Background and Objectives: In the soil science, echangble sodium percentage and sodium adsorption ratio are two different criteria to evaluate of soil alkality. For measured of ESP, it is essential to have soil Cation Exchange Capacity (CEC). But, CEC determined by using laborious method is expensive and time consuming. Developing a model that predicts ESP indirectly from a easily-measured properties to be more appropriate and economical. Researches showed a relationship between ESP and SAR. So, SAR can be allocated to predict of ESP. For this reason, many attempts have been made to predict ESP from soil. The specific goal of the research develop model to determining ESP based on SAR by OLS and BN models for Bonab soils in East Azarbaijan province, Iran. Materials and Methods: For arrived presented research, 209 soil samples were taken by grid survey (250×250) of Bonab, Iran. The site is located at mean 1300 m above mean sea level, in semiarid climate in the Northwest of Iran. Then, some soil chemical properties such as Sodium, calcium, magnesium, SAR and ESP of the soil samples were measured using laboratory experiments. Then, two model was developed by OLS and BN. OLS estimators are linear functions of the values of the dependent variable which are linearly combined using weights that are a non-linear function of the values of the explanatory variables. So the OLS estimator is respect to how it uses the values of the dependent variable only and irrespective of how it uses the values of the explanatory. So A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms can perform inference and learning in Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty. Results: The Coefficient of Determination (R2) and Root Mean Square error (RMSE) of the soil ESP-SAR model is reported 0. 99, 0. 71 and 0. 98, 1. 63 by OLS and BN respectively. Based on the statistical result, both of soil ESP-SAR model was judged acceptable. T-test were used to compare the soil ESP values predicted using the soil ESP-SAR model with the soil ESP values measured by laboratory tests. The paired samples t-test results indicated that the difference between the soil ESP values predicted by the model and measured by laboratory tests were not statistically significant (P>0. 05). Therefore, the soil ESP-SAR model can provide an easy, economic and brief methodology to estimate soil ESP. The GMER index also indicated low estimation of two selected land evaluation method. Conclusion: The results of present study illustrated that OLS and BN models can predict ESP with acceptable limits. OLS and BN are mathematical models between input and output variables and have the ability of modeling between ESP and SAR.

Predicting stock returns is one of the major issues to be discussed in the financial literature, and investment. Researchers have proposed various methods for predicting stock returns, that the most famous of them are the Capital Asset Pricing Model by Sharpe and Lintner, arbitrage pricing model by ross and three factors model by Fama and French. F& F three factor model as the most significant factor models in recent years great attention has been. Despite having many strengths of this model is based on the assumption of constant beta coefficient is founded, However, this assumption does not hold absolute in any circumstances. In this study, we tried to model with constant or variable coefficients fitted separately and then compare the accuracy each of them. For this purpose, the state space model and ordinary least squares (OLS) models were fit assuming constant and variable coefficients are used. This research will be done on listed companies in Tehran Stock Exchange for a period of 72 months (October 1385 to September1391). The results show that, compared to state-space model of a linear least squares model for predicting stock returns has a better performance, this means that Beta coefficients in three-factor is on the Tehran Stock Exchange are not constant.

Introduction: The quantitative structure-property relationship (QSPR) is a successful strategy for prediction of surfactant properties based on modeling between calculated descriptors from molecular structures of the surfactants and chemical or physical properties of the solution. There are a great number of molecular descriptors that have been used in such QSPR studies, which can be divided into six types, namely constitutional descriptors, topological descriptors, electrostatic descriptors, geometrical descriptors, quantum chemical descriptors and thermodynamic descriptors. There are some reports about the applications of QSPR approaches to predict the CMC of anionic, nonionic and Gemini surfactants.Aim: In the present work, the logCMC of some tetra-alkylammonium and alkylpyridinium salts was mathematically related to the molecular structure properties.Material and Methods: All critical micelle concentrations data of this investigation were obtained from a set of cationic surfactants. They are measured in water at 25 oC. The data set consists of 44 surfactants were divided into two groups with 29 tetra-alkyl ammonium and 15 alkylpyridinium salts. The 3D molecular structures generated by ChemDraw 2005 and optimized by AM1 rotuine of MOPAC. The molecular descriptors generated ChemSAR and Dragon ver 3.0Results: OLS regression analysis provided useful equations that can be used to predict the logCMC of cationic surfactants in this study. Model (I) which was used to estimate the logarithm of CMC tetra-alkyl ammonium surfactants using four structural descriptors could be represented as:logCMC=-1.0097 - 0.1258Lc – 0.0123VH + 0.0960AHG +0.0053RHCIn=20.R2 =0.9860,s2 =0.0210F =135, model (I)The logCMC of alkylpyridinium surfactants with three descriptors can be effectively predicted using following Eq. for model (II).LogCMC=6.0291 – 0.2461Lc – 0.0011VH + 0.0249RHCIModel(II), n = 10,R2 =0.09940,s2 =0.0098,F =159, model (II)simultaneous model, which was used to estimate logCMC all cationic surfactants using four molecular structure descriptors, could be represented as log CMC = -1.4055 - 0.1529Lc - 0.0101VH + 0.1214 AHG + 0.0063RHCI n =30, R2 =0.9820,s2 =F =173,final model where n is the number of compounds used for regression, R2 is the squared correlation coefficient, s2 is the standard error of the regression, and F is the Fisher ratio for the regression.Conclusion: The results indicate that the CMC decreases as the hydrophobic character (L and V) increases and CMC increases as the hydrophilic character (A) of the surfactant increases.

Objective: In this study, the ordinary least squares (OLS) regression model was applied to estimate the impacts of  land use and land cover (LULC) changes from 1999 to 2023 derived from Landsat satellite imagery on water productivity as a key ecosystem service essential for environmental sustainability. Focusing on the Lavasanat district in Tehran province, which has undergone rapid urbanization and severe land use/cover changes, this study determined the extent to which water production performance (runoff) responded to land use/cover changes, thereby providing significant information on the environmental consequences of land use/cover changes under increasing human pressures.Method: This study used Landsat satellite imagery to assess the trends in land use/cover (LULC) changes over time at a spatial resolution of 30 m. Water yield modeling was performed using the annual water yield index of the InVEST software. The model inputs included land use and cover maps from three different time series along with data on precipitation, potential evapotranspiration, soil depth, water availability for plants, and local biophysical tables. The results from the InVEST model were analyzed using an ordinary least squares (OLS) regression model to estimate the impact of land use/cover changes on water yield. This method allows for a detailed examination of the relationship between land use/cover changes and their impacts on the water yield.Results: The results showed that between 1999 and 2023, the area of green spaces, including agricultural lands, gardens, and pastures, decreased by 161.21 km², or 31 percent. Consequently, the annual water production (runoff) increased from 105 to 130 million cubic meters. In addition, the area of the minimum error zone decreased from 445.3 to 23.5 km², indicating a decrease in the reliability of the model. Such findings indicated the high variability and complexity of hydrological interactions and indicated the severe effects of overdevelopment and increasing land use/cover pressures.Conclusions: The results of this study showed that the water production capacity (runoff) of the ecosystem was vulnerable to changes in land use and land cover. These changes increased runoff, reduced water permeability in the soil, and disrupted the hydrological balance of the region. Therefore, it is necessary to use integrated modeling approaches and simultaneously pay attention to climate change, socio-economic factors, and land use/cover planning. Regional planning should also emphasize preservation and restoration of green spaces and smart management of urban development, which is an inevitable necessity to maintain ecosystem resilience and water resource sustainability.

Economic growth is the most important parameter to determine progress and the rate of development in every country. Man power is considered as an important parameter in all models of economic growth. Therefore, demographic variables play an important role in the economic growth of countries. During recent decades, demographic variables have experienced large fluctuations in Iran. Baby-boom in 60s, considerable decrease in the rate of fertility in 70s and its continuation and intensification in 80s has led to change in age structure of the country. The present paper seeks to examine the effect of these changes on economic growth. To this end, national product in considered as a function of capital and labor force and is estimated on the basis of Cobb-Douglas production function and ordinary least squares (OLS) method. The results indicate that capital and active labor force has a positive effect on economic growth and retired labor force has a negative effect on it. If seniority increases one percent, the economic growth, on average, will decrease 0.01, given that other conditions are fixed.

Background: This investigation elucidates the significance of radiation emergency medicine (REM) within South Korea, while addressing the multifaceted challenges linked to the education of medical personnel in the field of radiation emergency responses. The efficacy of REM training initiatives has undergone scrupulous evaluation through a variety of techniques, including, but not limited to, the application of the DISASTER Paradigm and engagement in simulation-based training exercises. Materials and Methods: The present research is structured to evaluate the incremental utility of REM training programs by applying the Difference-in-Difference (DID) estimation using OLS regression methodologies. Simultaneously, it aims to suggest potential improvements to existing training modules. Central to the methodology is the estimation of the DID model via the 'sm.ols' function in the Python programming environment. In the equation 'outcome ~ T_d + P_t + T_d * P_t', 'outcome' denotes the dependent variable under review, 'T_d' signifies the treatment dummy variable, and 'P_t' represents the period dummy variable. The interaction term 'T_d * P_t' elucidates the average effect of the treatment post-intervention, taking into account the temporal trend. Results: The conclusions drawn from this scholarly investigation have manifested negative net utilities across the three pivotal DISASTER Paradigm indicators (T, E, and R). Through the adept implementation of a Python-infused computational methodology, this study has yielded results characterized by precision and veracity. These insights furnish empirical evidence, indicating that the intervention in question may not have yielded an enhancement in the net utility for the designated target cohort. Conclusion: This scholarly inquiry underscores the efficacy and meticulous precision of OLS-DiD estimations executed via a Python-centric computational approach. The empirical findings emanating from this research serve to fortify a robust foundation for the strategic navigation of unique challenges within the intersecting realms of nuclear science and medical studies, with particular emphasis on advancing the field of radiation emergency medicine (REM) education.

The Mixed Least Squares Meshfree (MDLSM) method has shown its appropriate efficiency for solving Partial Differential Equations (PDEs) governing the engineering problems. The method is based on the minimizing the residual functional. The residual functional is defined as a summation of the weighted residuals on the governing PDEs and the boundaries. The Moving Least Squares (MLS) is usually applied in the MDLSM method for constructing the shape functions. Although the required consistency and compatibility for the approximation function is satisfied by the MLS, the method loss its appropriate efficiency when the nodal points cluster too much. In the current study, the mentioned drawback is overcome using the novel approximation function called Mapped Moving Least Squares (MMLS). In this approach, the cluster of closed nodal points maps to standard nodal distribution. Then the approximation function and its derivatives compute noting the some consideration. The efficiency of suggested MMLS for overcoming the drawback of MLS is evaluated by approximating the mathematical function. The obtained results show the ability of suggested MMLS method to solve the drawback. The suggested approximation function is applied in MDLSM method, and used for solving the Burgers equations. Obtained results approve the efficiency of suggested method.

The main goal of this paper is to study exchange rate impact on the export of dates as one of the most important exported horticultural products in Iran. For this purpose, ordinary least squares (OLS) method was used to estimate the relationship between volume of exported dates and other selected variables. The required data were collected from various official resources. Results showed that exchange rate is a critical factor and exporters reacted to its changes. In addition, other factors such as foreign currency written promise, exchange rate unification and stabilization policy, had impacts on volume of date’s exports. Outsourcing foreign policy that was always considered a short-term policy had negative effect on export. Unification of the exchange rate did not have significant impact on dates export. Also, exchange rate stabilization policy led to reduce the potential exporter’s income due to prevailing inflation in the country and increased production costs.

In this study, for the selection of the characteristics of the company that provides the incremental information to investors and financial analysts, the linear models are adapted by the ordinary Lasso method (Tibshirani, 1996), Adaptive Group LASSO (Zu, 2006) and the least squares method (OLS). The main objective of this research is to determine which method can predict the expected return on stock portfolios in the shortest time and using the least effective features. The research sample is1340observations, including 134companies listed in Tehran Stock Exchange, and the research variables from the financial statements of the companies and the stock market reports between 2008and 2018. The results of this study show that by employing the least squares regression method, 7 characteristics, the typical 5-characteristics LASSO method and in the Adaptive Group LASSO method, only 4characteristics, contain incremental information to predict the expected returns of stock portfolios. In the second place, by applying the Adaptive Group LASSO regression method, one can achieve the same results with using the least characteristics.