Purpose: Evaluating the credit of customers in credit institutions is one of the most important challenges of banks today. The lack of accurate evaluation of customers can lead to delayed maturity and ultimately burn the bank's claims and increase the credit risk. Overdue claims, which are known as non-current claims in the banking literature, are the result of credit risks. The banking and economic authorities always try to minimize the credit risk and non-current claims in order to avoid bankruptcy and its negative consequences. Bank credit is an efficient scale for calculating credit risks. But the judgment method is often used to determine the credit risk of customers. This type of Validation and decision-making is not as real as it should be; it has little accuracy and actually causes problems for banks in the process of handing over facilities. The purpose of this article is to present a detailed framework for the Validation of bank customers, which is done on a case-by-case basis for a number of recipients of facilities from Saderat Bank in the period of 2019-2021.Methodology: In this study, the censored multiple logistic regression technique is employed to rank customers based on creditworthiness and examine its impact on reducing non-current claims in Saderat Bank. The results obtained can facilitate the decision-making process regarding the credit rating of customers and the reduction of non-current claims in Saderat Bank. This purpose is fulfilled with discrete regression models in which the dependent variable selects binary values. A common approach in modeling binary choices is the use of multiple logistic probability models (censored regression), where it is assumed that there is an unobservable dependent variable (yi*) defined by a regression relationship.The statistical population of the present study includes the real customers in the Saderat Bank system who have received facilities and whose facilities are now in progress or have ended. The data on these customers cover the 2000 credit facilities of people who have referred to the bank. The data were collected from Saderat Bank branches during the period of 2019-2021. Also, the statistical sample of the research was divided into two categories of customers, creditworthy and non-creditworthy.Findings and discussion: Based on the estimation of the censored regression model, gender and age have a significant effect on the timely collection of loans. Women have performed better than men in repaying the loan, and, as age increases, the probability of not repaying the loan increases. The level of education and the repayment period also have positive effects on the timely collection of claims. Moreover, the amount of the granted loans and the interest rate of the loans have a negative and significant effect. Among the other investigated variables is the history of obtaining loans by the borrower, which has a positive and significant effect on the timely collection of loans. This factor can be a good sign of the eligibility of the borrower. The type of collateral deposited with the bank as a guarantee is another investigated variable with a significant impact on the non-repayment of loans. The loans obtained with property collaterals have better repayment conditions than joint collaterals. Finally, the average balance of the borrower when taking a loan has been found as an effective and significant factor in identifying his eligibility. On the other hand, the type of facility (capital or current) has no effect on timely collection or overdue claims.In addition, the results obtained from the censoring model for bad credit customers, in which the deferred class and the questionable access class are considered as dependent variables, show that the factors of gender, age and loan amount have positive and significant effects on the non-repayment of the loan. The repayment period has a negative and significant effect on the non-repayment of the loan. Also, the factors of the average balance of the applicant, the type of facility and the customer's work have no significant effect on the creditworthiness of bad credit customers of Saderat Bank. With these findings on the bank, it is better to identify bad customers and focus on other study variables such as loan repayment time, installment interval, number of installments, amount of each installment per month, loan extension, history of receiving loans, real estate collateral, and interest rate of loans.Conclusions and Policy Implications: Collection of the granted loan within the specified period of time points to the implementation of the correct methods and the use of resources to create the loans necessary for expanding economic activities and directing the bank resources to the correct investment locations. However, the implementation of inappropriate economic policies in the past years, the lack of financial disciplines, and the unfavorable economic conditions of the country have played a large role in the non-repayment of the granted loans. Therefore, the demographic and economic features of loan recipients are the key parameters for Validation, playing a central role in managing non-current claims and the credit risk of banks. It is, thus, suggested that, at the micro-level in the technical and economic evaluation of the projects for granting loans, banks should take into account the trend of macroeconomic variables, so that loans can be given based on the relevant forecasts and in such a way that the original resources and expected profit of the loans can be returned. Expansion of knowledge and research in the field of customer Validation and reduction of non-current claims of banks will have valuable results for organizations. Among them, one may mention the reduction of costs and the possibility of completing the money cycle in the bank and the country.

ABSTRACT Despite a wide range of components and criteria affecting travel behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan in Tehran, Iran, as the old, conventional, and new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered that factors such as car ownership, dependence and pro-liking for private cars, density and access to educational centers and parks, access to medical and service centers, and variety and density of retail stores had been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, and pro-liking for other than a private car, having a license, number of children under five years old, and age have influenced travel behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding factors affecting travel behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including land use, along with demographic-economic characteristics, travel behavior, and attitudes of people, has been raised by urban planners. Studies seek to find factors affecting travel behavior, especially land use characteristics. Despite presenting a wide range of components and criteria affecting travel behavior, the results of the studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which physical and non-physical characteristics affect travel behavior due to distinctions between different development patterns?   Methodology The present research method is analytical and experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, and walking as the travel behavior. According to the research's purpose, indicators and criteria affecting travel behavior were extracted after reviewing the theoretical and experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan as the old, conventional, and new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of land use, travel habits, and access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered which factors affecting travel behavior were due to the differences in study areas and which factors affect travel behavior regardless of development patterns.   Results and discussion This research aims to identify the factors affecting travel behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, and pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of factors affecting travel behavior, such as perceptually environmental characteristics of the neighborhood, dependence and pro-liking for personal cars, variety and density of retail stores, density and access to educational units and parks, access to medical and service centers, and car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between physical and non-physical factors (the same indicators in the same study areas) with travel behavior, the factors affecting travel behavior owing to different development patterns were identified. Factors such as car ownership, dependence and pro-liking for private cars, density and access to educational units and parks, access to medical and service centers, and variety and density of retail stores have been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, and pro-liking for other than a private car, having a certificate, number of children under five years old, and age have influenced on travel behavior regardless of the variation between neighborhoods (different physical development patterns).   Conclusion In In order to discover the factors affecting travel behavior due to the differences in patterns of physical development, this research has provided a more detailed analysis of the factors affecting travel behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel behavior and finding the main components affecting it, considering the extent of variables and data, can pave the way for professionals in transportation planning and urban planning, in addition to providing detailed methods and criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Body composition analysis is an important clinical marker for risk of obesity- influenced condition, also the anthropometry science survey relationship between athletic performance and functional anatomical systems output, such as local strength, power development anaerobic and endurance potentials.The purpose of this project was cross Validation regression equations and the accuracy determination for estimating body density (BD) in the male wrestlers, using noninvasive skinfold method. One hundred and forty youth athletes (age:23/4=0/24 years, heigh: (72.4=0.9 cm and weight: 79/4=2/2 kg, Mean= SEE) without any history physiological or skeletal diseases were participated during hydration condition. Fat thickness was measured at 9 sites, using the desitometric skinfold method. Predition equations for BD and body fat (BF) were cross-validated using SLOAN (EQ1), JACKSON (EQ2), LOHMAN&Brozek equations respectively. Conclusion: The best BD & BF selected equations by Sloan and Jackson equations are as follow:BD1= 0.97(BD3)-0.029 [R2=0.987, SEE=0.002, P< 0.001] BF1= 1.358(BF3)-1.089[R2=0.938, SEE=2/135, P<0.00l] These data indicated that EQ1&EQ2 generalized multiple regression equations are valid estimating anthropometric bd of Iranian male wrestlers.

‎The high-dimensional data analysis using classical regression approaches is not applicable, and the consequences may need to be more accurate. This study tried to analyze such data by introducing new and powerful approaches such as support vector regression, functional regression, LASSO and ridge regression. On this subject, by investigating two high-dimensional data sets (riboflavin and simulated data sets) using the suggested approaches, it is progressed to derive the most efficient model based on three criteria (correlation squared, mean squared error and mean absolute error percentage deviation) according to the type of data.

Background: The expansion of the use of audio and video media technology has faced society with many medical, cultural and economic challenges. In the meantime, excessive use of mobile phones can cause many problems. Objective: The aim of this study was to construct and validate a questionnaire on physical and psychological injuries of mobile phones. Method: The method of the present study was descriptive-correlational and confirmatory factor analysis. The statistical population of the study consisted of all citizens of Khorramabad with a diploma or higher in 1399, which was selected as a statistical sample by available sampling method of 200 people online and virtual. Results: The results showed that the factorized physical and psychological harm questionnaire of cell phone with 42 questions and 10 components (fear of loss, vibration syndrome, duck syndrome, nomophobia, insomnia, hearing problem, eye syndrome, tunnel and neck syndrome SMS) and has good reliability, validity and flexibility in terms of psychometrics. Discussion and Conclusion: The questionnaire of factor analysis of physical and psychological injuries of mobile phones can be used to identify and study the physical and psychological injuries of mobile phones.

The adsorption of zinc, lead, and copper ions onto silica gel adsorbent has been successfully carried out in this study. Linear regression of polynomial transformation from input variables was employed to model the correlation between estimator variables (adsorbent dose, initial concentration, contact time, and pH) and output variable (%removal). Although the R2 scores varied, overall, the models performed well in predicting metal ion removal. The regression coefficients of the models revealed that adsorbent dose and pH were the most significant factors for zinc and copper adsorption, while initial concentration and contact time also have a significant role in lead adsorption. Bayesian regression was used as a complementary approach to Response Surface Methodology (RSM), revealing different weight distributions for zinc and copper adsorption compared to RSM polynomial regression. The study concludes that copper and lead adsorption using RSM are more reliable compared to zinc, and suggests further optimization of factors or levels for more accurate results. The use of Bayesian regression provides valuable insights into variable weights and can improve the optimization process. Overall, this study provides useful information for designing efficient metal ion adsorption processes. This study provides useful insights for future research on the competition for metal ions in adsorption processes.