This research aims to study the readiness of graduates to match the labor market and the development of graduates to adapt and develop themselves to meet the policy of Thailand 4.0 by quantitative study. The quantitative method was used by the Faculty of Business Administration. Establishment A total of 20 establishments. The statistics used for data analysis were Frequency Percentage Mean. The research found that the surveyed enterprises were divided into private government and state enterprises. There are 7 government agencies 33.3% 11 enterprises 52.4% and 14.3% of state enterprises. There are 61.90476 and the duration of the establishment is 0-11 years. The availability of students for cooperative education is 3.4762 at the moderate level. The average score of 3.5218 was moderate. The overall composition of computer languages and programs is also the current popular SQL and Photoshop languages of the establishments. Findings from research on the readiness of graduate production. Of the Faculty of Business Administration Rajamangala University of Technology Phra Nakhon to support the move to Thailand 4.0 the current technology has changed dramatically. The curriculum and the academic development should be in line with the changes. Students should always develop themselves.

Thailand is an agricultural country. So that the water resources are important. The water management is very important for keep the water used in necessary time. The monsoon is causes a heavy rain. The monsoon prediction by the mathematical model is important. The accuracy of the forecasts by the predictability measurement method is very important. In this research improvement the predictability measurement for the winter monsoon prediction in the Southeast Asia by the shallow water model. The data from The Bjerknes Centre for Climate Research (BCCR) University of Bergen Norway. The global climate model is Bergen Climate Model (BCM) Version 2.0 (BCCR-BCM2.0) of the Intergovernmental Panel on Climate Change (IPCC) are used. The data for run the model on 19 December 2059 from the WCRP CMIP3 is the initial condition. The model is run for 7 days forecast. The Lyapunov exponent (LE) is the predictability measurement method for verify the efficiency of the model and improvement the largest Lyapunov exponent (LLE) by limit theorems for verify the efficiency of the model too. The results to show that the shallow water model can be prediction the winter monsoon for 4 days forecast is accuracy when compare with the BCCR-BCM2.0 model and measure the accuracy by LE. In addition the improvement the LLE by limit theorems can be measure the accuracy of the winter monsoon for 4 days forecast to same as the LE. Therefore the improvement the LLE by limit theorems can be measure the accuracy of the winter monsoon prediction in Thailand by the shallow water model are suitable.

The objectives of this research are to propose a model for estimating and detecting rice yields in Thailand to investigate the factors related to the rice yields to find the trend and to construct the maps of rice yields in Thailand. A linear mixed model (LMM) including the spatial effects which follow the conditional autoregressive model (CAR) and the time effects which follow the first-order linear autoregressive (AR(1)) model is proposed. The estimated rice yields are used to construct the rice yield maps in Thailand. The dependent variables are the rice yield in each month of each province. The factors considered are rainfall average temperatures and regions. The results indicate that the factors enfluencing the rice yields are rainfall average temperatures and regions (North Northeast South East West Central region is a refrence region). The trend also effects the rice yields. The rice yeilds maps are easy for readers to identify which areas have high or low yields and easy to compare the yields among areas by looking at their different colors.

The objectives of this research are to propose a model for risk assessment of Measles and Rubella morbidity in Thailand to investigate factors influencing on the Measles and Rubella morbidity rates and to construct the disease maps of those two diseases. The generalized linear mixed model (GLMM) in which the responses have a Poisson distribution and the spatial effects follow a multivariate conditional autoregressive model (MCAR) are adopted. The dependent variables are the number of Measles and Rubella disease patients in each province. The factors considered are average temperature the amount of rainfall and regions. The results show that the temperature rainfall and regions influence on the Measles and Rubella morbidity rates. Measles and Rubella maps are easy for readers to see which areas are at high risk and to compare the morbidity rates among those areas by looking at those different colors.

The purpose of this descriptive research was to study the information learning forms of students in Rajamangala University of Technology Phra Nakhon in Thailand 4.0. The sample was 380 students of Rajamangala University of Technology Phra Nakhon studying the first semester of academic year 2018. The research instrument was a questionnaire. The results showed that the students used information learning forms at a high level. Considering each item the students used information to generate the most reports. The students used information for relaxation and entertainment doing homework and knowledge exchange respectively. The students needed quality and specific content characteristics the source of information respectively. Most students required the information source that identified the source of the information or bibliographic information written from the authors direct experience compiled data from various sources and up-to-date information respectively. Most students required content from academic or scholarly authors comments or ideas and the concept or conclusion of the story. The students needed information learning forms in pictures on websites and YouTube respectively