The objective of this study is to obtain a model that maximize production of γ-linolenic acid (GLA) by the moss Physcomitrella patens employing response surface methodology (RSM). The RSM with a three-variables (sucrose potassium nitrate and glutamate concentrations) and five-level (-2 -1 0 1 and 2) central composite design (CCD) including 20 experimental runs was employed to optimize the medium composition. Results showed that the experimental data could be appropriately fitted into a second-order polynomial model with a coefficient of determination (R2) more than 0 .9 5 for GLA production. Analysis of variance (ANOVA) revealed that the model was highly significant (p<0.0001) and the effects of the sucrose (20-100 g/L) and glutamate (0.5-2.5 g/L) concentrations on GLA production were significant (p<0.05). The GLA production with the optimized culture medium (sucrose concentration of 62.92 g/L potassium nitrate of 0.80 g/L and glutamate concentration of 1 .4 2 g/L) by RSM increased 4.61 folds when compared with the standard BCD medium. This experimental value (16.37 mg GLA/L) fit well with the predicted values (16.20 mg GLA/L).

This research report describes the study of the development of finite element modeling for magnetoacoustic wave generation for non-destructive tomography. In the study the two-dimensional finite element model is used for the generation of magnetoacoustic wave in the mimic biological tissue model. The Gaussian pulse is applied as the excitation pulse in this research. The result of signal capturing at the boundary of mimic tissue model is used for the reconstruction cross-section image which is agreed to the original conductivity distribution.

Fin Field Effect Transistor (FinFET) technology has increasingly been adopted for use within integrated circuits thanks to its superior electrical integrity and scalability. However a steady increase in vulnerability of FinFET circuits to negative bias temperature instability (NBTI) has recently become one of the major lifetime reliability problems. In this paper we propose a simulation framework to address NBTI in combinational circuits implemented with 14-nm tri-gate FinFET predictive technology. Particle swarm optimization (PSO) algorithm is applied to construct an accurate long-term NBTI degradation model from cycle-by-cycle data history. The proposed simulation methodology provides accurate results with high efficacy in computation time compared to reference models. From the proposed framework the average delay degradation for all experimental circuits under normal operation after 10-year NBTI stress is approximately 6% yet temperature variation in the circuit strongly influences NBTI degradation.

In general for the refrigeration the cooling coil is almost used to be the device to decrease the temperature. Thus in order to design heat exchange we should study the heat transfer into the cooling coil. This research proposes the numerical using finite element method for calculating the temperature at any position of the cooling point for considering to porous media. After that to create an algebraic equation fmite element for writing the program to search the solution by estimating the temperature. The research demonstrates that the changing of temperature will be decreased to saturated condition that is the temperature around the region of the cooling coil model edge will be decreased. The longer time the lower temperature around each position will decrease respectively until the temperature at each position inside the model are the same as region of the cooling coil model edge. Therefore this search result can be the basic for the comparison with the similar problem of other

The problem of this research is automotive part companies do not have order because of the world economic crisis and effect to automotive industry. Automotive and automotive parts demand in the world decrease. Automotive Industry of Thailand received affect. Automotive part companies cannot export to foreign markets. The companies in Thailand need the forecasting of automotive part quantity demanded by foreign markets by selecting the most accurate model. This paper presents the modeling for forecasting demand for automotive part of foreign markets to Thailand with time series forecasting techniques. In this study the automotive part are wheels and including parts and accessories because Thailand exported the most in group of automotive parts. These five countries have consistently demanded the largest number of wheels and including parts and accessories are Japan China South Korea Germany and Indonesia. The research involves historical collecting data for the period of 1997 to 2008. The evaluation function of forecast error is used with MAPE. The results reveal that each country suits for each model in term of the chosen dimension. The government can use the forecasting demand for effective production planning by informing manufactures.