The correct estimation of river discharge is an important issue in forecasting of drought and floods, designing of water structures, dam reservoir operation and sediment control. For this reason, water resources managers used intelligent techniques such as Artificial Neural Networks and data mining methods such as Decision Tree to reliably estimate the discharge in a river. In this study, the Elman Neural Networks (ENN) and M5 model trees were used to forecast daily discharge of Aharchay River. The daily discharge data of Aharchay River measured at the Orange hydrometric station was used for modeling. The results showed that for the forecasting discharge of one day ahead, the ENN method presents more accurate results in compression with M5 model. For forecasting discharge of one day ahead, the best scenario of ENN model with a relatively complicated structure of 9-3-1 that indicating 9 nodes in input layer, 3 nodes in hidden layer and 1 node in output layer, the calculated error measures were R2=0. 90, RMSE=0. 028 (m3/s) and MAE=0. 001 (m3/s). The corresponding values for M5 model with only two input parameters including the discharge of current and last day, were R2=0. 83, RMSE=0. 734 (m3/s) and MAE=0. 317 (m3/s). Comparing the performance of ENN and M5 models indicated that, however the ENN approach may present more accurate results than the M5 model tree, but the M5 model provides more understandable, applicable and simple linear relation in forecasting daily discharge. In addition, the number of required input parameter for M5 model is less than ENN model. Thus, the M5 model tree can be used as an alternative method in forecasting daily discharge.

This study applied three Artificial Intelligence (AI) models to project the Ductile to the Brittle Transition Temperature (DBTT) of Functionally Graded Steels (FGS). These prediction models are Minimax Probability Machine Regression (MPMR) model, Genetic Programming (GP), and Emotional Neural Network (ENN) algorithms with strong prediction performance. The data of FGS type, crack tip configuration, the thickness of the graded ferritic zone, the thickness of the graded austenitic region, the distance of the notch from the Bainite or Martensite intermediate layer, and temperature were used as inputs in the establishment of the AI ​​models. Charpy impact test (CVN) values obtained from experiments used as output. The datasets have been divided into two groups: one for training and another for testing. The performance of the established AI models was evaluated through 16 statistical indicators and graphically used regression error characteristics, an area over the curve, Taylor diagrams, and scatter plots. As a result, the GP model showed superior prediction performance to other models. The primary objective of this study was to decrease the parameter count while also facilitating model comparisons. In this way, in areas with complex studies such as civil engineering; it allows the work to be completed more practically.

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets. Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter, then the performance of three ANN-based classifiers have been investigated. Materials and Methods: The input features of ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural networks based on Akaike information criterion (ENN-AIC) were extracted in the form of QMTI and T1 mean values from parametric maps. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria. Results: The results indicate that ENN-AIC-based classification method has achieved 90% accuracy, 92% sensitivity and 86% precision compared to other ANN models. NPV, FPR and FDR values were found to be 0. 933, 0. 125 and 0. 133, respectively, according to the proposed ENN-AIC model. A graphical representation of how to track actual data by the predictive values derived from ANN algorithms, was also presented. Conclusion: It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF. In addition, this research provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.

We study processes in which neutrinos can be generated as a possible source for the neutron emission form neutron stars. In the high energy limit and in the range em < E < Mw the cross sention for ge →ev`v is analytically calculated. We also calculate the cross section for gg→v`v in the noncommutative space. The obtained results in comparison with the cross section for gg→v`v and gg→gv`v in the commutative space could be quite important in the astrophysics. As a result the cross section for gg→v`v in the noncommutative spaces, in the range A = 100- 300Ge V, is comparable with its counterpart in the commutative space.  

Land cover always has changed due to human activities and natural phenomena, Intensive and variety of these changes in urban environments are more than others. The objective of this research was assessment the temporal and spatial changes for two coastal cities (Chalus and Babolsar) and two non-coastal cities (Ghaemshahr and Amol) in Mazandaran province with the view to compactness, complexity and centrality of urban form using landscape metrics. The methodology of this research was quantify method and the land use maps were produced in three classes (urban, cropland and water) by maximum likelihood classification using Landsat satellite images. For landscape change analysis 12 landscape metrics was used in the class and landscape level. The results show that the NP for cropland in four cities increased, which represent fragmentation, loss of continuity and interference in cropland. Additionally, increasing trend of number of patches was observed in two cities Ghaemshahr and Babolsar in landscape level showed fragmented structure in these cities. Also, ENN-MN decreased only for Ghaemshahr that means high centralization was occurred in this city. Generally, the significant difference was not observed between coastal and non-coastal cities with the view to compactness and complexity.

Background and objectives: Getting informed about of land uses patterns and knowing the changes of each land use, during the time is the one of the prerequisites for the correct uses of land. During the last decades many methods of this regard have been developed. One of these methods is the use of landscape metrics. Landscape metrics are quantitative indices that describe compositional and spatial aspects of landscape. The most important impose on these studies is that changes into landscape patterns strongly affected landscape function. Due to the special ecological position of the Arasbaran biosphere, and some destructive factors such as over-grazing, high acreage of agricultural fields and encroachment of human-made structures into natural ecosystems, the area has witnessed a noticeable rate of land degradation during the last decades. Therefore, the main objective of this study was analysis of land use and land cover changes using ecological landscape metrics. Materials and methods: At first, the multi temporal Landsat images dated 1990, 2002 and 2014 were provided. Based on former knowledge from study area and study aim, satellite images were classified in seven classes including high forest, low forest, agriculture, grassland, barren land, water and urban area. For quantitative landscape metrics for each land use map, we used Fragstate software. Selected metrics included CA, PLAND, NP, LPI, MN_SHAPE, ENN, IJI, DIVISION and SHDI. Results: The result of calculating CA, PLAND and NP metrics showed that forests were degraded and urban area and barren land were extended. Decreasing the index of the largest patch (LPI) and increasing indexes of contagion-interspersion (IJI), ENN and DIVISION for forest area indicates the destruction and disintegration of these lands. In landscape level the number of patches has increased over the time, which represents the intensity of negative and defamatory change during the study. Also, the increasing of Shannon's diversity index confirms the increasing diversity of patches in the regions as a result of increasing patch numbers and changes in landscape. Contribution of each category in altering other LULC categories was also calculated to provide an improved understanding regarding current LULC change processes in the area. Grassland is the most invasive category against low-density forest such that it occupies this category for 815 and 1, 219 hectares during both time intervals, respectively. Conclusion: The result of calculating metrics and change detection showed the changes in the second period (2002-2014) were more intense than the first period (1990-2002). Based on the results can be mentioned that during of study the landscape in Arasbaran region completely fragmented. Also consider spatial diversity and dispersion of land uses, there is a possibility of degradation and land use change in the future. Accordingly, there is a need to develop a suitable program to prevent unwanted changes in the landscape and maintain its spatial continuity. Development in the future should be programmed based on sustainable development principles and attention be pay to protection, maintenance and ecosystem management.

The aim of this study is to determine the status of forest spatial disturbance in Golestan National Park using remote sensing and landscape metrics. Detailed forest/non-forest map was prepared by on-screen digitizing method using Landsat 8 data. After determining the optimal area of gridding, the studied area was divided into 550 hexagonal zones with the area of 100 hectares each one. Spatial distribution, connectivity and composition of non-forest patches in each zone were measured using 12 spatial metrics (MESH, NP, PD, ENN-MN, LPI, FRAC-MN, CONTIG_MN, SPLIT, AREA-MN, DIVISION, PLAND and SHAPE-MN). After standardization of metrics values, principal component analysis was also performed in order to determine the effective metrics in disturbance and their associate weight in the spatial disturbance model. Afterwards, based on the prepared model, the disturbance index map was prepared and classified in four classes of disturbance including 1) without disturbance, 2) low disturbance 3) medium disturbance and 4) high disturbance. The results showed that the mentioned classes cover 48.53, 16.15, 19.90 and 15.41 percentage of the studied area, respectively. With the approach that used in this study, the disturbances map of the under management forest areas can be prepared periodically and used as one of its monitoring tools.

The Depression Anxiety Stress Scale (DASS) is a self-report measure of anxiety, depression and stress developed by Lovibond and Lovibond (1995) which is used in diverse settings. Lovibond and Lovibond try to discriminate between the constructs depression and anxiety by the scale. This scale proposes that physical anxiety and mental stress factor-out as two distinct domains. This screening and outcome measure reflects the past 7 days (Antony, Bieling, Cox, ENN, & Swinson, 1998).DASS has a 42-items form and a short form that includes 21-items. According to Brown et al. (1997) the subscales of the DASS-21 may measure the three dimensions specified in the tripartite model; low PA (DASS-Depression), physiological hyperarousal (DASS-Anxiety), and NA (DASS-Stress). The model posits a general distress or negative effect factor that is shared by both anxiety and depression, a physiological hyperarousal factor that is relatively unique to anxiety, and an anhedonia or low positive affect factor that is relatively unique to depression. In short form of the depression anxiety stress scale, each factor (depression, anxiety and stress) is measured by 7 items. Different researchers in their studies have reported an acceptable reliability and validity for the measure (Henry and Crawford, 2005; Antony et al., 1998; Brown et al., 1997).

It is essential to monitor the spatial and temporal patterns of urban development and identify factors to urban planning and sustainable development، especially in developing countries. The main purpose of this study was to identify the spatio-temporal changes in Kermanshah land-use patterns as a lever، control the changes of future developments the city. To produce a land use map from satellite images Landsat 7 and 8 sensors OLI and ETM+ Related to 1991 and 2016 were used. After processing and pre-processing، necessary land use map was prepared and maps Enter were FRAGSTATS 4. 2 software. The analysis of landscape patterns was done based on landscape ecology approach using spatial criteria (landscape metrics). The results showed that during the period studied residential area of 3304 hectares has been increased. More reduction amount is related to the user without covering 2492 hectares. Also، according to the results of the analysis metrics landscape has an increasein Residential User Metrics PLAND، NP، PD، LPI، TE. Farmer User has an increase in three Metrics MPS To the) 42/81 Hectare (، Shape-MN to the (0/03 Hectare) and ENN-MN to the (10/48 Hectare). Users without coverage has an increase Only Metrics MPS، TE، ED، LSI and Shape-MN. Tree cover in the five Metrics) NP، PD، LSI، IJI) increases while other metrics have decreased. The blue zone has increased only metric TE to the 81870 Meter.

The purpose of this study was to analyze the relationship between land use changes in different time periods using landscape metrics in the KoozehTopraghi Watershed located in Ardabil Province. For this purpose, three Landsat satellite images from 2000, 2010, and 2021 were received from the United States Geological Survey (USGS) database. After preparing the land use maps of the watershed by supervised classification and converting it to a raster format, the landscape metrics of Fragstats 8.2 software were calculated and quantified in two levels of landscape (22 metrics) and class (13 metrics). The kappa coefficient for the land use maps of the three years under study (2000, 2010, and 2021) was equal to 58.2, 75.0, and 59.2 %, respectively. The results showed that at the level of class, rainfed agriculture had the maximum value in the edge density metric, and irrigated agriculture had the maximum values in the average distance of the nearest Euclidean neighbor, the number of patches, and the total edges in all three years. Furthermore, at the landscape level, the fragmentation index decreased slightly in 2010 compared to 2000 and then increased in 2021. The largest patch index also showed a significant decrease in 2021, so it has reached from 43.34 in 2000 to 34.81 in 2021.