EVALUATING THE EFFECTIVENESS OF A MACHINE LEANING MODEL FOR RECOMMENDATION SYSTEM FOR GENERIC COMPETENCY DEVELOPMENT IN HIGHER EDUCATION

Generic competence (GC) is an important element in the development of students in tertiary education. Activities that contribute to the development of GCs are called GC Development Activities (GCDAs). This research investigates the effectiveness of a recommendation system for students to join GCDAs. At a self-financed higher education institution, the investigators evaluated the performance of various recommendation algorithms, including collaborative filtering and content-based recommendation, in providing systematic recommendations on the selection of GDAs. Then the investigators developed a machine learning model for building a recommendation system for generic competency development. The recommendation system was trained using the  GCDAs joined by the students in the past. Then it made recommendations to other students on what GCDAs are suitable for them. The recommendation system was tried by a selected group of students at the institution. Then the effectiveness of the system was measured in terms of the acceptance of the system by the students, and in terms of the improvement in GC of the students. The project will benefit students, academics, and institutions. Students, especially freshmen, will be able to further enhance their generic competence by selecting suitable activities to develop their GCs. Student advisors will be able to provide advice on non-academic development in an evidence-based and efficient manner. Educational institutions will also be able to use their resources more efficiently to provide student activities that consider students’ whole-person development

Factors affecting the adoption of online auctions by internet users in Hong Kong

This is an exploratory empirical study with the aim to identify the factors that affect the adoption of online auctions by Internet users in Hong Kong. The frameworks used were the TAM (Technology Acceptance Model), TCE (Transaction Cost Economics) and SERVQUAL (Service Quality). It was found that the dimensions that affected the customer’s perceived value of the online auction are benefits, costs, risks and service quality. Data was collected from four pilot focus groups, one online survey and a final focus group. The subjects in the focus groups were 21 undergraduates, whereas the subjects in the online survey were 152 internet users. The results of the pilot focus groups guided the design of the online survey. The results of the survey was analysed using the Kruskal-Wallis test. The final focus group was used to seek explanations to some issues arose from the online survey. It was found that the factors in the benefit dimension were liquidity, enjoyment, and price transparency. The factors in the cost dimension were time, effort, service charge and reputation of the user. The factor in the risk dimension was financial risk. The factors in the service quality dimension were efficiency and system availability. The final focus group revealed that the auctioneer’s role in policing the auction web site was important. For differences among the subjects, it was also found that the adult users consider their reputation in auction website, young adults are worried about financial risks, and female users are more concerned about financial risks than male users. The implications of these differences are discussed. The main academic contribution was the development of a questionnaire and a model which can be used in further research about other forms of auction

Patent applications for using DNA technologies to authenticate medicinal herbal material

Herbal medicines are used in many countries for maintaining health and treating diseases. Their efficacy depends on the use of the correct materials, and life-threatening poisoning may occur if toxic adulterants or substitutes are administered instead. Identification of a medicinal material at the DNA level provides an objective and powerful tool for quality control. Extraction of high-quality DNA is the first crucial step in DNA authentication, followed by a battery of DNA techniques including whole genome fingerprinting, DNA sequencing and DNA microarray to establish the identity of the material. New or improved technologies have been developed and valuable data have been collected and compiled for DNA authentication. Some of these technologies and data are patentable. This article provides an overview of some recent patents that cover the extraction of DNA from medicinal materials, the amplification of DNA using improved reaction conditions, the generation of DNA sequences and fingerprints, and the development of high-throughput authentication methods. It also briefly explains why these patents have been granted

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

Generic competency (GC) is an essential but often overlooked aspect of developing students in higher education. While there is much research about using technologies to develop discipline- specific skills for students, the use of technologies in GC development is insufficient. In particular, more research is needed on using technologies to predict student participation in GC development activities (GCDAs). Machine learning (ML) can use student characteristics, known as features, to predict their involvement in GCDAs. However, too many features will slow down the prediction process and reduce the ability to pinpoint the best features for prediction. This study explored an effective way to identify the minimal number of features essential for predicting student participation in GCDAs. The findings help educators develop recommendation systems to help students select the most beneficial GCDA for their holistic development. We collected 98 features from 9570 students from a community college. Then, we applied the Principal Component Analysis and SelectKBest algorithms to reduce the number of features from 98 to 8. Finally, we compared the accuracy of predictions using KNN and ANN based on the all-feature dataset with those based on the reduced-feature dataset. The results showed that the reduced-feature dataset maintained good prediction accuracy and enabled the educator to recommend the GCDAs to students. The findings could drive further research and development in applying machine learning technologies to enhance the recommendations for GCDAs for higher-education students

Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students’ generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students’ activity and academic records in the case higher education institution to find out three major types of patterns between the students’ participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paperPeer Reviewe

An integrated web medicinal materials DNA database: MMDBD (Medicinal Materials DNA Barcode Database)

<p>Abstract</p> <p>Background</p> <p>Thousands of plants and animals possess pharmacological properties and there is an increased interest in using these materials for therapy and health maintenance. Efficacies of the application is critically dependent on the use of genuine materials. For time to time, life-threatening poisoning is found because toxic adulterant or substitute is administered. DNA barcoding provides a definitive means of authentication and for conducting molecular systematics studies. Owing to the reduced cost in DNA authentication, the volume of the DNA barcodes produced for medicinal materials is on the rise and necessitates the development of an integrated DNA database.</p> <p>Description</p> <p>We have developed an integrated DNA barcode multimedia information platform- Medicinal Materials DNA Barcode Database (MMDBD) for data retrieval and similarity search. MMDBD contains over 1000 species of medicinal materials listed in the Chinese Pharmacopoeia and American Herbal Pharmacopoeia. MMDBD also contains useful information of the medicinal material, including resources, adulterant information, medical parts, photographs, primers used for obtaining the barcodes and key references. MMDBD can be accessed at <url>http://www.cuhk.edu.hk/icm/mmdbd.htm</url>.</p> <p>Conclusions</p> <p>This work provides a centralized medicinal materials DNA barcode database and bioinformatics tools for data storage, analysis and exchange for promoting the identification of medicinal materials. MMDBD has the largest collection of DNA barcodes of medicinal materials and is a useful resource for researchers in conservation, systematic study, forensic and herbal industry.</p

Oral health of community dwelling elders in Hong Kong

Introduction: The world’s population is ageing and the elders are vulnerable to oral health problems. This project aimed to observe the oral health status of community dwelling elders in Hong Kong, investigate the oral health impact on their quality of life and nutritional status, and provide oral health education and preventive service. Methods: Community dwelling elders (65 or above) were recruited from five community centers. An interviewer-administered questionnaire was completed to collect information on their socio-demographic background and oral health perception/practice. Their oral health related quality of life (OHQoL) and nutritional status were measured by using the Geriatric Oral Health Assessment Index (GOHAI) and Mini-Nutritional Assessment (MNA), respectively. Their tooth and periodontal status, oral hygiene and soft tissues were examined. Individualized advice, oral health education and scaling were provided. Results: A total of 195 elders participated. Despite their sound knowledge on dental caries, their awareness of the causes and risk factors of periodontal diseases was low. Several misconceptions of oral health were common and their expectation towards oral health was low. Although 81% brush their teeth at least twice a day, over 75% did not perform interdental cleaning mainly due to unawareness of the necessity (32%) and lack of skills (42%). Their dental visits were treatment-driven and mainly for pain-relieving and emergency care. Only 19% were regular dental attendees. The mean (SD) DMFT was 8.9 (7.8). Over 60% had periodontal pockets; 6% were edentulous and 38% had fewer than 20 teeth. The mean (SD) total GOHAI score was 56.4 (8.0); 60% reported negative impact of oral health on their quality of life. The mean (SD) MNA score was 25.2 (2.9); 30% had malnutrition or were at risk. Multivariate analysis showed that tooth loss and untreated decayed teeth (DT) were significant determinants of poor OHQoL; older age, higher education level, and poorer OHQoL were significantly associated with higher risk of malnutrition (all p<0.05). Conclusions: Oral health problems are common among community dwelling elders in Hong Kong. Their oral health awareness and practice are yet to be improved. Tooth loss and unmet treatment need for dental caries significantly compromise elders’ quality of life, which in turn increases their risk for malnutrition. Outreach oral health education and service are well received by the community.published_or_final_versio

Emission Factors for a Taxi Fleet Operating on Liquefied Petroleum Gas (LPG) as a Function of Speed and Road Slope

Real-driving emissions of NOx, CO, and THC, as well as fuel consumption (FC) were studied from 18 liquefied petroleum gas (LPG) fueled taxis operating in a metropolitan road network. Euro 2 to Euro 5 technology vehicles were measured with the use of portable emission measurement systems (PEMS). Statistical processing was implemented to derive mean emission levels for the different technologies. The taxis were measured from 6 months to 2.5 years after their catalysts and lambda sensors were replaced. The emission levels of Euro 4 taxis after catalyst replacement appear higher compared to pre-replacement levels, while pre-Euro 4 taxis emission levels were moderately reduced by the catalyst replacement. Overall, Euro 5 LPG taxis exhibit the lowest emissions, even below the respective regulated limits. The NH3 and N2O pollutant levels of a Euro 5 LPG taxi measured in the lab were found at about half its NOx emissions. Different integration methods of PEMS data were investigated toward the development of emission factors, including both time-based and distance-based approaches at different resolutions. Distance-based integration in sections of 500 m was considered suitable, as this provides a large dataset for statistical confidence and sufficient resolution for link-based modeling. Based on this, FC and emission factors of NOx, CO, and THC as a function of speed and road slope are presented, separately for vehicles considered as normal and high emitters. Volatile organic compounds speciation of Euro 5 taxis showed that methane and butane are the most abundant hydrocarbon species in the exhaust