Factor that influence knowledge sharing among undergraduate student Universiti Utara Malaysia

The objective of this study is to investigate the factors that influence knowledge sharing among students. The factors covered individual, classroom, and technological aspects. A questionnaire was used for collecting data. 100 students from School of Technology Management and Logistics (STML), UUM participated in this study. It was found that technology support, student’s ability to share and degree of competition with the classmates significantly influence knowledge sharing of students respectively. In contrast, student’s willingness to share, instructor support and technology availability have no influence on knowledge sharing of students

Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development.  Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health.  The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM).  The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah

Genetic diversity and differentiation of Aquilaria malaccensis Lam. using RAPD markers

Aquilaria malaccensis Lam. (family Thymelaeaceae) commonly known as agarwood or gaharu producing tree in Malaysia. The tree is being heavily exploited due to its highly valuable agar oil used in the production of high grade perfumes and traditional medicines. Consequently, their population in nature is threatened greatly. Conservation of this tree species is of the main concern, however, identification of A. malaccensis from other Aquilaria sp. based on morphology is very difficult and time consuming. This study aimed to determine the genetic diversity among three selected Aquilaria sp. namely A. malaccensis, A. sinensis (Lour.) Spreng. And A. subintegra Ding Hou using random amplified polymorphic DNA (RAPD) markers and to differentiate A. malaccensis from A. sinensis and A. subintegra. Out of ten RAPD primers, four primers (G12, R15, U13 and OPA 05) produced the most clear and reproducible bands. A total of 24 bands were scored from the four primers. Construction of dendrogram resulted in two major clusters; cluster I consisted of only A. malaccensis accessions, and cluster II consisted of A. subintegra and A. sinensis accessions. This indicates that A. subintegra is more closely related to A. sinensis while A. malaccensis is genetically distant from both. Species-specific bands for A. malaccensis were produced at 875, 1000 and 2500 bp by G12 primer, and at 2500 bp by OPA 05 primer. This study laid the foundation for a creation of rapid and cost effective molecular identification of A. malaccensis