during epidemics and cause systemic risks that influence the existing pattern of epidemic prevention. Therefore, it is necessary to explore the effect of natural disaster risk caused by climate change on the response to outbreaks in the context of the COVID-19 epidemic. This study will focus on individual-level epidemic prevention behaviors, taking as an example the significant risk of severe destructive flooding caused by heavy rains in Henan, China, on July 20, 2021, which claimed 398 lives, to explore the effect of floods on the preventive behaviors of residents in the hardest hit areas against COVID-19. Through the multi-stage stratified random sampling of the affected residents in Zhengzhou, Xinxiang, Hebi, Luoyang, Anyang, and other cities in Henan Province, 2,744 affected people were surveyed via questionnaires. Through the linear regression model and moderating effect analysis, the study found that after floods, the individual's flood risk perception and response behaviors significantly correlated with the individual's prevention behaviors against COVID-19. Specifically, both flood risk perception and response behaviors strengthened the individual's prevention behaviors. Furthermore, the study also found that community risk preparation behavior and social capital can moderate the above relationship to a certain extent. The research can guide risk communication under the compound risk scenario and prevent risky public behavior under the consistent presence of COVID-19 in the community.

Optimal design and reconfiguration of flower constellations: An application to global disaster management

Unpredictable natural disasters are inevitable, and creating a communication satellite constellation that can continuously cover the entire Earth during disasters seems practically impossible. On the other hand, building up dedicated constellations associated with disaster areas does not make sense, either technically or temporally. This paper presents an optimal approach to providing timely and continuous communication coverage by optimal design and reconfiguration of flower constellations (FCs). In particular, the proposed approach addresses the optimal design of an FC and its formation by optimal satellites’ transfer from the last constellation and the remaining satellites from the previous reconfigurations. The multi-objective evolutionary algorithm (NSGA-II) is used to find optimal FC designs that minimize the height of perigee while simultaneously minimizing the number of satellites. In addition, the optimum assignment for transferring satellites to the designed FC is to minimize the total Δv for the reconfiguration by the particle swarm optimization (PSO) algorithm. In the end, the feasibility of the proposed approach is demonstrated by comparison optimal results.

Disaster-Resilient Communities on Flood Plains and Their Agricultural Regeneration: A Case Study in Meinong Plain, Taiwan

Taiwan’s near-mountain alluvial plain is a high-risk area with frequent disasters, and residents have become more tolerant of the compound disasters that occur with overall environmental changes associated with the development of urbanization in recent years. This paper presents a case study of a near-mountainous alluvial plain in Southern Taiwan. The Hakka ethnic group is the main community in the study area and also the main research object. This case study illustrates the disaster resilience of the community to natural and artificial disasters. This study adopted two research approaches, namely historical geography and political economic geography, as well as community resilience theory. Research methods including case study, secondary literature analysis, fieldwork, and interviews were used. Through text analysis, it was found that (1) the community’s awareness of disaster avoidance was rooted in the experience of reclamation in the early 17th century; (2) communities have experienced artificial disasters caused by political and economic intervention, which have been transformed into disaster awareness and community resilience; (3) cumulative artificial disasters have a greater impact on communities than unpredictable natural disasters; and (4) the energy of community resilience and agricultural regeneration is based on the duality of disasters.

Optimal strategies to control human behaviors during a catastrophic event

<abstract><p>In a world the more and more affected by sudden, unpredictable natural and/or industrial disasters, with few or without warning signs, it is essential to understand, analyze and control population behaviors during such events.</p> <p>Our objective is to model and investigate the actions that can be deployed by operational staff during catastrophic events in order to optimize risk management, reduce panic and save lives. For this purpose, we propose and solve an optimal control problem by using Pontryagin's Maximum Principle. Finally, we determine the best control strategy in the realistic scenario of a tsunami on the French Riviera.</p></abstract>

Significance of the Application of Resilience-based Approach in Human Trafficking at the Area of Sub-Saharan Africa

It is beyond dispute that the effects of climate change can be experienced more frequently at all parts of the ecosystem. The current change of our environment contributes to unpredictable natural disasters, which results increased number of children victims by human trafficking in the devastated areas, that mostly affected the regions of Sub-Saharan Africa. As the overwhelming natural disasters destroy the education system and other social services, human traffickers may take their victims easily for mainly sexual exploitation. The resilience-based methods can produce solutions to this global challenge and reduce vulnerability and risk concerning the orphaned by natural disasters who can easily become exploited persons by human traffickers. The aim of the study is introducing and analysing the Geneva Convention that should be the essential frame of the resilience-based approach of human trafficking.

Assessing the role of industry 4.0 for enhancing swift trust and coordination in humanitarian supply chain.

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating “swift trust” and “coordination” between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including “Internet of Things and Big Data Analytics”. The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance.

Development of the method of determining the location of a short circuit in transmission lines

One of the serious problems of the reliable functioning of energy systems is the disruption of the performance of overhead and cable transmission power lines caused by various reasons. Most of the power outages occur in distribution networks. Particularly dangerous short-circuits in the supply and distribution network, due to unpredictable natural disasters. To ensure uninterrupted and reliable power supply to consumers, it is necessary to identify and eliminate the malfunction in a short time to reduce damage. Finding and troubleshooting a line is an important task. The authors proposed a method for quickly determining the location of a short circuit in power lines. The proposed method allows to reduce search time and increase the accuracy of determining the fault location.

Effect of health belief model on flood-risk educational approach among elementary school children in Malaysia.

Worsening climatic conditions can subsequently lead to the frequent occurrence of unpredictable natural disasters. The early-life educational approach is one of the non-structural mitigations in disaster management, which are the most effective efforts to promote early-life disaster awareness and enhance the knowledge transfer in disaster risk education. By using the health belief model (HBM), this study aims to examine the effectiveness of HBM on the flood-risk reduction (FRR) educational intervention by looking into the perceived susceptibility, severity, benefit and self-efficacy among elementary school children in Malaysia. This study utilised the one-group pre-test–post-test design by recruiting 224 elementary school children in the pre-FRR educational intervention programme, and 205 who undertook a post-intervention programme a month later. This study showed that the FRR educational intervention significantly improved (p < 0.001) the overall HBM components during the post-intervention, particularly in: (1) FRR knowledge, (2) perceived susceptibility, (3) perceived severity and (4) perceived benefits. The one-way analysis of covariance test showed that knowledge transfer intervention is effective to improve all the HBM components that include (1) FRR knowledge, F(38,127) = 2.517; (2) perceived susceptibility, F(6,191) = 6.957; (3) perceived severity, F(20,163) = 2.944; (4) perceived benefits, F(25,153) = 2.342 and (5) self-efficacy, F(7,189) = 12.526. The impact of integrating HBM into knowledge transfer intervention was seen to be effective and provide a positive knowledge enhancement among learners. Therefore, it is crucial to implement a consistent and sustainable educational intervention to harness formal education for community resilience at an early age.

A Bayesian Network Model for Seismic Risk Analysis.

Earthquakes are one of the most unpredictable natural disasters. A series of secondary and derived disaster events may occur afterwards and lead to even more consequences. In such situations, a seismic risk analysis that takes into account secondary and derived disaster events is vital in reducing the risks of such disasters. The absence of a holistic seismic risk analysis model-one that takes into account the derived disaster events-may mean that the serious consequences of the disaster chains set off by earthquakes are neglected. This article proposes a comprehensive seismic risk analysis that enables a better understanding of seismic disaster chains and rescue scenarios. The approach is based on a Bayesian network constructed using scenario-based methods. The final network structure is achieved by learning parameters. To determine the critical secondary disasters and the key emergency-response measures, probability adaptation and updating using the Bayesian model were performed. The practical application of the model is illustrated using the Wenchuan earthquake and the Jiuzhaigou earthquake in China. The two examples show that the model can be used to predict the potential effects of secondary disasters and the final seismic losses. The results of the model can help decisionmakers gain a comprehensive understanding of seismic risk and implement practical emergency-rescue measures to reduce risk and losses.

Impact of extreme weather conditions on plant genetic resources and livelihood in central Himalayan Region

Unpredictable extreme weather, climate events and natural disasters have the potential to upset the normal life processes of plants, animals and human being. Central Himalayan Region (CHR) is known for its biological richness as well as vulnerability to natural disasters. Focus areas of natural disaster studies are damage to human lives, live stock and property. Plant genetic resources hardly get place in natural disaster management. Keeping the glacial lake outburst flood (GLOF) during June 2013 in Kedarnath region of the Uttarakhand province of India in view, the present study was conducted with the aim of assessing the damage to livelihood and plant genetic resources and find out possible solution to conserve the available genetic diversity and minimize the damage to livelihood.