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Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

This study aims to test a conceptual model using public attitudes toward biomedicine and traditional Chinese medicine (TCM) to predict respondents’ medical treatment choice.

A quantitative online survey was conducted using quota sampling. Altogether 1,321 questionnaires from Hong Kong residents of age 15 years or above were collected.

Attitudes toward biomedicine in relation to TCM and perceived cost of TCM consultation were found to be significant variables in predicting respondents’ medical treatment choice of treatment. Perceived efficacy of TCM, however, was not a significant predictor. Older respondents, as well as respondents with higher education, were less likely to consult biomedicine first when ill. They were also less likely to consult biomedicine exclusively.

This study uses a convenience sample recruited through personal networks. The findings cannot be generalized to the rest of the population.

Respondents in the study generally perceived TCM’s efficacy to be high, but not high enough to make it the medical treatment of choice. To promote TCM in Hong Kong, there is a need to enhance trust in it. This can be achieved through strengthening scientific research and development of TCM, enhancing professional standards of TCM practitioners and educating the public about the qualifications of TCM practitioners. Strategic channel planning to reach potential target and reducing the time cost of TCM medication should be examined.

The study is the first to relate attitudes to and perceptions of TCM with medical treatment choices in Hong Kong.

The proposed DHHFLOWLAD is used to design a recommendation system, which aims to provide the most appropriate treatment to the patient under a double hierarchy hesitant fuzzy linguistic environment.

Based on the ordered weighted distance measure and logarithmic aggregation, we first propose a double hierarchy hesitant fuzzy linguistic ordered weighted logarithmic averaging distance (DHHFLOWLAD) measure in this paper.

A case study is presented to illustrate the practicability and efficiency of the proposed approach. The results show that the recommendation system can prioritize TCM treatment plans effectively. Moreover, it can cope with pattern recognition problems efficiently under uncertain information environments.

An expert system is proposed to combat COVID-19 that is an emerging infectious disease causing disruptions globally. Traditional Chinese medicine (TCM) has been proved to relieve symptoms, improve the cure rate, and reduce the death rate in clinical cases of COVID-19.

Traditional Chinese Medicine (TCM) is a complete medical healthcare system that encompasses acupuncture, acupressure, moxibustion, herbal medicine, diet, tui na massage, and exercises (tai chi and qigong) among other traditional therapies. It uses herbs and natural resources to produce the traditional medicines and focuses on maintaining the balance between body and mind. As such, many aspects of TCM can be considered as green and sustainable. While there is market demand for TCM in some countries and among some communities, some others are still not aware of TCM. Moreover, there are fewer discussions in the academic platforms on TCM. This case highlights the scenario of TCM based on Malaysia’s perspective and discusses its challenges and prospects.

Traditional Chinese medicine (TCM) had a long history and has been widely practiced worldwide. TCM includes acupunctures, herbal medicine and chiropractic. However, limited studies examined the relationship between knowledge, attitude, utilisation and satisfaction of TCM among the Hong Kong general public. This study has developed a research model which aims to examine the relationship between knowledge, attitude, utilisation and satisfaction of TCM in Hong Kong by using partial least square structural equation model.

An online-based questionnaire was distributed by using convenience sampling. The questionnaire consisted of five parts to collect the data regarding the knowledge, attitude, utilisation and satisfaction of TCM of respondents. The reflective measurement model and structural model were examined with SmartPLS 3.0 statistical software.

A total of 131 respondents completed the survey, and all data were valid after data screening and cleaning. Around 60% of the participants received TCM information from their friends and family members, and 42% from the internet. Likewise, there is positive relationship from the knowledge of TCM to the utilisation, from the attitude to the utilisation and from the utilisation of TCM to the satisfaction. However, the positive relationship of knowledge regarding TCM and attitude is not proven. A t-test and one-way analysis of variance showed no significant differences between gender and age groups on each measurement items.

This paper provides insights for researchers and policymakers to understand the significance of attitude and perception of the benefits of treatments in the use of TCM. The positive experience of TCM from other people is essential for enhancing the willingness to use TCM while education is also fundamental in promoting TCM to the public.

In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms underlying the occurrence, progression, alterations and outcomes of diseases. However, there is a dearth of research in the field of intelligent diagnosis concerning the analysis of MD.

In this paper, we propose a supervised Latent Dirichlet Allocation (LDA) topic model, termed MD-LDA, which elucidates the process of MDs identification. We leverage the label information inherent in the data as prior knowledge and incorporate it into the model’s training. Additionally, we devise two parallel parameter estimation algorithms for efficient training. Furthermore, we introduce a benchmark MD identification dataset, named TMD, for training MD-LDA. Finally, we validate the performance of MD-LDA through comprehensive experiments.

The results show that MD-LDA is effective and efficient. Moreover, MD-LDA outperforms the state-of-the-art topic models on perplexity, Kullback–Leibler (KL) and classification performance.

The proposed MD-LDA can be applied for the MD discovery and analysis of TCM clinical diagnosis, so as to improve the interpretability and reliability of intelligent diagnosis and treatment.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

China was the only developing country that participated in the human genome project and contributed 1 per cent of human genome sequencing in 2000. And it finished rice genome sequencing independently in 2002. China’s biomedical industry, however, remains largely an academic affair. The industry is characterized by its inability to support and commercialize innovative research, which in turn has resulted in the prevalence of generic drugs. Managers of Chinese firms have been focusing on the shortterm profits that can be generated by generics rather than the longer‐term potential profits arising from innovative research. But the viability of such short‐cut strategy is now called into question as the IPR infringements will mean hefty fines to the violators in the wake of China’s WTO accession. There is hence an urgent need to make the timely transformation from academic affair to commercialization. This paper examines the reasons why biomedical industry remains largely an academic affair in China by stacking China against the key success factors of biomedical industry in the world. It then suggests the ways to make the transformation by filling the gap between basic research and commercial products and cultivating the necessary business environment for biomedical drugs in China.

This chapter aims to make a contribution to recent debates on the ‘governance of security’ (Johnston & Shearing, 2003) by drawing upon empirical research conducted by the author and other writers on ‘plural policing’ and the construction of closed circuit television (CCTV) surveillance networks. The chapter attempts to avoid the tendency in some of the ‘governmentality’ literature to ‘airbrush out the state’ (Hughes, 2007, p. 184), whilst at the same time showing that the aims and intentions of dominant state forces and elites are not always realised in practice. The chapter also tries to avoid any simplistic notion of a shift in policing strategies from ‘crime fighting’ to ‘risk management’. The aim instead is to show how the construction of surveillance networks is blurring the boundaries of the ‘public–private’ divide along the ‘sectoral’, ‘geographical’, ‘spatial’, ‘legal’ and ‘functional’ dimensions (Jones & Newburn, 1998), giving rise to a plural policing continuum.