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).

The research purposes were to analyze and search for wastes in the planning process and transportation of the automobile parts of a transport company in Samut Prakan using Makigami Lean and ECRS Techniques. The informants used in this study were the support staff working the planning process and automobile part transportation for the car assembly factory in the industrial estate Rayong Eastern Seaboard. Six employees from different work units took part in the in-depth interview as the research tool for the data collection. Statistics used in the data analysis including percentage and workflow analysis with related management tools. The study indicated that searching for the waste in the technical planning process with Makigami Lean and ECRS Techniques could help reduce the work time for 1060 minutes in each step.

Metaheuristic optimization approach has become the new framework for control synthesis. The main purposed of the control design are input (command) tracking and load (disturbance) regulating. This research proposes an optimal proportional-derivative (PID) controller design for the brushless dc (BLDC) motor speed control system with input tracking and load regulating constrained optimization by using the cuckoo search (CuS) one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time the maximum overshoot settling time and steady-state error are set as inequality constraints for tracking purpose while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CuS will be compared with those obtained by the particle swarm optimization (PSO) and genetic algorithm (GA). From simulation results it was found that the parameters of CuS were optimal than both PSO and GA for BLDC motor speed control system satisfying the preset input tracking and load regulating constraints. In addition the simulation results are confirmed by the experimental ones from the BLDC motor speed control system developed in the laboratory.

The objective of this research is to propose method for optimization route selection by Analytic Hierarchy Process (AHP) and The Technique for Order Preference by Similarity to The Ideal Solution (TOPSIS) model. The model was applied to case study route selection from Laem Chabang Port in Chonburi to Bangpoo Industrial Estate in Samut Prakan based on the criteria: cost of transportation transit time risk of freight damaged risk of infrastructure and equipment and risk of other factors. AHP was used to determine the relative weight of criteria. Additionally TOPSIS was used to select the best alternative routes. The result showed that the AHP-TOPSIS model which is new approach for route selection flexible decrease discrepancy from weighting more efficient than one either AHP or TOPSIS and not too complicated models compared to the other methods in multiple criteria analysis.

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.