In the past two decades caries risk prediction has been and important subject for investigators. Caries risk assessment is an effective method for the prediction of future caries. By CRA the high-risk groups are determined. Then the prevention programs in relation to high-risk groups, be perform. This article is the results of a research project which has been performed by the author in Tehran. 218 primary school children (7 years old) have participated in this study. They have been examined for caries risk factors in three stages: 1- By a questionnaire for personal and socio demographic information. 2- Bite wing radiography to diagnosis the incipient proximal caries. 3- Dental examination of the student. Results of the research showed 34 person (15.6%) were high-risk, 63 person were low-risk and 121 person (55.5%) were moderate risk. Some of the caries risk factor have been also evaluated.

Background: Accumulation of heavy metals and increasing their concentration and reaching them to the danger range can threaten human health by entering to food chain. Thus, continuous monitoring of pollutants consentration in agricultural products is important. So, the aim of this study was to investigate the heavy metals concentration and risk assessment resulting from them in agriculture product with two types of irrigation in farms of Dayyer city which is one of the counties in Boushehr province.Material and Methods: Samples of agriculture product were harvested in three in three categories vegetables included: leafy (lettuce, spinach, cabbage), tuber (onion, potato), and fruit (tomato, green pepper) from farms of Dayyer city with two types of irrigation included River Water Irrigation (RWI) and Ground Water Irrigation (GWI). Totally 320 samples were analyzed by using (ICP-OES -Spectrum Arcos) after acidic digestion steps and extraction of heavy metals from samples. Then, the obtained results were analyzed by diffrent equations and statistic softwares.Results: results showed that the concentrations of metals were 68.75 (47.71), 277.62 (196.12), 32.68 (18.31), 42.19 (17.09), 1.76 (0.87) and 12.04 (5.83) mg kg-1 for Zn, Mn, Cu, Cr, Cd and Pb in RWI (and GWI), respectively in irrigated farms with river water (farms irrigated by ground water). The concentration of heavy metals was significantly higher in the soil of farms which irrigated with river water than those which irrigated with ground water (P<0.05). Also, results showed that in farms which irrigated by ground water, the highest Target hazard quotient (THQ) have been reported in spinach with mentioned values; Zn (0.1150), Cd (1.2846), Cu (0.2030) and Pb (1.9141).Conclusion: The findings of this study showed that the concentration of heavy metals in soil and agricultural products which irrigated with contaminated water with urban and industrial sewage was higher than soil and agricultural productswhich irrigated with ground water. Also results of this study showed that the hazard for consumers health in agricultural products that were irrigated with water contaminated by industrial and urban sewage was higher than which irrigated with ground water. The researchers of this study suggest that we seriously should be avoided from entering the urban and industrial sewage to water which irrigate the agricultural products.

Financial and economic decisions are always at risk due to future uncertainties. Therefore, one way to help investors is to provide investment risk forecasting models. But as these projections are closer to reality, decisions that are based on such predictions will be more correct. The main purpose of the present experimental study is to predict systematic risk with emphasis on financial and non-financial variables. The statistical population of this research is the companies accepted in Tehran Stock Exchange. The data of the study consist of 552 Firm-year from 2012 to 2017. To test the hypotheses, the artificial neural networks approach and night worm algorithms, decision tree and regressor of backup vector machine have been used. The results of this study showed that the results obtained from the hypothesis test showed that all three of the algorithms have the power to explain systematic risk.

The economic downturn in recent years has had a significant negative impact on corporates performance. In the last two years, as in the last years of 2010s, many companies have been influenced by the economic conditions and some have gone bankrupt. This has led to an increase in companies' financial risk. One of the significant branches of financial risk is the emph{company's credit risk}. Lenders and investors attach great importance to determining a company's credit risk when granting a credit facility. Credit risk means the possibility of default on repayment of facilities received by a company. There are various models for assessing credit risk using statistical models or machine learning. In this paper, we will investigate the machine learning task of the binary classification of firms into bankrupt and healthy based on the emph{spectral graph theory}. We first construct an emph{adjacency graph} from a list of firms with their corresponding emph{feature vectors}. Next, we first embed this graph into a one-dimensional Euclidean space and then into a two dimensional Euclidean space to obtain two lower-dimensional representations of the original data points. Finally, we apply the emph{support vector machine} and the emph{multi-layer perceptron} neural network techniques to proceed binary emph{node classification}. The results of the proposed method on the given dataset (selected firms of Tehran stock exchange market) show a comparative advantage over PCA method of emph{dimension reduction}. Finally, we conclude the paper with some discussions on further research directions.

The objective of this study was to design and validate a model for assessing financial distress risk. The research method employed was based on an econometric model using the GARCH framework, implemented through EViews software version 10. The statistical population consisted of listed banks during the period from 2012 to 2023, and the research sample included data relevant to the financial distress risk assessment model. Among the key findings of the study, it was observed that based on the regression coefficients of the criteria, bank credit risk and bank liquidity risk—with coefficients of 99% and 95%, respectively—contributed significantly to the reduction of financial distress risk in listed banks. Additionally, changes in net income to total bank assets and net working capital to total bank assets demonstrated highly significant impacts on the reduction of financial distress risk, with calculated coefficients of 99% and 94%, respectively. Furthermore, bank market risk, market value to book value of the bank, and total liabilities to total assets of the bank, each with a 91% coefficient, showed similarly significant effects in reducing financial distress risk in listed banks. In contrast, operational risk of the bank, interest rate risk, net income to shareholders' equity, and the total asset size of the bank ranked next with approximate coefficients of 80%. Moreover, according to the power-dependence chart, bank liquidity risk exhibited the highest influence power at 100% and the lowest dependence level at 17%, placing it in the “independent” zone (low dependence, high influence). In fact, bank credit risk and operational risk showed high influence power—83% and 67%, respectively—and moderate dependence levels of 50%, positioning them in the “linkage” zone (high influence, moderate dependence). Conversely, net working capital to total assets, net income to total assets, and market risk of the bank exhibited the highest dependence level at 100% and the lowest influence power at 50%.

In the response phase the most important step is to assess disaster after occurrence. By disaster assessment, we can obtain all the needed information for controlling, decision-making and also disaster planning. Lack of disaster assessment causes you to make unsuitable decisions based on limited or inadequate data which leads to imperfect disaster response. According to IFRC, disaster response operation will have an unsuitable function without disaster assessment. This paper presents the principles and concepts of disaster assessment.

Risk assessment is always considered one of the financial decision-making processes in any market, which is usually associated with significant challenges for investors due to the impossibility of predicting all effective factors in the market. Risk tolerance is considered as a mental and functional basis in investment, which, due to its integration with individual characteristics in similar conditions, may result in noticeable differences in returns for investors. The aim of this research is to evaluate the financial risk tolerance of investors in the capital market. The present research is qualitative and methodologically developmental, therefore text analysis processes were utilized to first identify research related to financial risk tolerance assessment and then determine its components and themes. Furthermore, a Delphi analysis was used to determine the theoretical consensus boundary so that a theoretical framework for the research could be presented thereafter. The results of the study indicated the existence of two main components of cognitive and behavioral drivers of financial risk tolerance of investors, identifying a total of 12 themes as the main components of the research through two stages of Delphi analysis.

The purpose of this study is to investigate the effect of Weinstein and Hume's self-control learning strategies on financial fraud risk assessment. Self-control learning strategies are all psychological efforts to control a person's processes, functions, and inner state that the learner uses during learning. Therefore, its purpose is to help learn, organize, store knowledge and skills, as well as facilitate their use in the future. Weinstein and Hume's self-control learning strategies include self-control strategies for mental training, semantic development, and organization. The statistical sample of this research includes 519 auditors working in auditing firms, members of the Iranian Institute of Certified Public Accountants of Iran, and the Auditing Organization in 2021, who are selected by a simple random sampling method. The research method is a descriptive-survey and the instrument used in the research is a standard questionnaire. to statistically analyze the data and test the hypotheses, structural equation modeling is used by LISREL software. The results show that there is a significant relationship between auditors' self-control learning strategies by Weinstein and Hume method and financial fraud risk assessment. Also, self-control learning strategies, mental training, semantic development, and organization have the greatest impact on financial fraud risk assessment, respectively.

In this research, a new model for valuation of financial contracts is designed by using guaranteed and participation rates. In the traditional methods, valuation of these contracts is determined with the use of minimum guaranteed rate of returns such as risk free rate. However, the proposed model is based on the valuation of these contracts by guaranteed and participation rates as well as the customer's risk tolerance and investment risk. In this model, first an asset allocation model is considered in order to model market elements such as certificate of deposit, bonds and stocks. Then, conditional value at risk, as a proper risk measure, is used to determine guaranteed and participation rates and fair valuation of the contracts. To improve the validity, the model is run for both the capital markets of Iran and the United States of America. The variables used in this study are interest rate of banks, bonds and stock index from 1998 to 2012 in Iran and from 1980 to 2012 in America.

Objective: Fraudulent financial reporting is a major concern of the audit profession. In this regard, auditors are responsible according to auditing standards. Given the importance of identification and assessment of the risk of fraud in an audit of financial statements and the importance of responses of auditors, the purpose of this study is to provide a model for identification and assessment of the risks of material misstatement due to fraud in an audit of financial statements. Methods: In order to achieve this goal, firstly, researches in this field were studied to identify the effective indicators on assessment of the risk of fraud and other effective indicators were extracted by interviewing experts. Experts' opinion on the extracted indicators was collected through a questionnaire and were agreed upon through using Fuzzy Delphi method and expert opinion poll. Subsequently, confirmatory factor analysis test was used to determine the factor load of each indicator of each component. The relationship between the influence of different indicators on assessment of the risk of fraud in the form of Structural Equation Modeling (SEM) was tested and the research model was presented. Results: The results of the study showed that 146 out of 162 indicators extracted from theoretical foundations and researches and interviews, in the form of 5 components, reached the Delphi group general consensus and the results of performing a confirmatory factor analysis showed 144 indicators were of sufficient importance. Conclusion: The components that influence the assessment of the risk of fraud in an audit of financial statements, importance respectively, are: the characteristics of the client, the characteristics of the audit firm, the characteristics of performing the audit, the environmental characteristics and the personal characteristics of the auditor. Of the 53 indicators approved in the component of client characteristics, five important indicators are: lack of sufficient managers experience; lack of fair dealing with staff; lack of appropriate work environment for employees; lack of job security; ignoring the need to monitor or reduce the risks associated with assets misappropriation. Of the 23 indicators approved in the component of audit firm characteristics, five important indicators are: having sufficient auditing tools; auditor access to databases; giving auditors reasonable rights and benefits; adequate investment in auditing; and fair dealing with Auditors. Of the 27 indicators approved in the component of the characteristics of performing the audit, five important indicators are: adjusting the audit plan and auditing tests in responses to fraud risk factors; adequate understanding of the client and its environment, including internal controls; appropriate allocation and oversight of the audit team; the discussion among audit team members; and auditor access to databases. Of the 19 indicators approved in the component of environmental characteristics, five important indicators are: lack of culture of accountability and answering; sophisticated rules and regulations; numerous laws and regulations; lack of independent oversight bodies; and inexpressive laws and regulations. Of the 22 indicators approved in the component of personal characteristics of the auditor, five important indicators are: Ability to properly understand evidence and identify evidence inconsistent with other evidence; knowledge and awareness of fraud; responsibility; confidence; ability to identification of intentional identified misstatements.