Presently the rail transit system plays a major role in the commercial world that they currently use diesel engine not only causing to a lot of fuel consumption but also generating numerously pollution. Therefore this project aims to study the break-even point of investment under comparison of brake specific fuel consumption and pollution of locomotive engines. By experimental works there are 3 types of locomotive engine: Pielstick M.T.U. and Caterpillar which are the difference of the combustion chamber and the fuel injection systems. The simulation load on the static tests (Dynamometer) was used following the regulation characteristic curve which is standard of State Railway of Thailand. As a result of testing the engine using common rail type of fuel injection system M.T.U. was the lowest brake specific fuel consumption at all of engine by the average of all engine speed 206 g/kW-h of brake specific fuel consumption. It is lower than Caterpillar (Semi common rail with direct injection) which is 314 g/kW-h equivalent 34.39 % while Pielstick engine a mechanism fuel injection system is 389 g/kW-h equivalent 47.04 % For pollution area there are Hydrocarbon (HC) Nitrogen Oxide (NOX) and black smut. The engine with direct injection system relieved the previous substance less than the pre-combustion engine. Moreover the compound of carbon monoxide (CO) was found that the pre-combustion engine (Pielstick) discharged carbon monoxide (CO) less than the direct injection engine (Caterpillar) whereas the common rail engine emancipated same substan cenear by with the pre-combustion engine (Pielstick). However the new technology of the engines is high maintenance costs. Therefore a number of applications have to be needed to make a quantitative value for the investment.

This paper discusses the mixed alloy silver pink by spending innocent Alloy capital ratio of 58.00% and check the analysis of color against Silver 925 under the metal content of about 58.40% alloy 41.60% result. According to the study the researchers used to hold above ingredients prototype to develop a mixture of silver to feature pink by analyzing the color (Color Analysis) tested the color of silver mixed pink capital ratio of 116.80 alloy. 83.20 wt.% has a shiny bright color space the L 67.17 a = 9.60 b = 2.04 a compound that can be used to produce silver mixed pink. In the manufacturing industry has for the user.

The objectives of this research are to propose a proper model for defective products in an autoparts manufacturing industry to apply the proposed model to the defective products data to investigate the factors related to the machines producing defective products and to compare the proposed model with the generalized estimating equations model (GEE). The proposed model is a generalized linear mixed model (GLMM) in which the dependent variables have a Poisson distribution. The parameter are estimated using a Bayesian method. The data were collected from 12 machines 15 days for each machine from an autoparts manufacturing factory. The data collection dates vary depending on the working steps and the product types. The results show that the proposed model fits the data. The factors related to the machines producing defective products are working steps workers and product types. According to the estimates of the factors effects the working step producing the largest number of defective products is the working step 2 followed by the working steps 3 1 and 4 respectively. The worker producing the largest of defective products is the worker 4 followed by the workers 10 5 8 6 2 3 1 7 and 9 respectively. The product type 3 has the largest number of defective products followed by the product types 2 and 1 respectively. If the population-averaged effects are focused the GEE is applied. However the subject-specific is focused the GLMM is applied.

The objective of this research is to use a stochastic process for building a model using Bayesian parameter estimation. The model is applied to the of crash prices and future prices of rice in Thailand for 120 moths including monthly closing average price of white rice 5% non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105. The data are time series data containing autocorrelation trend seasonality and outliers. The algorithms are turned into codes in OpenBugs and the performance of the model is evaluated using simulation in R. The model with a Weibull cumulative distribution function trend the model with an exponential cumulative distribution function trend and the exponential smoothing model are compared. The root mean squared error (RMSE) mean absolute percentage error (MAPE) and mean absolute error (MAE) are used as the criteria. The results show that the model with the model with an exponential cumulative distribution function trend gives smallest RMSE MAPE and MAE for the crash prices of non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105 for both model fitting part and model validation part. The model with a Weibull cumulative distribution function trend gives the smallest RMSE MAPE and MAE for the closing average price of white rice 5% for both model fitting part and model validation part.

The objectives of this research are to propose a Bayesian model for spatial time series analysis to apply the proposed model to forecast a monthly rubber yield in Southern provinces of Thailand and to compare the performance of the proposed model with the classical Holt-Winters Additive Exponential smoothing. The proposed model is a linear mixed model (LMM) with spatial effects which follow a conditional autoregressive model (CAR). Dummy variables are used for seasonal effects. A Bayesian method is used for parameter estimation. The estimated monthly yields are used for the monthly rubber yield forecasting. The dependent variables are the rubber yield in each month of each province. The data are secondary data at a provincial level. The factors considered are spatial effects heterogeneity effects and seasonal effects. The results show that the factors influencing on the amount of rubber yields are spatial heterogeneity and seasonal effects. The proposed model is proper and forecast accurately. Using the mean absolute error (MAE) the proposed model has a better performance compared to the classical Holt-Winters Additive Exponential smoothing in both model fitting and model validating. The proposed model should be the first consideration for spatial time series forecasting.