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.

Synthesis of polystyrene microspheres and functionalization with Pd 0 nanoparticles to perform bioorthogonal organometallic chemistry in living cells

Abstract: We have developed miniaturized heterogeneous Pd(0)-catalysts (Pd(0)-microspheres) with the ability to enter cells, stay harmlessly within the cytosol and mediate efficient bioorthogonal organometallic chemistries (e.g., allylcarbamate cleavage and Suzuki-Miyaura cross-coupling). This approach is a major addition to the toolbox available for performing chemical reactions within cells. Here we describe a full protocol for the synthesis of the Pd(0)-microspheres from readily available starting materials (by the synthesis of size-controlled amino-functionalized polystyrene microspheres), as well as for their characterization (electron microscopy and palladium quantitation) and functional validation ('in solution' and 'in cytoplasm' conversions). From the beginning of the synthesis to functional evaluation of the catalytic device requires 5 d of work.

Extreme Learning Machine Model for State-of-Charge Estimation of Lithium-Ion Battery Using Gravitational Search Algorithm

Abstract: This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model.

Negative Binomial-Lindley Distribution and Its Application

Abstract: Problem statement: The modeling of claims count is one of the most important topics in actuarial theory and practice. Many attempts were implemented in expanding the classes of mixed and compound distributions, especially in the distribution of exponential family, resulting in a better fit on count data. In some cases, it is proven that mixed distributions, in particular mixed Poisson and mixed negative binomial, provided better fit compared to other distributions. Approach: In this study, we introduce a new mixed negative binomial distribution by mixing the distributions of negative binomial (r,p) and Lindley (θ), where the reparameterization of p = exp(-λ) is considered. Results: The closed form and the factorial moment of the new distribution, i.e., the negative binomial-Lindley distribution, are derived. In addition, the parameters estimation for negative binomial-Lindley via the method of moments (MME) and the Maximum Likelihood Estimation (MLE) are provided. Conclusion: The application of negative binomial-Lindley distribution is carried out on two samples of insurance data. Based on the results, it is shown that the negative binomial-Lindley provides a better fit compared to the Poisson and the negative binomial for count data where the probability at zero has a large value.