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.

Background and Aim: Undercounting is a common problem in surveillance systems and registries. One of the procedures has been used for assessing sensitivity of a surveillance system or completeness of a registry is capture-recapture method. The objective of the present study was to estimate the number of deaths due to road traffic injuries applying capture-recapture method and using three data sources: police, legal medicine organization and hospital.Material and Methods: All of the deaths due to traffic injuries occurred within Kerman district in the year 2000 were derived from police, Shahid Bahonar hospital and Kerman legal medicine data sources. Matching cases between the lists was based on three characteristics: first name, family name and date of accident. Loglinear model was used for statistical analysis.Results: The total number of identified cases was 471; the best fitted loglinear model estimated the actual number of deaths as 596 (CI 95%: 543-686). Based on Iranian Statistical Center estimates, the population of Kerman district in the year 2000 has been 644673; so the cause-specific mortality rate of traffic injuries is estimated as 92 (CI 95%: 84-107) per 100,000 population. Therefore the proportion of deaths egistered in police, legal medicine and Shahid Bahonar hospital are 16%, 58% and 48% respectively. The coverage of total deaths (471) is about 79%. Conclusion: The findings showed that none of the data sources had enough coverage of all deaths due to traffic injuries. Capture-recapture estimates can help for obtaining better estimates.

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary, multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpose, multinomial log it regression (MLR) is considered as the basis. Then, the MLR is converted to Poisson GLM with log link. Two methods including Multivariate exponentially weighted moving average (MEWMA) statistics, and Likelihood ratio test (LRT) statistics are proposed to monitor MLR profiles in phase II. Performances of these three methods are evaluated by average run length criterion (ARL). A case study from alloy fasteners manufacturing process is used to illustrate the implementation of the proposed approach. Results indicate satisfactory performance for the proposed method.

Fictionalism about scientific Models is a philosophical approach according to which many important questions about the nature and function of Models can be answered by taking Models as fiction, without any ontological commitments to Models. In this approach, fiction is a technical term denotes on a particular kind of imagination in which the participant due to the presence of prop is engaged in the pretence action: she takes some false statements as true and participates in the constructed fictional world. In this paper, after elaborating Walton's account of fiction and make-believe games and his aim to cover metaphors by the same mechanism of pretence, and by focusing on Camp's criticisms and her point to distinguish metaphorical imaginations from fictional ones, we will reach the conclusion that Walton's account is not appropriate to cover metaphors. Next, we will consider Frigg's use of Walton's account to analyse scientific Models. It will argued that Frigg's framework is inadequate to cover metaphorical Models, which in parallel with fictional ones play a crucial role in science.

In this research, generalized linear Models are used for modeling the trip modal split of Tehran residents. Characteristics of trip and traveler are used for calibrating the model. 434 questionnaires are distributed between random samples of students of Tehran. Selected students were asked to fill one questionnaire form about trips of their family and the other questionnaire about trips of their neighbor. The gathered data of questionnaires are analyzed and using a log linear model, effect of trip destination, trip goal, and trip time on the modal split is studied. Sample size is determined as it can produce the necessary information with sufficient accuracy. It is emphasized that the sample size is dependant to the information and accuracy which is needed for decision making. It has been shown that a sample size of 400 families in Tehran is enough for determining the trip goals. For determining the trip destinations, Iran is divided into 8 regions and trip destination of Tehran residents is categorized in this regions. The ANOVA method is used for checking the effect of trip region, trip goal and trip time on the modal split. It has been shown that the 3 mentioned parameters can affect trip modal split. Analysis of gathered data shows that trip goals can be classified in the below categories: recreation, with 48%, meeting the relations and friends, with 19%, pilgrimage, with 18%, work, with 10%, and other goals including studying, business, and treatment with 5%. Using the generalized log linear Models, dependency of trip region, trip goal, and trip time on the use of private car for intercity trips is studied. Pair combination of trip goal and trip time parameters, shows that most of the residents prefer to use private cars for traveling to Tehran region for recreation. In addition, most of the residents prefer to travel to the North region by their private cars for recreation.

Lactation length is different in individual cows, which is generally converted to a 305-day standard using curve fitting Models for genetic and management practices. Individual curves do not have a standard shape in all cases, and can deviate from the standard pattern according to factors such as individual differences, and type of fitted Models. These non-standard curves, called atypical, resulted from incorrect estimated parameters of the curves; which consist of: continuously increasing or decreasing and reversed standards. This study was conducted to investigate the importance of atypical curves in estimation of 305-day milk production, by fitting two nonlinear Models? Wood (empirical) and Pollott (biological), on 7659 and 6692 test-day milk yield of 977 and 776 first calving Iranian Simmental and Jersey cows, during 2007-2020, using R software. Different patterns obtained based on the combination of increasing (b) and decreasing (c) phase parameters of curves. The number of standard curves from the Pollott and Wood Models were 85.5% and 62.2% for Simmental, and 83.1% and 70.6% for Jersey cows, respectively. Only continuously increasing curves were observed in both breeds in Pollott model (14.8% and 16.9%, Simmental and Jersey cows, respectively); Whereas in Wood model, all 3 groups of atypical curves were observed, which the reversed standard was the most (22.3% and 16.5%, Simmental and Jersey cows, respectively). Based on the findings, at the time of standardizing the production of dairy cows (national evaluations), not only differences between breeds, but also special attention to the production of atypical curves, should be paid (to correct or discard them).