Mental care has become crucial with the rapid growth of economy and technology. However, recent movements, such as green technologies, place more emphasis on environmental issues than on mental care. Therefore, this study presents an emerging technology called orange computing for mental care applications. Orange computing refers to health, happiness, and physiopsychological care computing, which focuses on designing algorithms and systems for enhancing body and mind balance. The representative color of orange computing originates from a harmonic fusion of passion, love, happiness, and warmth. A case study on a human-machine interactive and assistive system for emotion care was conducted in this study to demonstrate the concept of orange computing. The system can detect emotional states of users by analyzing their facial expressions, emotional speech, and laughter in a ubiquitous environment. In addition, the system can provide corresponding feedback to users according to the results. Experimental results show that the system can achieve an accurate audiovisual recognition rate of 81.8% on average, thereby demonstrating the feasibility of the system. Compared with traditional questionnaire-based approaches, the proposed system can offer real-time analysis of emotional status more efficiently.

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Abstract. Biological water quality monitoring is an emerging technology. The change of water quality can be quickly tested by using the sensitiveness of aquatic organisms to water environmental change. However, how to extract semantics of fish behavior from the video data is the key technical point of achieving water quality testing. On the base of quantifying fish behavioral characteristics, the essay puts forward the semantic descriptive model of fish behavior. By grouping the parameter of the amount and average height of multitarget fish movement and extracting the semantics of each group, the semantic descriptive network of fish behavior characteristics and water quality finally was set up. The experimental data show that the semantic descriptive network can better characterize the water quality in high temperatures. This provides the theoretical base for the applications of biological water quality tests by the behaviors of fish groups.

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