Health professions preventing and controlling Coronavirus Disease 2019 are prone to skin and mucous membrane injury, which may cause acute and chronic dermatitis, secondary infection and aggravation of underlying skin diseases. This is a consensus of Chinese experts on protective measures and advice on handcleaning-and medical-glove-related hand protection, mask-and goggles-related face protection, UV-related protection, eye protection, nasal and oral mucosa protection, outer ear, and hair protection. It is necessary to strictly follow standards of wearing protective equipment and specification of sterilizing and cleaning. Insufficient and excessive protection will have adverse effects on the skin and mucous membrane barrier. At the same time, using moisturizing products is highly recommended to achieve better protection. K E Y W O R D SCOVID-19, healthcare workers, protect, SARS-CoV-2, skin and mucous membrane barrier

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

Idea generation is significant in design, but coming up with creative ideas is often challenging. This paper presents a computer-based tool, called the Combinator, for assisting designers to produce creative ideas. The tool is developed based on an approach simulating aspects of human cognition in achieving combinational creativity. It can generate combinational prompts in text and image forms through combining unrelated ideas. A case study has been conducted to evaluate the Combinator. The study results indicate that the Combinator, in its current formulation, has assisted the tool users involved in the case study in improving the fluency of idea generation, as well as increasing the originality, usefulness, and flexibility of the ideas generated. The results also indicate that the tool could benefit its users in generating high-novelty and high-quality ideas effectively. The Combinator is considered to be beneficial in expanding the design space, increasing better idea occurrence, improving design space exploration, and enhancing the design success rate.

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