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.

Introduction: Accreditation is a comprehensive and supportive model for assessing hospital performance. The present study aimed to assess the methodology of scoring hospital accreditation standards in Iran. Method: This research is a qualitative study in 2017. The main scope was to interview the experts in the area of policy and accreditation of hospitals in Iran. Accordingly, semi-structured interviews were conducted with 25 experts and hospital accreditation assessors. A framework analysis was used to analyze qualitative data. Results: In this study, six subjects were identified as the key factors influencing the hospital accreditation method. Standards Classification, Scoring, Weighting, Overall Score Ranking, Role of Assessors, Evaluation Method. In the current method, the overall score did not determine the difference between hospitals. Weighting was not clearly specified. The executive steps which were guidelines of implementation were scored. The decision making methods of the assessors were not the same, and the assessments were mandatory and not carried out by an independent organization. Conclusion: In the area of classification of standards and the method of scoring and weighting in the current accreditation evaluation, there needs to be changes. Required measurable elements in the accreditation standards will be eliminated. The precondition for entering the accreditation process will be to obtain the necessary permits before accreditation. It is suggested that the Likert scale be used for scoring. It’ s recommended that the weight of patient safety standards must be considered more.

Purpose: Observation is one of the important pillars of futures studies and the introduction of projects and researches. If it likened to a building, correct observation will be like a solid and reliable foundation for it. Different researchers have presented several observation models. These models are presented based on the surrounding conditions and characteristics of each research and are different from each other.The main purpose of the current research is to calculate the evaluation indicators of observation models and to identify the characteristics, strengths and weaknesses of 36 existing models, focusing on last ten years and common models in the past based on the aforementioned indicators, and, other goals Like determining the scoring relationship, observation models were also followed.Method: This research is practical from the point of view of the goal and is considered as mixed research and it uses the methods of library studies, expert panel and questionnaire.Findings: In this research, 36 models were extracted and explained from among the existing observation models, and twelve final indicators were determined based on the opinion of experts in order to evaluate them. The mentioned indicators were weighted based on their importance by experts and a relationship was presented to score the observation models and finally 36 observation models were scored based on it.Conclusion: The results show that the three observation models of Momenizahed, Sakalidis and Cuhls got the most points. Based on the final evaluation indicators, observation models were introduced that had more strengths in each of them.

Nowadays, computer network attacks are considered as the most serious issue in everybody’s life. Network hardening is a necessity which requires prioritizing vulnerabilities and finding the most dangerous ones. So, risk assessment should be done as much accurate as possible. The most widely used system for risk assessment is CVSS (Common Vulnerability Scoring System) that has some difficulties which make risk analysis inaccurate. Limited number of risk scores is the most critical issue which causes inefficient vulnerability discrimination. Here one novel approach is proposed which amends the Impact formula of CVSS by the aim of increasing risk estimation accuracy and scores diversity.

Evaluation of land capability is a way of knowledge of nature and preventing its destruction. The Logic scoring of preference method is one of the new approaches in land evaluation. This method consists of three main components: the attributes tree, the preliminary criteria and the aggregation structure. Criteria and sub-criteria of the conservation model, criteria weightings and standardization functions were determined by the resources reviewing, environmental attributes of the area and consulting with local experts. Then, the decision tree is formed, after that, the sub-criteria, criteria and attributes were weighted. Their replacement capability was determined. Replaceability or simultaneity means identifying importance of criteria and sub-criteria in decision making. So, more or less effect of criteria was determined on the final decision by the ranges of positive and negative numbers. Aggregation of criteria and sub-criteria were done by Weighted Average Power Method. Conservation maps were prepared with two scenarios without_with the replaceability criteria. Scenario result of without replaceability shows the region's conservation value better than the replaceability scenario. The research results showed that rivers and aquatic ecosystems, scarce native plants and Mountains with high slope and shallow soil are sensitive to degradation. The proposed GIS-based LSP method is an enhanced MCE approach that represents an excellent tool for discussion and deliberation among stakeholders, decision makers, land-use planners and other experts.

Background: Recognition of influential factors is necessary for predicting the severity of cholecystectomy. We aimed to evaluate the diagnostic power of the proposed scoring system for predicting the severity of laparoscopic cholecystectomy. Materials and Methods: In this cross-sectional study, there were 122 patients who underwent cholecystectomy in Shahid Beheshti Hospital during 2018. Prior to surgery, accurate history, and clinical, laboratory, and ultrasound information were recorded and scored on a checklist. Finally, the difficulty of cholecystectomy was divided into three categories of easy (less than 60 minutes, uncomplicated) and difficult (between 60 and 90 minutes, divided into controllable) and very difficult (between 90 and 120 minutes with uncontrollable complications or open surgery(. Data were analyzed in SPSS software, version 22, and the indicators were calculated for the purpose of the study. Results: There were 28 (22. 4%) men and 97 (77. 6%) women. The mean ± SD ages of the patients in easy and difficult operations were 44. 71 ± 13. 56 and 44. 52. 14. 56 years, respectively (p = 0. 939). The patients’ sex and history of acute cholecystitis attacks did not have a significant relationship with the difficulty of laparoscopic cholecystectomy (p ≥ 0. 05). However, between the palpable gallbladder (p > 0. 0001(, impacted stone (p > 0. 0001(, gallbladder wall thickness (p > 0. 0001(, and body mass index above 30 (p > 0. 0001( there was a statistically significant relationship between the difficulty of operation. There was also a significant relationship between AST (p = 0. 034(, T Bill (p = 0. 008( WBC (p < 0. 0001), and the difficulty of laparoscopic cholecystectomy, but between ALT (p =0. 065), the severity of cholecystectomy was not significantly related. Sensitivity (86%), specificity (58%), positive predictive value (44%) and negative predictive value (91%) were calculated for the scoring system for the difficulty of laparoscopic cholecystectomy. Conclusion: To predict the difficulty of laparoscopic cholecystectomy, we strongly recommend the preoperative scoring system. It is also recommended to conduct further studies on the use of the scoring system during surgery.

Today, metabolic syndrome in the age group of children and adolescents has become a global concern. In this work, a data mining model is used in order to determine a continuous metabolic syndrome (cMetS) score using the linear discriminate analysis (cMetS-LDA). The decision tree model is used to specify the calculated optimal cut-off point cMetS-LDA. In order to evaluate the method, the multi-layer perceptron neural network (NN) and the support vector machine (SVM) models are used, and the statistical significance of the results is tested with the Wilcoxon signed-rank test. According to the results of this test, the proposed CART is significantly better than the NN and SVM models. The ranking results in this work show that the most important risk factors in making cMetS-LDA are WC, SBP, HDL, and TG for the males, and WC, TG, HDL, and SBP for the females. Our research work results show that the high TG and central obesity have the greatest impacts on MetS, and that FBS has no effect on the final prognosis. The results obtained also indicate that in the preliminary stages of MetS, WC, HDL, and SBP are the most important influencing factors that play an important role in forecasting.