Background and Aim: Measuring the outcome of chronic diseases such as Multiple sclerosis is an important factor in assessment of disease impact on different dimensions of quality of life and in evaluation of therapeutic interventions. The aim of this study was to perform the cross-cultural adaptation of the MSIS-29 which is a MS-specific outcome measure for Iranian patients.Materials and methods: The Iranian adaptation process of the MSIS-29 included 5 steps. To evaluate psychometric properties of the translated version, the questionnaire was administered to a consecutive sample of 96 patients with clinically definite MS referred to our out-patient clinic. Test-retest reliability was assessed in a sub-sample consisted of 30 patients. These patients completed the questionnaire on two occasions separated by a 7-day interval. The Iranian version of the SF-36 was also administered to this sub-sample in order to evaluate the validity of translated MSIS-29.Results: Statistical analysis indicated that the Persian version of the MSIS-29 had high internal consistency (cronbach's alpha coefficients> 0.70) and test-retest reliability (intra-class correlation coefficients >0.70) and a good validity.Conclusion: The Persian version of the MSIS-29 is a reliable and valid instrument for measuring MS outcome in Iranian patients. It can be used in clinical trials and cross-sectional studies.

In this paper, first, the equations of motion for a rectangular isotropic plate have been derived. This derivation is based on the Von Karmarn theory and the effects of shear deformation have been considered. Introducing an Airy stress function, the equations of motion have been transformed to a nonlinear coupled equation. Using the Galerkin method, this equation has been separated into position and time functions. By means of the dimensional analysis, it is shown that the orders of magnitude for nonlinear terms are very small, with respect to linear terms. Then, for the first time, the invariant manifold theory has been applied to the plate problem and it is proved that the nonlinearities are stiffness and inertia types. Finally, the Multiple scale method is applied to the equations of motion and closed-form relations for the nonlinear natural frequencies and mode shapes of the plate are derived. The obtained results are in good agreement in comparison with numerical methods. Using the obtained relation, the effects of initial displacement, thickness and dimensions of the plate on nonlinear natural frequencies and displacements have been investigated. These results are valid for a special range of the ratio of thickness to dimensions of the plate, which is a characteristic of the Multiple scale method.

Nonlinear vibration analysis of the reaction wheel based on rotating shaft/disk model with elastic bearings which subjected to temperature changes is investigated. The rotating shaft has been modeled by the Timoshenko beam theory and elastic bearing model consists of a nonlinear spring and linear damping characteristics. Gyroscopic effects and disk imbalance forces, as well as the couplings associated with the shear deformation have been considered in the equation of motion. Temperature changes (increase or decrease) is appeared in system as an axially load when the supports of the rotor do not allow the shaft to move in axial direction. The method of Multiple scales has been applied directly to solve nonlinear mathematical model and hence obtain the nonlinear dynamic response of the rotor system. Numerical simulations are based on a real reaction wheel parameters. The resonant curves have been plotted in time and frequency domain and discussed w/wo temperature changes. It has been shown that temperature changes significantly affects the dynamic behavior of the rotor system leading to resonant hard spring type curves.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and hybrid deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed multi-scale deep neural network uses a combination of an embedding layer, a modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM network.