The University has secured an impressive HK$16.1 million for 18 projects from the Research Grants Council (RGC) under the Competitive Research Funding Schemes for the Local Self-financing Degree Sector. All except an Inter-Institutional Development Scheme project came from the Faculty Development Scheme (FDS), which in total won HK$15.7 million, the highest amount of all competing institutions. Several of these projects were granted more than HK$1 million individually, with one of them achieving the highest FDS grant!
Enhancing accuracy of paper-based microfluidics in bacteria detection and toxicity measurement
The highest-awarding project, submitted by Dr Chen Jianlin of the School of Science and Technology, seeks to improve the accuracy of an increasingly common type of device for measuring bacteria and toxicity levels. Addressing the unexplained condition that chemical indicators such as resazurin tend to be reduced much more slowly when transported to paper-based microfluidic devices than in free solutions, Dr Chen’s project will offer a theoretical answer to this discrepancy.
Ecological functions of microbial communities attached to the aerial roots of grey mangroves
In environmental sciences, Prof. Fred Lee of the School of Science and Technology focuses on an increasingly threatened native mangrove species in the Futian National Nature Reserve, namely Avicennia marina (grey or white mangroves). A. marina is distinguished by its aerial roots, known as pneumatophores, which are colonized with diverse microorganisms. By analysing their structure, the project will shed light on the role of these microbial communities in the survival of A. marina, and strategies to conserve the species.
On an additional note, the University’s marine and coastal research work is expanding with two exciting developments. First, HKMU has become a member institution of the State Key Laboratory of Marine Pollution located at CityU. Second, it has signed a framework agreement with the Futian National Nature Reserve Management Bureau and Shenzhen University for establishing a Greater Bay Area Mangrove Wetland Research and Development Centre in the Futian reserve. For HKMU, this has been a major step forward since it set up the University Research Facilities for similar research work with RGC funding on campus.
Learning analytics intervention system for Python programming courses
In the field of learning analytics (LA), Dr Samuel Choi of Lee Shau Kee School of Business and Administration attempts to build an interactive platform for students to learn Python programming. This will be the first platform that considers all five stages of the LA cycle — capturing, reporting, predicting, acting and refining, and will make an exemplary LA model that achieves educational intervention through the technique of reinforcement learning.
Detecting rumours on social media by refined multimodal deep learning
Another project dealing with machine learning, led by Dr Keith Lee of the School of Science and Technology, is aimed at detecting rumours on social media. Multimodal deep learning describes the process by which machines ‘learn’ multimodal data, such as text and images in the case of social media. This can be challenging because of the varied ways in which different kinds of data are extracted, and the project will devise a new machine learning model capable of fusing multimodal data.
Cross-cultural study of depression in high-ability adolescents
Dr Amy Cheung of the School of Arts and Social Sciences is looking to expand the extant scholarship on depression in high-ability adolescents in two dimensions: first, by taking a large, cross-cultural sample beyond the previously US-dominant scope, and second, by including and identifying cognitively strong academic underachievers. The results will form the basis of future longitudinal studies.
Strictly Necessary Cookies
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.