In this study earthquake reconnaissance has identified shear failure of reinforced concrete columns as a primary cause of collapse of older existing reinforced concrete frames. In this research, different factors which affect the shear failure have been studied. The MODELS presented by previous researchers and related organization (i.e. ACI, FEMA) have been evaluated. Then by using the results of 56 cyclic loading tests on rectangular columns, a new model has been presented. This proposed model has the advantage of separating different modes of column failure.

Mental MODELS are the reference frame works in which people interpret the world around them. In this paper, mental MODELS of two main stakeholder groups (forestry experts in Forest, Rangeland, and Watershed Organization and the local herders of Lire-Sar watershed) were analyzed in the context of forest DEGRADATION. The required data have been obtained through semi-structured interviews with individual members of each group. The results were conceptualized in the system dynamics approach and the cause and effect diagram of each groups were derived. The results of the mental MODELS diagraming of the two groups show that each group blames the other group for the DEGRADATION process and they are not expected to revise their actions. Organizing training courses about the goals and objectives of the forestry projects for the local herders and villagers, and improving the forestry organization experts capacity and capability in dealing with the mental MODELS that are based on the policies and decision makings, would be useful in creating a proper atmosphere for these two groups to understand the priorities and objectives of each other.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language MODELS and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression MODELS to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator MODELS were developed. Results showed that multiple regression MODELS are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression MODELS was verified with the published data of one researcher. The findings indicated that these MODELS are accurate enough for estimating the CR of stones. Consequently, the multiple regression MODELS provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

The objective of the present study was to investigate the performance of some of the extracted information for mapping land DEGRADATION using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land DEGRADATION map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land DEGRADATION covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS MODELS increases the ability to identify land DEGRADATION.

This study aimed to estimate the genetic parameters of body weight traits in Markhoz goats, using B-spline random regression MODELS. The data used in this study included 19549 records collected during 29 years (1992-2021) in Markhoz goat Breeding Research Station, located in Sanandaj, Iran. The model used to analyze data included fixed effects (year of birth, sex, type of birth and age of dam) and random effects including direct additive genetic, maternal additive genetic, permanent environmental and maternal permanent environmental assuming homogeneous and heterogeneous residual variance during the time. Akaike (BIC) and Bayesian (BIC) information criteria were used to compare the MODELS and bspq.4.4.4.4 was selected as the best model. The direct heritability values for birth, 3-month, 6-month, 9-month and 12-month weights were estimated to be 0.14, 0.16, 0.08, 0.28 and 0.26, respectively. Genetic correlation between body weights at birth and 3-month, birth and 6-month, birth and 9-month, birth and 12-month, 3-month and 6-month, 3-month and 9-month, 3-month and 12-month, 6-months and 9-month and 9-month and 12-month were 0.22, 0.38, 0.21, 0.56, -0.26, 0.30, 0.62, 0.86 and 0.77, respectively. The highest phenotypic correlation was between the weight of 9-month and 12-month (0.82) and the lowest correlation was between birth weight and 3-month and 6-month (0.12). The results showed that the 9-month weight is a good criterion for selection in Markhoz goats.

Monitoring and evaluation of transformers are essential to prevent their insulation failure. In this paper, the use of 2-furan-carboxaldehyde (2-FAL) and methanol (MeOH) concentrations as the main products of paper insulation DEGRADATION, and insulation condition markers, has been studied. In order to study the DEGRADATION process and production of DEGRADATION products, the thermal aging process of the transformer insulation system was implemented in laboratory conditions. The results of laboratory studies show that in the early stages of DEGRADATION the amount of MeOH is significant compared to 2-FAL. Also, the estimation of the degree of polymerization (DP) in the early stages of DEGRADATION (DP>800) through MeOH concentration and with decreasing DP (DP <800) through 2-FAL concentration is closer to the real value. The results of studies performed on 35 distribution transformers confirm the production of significant amounts of MeOH in the early stages of DEGRADATION. Also, the estimated DP values for the studied transformers were obtained through 2-FAL and MeOH concentration. The results show that estimating the amount of DP through MeOH concentration is associated with a probability of error of about 9% compared to estimating DP through 2-FAL concentration.