The objectives of this research were to create a model for time sereries data prices and yields of natural rubber to be more accurate prediction and to compare the proposed model with the classical time series models such as the Holt-Winters Additive Exponential smoothing model (Holt-Winters ES) and the seasonal autoregressive integrated moving average model (SARIMA). The proposed model was a linear mixed model (LMM) consisting with trend autoregression outliers and sesonal terms. Dummy varialbles and Fourier seasonal effects were studied. Bayesian estimation was used and the estimated prices and yields were used for prediction. The results show that the proposed model with a Fouries seasonal effect is the most appropriated comparing with the dummy seasonal effect the Holt-Winters ES and the SARIMA having the least mean absolute values (MAE) both fiting and validating parts. The proposed model with sesaonal dummy variables should be the first consideration for forecasting spatial time series with a seasonal component.

Recently the Thai industry has continually grown up and also plays a role to economic growth in the country which affects the many of automotive components to be in the market. While the degeneration and waste of automotive components had increased as well. Therefore a crash box is one of the automotive parts which damage from the accident. Consequently the researchers have created a prototype material to develop into the crash box to reduce the environment problem from automotive parts. The random mixture of water hyacinth and husk was attempted to up to 5 ingredients. By forming a solid cylinder and 200 mm of long was used as prototype of the crash box in this research. Thus the energy absorption using compressing test was attempted to by the universal testing machine. The results showed that using polymers as a binder with 50% of water hyacinth and 50% of husk was the better ability of energy absorption. Regarding this ingredient 1612444 J/m3 of the energy absorption was referred to indicate that is the best ability of energy absorption. It is probably reduce the severity of the impact in the event of pedestrian collision.

This research presents a study of the compressive strength tensile strength flexural strength and water absorption of Cellular Lightweight Concrete containing fine rice hush-bark ash. Portland cement (CT) is partially replaced with pozzolans 20 30 and 40% by weight of the cementitious materials. The results show that the compressive strength tensile strength flexural strength and low water absorption of Cellular Lightweight Concrete improves substantially with partial replacement of Portland cement with rice hush-bark ash. Cellular Lightweight Concrete mix cement containing 20 and 30% of pozzolans can be used to make cementitious materials

This research presents a study of the use of classified fly ash and ground rice husk ash as a pozzolanic material in producing high-strength concrete. Portland cement (CT) is partially replaced with classified fly ash and ground rice husk ash by weight of the cementitious materials. Compressive strength porosity and chloride penetration were investigated. The results show that the compressive strength resistance to chloride and low porosity of high-strength concrete improves substantially with partial replacement of Portland cement with fly ash and rice husk ash.

The objectives of this research are to propose a linear mixed model (LMM) with spatial correlation for an analysis of rice yields in Thailand to estimate the rice yield in each month of all provinces in Thailand to investigate factors influencing on the rice yields and to construct the maps of rice yields. A linear mixed model (LMM) in which the spatial effects follow the conditional autoregressive model (CAR) is used. The estimated rice yields are used to construct the rice yield maps. The dependent variables are the rice yield in each month of each province. The data are secondary data at a provincial level. The factors considered are rainfall averaged temperatures and regions. The results show that the factors influencing on the rice yields are rainfall averaged temperature and region. The amount of regional effects ordering from largest to smallest values are northestern region southern region western region central region eastern region and northern region respectively. The top ten provinces and months with high yields ranking from largest to smallest values are Ubon Ratchathani in November (1328000 ton) Surin in Novermber (1070000 ton) Si Sa Ket in Novermber (1023000 ton) Buri Ram in November (939400 ton) Nakhon Ratchasima in November (907500 ton) Roi Et in Novermber (852800 ton) Chiang Rai in Novermber (769800 ton) Khon Kaen in Novermber (692800 ton) Maha Sarakham in Novermber (676200 ton) Udon Thani in Novermber (620600 ton) respectively. The rice yeilds maps are easy for readers to identify which areas have high or low yields. They are a useful tool for planning decision making and implementing plans to help rice famers to increase their rice production.