Safety Improvement Measures Research Articles

Closed-form P-I diagram based on flextural and shear failure for ptotective wall

Protective walls are crucial structures in mitigating damage from explosions, particularly in environments such as gas refueling stations. However, evaluating their performance typically involves intricate numerical analyses to derive Pressure-Impulse (P–I) diagrams. This paper presents a novel closed-form solution for deriving P–I diagrams specifically tailored for protective walls, considering flexural and shear resistance. By assuming perfect plastic resistance, the proposed solution offers an efficient alternative to numerical analysis, particularly beneficial in preliminary design stages. The validity of the closed-form solution is demonstrated through comparisons with numerically derived P–I diagrams, showing promising results across various design parameters. This approach enhances the accessibility and ease of designing protective walls for blast resistance, contributing to improved safety measures in critical infrastructure.

Assessment of Factors Affecting the Safety Performance in Construction Projects

Abstract: This study analyzes the 152 responses of clients, contractors, and consultants within the construction industry in order to investigate safety performance factors within the construction industry. The data for the study were gathered through the use of a structured questionnaire, and statistical analysis, including ANOVA tests, was performed on it in order to evaluate the possibility of differences between the respondents in terms of their experience and designation. The findings highlight significant differences in perceptions among the various groups and suggest that areas of focus for improvement include safety training, availability of equipment, communication, and adherence to safety standards. The study emphasizes the significance of ongoing evaluation and improvement of safety measures and advocates for increased attention to the cultivation of a culture that places an emphasis on safety in construction projects. These findings provide valuable insights for improving overall safety performance and contribute to a better understanding of the dynamics of safety in the construction industry, which is a sector that is currently understudied.

SAFEPARK: Vehicle Detection and Traffic Violation Parking Management System

Abstract: The escalating motorcycle accident rates highlight the pressing need for improved safety measures. Helmets, a crucial safety gear, are often neglected, contributing significantly to fatalities. This paper addresses the pervasive issue of noncompliance with motorcycle safety rules, focusing on helmet usage and triple riding. Existing systems for monitoring lack precision, prompting our proposed Bike Traffic Violation System. Leveraging Haar Cascade and YOLOv3 models, it identifies motorcycles, detects riders without helmets, instances of triple riding, and even empty parking spots with unprecedented accuracy. The Machine Learning component employs a Support Vector Classification model, bolstered by 4-fold cross-validation, ensuring robustness. This innovative system provides real-time insights into traffic violations, enabling prompt interventions by law enforcement agencies. It overcomes manual identification shortcomings, offering a comprehensive solution for enforcement and awareness campaigns. In summary, our Bike Traffic Violation System not only advances automated traffic rule monitoring but introduces a novel methodology for precise detection, significantly contributing to enhanced road safety and accident prevention.

Sustainability Assessment of Machinery Safety in a Manufacturing Organization Using AHP and CART Methods

Machine safety is not only a prerequisite for successful production but also the foundation for the sustainability and growth of any manufacturing organization. The latest approaches in this rapidly developing field integrate effective risk management tools and strategies into occupational health and safety (OHS) management systems. The study, through a real example from practice, describes the use of the analytic hierarchy process (AHP) method for machine safety improvement, considering the possible types of losses. Classification and Regression Tree Analysis (CART) was applied to assess the efficiency, cost-effectiveness, and, therefore, the overall sustainability level of the relevant safety measures. These were proposed risk reduction measures that typically raised uncertainty among managers regarding the estimation of cost-effectiveness. The advantage of the application decision tree approach is the possibility to identify and establish relatively homogeneous groups of undesirable events and their impact on the organization’s objectives. A comprehensive model has been developed to support management decision making in manufacturing organizations towards implementing and improving safety measures in line with manufacturing sustainability goals.

Design and Implementation of an Iot-Based Safety and Energy Efficient System

The paper provides a safe and power-efficient Internet of Things (IoT)-based solution. With the growing concerns about environmental sustainability and the demand for improved safety measures in today’s society, it has become critical to focus on building systems that decrease energy consumption and assure the safety of humans and the environment. The proposed system employs cutting-edge technologies and novel approaches that will result in optimal energy usage with minimal or no human involvement thereby assuring the safety of people. The proposed system leverages on existing IoT technologies and is comprised of smart sensors, energy-efficient gadgets, and a centralized control unit. Therefore, it is able to demonstrate cutting edge power control strategies including power gating and dynamic central power regulation which control the usage of gadgets to operate at optimal power levels vis-a-vis the real-time demand to achieve energy efficiency. The system is also able to collect data on occupancy, lighting, temperature, and power consumption trends which can assist in making data-driven choices that improve the usage of energy. The proposed system provides large energy savings without sacrificing user comfort by dynamically altering lighting, and cooling systems based on occupants and ambient factors. Multiple simulations and real-world trials in homes and institutions’ buildings were used to assess the efficacy of the suggested system. The findings revealed substantial reductions in energy consumption without jeopardizing safety.

Analysis of Fishing Vessel Accidents and Suggestions for Safety Policy in South Korea from 2018 to 2022

Commercial fishing, one of the most dangerous sectors globally and, particularly, in South Korea, faces fatal accident rates significantly exceeding other industries, and an urgent improvement in safety measures is necessary to reduce these numbers effectively. The main objective of this study is to analyse fishing vessel accidents in South Korea and present the direction of safety policy for fishing vessels based on the lessons learnt from previous accidents, and, ultimately, to contribute to reduce fishing vessel accidents. For this purpose, we have analysed the marine accident data from the last five years provided by the Korea Maritime Safety Tribunal (KMST), the statistical yearbook of oceans and fisheries provided by the Ministry of Oceans and Fisheries (MOF), and the statistical survey of marine fisheries provided by the Korean Statistical Information Service (KOSIS), using both quantitative and qualitative approaches. After thorough review and analysis of the accident data, we have proposed seven suggestions for the direction of future safety policy for fishing vessels in South Korea: adopt international safety benchmarks, focus on occupational and collision accidents, tailor safety measures to vessel size and age, enhance lookout practices to prevent collisions, implement comprehensive strategies to mitigate human errors, and address communication issues among Korean and international crews.

Yard Work and Gardening: A Pastime with Potential for Craniofacial Injury.

Yard and garden tools are increasingly used by Americans. Despite the growing popularity of gardening as a leisure activity, the rate of injury associated with these tools remains remarkedly high. Previous research has highlighted the risks associated with improper handling of tools and associated potential long-term consequences. This retrospective study aims to provide a comprehensive analysis of the craniofacial injuries caused by yard and garden equipment. The investigation will emphasize the need for improved safety measures and regulations. Publicly available data from the National Electronic Injury Surveillance System (NEISS) database were utilized to identify craniofacial injuries related to yard and garden tool handling during a 10-year period (2013-2022). Study population included patients aged 1 to 85+ years who visited emergency departments secondary to these injuries. Data analysis involved queries for specific products and diagnostic codes related to craniofacial injuries. Over the study period, a total of 588,384 craniofacial injuries associated with yard and garden tools were recorded, averaging 58,838 injuries annually. Men accounted for a significantly higher proportion of injuries (69.2%) compared to women (30.8%). Elderly population, particularly those aged 60 to 64 years, experienced the highest incidence of craniofacial injuries. Cranium was the most commonly affected anatomical location, comprising 34% of all injuries. Lacerations and contusions were the most frequent injury diagnoses. Greenhouse or gardening supplies and lawnmowers are the most common causative agents, respectively. Our study highlights the persistent incidence of craniofacial injuries associated with yard and garden tool handling emphasizing the need for standardized safety regulations and guidelines. Sociological and demographic factors, such as differences in gardening habits and behavioral patterns between genders, contribute to the observed disparities in injury rates. Understanding the underlying factors contributing to these craniofacial injuries is crucial to promoting safer practices and reducing the overall burden associated with yard and garden tools.

Improvement of Curvature Change Rate (Parameter) in Two-Line Rural Roads

The studies in various countries show that one of the main causes of accidents on rural roads still remains the drivers’ errors in choosing safe speed, which can be attributed to the errors of perceiving the road alignment. The current practice shows that the engineering safety improvement measures as well as accident prevention measures such as intersection design, traffic signs and equipment, facilities for vulnerable road users, access management, and human behaviour are not always effective. Probably, one of the alternatives to solve this problem is a design methodology that is used in the European Union countries and based on road safety criteria and road safety module. This methodology is aimed at ensuring design consistency between the elements of road alignment in a way that the driver intuitively chooses safe speed or the probability of errors in perceiving the road alignment is minimum. The article presents experimental research carried out in Vilnius Gediminas Technical University, the objective of which is to determine the accuracy of road safety module and to suggest possible alternatives for its improvement.

The evolution of embedded systems in automotive industry: A global review

The evolution of embedded systems in automotive industry: A global review

Lateral Overturning and Backward Rollover of Agricultural Tractors: A Review

Tractor accidents caused by lateral overturning and backward rollover during agricultural activities and general driving are common. In this study, various research cases were analyzed to identify the factors influencing the lateral overturning and backward rollover of tractors and to examine their static and dynamic stability. Studies on the analysis of the major causes of these incidents and evaluation of tractor safety were compiled. Test methods, including actual tests and simulations, were categorized, and the characteristics of lateral overturning and backward rollover safety of tractors in different studies were examined. Additionally, safety improvement measures were proposed by identifying and summarizing the causes of accidents involving agricultural machinery. Tractor safety was evaluated primarily by conducting actual tractor and simulation tests. These tests were classified into field tests, tests on scale models, spreadsheet programs, and 3D simulation programs. The primary causes of lateral overturning and backward rollover were unstable center of gravity, extremely high driving speed, and ground conditions. Given the considerable number of studies dedicated to evaluating tractor safety, various technologies aimed at preventing lateral overturning and backward rollover incidents are expected to be applied to tractors in the future. The production and testing of safe agricultural machinery are expected to contribute to a reduction in accident rates.

Mitigating Cross-Species Viral Infections in Xenotransplantation: Progress, Strategies, and Clinical Outlook.

Xenotransplantation holds great promise as a solution to address the critical shortage of organs, but it raises concerns regarding the potential transmission of porcine viruses to recipients, leading to infections and even zoonotic diseases. Data used in this review were mainly from literature of Pubmed database. Keywords included xenotransplantation, infection, virus, and epidemiology. The original articles and critical reviews selected were relevant to this review's theme. We review the major viral infections of concern in xenotransplantation, their risk of transmission, diagnosis, treatment, and ways to prevent infection. Then, we pivot to a comprehensive overview of the current status of xenotransplantation. In addition, we offer our own insights and recommendations for propelling xenotransplantation forward, transitioning from preclinical experiments to the critical phase of clinical trials. Viral infections pose considerable safety concerns within xenotransplantation, particularly with the possibility of emerging or currently unidentified viruses. Clinical trials serve as a crucial platform to progress the safety standards of xenotransplantation. However, further studies and dedicated efforts are required to effectively translate findings into practical applications that can improve safety measures in this field.

Maritime trajectory mining: An automatic zones of interests discovery and annotation framework⋆

As global traffic continues to grow, the identification of areas of particular significance, known as Zones of Interest (ZOI), becomes crucial for optimizing transportation systems and analyzing mobility patterns. In the maritime domain, effective ZOIs discovery is essential for enhancing route planning, improving safety measures, and managing resources efficiently. Within the context of trajectory mining, these ZOIs provide valuable insights into movement behaviors and operational efficiencies. In this paper, we present a framework for discovering and annotating ZOIs within maritime trajectories. The proposed approach involves processing raw positional data to initially identify candidate ZOIs, which are subsequently refined using contextual information. By leveraging real georeferenced vessels trajectories, collected from thousands of commercial ships, this framework proposes a structure of elements that will be implemented as part of the TNTM French project. While this research contributes to maritime field by providing a method for ZOIs discovery and annotation, it can be generalized to various application domains that may leverage of mobility data analytics.

재난안전의무보험제도 개선방안에 관한 연구

In recent times, there has been a notable increase in large-scale fires affecting vulnerable groups, such as the Jecheon Sports Center fire, Ulsan residential and commercial apartment fire, Namyangju residential and commercial apartment fire, and Miryang Sejong Hospital fire. This has heightened public demand for improved safety measures and the need for a new insurance policy to ensure rapid economic recovery and secure living conditions. To address this, our study proposes ways to enhance the support system for victims of disasters in vulnerable facilities. We also aim to optimize the synergy between policy insurance and private insurance by refining the disaster responsibility insurance system, which serves as a social safety net. This will be achieved through a comprehensive analysis of disaster safety insurance practices both domestically and internationally, as well as an examination of disaster responsibility insurance operations from 2018 to 2020.

Research on prediction of slurry migration distance in aggregate stacking based on GA-PSO-BPNN algorithm

After the water inrush accident in coal mine tunnels, early-stage pouring of aggregate forms a high-resistance, low-permeability aggregate stacking, transforming the pipeline flow into percolation. In the later stage, grouting is carried out into the interior of the aggregate stacking, effectively accumulating and solidifying the cement slurry. Among these, whether the slurry can migrate over long distances and fill the voids inside the aggregate stacking is the critical determinant of the success or failure of sealing. To quantitatively analyze the migration distance of slurry inside the aggregate stacking after grouting, a single-hole grouting test platform was established, and an orthogonal experiment was designed with grouting pressure, water cement ratio, and aggregate stacking porosity as influencing factors. Based on 25 sets of experimental measurements, four neural network prediction models suitable for studying the slurry migration distance within the aggregate stacking were constructed separately as back propagation neural network (BPNN), genetic algorithm (GA) combined BPNN, particle swarm optimization (PSO) combined BPNN, and GA-PSO combined BPNN. Evaluation criteria such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination (R2) were used for comparative analysis of the calculation errors and prediction accuracy of each model. From the perspective of neural network prediction results, the weight value of each influencing factor was analyzed, and the ranking was as follows: grouting pressure > aggregate particle size > water cement ratio, with grouting pressure being the primary controlling factor. The study demonstrates that the GA-PSO-BP model exhibits the best prediction performance, with an average relative error of only 1.59% and an R² of 0.998. This neural network model overcomes issues such as slow learning and getting stuck in tricky spots in BP neural networks. The prediction model shows high accuracy and stability, enabling more effective and accurate prediction of slurry migration distances, making it worthy of dissemination and application. This study can improve safety measures by reducing waste, expediting disaster management efforts, and minimizing environmental hazards associated with mining incidents.

Learning to manage the unexpected: applying Weick and Sutcliffe’s HRO principles to oil tanker accidents

PurposeWeick and Sutcliffe identified five principles that enable high-reliability organizations (HROs) to address environmental complexity and manage unexpected events. The current study aims to adopt this sensemaking perspective to analyze accidents within a typical HRO sector, namely maritime transport.Design/methodology/approachThrough a retrospective case study analysis, this study focused on seven oil tanker accidents, using them as illustrative examples.FindingsFindings show how the five principles contributed to the accidents' occurrence, explaining how failures in sensemaking affected the crew's capability to both prevent errors and cope with their consequences, thus leading to disasters.Research limitations/implicationsOverall, the study offers an applicative contribution showing how this model may provide a reliable framework for analyzing the psychosocial factors affecting an accident. This approach deepens the understanding of how latent factors are enacted and how the prevention and error management phases interrelate within a comprehensive flow of the entire accident sequence. Furthermore, the study emphasizes consistent patterns that emerge across multiple accidents within the same sector, in order to learn valuable lessons to improve safety measures in the future.Originality/valueThis study constitutes an exemplary application in support of how Weick and Sutcliffe’s model is valuable for investigating HROs. It offers a second-order interpretative framework to understand accidents and underscores the interplay among these factors during the dynamic development of an accident.

The impact of electric scooters in Melbourne: data from a major trauma service.

The proliferation of electric scooters globally has been associated with an increase in related injuries and consequent economic burden. This study aims to assess the injury patterns and the economic impact associated with electric scooter use in Melbourne, Australia. A retrospective cohort study was conducted using hospital and registry data from January 2022 to January 2023. Data collected included demographic details, alcohol and helmet use, injury type and severity, operative treatment provided, and direct medical costs. The economic impact (in AUD) of the patient's emergency presentation and hospital admission was calculated. During the study period, 256 electric scooter related injuries were recorded, comprising 247 riders and nine pedestrians. The majority of patients were males (69%) with a median age of 29.5 (15-78). Alcohol use was reported by 34% and helmet use by 33%. Injuries most commonly affected the upper limb (53%) and head (50%), with abrasions (75%) and fractures (48%) being the most common type of injury sustained. The total hospital cost was $1 911 062, and the median cost was $1321.66 per patient (IQR: $479.37-$5096.65). Electric scooter usage, as observed through patient presentations to the Royal Melbourne Hospital, is associated with a considerable number of injuries, primarily among young males, and an ensuing substantial economic burden. The findings underscore the urgent need for improved safety measures to minimize electric scooter-related injuries and their clinical and economic repercussions.

Redox imbalance and inflammation: A link to depression risk in brazilian pesticide-exposed farmers

This study aims to elucidate the mechanisms linking occupational pesticide exposure to depression among rural workers from Maravilha, Brazil. We assessed the mental health, oxidative, and inflammatory profiles of farmers exposed to pesticides (N = 28) and compared them to an urban control group without occupational exposure to pesticides (N = 25). Data on sociodemographic, occupational history, and clinical records were collected. Emotional states were evaluated using the State-Trait Anxiety Inventory (STAI) and Beck Depression Inventory (BDI). Biochemical, hematological, inflammatory, and redox parameters were examined in blood samples from both groups. Results showed educational disparities between groups and unveiled a concerning underutilization of personal protective equipment (PPEs) among farmers. Glyphosate was the predominant pesticide used by farmers. Farmers exhibited higher BDI scores, including more severe cases of depression. Additionally, elevated levels of creatinine, ALT, AST, and LDH were observed in farmers, suggesting potential renal and hepatic issues due to pesticide exposure. Oxidative stress markers, such as increased lipid peroxidation and superoxide dismutase (SOD) activity, along with decreased catalase (CAT) activity and ascorbic acid levels, were noted in the pesticide-exposed group compared to controls. Elevated levels of inflammatory cytokines, particularly IL-1β, IL-6 and TNF-α, were also observed in pesticide-exposed group. Our findings suggest that inflammation, oxidative distress and lower educational levels may be associated with depression in pesticide-exposed farmers. This study highlights the impact of occupational pesticide exposure on the mental health of rural workers. The underuse of PPEs and the link between depressive symptoms, inflammation, and oxidative stress underscore the urgent need for improved safety measures in agricultural practices. Addressing these issues will contribute to a deeper understanding of the intricate relationship between environmental exposures and mental health outcomes.

Analysis of Stakeholders Perceptions of The Impact of Crisis on Tourism in Ogun State, Nigeria

Ogun State, Nigeria is experiencing crises, including kidnapping and terrorism that can impact its destinations. Thus, this study examined the perceptions of 133 tourists on the influence of these crises on their visitation to an attraction in the state, using a questionnaire survey. Two attraction officials were also interviewed on whether such crises impact revenue generation and employment. Results from the questionnaires which were analyzed using PSPP frequency distribution show that although terror crises had no significant influence on tourists' visitation to the attraction, they were concerned with the level of insecurity at the destination. Also, the transcribed interview outcomes reveal that, although similar crises had no significant impact on tourism, its perceptions can affect revenue generation and employment, when destinations are poorly projected. Consequently, these findings suggest the need for improved safety measures and mechanisms for promoting safe places at the destination by the authorities, for improved tourist arrivals, revenue, and employment opportunities.

AutomatedFace Mask Detection Using Pretrained CNN

In recent times, the use of face masks has emerged as a critical subject. Automated facial mask detection can curb the transmission of the COVID-19 virus and SARS-VIRUS within communal areas by identifying individuals who are not utilizing masks. In this work, a pretrained Convolutional Neural Network (CNN) with model ResNet-50, which is initially trained on the Image Net competition data, is utilized. This model is augmented with a 300-linear layer network and fine-tuned on a dataset that comprises 1,000 facial images. During the evaluation of the validation dataset consisting of approximately 800 face images, the model achieved an impressive 99% accuracy. Its primary objective is to discover if an individual is wearing a facial mask using a cropped image of their face. By leveraging such advanced technologies, we can contribute significantly to public health and safety measures in the ongoing battle against COVID-19 and SARS-VIRUS.Keywords:CNNCOVID-19Face Mask Detection (FMD) SARSDOI: 10.22401/ANJS.26.4.12*corresponding author email: farah.saad@nahrainuniv.edu.iqThis work is licensed under a Creative Commons Attribution 4.0 International License1.Introduction COVID-19 (CO ronaVIrus Disease of 2019) and SARS(Severe Acute Respiratory Syndrome) are two viral respiratory illnesses caused by coronaviruses, both of which have had important global impacts on public health [1]. These contagious diseases emerged in different time frames but share similarities in their mode of transmission and clinical presentation, prompting the search for effective non-pharmaceutical strategies to mitigate their spread [2]. One essential strategy to reduce the transmission of respiratory droplets, which can carry the viruses, is the widespread adoption of face masks in public spaces. However, monitoring and ensuring compliance with mask-wearing practices in crowded areas can be challenging, particularly in densely populated regions. Automatic face mask detection (FMD) systems powered by advanced technologies offer a potential solution to this challenge [3]. Convolutional Neural Networks (CNNs) and deep learning techniques have demonstrated remarkable success in computer vision tasks, including image classification [4]. In this context, utilizing pretrained CNN models, such as ResNet-50, has shown promising results for identifying faces wearing or not wearing masks. In this work, we explore applying a pretrained ResNet-50 CNN model fine-tuned on a well-balanced dataset of 12,000cropped photos of faces with and without masks. The goal is to create an accurate and reliable FMD (Face Mask Detection) system that can automatically identify individuals who are not wearing masks in public spaces. Through this work, we aim to contribute to the expanding realm of research on FMD as a crucial tool to prevent the spread of respiratory illnesses. The successful implementation of such technology has the ability to play a significant role in safeguarding public health, improving safety measures, and aiding in the ongoing battle against COVID-19 and SARS in various community settings.

Enhancing Project Management through Exploratory Factor Analysis of Safety Performance in Dubai's Construction Industry

The construction industry in Dubai, known for its rapid growth and towering skyscrapers, has faced significant challenges ensuring the safety of its workforce. The purpose of this study was to enhance project management practices through an exploratory factor analysis of safety performance within this sector. This research aimed to identify the underlying factors that contribute to safety outcomes and to provide a data-driven foundation for improving safety measures. Addressing the problem of frequent construction accidents and the high rate of safety incidents, this study hypothesized that a multifaceted approach to safety analysis would reveal complex interdependencies affecting worker safety. The methodology involved a comprehensive review of incident reports and safety records from various construction projects in Dubai over the past five years, coupled with surveys and interviews with industry professionals. The data underwent exploratory factor analysis to ascertain the primary variables impacting safety performance. Findings indicated that key factors affecting safety included worker training, communication efficacy, adherence to safety protocols, and the use of protective equipment. Surprisingly, statistical analysis revealed that projects with high safety investments did not always correlate with lower incident rates, suggesting the importance of qualitative factors over mere financial commitment. The study concludes that enhancing safety performance in Dubai’s construction industry requires a holistic approach that goes beyond financial investment in safety. Recommendations include implementing regular, targeted training programs, improving on-site communication channels, strict enforcement of safety protocols, and fostering a safety-centric organizational culture. In light of these conclusions, the study advocates for policy revisions at the regulatory level and a shift in the industry’s safety paradigm. The recommended strategic changes aim to not only mitigate the risks inherent in the construction industry but also to enhance the overall management of construction projects in the dynamic landscape of Dubai. Keywords. Project Management, Exploratory Factor Analysis, Safety Performance, Construction Industry, Dubai