National University of Malaysia

About: National University of Malaysia is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Heat transfer. The organization has 26593 authors who have published 41270 publications receiving 552683 citations. The organization is also known as: NUM & Universiti Kebangsaan Malaysia.

A review of integrated photocatalyst adsorbents for wastewater treatment

Abstract: Photocatalysis has the best potential to replace the conventional wastewater treatment technology due to its utilization of visible light to photo-degrade organic and inorganic contaminants. However, when applied in slurry form, agglomeration of nanoparticle will lead to serious decrease in photocatalytic performance due to hinderance effect. By combining the photocatalyst and adsorbents, which is designated as integrated photocatalyst adsorbent (IPCA), an adsorbent material which also degrades toxic organic compounds in the presence of UV/visible light irradiation could be produced. The compound does not only preserve all the interesting characteristics of both individual components, but also overcomes serious drawbacks, such as low absorptivity, rapid recombination of photogenerated electrons and hinderance effect of photocatalyst when applied in slurry form. There are several criteria that must be obeyed by the absorbent material used such as high absorption capacity to target compound, reasonable transparency to UV–vis light, high surface area, inhibition of photocatalyst leaching and good stability with dispersing solvent. In this review article, the authors presented an overview on the application of photocatalyst, adsorbents and integrated photocatalyst adsorbents for wastewater treatment. Moreover, the discussions were also focused on the major adsorbent which has been integrated with photocatalyst such as carbon, clays, zeolite matrix materials and others. Additionally, the mechanisms of the adsorption of emerging organic contaminants with adsorbents in IPCA were also discussed to clearly understand the possible interactions between organic contaminants and IPCA. Outlook on IPCA study were also discussed to further broaden the prospective of this technology.

Wound Healing Properties of Selected Natural Products.

Abstract: Wound healing is a complex process of recovering the forms and functions of injured tissues. The process is tightly regulated by multiple growth factors and cytokines released at the wound site. Any alterations that disrupt the healing processes would worsen the tissue damage and prolong repair process. Various conditions may contribute to impaired wound healing, including infections, underlying diseases and medications. Numerous studies on the potential of natural products with anti-inflammatory, antioxidant, antibacterial and pro-collagen synthesis properties as wound healing agents have been performed. Their medicinal properties can be contributed by the content of bioactive phytochemical constituents such as alkaloids, essential oils, flavonoids, tannins, saponins, and phenolic compounds in the natural products. This review highlights the in vitro, in vivo and clinical studies on wound healing promotions by the selected natural products and the mechanisms involved.

The Global State of the Genetic Counseling Profession.

Abstract: The profession of genetic counseling (also called genetic counselling in many countries) began nearly 50 years ago in the United States, and has grown internationally in the past 30 years. While there have been many papers describing the profession of genetic counseling in individual countries or regions, data remains incomplete and has been published in diverse journals with limited access. As a result of the 2016 Transnational Alliance of Genetic Counseling (TAGC) conference in Barcelona, Spain, and the 2017 World Congress of Genetic Counselling in the UK, we endeavor to describe as fully as possible the global state of genetic counseling as a profession. We estimate that in 2018 there are nearly 7000 genetic counselors with the profession established or developing in no less than 28 countries.

Hybrid feature selection based on enhanced genetic algorithm for text categorization

Abstract: An enhanced genetic algorithm (EGA) is proposed to reduce text dimensionality.The proposed EGA outperformed the traditional genetic algorithm.The EGA is incorporated with six filter feature selection methods to create hybrid feature selection approaches.The proposed hybrid approaches outperformed the single filtering methods. This paper proposes hybrid feature selection approaches based on the Genetic Algorithm (GA). This approach uses a hybrid search technique that combines the advantages of filter feature selection methods with an enhanced GA (EGA) in a wrapper approach to handle the high dimensionality of the feature space and improve categorization performance simultaneously. First, we propose EGA by improving the crossover and mutation operators. The crossover operation is performed based on chromosome (feature subset) partitioning with term and document frequencies of chromosome entries (features), while the mutation is performed based on the classifier performance of the original parents and feature importance. Thus, the crossover and mutation operations are performed based on useful information instead of using probability and random selection. Second, we incorporate six well-known filter feature selection methods with the EGA to create hybrid feature selection approaches. In the hybrid approach, the EGA is applied to several feature subsets of different sizes, which are ranked in decreasing order based on their importance, and dimension reduction is carried out. The EGA operations are applied to the most important features that had the higher ranks. The effectiveness of the proposed approach is evaluated by using naive Bayes and associative classification on three different collections of Arabic text datasets. The experimental results show the superiority of EGA over GA, comparisons of GA with EGA showed that the latter achieved better results in terms of dimensionality reduction, time and categorization performance. Furthermore, six proposed hybrid FS approaches consisting of a filter method and the EGA are applied to various feature subsets. The results showed that these hybrid approaches are more effective than single filter methods for dimensionality reduction because they were able to produce a higher reduction rate without loss of categorization precision in most situations.