Purpose This paper reports on a critical literature review, which aimed to identify, understand and qualify barriers that hinder the release of open government data (OGD) in China. Moreover, the purpose of this paper is to develop and propose a theoretical framework, which can be adopted as a basis for empirical investigation in the future, and to articulate mitigating strategies. Design/methodology/approach This study adopted an inductive qualitative approach, retrieving 42 academic articles from three main Chinese academic databases: CNKI, Wanfang and CQVIP. A thematic analysis approach was employed for the literature analysis. Findings The literature analysis pointed to 15 barriers to the release of OGD in China. Furthermore, the barriers emerged in the following three main themes: institutional barriers, data integrity and quality barriers, and user participation barriers. Originality/value This paper reports on one of the early research efforts into the problems of releasing OGD in China. Although this study focusses on Chinese context and issues, the findings and lessons learnt can be shared across international borders.
PurposeTo analyze the current situation of the Hong Kong textile and clothing industries; to investigate the influential factors that can assist Hong Kong to develop as Asia's fashion hub; and to plan for the future development of Hong Kong as a regional centre for fashion.Design/methodology/approachThis paper provides an overview of the Hong Kong textile and clothing industries by using the SWOT analysis. Based on the findings of the analysis, some recommendations for future development are provided. The sources of this study are from the government publications, journal articles, book reviews and advice of practitioners.FindingsThe Hong Kong textile and clothing industries have their strengths and weaknesses. To be a regional centre for fashion, developing an original brand manufacturing (OBM) business is a preferred approach.Research limitation/implicationThis paper provides a general review of the Hong Kong textile and clothing industries. To gain a more in‐depth understanding of the industries and to explore some more solutions for the research problems, it is important to further investigate these issues by attempting in‐depth interviews with the Hong Kong textile and clothing industrialists.Practical implicationsThis paper provides a very useful source of information and impartial advice for the Government, industry players, academic institutions and the media. The formation of Asia's fashion hub is dependent upon the cooperation and effort of these four parties.Originality/valueThis paper analyses some of the factors contributing to Hong Kong's success and draws conclusions on strategic directions for the sustainability.
BackgroundAccording to traditional Chinese medicine theory, a Qi-deficiency constitution is characterized by a lower voice frequency, shortness of breath, reluctance to speak, an introverted personality, emotional instability, and timidity. People with Qi-deficiency constitution are prone to repeated colds and have a higher probability of chronic diseases and depression. However, a person with a Balanced constitution is relatively healthy in all physical and psychological aspects. At present, the determination of whether one has a Qi-deficiency constitution or a Balanced constitution are mostly based on a scale, which is easily affected by subjective factors. As an objective method of diagnosis, the human voice is worthy of research. Therefore, the purpose of this study is to improve the objectivity of determining Qi-deficiency constitution and Balanced constitution through one’s voice and to explore the feasibility of deep learning in TCM constitution recognition.MethodsThe voices of 48 subjects were collected, and the constitution classification results were obtained from the classification and determination of TCM constitutions. Then, the constitution was classified according to the ResNet residual neural network model.ResultsA total of 720 voice data points were collected from 48 subjects. The classification accuracy rate of the Qi-deficiency constitution and Balanced constitution was 81.5% according to ResNet. The loss values of the model training and test sets gradually decreased to 0, while the ACC values of the training and test sets tended to increase, and the ACC values of the training set approached 1. The ROC curve shows an AUC value of 0.85.ConclusionThe Qi-deficiency constitution and Balanced constitution determination method based on the ResNet residual neural network model proposed in this study can improve the efficiency of constitution recognition and provide decision support for clinical practice.
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