Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

a b s t r a c tThe ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.

Computational thinking (CT) concepts are newly introduced concepts in the Malaysian curriculum. This study is therefore designed to investigate Malaysian teachers' perception on the integration of computational thinking skills in their teaching and learning practices. A survey form was designed based on the Technological Acceptance Model (TAM) and was disseminated throughout Malaysia to gauge teachers' perception on CT based on the perceived usefulness of CT, perceived ease of CT integration into teaching and learning practices, teachers' attitude towards CT and their intention to use CT in their classrooms. A total of 166 primary school teachers participated in the survey and the data was analysed using Structural Equation Modelling (SEM). This study managed to predict Malaysian teachers' intention in integrating computational thinking skills in their classroom practices via two significant determinants, namely the perceived ease of integration and positive attitude towards computational thinking. This study is important because it highlights factors affecting teachers' perception on the newly improvised curriculum, and is an effort to support CT delivery in Malaysian classrooms.

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This paper focuses on the formulation of a deterministic 2019-nCov transmission model by considering the exposed and recovered populations with immunity. The scenario of the simulation is depicted based on the patient zero in Malaysia. The transmission model is found to be able to predict the next confirmed case given a single case introduced in a fully susceptible population. The mathematical model is developed based on the SEIR model which has susceptible, exposed, infectious and recovered populations. The system of equations which were obtained were solved numerically and the simulation results were analyzed. The analysis includes the impact of the disease if no control is taken.