High-entropy alloys (HEAs) are attracting attention because of their excellent properties and functions; however, they are relatively expensive compared with commercial alloys. Therefore, various efforts have been made to reduce the cost of raw materials. In this study, MIM is attempted using coarse equiatomic CoCrFeMnNi HEA powders. The mixing ratio (powder:binder) for HEA feedstock preparation is explored using torque rheometer. The block-shaped green parts are fabricated through a metal injection molding process using feedstock. The thermal debinding conditions are explored by thermogravimetric analysis, and solvent and thermal debinding are performed. It is densified under various sintering conditions considering the melting point of the HEA. The final product, which contains a small amount of non-FCC phase, is manufactured at a sintering temperature of 1250oC.

본 연구는 국내 플라워숍에서 절화의 판매가격표시제도를 활용하는 것에 대한 소비자와 판매자의 인식 및 만족도를 알 아보고 국내 화훼시장에서의 효과적인 판매가격표시제도의 활용방안을 모색하고자 국내 소비자 237명, 판매자 32명을 대상으로 설문을 진행하였다. 소비자와 판매자 모두 절화의 가격이 절화 상품의 포장법과 함께 가장 높은 구매 결정 요인 으로 나타났다. 플라워숍의 절화 판매가격표시제도 활용 장점 에 대해 소비자가 판매자보다 높게 인식하고 있었으며, 소비 자와 판매자 모두 판매가격표시제도를 활용함으로써 소비자 의 합리적인 절화 소비 및 절화 가격 신뢰에 가장 큰 도움을 준다고 응답하였다. 소비자는 절화 판매가격표시제도에 대한 불편한 점이 모두 보통(3.00) 이하로 나온 것에 비해, 판매자 의 경우 매번 바뀌는 가격(4.25) 및 가격 표기 기준의 애매함 (3.78), 가격 표기의 번거로움(3.69)을 불편한 요인으로 인식 하였다. 효과적인 판매가격표시제도 방법에 대한 설문에서 소 비자는 한 송이, 한 단 보다는 3~4줄기의 한 묶음씩 판매를 선호하였고, 판매자는 판매 단위에 따른 선호 차이가 없었다. 가격 표시 방법에는 소비자와 판매자 모두 화병에 가격을 표 시하는 것을 가장 선호하였다. 전반적인 절화 판매가격표시제 도에 대하여 소비자들은 판매자들이 기대하는 이상으로 만족 도가 높았으며(P<0.001), 특히 절화 품질(3.75)보다도 절화 가격(3.97)에 대한 만족도가 더 높아지는 것으로 나타났다. 이 를 통해, 절화 판매가격표시제도가 활성화된다면 소비자들의 절화 가격에 대한 신뢰도와 만족도를 높여줄 것이며 국내 화 훼산업 활성화에 도움이 될 것으로 판단한다.

The aim of this study was to determine the therapeutic effects of beauty care on negative mental health, including stress and depression. Nail care, massage care, and makeup were used as programs for beauty therapy. Qualitative research was conducted with six female participants over five-month period. After interviewing the subjects in advance, beauty care treatments were performed every week 4 weeks in the following order: nail care, massage, and makeup. The results are as follows, First, the participants perceived beauty care in the form of 4 concepts: “courtesy in social life,” “investment in oneself,” “self-satisfaction,” and “self-care.” Second, the effects of beauty therapy were categorized as “psychological effects,” “confidence,” “behavioral changes,” “evaluation of others,” and “positive social effects.” Third, each subject showed different psychological effects during the process when the function of the therapy took effect through the beauty care treatment. It was confirmed that confidence levels increased as a result of treatment through the process of becoming re-aware of oneself. Positive statements from the participants included: “I want to go out,” “I have become kind and positive to others,” “I have become more active in a given task,” and “It seems that my work ability is improving.” Finally, t-test results for selfesteem, depression, and stress showed there were significant differences in self-esteem and depression. This confirmed that self- esteem increased, and depression decreased after the beauty care treatment.

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.