Safety Management Research Articles

The fire hazard preparedness of special needs schools in the North West Province, South Africa

Fire disaster preparedness has been reported as critical in schools, especially for schools that have learners with special needs. This study, therefore, through a quantitative research approach, collected data from 88 participants across nine special needs schools (SNS) in the North West Province of South Africa to assess the fire preparedness of these schools. Data were collected using a structured questionnaire. The study applied descriptive and inferential statistics using R Statistical Software package to analyse questions that gauged the fire preparedness of the sampled schools. Four generalised linear and one ordinal regression models were applied to the data and found statistically significant differences for some predictors across the four applied GLMs. Significant results were found for the school fire safety management model applied to the Disaster Preparedness Strategy framework (χ2 = 206.7; p = 0.01; R2 = 90.5%). However, a significant difference was only found in the question of early warning systems across the eight predictor variables for the framework.Contribution: The maintenance and recordkeeping model showed significant results in terms of record maintenance and the frequency of fire safety equipment inspections. While compliance with specific fire safety regulations is essential, broader engagement and continuous improvement in early warning systems are necessary for a more resilient disaster preparedness strategy. This study addresses a critical gap in understanding and improving fire hazard preparedness in these schools. The authors therefore recommend intervention from the authorities to assist these schools to prepare for fires.

Experimental Study on the Thermal Behavior Characteristics of the Oxidative Spontaneous Combustion Process of Fischer–Tropsch Wax Residue

Coal-to-liquid technology is a key technology to ensuring national energy security, with the Fischer–Tropsch synthesis process at its core. However, in actual production, Fischer–Tropsch wax residue exhibits the characteristics of spontaneous combustion due to heat accumulation, posing a fire hazard when exposed to air for extended periods. This significantly threatens the safe production operations of coal-to-liquid chemical enterprises. This study primarily focuses on the experimental investigation of the oxidative spontaneous combustion process of three typical types of wax residues produced during Fischer–Tropsch synthesis. Differential Scanning Calorimetry (DSC) was used to test the thermal flow curves of the three wax residue samples. Kinetic analysis was performed using the Kissinger–Akahira–Sunose (KAS) and Flynn–Wall–Ozawa (FWO) methods to calculate their apparent activation energy. This study analyzed the thermal behavior characteristics, exothermic properties, and kinetic parameters of three typical wax residue samples, exploring the ease of reaction between wax residues and oxygen and their tendency for spontaneous combustion. The results indicate that Wax Residue 1 is rich in low-carbon chain alkanes and olefins, Wax Residue 2 contains relatively fewer low-carbon chain alkanes and olefins, while Wax Residue 3 primarily consists of high-carbon chain alkanes and olefins. This leads to different thermal behavior characteristics among the three typical wax residue samples, with Wax Residue 1 having the lowest heat release and average apparent activation energy and Wax Residue 3 having the highest heat release and average apparent activation energy. These findings suggest that Wax Residue 1 has a higher tendency for spontaneous combustion. This research provides a scientific basis for the safety management of the coal chemical industry, and further exploration into the storage and handling methods of wax residues could reduce fire risks in the future.

Application and Research of Mine Safety Management Strategy Based on the BP Network Model

Coal mine safety production is undoubtedly the cornerstone of the healthy, table, and sustainable development of China’s coal industry, and it also profoundly affects the effective implementation of the national energy strategy. In this context, exploring the application of network models in coal mine safety management strategies not only has significant practical significance, but also has profound development value. By integrating these factors, we can have a more comprehensive understanding of the causes of coal mine safety accidents and provide a scientific basis for formulating effective preventive measures. By collecting and analyzing various data in real-time during the coal mine production process, we can promptly detect abnormal situations, predict potential safety risks, and take corresponding measures for intervention and prevention. This dynamic monitoring and early warning mechanism can not only improve the safety of coal mine production, but also enhance the efficiency and efficiency of coal mine production. In the analysis process, we particularly focused on the advantages of Back Propagation (BP) neural network models in mine safety evaluation. BP neural network models can handle uncertainty and fuzzy information and have strong learning and adaptive capabilities. By constructing a safety evaluation model based on BP neural networks, we can more accurately evaluate the safety risks in the coal mine production process, providing strong support for formulating more scientific and reasonable safety management strategies.

Analysis of Computed Tomography Scans for Radiation Safety Management in the Republic of Korea

Background: Computed tomography (CT) scans in the Republic of Korea have spiked, reaching approximately 9 million examinations annually in 2017. CT provides significant medical benefits, but radiation exposure remains a concern. This study aims to analyze CT scans in Korea, as a basis study for radiation safety management.Materials and Methods: The raw data of total CT scans was obtained from the Health Insurance Review & Assessment Service and analyzed by CT scan type, patients’ age and sex, and medical facility type. CT scans trends were analyzed considering the disease incidence.Results and Discussion: In 2017, CT scans accounted for 8,977,300. Usage <i>per capita</i> was 0.18 in males and 0.17 in females. CT scans increased with age until the 50- to 59-year-old groups, then decreased. CT scans were high in abdominal/pelvic (35%), chest (26%), and head (22%) regions due to higher disease rates. Head CT was most frequently used for infants and children. Abdominal/pelvic, chest, and spine CT were more frequent for older groups. The CT scans in the upper and lower extremities was high in child and juvenile groups. Chest and abdominal/pelvic CTs were higher in males, whereas spine, whole spine, and CT densitometry were higher in females. The proportion of CT scans of tertiary and general hospitals, hospitals, and clinics accounted for ≥80%, 13%, and 5%, respectively. Abdomen/pelvis, chest, and head/ neck CTs were mostly conducted in tertiary and general hospitals, spine CT in hospitals and clinics, extremity CT in hospitals, and CT densitometry in clinics.Conclusion: The trend of CT scans varied based on the incidence rate for each patient’s sex and age, and serious illness diagnosis by medical facility type. The results of this study provide data and guidance for evaluating the radiation exposure of the Korean population by CT and developing management policies for medical radiation safety.

NON-STANDARD RISK MODEL FOR APPLICATIONS IN RAILWAY TRANSPORT

In risk management in railway transport, standard risk models are usually used based on its typical definition and discrete quantification. This approach allows for easy justification of the adopted model, most often by referring to appropriate norms or standards (such as IRIS). The scientific approach does not disqualify the practical use of standard risk models, but its disadvantages (especially typical risk matrices, including their subjectivity) are increasingly being pointed out. In risk management procedures, most frequently one model is used to assess the risk of all identified hazards. This may turn out to be a mistake, considering the specific characteristics of the hazards. A risk model applied to one hazard may not be adequate to assess the risk of another. Therefore, it should be individually adapted both in terms of variables and the ranges of their measurement values. For some hazards, it will even be necessary to develop or adopt non-standard models. The aim of the article is to present non-standard risk models that provide a base for their easy implementation in safety management procedures used by railway entities.

Enhancing work safety behavior through supply chain safety management in small and medium sized manufacturing suppliers.

Enhancing work safety behaviors among Small and Medium-Sized Manufacturing Suppliers (SMMs) is crucial for establishing a more secure and efficient supply chain. Since the foundation of safety lies in positive prevention, it is crucial that SMMs adopt proactive and pro-social work safety behaviors that transcend mere compliance with standard regulations. Present-day diversified supply chain safety management apply varying degrees of pressures on SMMs. The effectiveness of such pressures, as well as their potential to spawn advanced safety behaviors in SMMs, is a matter of investigation. This research investigates the impact of Supply Chain Safety Management Pressure(SCSMP) on the SMMs' Work Safety Behaviors(WSB). A theoretical framework is constructed, grounded in institutional theory and theory of planned behavior, which earmarks three distinct dimensions of SCSMP: Coercive Pressure(CP), Mimetic Pressure(MP), and Normative Pressure(NP). The survey of 265 SMMs facilitated an assessment of the SMMs' Willingness for Responses(WR), which includes their Willingness for Adaptive Responses(WAR) and Willingness for Co-creative Responses(WCR). Subsequently, the resulting WSB entail Safety Compliance Behavior(SCB), Proactive Safety Behavior(PSB), and Pro-social Safety Behavior(PsSB). Among these, SCB is categorized as a basic safety behavior, while PSB and PsSB which emphasize voluntary, active, and cooperative actions, are classified as advanced safety behaviors. Our research findings underscore the substantial influence of the SCSMP in shaping WSB. WR serves as a critical intermediary, connecting external pressures with internal organizational practices. In particular, WCR is instrumental in the formation of advanced safety behavior. The theoretical contribution of this research is manifested in its enhancement of our comprehension regarding the determinants of WSB among SMMs. Furthermore, it addresses the literature gap in elucidating the effectiveness of supply chain safety management and the mechanisms behind the formation of WSB. The practical significance lies in elevating the overall safety standards throughout the supply chain and minimizing safety-related hazards among SMMs.

Balancing plant safety and efficiency through innovative engineering practices in oil and gas operations

In the oil and gas industry, balancing plant safety with operational efficiency is a critical challenge that requires innovative engineering practices. This paper explores how advanced engineering techniques can enhance both safety and efficiency in oil and gas operations. By integrating cutting-edge technologies and methodologies, companies can achieve a harmonious balance between protecting personnel, equipment, and the environment, while also optimizing productivity and resource utilization. Key innovative engineering practices discussed include the implementation of real-time monitoring systems and predictive analytics to enhance safety and operational efficiency. Real-time monitoring, powered by Internet of Things (IoT) sensors, allows for continuous tracking of plant conditions, detecting anomalies before they escalate into significant issues. Predictive analytics, utilizing machine learning algorithms, further aids in forecasting potential failures and scheduling maintenance proactively, thereby minimizing downtime and operational disruptions. The paper also examines the role of risk-based approaches and safety management systems in improving plant safety. Techniques such as quantitative risk assessment (QRA) and safety integrity level (SIL) analysis are pivotal in identifying potential hazards and designing mitigation strategies. These practices ensure that safety measures are proportionate to the actual risk, avoiding over-engineering while addressing critical safety concerns. Moreover, the integration of automation and digital twin technology is discussed as a means to enhance both safety and efficiency. Automation reduces human error and improves precision in operations, while digital twins provide a virtual replica of physical assets, enabling simulation and optimization of plant operations in a risk-free environment. Case studies illustrate successful implementations of these practices, highlighting their impact on reducing incidents and improving operational performance. The paper concludes by emphasizing the need for ongoing innovation and collaboration between engineers, safety experts, and operational managers to continuously advance safety standards and operational efficiency in oil and gas operations.

Experimental Case Study on Mixed Ignition According to the Law of Safety Management of Hazardous Materials

Experimental Case Study on Mixed Ignition According to the Law of Safety Management of Hazardous Materials

Validation of a Self- Administration of Medications Assessment Tool for Home Dwelling Older Adults During an Elective Admission for Rehabilitation

Abstract Background Safe medication management is challenging among older adults due to an increased prevalence of multiple chronic diseases often requiring complex multi-drug pharmacotherapy. Poly-pharmacy can increase the risk of non-adherence to medications in home dwelling older adults, reported as ranging from 6 to 55%. During hospital stays older adults may lose control over their medication management due to the shift in responsibilities from patient to healthcare professionals. Self-administration of medication (SAM) can increase patients’ understanding about their medication and promote independence and autonomy in the hospital setting. Methods From January -December 2023, all elective admissions were assessed for inclusion in SAM during their inpatient stay (n-74). An assessment tool, validated for use in the residential care setting was used. The tool, comprising of 17 items assesses the patients experience of self- medication, capability to self-medicate (cognition and function) and knowledge of their medications. Criteria for inclusion in SAM included older adults who self-administered their medication at home prior to admission and assessed as suitable using the assessment tool. Medication reconciliation was undertaken by a pharmacist for all patients prior to commencing SAM. Results 42% of admissions (n=31) were deemed suitable for SAM during their inpatient stay. Mean age 79 years (age range: 68-96 years). Number of medications > 5 medications daily. 90% (n-28) successfully completed SAM throughout their stay with 10% (n-3) reverting to nurse administration due to changes in medication during their inpatient stay Conclusion The use of the assessment tool supported a 90% success rate for SAM in this cohort. Further testing is recommended to evaluate the tool in other in-patient settings. An evaluation of SAM for the prospective of older adults and nursing team is also recommended.

Competencies for Human-Robot Collaboration in the Construction Industry – Academia’s Perspective

ABSTRACT This paper investigates the perceptions of Construction Engineering and Management instructors regarding the competencies for Human-Robot Collaboration (HRC) in construction to determine knowledge areas, skills, and abilities prioritized for facilitating effective collaboration between humans and robots in the industry. A two-round Delphi study was employed to evaluate the perceptions of construction instructors regarding HRC competencies. This study’s findings revealed that human-robot collaboration knowledge areas prioritized by the instructors include HRC ethics and regulation, robot anatomy and technical specifications, construction robot applications, sensors, and task planning. The instructors prioritized skills such as task planning, application of machine learning algorithms, safety management, human-robot interface proficiency, and effective communication. HRC abilities prioritized include decision-making, continuous learning, critical thinking, attention to detail, analytical aptitude, and adaptability. This research established the competencies prioritized by the instructors for implementing HRC in the construction industry and recommended future research directions.

Driving risk prediction of urban arterial and collector roads using multi-dimensional real-time data

With the objective of predicting driving risks on urban arterial and collector roads, this paper primarily collects and processes vehicle trajectory data and driver-vehicle-road-environment data. Using Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and K-means, risk scores and levels for the target roads are determined. An original dataset comprising 10,320 samples is established, with risk levels as labels and multi-dimensional data as features. SHapley Additive exPlanations (SHAP) analysis is utilized to extract important features. The performance of four ensemble models are compared, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Stacking. Three data resampling techniques are integrated to address data imbalance. The combination of LightGBM and Deep Convolutional Generative Adversarial Network (DCGAN) outperforms other approaches, showing potential for real-time risk prediction. Besides, DCGAN is employed to update the multi-dimensional features. The models' predictive performance is evaluated under various update ratios using F1-score and training time, with LightGBM demonstrating superior accuracy and efficiency compared to its counterparts (RF, XGBoost, and Stacking). This study develops a driving risk prediction system based on multi-dimensional features, offering a theoretical foundation and technical approach to enhance urban road traffic safety management.

PROBLEMS OF ANALYZING OCCUPATIONAL INJURIES IN THE LIGHT AND TEXTILE INDUSTRIES

Problem statement. One of the main functions of the occupational health and safety management system at enterprises is to analyze occupational injuries and diseases that occur to employees as a result of their work duties in the event of an inadequate state of occupational health and safety at the enterprise. The light and textile industry in Ukraine is characterized by uncertain working conditions, as it is mostly based on small enterprises. The lack of analysis of occupational injuries and their causes complicates the process of developing occupational safety and health measures for enterprises operating in the light and textile industry, respectively. The purpose of the article is to identify the reasons for the complication of the process of analyzing occupational injuries and the issues of analyzing the causes of injuries in the light and textile industries. Conclusions. The analysis of occupational injuries in the light and textile industry has revealed an upward trend in the number of accidents in this sector. Given that over the past four years, the State Statistics Service has not separated the light industry into a separate statistical data collection point, this makes it more difficult to analyze injuries in this area. It was also found that during 2020−2024, the State Labor Service registered fatal and group accidents that were not related to work in the light and textile industry, but were classified as such because the production itself is engaged in light industry. At the same time, the type of work performed by the victim is more likely to be classified as construction or cargo rather than tailoring. The analysis identified the main problems that prevent the identification of the exact causes of accidents in the light and textile industry.

The Story of a Failed Food Safety Management, Whistleblowing, and Governance

The Story of a Failed Food Safety Management, Whistleblowing, and Governance

Pest Control and Safety Management Monitoring in Outdoor Plantation Using Unmanned Aerial Vehicle Captured Images

The National Institute of Ecology in Seocheon, Chungcheongnam-do, exhibits and studies Korea’s diverse ecosystems to communicate the importance of biodiversity conservation to the general public. The site faces the challenge of preserving the natural environment while ensuring visitor convenience. This requires systematic data collection to quickly detect and respond to threats from pests and natural disasters. In this study, high-resolution orthoimages were acquired to monitor ecological changes using unmanned aerial vehicles. Images were captured through flights monthly from March to July 2024. Digital elevation models and orthoimages were generated to identify dead trees and assess areas of damage caused by heavy rainfall. The study covered an area of 998,655 m2, and the image resolution was 3.6 cm/pixel. The orthophotos were useful in identifying plant pest damage. They also helped evaluate damage caused by rain, showing that the total area of damage was 29,384 m2, mainly due to soil erosion. Furthermore, it is expected that the accumulation of such unmanned remote sensing image data can also be applied to the safety management of various natural conservation areas and public facilities.

Anlotinib plus oral fluoropyrimidine S-1 in refractory or relapsed small-cell lung cancer (SALTER TRIAL): a multicenter, single-arm, phase II trial

BackgroundPatients with small-cell lung cancer (SCLC) have few treatment options and dismal overall survival (OS) after failed platinum-based chemotherapy.MethodsThe eligibility criteria of this phase II clinical trial included patients with measurable disease, age of 18 to 75 years, a confirmed diagnosis of disease progression or recurrence after prior platinum-based chemotherapy with a pathologically proven diagnosis of SCLC. Patients were treated with anlotinib at a dosage of 12 mg once daily (QD) and S-1 at 60 mg twice daily (BID) for 2 weeks, followed by a 1-week treatment-free interval. After six cycles of the above treatment, patients continued the maintenance therapy using S-1 monotherapy at 60 mg/ BID for 2 weeks, followed by a 1-week treatment-free interval until disease progression.ResultsFrom March 2019 to June 2020, a total of 71 patients were initially assessed for eligibility in this study. Out of these, 52 patients who met the inclusion criteria were enrolled, and 48 patients received at least two doses of the study drug. The median follow-up time was 25.1 months. The ORR was seen in 21 patients (43.8%). The median PFS was 4.5 months (95% CI, 3.5–5.5 months), and the median OS was 5.9 months (95% CI, 4.6–7.3 months). The most common grade 3–4 treatment-related adverse events were thrombocytopenia (16.7%), anemia (14.6%), neutropenia (14.6%), and hypertension (10.4%). No treatment-related death occurred.ConclusionsThe combination of anlotinib with oral fluoropyrimidine S-1 demonstrated notable activity in relapsed or refractory SCLC, showing a favorable ORR and an acceptable, manageable safety profile.Trial registrationThis trial was registered with ClinicalTrial.gov (NCT03823118) on 3 January 2019.

THE STRATEGIC ROLE OF FPSO IN DEEPWATER EXPLORATION: INTEGRATING SOCIAL MANUFACTURING SYSTEMS WITHIN INDUSTRY 5.0

This study analyzes the role of Floating Production, Storage, and Offloading units (FPSO) in deepwater oil and gas exploration within the context of Industry 5.0, focusing on integrating social manufacturing systems. Reviewing common offshore exploration methods, the study identifies FPSOs as key catalysts for innovation, energy efficiency, and sustainability. The research concludes that combining FPSO with Industry 5.0 can optimize horizontal and vertical production processes, enhancing predictive maintenance, advanced safety, and waste management. Continuous innovation is highlighted as vital for addressing future challenges in deepwater exploration.

Association between marine corps safety management system assessment results and injury rates and outcomes

Occupational health and safety management systems (OHSMSs) have been proposed as a method of managing human health and safety risk and reduce injuries and illnesses. The investigation objective was to determine the association between OHSMS performance, as measured by scores from assessments and inspections, and mishap rates and mishap outcomes. Results for 55 OHSMS assessments/inspections across 15 organizations from the years 2017–2022 were analyzed. Results were paired with 545 mishaps and according to mishap classification, mishap category, and mishap severity. Correlation analysis was conducted to determine strength and significance of association between OHSMS performance and mishap rates. Improved OHSMS performance, as measured by scores from assessments and inspections, was associated with lower severity mishap occurrence such as reduced rates of fatalities, permanent total and partial disability, and property damage. Mishaps involving fatalities and permanent total disability had the strongest association with measured OHSMS performance. This research indicates that higher levels of OHSMS performance may help reduce severe worker injuries and illnesses and reduce mishap costs.

Construction Safety Management of High-Rise Buildings under the Basic Principles of Disaster Sociology

In order to reduce the risk of accidents in high-rise building construction, firstly, the potential disaster risk factors in the construction of high-rise buildings were systematically analyzed, including the distortion of accident information transmission, the lack of morality during disasters, the danger of high-altitude work, the complexity of the intersection of types of work, and the non-professionalism of construction management. Secondly, this paper focuses on the analysis of the current problems in the construction safety management of high-rise buildings, and considers the social risk factors, including the distortion of safety accident transmission, the lack of morality during disasters and the disaster safety awareness of construction personnel. Finally, from the perspective of disaster sociology, specific improvement measures for the safety management of high-rise building construction are proposed. The results show that social risk factors are equally important in construction safety management, which not only enriches the theoretical basis of construction safety management of high-rise buildings, but also provides a specific theoretical reference for safety management in practice.

Analysis on pulse rate variability for pilot workload assessment based on wearable sensor

AbstractThe workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real‐time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA‐TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA‐TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k‐nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing > takeoff > cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

Learning-based framework for industrial accident prevention: fuzzy cognitive mapping approach

PurposeDespite efforts to improve safety management practices in industrial companies, major accidents seem to be inevitable. Many accidents still occur because companies are unable to learn from past occurrences due to ineffective incident and accident learning processes. This study proposes a learning-based framework for industrial accidents investigation and contributes to accident prevention research.Design/methodology/approachThe proposed learning process includes the analysis of the industrial accident using the Event Tree Analysis (ETA) method, capitalisation of causative factors using the Swiss Cheese Model (SCM), and finally modelling the relationships among the accident causative factors and analysing their causality using the Fuzzy Cognitive Mapping (FCM) technique and running learning scenarios.FindingsThe proposed learning process was applied to an industrial accident, and the results showed that human unsafe behaviours and unsafe supervision were the principal causative factors of the blowout accident.Practical implicationsThe proposed learning-based framework provides a structured approach for oil and gas companies to systematically analyse and learn from past accidents, enhancing their prevention strategies. Theoretically, the framework bridges the gap between theory and practice by demonstrating how established accident analysis methods can be combined and applied in a real-world industrial context.Originality/valueThe proposed learning process combines accident analysis and investigation techniques with simulations for an in-depth and robust learning-based framework for accident prevention.