There are two categories in which educator can deliver open distance learning (ODL) via an internet connection, namely Synchronous Online Learning (OL-sync) and Asynchronous Online Learning (OL-async). At Universiti Teknologi MARA Cawangan Pulau Pinang (UiTMCPP), lecturers could conduct ODL in OL-sync or OL-async due to MCO which was enforced on March 18, 2020. Therefore, this study was conducted to compare the influence of different learning styles with OL-sync and OL-async on UiTMCPP students' academic achievement during the COVID-19 crisis. This study will present the data analysis of research quantitatively. Independent sample ttest is going to employ, and the result indicates a significant difference between the mean of the two online learning approaches with a significant value of 0.004. Therefore, we can conclude that the OL-synch approach gives better result in students' academic performance. It is consistent with the findings of Duncan (2012), which recommend that online learning of the synchronous approach will give a better academic performance for the students.

The missing value in the dataset has always been the critical issue of accurate prediction. It may lead to a misleading understanding of the scenario of air pollution. There might only be a small number of missing (5% to 10%) answers to each problem, but the missing details may vary. This research is focused mainly on solving long gap missing data. Single missing value imputation means replacing blank space in the monitoring dataset from chosen Department of Environment (DoE) monitoring station with the calculated value from the best technique for long gap hours. The variable that is mainly being a monitor is PM10. The technique focused on this research is the single imputation technique. Furthermore, this technique was tested on the Tanjung Malim monitoring station dataset by fitting with five performance indicators. The result was compared with the previous study, whether it is the best used for long gap hour data. Four stages need to be followed to complete this research. The steps are data acquisitions, characteristic analysis of missing value, single imputation approach, verification of approach and suggestion of the best technique. This research used four existing imputation techniques: series mean (SM), mean of nearby points (MNP), linear trend (LT), and linear interpolation (LIN). This research shows that the interpolation technique is the best technique to apply particulate matter missing data replacement with the least mean absolute error and better performance accuracy.

Ergonomics knowledge helps in its right application and contributes significantly to the general well-being and safety of students.  Ergonomics in Malaysia is a relatively new concept and yet to be considered an essential component of most organizations. The purpose of this study is to examine the engineering students in Universiti Teknologi MARA Cawangan Pulau Pinang (UiTMCPP),  Malaysia on their ergonomics knowledge level. Questionnaires were distributed to 246 engineering students of mechanical, civil, chemical, and electrical in UiTMCPP and the responses were analyzed using SPSS version 15. The result of their ergonomics knowledge found that the average mean score and standard deviation were 2.74 and 1.21 respectively. It shows that their ergonomics knowledge level was moderate. The average mean score of the steps taken by the university management is 4.03 which is a high level. The university management and respective school must give regular ergonomics education and training for the students as well as university staff to increase the ergonomics knowledge to a better level.

Air pollution is a considerable health danger to the environment. The objective of this study was to assess the characteristics of air quality and predict PM10 concentrations using boosted regression trees (BRTs). The maximum daily PM10 concentration data from 2002 to 2016 were obtained from the air quality monitoring station in Kuching, Sarawak. Eighty percent of the monitoring records were used for the training and twenty percent for the validation of the models. The best iteration of the BRT model was performed by optimizing the prediction performance, while the BRT algorithm model was constructed from multiple regression models. The two main parameters that were used were the learning rate (lr) and tree complexity (tc), which were fixed at 0.01 and 5, respectively. Meanwhile, the number of trees (nt) was determined by using an independent test set (test), a 5-fold cross validation (CV) and out-of-bag (OOB) estimation. The algorithm model for the BRT produced by using the CV was the best guide to be used compared with the OOB to test the predicted PM10 concentration. The performance indicators showed that the model was adequate for the next day’s prediction (PA=0.638, R2=0.427, IA=0.749, NAE=0.267, and RMSE=28.455).