Significant Number Of Fatalities Research Articles

SOS Smart Reminder and Emergency Detection Device Using ESP8266 and MPU6050

Research indicates that approximately 85% of deaths are caused by heart attacks, strokes, shortness of breath, and other medical conditions could be prevented through timely treatment. Hence, immediate access to an easily reachable SOS button plays a vital role in providing prompt assistance. Vehicle accidents result in a significant number of fatalities. Delays in reaching the hospital contribute to about 76% of deaths in each vehicle category. An urgent solution is needed: a gadget or technology capable of detecting collisions and promptly alerting their local guardians. This would enable immediate medical assistance, potentially saving lives and reducing the fatality rate associated with such accidents. The current work is driven by three key factors: the necessity for a dedicated device to remind patients with chronic illnesses to take their medications at the prescribed times, the absence of a convenient SOS button readily accessible during emergencies, and the delay in communication between accidents and contacting guardians. The authors in this study emphasizes the importance of timely medical care and interventions which significantly decrease mortality rates associated with these conditions. Not adhering to medication schedules and taking incorrect medicines at inappropriate times can have serious consequences. A specialized reminder device that prompts individuals with medication names at specific times is necessary to ensure proper administration and improve health outcomes.

Investigation of the Effect of Aerosol Deposition by Applying Electrostatic Fields

Particulate matter (PM) constitutes a significant risk factor for environmental health regarding ambient air quality. An epidemiological investigation has determined that inadequate air quality is associated with the development of lung and cardiovascular diseases, chronic ailments, respiratory infections, and a significant number of fatalities on a global scale. According to the World Health Organization (WHO), the annual mortality rate due to air pollution is around 7 million individuals. The necessity for researching air quality, climate change, and the challenges posed by particulate matter (PM) is widely recognized. While reducing ambient air quality, it is essential to consider the limitations of current technical control methods. This article focuses on developing and implementing advanced technology to mitigate particle pollution in urban environments. The comparison of empirical data and computational simulations has demonstrated the efficacy of utilizing pulsed radio waves to reduce particulate matter. The control technique exhibits a threefold increase in the rate of PM concentration reduction compared to gravity settling. The approach's efficacy was evaluated through controlled trials conducted in controlled chambers and urban environments, demonstrating up to 50% reductions. The validation of the implemented test case results of the control technology was performed using historical data while considering the existence of radio waves. The coagulation process demonstrated and verified the effectiveness of reducing particle matter. The employed methodology has been shown to encourage outcomes concerning mitigating particulate matter contamination within urban and industrial environments.

Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data

Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost techniques. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic–metaheuristic approach that integrates the heuristic knowledge of the Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets, yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.

Causes of safety risk during tower crane operations on construction sites in Vietnam

The construction industry holds significant importance for any nation; however, it remains a high-risk sectorcharacterized by a significant number of fatalities and grave injuries. Among these risks, tower crane operationsstand out as a leading contributor to the heightened risk rate. This research paper endeavors to assess the safety risks inherent in tower crane operations at construction sites in Vietnam. This assessment is conducted from the perspective of various stakeholders involved in tower crane activities, namely managers, operators, and workers. To achieve this, a meticulously structured questionnaire utilizing a five-point Likert scale was formulated and distributed to gather insights from 78 proficient individuals engaged in tower crane-related tasks. The outcomes from all participants collectively underscore 21 distinct causes that give rise to safety risks during tower crane operations in Vietnam. Nonetheless, noticeable differences in opinions appear when looking at answers from managers, operators, and workers. The operators and workers showed that “Winds (wind intensity seriously affects the lifted load and tower crane itself)” are the most common safety risk. On the other hand, the managers highlighted the issue of whether the “Tower crane operator is an in-house staff member or outsourced” as their main concern. The managers, operators and workers showed that “Experience and skills of the operator during operating tower crane”, “Maintenance management degree of the tower crane and lifting aids”, and “Overhead power lines in the tower crane’s work region” are the most severe cause of safety risk, respectively. Operators and workers pointed out that the "Degree of maintenance management for tower cranes and lifting aids"has the highest safety risk level. However, managers highlighted that “Winds (wind intensity seriously affects the lifted load and tower crane itself)” pose the highest safety risk level.

Rationale for combined therapies in severe-to-critical COVID-19 patients.

An unprecedented global social and economic impact as well as a significant number of fatalities have been brought on by the coronavirus disease 2019 (COVID-19), produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Acute SARS-CoV-2 infection can, in certain situations, cause immunological abnormalities, leading to an anomalous innate and adaptive immune response. While most patients only experience mild symptoms and recover without the need for mechanical ventilation, a substantial percentage of those who are affected develop severe respiratory illness, which can be fatal. The absence of effective therapies when disease progresses to a very severe condition coupled with the incomplete understanding of COVID-19's pathogenesis triggers the need to develop innovative therapeutic approaches for patients at high risk of mortality. As a result, we investigate the potential contribution of promising combinatorial cell therapy to prevent death in critical patients.

Road Accident Severity Prediction using Machine Learning

Road accidents have become a major concern globally, causing a significant number of fatalities and injuries every year. The aim of this project is to predict road accidents severity using machine learning techniques, in order to reduce their occurrence and mitigate the associated risks. The project uses data collected from various sources such as accident reports, weather conditions, and road infrastructure to train and evaluate various supervised learning algorithms and predict the accident severity. Four algorithms were compared, including Decision Tree, Naive Bayes, Random forest . Most probably occurring road accident locations are identified and that particular region is indicated as black pot. The proposed method can be used to provide real-time risk information to road users, helping them to make informed decisions and avoid potential accidents. The project highlights the importance of using machine learning techniques in road safety analysis, providing a foundation for further research in this field

Carbapenem-resistant biofilm-producing multidrug resistant Klebsiella pneumoniae induced hospital-acquired infections and analysis of resistant genes

In recent years, drug resistance has caused a significant number of fatalities. This review focuses on the emergence of medication resistance in Klebsiella pneumoniae strains associated with biofilm development, neonatal sepsis, aging, and secondary K. pneumoniae infection in COVID-19 cases. This review is an exclusive survey of >200 research articles and >100 review articles indexed in the Web of Science list with the keywords “K. pneumoniae,” “drug resistance,” “sepsis,” “neonatal sepsis,” “sepsis in older age,” “secondary infection,” “biofilm production,” “secondary infection in COVID-19,” “drug resistance genes,” and “whole genome sequencing”. In order to protect antibiotic susceptibility and to provide an overview of the biofilm forming mechanisms among gram-negative bacteria, the review discusses the function of multidrug-resistant K. pneumoniae in the production of biofilms. Additionally, K. pneumoniae’s role in contemporary cases of sepsis, spread of medication resistance in hospitals, and sepsis caused by K. pneumoniae in the geriatric age group are discussed. A summary of current approaches and the identification of drug resistance genes utilizing a whole genome sequencing technique are also reviewed in this article.

Status and Individual View toward Lightning among University Students of Bangladesh

Bangladesh has seen a significant number of fatalities and injuries related to lightning in the past few years, which indicates that lightning has become a deadly hazard. This cross-sectional study aims to determine university students’ self-rated status about lightning. Additionally, it evaluates these students’ views toward lightning through knowledge, attitude, and practices (KAP). A total of 1274 university students participated in an online KAP survey. Where appropriate, the Kruskal–Wallis or Mann–Whitney U test, Spearman’s rank correlation, and logistic regression models were performed. About 90% of university students perceive lightning as a dangerous event, and 38% rated their places unsafe. More than half of the survey population reported frequent lightning; most (84%) did not have lightning safety precautions, and a small portion (26%) received warning messages. Individuals encountering frequent lightning consider lightning-prone areas much more dangerous compared to the individuals encountering occasional lightning. Students living in tin sheds assessed lightning as a dangerous event (4.78 ± 0.53) and having unsafe surroundings (2.44 ± 0.98). Many individuals have enough knowledge (63%), developed positive attitudes (93%), and effective preventative practices (77%). The logistic regression analysis indicated that having adequate information and a good attitude can assist individuals in practicing lightning safety; also, student’s Gender, living with family, residential unit, university type, study year, major field, and having lightning-related subjects in university curricula as significant predictors. Females demonstrated better lightning practice than males. Additionally, lightning-related courses in university curricula are critical for educating students about lightning. Behavioral improvements among these students will require substantial lightning campaign actions coupled with effective education.

Knowledge, Attitude, and Practices towards Lightning in Bangladesh

Despite the significant number of fatalities and injuries, there is currently a lack of data on public knowledge, attitudes, and practices regarding lightning in Bangladesh. This study aims to assess the public’s knowledge, attitude, and practices (KAP) towards lightning in the country. A total of 1641 individuals participated in an online KAP survey. Where appropriate, the Kruskal–Wallis or Mann–Whitney U test, Spearman’s rank correlation, and logistic regression models were performed. About 65% of the survey population reported frequent lightning; most (72.64%) did not receive any warning messages, and small proportions (22.12%) had lightning safety precautions. Individuals with numerous lightning experiences perceived considerably unsafe places against lightning compared to infrequent lightning experiences (p < 0.05). Respondents reported good knowledge (56.06%), positive attitudes (82.27%), and good preventive practices (72.33%). The logistic regression demonstrated that having good knowledge and positive attitudes can help people practice lightning safety. Females demonstrated better lightning attitudes and practices compared to males. In comparison to urban residents, rural residents had poor practices. In addition, individuals’ educational levels might also play a crucial role in preparing them for lightning. Overall, extensive lightning campaign activities combined with effective education are required for the behavioral changes in this lightning-vulnerable society.

ANALYZING THE FACTORS INFLUENCING ROAD TRAFFIC ACCIDENT SEVERITY: A CASE STUDY OF KHULNA CITY

Road traffic accident occurrences are terrible global phenomena all over the world. With more and different motorized vehicles, Bangladesh and Khulna city face a more significant number of fatalities and injuries. The metropolitan city is one of the riskiest places for road accidents because of its higher population density than in any other location. Road accidents in Khulna city are a significant problem, and they are increasing day by day. So, to reduce the severity of road accidents, the factors influencing road accident severity in Khulna city must be studied and analyzed. Binomial logistic regression is selected and applied to predict the risk or severity of 266 accidents from 2010 to 2019, collected by the Traffic Branch of the Deputy Police Commissioner of Khulna Metropolitan Police. The response variable for this study is accident severity (fatal and non-fatal). According to the study's findings, the number of injuries, number of fatalities, vehicle velocity, accident time, collision type, type of vehicle, and vehicle ownership influence the severity of road traffic accidents in Khulna. This study and its recommendations will aid in developing and implementing future road traffic policies and safety measures.

Dietary Isothiocyanates, Sulforaphane and 2-Phenethyl Isothiocyanate, Effectively Impair Vibrio cholerae Virulence.

Vibrio cholerae represents a constant threat to public health, causing widespread infections, especially in developing countries with a significant number of fatalities and serious complications every year. The standard treatment by oral rehydration does not eliminate the source of infection, while increasing antibiotic resistance among pathogenic V. cholerae strains makes the therapy difficult. Thus, we assessed the antibacterial potential of plant-derived phytoncides, isothiocyanates (ITC), against V. cholerae O365 strain. Sulforaphane (SFN) and 2-phenethyl isothiocyanate (PEITC) ability to inhibit bacterial growth was assessed. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values indicate that these compounds possess antibacterial activity and are also effective against cells growing in a biofilm. Tested ITC caused accumulation of stringent response alarmone, ppGpp, which indicates induction of the global stress response. It was accompanied by bacterial cytoplasm shrinkage, the inhibition of the DNA, and RNA synthesis as well as downregulation of the expression of virulence factors. Most importantly, ITC reduced the toxicity of V. cholerae in the in vitro assays (against Vero and HeLa cells) and in vivo, using Galleria mellonella larvae as an infection model. In conclusion, our data indicate that ITCs might be considered promising antibacterial agents in V. cholerae infections.

Hamiltonian Monte Carlo with Random Effect for Analyzing Cyclist Crash Severity

Vulnerable traffic users, such as bikers and pedestrians, account for a significant number of fatalities on the roadways. Extensive research has been conducted in the literature review to identify factors to those crashes. Studying factors to those crashes is especially important in the Western state in the US, due to one of the highest fatality rates in the nation and its unique geographic conditions. The first step in identifying factors to the severity of cyclist crashes is to find the underlying factors to that type of crash, while accounting for the heterogeneity in the dataset. Various techniques such as mixed parameter or mixed effect models have been employed in the literature to account for the heterogeneity of the dataset. In the mixed effect model, often the random effect parameter has been assigned subjectively, and based on some attributes and engineering intuitions. Those assignments are expected to account for the heterogeneity in the dataset and enhancement of the model fit. However, a question might arise whether those factors could account for an optimum amount of the heterogeneity in the dataset. A more reasonable way might be to let the algorithm such as the finite mixture model (FMM) to identify those clusters based on parameters of the Gaussian model, means and covariance matrices of the dataset, and allocate each observation to the related clusters. Thus, in this study, to capture optimum amount of heterogeneity, first we implemented the finite mixture model in the context of maximum likelihood, due the label switching issue of the method in the context of the Bayesian method. After assignment of the parameters to the observation, the main method of Hamiltonian Monte Carlo (HMC) with random effect was implemented. The results highlighted a significant improvement in the model fit, in terms of Widely Applicable Information Criterion (WAIC). The results of this study highlighted factors such as older biker age, increased number of lanes, nighttime travelling, increased posted speed limit and driving while under emotional conditions are some factors contributing to an increased severity of bikers’ crash severity. Extensive discussion has been made regarding the methodological algorithms and model parameters estimations.

The Hyperlipidaemic Drug Fenofibrate Significantly Reduces Infection by SARS-CoV-2 in Cell Culture Models.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic has caused a significant number of fatalities and worldwide disruption. To identify drugs to repurpose to treat SARS-CoV-2 infections, we established a screen to measure the dimerization of angiotensin-converting enzyme 2 (ACE2), the primary receptor for the virus. This screen identified fenofibric acid, the active metabolite of fenofibrate. Fenofibric acid also destabilized the receptor-binding domain (RBD) of the viral spike protein and inhibited RBD binding to ACE2 in enzyme-linked immunosorbent assay (ELISA) and whole cell-binding assays. Fenofibrate and fenofibric acid were tested by two independent laboratories measuring infection of cultured Vero cells using two different SARS-CoV-2 isolates. In both settings at drug concentrations, which are clinically achievable, fenofibrate and fenofibric acid reduced viral infection by up to 70%. Together with its extensive history of clinical use and its relatively good safety profile, this study identifies fenofibrate as a potential therapeutic agent requiring an urgent clinical evaluation to treat SARS-CoV-2 infection.

Modeling the COVID-19 Pandemic Dynamics in Iran and China

The epidemic outbreak COVID-19 was first detected in the Wuhan city of China and then spread worldwide. It is of great interest to the researchers for its high rate of infection spread and its significant number of fatalities. A detailed scientific analysis of this phenomenon is yet to come. However, it is of interest of governments and other responsible institutions to have the right facts and figures to take every possible necessary action such as an arrangement of the appropriate quarantine activities, estimation of the required number of places in hospitals, assessment of the level of personal protection, and calculating the rate of isolation of infected persons, among others. In this article, we compare the COVID-19 pandemic dynamics between the two most affected Asian countries Iran and mainland China. We provide a convenient method of data comparison that can be helpful for both governmental and private organizations to arrange the appropriate quarantine activities. Furthermore, a statistical model is suggested to provide the best characterization of the COVID-19 daily deaths data of Iran and China. By analyzing daily death events, we observed that the proposed model provides a better description of the COVID-19 events, and therefore, can be used as a good candidate model for predicting them.

Illegal practice of abortions

Abortions that are unsafe or illegal are one of the leading causes of violations of women's and children's human rights throughout the globe. Lack of access to safe abortion procedures results in a significant number of fatalities and severe immobility. In India, it is estimated that 15.6 million pregnancies occur on a regular basis. A significant portion of them are thought to be dangerous. Unsafe fetal removal is the third leading cause of maternal death, claiming the lives of 10 women every day and leaving thousands more with morbidities. There is a need to strengthen access to abortion advantages while also preventing women's passing and inabilities. In 2002, as part of the Abortion Assessment Project, the Abortion Assessment Project led an immense focus on incited fetal removal in India. The exams, which were a significant part of this project, assessed 6.4 million preterm newborns in India each year. Even while the act of removing a fetus is not inherently immoral, the unlawful manner in which it is carried out makes it highly damaging and should be banned.

ОБЕСПЕЧЕНИЕ ВЗРЫВОБЕЗОПАСНОСТИ ЖИЛЫХ ДОМОВ

Purpose. The article describes constructive and technical solutions for preventing and reducing the consequences of household gas explosions in residential multi-apartment buildings equipped with a fixed gas supply system. Methods. The statistical data on household gas explosions in residential gasified multi-apartment buildings on the territory of Russia have been analyzed. The comparison of existing technical solutions and measures aimed at preventing an emergency explosion and reducing the explosion consequences (gas leak sensor, ventilation system, safety structures of the hinged and swing type) has been carried out. Findings. The data showing the timeliness of this problem due to the significant number of fatalities and large property damage have been obtained. In particular, during the period from 2017 to 2020, 37 explosions of household gas occurred in residential multi-apartment buildings, causing death of 61 people. Despite many positive aspects, the methods being currently used do not provide an adequate level of explosion safety, and therefore the authors see it appropriate to introduce measures aimed at reducing consequences in the event of gas explosion. One of such solutions is the use of a window opening as a safety structure. The main disadvantage, which does not allow us to consider blind glazing as a safety design, is the restriction on the glass thickness (4 mm). In addition, the drawback of using the given construction is low thermal insulation ability and high risk of causing damage to people near the building by glass fragments in the event of an explosion. This disadvantage of modern double-glazed windows can be eliminated by modifying the window frames with the help of special parts or weakened fasteners, which, if excessive pressure occurs, would ensure timely opening of the structure. An example includes safety structures on hinges, which have the ability to rotate these structures when exposed to excessive pressure. Another type of a safety construction is a swing safety design. At a given excess pressure on the working sash, the locking devices, which release the frame rotary sash, are triggered.Research application field. The results of the study can be used in the working out new technical solutions designed to prevent an emergency explosion and reduce the consequences of a potential explosion. Conclusions. The conducted research leads to the conclusion that further work is necessary to study the parameters of internal emergency explosions of household gas in residential buildings under various accident scenarios in order to develop optimal solutions to minimize the consequences of an explosion.

Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.

Living in a Multi-Risk Chaotic Condition: Pandemic, Natural Hazards and Complex Emergencies.

Humans are living in an uncertain world, with daily risks confronting them from various low to high hazard events, and the COVID-19 pandemic has created its own set of unique risks. Not only has it caused a significant number of fatalities, but in combination with other hazard sources, it may pose a considerably higher multi-risk. In this paper, three hazardous events are studied through the lens of a concurring pandemic. Several low-probability high-risk scenarios are developed by the combination of a pandemic situation with a natural hazard (e.g., earthquakes or floods) or a complex emergency situation (e.g., mass protests or military movements). The hybrid impacts of these multi-hazard situations are then qualitatively studied on the healthcare systems, and their functionality loss. The paper also discusses the impact of pandemic’s (long-term) temporal effects on the type and recovery duration from these adverse events. Finally, the concept of escape from a hazard, evacuation, sheltering and their potential conflict during a pandemic and a natural hazard is briefly reviewed. The findings show the cascading effects of these multi-hazard scenarios, which are unseen nearly in all risk legislation. This paper is an attempt to urge funding agencies to provide additional grants for multi-hazard risk research.

Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China

Background. The epidemic outbreak caused by coronavirus COVID-19 is of great interest to researches because of the high rate of the infection spread and the significant number of fatalities. A detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in the questions of the epidemic duration, the expected number of patients and deaths. Long-time predictions require complicated mathematical models that need a lot of effort to identify and calculate unknown parameters. This article will present some preliminary estimates.Objective. Since the long-time data are available only for mainland China, we will try to predict the epidemic characteristics only in this area. We will estimate some of the epidemic characteristics and present the dependen­cies for victim numbers, infected and removed persons versus time.Methods. In this study we use the known SIR model for the dynamics of an epidemic, the known exact solution of the linear differential equations and statistical approach developed before for investigation of the children disease, which occurred in Chernivtsi (Ukraine) in 1988–1989. Results. The optimal values of the SIR model parameters were identified with the use of statistical approach. The numbers of infected, susceptible and removed persons versus time were predicted and compared with the new data obtained after February 10, 2020, when the calculations were completed.Conclusions. The simple mathematical model was used to predict the characteristics of the epidemic caused by coronavirus in mainland China. Unfortunately, the number of coronavirus victims is expected to be much higher than that predicted on February 10, 2020, since 12289 new cases (not previously included in official counts) have been added two days later. Further research should focus on updating the predictions with the use of up-to-date data and using more complicated mathematical models.

An IOT based Accident Severity Prediction Mechanism using Machine Learning

The significant number of fatalities and serious injuries caused by traffic accidents around the world is a worrying problem. Developing nations typically bear a heavier weight of casualties. As a result, developing a model to forecast the likelihood of accidents is extremely difficult. However, the application of machine learning algorithms is one of the significant techniques to forecast the seriousness of such events. As a result, the main goal of the suggested thesis is to automate the process of accident detection by evaluating the severity levels and filtering a set of influential factors that could cause a road accident and generating them using IoT. SMOTE's theoretical notions are put into practice in order to address data imbalance and to ensure that the dataset is balanced. In a later step, the dataset is put to use in the process of building a framework that is constructed from five machine learning algorithms and one stacking algorithm. In the final step of the process, a study is conducted using variables such as the state of the weather and the varying degrees of severity that can have a role in the occurrence of traffic accidents. According to the findings of the experimental analysis that was carried out as part of the research project, the random forest model generated a higher level of accuracy than any of the other models that were put into use, achieving 74%.