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This study seeks to examine the motivational and socio‐demographic characteristics of meetings, incentives, conventions and exhibitions (MICE) visitors to Taiwan in order to identify salient market subgroups or segments. The aim is to establish results with relevance to Asian destinations and with some more general applicability.

Based on the literature and expert input a questionnaire was designed and pretested. Using convenience sampling, data were collected from MICE visitors to Taipei (Taiwan). Principal components, hierarchical cluster, K‐means, chi‐square and ANOVA analyses of the data provide information about MICE visitors and their segments.

Three motivation‐based MICE segments are identified. Variable values suggest the following segment names – value seekers, no‐value seekers, and education seekers. Significant socio‐demographic differences are found between the segments. Results provide insight into MICE visitor decision making showing the role of business, education and leisure‐related motives. Discussion focuses attention on implications of the results for the development of tourism strategies.

The findings enhance understanding of the motivations of MICE visitors. The information adds to the knowledge that destination marketers can consider in developing a competitive edge. Since Taipei's MICE visitors should be similar to those of Asian competitors, results contribute to a better understanding of MICE business, education and leisure‐related motives and activities in Asia.

The wine business is a fast growing industry in Taiwan particularly since the government deregulated the monopoly system, which in turn has induced fierce competition. Affective theory suggests that consumer emotion affects mood and purchasing behavior. The purpose of this paper is to propose that wineries may attract diverse customers by using pleasant atmospheric cues.

In total, 346 samples from four famous wineries of two different types were collected and customers' mood and emotional factors relating to the wineries were explored, and the relationship between the factors of winery atmosphere, customer satisfaction, and purchasing behavior were examined.

Test results confirm that a pleasant atmosphere attracts and strengthens the customer's affective commitment, which in turn strengthens the customer's repetitive purchasing intention.

Samples from four wineries may be too limited to allow generalisability of research findings, although these wineries attracted the majority of winery visitors.

Wineries should target and attract broader customer segments to include general tourists by creating the winery as a place of interest for leisure activities. The inclusion of customer‐centric factors in marketing campaigns in response to customer demands is needed in spanning market boundaries.

This paper expands the conventional wisdom of attracting wine consumers through wine tasting to include the overall attractiveness and appeal of the winery. Involving more diversified customer types as target groups will effectively broaden the winery's business scope and turnover.

Drug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.

The literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.

The proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.

The drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.