Unsafe Behavior Research Articles

Prevalence of risky sexual behavior and associated factors among Injibara University students, Northwest Ethiopia.

Engaging in risky sexual behaviors can lead to HIV infection, sexually transmitted infections, and unintended pregnancy among youths. University students had greater sexual risks for many reasons. Therefore, this study aimed to assess the magnitude and associated factors of risky sexual behaviors among regular undergraduate students at Injibara University, Northwest Ethiopia. A cross-sectional study was conducted at Injibara University from 20 January to 30 2020. Multistage sampling was employed to select 770 students. Data were collected using a semistructured self-administered questionnaire. A binary logistic regression model was used to identify factors associated with risky sexual behavior. Adjusted odds ratios with 95% CIs were determined, and variables with P-values <0.05 were considered significant. A total of 770 students participated in the study, providing a response rate of 100%. In this study, 294 (38%, 95% CI: 35%, 42%) students engaged in risky sexual behaviors. Risky sexual behavior was significantly associated with not tested for HIV (AOR = 1.62, 95% CI: 1.15-2.31), peer pressure (AOR = 1.90, CI: 1.37-2.64), basic HIV knowledge (AOR = 2.16, CI: 1.65-2.89), substance use (AOR = 3.56, CI: 2.11-6.06), watching pornography videos (AOR = 1.58, CI: 1.11-2.23), and HIV risk perception (AOR = 1.37, CI: 1.02-1.91). A substantial proportion of university students in this study engaged in unsafe sexual behavior. Risky sexual behaviors are more likely to occur when students are under peer pressure, use substances, have no perceived HIV risk, watch pornography, and have inadequate basic HIV knowledge. Therefore, tailored strategic interventions such as life skill training should be designed to bring about positive behavioral changes among university students.

Risky Sexual Behaviours and Knowledge on Contraceptive Use and Family Planning Among Women Who Use Drugs (WWUDs) in Malaysia

Women who use drugs (WWUDs) face elevated risk of intimate partner violence (IPV) and unsafe sexual behaviours. This prevalence study, involving 200 WWUDs in a mandated drug rehabilitation program, examined risky sexual behaviours, family planning knowledge among IPV victims, and barriers to contraceptives or condoms use. The majority were Malays (86%, n = 171/200), averaging 32.3 years of age (SD = 8.61). Over half had experienced IPV in their lifetime (53%, n = 105/200). IPV victims had higher odds of unwanted pregnancies, prior abortions, concurrent drug use with partners, drug use before sex, and cohabitation with male partners who used drugs. Sixty percent were uninformed of female condoms, and most disliked using male condoms due to performance concerns. Despite better family planning knowledge among IPV victims, WWUDs had limited awareness of contraceptive use. Interventions should prioritize empowering and educating WWUDs, particularly IPV victims, on IPV prevention and the risks associated with unsafe sexual behaviour during rehabilitation.

Improving the professionalization level of a new generation of construction workers to reduce their unsafe behaviour: an empirical study in China

Objectives. Improvement of the professionalization level for the new generation of construction workers (NGCWs) is critical to upgrade the construction industry. It also provides a new approach to reduce their unsafe behaviour. The purpose of this study is to analyse the correction mechanism of the professionalization level on NGCWs’ unsafe behaviour. We examined the mediating role of work–family conflict and job burnout on impacting NGCWs’ unsafe behaviour. The moderation effect of the NGCWs’ perceived organizational support was also examined. Methods. The cross-sectional study was conducted based on structural equation modelling, confirmatory factor analysis and regression analysis with a sample of 496 NGCWs in China. Results. The professionalization level can effectively correct NGCWs’ unsafe behaviour. Job burnout can independently mediate the relationship between professionalization level and unsafe behaviour and act as a serial mediator for work–family conflict. Moreover, the high level of perceived organizational support will effectively suppress the effect of work–family conflict on job burnout. Conclusions. Improving the professionalization level is an effective way to correct NGCWs’ unsafe behaviour. Moreover, management should help the NGCWs balance work and family, to alleviate burnout. Additionally, it is suggested that management should ensure the NGCWs perceive the organizational support.

LLMGuard: Guarding against Unsafe LLM Behavior

Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.

Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees (Abstract Reprint)

Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using reinforcement learning often fail to generalize to novel environments due to unsafe behavior. In this paper, we propose Sim-to-Lab-to-Real to bridge the reality gap with a probabilistically guaranteed safety-aware policy distribution. To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis. In Sim-to-Lab transfer, we apply a supervisory control scheme to shield unsafe actions during exploration; in Lab-to-Real transfer, we leverage the Probably Approximately Correct (PAC)-Bayes framework to provide lower bounds on the expected performance and safety of policies in unseen environments. Additionally, inheriting from the HJ reachability analysis, the bound accounts for the expectation over the worst-case safety in each environment. We empirically study the proposed framework for ego-vision navigation in two types of indoor environments with varying degrees of photorealism. We also demonstrate strong generalization performance through hardware experiments in real indoor spaces with a quadrupedal robot. See https://sites.google.com/princeton.edu/sim-to-lab-to-real for supplementary material.

Group cognitive characteristics of construction Workers’ unsafe behaviors from personalized management

Unsafe behavior management is an effective way to prevent construction safety accidents. However, unsafe behavior management lacks personalization and needs to incorporate cognitive factors. Therefore, this study explored the group cognitive characteristics of workers' unsafe behaviors under common individual factors. Firstly, the cognitive model of unsafe behavior was validated using structural equation modeling and bootstrap techniques. The results showed a distal mediating between the cognitive processes of unsafe behaviors. Second, the cognitive processes of workers’ unsafe behaviors were analyzed using multiple group analysis for three individual factors: age, work experience, and education. The results showed that age is not a significant individual factor in group cognitive characteristics, education and work experience are significant individual factors with group cognitive characteristics of unsafe behavior. Workers with shorter experience are prone to cognitive failure in risk identification, others' influence, self-efficacy, and work skills. Workers with longer experience are prone to cognitive failure in safety awareness, others' pressure, and execution ability. Low-education workers are prone to cognitive failure in risk identification and pursuing energy-saving. High-education workers are prone to cognitive failure in safety awareness, self-efficacy, and work skills. Finally, targeted measures are given for significant individual factors based on group cognitive characteristics of unsafe behavior in order to construct widely used personalized group cognitive management for unsafe behavior.

Detection of Safety Signs Using Computer Vision Based on Deep Learning

Safety signs serve as an important information carrier for safety standards and rule constraints. Detecting safety signs in mines is essential for automatically early warning of unsafe behaviors and the wearing of protective equipment while using computer vision techniques to realize advanced safety in the AI and IoT era. This work aims to propose an improved YOLOV4-tiny safety signs detection model applying deep learning to detect safety signs in mines. The dataset employed in this study was derived from coal mines and analogous environments, comprising a total of ten types of safety signs. It was partitioned into training, validation, and test sets following a distribution ratio of (training set + validation set) to test set = 9:1, with the training set to validation set ratio also set at 9:1. Then the attention mechanism ECANet was introduced into the model, which strengthened the network’s learning of places that need attention. Moreover, the Soft-NMS algorithm was used to retain more correct prediction frames and optimize the detection model to further improve the detection accuracy. The Focal Loss function was introduced to alleviate the problem of category imbalance in one-stage safety signs detection. Experimental results indicate that the proposed model achieved a detection precision of 97.76%, which is 7.55% and 9.23% higher than the YOLOV4-tiny and Faster RCNN algorithms, respectively. Besides, the model performed better in the generalization because it avoided the over-fitting phenomenon that occurred in the YOLOV4-tiny and the Faster RCNN. Moreover, the advantages of the improved model were more prominent when detecting small target areas and targets under dim conditions in coal mines. This work is beneficial for the intelligent early warning system with surveillance cameras in coal mines.

Machine learning-based multimodal fusion recognition of passenger ship seafarers’ workload: A case study of a real navigation experiment

Passenger ships have complex transportation systems and seafarers face high workloads, making them susceptible to serious injuries and fatalities in the event of accidents. Existing unimodal workload recognition for seafarers mainly focuses on fixed load induction in bridge simulators, whereas a multimodal approach using multi-sensor data fusion can overcome the reliability and sensitivity limitations of a single sensor. To accurately identify the workload of seafarers, we propose a machine learning-based multimodal fusion method at the feature layer and utilise the Gini index to determine the feature weight of the multimodal data. Through a real ship navigation experiment, the subjective workload assessment technique (SWAT) was employed to collect the continuous workload scores of 24 seafarers in daily tasks. Further, the Dempster–Shafer evidence theory was used to integrate these scores with the unsafe behavior probability of seafarers to obtain a calibrated workload. Electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) signals were collected in real time, and a high-dimensional feature matrix was extracted to construct the workload recognition model. Random forest, XGBoost, and back-propagation neural networks were used to establish multimodal fusion workload recognition models at the feature-fusion stage, and the model performances were compared. The results showed that the multimodal fusion based on EEG, ECG, and EDA had an excellent recognition accuracy. The XGBoost algorithm has better performance with an accuracy of 85.72%, which is an increment of 9.49% compared to that of the unimodal algorithm, and this improvement passed the statistical significance test. Important features suitable for multimodal fusion recognition were also analysed. The findings of this study have profound significance for enhancing risk control measures pertaining to human factors and ensuring navigation safety.

Self-management of Risk for the Prevention of Traffic Accidents from a Health Perspective: A Qualitative Study.

Introduction: In the world, deaths and injuries caused by traffic collisions have been considered a public health problem. In Colombia, 7.238 fatalities were recorded in 2021, with motorcycle riders representing the largest group of victims at 59.7%. Methods: The aim of this qualitative phenomenological study is to describe the risky experiences and deliberate actions of diverse road users that influence the self-management of the risk of traffic collisions. Results: Data were obtained from 22 participants: motorists, pedestrians and drivers. The content analysis describes various human conditions that affect self-management of the risk of traffic accidents, such as unsafe behaviors, non-compliance with traffic regulations by the different road actors, competitive culture among drivers, eagerness, among others. Additionally, factors related to care were determined: healthy recreational activities, promoting the value of one's own life and that of others, adequate time management and preventive behaviors by some road users. Conclusion: This research provides information on social and cultural aspects, experiences and risky behaviors of different road actors that influence the incidence of traffic accidents in Colombia.

A Fast and Robust Safety Helmet Network Based on a Mutilscale Swin Transformer

Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; however, they require real-time recognition automation solutions based on computer vision. Although various deep-learning-based models have recently provided new ideas for identifying helmets in traffic monitoring, few solutions suitable for industry applications have been discussed due to the complex scenarios of construction sites. In this paper, a fast and robust network based on a mutilscale Swin Transformer is proposed for safety helmet detection (FRSHNet) at construction sites, which contains the following contributions. Firstly, MAE-NAS with the variant of MobileNetV3’s MobBlock as a basic block is applied to implement feature extraction. Simultaneously, a multiscale Swin Transformer module is utilized to obtain the spatial and contexture relationships in the multiscale features. Subsequently, in order to meet the scheme requirements of real-time helmet detection, efficient RepGFPN are adopted to integrate refined multiscale features to form a pyramid structure. Extensive experiments were conducted on the publicly available Pictor-v3 and SHWD datasets. The experimental results show that FRSHNet consistently provided a favorable performance, outperforming the existing state-of-the-art models.

The impact of identity conflicts among Chinese knowledge-based miners on unsafe behavior: A moderated chain mediation model.

In the context of smart mine construction, coal mine safety management is imposing stringent requirements on the safety competence of miners. To meet these demands, coal mine enterprises have initiated proactive measures to recruit a new generation of knowledge-based miners who possess high qualifications and specialized backgrounds. This study aims to explore the underlying mechanisms of identity conflict (IC) and its influence on unsafe behavior (USB) among KBMs within the context of China's smart mining initiatives, as well as to identify the role of potential mediating and moderating variables in this relationship. An empirical study was conducted on a sample of 304 KBMs with a college degree or above, selected from multiple coal mines in Shanxi Province, China. Hierarchical regression analysis and the Bootstrap method were employed for data analysis. This model incorporated IC as the antecedent variable and meticulously investigated the mediating effects of role breadth self-efficacy (RBSE) and psychosocial resources (PSR), along with the moderating effect of positive explanatory bias (PEB). The findings indicate a significant positive correlation between IC and USB among KBMs. RBSE and PSR were found to mediate the relationship between IC and USB. Moreover, this mediating effect further influenced the relationship between IC and USB through a chain mediating effect. Additionally, PEB strengthened the positive impact of KBMs' RBSE on PSR. These results provide a broader perspective on the antecedent variables associated with KBMs' USB and provide valuable insights and practical management strategies enhancing the safety management practices within coal mining enterprise.

The Breakfast and Dressing Conflict Task: Preliminary Evidence of Its Validity to Measure Online Self-Awareness After Acquired Brain Injury.

Impaired self-awareness (SA) of deficits after an acquired brain injury (ABI) severely affects patients' independence in activities of daily living (ADLs). However, any assessment tool permits an exhaustive evaluation of SA in the context of ADLs. To study the validity of the Breakfast and Dressing Conflict Task (BD Conflict Task) to assess online SA (awareness of performance in the context of a given task) in patients with ABI; to study its interactions with offline SA (general awareness); and to test the validity of a simplified measure of performance monitoring, the ADL Conflict-Monitoring Index. Convergent validity and correlational study. Research laboratory, hospitals, and homes. Thirty patients with ABI and 28 neurologically healthy controls. Using the BD Conflict Task, measures of emergent awareness, self-regulation, anticipatory awareness, and self-evaluation were assessed and their convergent validity and relationship with offline SA were analyzed. The ADL Conflict-Monitoring Index was calculated, and its convergent validity was tested. The online SA variables of the BD Conflict Task showed convergent validity with traditional online SA measures. Offline SA correlated with emergent and anticipatory awareness in the Breakfast Task. The ADL Conflict-Monitoring Index proved to be a valid measure of patients' performance monitoring. These preliminary findings suggest that the BD Conflict Task is a valid tool to assess online SA in patients with ABI and provide further understanding of the online SA-offline SA interaction. Furthermore, the ADL Conflict-Monitoring Index may be a valid and easy-to-use monitoring measure in clinical settings. Plain-Language Summary: Patients with acquired brain injury (ABI) and reduced awareness of their cognitive deficits face problems performing activities of daily living (ADLs) and may show signs of unsafe behaviors. Being aware of one's own abilities involves anticipating problems before starting a task, detecting and correcting errors during the task, and evaluating performance afterward. This study provides preliminary validity for the Breakfast and Dressing Conflict Task, a new tool that assesses aspects of self-awareness simultaneously in the context of familiar and significant ADLs. Furthermore, the tool simplifies the assessment of detecting and correcting errors with an easy-to-use index, making it suitable for use in clinical settings.

Leveraging Continental Norms and Mechanisms to Enhance Barrier-Free Access for Pedestrians with Disabilities in Kenya

When it is realised meaningfully, barrier-free access enables pedestrians with disabilities to use streets without being impeded by non-existent or poorly maintained sidewalks, inaccessible overpasses or underpasses, crowded sidewalks, lack of traffic controls, lack of aids at street crossings, unsafe motorist behaviour, and poor signage and lighting. While Kenya has laws in place that are intended to facilitate barrier-free access, in reality, these laws are not implemented, resulting in the violations of rights of pedestrians in general, and pedestrians with disabilities in particular. Using the lived experiences of pedestrians with disabilities, this article reflects on the policy, legislative, and practical contexts which undermine access. It shows that despite the range of policy and legal instruments which Kenya has adopted or enacted to ensure the public in general can access streets, pedestrians with disabilities enjoy arising benefits only marginally. The article’s thesis is that continental policy and normative instruments and institutions may impel Kenya towards ensuring that pedestrians with disabilities have meaningful barrier-free access.

The Role of Socioeconomic and Cognitive Status in Determining Traffic Behaviour of Elderly Pedestrians in Iran: A Cross-Sectional Study

Elderly pedestrians are among the most vulnerable groups in terms of traffic-related injuries. This study aimed to investigate traffic behaviour and its determinants among elderly pedestrians in Iran. This cross-sectional study examined the traffic behaviour of 600 elderly pedestrians in Zanjan, Northwestern Iran. The participants were selected using the multistage random sampling method. Pedestrians’ traffic behaviour, socioeconomic status (SES), and cognitive function were assessed using the Pedestrian Behaviour Questionnaire (PBQ), SES questionnaire, and Abbreviated Mental Test score (AMTS), respectively. Multiple linear regression was used to investigate the association of total score of pedestrian traffic behaviour with socioeconomic and cognitive status while adjusting for potential confounding roles of demographic variables. More than 90 percent of elderly pedestrians showed unsafe traffic behaviour. This included traffic violations and failure to adhere to traffic rules. Almost half the elders were aggressive and distracted when walking and crossing the street. A higher total score of PBQ was associated with a higher cognitive status, higher SES, higher levels of education, and walking more than 1 hour a day. The majority of the elders showed unsafe traffic behaviour and elderly pedestrians with high SES and normal cognitive function were more likely to exhibit safe behaviour than those with low SES and cognitive dysfunction. Interventions are needed to improve the traffic behaviour of elderly pedestrians especially those with low SES levels.

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Due to factors such as the availability of assembly equipment, technology, and site management level, prefabricated building construction safety accidents often occur. To ensure the safety of prefabricated buildings and effectively reduce the accident rate, the concept of resilience is introduced into the safety management of prefabricated buildings. Based on the resilience absorption capacity, adaptation capacity, recovery capacity, and optimization capacity, a comprehensive evaluation index system for the safety resilience of prefabricated buildings is established. By combining prior knowledge with structural learning and parameter learning, a dynamic Bayesian network (DBN) model is constructed to dynamically evaluate the safety resilience of prefabricated buildings. Through forward causal reasoning and backward diagnostic reasoning, the dynamic safety resilience value of prefabricated buildings and the chain of maximum failure causes are obtained. Finally, by conducting a sensitivity analysis on the target nodes, the key influencing factors of the safety resilience of prefabricated construction are identified, and improvement suggestions for enhancing resilience are proposed. The results indicate that establishing a resilience safety culture, preventing unsafe behaviors of personnel, safety management, and supervision on the construction site, emergency management actions, and building a risk management information system are crucial factors influencing the safety resilience of prefabricated buildings. The enhancement of absorption capacity has the greatest impact on the safety resilience of prefabricated buildings.

Evaluation of risk factors affecting the safety of coal mine construction projects using an integrated DEMATEL-ISM approach

Purpose The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.Design/methodology/approach The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.Findings The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.Research limitations/implications Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.Social implications The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.Originality/value Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Study on the Influence of Safety Culture Level on The Unsafe Behavior of Coal Miners

In order to explore the influence of safety culture level on the unsafe behavior of coal miners, the structural equation model method is used to study. Summary combing the internal and external research results, put forward the measure of safety culture level of safety atmosphere, safety attitude, safety awareness, safety training four influencing factors, construct structure equation model, proposed model analysis hypothesis, the introduction of unsafe psychology as a intermediary variable, according to Henan, Shanxi coal mine 226 effective sample data, using AMOS software verification analysis. The results show that the safety atmosphere, safety attitude, safety awareness and safety training all have negative effects on the unsafe behaviors, and the unsafe psychology is positively related to the unsafe behaviors. According to the results of the model analysis, the corresponding intervention countermeasures will have a positive effect on preventing accidents in coal mine.

The Influence of Unsafe Behavior and Workspace Arrangement on Occupational Accidents Among Workers at Pt Maruki International Indonesia Makassar

Objective: Workplace accidents are unplanned and uncontrolled incidents that occur during work due to unsafe actions and unsafe conditions, leading to the interruption of work activities. This study aims to investigate the influence of behavior and workspace layout on workplace acidentes.&#x0D; &#x0D; Method: This research employs a quantitative approach with a case-control study design. Questionnaires were distributed to 57 workers, comprising 19 cases of workplace accidents and 38 controls without workplace accidents at PT Maruki Internasional Indonesia. Collected data include gender, age, tenure, behavior, and workspace layout. The gathered data in Excel tables will be analyzed using chi-square tests and logistic regression analysis.&#x0D; &#x0D; Result: The most common type of workplace accident is found among male workers (OR=4.065 [CI 95%: 0.462-35.752]; p=0.247). Unsafe work behavior (OR=18.417 [CI 95%: 3.656-92.779]; p=0.000) and workspace layout (OR=5.385 [CI 95%: 1.587-18.264]; p=0.010) are significant factors. Further logistic regression analysis reveals that variables influencing workplace accidents at PT Maruki Internasional Indonesia are work behavior and workspace layout&#x0D; Conclussion: Behavior and workspace layout are the most influential risk factors for workplace accidents, with unsafe work behavior being the most significant risk fator.&#x0D; &#x0D; Implication of the research: This research can contribute to raising public awareness about the importance of workplace accident impacts and encourage people to be more cautious while working.

원청의 안전지원이 협력사업장 근로자의 안전행동수준에 미치는 영향

According to the Ministry of Employment and Labor, 40% of fatal accidents due to industrial accidents between 2017 and 2021 were at partner workplaces, and 62.6% of all accidents (as of 2021) were preventable by following basic safety rules, such as falling, getting caught, or colliding. Therefore, this study investigated the effect of the prime companies’ safety support on the safety culture creation and safety behavior of workers in partner workplaces as a way to establish a safety culture in partner workplaces. This study was designed to examine the effect of safety support from prime companies on safety behavior level of workers at partner workplaces. It is a cross-sectional study using the Korea Occupational Safety and Health Agency’s 2021 Occupational Safety and Health data. The sample for the study is 739 manufacturing partner workplaces. Data were analyzed using descriptive statistics and hierarchical multiple regression analysis using SPSS (ver.21). According to this study, there were labor unions in 91 places and 13.9% in 94 places with departments dedicated to industrial safety and health, indicating that the establishment of an industrial safety and health system was generally insufficient. In addition, this study examined the effect of the prime companies' safety support on the safety culture of partner workplaces and the safety behavior of their workers. As a result of the hierarchical multiple regression analysis, safety support from prime companies had a positive (+) effect on safety culture at partner workplaces. Leadership of management appear to have a partial mediating effect on safety support from prime companies and safety behavior level at partner workplaces. Safety communication and education·systems appear to have a mediating effect on safety support from prime companies and safety behavior level at partner workplaces. According to this study, active safety support from the prime companies can revitalize the safety culture of the partner workplaces, thereby improving the level of safety behavior of workers to prevent accidents. Since partner workplaces generally lack safety capabilities compared to the prime companies, they can spread the safety culture of partner workplaces through safety support from the prime companies, and it can be expected to be effective in preventing industrial accidents by controlling the unsafe behavior of workers by establishing a safety culture.

An optimisation model to locate level crossings in railway lines at mines to minimise the total weighted-walked distance

The location of level crossings for pedestrian circulation is a safety issue due to potential human losses and material damage. This problem has become a concern in urban and industrial environments due to the characteristics of rail systems and unsafe pedestrian behaviour. Research reveals that psychological and infrastructural factors influence pedestrians' decision-making when crossing railroad lines. One of these is the convenience that leads pedestrians to cross at the nearest point. However, investigations to date only study the impact of location on the risk and severity of crashes and pedestrian rule violations. The current research is quasi-experimental, i.e., no optimisation tool has been developed to determine which crossing points pedestrians should follow. Therefore, this research develops a mathematical model for locating level crossings at railways to minimise the total weighted-walked distance. A genetic algorithm was proposed for solving the model, especially for large-size problems. The algorithm parameters were calibrated using the design of experiments and ten instances based on the characteristics of the area under study. The obtained results provide (1) the location of the level crossings and (2) the route pedestrians should follow for each origin-destination pair based on the facilities adjacent to the rail lines. A sensitivity analysis was performed using the ten instances to determine how the location of the level crossings changes with modifications in the facilities' location. Results showed that the model provides alternative solutions according to the problem's size. This study delivers a methodology for practitioners and stakeholders of transportation systems.