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In order to predict the behavior of soil-related phenomena, it is necessary to have knowledge about unsaturated flow and using models that provide optimal estimates of the retention curve and hydraulic conductivity of soils. Despite the widespread use of the classic van Genuchten-Mualem model (VGM), this model usually performs poorly in predicting hydraulic conductivity and modification of some of its parameters seems necessary. In this research, a number of 283 soils from different texture of the UNSODA bank were selected and divided into two sections of calibration and validation sections and their soil parameters were extracted and categorized. Then, by defining the modified unsaturated hydraulic conductivity (Ksc) instead of the saturated hydraulic conductivity (Ks) and determining the limits for l and n parameters, the hydraulic conductivity-moisture function of this model were solved using 24600 pairs of points li and nj for each soil of the three main soil texture classes. In the following, the optimal l value (l̂) of each texture class was selected based on the minimum value of the hydraulic conductivity estimation error using the root mean square error (RMSE) index and the n values that create the minimum errors were selected as the optimal pore size distribution coefficients of the hydraulic conductivity-moisture function (n̂opt). In order to create pedotransfer functions for estimating n̂opt, we ran stepwise regression in MATLAB software considering the condition of statistical significance (P-value=0.05) for independent variables and functions for each soil texture class. After creating pedotransfer functions, the results of the proposed method of this research (MVGM) were compared with the VGM model results using RMSE and Nash-Sutcliffe (NSE) indices. The results showed that in both sections of creating and validation functions, the MVGM performed better in estimating hydraulic conductivity and had a higher efficiency index for all classes of soil texture.

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.