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.

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.

1. Introduction: From 1960s several attempts have been made to measuring the rock brittleness index BI. Schwartz (1964) using results of a series of triaxial tests on rock samples, stated that the rock’ s behavior from frangibility to ductility happens in 4. 3 ratios of principal stresses. Altindag (2002; 2003) introduced a new method for prediction of the BI by the division of the uniaxial compressive strength (UCS) of the rock to Brazilian tensile strength (BTS). In the late 1960s punch penetration test (PPT) introduced by Handewith (1971) to measure some physical properties of rock sample related to hardness and toughness of rock. Yagiz (2006) stated that the PPT’ s results for measuring the BI have a very high correlation with TBM penetration rate. Although the PPT has very delightful results, application of this test is very expensive and needs much time as well...

The topic dividend policy is one of the most leading issues in modern corporate finance affecting the firm value. The results of linear methods and regression could not satisfy researchers in forecasting of financial issues such as dividend policy. In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate Artificial Neural Network using a sample of 183 companies listed in the Tehran Stock Exchange through for the years 2011_2015. This study shows that the application of the multivariate neural network model results in forecasts that are more accurate than Univariate neural network forecasting models. Our findings show that forecast of a multivariate ANN incorporating Marsh and Merton (1987) variables is more accurate than univariate ANNs. Therefore, based on the results of the study we suggest that shareholders, investors and other stakeholders use multivariate ANNs to predict dividend policy of companies listed in Tehran Stock Exchange.

An attempt has been made in this paper to investigate the effect of particle size distribution on coal flotation kinetics. The effect of particle size (Ps) on kinetics constant (k) and maximum theoretical flotation recovery (RI) was investigated while other operational parameters were kept constant. The relationship between flotation kinetics constant and theoretical flotation recovery with particle size was estimated with nonlinear equations. Analysis of variance showed that the effect of particle size on the kinetics constant was statistically significant at 95% confidence level. However, it was not significant on maximum theoretical flotation recovery (RI). Different regression methods were conducted in order to model the effect of coal particle size on flotation kinetics. Results indicated that the quadric regression method gave better prediction of the cumulative recovery for different particle size fractions. The correlation coefficient (R2) values of this model were 0.99, 0.996, 0.98, 0.98 and 0.97 for average of particle sizes of 37.5 mm, 112.5 mm, 225 mm, 400 mm and 625 mm respectively.

Background and Objectives: One of the essential factors in the programming and management of water resources is predicting the amount of runoff. Increasing the accuracy in predicting runoff will increase the efficiency of programming and management,therefore, improving the modeling of discharge prediction is a requisite issue. The first aim of this study is to evaluate the efficiency of the multivariable linear regression, M5 decision tree, and time series in predicting the river runoff. The second aim is to analyze the modeling time step (monthly or seasonal) and the effects of model inputs (one delay steps variable against several delay steps variable) on the accuracy of the studied models. Material and Methods: Navrood watershed located in the west part of Guilan province is chosen for the study area in this research. Required data is collected from Kharjgil (1368-1398) and Kholian (1375-1397), including monthly river flow, rainfall, and temperature from Guilan regional water company. The amount of runoff is predicted in two approaches by the received data in monthly and seasonal time steps sing three models of multivariable linear regression, time series, and M5 decision tree. In the first approach, input variables to the model were river flow, rainfall, and temperature with three steps delay. In the second approach, the only variable was river flow with three steps delay. The model evaluation criteria in this research are the mean bias error (MBE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (𝑅, 2). Results: In the first approach and in monthly timestep, M5 decision tree is selected model with MBE-NSE equal to-0. 04, 0. 80 (train) and 0. 01, 0. 72 (test) in Kharjgil station, and-0. 01, 0. 79 (train) and 0. 00, 0. 86 (test) in Kholian station. In the seasonal time step, the criteria for the M5 decision tree in Kholian station are equal to 0. 02, 0. 78 (train),-0. 02, 0. 86 (test), and in Kholian station are-0. 01, 0. 79 (train), 0. 00, 0. 86 (test). This model was the best in this study for the first approach in the seasonal time step. The second approach has led to different findings considering both monthly and seasonal time steps. In the second approach, the criteria in monthly time step for time series model during train and test in Kharjgil station are respectively-0. 05, 0. 47 and 0. 10, 0. 52 and in Kholian are-0. 02, 0. 63 and 0. 2, 0. 49. The selected model criteria for seasonal time step considering train and test are-0. 42, 0. 58 and 0. 06, 0. 83 in Kharjgil station, and 0. 09, 0. 40 and-0. 10, 0. 62 in Kholian station. The time series model is selected in the second approach in the seasonal time step. Conclusion: The findings of this research have shown that in both stations and time steps, the M5 decision tree model has shown a higher accuracy in prediction than the two other models in the first approach. Meanwhile, the decision tree model does not show accurate results in the second approach. Alternatively, compared to two other models in both stations and both time steps, the time series model had a higher accuracy. Findings of this research have emphatically shown that specific approaches in choosing the model's inputs can effectively influence the selected model and the accuracy of modeling.

Introduction: Dam failure and its flooding is one of the destructive phenomena today. Therefore, estimatingthe peak outflow (QP) with reasonable accuracy and determining the related flood zone can reduce risks. Qp of dam failure depends on important factors such as: depth above breach (Hw), volume of water above breachbottom at failure (Vw), reservoir surface area (A), storage (S) and dam height (Hd). Various researchers haveproposed equations to estimate QP. They used the regression method to obtain an appropriate equation. Regression is a mathematical technique that requires initial test and diagnosis. These researchers present a newregression model for a better estimation of Qp.Materials and Methods: The data used in this study are related to 140 broken dams in the world for 34 ofwhich sufficient data are available for analysis. Dam failure phenomenon is a rapidly varied unsteady flow that isexplained by shallow waters equations. The equations in the one-dimensional form are known as Saint-Venantequations and are based on hydrostatic pressure distribution and uniform flow under rectangular steepassumption. Although hydraulic methods to predict the dam failure flood have been developed by differentsoftware, due to the complex nature of the problem and the impossibility of considering all parameters inhydraulic analysis, statistical methods have been developed in this field. Statistical methods determine theequations that can approximate the required factors from the observed parameters. Multiple regression is a usefultechnique to model effective parameters in Qp, which can examine the statistical aspects of the model. This workis done by different tests, such as the model coefficients necessity test, analysis of variance table and it createsconfidence intervals. Data analysis in this paper is done by SPSS 16 software. This software can provide fitmodel, various characteristics and related tests in the Tables.Results and Discussion: This paper proposes a new relationship with better estimation of discharge peak (Qp) based on Hw and Vw factors. Results showed how to choose the appropriate model (fitting the model) and theinitial required tests, according to the diagnostic model. And it compares the estimated error (relative efficiency) of the researchers’ models with the proposed models. The number of models can be classified to threeconvenient linear, multiplicative and transformed bases on Vw, Hw and Qp (nonlinear terms Qp). The best modelsfor each of the three models were selected. Their corrected determination coefficients (Adj R2) are close togetherand are between 0.86 until 0.864. The relative efficiency criteria based on the root mean square error (RMSE) was used to determine the best model. This standard was also used for other researchers’ models. RMSE of the three models presented in this article is lower than that of other models (from 745 to 759). Diagnostics analysis of the three models is not possible due to the large volume, so some statistical analysis for the model 2 arepresented in detail. The results are given in the following Tables. Test level has been assumed to be 5%. Fromthe point view of hydraulics, it can be said that the final equation for Qp should be proportional to Hw 1.5. Soalthough the model (2) has the lowest RMSE, but the model (3) of the hydraulics viewpoint seems more logicaland its RMSE is not very different from the model (2), so this model can be selected as the best model. Figure 1show diagnostics diagrams of model (3). The right Figure shows the homogeneity of residuals (follow the normallaw) as a histogram. This homogeneity is confirmed by the crouch graph (center Figure). The left graph showsthe stabilization of residual variance. According to the preliminary and diagnostics tests results, the model (3)has been selected. Its determination coefficient (0.864) also shows good strength.Conclusion: In this study, data from 140 broken dams were used to provide an appropriate model forestimating the peak outflow of dam failure. Standard statistical principles including preliminary tests, diagnosticand the efficiency of the models are the innovations of this paper. Analysis showed that the three models arecompetitive, and that the best of them was selected. The determined coefficient of these models was from 0.86 to0.864 ranges. Relative efficiency was calculated by the RMSE index. The results showed that these models aremore accurate than the models presented by other researchers. The model (3) was presented in this research, thebest result was estimated for Qp and its error was less than the other models.