Improving participation in voluntary activities in Iran is important because increasing productivity among young people, given the country's young population, contributes to community sustainability. Therefore, a better understanding of volunteer motivation in Iranian youth is needed. The Volunteer functions Inventory (VFI) for assessing volunteer motivations has good psychometric properties and is adapted to several languages, but no validated Iranian translation yet exists. The purpose of this study was to investigate the psychometric Characteristics of voluntary functions inventory in members of Iranian Red Crescent Society. Sample size was 595 members of Youth Organization of Iranian Red Crescent Society from 31 provinces and 175 cities of Iran that were selected by multi-stage cluster sampling method and responded to voluntary functions inventory. Data were collected using a demographic sample and voluntary functions inventory (VFI). Confirmatory factor analysis using principal components method was used for data analysis. The results of the present study showed that the voluntary functions inventory had validity and reliability. Also, the factor structure showed that 29 items and 6 factors well assess people's attitudes to volunteering, and the structure of this inventory was well-fitted and confirmed all goodness of fit models. The present study provides the use of the Iranian translation of the Voluntary functions Inventory (6 scales and 29 items) to assess volunteer motivation among young Iranian volunteers.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In this paper, we introduce open cellular learning automata and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. The numerical results also confirm the theory.  

The aim of inequalities is to develop tools for analyzing the problems in pure and applied mathematics. Our primary objective in this research is to introduce some new type inequalities connected with Caputo fractional derivative. In the light of the operator, we extend and generalize some important inequalities to this fractional calculus dealing with synchronous functions.

Application of the nonlinear H∞ identification method to identify a synchronous generator model is investigated in this paper. The linear H∞ identification method has been well established in the literature for robust modeling despite noise and system uncertainties. Since many practical systems such as synchronous generators are nonlinear, linear models identified for particular operating conditions do not perform well for other operating conditions. To overcome this shortcoming, the linear H∞ identification method has been modified to cover some nonlinearities of the systems such as saturation in synchronous machines. The derived proposed algorithm is then applied to a seventh order nonlinear model of a synchronous machine with saturation effect. In this study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous machine. Simulation results show good accuracy of the identified models.

In this paper, an additive self-tuning (ST) control scheme is presented for a static synchronous series compensator (SSSC) to improve performance of conventional PI control system for damping sub-synchronous resonance (SSR) oscillations. The active and reactve series compensation are provided by a three-level 24-pulse SSSC and fixed capacitor. The proposed ST controller consists of a pole shift (PS) controller and a recursive least squares (RLS). The RLS identifier algorithm is used to estimate parameters of auto regressive moving average exogenous (ARMAX) model. With this scheme, there is no any necessity for retuning the PI controller parameters under different operational circumstances of the system. The SSR analysis are performed using eigenvalue analysis, transient simulation and FFT analysis. The considered test system is adapted from IEEE First Benchmark Model. As shown in the results, the proposed additive ST controller has an effective and acceptable performance for damping of SSR under different disturbances and operating conditions. It should be noted here that all digital simulations have been done by using MATLAB.