در این مقاله، الگویی جامع برای ارزیابی عملکرد و رتبه بندی شرکت های توزیع برق معرفی می شود. در رویکرد پیشنهادی، مدل های تحلیل پوششی داده ها (Data Envelopment Analysis) بعنوان مدلی ناپارامتریک و حداقل مربعات معمولی تصحیح شده (Corrected Ordinary Least Squires) بعنوان مدلی پارامتریک، توسط مدل تحلیل مولفه های اصلی (Principal Component Analysis) با هم ترکیب می شوند تا رتبه های دقیق شرکت های توزیع برق بدست آمد.در این روش، ابتدا درجات کارایی شرکت های توزیع برق ایران را با استفاده از مدل های تحلیل پوششی داده ها (DEA) و حداقل مربعات معمولی تصحیح شده (COLS) بدست می آوریم و سپس جواب های حاصل از دو مدل DEA و COLS بعنوان شاخص های ورودی در مدل تحلیل مولفه های اصلی (PCA) مورد استفاده قرار می گیرند. در این مقاله، 38 شرکت توزیع برق در ایران را با استفاده از رویکرد پیشنهادی ربته بندی می کنیم و سپس جواب های حاصل را با استفاده از رویکرد میانگین هندسی درجات کارایی حاصل از مدل های DEA و COLS تصدیق و تعیین اعتبار می کنیم.

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.

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.

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.

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