High Casualty Rate Research Articles

Recovering psychological trauma in coal mining communities

ABSTRACT To date, there has been little research on the history of work-related mental health issues among coal workers. Much of the coal extraction industry peaked during the early twentieth century and then began its precipitous decline. Mining has high casualty rates, and the medical field did not understand the long-term effects of psychological trauma on mine workers until recently. Newspaper articles and other historical records provide accounts of accidents and tragedies. Heroism is often emphasized in recalling tragic episodes at the expense of recording the emotional stress suffered by survivors. A diverse set of historical sources provides a context for work-related mental health issues in the coal extraction industry. Probing a set of historic oral histories of coal workers and creating analogies to current studies provide additional clues about the under-told story of these historic actors’ general health and well-being. While incorporating oral histories from other regions, focusing on the anthracite coal industry in northeastern Pennsylvania allows us to identify historical actors and helps us think differently about other working-class communities impacted by industrial capitalism.

Corrupting the Battlefield: How Corruption Influences Belligerents’ Battlefield Performance

In some modern conflicts, stronger militaries have displayed poor performance in conflicts, as evidenced by slow military offensives, high battlefield casualty rates, failure to secure planned objectives and defeat in the war. Poor conflict performance, in these cases, is attributed to material and certain non-material state characteristics such as regime type, economic development, and ideology. Current explanations of conflict performance, however, have yet to integrate empirical evidence identifying another state characteristic as a negative influence on state capacity: corruption. To address this gap, we identify direct and indirect theoretical channels by which state-level corruption impacts conflict performance. Corruption decreases the war material available to troops on the ground and negatively impacts strategic decision-making by commanders. An analysis of states in interstate conflicts from 1816 to 1990 confirms the relationship between corruption and several measures of conflict performance. Case evidence from the First Russian-Chechen War is used to illustrate the mechanisms of our argument.

Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics

As vehicles with high accident and casualty rates within the road transportation system, engineering vehicles have been receiving much attention and emphasis in terms of safety. Accurate analyses and evaluations of risk factors in vehicle operation are imperative for enhancing the management level of engineering vehicles. This study explores the differences between various types of drivers by analyzing the driving characteristics of professional drivers. The evaluation index system is developed and quantified by integrating factors related to engineering vehicle drivers, road environment, and industry management. Additionally, the risk assessment model is developed using the error backpropagation algorithm. The optimal model is determined by comparing the number of nodes in different hidden layers, the activation function, and regularization optimization. The prediction accuracy of this model’s coefficient of determination is 0.912, indicating that the model has validity. This study is conducive to improving the safety level of engineering vehicle operation in order to reduce the rate of vehicle traffic accidents, the severity of accidents, and the consequences of losses. It also has practical application value in safeguarding social security.

Community Preparadness For Earthquake Natural Disasters on Empirical Study

Background: Indonesia is in a region prone to natural disasters, including earthquakes. Earthquakes often result in loss of life and property damage. One of the main factors contributing to a high casualty rate is the lack of knowledge, attitude, and behavior toward disasters and insufficient preparedness to anticipate such events. Method: A quantitative correlation study used a cross-sectional approach. The sample was selected using stratified random sampling, resulting in 16 samples from each educational level (elementary, junior high, high school, and university), totaling 64 respondents. Data analysis was conducted using the Chi-Square test. Results: The characteristics of the respondents of the gender, the most common are men 56.3%, the most common age group is in the 36-45 years old range 45.3%, then in the occupational group the most common are housewives with 26.6%. The study found that the majority of the community had sufficient knowledge (p-value 0.001), good attitudes (p-value 0.029), and sufficient behavior (p-value 0.001) towards earthquake preparedness. This indicates a significant relationship between the level of knowledge, attitude, and behavior with preparedness for earthquake natural disasters. Conclusion: Knowledge level, attitude, and behavior influence community preparedness for natural disasters, including earthquakes.

Predictive Model for Hydrostatic Curves of Chine-Type Small Ships Based on Deep Learning

Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents involve small ships (vessels with gross tonnage of less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship’s seaworthiness. Despite the high frequency of capsizing accidents involving them, small ships are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which are required data for stability calculations.

Evaluation of rescue management in incidents with a high casualty rate – results of own research

Evaluation of rescue management in incidents with a high casualty rate – results of own research

Review on NIPAH Virus

Nipah contagion is an acute febrile illness that can beget fatal encephalitis. It's an arising zoonotic paramyxovirus aboriginal to south- east Asia and the western Pacific, and can be transmitted by its primary force of fruit batons, through intermediate beast vectors and by mortal- to- mortal spread. Outbreaks of Nipah contagion encephalitis have passed in Malaysia, Singapore, Philippines, India and Bangladesh, with the most recent outbreak being in Kerala, India in late. Extremely high case casualty rates have been reported from these outbreaks, and to date no vaccines or remedial operation options are available. Combining this with its propensity to present non-specifically, Nipah contagion encephalatis present a grueling opinion that should not be missed in cases returning from aboriginal regions. Raising mindfulness of the epidemiology clinical donation and threat factors of constricting Nipah contagion is vital to honor and manage implicit outbreaks of this complaint in the UK

Kinematic responses of child as second rider of electric-two-wheelers under lateral impact with vehicle

Electric-two-wheeler (E2W) related accidents have become a major safety concern on road due to the growing prevalence and the high casualty rate. Most existing studies focus on drivers of the E2W, while ignore the second rider (usually a child) as passenger. This study aims at investigating the kinematic response of the child rider upon vehicle impact and analyzing how motion patterns are influenced by the geometric parameters of the vehicle and E2W. A computational framework was established for the intended task. We modeled the E2W-rider system in Madymo, including an E2W with parametric geometry and two riders, one adult and one child respectively. This study focuses on lateral impact in terms of the accident scenarios, as the case dominates in the field data reports. Vehicle types, seating height of the E2W and sitting position of the child rider were considered as variables in the simulation matrix. Results show that the relative height between child’s sitting and vehicle hood front-edge, and the sitting position (back-seated or front-seated) are two main influencing parameters on kinematic responses of child rider. The child rider tends to bounce higher on hood upon impact when sitting above the hood front-edge, while might be laterally pushed away by the car-front when sitting below the hood front-edge. Meanwhile, back-seated child rider is more likely to rise higher and rotate faster upon impact compared to a front-seated one. These findings may guide safe riding and safety countermeasure development for child riders of E2W.

Developing a Field-oriented Analytical Safety Assessment Model for Construction Workers

Several workplace injuries and fatalities are annually reported in the construction industry, and various efforts have been made to ensure safety. However, the construction industry still has higher casualty rates than other industries, and a preemptive safety management system that considers the safety risk factors at the construction site is deemed a necessity. Therefore, this study proposed a model that can quantitatively evaluate the safety of construction workers. A total of 18 safety risk factors consisting of three groups was derived from disaster-case data through a literature review, and was consulted to experts based on a survey to verify the suitability. Based on the results from the survey, 13 safety risk factors were selected, and the weights for each factor were derived. Moreover, an Analytic Network Process was used to derive the weights. Afterwards, a safety risk scale for each factor was derived based on the utility function from the accident case data provided by Korea Occupational Safety and Health Agency and opinions provided by expert. Finally, a safety assessment index for construction workers was proposed by combining the derived weights and safety risk scales. Accordingly, this study facilitates identifying construction workers with a high probability of suffering accidents in advance. Therefore, this study can contribute to preventing accidents at the construction site by supporting customized safety management for workers in hazardous environments.

District-level analysis of socio-demographic factors and COVID-19 infections in Greater Accra and Ashanti regions, Ghana.

Since December 2019 when COVID-19 was detected, it took the world by surprise in terms of spread and morbidity/mortality. The high rate of spread and casualties recorded from COVID-19 called for research in all directions to find ways to contain and reverse the incidences. It is against this background that this paper sought to measure the association of the socio-demographic factors in the hard-hit districts in Greater Accra and Ashanti to analyze its relationship with the novel COVID-19 virus. Data on COVID-19 cases from 35 Districts in both Greater Accra and Ashanti Regions were collected from the Ghana Health Service and population data from Ghana Statistical Service. Descriptive statistics and regression analysis were generated using R. We found that some socio-demographic variables have an association with COVID-19 infections. For example, age and religion especially Christianity and Islam pose risk to COVID-19. The population aged 15-64 was particularly at high risk of infections due to the high level of movement of this age group. We, therefore, recommend that places of congregation such as Churches and Mosques be targeted for vigorous sensitization on COVID-19 protocols and prevention. Also, districts with a high population between the ages of 15-64 should step sensitization efforts to educate their inhabitants on the need to reduce travel and related activities to curb the spread of the virus.

An exploratory study of a law enforcement model for vehicle speed in Tshwane Metropolitan Municipality, South Africa

This study explores the law enforcement model for vehicle speed in Tshwane municipality of South Africa. South Africa has comparatively high road traffic crashes and casualty rates. Most of these crashes are associated with illegal speeding. This study was designed against the background of protracted road traffic crashes the triple causative factors as well as existing traffic law enforcement systems and processes aimed at managing illegal speeding. Data were collected from a sample of 28 senior law enforcement officials from the Tshwane Metropolitan Police Department (TMPD) traffic division, the Gauteng Traffic Police and the National Traffic Police. Qualitative data were analysed using thematic analysis’ open and axial coding processes on Atlas.ti.8. In its findings, the study developed a model that shows that illegal speeding and consequential road crashes were caused by an interaction of multiple factors. These were poor human behavioural elements as the main factor, road-environment factors and vehicle-related factors in the presence of inadequate and uncoordinated traffic law enforcement, weak penal systems for offenders, corruption, poor driver training and low road safety education.

World War II, the Baby Boom, and Employment: County-Level Evidence

This paper examines the impact of male casualties due to World War II on fertility and female employment in the United States. We rely on the number of casualties at the county-level and use a difference-in-differences strategy. While most counties in the U.S. experienced a Baby Boom following the war, we find that the in- crease in fertility was lower in high-casualty rate counties than in low-casualty rate counties. Analyzing the channels through which male casualties could have decreased fertility, we provide evidence that county male casualties are positively related to 1950s female employment and household income.

COVID-19 Pandemic and Counter Insurgency (COIN) Operations in the Northeast of Nigeria : Imperative of Technology

Boko Haram insurgency brings with it unprecedented consequences, particularly in records of multiple deaths and devastating effects on socio-economic dynamics of the nation. Since no state would stay without responding to threats to its national security, the federal government of Nigeria adopted both kinetic and non-kinetic approaches to counter the impact of terrorism on its shores. With several attacks from Boko Haram and the resultant consequences in terms of deaths, displacement and economic damages, the group has remained a potent force particularly in the North East and the Sahel region. In the midst of these contending realities is the Corona Virus Disease (COVID -19) that broke out from Wuhan, China in December 2019. Within the space of few months, the outbreak spiraled into a public health emergency and global pandemic; with rapid spread, high casualty rate and negative impacts on global health and economy. Accordingly, Nigeria like other nations was hit by the virus, causing disruption in its already fragile economy coupled with internal security threats. As a measure of containment, the global lockdown measures resulted in less economic patronage which translated into vulnerabilities like crime rate increase. While other countries were seeking answers to the question of health security, Nigeria was faced with the double tragedy of Boko Haram on one hand; and health insecurity on the other with attendant consequences. In this view, application of technology in combating insecurity as well as health emergencies will be the way to go, particularly, in a world that is technologically driven. This paper examines the imperative of technology in counter insurgency operations. It will adopt the historical approach in its analysis to build a pragmatic approach to reversing the trend.

Effect of Residential Building Wind Retrofits on Social and Economic Community-Level Resilience Metrics

Tornadoes occur at a high frequency in the United States compared with other natural hazards but have a substantially small footprint. A single high-intensity tornado can result in high casualty rates and catastrophic economic and social consequences, particularly for small- to medium-size communities. Comprehensive community resilience assessment and improvement requires the analyst to develop a model of interacting physical, social, and economic systems and to measure outcomes that result from specific decisions made. These outcomes often are in the form of metrics such as the number of people injured or the number of households and/or businesses without water, but it has been recognized that most community resilience metrics have socioeconomic characteristics. In this study, for the first time, a fully quantitative interacting model is used to examine the effect of a tornado damaging physical infrastructure (buildings and electrical power network) and the effects on the population and the local economy for a real community. Then, three residential building retrofit strategies are considered as alternatives to improve community resilience, and the metrics for the physical, economic, and social sectors are computed. An illustrative example is presented for the 2011 Joplin tornado in a new open-source Interdependent Networked Community Resilience Modeling Environment (IN-CORE), with a computable general equilibrium (CGE) economics model that computes household income, employment, and domestic supply before and after the tornado. Detailed demographic data was allocated to each structure to enable the calculation of resilience metrics related to population dislocation impacts from the tornado. The results of these analyses stemming from building damage estimation have a logical trend, but the substantial contribution of this work is that, for the first time, the effect of retrofit strategies for tornado loading can be quantified in terms of their effects on socioeconomic metrics.

An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations

Car-electric bicycle (e-bike) accidents have been the subject of strong attention due to the widespread usage of e-bikes and a high casualty rate for their riders. Manually conducted accident reconstruction is based on the trial-and-error method with a limited number of parameter combinations, which makes it time-consuming and subjective. This paper aims to develop an intelligent method for accurate, high-efficient reconstruction of accidents involving cars and e-bikes. First, an automatic operation framework, which can drive the MADYMO program and perform results analysis automatically, was built with four multi-objective optimization algorithms available – NSGA-Ⅱ, NCGA, AMGA, and MOPS; The optimization condition was controlled with 12 design variables, 5 objective functions, and 3 constraints. Then, a real e-bike accident with surveillance video was reconstructed through the proposed framework to verify its validity using comparisons of simulated and actual rest positions, initial variables, kinematic response, and head injury. Lastly, the simulation data were used to study the effects of the initial variables on objectives with a multiple linear regression model. The results showed that it took only about 24 h in total for optimization with 480 automatic operations. Optimal conditions were searched at run numbers of 469, 430, 323, and 474 for NSGA-Ⅱ, NCGA, AMGA, and MOPS, respectively. NSGA-Ⅱ had the best performance for e-bike accident reconstruction with average errors of objectives below 5%; Good consistencies for the rider’s kinematic response in three stages after collision were observed between simulations and screenshots from the surveillance video, as well as for velocities between the simulation and those estimated from the surveillance video and for head injury between the simulation and the medical report. In contrast to the subjective trial-and-error method that highly depends on the analyst’s intuition and experience, this intelligent method is based on multi-objective optimization theory, with which results can be optimized in terms of the automatic change of initial variables. All the above comparisons demonstrate that the method is valid for effectively improving efficiency without simultaneously compromising accuracy. This intelligent method, coupling automatic simulation and multi-objective optimization, can also be applied to other accident reconstructions, and the significant order of initial variables’ effects on objectives can provide recommendations for further reconstructions.

Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors

BackgroundTyphoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons.MethodsIn this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors.ResultsBoth natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces.ConclusionsThese results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons.

World War II, the Baby Boom and Employment: County Level Evidence

This paper examines the impact of male casualties due to World War II on fertility and female employment in the United States. We rely on the number of casualties at the county-level and use a difference-in-differences strategy. While most counties in the U.S. experienced a Baby Boom following the war, we find that the in- crease in fertility was lower in high-casualty rate counties than in low-casualty rate counties. Analyzing the channels through which male casualties could have decreased fertility, we provide evidence that county male casualties are positively related to 1950s female employment and household income.

Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents

As the construction industry experiences a high rate of casualties and significant economic loss associated with accidents, safety has always been a primary concern. In response, several studies have attempted to develop new approaches and state-of-the-art technology for conducting autonomous safety surveillance of construction work zones such as vision-based monitoring. The current and proposed methods including human inspection, however, are limited to consistent and real-time monitoring and rapid event recognition of construction safety issues. In addition, the health and safety risks inherent in construction projects make it challenging for construction workers to be aware of possible safety risks and hazards according to daily planned work activities. To address the urgent demand of the industry to improve worker safety, this study involves the development of an audio-based event detection system to provide daily safety issues to laborers and through the rapid identification of construction accidents. As an evidence-driven approach, the proposed framework incorporates the occupational injury and illness manual data, consisting of historical construction accident data classified by types of sources and events, into an audio-based safety event detection framework. This evidence-driven framework integrated with a daily project schedule can automatically provide construction workers with prenotifications regarding safety hazards at a pertinent work zone as well as consistently contribute to enhanced construction safety monitoring by audio-based event detection. By using a machine learning algorithm, the framework can clearly categorize the narrowed-down sound training data according to a daily project schedule and dynamically restrict sound classification types in advance. The proposed framework is expected to contribute to an emerging knowledge base for integrating an automated safety surveillance system into occupational accident data, significantly improving the accuracy of audio-based event detection.

Discrimination, Migration, and Economic Outcomes: Evidence from World War I

Are the costs of discrimination mainly borne by the targeted group or by society? This paper examines both individual and aggregate costs of ethnic discrimination. Studying Germans living in the U.S. during World War I, an event that abruptly downgraded their previously high social standing, we propose a novel measure of local anti-German sentiment based on war casualties. We show that Germans disproportionally fled counties with high casualty rates and that those counties saw more anti-German slurs reported in newspapers. German movers had worse occupational outcomes after the war but also the discriminating communities paid a substantial cost. Counties with larger outflows of Germans, who pre-war tended to be well-trained manufacturing workers, saw a drop in average annual manufacturing wages of 1-7% which persisted until 1940. Thus, for discriminating communities, a few years of intense anti-German sentiment were reflected in worse economic outcomes that lasted for more than a decade. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Performance of International Tourists’ Perception and Behavior toward Road Safety in Phuket, Thailand

When traveling in a foreign country, road transport is most frequently and conveniently used particularly, but road conditions and manners are stunningly different among countries, which increases potential risks to meet accidents. If tourists feel such risks, it may be that they will not be repeaters and their impressions will also be shared with others, leading to a potential decrease of tourists. Phuket, Thailand, a worldly wellknown beach resort, faces problems in road conditions and manners, with a high rate of traffic accidents and casualties, and governmental actions have to be taken. This paper reports a questionnaire survey result about how international tourists feel and understand road conditions in Phuket during their stay, which is expected to offer good evidence on which governmental actions should be based. The survey results revealed (mostly through the behaviors and recommendations) that the majority of international tourists to Phuket felt risks in terms of road conditions, regardless of whether they drove by themselves or not, and most of them wanted the basic information on road conditions as well as regulations, so that they could prepare themselves. Relations between demographic characteristics and questionnaire replies were also investigated, suggesting that there be a necessity of reducing risks particularly for elderly tourists, and that the driving manner of local residents including professional drivers should be improved.