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In this technology advance era, most of the counties are trend to promote high technological in business development for increase the profitability and market value. The information technology (IT) project having a dynamic and complex nature caused high uncertainty and trend to be risky in any resources or capital loss. There are many risk factors is difficult to manage and mitigate by the project manager unless they try to accept or avoid it in the implementation process. Regardless of the previous studies have focused on the critical success factors in information system and project till date, there are fewer studies emphasis on critical risk factor in IT projects. To address these issues above, this paper aims to Prioritize the main Critical risk factors (CRFs) affecting the IT projects in Malaysia based on Pairwise comparison. This study is conducted through experts in the IT field company responded to the Pair-wise comparison survey. Besides that, the research questionnaire is a close-ended survey and 22 CRFs have been converted in a matrix form of a table for the respondents to answer based on their judgment, perception, and experience in IT projects. The result showed that the most significant important CRFs affecting IT projects is task distribution and resource coordination, and lack of communication while the finding can contribute to the IT company, other researchers and government sector who intend to expand their opportunities in the IT projects [ABSTRACT FROM AUTHOR]

Domestic palm oil consumption in Malaysia increased to about 3.37 million metric tons in 2022. This number is expected to increase further due to population growth and consumer preferences for palm oil cooking oil. However, in 2016, the government withdrew its subsidy on palm cooking oil, increasing its price by 48.9%. Recently, the government proposed to use a floating price for palm cooking oil. These changes have left direct and indirect impacts on food producers and consumers. Therefore, this paper examines the impact of an increase in the price of palm cooking oil on food producers (directly) and consumers (indirectly). This study applied the Leontief price modelling technique and simulated a scenario for the price changes in the production cost. The simulation results indicated that costs and prices changed. The results found that the most significant cost change came from restaurants, 52.0%, while other price changes were from oil palm (29.6%) and restaurants (17.4%). This study also used the profit index to calculate the profit food producers and services gained. Based on this scenario, food producers should revise their prices of goods and services since they still gain profit. It was recommended that the government revise the price of palm cooking oil by providing subsidies or incentives to the food producer or consumer. The fluctuation in palm oil prices impacts the price of palm cooking oil in the market. If the government does not revise cooking oil prices, it will affect food consumers and producers. [ABSTRACT FROM AUTHOR]

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models. [ABSTRACT FROM AUTHOR]

Several mathematical models have been developed in the past to consider reliability when designing energy systems. However, these models focus on the system reliability which only considers the operability of the system. Hence, a reliability indicator called supply reliability is introduced to explicitly assess an energy system's reliability meeting the desired energy demand. Besides, previous models can only allocate equipment with identical sizes and equipment reliability. This could overdesign systems and ignore the possibility of allocating a combination of different equipment sizes and reliability. This paper presents a combinatory matrix methodology for allocating equipment with different sizes and reliabilities to improve supply reliability of an energy system design. This combinatory matrix method is a novel feature that allows decision-makers to efficiently evaluate various configurations of equipment based on size and reliability. A case study consisting of a power distribution network is first presented and solved as a pedagogical example to illustrate the methodology and features of the proposed approach. Then, a large-scale power system case study in Malaysia combining existing conventional power equipment and a palm-based bioelectricity supply chain is solved. Based on the optimized results, the redundancy allocated increased the energy generated from the bioelectricity supply chain by 74.14% and cost by 82.96% to achieve the desired energy demand at high supply reliability. This allows decision-makers to determine the cost associated with improving supply reliability for an energy system. [ABSTRACT FROM AUTHOR]

Background: Plasmodium knowlesi, a simian malaria parasite infection, increases as Plasmodium falciparum and Plasmodium vivax infections decrease in Johor, Malaysia. Therefore, this study aimed to identify the distribution of vectors involved in knowlesi malaria transmission in Johor. This finding is vital in estimating hotspot areas for targeted control strategies. Methods: Anopheles mosquitoes were collected from the location where P. knowlesi cases were reported. Cases of knowlesi malaria from 2011 to 2019 in Johor were analyzed. Internal transcribed spacers 2 (ITS2) and cytochrome c oxidase subunit I (COI) genes were used to identify the Leucosphyrus Group of Anopheles mosquitoes. In addition, spatial analysis was carried out on the knowlesi cases and vectors in Johor. Results: One hundred and eighty-nine cases of P. knowlesi were reported in Johor over 10 years. Young adults between the ages of 20–39 years comprised 65% of the cases. Most infected individuals were involved in agriculture and army-related occupations (22% and 32%, respectively). Four hundred and eighteen Leucosphyrus Group Anopheles mosquitoes were captured during the study. Anopheles introlatus was the predominant species, followed by Anopheles latens. Spatial analysis by Kriging interpolation found that hotspot regions of P. knowlesi overlapped or were close to the areas where An. introlatus and An. latens were found. A significantly high number of vectors and P. knowlesi cases were found near the road within 0–5 km. Conclusions: This study describes the distribution of P. knowlesi cases and Anopheles species in malaria-endemic transmission areas in Johor. Geospatial analysis is a valuable tool for studying the relationship between vectors and P. knowlesi cases. This study further supports that the Leucosphyrus Group of mosquitoes might be involved in transmitting knowlesi malaria cases in Johor. These findings may provide initial evidence to prioritize diseases and vector surveillance. [ABSTRACT FROM AUTHOR]

Although the functional and institutional distributions of income are integrally connected to individual living standards and other development policy objectives, these dimensions are rarely given prominence or even accommodated within standard national accounting frameworks. This paper summarizes research on the estimation of a social accounting matrix (SAM) for Malaysia for 1970 in which the distribution of income between different factors and socio-economic groups is identified. It is the latest of a series of case studies involving some of the authors and is, perhaps, the most detailed of its kind. The study departs from the Nations SNA guidelines at various points. The SNA basically proposes a commodity balance approach to national income accounting. In giving equal emphasis to income/outlay accounts as to the production accounts, the present study has brought together data from two major primary sources: a household expenditure survey and a production survey. Their combination poses several problems which are discussed in the paper. It leads to an integrated picture, in matrix form, of the interrelationships between income distribution and production structure in the Malaysian economy. Both the factor and household accounts in our SAM are disaggregated according to race and the geographic distinction between Peninsular and East Malaysia, with an urban/rural split within Peninsula Malaysia. The Peninsula labor force is further disaggregated by education level, while its households are then subdivided according to the employment status of main income earners. Arguments for and against these choices are presented. Some other aspects of the study can be noted. First, the distinction drawn between East and Peninsular Malaysia is desirable not only because of the inherent interest of the regions but also because of large differences in data availability and hence in estimation methods. Secondly, to complete our SAM it was necessary to estimate inter-household transfers, being the institutional analogue of inter-industry commodity flow. And finally an attempt has been made to impute the labor component of unincorporated business income. These, then, are the major problems which had to be overcome in our attempt to quantify the generation, distribution, and redistribution of income within Malaysia in a SAM framework. [ABSTRACT FROM AUTHOR]

Sustainable water demand management has become a necessity to the world since the immensely growing population and development have caused water deficit and groundwater depletion. This study aims to overcome water deficit by analyzing water demand at Kenyir Lake, Terengganu, using a fuzzy inference system (FIS). The analysis is widened by comparing FIS with the multiple linear regression (MLR) method. FIS applied as an analysis tool provides good generalization capability for optimum solutions and utilizes human behavior influenced by expert knowledge in water resources management for fuzzy rules specified in the system, whereas MLR can simultaneously adjust and compare several variables as per the needs of the study. The water demand dataset of Kenyir Lake was analyzed using FIS and MLR, resulting in total forecasted water consumptions at Kenyir Lake of 2314.38 m3 and 1358.22 m3, respectively. It is confirmed that both techniques converge close to the actual water consumption of 1249.98 m3. MLR showed the accuracy of the water demand values with smaller forecasted errors to be higher than FIS did. To attain sustainable water demand management, the techniques used can be examined extensively by researchers, educators, and learners by adding more variables, which will provide more anticipated outcomes. [ABSTRACT FROM AUTHOR]

Loss of spatial data with a long gap is a significant limitation for remote sensing analyses using satellite-based monitoring of oceanography. This limitation could not be ignored as it may affect the subsequent analysis and modeling of the data. Hence, this gap needs to be improved by filling the spatial gap in the satellite datasets. In this research, Data Interpolating Empirical Orthogonal Functions (DINEOF) is applied to fill the spatial gap and has successfully worked in the reconstruction of missing data of chlorophyll-a for monitoring harmful algal blooms (HABs) in Sepanggar Bay located at coastal water of Kota Kinabalu, Malaysia. The original chlorophyll-a pixels are used to assess the accuracy of the predicted data. Then, the DINEOF model is compared with the Spatio-temporal Kriging model for validation purposes. The results obtained show that the DINEOF model has the highest Pearson correlation coefficient, 0.9940 and the smallest values of Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD) which are 0.2770 and 0.0155 respectively. Therefore, this proved that the DINEOF model is more effective for filling spatial long gaps. [ABSTRACT FROM AUTHOR]

Analyzing commodity prices contributes greatly to traders, economists and analysts in ascertaining the most feasible investment strategies. Limited knowledge about the price trend of the commodities indeed will affect the economy because commodities like palm oil and gold contribute a huge source of income to Malaysia. Therefore, it is important to know the optimal price trend of the commodities before making any investments. Hence, this paper presents a logic mining technique to study the price trend of palm oil with other commodities. This technique employs 2-Satisfiability based Reverse Analysis Method (2-SATRA) consolidated with 2-Satisfiability logic in Discrete Hopfield Neural Network (DHNN2-SAT). All attributes in the data set are represented as a neuron in DHNN which will be programmed based on a 2-SAT logical rule. By utilizing 2-SATRA in DHNN2-SAT, the induced logic is generated from the commodity price data set that explains the trend of commodities price. Following that, the performance evaluation metric; error analysis and accuracy will be calculated based on the induced logic. In this case, the experimental result has shown that the best-induced logic identifies which trend will lead to an increase in the palm oil price with the highest accuracy rate. [ABSTRACT FROM AUTHOR]

Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning. [ABSTRACT FROM AUTHOR]

Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value. [ABSTRACT FROM AUTHOR]

The aim of this paper is to determine whether the propagation and transmission mechanism of Malaysian monetary policy differed during the Asian Financial Crisis of 1997/1998 and the Global Financial Crisis of 2007/2008. The methodology employs a time-varying vector autoregression framework. The primary result is that despite having no evidence of time-variation within the propagation mechanism of Malaysian monetary policy the average contribution of a monetary policy shock to the variability of each macroeconomic variable: real GDP, inflation and the nominal effective exchange rate, differs between the two crises. This finding suggests that despite the propagation mechanism being relatively constant, Malaysia’s monetary policy transmission mechanism evolves over time. We believe that the main mechanism driving this evolution is the time-variation in the variance-covariance matrix of the shocks of the model, not the coefficients. We also find some evidence that the implementation of capital controls reduced the influenceability of monetary policy on the Malaysian economy. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of foreign shocks upon domestic macroeconomic fluctuations and monetary policy, and examines the effectiveness of domestic monetary policy as a stabilization policy in Malaysia. Monetary policy variables (interest rate and money supply) have been measured through a non-recursive structural VAR (SVAR) identification scheme, which allows the monetary authority to set the interest rate and money supply after observing the current value of foreign variables, domestic output and inflation. The results show the important role of foreign shocks in influencing Malaysian monetary policy and macroeconomic variables. There is a real effect of monetary policy, that is, a positive shock in money supply increases domestic output. In contrast, a positive interest rates shock has a negative effect on domestic output growth and inflation. The effects of money supply and interest rate shocks on the exchange rate and stock prices are also consistent with standard economic theory. In addition, domestic monetary policy is able to mitigate the negative effect of external shocks upon domestic economy. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive neurofuzzy interface system (ANFIS) approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorporated an enhanced feature extraction method called principle component analysis (PCA) to reduce the input features to the ANFIS. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the ANFIS output compared to that of the MNE method. The ANFIS output provides promising results in terms of accuracy and computation time. Furthermore, it can be concluded that the ANFIS with enhanced feature extraction method reduces the time taken to train the ANFIS without affecting the accuracy of the results. [ABSTRACT FROM AUTHOR]

The key objective of this study is to determine and analyze the impacts of inbound tourism on the Malaysian economy using an input-output analysis. With the view of accomplishing the key objective, this study also explores the economic potential of inbound markets for the Malaysian tourism industry. As central to any economic impact analysis, tourism multipliers are estimated from the inverse of Leontief’s input-output matrix. The empirical results reveal two interesting observations concerning inbound tourism impacts on Malaysian economy. These are, firstly, tourism sectors that produce larger multipliers do not generate larger total economic impact, and secondly, inbound markets with highest per capita do not yield highest economic impact. Therefore, this study concludes that an economic sector with lower multipliers should not be neglected and that the contribution of inbound tourism market generating lower per capita expenditure should not be ignored when preparing appropriate tourism marketing and public policies for developing economies. [ABSTRACT FROM AUTHOR]

According to Mundell (1961), an optimum currency area is an economic unit composed of regions affected symmetrically by disturbances and between which labour and other factors of production flow freely. The issues of forming a single currency in the ASEAN countries have long been debated by the government of the member of ASEAN. Ironically, these countries would also choose not to surrender their own county's currency which made the idea of forming an OCA nearly impossible. It is also worth to note that the formation of an OCA within the ASEAN members may not be as simple as the European Union due to the mix results obtained by previous studies. This paper will examines the feasibility of forming an Optimum Currency Area (OCA) among the ASEAN-5, which are Indonesia, Malaysia, Philippines, Singapore and Thailand, by testing the symmetry of underlying structural shocks. This paper will further suggest the feasible sub regions to form the OCA within these ASEAN-5.The results imply that it would be difficult for the ASEAN-5 to form a currency union, however there is a possibility for Malaysia, Singapore and Thailand to spearhead the formation of the single currency. [ABSTRACT FROM AUTHOR]

This study empirically examines the short- and long-run dynamic causal linkages between Malaysia and its major trading partners (the United States, Japan, Singapore, China, and Thailand) based on a two-step estimation, Autoregressive distributed lag (ARDL) and Generalized Method of Moments (GMM) during the period 1992-2008. The study documents that the stronger the trade ties among the countries, the higher the degree of comovements among their stock markets. The Japanese stock market, to some extent, is found to be more important than the United States over these markets. In designing stock market policies, each country should take into consideration of any shocks in its major trading partners. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to identify the real power transfer between generators and load using modified nodal equations. It further focuses on creating an appropriate Radial Basis Function Network (RBFN) to solve the problem in a simpler and faster manner. For this purpose, two separate stages training algorithm have been chosen for the proposed RBFN power transfer allocation technique. Almost all system variables obtained from load flow solutions are utilised as input to the neural network. The validation of the proposed methodology is demonstrated by using the actual 25-bus equivalent power system of south Malaysia, to illustrate the effectiveness of the RBFN technique compared to that of the modified nodal equations method. [ABSTRACT FROM AUTHOR]

By evaluating the Malaysian consumption patterns, this article measures capital mobility in Malaysia with three main trading countries, namely the United States, Japan and Singapore. The results indicate that Malaysia's national consumption was fully integrated with the United States over the period 1960-2000. Since the consumption models are based on a high degree of capital mobility, there is some evidence that this condition is met in the Malaysian data. This indicates that Malaysia exhibited a substantial amount of financial openness despite periodic exchange controls. [ABSTRACT FROM AUTHOR]

The United Nation's Decade on Ecosystem Restoration 2021–2030 aims to halt ecosystem degradation to achieve Sustainable Development Goals (SDGs) by 2030. In Malaysia, the concept of sustainable forest management (SFM) has been practiced since 1901. In this study, we evaluated the genetic diversity of the native dipterocarp timber tree Shorea acuminata in a rehabilitated area at Kenaboi Forest Reserve (Kenaboi FR). The rehabilitated area was formerly a degraded forest managed with the taungya restoration system for 50 years. All trees with diameter at breast height (DBH) of 5 cm and over were measured, tagged and identified in a one-hectare study plot. A total of 132 inner bark samples were collected for DNA extraction. Four SSR markers (Sle280, Sle392, Sle475 and Sle566) and two EST-SSR markers (SleE07 and SleE16) were used to analyse 95 good-quality DNA samples. Genetic diversity parameters including maternal contribution were determined for 75 samples. The genetic diversity of big trees (He = 0.656 ± 0.19) and small trees (He = 0.652 ± 0.17) were high and both were in genetic equilibrium, with Fis values of the big trees being 0.035 and small trees being 0.164. Clustering analysis based on Jaccard's similarity values (at 95% confidence level) confirmed that big trees in the Kenaboi FR rehabilitated area had originated from genetically diverse seed trees of the Sungai Menyala Forest Reserve which were used as the planting stock for the taungya restoration system. Maternal contribution showed that the allele contribution of the small trees came from the planted S. acuminata trees within the study area. The high genetic diversity of small trees in this study provides strong evidence that the existing big trees would be suitable for a genetically diverse seed collection to rehabilitate other degraded forests. Sustainable forest management must emphasise genetic diversity in order to ensure the resilience of rehabilitated forest ecosystems. [ABSTRACT FROM AUTHOR]

A ban on palm oil imports by the European Union has become a problematic issue, especially for palm oil producers' countries. Oil palm has been widely used in many sub-sectors, and any changes in the production side may affect many sectors that use oil palm as an input factor in their productions. This study explores the chain of the oil palm sector on the other sub-sectors in Malaysia by using a value-added multiplier method and network modeling. The study focuses on the specific oil palm sub-sector and oils and fats sub-sector in the Malaysian economic structure based on the Malaysian Input-Output 2015 Table. Network visualization and all the analyses involving network methods were developed and performed using UCINET and GEPHI software. The value-added multiplier results explained that the net value between output multiplier and import multiplier is vital to depict the real impact of net resources used as an input factor in the oils and fats and oil palm sub-sectors. The high-density value level shows that the Malaysian oil palm sector has high connectivity in the economic system. From the network visualization analysis, the oils and fats sub-sector has a high level of integration with other sectors within the network. Meanwhile, the oil palm sub-sector categorized in the periphery structure group has a low level of integration in the input-output network. This is due to the high value-added demand for oil palm in the oils and fats sub-sector in the manufacturing sector. Overall, most of the sub-sectors in Malaysia are highly interconnected due to the high clustering ratio. Therefore, ensuring sufficient oil palm production is vital for sustainable production of other sub-sectors. [ABSTRACT FROM AUTHOR]

This study shows that tin export tax results in negative impacts on production, export, and employment. The domestic producers have to bear more of the burden of export tax since the supply is less elastic than the export demand with respect to own-price. The reduction in domestic tin output as a result of export tax would mean that fewer workers would be employed in the industry. Therefore, it is suggested that the tin export tax be reduced substantially to prevent more miners from leaving the industry and reduce the unemployment problem. The study also indicates that Malaysian producers of tin tend to over produce when there is an increase in the demand for tin due to an increase in the number of mines, the price of aluminum, and the world production index. This suggests that tin producers should also pay more attention towards production planning so that the excess supply of tin could be reduced to prevent the tin price from falling. [ABSTRACT FROM AUTHOR]

Electric supply is listed as one of the basic amenities of sustainable development in Malaysia. Under this key contributing factor, the sustainable development goal aims to ensure universal access to an affordable, clean, and reliable energy service. To support the generation capacity in years to come, distributed generation is conceptualized through stages upon its implementation in the power system network. However, the rapid establishment growth of distributed generation technology in Malaysia will invoke power quality problems in the current power system network. In order to prevent this, the current government is committed to embark on the development of renewable technologies with the assurance of maintaining the quality of power delivered to consumers. Therefore, this research paper will focus on the review of the energy prospect of both fossil fuel and renewable energy generation in Malaysia and other countries, followed by power quality issues and compensation device under a high renewable penetration distribution network. The issues and challenges of distributed generation are presented, with a comprehensive discussion and insightful recommendation on future work of the distributed generation. In accordance with the addressed highlights in this paper, it would serve as the criterion on upcoming revolution of distributed generation integrated along with the traditional network in Malaysia. [ABSTRACT FROM AUTHOR]