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.

Introduction: Economic growth and public welfare are some of the macroeconomic goals for any country. Health economics is one of many parameters used as a criterion for action in the evaluation of this important subject. The present study has been examined the relationship between health economics factors and non-oil economic growth in Iran.Materials and Methods: In this study, an attempt has been made to estimate non-oil economic growth elasticity to health economics factors by applying DYNAMIC ORDINARY LEAST SQUARES ((DOLS)) approach from 1996:1 to 2010:4.Results: The empirical results suggest that among four factors in the field of health economics in this survey, two factors, and i.e. Total health expenditures (as percentage of GDP) and population age 65 and above as a percentage of the total population have been positively and significantly effect on Iranian non-oil economic growth during the period of study. According to the theories and previous empirical studies, other known variables in economic growth models such as financial development (banking sector credit available to the private sector as a percentage of GDP) and foreign trade like export and import have also had a significantly positive effect on Iranian non-oil economic growth.Conclusion: Thus, it seems that the authorities and policymakers in the field of healthcare may prepare the ground for a boosting in the economic growth of Iran through increasing healthcare and health expenditures by orientation of development and giving depth to the healthcare infrastructures and system in the future.

This paper aims to explain the real-fiscal linkages by identifying the macroeconomic determinants of government revenue in Iran. To achieve this, the DYNAMIC ORDINARY LEAST SQUARES ((DOLS)) method was applied using data from 1972 to 2020. The findings indicate that inflation, centralization, the divergence of public prices from market prices, and the concentration of tax collection on labor income tax all have positive effects on government revenue in Iran. In contrast, economic growth and trade openness have negative effects on government revenue. Overall, these findings support implementing an economic growth strategy that is internally comprehensive within the tax system and externally coherent in terms of the economics of growth, and to which the government has a “tight commitment”. “Internally comprehensive” refers to establishing a mature progressive tax system that includes an inflation-adjusted comprehensive income tax along with automatic complete indexation. When complete and explicit automatic indexation is lacking, fiscal authorities may still benefit from inflation, which can lead to persistent political incentives for inflation inertia. “Externally coherent” refers to ensuring that aspects such as openness, decentralization of macro-fiscal decision-making, and the efficient size of government align with the comprehensive tax system to promote economic growth.

This paper investigates the impacts of economic and financial developments on environmental pollution for 6 OPEC countries using panel cointegration and panel long-run estimator ((DOLS)) over the period 1980–2008. The results shows that both Economic and financial development have significant effect on the environmental quality in selected OPEC economies. The findings not support the existence of an environmental Kuznets curve in the case of 6 OPEC countries. While conform existing an inverted U-shaped relationship between financial development and environmental pollution.

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.

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.

Asymmetric effects of oil shocks mean the difference between the positive and negative effects of oil shocks. Empirical studies show that these asymmetric effects can affect economic growth in oil-exporting countries as well as importing countries. In this regard, this paper tries to investigate the asymmetric effects of oil shocks on economic growth in oil-exporting countries (including Iran) during the period 1980-2011 by using hidden panel Cointegration. This approach, in addition to analyzing the long-term non-liner relationship between the variables, has another important capability for modeling asymmetry between different variables. Firstly, it has been shown that there is a long relationship between cumulative positive and negative components in crude oil prices and GDP in these countries by using Kao panel co-integration (hidden co-integration verification). Then, their long- run asymmetric relationships are measured by using DYNAMIC ORDINARY LEAST SQUARES ((DOLS)). The results of this study show the negative effects of oil shocks are more than the positive effects on the economic growth of oil-exporting countries.

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.