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.

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.

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.

In Marine discussions, the study of sea surface temperature (SST) and study of its spatial relationships with other ocean parameters are of particular importance, in such a way that the accurate recognition of the SST relationships with other parameters allows the study of many ocean and atmospheric processes. Therefore, in this study, spatial relations modeling of SST with Surface Wind Speed (SWS), Chlorophyll a Concentration, latitude and longitude in Oman Sea between 2003 to 2016 was performed by Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) method available in ArcGIS software and the outputs of the two methods were compared. The results of the OLS method showed that the Surface Wind Speed variable had the most effect on estimating SST values in the Oman Sea, and other variables had shown a low effect on the SST estimation. But in the GWR model, it was found that the longitude variable had the most effect in the estimation of SST values and had a positive relation with SST. In this model, the SWS variable has a positive relationship with SST, but its impact is less in compared with OLS model. Other variables also have a negative relationship with SST. Subsequently, using the local explanation coefficient (R2), it was determined that the GWR model had a higher accuracy than the OLS model for estimating SST values in the Oman Sea, so that the GWR model justify 85% of SST spatial changes in the Oman Sea, but the OLS model justifies only 55% of spatial variations of this parameter. The higher accuracy of the GWR model in the estimation of SST values was found in the eastern and western parts of the Oman Sea and this model was less accurate in the central part of the sea.

In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of the proposed algorithm through simulation and real data analysis. Since the proposed algorithm uses rank values rather than the actual values of the observations, it is extremely robust to the outliers and suffers less from the presence of noise than the other algorithms.

Image magnification is one of the current issues of image processing in which keeping the quality and structure of images is the main concern. In image magnification, it is necessary to insert information in extra pixels. Adding information to an image should be compatible with the image structure with- out making artificial blocks. In this research, extra pixels are estimated using the surface of least squares, and all the pixels are reviewed according to the suggested edge-improving algorithm. The suggested ethod keeps the edges and minimizes the magnified image opacity and the artificial blocks. Numerical results are presented by using PSNR and SSIM fidelity measures and compared to some other methods. The average PSNR of the original image and image zooming is 32.79 which it shows that image zooming is very similar to the original image. Experimental results show that the proposed method has a better performance than others and provides good image quality.