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This research aims to collect human body variables via 2D images captured by digital cameras. Based on those human variables, the forecast and recommendation of the Digital Camouflage Uniforms (DCU) for Taiwan's military personnel are made.

A total of 375 subjects are recruited (male: 253; female: 122). In this study, OpenPose converts the photographed 2D images into four body variables, which are compared with those of a tape measure and 3D scanning simultaneously. Then, the recommendation model of the DCU is built by the decision tree. Meanwhile, the Euclidean distance of each size of the DCU in the manufacturing specification is calculated as the best three recommendations.

The recommended size established by the decision tree is only 0.62 and 0.63. However, for the recommendation result of the best three options, the DCU Fitting Score can be as high as 0.8 or more. The results of OpenPose and 3D scanning have the highest correlation coefficient even though the method of measuring body size is different. This result confirms that OpenPose has significant measurement validity. That is, inexpensive equipment can be used to obtain reasonable results.

In general, the method proposed in this study is suitable for applications in e-commerce and the apparel industry in a long-distance, non-contact and non-pre-labeled manner when the world is facing Covid-19. In particular, it can reduce the measurement troubles of ordinary users when purchasing clothing online.

Combining the collected human body variables by a 3D body scanner and the research results of medical computed tomography (CT) imaging, this research aims to develop a military bulletproof vest that is both protective and fit. In particular, the protective part must be able to cover the vital human internal organs completely. The results of this research help to make military bulletproof vests of different sizes for Taiwanese male and female soldiers. At the same time, the research results can provide a reference for the industry of making special-purpose clothing.

17 important human body variables of 988 participants (male: 716, 72.5%; female 272, 27.5%) are used for the analysis. The K-means algorithm firstly builds clusters of different body shapes for both sexes; the silhouette coefficient helps to determine the optimal number of clusters to be six. Thus, the standard size of the bulletproof vest for soldiers is determined. The specifications of the bulletproof vest's inner core and textile vest are calculated for each cluster user. Our research then makes twelve prototypes of the bulletproof vest. After that, 12 subjects are invited to try on the new version (the vest designed in this study) and the old version (the vest currently used) to contrast the differences between the two.

According to the index of the silhouette coefficient, the optimal number of clusters is determined to be six for both male and female clusters. Therefore, this study has designed six sizes of the bulletproof vest for male and female soldiers in Taiwan. After trying the new and old vests on, the subjects all indicate that the new vest fits better than the old one. In addition, the coverage of the bulletproof vest designed in this study is 94.38% for male users and 92.75% for female users.

The design of bulletproof vests must take note of the fit of the clothing itself and its protective function. Apart from the size design of general clothing only focusing on the human shape exteriorly, the bulletproof vest also needs to pay attention to the relative positions of vital organs inside the human body. Besides, for practical applications, it is quite effective to use the silhouette coefficient to determine the results of cluster analysis. Thus, the value of this research lies in the cross-field combination, enabling the integration of body measurement, data science and clothing design. Generally, bulletproof vests of newly designed sizes can meet the requirements of Taiwan's military. The research results can be used in the development of various military clothing for Taiwanese military personnel. At the same time, the results can be provided to the clothing industry as relevant parameters for designing unique functional clothing.

The purpose of this paper is to examine the relationship among job training satisfaction (JTS), job satisfaction (JS) and turnover intention (TI) and moreover, the role of JS in the relationship between the JTS and TI.

A survey was used in this study for a sample size of 115. The principal component analysis was carried out to examine the factors in JTS and JS. The regression and mediation analyses were applied to assess the relationship among the JTS, JS and TI.

A significantly negative relationship between JTS and TI was found and mediated by JS. The paper also suggests that JS is positively related to JTS but is negatively related to TI, which is in accordance with what have been reported in the earlier studies.

This study reinforced the significance of job training in human resource management practices. The employees’ satisfaction with job training that would lead to higher JS and lower TIs was emphasized. Therefore, it becomes important for HRD professionals to consider both the delivery of the training to the employees and the employees’ satisfaction with the training approach when designing the training program.

While many different variables have been studied in the relationship with TI, the JTS is rarely discussed. This paper attempts to examine the relationship among JTS, JS and TI with addition of new insights.