Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data

Tracking virus outbreaks in the twenty-first century

Emerging viruses have the potential to impose substantial mortality, morbidity and economic burdens on human populations. Tracking the spread of infectious diseases to assist in their control has traditionally relied on the analysis of case data gathered as the outbreak proceeds. Here, we describe how many of the key questions in infectious disease epidemiology, from the initial detection and characterization of outbreak viruses, to transmission chain tracking and outbreak mapping, can now be much more accurately addressed using recent advances in virus sequencing and phylogenetics. We highlight the utility of this approach with the hypothetical outbreak of an unknown pathogen, 'Disease X', suggested by the World Health Organization to be a potential cause of a future major epidemic. We also outline the requirements and challenges, including the need for flexible platforms that generate sequence data in real-time, and for these data to be shared as widely and openly as possible

Attentional focus strategies to improve motor performance in older adults : a systematic review

202307 bcchVersion of RecordRGCOthersGeneral Research Fund of Shanghai Normal UniversityPublishe

Deconstructing Patterns of Social Stigma towards people living with mental illness: A Latent Class Analysis in Hong Kong

Poster presentationTheme: Destigmatization of mental illness and health problem

A factor mixture analysis of social stigma toward people with mental illness

Meeting Theme: Advancing the National Prevention Strategy Through Behavioral Medicine InnovationBackground: Social stigma toward people living with mental illness and the associated treatment avoidance are acute in the Chinese communities. There is an urgent need to identify ways to eradicate stigma and promote the understanding of people living with mental illness (PLMI). The present study explored the cluster patterns and attributing factors of social stigma in Hong Kong using a factor mixture analysis. Methods: Participants were a university sample of 218 Chinese adults (mean age = 22.4 years, SD = 6.1). They filled in a self-report questionnaire which measured the attribution factors of social stigma, social distance, and interpersonal reactivity toward PLMI. The Attribution Questionnaire was used to assess nine stigmatizing attitudes toward PLMI: pity, danger, fear, blame, segregation, anger, help, avoidance, and coercion, on a 9-point rating scale. Latent profile analysis and factor mixture analysis were carried out using Mplus 7 and the identified classes were validated by comparing their demographics and attributing factors using a stepwise distal outcome approach. Results: Two latent classes were identified in the factor mixture models with good classification accuracy. The majority of the participants (N = 175, 80.2%) belonged to the low-stigmatizing class with low to moderate degrees of expression of stigmatizing attitudes toward PLMI. The high-stigmatizing class (N = 43, 19.8%) displayed moderate to high degrees of expression of stigmatizing attitudes toward PLMI. Compared to the low-stigmatizing class, participants in the high-stigmatizing class was more likely to be male, younger, and reported significantly higher social distance, personal distress, and empathetic concern. Discussions: The different group profiles elucidated the complex interactions among emotions, thoughts, and behavior of social stigma toward PLMI. An appreciation of the complexity in stigma patterns enhances psychiatric services through tailored education and promotion initiatives. Acknowledgement: This study was supported by the Public Policy Research Scheme, Research Grants Council (HKU 7006-PPR-11)