安全科学与韧性(英文)最新文献

The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change. This study employs cellular automata principles to analyze forest fire behavior, taking into account meteorological elements, combustible material types, and terrain slopes. The Wang Zhengfei model is utilized to compute fire spread speed, and a multifactor coupled forest fire model is developed. Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min, which is consistent with the experimental results. Using the forest fire in Anning city, Yunnan Province, as a case study with a mean burned area of 2281 ha, the burned area, rate of change in burned area, and burning area demonstrated an increasing trend, with fluctuating states in the rate of change of the burning area. Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed, vegetation type, and maximum slope reveals that under wind influence, the fire site adopts an elliptical shape with the downwind direction as the major axis. Quantitatively, when the wind speed increases from 2 m/s to 10 m/s, the burned area expands by a factor of 1.37. The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types, and the burned area increases by a factor of 1.92 when the maximum slope increases from 5° to 25°.

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice.

Continuous Renal Replacement Therapy (CRRT) serves as an intervention strategy for the management of acute kidney injury (AKI) in critically ill patients. However, owing to its complex nature and the potential for complications, the implementation of CRRT demands continuous monitoring to prevent patient safety risks. This study aims to identify and validate prevalent risks linked to CRRT within a real-world clinical setting, intending to propose preventive measures grounded in expert insights. To systematically categorize and visually depict the risks, their consequences, preventive measures, and recovery controls, our study employed the Bowtie method in conjunction with the Systems Engineering Initiative for Patient Safety (SEIPS) model. In addition to considering patient-related factors that exhibit variability among critically ill individuals, our key findings showed that the most influential risks impacting the effective delivery of CRRT are incidents of clotted filters, bleeding risks arising from the necessity of anticoagulation for filter efficacy, vascular catheter-related bloodstream infections, variations in proficiency levels among healthcare professionals regarding CRRT modalities, especially in operating the CRRT machines, high nursing workload, frequent nursing turnover, occurrences of hypophosphatemia, variability in CRRT prescribing patterns, and issues related to communication among stakeholders. This research sheds light on the primary risks associated with CRRT and provides practical and viable strategies for effective management. Furthermore, the Bowtie diagram developed as part of this study serves as a valuable tool for visually representing the healthcare system and facilitating the identification of system-related risks within healthcare settings.

Safety pictograms are essential tools for identifying workplace hazards by providing critical information about health hazard risks, fire safety, emergency evacuation, and accident prevention. Effective safety pictogram training programs are necessary to enhance workers' knowledge of these pictograms. This study evaluates the effectiveness of a safety pictogram training program on the comprehension and retention of knowledge among engineering students. A total of 262 participants were asked to predict the meaning of 22 safety pictograms regulated by International Organization for Standardization (ISO) 7010 before and after a one-hour online training session. A follow-up test was administered six months later to assess long-term knowledge retention. Results showed that the average comprehension rate increased from 60.1 % before training to 68.3 % after training, with a retention rate of 66.0 % six months after training. The study found that training positively affected comprehension of emergency and mandatory action pictograms, while lower scores were observed for warning pictograms. Statistical tests revealed a significant effect of training on comprehension levels 16 out of 22 pictograms, with an average increase in comprehension of 11.2 %. Of these 16 pictograms, the comprehension level of 10 pictograms increased after training and remained at the same level six months later. However, the scores decreased slightly six months after the intervention, indicating the need for continued reinforcement or retraining. These findings have important implications for safety education and training programs, particularly in industries where safety hazards are widespread. The positive impact of training on comprehension scores highlights the ongoing need to improve safety pictogram comprehension to consistently meet standard acceptance criteria. Future training programs may need to focus on categories such as warning pictograms and fire equipment and fire action pictograms, which exhibited lower comprehension scores, to ensure better employee understanding.

Excessive dependence on fossil fuels has precipitated various challenges, including Greenhouse Gas (GHG) emissions, health hazards, and the depletion of natural resources. Such perils underscore the importance of conducting requisite risk assessments for alternative fuel sources like Compressed Natural Gas (CNG) to ensure their safe utilization. This study undertakes a quantitative risk assessment encompassing diverse facets of the CNG sector holistically, aiming to pinpoint, analyze, and appraise risks, thus empowering policymakers to devise targeted mitigation strategies. To achieve this goal, the collected data undergoes analysis via an integrated approach combining the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Technique of Order Preference by Similarity to Ideal Solution (F-TOPSIS). The study's findings reveal a heightened risk of explosion within the CNG sector owing to its highly combustible nature. Additionally, it computes an overall risk index of 0.266 for the CNG sector in a developing nation like Pakistan, indicating a relatively lower risk level compared to other fuel sources. Policymakers are thus advised to undertake requisite measures concerning infrastructure, customer safety, and environmental and economic stability to accrue both immediate and long-term benefits. The application of hybrid techniques for the risk assessment of the CNG sector in the case of a developing country marks the novelty of this study and a study of the first of its kind.

Earthquakes are major catastrophes that cause great life and economic losses to human society and environment. This paper reviews and synthesizes relevant studies, drawing from a systematic examination of 4229 articles from the Web of Science core collection (1982–2023). Employing the CiteSpace visualization and analysis tool, current research and emerging trends in seismic risk assessment are discussed and analyzed. This paper provides a holistic overview of principal contributions, knowledge sources, interdisciplinary characteristics, and principal research topics in this field. Additionally, we propose key technologies that are in urgent need of enhancement, including data availability, quantity and quality of data, interpretability of machine learning models, performance improvement of machine learning methods and application of foundation models, as well as real-time risk assessment techniques. These insights support both theoretical understanding and practical applications of seismic risk assessment and damage analysis.

During emergency evacuation, it is crucial to accurately detect and classify different groups of evacuees based on their behaviours using computer vision. Traditional object detection models trained on standard image databases often fail to recognise individuals in specific groups such as the elderly, disabled individuals and pregnant women, who require additional assistance during emergencies. To address this limitation, this study proposes a novel image dataset called the Human Behaviour Detection Dataset (HBDset), specifically collected and annotated for public safety and emergency response purposes. This dataset contains eight types of human behaviour categories, i.e. the normal adult, child, holding a crutch, holding a baby, using a wheelchair, pregnant woman, lugging luggage and using a mobile phone. The dataset comprises more than 1,500 images collected from various public scenarios, with more than 2,900 bounding box annotations. The images were carefully selected, cleaned and subsequently manually annotated using the LabelImg tool. To demonstrate the effectiveness of the dataset, classical object detection algorithms were trained and tested based on the HBDset, and the average detection accuracy exceeds 90 %, highlighting the robustness and universality of the dataset. The developed open HBDset has the potential to enhance public safety, provide early disaster warnings and prioritise the needs of vulnerable individuals during emergency evacuation.

The global economic crisis of 2008–2013 led to the emergence of the concept of resilience, which focuses on the ability of socio-economic system store cover socially, economically, and environmentally after external impacts. The COVID-19 pandemic spurred scholarly interest in regional resilience as a new conceptual framework for the sustainability theory. This paper aims to examine the influence of the pandemic on the trends and geography of regional resilience studies. We analyzed data derived from Science Direct and used VOSviewer to perform clustering and bibliometric network analysis. The countries that suffered the most from the pandemic and showed the largest regional socioeconomic disparities have become new centers of knowledge on regional resilience. Moreover, the pandemic has led to a visible shift in the research focus. Thus, after 2020, more attention has been paid to the structural and topological characteristics of regions that enable them to reorganize their resources more effectively in times of crisis. This study investigates the potential of the resilient development concept as a framework for gaining insights into the factors supporting regional adaptability.