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

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

Environmental effects on AGN activity via extinction-free mid-infrared census

How does the environment affect active galactic nucleus (AGN) activity? We investigated this question in an extinction-free way, by selecting 1120 infrared galaxies in the AKARI North Ecliptic Pole Wide field at redshift z ≤ 1.2. A unique feature of the AKARI satellite is its continuous 9-band infrared (IR) filter coverage, providing us with an unprecedentedly large sample of IR spectral energy distributions (SEDs) of galaxies. By taking advantage of this, for the first time, we explored the AGN activity derived from SED modelling as a function of redshift, luminosity, and environment. We quantified AGN activity in two ways: AGN contribution fraction (ratio of AGN luminosity to the total IR luminosity), and AGN number fraction (ratio of number of AGNs to the total galaxy sample). We found that galaxy environment (normalised local density) does not greatly affect either definitions of AGN activity of our IRG/LIRG samples (log LTIR ≤ 12). However, we found a different behavior for ULIRGs (log LTIR > 12). At our highest redshift bin (0.7 ≲ z ≲ 1.2), AGN activity increases with denser environments, but at the intermediate redshift bin (0.3 ≲ z ≲ 0.7), the opposite is observed. These results may hint at a different physical mechanism for ULIRGs. The trends are not statistically significant (p ≥ 0.060 at the intermediate redshift bin, and p ≥ 0.139 at the highest redshift bin). Possible different behavior of ULIRGs is a key direction to explore further with future space missions (e.g., JWST, Euclid, SPHEREx)

Prevention of age-related macular degeneration

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the developed world. Although effective treatment modalities such as anti-VEGF treatment have been developed for neovascular AMD, there is still no effective treatment for geographical atrophy, and therefore the most cost-effective management of AMD is to start with prevention. This review looks at current evidence on preventive measures targeted at AMD. Modalities reviewed include (1) nutritional supplements such as the Age-Related Eye Disease Study (AREDS) formula, lutein and zeaxanthin, omega-3 fatty acid, and berry extracts, (2) lifestyle modifications, including smoking and body-mass-index, and (3) filtering sunlight, i.e. sunglasses and blue-blocking intraocular lenses. In summary, the only proven effective preventive measures are stopping smoking and the AREDS formula