Journal of Science Ladkrabang

Permutations of Candy Crush Game

Narit Yudrum — 2024-06-29

Candy Crush game was launched on Facebook in 2012 and it has been a famous game which is played on various mobile and web platforms. In Candy Crush game, differently colored candies are arranged in a grid. To clear a level, the player need to swap adjacent candies in order to match three or more candies of the same color. In this article, we show the permutations of the candies in Candy Crush game for students or the person who interested in combinatorics to get the new examples of applying permutations to solve something through the fun game.

On the Diophantine Equation p^x+(p+1)^y=z^2, where p is Prime

Chantana Wannaphan — 2024-06-29

In this paper, we study all non-negative integer solutions of the Diophantine equation , where is a prime number. The research results showed that 1) If , then the equation has only two solutions, namelyy , 2) if , then the equation has only solutions in the form of or , where is a positive integer, 3) if , then the equation has only two solutions, namely , 4) if and is even, then the equation has no solution, and 5) if , then the equation has only one solution. That is .

A Mathematical Model of Melioidosis Transmission with Effect of Wearing Boots

Yada Ponggun — 2024-06-29

Melioidosis is a major public health problem in almost tropical countries. Melioidosis is an infectious disease caused by bacterium from contaminated water sources and soil. Most of the patients are farmers who are at risk of infection through contact with soil and water while working. In this research, we developed and analyzed a mathematical model of melioidosis transmission with effect of wearing boots. We founded two equilibrium points: the disease-free equilibrium point and the epidemic equilibrium point. The stability conditions of both equilibrium points, which depended on the basic reproductive number (RO) and Ri when i = 1, 2 ,3, were investigated. The disease-free equilibrium point is locally asymptotically stable when R0<1 and R1<1, whereas the endemic equilibrium point is locally asymptotically stable when R0>1, R2>1 and R3>1. In conclusion, the numerical analysis showed that wearing boots is the factors affecting the control of melioidosis epidemic. Therefore, wearing boots should be encouraged to reduce the spread of melioidosis.

Forecasting Model for Thailand's Government Revenue During the Recovery of the COVID-19 Situation

Watha Minsan — 2024-06-29

The objective of this research is to identify an appropriate forecasting model for government revenue time series data across four sectors in Thailand: the Revenue Department, the Excise Department, the Customs Department, and the other agencies such as the Stat Enterprises and the Treasury Department. The study utilizes publicly available data from the website Ministry of Finance Open Data, covering the period from October 2012 to April 2023, totaling 127 months. The data set is divided into two subsets: a training data set spanning from October 2012 to April 2022 (115 months) and a testing data set from May 2022 to April 2023 (12 months). Five statistical forecasting methods are employed: additive trend-based linear with seasonal decomposition, additive trend-based logarithmic with seasonal decomposition, smoothing with Holt-Winters' additive method, the Box-Jenkins method, and a combined forecasting approach using regression analysis. The performance of these models is evaluated using the Mean Absolute Percentage Error (MAPE) metric applied to the testing data set. The findings of the study reveal that smoothing with Holt-Winters' additive method is the most suitable model for forecasting the Revenue Department, the Excise Department, and the other agencies. Additionally, the Box-Jenkins method with a SARIMA(2,1,0)(0,0,2)₁₂ model is identified as the most appropriate choice for forecasting the Customs Department.

Rainfall Forecasting Model in Nakhon Si Thammarat Province: Case Study Using Time Series Method

Warangkhana Riansut — 2024-06-29

December 2022. The time series was divided into 2 sets. The first series from January 2012 to December 2021 was used to create the forecasting models using the Box and Jenkins method, the simple seasonal method, Winters’ additive method, and Winters’ multiplicative method. The second series was from January to December 2022 for selecting the forecast model based on the lowest Root Mean Square Error (RMSE). This study found that a model from the Box and Jenkins method, SARIMA (0, 0, 0) (1, 1, 0)₁₂ and no constant term, had the lowest RMSE value.

Truck Routing for Waste Collection: A Case Study of Tha Ruea District, Phra Nakhon Si Ayutthaya Province

Kanogkan Leerojanaprapa — 2024-06-29

At present, waste collection is a routine and one of the major municipal functions that needs to improve efficiency. Tha Ruea Subdistrict of Phra Nakhon Si Ayutthaya Province aims to improve efficiency of the vehicle routing of garbage trucks. Therefore, the objective of this research is to suggest the optimal route by dividing this study into two scenarios: Scenario 1: re-route based on four original route zones (A - D zone) and Scenario 2: combining into 2 route zones (E and F zone) before re-routing. This research compared the original transport routes with the Nearest neighbor algorithm, Saving algorithm, Evolutionary algorithm, and Linear programming method. The results concluded that in the first scenario, the Linear programming approach provides the shortest routes in all zones and shows the same route based on Evolutionary algorithm for C zone by reducing the total distance by 0.25, 1.67, 0.72, and 0.7 kilometers, respectively. For Scenario 2, use routing zone integration by merging zones A and B into route E zone and combining zones C and D into route F zone. The study found that the Linear programming approach results in the shortest route in both zones, which can be reduced from the original route by 16.51 kilometers and 15.39 kilometers, or by 43.54% and 38.15% respectively. The total distance was reduced from 78.27 kilometers to 46.37 kilometers per day or decreased by 40.76%.

Data Logger System in Sugarcane Using Internet of Things

Anuwat Jaidee — 2024-06-29

This research presents the development of an environmental data collection system in sugarcane fields using Internet of Things technology (IoT technology). The study involved designing and installing soil moisture sensors at three points in a 1-acre experimental sugarcane plantation. Additionally, a weather monitoring station was installed to collect data on temperature, air humidity, sunlight, wind speed, wind direction, and rainfall. All data was stored in a database system to solve the problem of errors in storing weather data in sugarcane fields and for yield forecasting, annual production assessments, disease and storm monitoring, and sugarcane field status tracking. The results of the experiments showed that sugarcane yield increased significantly at 15.03%. The Internet of Things-based environmental data collection system in sugarcane fields outperformed manual data recording significantly. It provided real-time data updates hourly. However, during adverse weather conditions or insufficient sunlight, the equipment and system experienced reduced efficiency and intermittent data transmission. The researchers recommend increasing battery capacity or integrating the system with a basic electrical supply to ensure continuous data collection and to support the development of automated irrigation systems for the sugarcane and sugar production industry in the future.

Study of the 5th Wave of COVID-19 Outbreak in Thailand Using Mathematical Model

Pannathon Kreabkhontho — 2024-06-29

Covid-19 is a contagious disease that is transmitted from person to person through small droplets released when coughing or sneezing. Currently, Thailand is experiencing an outbreak of the Omicron variant of the Covid-19 virus in its fifth wave, with a high number of infected individuals compared to previous waves. Therefore, we are interested to study the spread of the disease and the effectiveness of the three-dose vaccination using Mathematical model. To analyze the spread and study the impact of vaccination, we developed a mathematical model consisting of eight population groups: Susceptible population (S), Vaccinated population at risk (Sv), Infected population with mild symptoms (I), Hospitalized population with severe symptoms (H1), ICU-admitted population in critical condition (C), Hospitalized population recovering from ICU treatment (H2), Recovered population (R), Death population (D). In our investigation of COVID-19 spread and the effectiveness of vaccination, we determined crucial epidemiological factors, including the endemic equilibrium and the basic reproduction number (R0). We then conduct mathematical and numerical analysis of the model. Our findings indicate that the endemic equilibrium is stable whenever R0>1 and unstable when R0<1. Additionally, we performed sensitivity analyses on the model, examining the impact of the basic reproduction number. Notably, parameters τi,mi, and γ demonstrated significant influence on the infection rate (basic reproduction number), while parameters β,η, and sv^i primarily influenced the magnitude of the infected population during an epidemic. Moreover, the first and second vaccine doses have similar effect in controlling the spread of COVID-19, while the complete three-dose vaccination demonstrated superior effectiveness in mitigating transmission compared to vaccination with only one or two doses.

Model Comparison for Predicting Beer Sales Volume: a Case Study of a Certain Brand of Beer

Janjira Piladaeng — 2024-06-29

The beer market is a sizable and important market in the alcoholic beverage industry. Accurate prediction of beer sales volume is crucial for entrepreneurs to employ in production planning and develop marketing strategies. As a result, this research aims to study models for predicting the sales volume of a certain brand of beer using multiple linear regression, decision trees, and random forest methods. The accuracy of the models is compared using the Root Mean Square Error (RMSE). The study utilizes monthly data from 2018 to 2021, consisting of 12 independent variables. The statistical analysis is conducted using the R programming language. The results reveal that the most suitable and efficient model for prediction is the multiple linear regression model, which uses a stepwise variable selection based on the Akaike Information Criterion (AIC). The significant independent variables influencing beer sales prediction include 1) the sales volume of the 1st competitor, 2) the third-tier selling price of the 1st competitor, 3) the third-tier selling price of the 2nd competitor, 4) the retail trade index, 5) the consumer price index for tobacco and alcoholic beverages, and 6) the population of Thailand aged 15 and above.

Comparison of Model Performance between Penalized Regression and Machine Learning for Diesel Prices in Thailand

Kulwadee Wahanarat — 2024-06-29

This research study was to compare of model performance for the price of diesel B7, diesel B10, and diesel B20 and find a suitable model to forecast the price of diesel B7, diesel B10, and diesel B20 using daily data from January 1, 2020 to December 31, 2022. The factors used in the study were crude oil prices in the world market, consumption of diesel B7, diesel B10, and diesel B20, exchange rate, consumer price index, oil fuel fund rate of diesel B7, diesel B10, and diesel B20, and crude palm oil prices. Then, compare models to find the best model by measuring the model’s performance with the Root Mean Square Error (RMSE) using Stepwise Regression, Penalized Regression such as Ridge Regression, Lasso Regression, Adaptive Lasso Regression, Elastic Net Regression, and Machine Learning such as Support Vector Regression and Random Forest. According to the research results, Random Forest is the most suitable method for forecasting the price of diesel B7, diesel B10, and diesel B20 with RMSE values of 0.379, 0.3833, and 0.3539, respectively.