姓名：马来好
职称：高级实验师
学位：博士
电子邮箱：malaihao@dlmu.edu.cn
工作经历：
2015年3月—2018年7月 bat365在线中国官网登录入口 助理实验师
2018年7月—2024年7月 bat365在线中国官网登录入口 实验师
2024年7月—至今 bat365在线中国官网登录入口 高级实验师
教学情况
辅机综合训练（1）、辅机拆装、液压设备拆装、船舶辅机实验等
研究方向
主要从事船舶机电一体化、海上运输风险、安全与可靠性等方面的研究工作
主持科研项目
[1] 国家自然科学基金青年基金项目：面向不确定性风险扰动的北极海上运输系统安全屏障设置方法研究，2025-2027.
[2] 中国博士后科学基金面上项目：危化品供应链风险共振传播特性与安全屏障靶向设置方法研究，2023-2025.
[3] 辽宁省博士科研启动项目：基于多源数据融合驱动的北极海上航行不确定性风险冲击韧性研究，2025-2027
[4] 交通运输部（交通运输行业重点科技项目清单）项目：水路运输安全生产重大风险防控体系效能评估技术研究，2020-2022.
[5] 辽宁省社会科学规划基金项目：基于功能共振视角的辽宁省危化品供应链安全韧性靶向提升研究，2022-2024
[6] 中国科协科技智库青年人才计划项目，2022.
代表性学术论文
近5年以第一/通讯作者发表JCR一区论文20余篇，主要代表性论文包括：
[1] Ma, L., Ma, X., Du, Q., & Zhang, R. (2025). Investigation of the severity of maritime accidents considering the interaction between human factors and operating conditions: A case study on collision accidents in China. Reliability Engineering & System Safety, 111533.
[2] Wang, T., Ma, X., & Ma, L*. (2026). A methodology to quantify risk-induced functional variability in Arctic maritime accident emergency response. Process Safety and Environmental Protection, 108615.
[3] Ma, L., Ma, X., Du, Q., & Chen, L. (2025). A novel framework for explaining the formation patterns of ship collisions based on regional disaster system theory. Journal of Marine Engineering & Technology, 1-20.
[4] Wang, T., Ma, X., Ma, L*., & Chen, L. (2025). A dynamic Bayesian network-based model for dynamic deduction of maritime emergency scenarios in the Arctic: A case of ship groundings. Ocean Engineering,339, 122090.
[5] Ma, L., Ma, X., Chen, L., Zhang, R., & Zhang, J. (2025). A methodology to quantify risk evolution in typhoon-induced maritime accidents based on directed-weighted CN and improved RM.Ocean Engineering,319, 120303.
[6] Ma, L., Ma, X., Zhang, R., & Du, Q. (2025). Investigation of distinct and joint contributions of human factors and operational conditions to different types of maritime accidents.Ocean & Coastal Management,267, 107750.
[7] Wang, T., Ma, L*., & Ma, X. (2025). Emergency scenario evolution of Arctic maritime accidents using data-driven dynamic Bayesian network.Transportation Research Part D: Transport and Environment,146, 104845.
[8] Ma, L., Ma, X., Wang, T., Chen, L., & Lan, H. (2024). On the development and measurement of human factors complex network for maritime accidents: A case of ship groundings.Ocean & Coastal Management,248, 106954.
[9] Ma, L., Ma, X., Wang, T., Zhao, Y., & Lan, H. (2024). A data-driven approach to determine the distinct contribution of human factors to different types of maritime accidents.Ocean Engineering,295, 116874.
[10] Ma, L., Ma, X., & Chen, L. (2024). A data-driven Bayesian network model for pattern recognition of maritime accidents: A case study of Liaoning Sea area. Process Safety and Environmental Protection, 189, 115-133.
[11] Ma, L., Chen, L., Ma, X., Wang, T., & Zhang, J. (2024). Incorporating human and organizational failures into the formation pattern for different Arctic maritime accidents using a data-driven Bayesian network. Ocean Engineering, 312, 119125.
[12] Wang, T., Ma, L*., Ma, X., & Zhao, Y. (2024). Risk evolution and prevention and control strategies in emergency responses for Arctic maritime transportation. Ocean Engineering, 313, 119580.
[13] Ma, L., Ma, X., & Chen, L. (2024). Risk evolution from causes to consequences of engine room fires on ships by mapping bow-tie into fuzzy Bayesian network. Journal of Marine Engineering & Technology, 23(6), 423-438.
[14] Wang, T., Ma, X., Ma, L*., & Zhao, Y. (2023). An emergency port decision-making method for maritime accidents in arctic waters. Journal of Marine Science and Engineering, 11(7), 1330.
[15] Ma, L., Zhang, H., Zheng, W., Shi, H., Wang, C., & Xie, Y. (2022). Investigation on the effect of debris position on the sensitivity of the inductive debris sensor. IEEE Sensors Journal, 23(5), 4438-4444.
[16] Ma, L., Ma, X., Lan, H., Liu, Y., & Deng, W. (2022). A data-driven method for modeling human factors in maritime accidents by integrating DEMATEL and FCM based on HFACS: A case of ship collisions. Ocean Engineering, 266, 112699.
[17] Ma, L., Ma, X., Lan, H., Liu, Y., & Deng, W. (2022). A methodology to assess the interrelationships between contributory factors to maritime transport accidents of dangerous goods in China. Ocean Engineering, 266, 112769.
[18] Zhang, H., Ma, L*., Shi, H., Xie, Y., & Wang, C. (2022). A method for estimating the composition and size of wear debris in lubricating oil based on the joint observation of inductance and resistance signals: Theoretical modeling and experimental verification. IEEE Transactions on Instrumentation and Measurement, 71, 1-9.
[19] Ma, L., Shi, H., Zhang, H., Li, G., Shen, Y., & Zeng, N. (2020). High-sensitivity distinguishing and detection method for wear debris in oil of marine machinery. Ocean Engineering, 215, 107452.
[20] Ma, L., Zhang, H., Qiao, W., Han, X., Zeng, L., & Shi, H. (2020). Oil metal debris detection sensor using ferrite core and flat channel for sensitivity improvement and high throughput. IEEE Sensors Journal, 20(13), 7303-7309.
