This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life machine type 6 worker group 3 and 4 working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimation

This research aims to investigate the cause of the accident that occurred on public passenger van by in-depth accident investigation method. The Method is a powerful and reliable standards and generally accepted. Researchers conducted a thorough review and collect information about bus accident resulting in the death and injury. The results of the analysis showed that characteristics of majority of the accidents occur only one public passenger van(Single-Vehicle Accident). Factors related to the cause of the accident was is mistakes and shortcomings and the interaction between people and road. However considering the overall error of the defect (the driver) is the most of the accident. The analysis of the cause and severity of the accident was found the Deaths by the number of many loss due to the Burned and Physical shocks . Result in death. And Door of the public passenger van was damaged out of the car to make it slow. And most of the passengers could not escape in time.

This article presents texture analysis of crispy snack products by using Acoustic Emission (AE) to measure sound that occurred when the products were chewed or broken. This technique has currently gained great interest from food scientists. A sound emitted from food was found to be related with microstructure and crispness. Measurement of sound emitted from food when it was chewed consists of three main steps: 1) Apply force on piece of food with a texture analyzer; 2) Detect sound signal with an acoustic envelop detector; and 3) Interpret the collected data of mechanical force and acoustic emission.

The objectives of this research were to select the most appropriate forecasting model for uncertain demand of hot-rolled steel and to find the purchase amount and the reorder point of some raw materials in order to minimize the total cost of production in an iron and steel production planning. The three forecasting models which were exponential model ARIMS model and the proposed Bayesian model were studied. An iron and steel factory in Samutprakarn province was a prototype for this study. Because of their uncertain high demand the Steel H-Beam Steel plate steel round bar and black steel round bar with various sizes were selected and their parameters including demand rate inventory cost back order cost and setup cost were collected. The study found that the proposed Bayesian forecasting model is the most appropriate. The predicted demand and all parameter values were used to find the purchase amounts and the reorder points of the raw materials under the (rQ) policy of the stochastic inventory model.

The purposes of the research were to conduct an error analysis in paragraph writing of the students of the faculty of Liberal Arts Rajanangala University of Technology Phra Nakhon to collect information to be used as a guideline for English teaching and learning and for English curriculum development for undergraduate students of the faculty. The simple-random sample group of 36 students was 40% of the total population 88 fourth-year students in the academic year 2010. The tools were a paragraph -writing worksheet and a form prepared to record the frequency of each type of errors ofeach sample The results of the study indicated that among four main types of errors : 1) paragraph format 2) paragraph structure 3) Grammar and sentence structure and 4) punctuation and mechanics it was found that errors in grammar and sentence structure were most frequently found (58.72%). This included 11 types of errors in the order of frequency found as follows: word choice verb forms detelminers sentence fragments prepositions singular/plural nouns verb-tenses run-on sentences subject-verb agreement wrong word order and use of reference terms accordingly.