In order to effectively analyze and control use-related risk of medical devices, quantitative methodologies must be applied. Failure Mode and Effects Analysis (FMEA) is a proactive technique for error detection and risk reduction. In this article, an improved FMEA based on Fuzzy Mathematics and Grey Relational Theory is developed to better carry out userelated risk analysis for medical devices. As an example, the analysis process using this improved FMEA method for a certain medical device (C-arm X-ray machine) is described.

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Human behavior understanding is of great importance for a variety of applications, such as personalized recommendations, smart home, urban planning, and anti-terrorism. Although there has been significant progress on the understanding of human behaviors, we still face a number of theoretical and technical challenges that need be further explored. In this article, we first outline the basic research process of human behavior understanding based on existing studies, and illustrate important issues in each step. Afterwards we describe main research challenges from the aspects of human behavior itself, the behavior related data, as well as the modeling and evaluations, respectively. Then we identify and explore ten most important fundamental open problems in this field. The proposed problem list is expected to provoke innovative studies on human behavior understanding, e.g., theory improvement and data collaboration. In this article, we also discuss possible ways that would be helpful for resolving the challenging problems.

The environmental and economic pressures caused by energy consumption have led to an increased consciousness of energy-saving in the manufacturing industry. To this end, this paper focuses on an unrelated parallel machine problem with multiple auxiliary resources, which is a typical configuration in the lithography process of wafer fabrication. By comprehensively considering the jobs with different processing demands, setup times, ready times, resource constraints and energy consumptions, a scheduling model with the objective functions of minimizing the total energy consumption of the system and the total weighted completion time is developed. Based on the mathematical model, a novel modified multi-objective artificial immune algorithm integrated with the non-domination sorting strategy is proposed to solve the problem. Furthermore, to improve the performance of the proposed algorithm, clone operators, neighborhood search operators, and elite preservation operators are applied to the algorithm. The experimental results and analysis validate that the presented algorithm is efficient and effective.

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