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The response surface methodology was employed to optimize the effects of pH, temperature (˚C), time (min) and the ratio of enzyme to substrate (% of substrate) on the hydrolysis process of cuttlefish muscle by alcalase. Central composite rotatable design with 5 levels and 4 factors and α=2 was used for the optimization of the process to gain the highest degree of hydrolysis. pH, temperature, time, enzyme concentration, interaction of temperature-enzyme concentration, square of pH, temperature, time and enzyme concentration had significant effects on the process. The R2 = 0.95, lack of fit < 0.05 and adeq-Precision of 14.16 for the model showed that the model could explain the variability within the range of values. The optimum condition for 42.0117 % of degree of hydrolysis was determined by Design Expert as pH 8.19, temperature 50.23, time 129.62 and enzyme2.15%.

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Optimization of protein hydrolysate from head and arms of cuttlefish (Sepia pharaonis) was examined. For this purpose, response  surface  methodology  (RSM)  was  employed  to  investigate  the effects  of different  operating  conditions  on  hydrolysis  process  of  cuttlefish protein by the application of alcalase enzyme. A Box-Behnken design with three factors at three levels was used for hydrolysis optimization and to check any individual or interaction effects between the experimental factors. In this method, the effects of three independent variables, including temperature, pH and enzyme to substrate ratio, were investigated on hydrolysis rate as a surface response. The mathematical model showed a good fitness with experimental data. Optimum conditions for temperature, pH and enzyme quantity were determined as 54.33 ˚C, 8.49 and 1.97 %,  respectively, which caused nearly 14.5 % hydrolysis degree. Based on the lack of fitness factor which was not significant, it was deduced that the resulted model was capable of prediction at different studied levels of variables. In this study, in order to confirm the conditions that proposed by mathematical equation, the hydrolyzed protein was produced accordingly at which resulted in a 16.8% hydrolysis degree. This finding was according to the aim of present trial by producing a protein hydrolysate with maximum hydrolysis degree. Then the functional properties of protein hydrolysate powder from optimized conditions were measured. Functional properties of this protein powders indicated a good solubility, but weak levels of emulsifying and foaming capacities.

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A suitable design is one design can achieve to its aims with minimum cost and needing to less computing time. In civil engineering due to survey of large scale structures and large number of design variables, it is so hard achieving to such design only based on experience and therefore optimization methods came to help designer as useful tools in order to find an economic and efficient design. Structural optimization can be defined as a process of dealing with the optimal design of various structures. Ausual objective function is the weight of the structure. In general, there are three main categories in structural optimization applications, namely, size, topology and geometry (shape) optimization.Cellular automata (CA) is a computationally efficient and robust tool to simply implement complex computations. As CA is simple to be implemented and can deal with complex problems without extensive mathematical computations, it is widely used in various fields of science and engineering.In recent years, various meta-heuristic inspired optimization methods have been developed.Almost all of metaheuristic algorithms come up with an idea of employing a particular process or event in nature as a source of inspiration for the development of optimization algorithm. The Cuttlefish algorithm is inspired based on the color changing behavior of cuttlefish to find the optimal solution. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors.In this article, cuttlefish algorithm (CFA)combined with cellular automata (CA) and were used for optimization truss structures.First, cellular automata and the Moor neighboring cells are defined and to the number ofsquares of the cell number ofcellular automata lattice( )is selected from the  best population. Then, the variables vector and their objective function of selected population are placed in each cell of the cellular automata.In a Moor neighboring, nine cells are compared to each other and the best answer ( )is selected and that is used to create new population.Finally, the best person in thenew population will be selected and itreplacedwith the worst person in the cellular automata, and thus the cellular automatais updated. Some benchmark numerical examples were solved using the CFA and CA-CFA algorithms, and the results of the numerical examples showed that the enhanced algorithm performancesbetter in size and topology optimization of truss structures than cuttlefish algorithm and other methods introduced in the literature. Finally, it can be concluded that the convergence speed of the improved algorithm compared with previous approaches is higher and its ability to achieve the desired values is better too.