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| 文献清单：2024年高引文章 | MDPI Safety |
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期刊名：Safety
期刊主页：https://www.mdpi.com/journal/safety
本期文献清单我们为您精选2024年发表于Safety期刊的10篇高引文章。希望能为相关领域学者提供新的思路和参考，欢迎各位学者阅读转发。
1. Navigating the Power of Artificial Intelligence in Risk Management: A Comparative Analysis
AI赋能风险管理：一场深度对比解析
https://www.mdpi.com/2313-576X/10/2/42
Yazdi, M.; Zarei, E.; Adumene, S.; Beheshti, A. Navigating the Power of Artificial Intelligence in Risk Management: A Comparative Analysis.Safety2024, 10, 42.https://doi.org/10.3390/safety10020042
2. Perceived Factors Affecting the Implementation of Occupational Health and Safety Management Systems in the South African Construction Industry
南非建筑业职业健康与安全体系落地难：哪些因素在作祟？
https://www.mdpi.com/2313-576X/10/1/5
Kunodzia, R.; Bikitsha, L.S.; Haldenwang, R. Perceived Factors Affecting the Implementation of Occupational Health and Safety Management Systems in the South African Construction Industry.Safety2024, 10, 5.https://doi.org/10.3390/safety10010005
3. Robotics, Artificial Intelligence, and Drones in Solar Photovoltaic Energy Applications—Safe Autonomy Perspective
机器人、AI与无人机在太阳能光伏中的应用：安全自主视角
https://www.mdpi.com/2313-576X/10/1/32
Olayiwola, O.; Elsden, M.; Dhimish, M. Robotics, Artificial Intelligence, and Drones in Solar Photovoltaic Energy Applications—Safe Autonomy Perspective.Safety2024, 10, 32.https://doi.org/10.3390/safety10010032
4. Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories
基于深度学习的制造教学实验室个人防护装备正确使用与充足性检测
https://www.mdpi.com/2313-576X/10/1/26
Ludwika, A.S.; Rifai, A.P. Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories.Safety2024, 10, 26.https://doi.org/10.3390/safety10010026
5. Nonlinear Analysis of the Effects of Socioeconomic, Demographic, and Technological Factors on the Number of Fatal Traffic Accidents
社会经济、人口与技术因素对致命交通事故数量的非线性影响分析
https://www.mdpi.com/2313-576X/10/1/11
Sohaee, N.; Bohluli, S. Nonlinear Analysis of the Effects of Socioeconomic, Demographic, and Technological Factors on the Number of Fatal Traffic Accidents.Safety2024, 10, 11.https://doi.org/10.3390/safety10010011
6. Digital and Virtual Technologies for Work-Related Biomechanical Risk Assessment: A Scoping Review
数字与虚拟技术在工作相关生物力学风险评估中的应用：一项范围综述
https://www.mdpi.com/2313-576X/10/3/79
Anacleto Filho, P.C.; Colim, A.; Jesus, C.; Lopes, S.I.; Carneiro, P. Digital and Virtual Technologies for Work-Related Biomechanical Risk Assessment: A Scoping Review.Safety2024, 10, 79.https://doi.org/10.3390/safety10030079
7. Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models
基于HFACS与贝叶斯网络模型分析人为失误对卡车事故严重程度的影响
https://www.mdpi.com/2313-576X/10/1/8
Waskito, D.H.; Bowo, L.P.; Kurnia, S.H.M.; Kurniawan, I.; Nugroho, S.; Irawati, N.; Mutharuddin; Mardiana, T.S.; Subaryata. Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models.Safety2024, 10, 8.https://doi.org/10.3390/safety10010008
8. Exploring Students’ and Teachers’ Insights on School-Based Disaster Risk Reduction and Safety: A Case Study of Western Morava Basin, Serbia
探索师生对学校灾害风险降低与安全的见解：以塞尔维亚西莫拉瓦盆地为例
https://www.mdpi.com/2313-576X/10/2/50
Cvetkovi?, V.M.; Nikoli?, N.; Luki?, T. Exploring Students’ and Teachers’ Insights on School-Based Disaster Risk Reduction and Safety: A Case Study of Western Morava Basin, Serbia.Safety2024, 10, 50.https://doi.org/10.3390/safety10020050
9. Risk Analysis of Underground Tunnel Construction with Tunnel Boring Machine by Using Fault Tree Analysis and Fuzzy Analytic Hierarchy Process
基于故障树分析与模糊层次分析法的TBM地下隧道施工风险分析
https://www.mdpi.com/2313-576X/10/3/68
Koohathongsumrit, N.; Chankham, W. Risk Analysis of Underground Tunnel Construction with Tunnel Boring Machine by Using Fault Tree Analysis and Fuzzy Analytic Hierarchy Process.Safety2024, 10, 68.https://doi.org/10.3390/safety10030068
10. Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method
基于集成机器学习与SHAP方法的实时交通及环境因素对连环撞车严重程度的影响分析
https://www.mdpi.com/2313-576X/10/1/22
Samerei, S.A.; Aghabayk, K.; Montella, A. Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method.Safety2024, 10, 22.https://doi.org/10.3390/safety10010022
Safety期刊介绍
主编：Raphael Grzebieta, University of New South Wales, Sydney, Australia
Safety（ISSN: 2313-576X）是一个经过严格同行评审的国际性开放获取期刊，主要关注工业和人类健康安全领域，包括但不限于以下话题：提升公共与工业安全及人类健康的先进科学与技术，风险评估，职业健康与安全，环境健康与安全，空中、海上及陆地交通工具的安全移动，安保、暴力事件与应急响应方面的伤害预防，以及基于证据的决策制定与安全哲学等多个研究方向.
2025 Impact Factor: 2.4
2025 CiteScore: 3.9
Time to First Decision: 34.4 Days
Acceptance to Publication: 6.6 Days
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