Search

Since December 2019, COVID-19 has quickly taken on a massive global form led to the World Health Organization (WHO) classified COVID-19 as a pandemic outbreak as a result. Due to a lack of information about the virus and the absence of medical services in the community during the early stages of this outbreak, the coronavirus spread quickly. Consequently, it becomes extremely difficult to control the influence of the disease outbreak. Thus, this study was aim to predict the peak numbers of the infected population on the first, the second, the third waves and endemic phase by utilizing a Susceptible-Infected-Recovered (SIR) predictive model between 25th January and 16th February 2020, corresponding to the entire first wave, the second, between 27th February 2020 and 30th June 2020, corresponding to part of the second wave, the current third wave began on 7th September 2020 and 1st April 2022, corresponding to the endemic phase, still present at the time of writing this article. The model retrieved the data from a reliable source on the Internet and its design is based on certain assumptions. The estimated reproductive value in this model for all simulations is 5.1, which are interconnected factors that have contributed to the rapid increase in the number of COVID-19 cases. This led to the outbreak of a highly contagious disease. The number of infected populations increase with the rate of disease transmission rate of infected increases and vice versa and the spread of COVID-19 from first, second, third waves and endemic reached maximum level in a very short time. The COVID-19 endemic in Malaysia is predicted to peak by the early of March 2020 for the first wave, mid of April 2020 for the second wave, early of October 2020 for the third wave and around April 22, 2022, for the endemic phase. The peak infected was predicted at 16,280,000 persons out of a total of susceptible individuals 33,573,874. Therefore, in addition to maintaining control measures at least until the anticipated peak time has passed, proper crisis management and efficient resource use are essential for successfully combating the endemic.

Carbon dioxide (CO2) is known as one of the largest sources of global warming. One of the ways to curb CO2 emissions is by considering the environmental aspect in the supply chain management. This paper analyses the influence of carbon emissions on the Inventory Routing Problem (IRP). The IRP network consists of a depot, an assembly plant and multiple suppliers. The deterministic demands vary and are determined by the assembly plant. Fixed transportation cost, fuel consumption cost and inventory holding cost are used to evaluate the system’s total cost in which fuel consumption cost is determined by fuel consumption rate, distance, and fuel price. Backordering and split pick-up are not allowed. The main purpose of this study is to analyze the distribution network especially the overall costs of the supply chain by considering the CO2 emissions as well. The problem is known as Green Inventory Routing Problem (GIRP). The mixed-integer linear programming of this problem is adopted from Cheng et al. wherein this study a different Hybrid Genetic Algorithm is proposed at mutation operator. As predicted, GIRP has a higher total cost as it considered fuel consumption cost together with the transportation and inventory costs. The results showed the algorithm led to different sequences of routings considering the carbon dioxide emission in the objective function.

Communication is the most important key factor for a student to lead effectively because it helps them to express their opinion. Some students are struggling to socialise with other students due to a lack of communication skills. It is important for students to have effective communication because it helps them develop their leadership skills and can also improvise their self-confidence. The purpose of this study was to identify the level of effective listening, performing tasks completely and effectively, clarity of thought and expression, and leadership skills, to assess the significant difference of demographic variables (gender, age, and semester) on effective listening, performing tasks completely and effectively, clarity of thought and expression and leadership skills, to determine the relationship between effective listening, performing tasks completely and effectively, clarity of thought and expression and leadership skills, and lastly to examine whether effective listening, performing tasks completely and effectively, clarity of thought and expression relate toward leadership skills. The random sample of 155 respondents is from the Faculty of Computer and Mathematical Science (FSKM) at UiTM Perlis. To achieve all the objectives, the researcher has chosen to apply the frequency test, the independent t-Test, the one-way ANOVA, the correlation, and regression performed by the Statistical Package for Social Sciences Version 20.0. (SPSS). Based on the findings, there is a positive relationship between the role of effective communication and leadership skills, but a great deal of determination is still needed to establish leadership criteria among undergraduates, as a few students have not been aware of the importance of leadership skills in their future careers. Moreover, the analysis shows that clarity of thought and expression are the most important roles that contribute to leadership skills. The role of effective communication in enhancing leadership skills among university students must be properly studied so that students can develop their ability to soon become major leaders.

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

Predictions of future events must be incorporated into the decision-making process. For tourism demand, forecasting is very important to help directors and investors to make decisions in operational, tactical, and strategic decisions. This study focuses on forecasting performance between Fuzzy Time Series and ARIMA to forecast the tourist arrivals in homestays in Pahang. The main objective of this study is to compare and identify the best method between Fuzzy Time Series and Autoregressive Integrated Moving Average (ARIMA) in forecasting the arrival of tourists based on the secondary data of tourist arrivals to homestay in Pahang from January 2015 to December 2018. ARIMA models are flexible and widely used in time-series analysis and Fuzzy Time Series which do not need large samples and long past time series. These two methods have been compared by using the mean square error (MSE) and mean absolute percentage error (MAPE) as the forecast measures of accuracy. The results show that Fuzzy Time Series outperforms the ARIMA. The lowest value of MSE and MAPE was obtained from using the Fuzzy Time Series method at values 2192305.89 and 11.92256, respectively.

Malaysia enters the endemic phase of Covid-19 on 1st April 2022. Since then, the Malaysia’s economy has started to recover, and domestic tourism has also restarted. Tourism is one of the factors that contributed to the economy. Avoiding the headache to plan their own trips, tourists usually count on tour operators to turn their dream vacations into realities. Booking a tour is the easiest way for a traveller to experience a foreign land and at the same time they could enjoy their vacation and visit the best spots. The main objective of this study is to determine the main criteria of a tour packages in Langkawi Island by using Fuzzy Analytical Hierarchy Process (FAHP). The five criteria identified are transportation, cost, food service, accommodation, and tour guide. Results showed that the transportation is the most important criteria a tourist look forward in the selection of a tour package.