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Malaysia has great geo-economic advantages, especially in becoming a major logistics and investment hub. However, as operational risk events create uncertainties, logistics companies suffer from supply and demand issues which affect their bottom lines, customer satisfaction and reputations. This is a pioneer paper to propose the optimization of the efficiency of listed logistics companies in Malaysia with operational risk factor using a data envelopment analysis (DEA) model. The basic indicator approach (BIA) is used as an output indicator for the operational risk capital requirement factor in the proposed model. This paper has practical and managerial implications with the identification of potential improvements for the inefficient listed logistics companies based on the optimal solution of the DEA model. This proposed model can be applied in emerging fields such as finance and project-based construction companies, where operational risk is a high concern. [ABSTRACT FROM AUTHOR]

Light rail transit (LRT) systems are vital aspects of the worldwide endeavor to achieve transport sustainability and have been essential in enhancing the economies of urban areas. Issues such as pollution, the risk of road accidents, and traffic congestion could be resolved using this progressive alternative. The statistics showed that only 20% of the commuters in Malaysia use public transport, including LRT, and 80% use private transportation. It is relatively low compared to other Asian countries. High-quality service is essential to improve users' perceived satisfaction with the provided services and increase LRT ridership. The objective of the present study is to acquire an understanding of which factors are crucially influential on users' perceptions of satisfaction. In-person questionnaires were utilized to obtain the information for this paper, with a total of 417 LRT riders in Malaysia's Klang Valley surveyed. This study adopted the factor analysis, correlation test, and artificial neural network (ANN) model. Eight elements related to the quality of service were extracted to ascertain how they influenced the perceived satisfaction of users: information signs, ticket-based services, amenities, safety, employee performance, speed, comfort, and the service details available to riders. Each factor was significantly related to the perceptions of satisfaction, according to the correlation test. Finally, the ANN model shows that the dominant factors determining the LRT users' perceived satisfaction are the signage, amenities, and provision of information. The findings of this research should benefit the providers of services, policy makers, and planning departments by enabling them to formulate successful approaches that ensure user satisfaction is enhanced and the number of riders on the LRT increases. [ABSTRACT FROM AUTHOR]

Income distribution models can be useful for describing the economic properties of a population. In this study, three-part composite Pareto models are fitted to the income distribution in Malaysia for the years 2007, 2009, 2012, 2014, and 2016. The three-part composite Pareto models divide the population into three parts, each following a different distribution model. The lower part follows the inverse Pareto distribution, the upper part follows the Pareto distribution, and the middle part follows another unspecified distribution model. For application in income data, the use of Gaussian mixture distribution is proposed for the middle part, making the inverse Pareto–Gaussian mixture-Pareto distribution model semi-parametric. From the model, it is found that the levels of income inequality in the lower and upper income groups decrease over the period of study. Additionally, the proportion of data following the inverse Pareto distribution in the model is highly correlated with the official absolute poverty incidence. [ABSTRACT FROM AUTHOR]

Stock market predictions are a challenging problem due to the dynamic and complex nature of financial data. This study proposes an approach that integrates the domain knowledge of investors with a long-short-term memory (LSTM) algorithm for predicting stock prices. The proposed approach involves collecting data from investors in the form of technical indicators and using them as input for the LSTM model. The model is then trained and tested using a dataset of 100 stocks. The accuracy of the model is evaluated using various metrics, including the average prediction accuracy, average cumulative return, Sharpe ratio, and maximum drawdown. The results are compared to the performance of other strategies, including the random selection of technical indicators. The simulation results demonstrate that the proposed model outperforms the other strategies in terms of accuracy and performance in a 100-stock investment simulation, highlighting the potential of integrating investor domain knowledge with machine learning algorithms for stock price prediction. [ABSTRACT FROM AUTHOR]

A fundamental issue in manufacturing systems is moving a local manufacturer into a supply chain network including wholesalers and retailers. In this research, a 3-phase framework is proposed to determine the food consumption pattern in food supply chains. In the first stage of this research, the consumer, availability and society factors for product classification according to the features of populations in Malaysia are identified (phase 1). Then, using statistical analysis, the effective factors are recognised (phase 2). In the third phase, the product clusters are recognised using a hybrid PCA and agglomerative clustering method. For this purpose, different clusters for the training step are used. The outcomes indicated that Age (0.94), City (0.79), Health Benefit Awareness (0.76) and Education (0.75) are the most effective factors in product consumption patterns, respectively. Moreover, the efficiency of the outcomes is evaluated using the Silhouette Coefficient, indicating that the proposed algorithm could provide solutions with a 68% score. Moreover, using Calinski-Harabasz Index, it was found that the algorithm provided more logic scores while the number of product patterns was 3 for the studied region (707.54). [ABSTRACT FROM AUTHOR]

Due to the significant number of distributed generators in the electric power system, islanding detection requirements are becoming an increasingly prominent aspect of the power system. The island detection system depends on accurate threshold determination since an incorrect threshold might result in a hazardous situation. To evaluate the proposed method's capacity to discriminate between different events, this study examined different unintentional islanding conditions such as under frequency and over frequency. The purpose of this study is to establish the threshold of the under and over frequency island conditions. The under frequency island condition happens when the distributed generator (DG) capacity exceeds the amount of connected load; on the other hand, the over frequency island condition happens during a higher connected load compared to the capacity of the DG. The contribution of this research is to propose an unintentional island threshold setting technique based on bus voltage angle difference data of the phasor measurement unit (PMU). In the PowerWorld simulator, the Utility Kerteh (location: Terengganu, Malaysia) bus system has been designed and simulated in this work. The test system has four distinct islanding scenarios under two conditions, and the performance of the proposed methods demonstrates that for the under frequency islanding conditions the scenario's threshold can be taken at a minimum of 40 milliseconds (ms) and a maximum of 60 ms, while the over frequency condition island threshold can be placed at a minimum of 60 ms and a maximum of 80 ms depending on the scenarios. Therefore, the proposed technique will be contributed to increase the reliability of the overall distribution grid so the unintentional island can be detected faster in terms of time. [ABSTRACT FROM AUTHOR]

This paper discusses scattered data interpolation by using cubic Timmer triangular patches. In order to achieve C1 continuity everywhere, we impose a rational corrected scheme that results from convex combination between three local schemes. The final interpolant has the form quintic numerator and quadratic denominator. We test the scheme by considering the established dataset as well as visualizing the rainfall data and digital elevation in Malaysia. We compare the performance between the proposed scheme and some well-known schemes. Numerical and graphical results are presented by using Mathematica and MATLAB. From all numerical results, the proposed scheme is better in terms of smaller root mean square error (RMSE) and higher coefficient of determination (R2). The higher R2 value indicates that the proposed scheme can reconstruct the surface with excellent fit that is in line with the standard set by Renka and Brown's validation. [ABSTRACT FROM AUTHOR]

This study proposes the concept of severity as an alternative measure of extreme air pollution events. Information about severity can be derived from the cumulative effect of air pollution events, which can be determined from unhealthy Air Pollution Index (API) values that occur for a consecutive period. On the basis of the severity, an analysis of extreme air pollution events can be obtained through the application of the generalized extreme-value (GEV) model. A case study was conducted using hourly API data in Klang, Malaysia, from 1 January 1997 to 31 August 2020. The block-maxima approach was integrated with information about monsoon seasons to determine suitable data points for GEV modeling. Based on the GEV model, the estimated severity levels corresponding to their return periods are determined. The results reveal that pollution severity in Klang tends to rise with increases in the length of return periods that are measured based on seasonal monsoons as a temporal scale. In conclusion, the return period for severity provides a good basis for measuring the risk of recurrence of extreme pollution events. [ABSTRACT FROM AUTHOR]

The bicycle is a forgotten and neglected mode of transport even though it offers numerous individual, social, and environmental benefits over motorised transport. This research seeks to determine the factors influencing students to use bicycles for commuting and focuses on the strategies that encourage bicycling in a university setting. This study proposes the novel model framework by adopting Ajzen's theory of planned behaviour. We modified the theory of planned behaviour by adding two new constructs, perceived barriers and habit. The respondents in this study are 422 students from Universiti Kebangsaan Malaysia in Selangor, Malaysia. The structural equation model in this study showed that the main attitudinal constructs, namely attitudes, subjective norms and perceived behavioural control, have a significant positive and direct influence on the students' willingness to cycle. In addition, the perceived barriers have a profound negative and indirect impact on behaviour intention through attitude, perceived behavioural control and habit. The perceived barriers did not have a significant influence on the subjective norms. However, habit has a profound positive and direct effect on three main attitudinal constructs (attitude, subjective norms and perceived behavioural control). This element could indirectly influence the behaviour intention of cycling in a university setting. Finally, this study has identified the physical, educational and economic policies for promoting bicycle use on university setting. It also contributes beneficial information that authorities, policymakers, planners and researchers could use to formulate effective strategies for increasing bicycle use on university setting and promote green and sustainable university settings. [ABSTRACT FROM AUTHOR]

Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide supply chain. For this purpose, the appropriate locations of the wholesaler's center points were found according to the geographical and population features of the markets in Malaysia. To this end, four wholesalers on the left island of Malaysia are recognized, which were located in the north area, right area, middle area, and south area. Similarly, two wholesalers were identified on the right island, which were in Sarawak and WP Labuan. In order to evaluate the performance of the proposed method, its outcomes are compared with other unsupervised-learning methods such as the WARD and CLINK methods. The outcomes indicated that K-means could successfully determine the best locations for the wholesalers in the supply chain network with a higher score (0.812). [ABSTRACT FROM AUTHOR]

Geopolitical uncertainties have been a concern for global economies and financial markets' participants. By employing Markov switching regression and quantile regression, we investigated the effect of global and country-specific geopolitical uncertainties on Malaysian Conventional and Islamic stock returns in different market conditions. The estimated results of the Markov switching regression show that Malaysian conventional and Islamic stocks react differently to global and country-specific geopolitical uncertainties under different market volatility conditions, implying volatility dependent exposures and reactions to global and country-specific geopolitical uncertainties. The quantile regression results also reveal that Malaysian conventional and Islamic stocks respond differently to global and country-specific geopolitical uncertainties at different market stages. The empirical findings, therefore, indicate a heterogeneous and non-linear stock reaction to geopolitical uncertainties, providing new insights into geopolitical uncertainties and stock return relationships. Hence, the results will be valuable for asset pricing and investments in an emerging market such as the Malaysian market. [ABSTRACT FROM AUTHOR]