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Assessment of Fire Safety Management for Special Needs Schools in South Africa

The safety and well-being of learners with special educational needs in South Africa remain a paramount concern, significantly impacting their constitutional rights and dignity. Despite legislative commitments aimed at fostering inclusive education, a pervasive absence of adequate fire safety measures in special needs schools (SNSs) in South Africa has persisted, leading to the vulnerability of these learners. Tragic incidents, such as fatal fires in these schools, as reported in the literature, underscore the urgent need for immediate intervention to ensure the safety and security of these learners, especially with regards to fire hazards. This study, conducted within the Northwest Province of South Africa, assessed the state of fire safety management in SNSs by applying a series of chi-squared (χ2) tests of independence for categorical variables, descriptive statistics, and regression analysis using the Statistical Package for Social Scientists (SPSS), Version 20 and found that limited access to power is the potential root cause of fires in these schools; also, the limited amount of fire safety initiatives was a problem. In addition, the ordinal regression showed a statistically significant relationship when the question of to what extent the learners in the sampled schools are involved in fire safety programs was regressed with the questions of whether management and stakeholders were involved in fire safety programs and also on taking part in the physical fire safety programs (χ2 = 47.412; df = 2; p < 0.001; R2 = 70.5%). Furthermore, fire safety management was not sufficiently implemented in the sampled schools and the safety legislations of the country were not implemented accordingly when it came to fire safety. This study identified root causes of fire risks, gauged stakeholders’ awareness and involvement in fire safety management, and advocated for more stringent safety policies and practices within the SNS based on the above findings.

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Navigating the Power of Artificial Intelligence in Risk Management: A Comparative Analysis

This study presents a responsive analysis of the role of artificial intelligence (AI) in risk management, contrasting traditional approaches with those augmented by AI and highlighting the challenges and opportunities that emerge. AI, intense learning methodologies such as convolutional neural networks (CNNs), have been identified as pivotal in extracting meaningful insights from image data, a form of analysis that holds significant potential in identifying and managing risks across various industries. The research methodology involves a strategic selection and processing of images for analysis and introduces three case studies that serve as benchmarks for evaluation. These case studies showcase the application of AI, in place of image processing capabilities, to identify hazards, evaluate risks, and suggest control measures. The comparative evaluation focuses on the accuracy, relevance, and practicality of the AI-generated findings alongside the system’s response time and comprehensive understanding of the context. Results reveal that AI can significantly enhance risk assessment processes, offering rapid and detailed insights. However, the study also recognises the intrinsic limitations of AI in contextual interpretation, advocating for a synergy between technological and domain-specific expertise. The conclusion underscores the transformative potential of AI in risk management, supporting continued research to further integrate AI effectively into risk assessment frameworks.

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A Risk Assessment Framework Based on Fuzzy Logic for Automotive Systems

Recent advancements in the automotive field have significantly increased the level of complexity and connectivity of modern vehicles. In this context, the topic of cybersecurity becomes extremely relevant, as a successful attack can have an impact in terms of safety on the car navigation, potentially leading to harmful behavior. Risk assessment is typically performed using discrete input and output scales, which can often lead to an identical output in terms of risk evaluation despite the inputs presenting non-negligible differences. This work presents a novel fuzzy-logic-based methodology to assess cybersecurity risks which takes attack feasibility and safety impact as input factors. This technique allows us explicitly model the uncertainty and ambiguousness of input data, which is typical of the risk assessment process, providing an output on a more detailed scale. The fuzzy inference engine is based on a set of control rules expressed in natural language, which is crucial to maintaining the interpretability and traceability of the risk calculation. The proposed framework was applied to a case study extracted from ISO/SAE 21434. The obtained results are in line with the traditional methodology, with the added benefit of also providing the scatter around the calculated value, indicating the risk trend. The proposed method is general and can be applied in the industry independently from the specific case study.

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Altered Haematological Parameters in Gasoline Station Workers Due to Benzene Exposure

Benzene is harmful to human health and early detection of haematological alterations is important in preventing adverse health effects. This study aimed to investigate the biomarkers of benzene exposure and its effects due to haematological alterations. Gasoline station workers with potential risks according to the biomatrix concerning benzene exposure underwent blood and urine evaluation for the biological monitoring of urinary trans, trans-muconic acid (tt-MA), and haematological and biochemical parameter evaluation. The results were analysed for correlations between biological and haematological effects. The tt-MA biomarker was detected in some workers and approximately 50% of workers had a blood profile that showed abnormal parameters with respect to the haemoglobin (Hb), haematocrit (Hct) and white blood cell parameters, which were outside the normal range. A significant correlation was observed between the tt-MA biomarker’s level and the levels of the haematological and biochemical parameters, which were Hb, Hct, eosinophil, neutrophil, SGOT and blood creatinine. The level of urinary tt-MA as a marker of benzene exposure correlated with haematological and biochemical changes in the blood, suggesting that the gasoline station workers were affected by benzene exposure. Moreover, the current study suggests that early detection of haematological abnormalities may be possible by analysing biomarkers of their effects through regular health surveillance of workers.

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Tailored Incident Investigation Protocols: A Critically Needed Practice

Construction scholars and practitioners have identified a repetitive pattern of direct causes leading to both fatal and non-fatal injuries among construction workers. Over the years, direct causes such as falls, electrocutions, and being struck have consistently represented a substantial proportion of recorded and reported injuries in the United States. One potential factor contributing to this repetition is the absence of root cause investigations for incidents. Incident investigations should focus on system deficiencies and shortcomings instead of individual behaviors. While the identification of incident root causes provides the needed information to eliminate the direct causes, it is inherently complex. Recently, the use of tailored incident investigation protocols as a practical and systematically conducted method was suggested to uncover the root causes of incidents, subsequently assisting in reducing their recurrence. To illustrate the feasibility of such an approach, this article provides a step-by-step guide to creating a tailored investigation protocol for revealing the root causes of arc flash incidents by utilizing a panel of safety experts. In addition, this study demonstrates the feasibility of developing tailored investigation protocols for other common causes, such as falls and electrocutions. Tailored investigation protocols streamline the identification of potential root causes to a manageable number, relying on subject matter experts. Consequently, they enhance learning from incidents by mitigating investigators’ biases and potential lack of experience. Safety practitioners can use the method presented in this article to create tailored investigation protocols based on their working environment to improve learning for occupational injuries.

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