Surface soil shear strength is an effective parameter in soil erosion and runoff modeling, and known as soil erodibility index. This study was conducted to investigate and predict soil shear strength in central Zagros region using multiple linear regressions (MLR). The study site was selected in rangeland and degraded-rangeland landuse with an area of 23562 ha. Based on the maps of geology, topography, landuse and soil capability, 18 Land Unit Tracts (LUT) was created. Supervised random sampling was used to collect samples in every land unit. A total of 120 samples were collected in triplicates to determine sample variability in each LUT. Soil samples were collected from the 0-3 cm of soil depth. Routinely measured (available) parameters included particle size distribution, soil organic carbon, CaCO3 in addition to slope, aspect; elevation and normalized difference vegetation index (NDVI) were used. Three MLR models were tested and investigated for soil shear strength in this study. The performances of the different models were evaluated using spearman’s correlation coefficient (r) between the observed and the estimated values, normalized mean square error (NMSE), mean absolute error (MAE), geometric mean of error ratio (GMER) and geometric standard deviation of the error ratio (GSDER). Results showed that land degradation significantly reduced surface soil shear strength. Addition of slope, aspect, elevation and NDVI to soil attributes as input parameters improved the performance criteria for soil shear strength.

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.

Some Iranian weather stations do not have sufficient instruments to measure climatic parameters such as pan evaporation, solar radiation and sunshine hours and/ or lack long-term and continuous data. A Pedo-transfer function (PTF) is an indirect method to predict parameters that are difficult to measure. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) were used to develop the PTFs. In this study, application of PTFs for estimation of climatic parameters was conducted for synoptic weather station of Tabriz. The objective of this study was to predict monthly parameters as solar radiation, evaporation, and sunshine hours from readily available parameters of weather. The results indicated that PTFs estimated solar radiation, evaporation, and sunshine hours well. The performance of both MLR and ANN were almost similar, only for solar radiation, ANN predictions were better than regression equation. The estimation of pan evaporation and sunshine hours were better than solar radiation in both PTF functions (MLR and ANN).

The main goal of the present study is to identify characteristics of the inter-station horizontal magnetic responses and the vertical magnetic data, as two types of magneto telluric transfer functions, in the modeling procedure. Through consideration of model responses and two-dimensional inversion of synthetic data, sensitivity of the data components in detecting different geophysical structures is investigated. An inversion of the magnetic transfer functions related to a real data set has been also accomplished, and the importance of a reference selection in analysis of the inter-station horizontal magnetic data has proved. Analysis of the results of the synthetic tests confirms the efficiency of the modeling based on horizontal magnetic responses and indicates more accuracy of this type of data, especially in terms of the resolution of the deep targets. There is not a great difference in the resolution of the structures between two datasets, as closely located anomalies are examined. Furthermore, both transfer functions can also sense vertical conductivity distribution.

Determination of saturated hydraulic conductivity Ks is needed for many studies in water movement and solute transport in the soil. Although some advances are made for direct measurements of ks, they are usually time consuming and costly. Some attempts have been made to indirectly predict the saturated hydraulic conductivity from the easily/readily obtainable parameters. The so-called pedo-transfer functions (PTF) proved to be a valuable tool to predict the Ks from the available soil data. The objective of this study was to derive some PTFs for different soil textures, requiring minimum input parameters. Consequently, 45 soil samples were taken and their Ks were directly measured, using the Geulph permeameter. The multilinear regression method was used to develop the pedo-transfer functions. The measurements were consisted of bulk density, particle density, field capacity, effective porosity (θe), particle size distributions, effective particle diameters, geometric mean diameter (dg) and geometric standard deviation of soil particles (δg). The normality and multi-colinearity of the parameters were tested by Minitab package. Three PTF functions were then derived, using different input parameters. The results indicated that accuracy of the derived PTFs can well predict (R2=0.73) the Ks from θe, dg and δg.

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.

The inertia of wave measurement buoys impresses transfer functions on the wave measurement data (i.e. heave, wave slopes with respect to the horizontal axes). This effect causes difference or error between the measured and actual wave data. Calculation or estimation of the buoy transfer functions and affecting the inverse of them, makes it is possible to achieve more accurate wave data. In this paper, an algorithm for estimation of the buoy transfer functions using in-situ wave data is introduced and the simulation results are presented. The effect of the buoy transfer functions on the spectral parameters is also investigated. This algorithm uses the intrinsic properties of the sea waves.