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.

The objective of this research is to study the factors affecting the academic position of faculty members of the faculty of science and technology Rajamangala University of Technology Phra Nakhon which consists of 5 divisions; Division of Environmental Science and Technology Division of Industrial Materials Science Division of Computer Science Division of Science and Division of Mathematics and Statistics. By using SWOT Analysis tools it is found that the main strengths are the progress in the professions increasing of the development of their knowledge potential and fame. The main weakness is the lack of understanding of how to write teaching documents lack of time and lack of interesting research topics. An opportunity is the mandatory criteria of the university. The faculty has financial support for academic positions and other rights from academic positions. The threat is the lack of research budget. The faculty does not have specialized counseling officers and frequently change the assessment criteria. We use the results of the SWOT analysis to group relationships with the TOWS Metrix tool analyzed in the form of a matrix relationship to define different types of strategies to suggest the results of the study in formulating policies to help support and encourage faculty members of the faculty that can further be determined for academic.

The main objective of this research is to study the factors that affect the work efficiency of personnel. Faculty of Science and Technology Rajamangala University of Technology Phra Nakhon This will lead to suggestions for the development of personnel to be more efficient in their operations. Data was collected between October 2018 and September 2019 which was a survey research using a questionnaire consisting of 4 parts: 1) general information of the respondents which was demographic characteristics 2) Satisfied with the operational efficiency 3) quality of working life and 4) Other suggestions with a sample of the research as Personnel under the Faculty of Science and Technology Rajamangala University of Technology Phra Nakhon amount 50 people. Data analysis used descriptive statistics for demographic data. And use statistical methods for analysis of relevant factors and testing hypotheses. The results of the study showed that the factors of work success of the Faculty of Science and Technology personnel Rajamangala University of Technology Phra Nakhon Able to work according to the set goals Without having a backlog And proud of the work Able to solve immediate problems Which is considered to be very good therefore Faculty of Science and Technology Should focus on personnel development in the workplace In order to further improve the operational efficiency of the personnel.

The research aimed to analyze and compare contents of accumulated residue of chlorpyrifos chromium lead and cadmium in five varieties of vegetables in Loei River Loei Province. Five varieties of vegetables such as Vegetable fern (Diplazium esculentum Limnocharis flava (L.)) Limnocharis flava (L.) Buch Lasia spinosa (L.) Thwaites swamp cabbge (Ipomoea aquatica Forsk) and water mimosa (Neptunia oleracea Lour.) were collected during winter summer and rainy seasons from Phu Luang Mueang Loei and Chiang Khan District. Chlorpyrifos was analyzed by high performance liquid chromatography (HPLC). The results showed that the highest chlorpyrifos contents were found in vegetables in winter and summer season in the amount of 0.04±0.00 mg/kg. Analysis of all three heavy metals were carried out dry ashing at 500C and acid digestion with nitric acid prior to analyzing by flame atomic absorption spectrophotometry (FAAS). The results showed that cadmium accumulation in the highest content among all metals in water mimosa (Neptunia oleracea Lour.) in summer season from Phu Luang District (1.83±0.01 mg/kg). The second highest metal content was lead which has found in swamp cabbge (Ipomoea aquatica Forsk) in summer from Mueang Loei District (1.40±0.08 mg/kg). chromium showed the lowest content in Limnocharis flava (L.) Buch in winter from Mueang Loei District (0.47±0.04 mg/kg). The contents of chlorpyrifos and heavy metals were not exceed the legal limits set by International Standard Food and Ministry of Republic Health No.98 of Thailand except Ipomoea aquatica Forsk which collected in summer that showed the lead content higher than the legal limits.

In this paper proposed remote sensing using Normalized Difference Vegetation Index from NOAA STAR cluster value from k-means temperature rainfall number of rainy days and runoff to create runoff prediction model using Artificial Neural Network (ANN) and evaluated runoff models with the R2 and RMSE. The results show that the using of cluster value with other parameters to create predictive models can enhance forecasting results. When using Normalized Difference Vegetation Index with temperature value at lag time 1-2 month and cluster value at lag time 1 month to create model with ANN we have got the best performance which are RMSE=0.09 and R2=0.743. The experimental results shows that remote sensing data and cluster value from k-means can be used to predictive the runoff effectively.