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In this research, changes in potato texture during storage were evaluated by laser light backscattering imaging method and using physical and mathematical models according to the results of mechanical destructive tests. Using the characteristics of the images, we tried to develop models for predicting the mechanical properties of potatoes. At first, the backscattering images taken from 594 potatoes. Backscattering profiles obtained for each sample. Cylindrical samples extracted from potatoes and subjected to uniaxial compression test. By fitting the first order Gaussian model to the obtained data from the final image and calculating the frequency of repetition of different intensities in the images, the parameters related to the absorption and scattering of reflected light from the tissue of the tubers obtained at three wavelengths of 650 nm, 780 nm and 980 nm and in six months of the cold storage. The behavior comparison of the mechanical test results and the changes in the Gaussian model fitting estimated parameters and the intensity frequencies during storage showed the effect of mechanical structure changes on the optical properties of the samples. Five models with satisfactory results obtained to predict the tangent modulus of elasticity (R=0.827, 0.815), secant modulus of elasticity (R=0.808), toughness (R=0.874), maximum fracture force (R=0.811) with the fuzzy-neural inference system. The results in this study could mainly useful for understanding the effect of tissue changes during storage on light scattering and absorption. It can be useful to design high-performance sensors and quality determination systems for non-destructive mechanical properties evaluation of potato tubers.

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In the present paper, the results of an approximation method named structural index used as the first step, the process of design and optimization of stiffened panel with Gaussian type surrogate model are carried out. Modeling phase is based on the finite element analyses of the structure. Nonlinear buckling load is set as the design constraint. The surrogate model is employed to reduce the number of finite element analyses in the optimization process. Therefore time of design process is reduced. Using infill points in the modeling and optimization process, converging to local optima is ensured. Introducing a novel technique, finding the global optimum of the surrogate model is guaranteed. The approach of surrogate based optimization is illustrated using two test problems. Also the sensitivity of the response to the initial sampling plan is investigated. Convergence criteria usually used in surrogate based optimization is modified to speed the convergence but is not affected the quality of the response. Design optimization process is presented for two types of stiffened panels. In type 1 stiffened panel with 4 design variables, the initial training set is constructed using 55 points. The response is obtained after addition of 5 infill points. For type 2, the initial sampling plan is selected to be 58 points. The optimization process is stopped after adding 173 infill points. Finally, obtained results are compared with the results of structural index method and an approach toward global optimum of the compression stiffened panel is introduced with the characteristics of optimum structure.