Background: Clinical laboratories need to manage resources properly and scientifically to survive in today's highly competitive environment. In this context, scientific-economic principles should be considered to determine the PROFITability or loss of laboratories. Thus, in this study, the NET PROFIT of laboratory services was measured based on scientific-economic principles. Methods: This was an applied research with descriptive-retrospective approach. A laboratory was selected from 61 laboratories of Kerman, Iran, which performed the highest number of tests among the laboratories of this city. In addition, due to easy access, it was the most visited laboratory by patients. The present study had 2 main phases: (1) measuring the price of services and (2) calculating the NET PROFIT of the studied laboratory. Data analysis was performed using activity-based costing (ABC) as an econometric model and Excel software. Results: The highest charges were related to direct costs (78. 28%); consumable goods (47. 26%) and professional and logistic human resources (46. 31%) had the highest share of these costs. In the test groups, the most expensive tests belonged to the hormones (23. 03%) and clinical chemistry (20. 84%). Total cost, revenue, and the NET PROFIT of the studied laboratory were 641 645, 1 390 942, and 749 297 USD, respectively. After doing sensitivity analysis (50% increase in the frequency of tests), the following values were obtained: 987 071, 2 086 413, and 1 099 342, respectively. Conclusion: Some test groups in the studied laboratory were not PROFITable, and this was due to the high cost of these tests and illogical tariffs. One way to overcome this problem is to increase the frequency of laboratory tests.

One of the most common criterion based on value which has been considered in recent years, is called EVA. This study has analyzed the ability of EVA to present information related to the firm's value and tries to compare it with some other criterion such as operational PROFIT and not PROFIT. In fact this paper is aimed to determine the correlation coefficient between the economic values added, NET PROFIT and operating firm's market values in Tehran stock exchange.This research is an empirical one, which investigates the relation between variables by using regression method and Pearson's correlation coefficient. The methodology of this research is past event study.Population of this study includes all Iranian firms listed in Tehran stock exchange, except investing and financial firms. We use the data of 77 firms over the period 1380-1386 selected from Tehran stock exchange. The results of this study has shown that although the coordination of EVA with market value is remarkable, but compared with operational and NET PROFIT, it is less capable.

According to literature, value of an asset is equal to the NET present value of future cash flows expected from that asset. Therefore, it appears that historical data would not dictate to determine the value of an asset. But, it is witnessed in the market that investors make their decisions based on the historical financial statements [2]. Meanwhile, Stewart [8] claims that economic value added, which is calculated using historical data, can precisely determine the market value added and, therefore, we should cease using other yardsticks such as accounting NET PROFIT. On this basis, first it was attempted to test this claim in the Tehran Stock Exchange Secondly, determine what is the relative degree of workability of this measure with the accounting NET PROFIT, which is a simple and traditional yardstick. The result indicates that in the Tehran Stock Exchange, both the economic value added, and the accounting NET PROFIT are weak and inefficient measures in determining the market value added.

The objective of this research is to design an optimal model for Build-Operate-Transfer (B.O.T) contracts, considering uncertainty conditions. In this study, mathematical modeling was conducted using MATLAB software and the Particle Swarm Optimization method. Hypothetical data related to a combined cycle power plant project were analyzed as a case study. The construction cost of the power plant was estimated at $120 million, and it was projected that the project’s annual revenue would increase from $10.74 million in the fifth year to $46.64 million by the eighteenth year. Operating costs also rose from $1.68 million in the fifth year to $19.90 million by the thirtieth year. The results showed that the cumulative NET financial flow for the government reached $273.32 million by the thirty-third year, while the private sector’s cumulative NET financial flow increased to $157.66 million by the thirty-second year. The proposed model, using historical data and information obtained from similar projects, was able to reduce risks associated with revenue fluctuations and provide a more accurate prediction of annual PROFITs. Based on the analysis, the internal rate of return (IRR) was calculated at 12% for the government and 25% for the private sector. Using the proposed model, the economic lifespan of the project was estimated to be 33 years from the government’s perspective and 32 years from the private sector’s perspective. The optimal point for transferring ownership of the project was determined to be in year 20.8. The findings indicated that the proposed model for B.O.T contracts, by reducing uncertainty and accurately forecasting financial flows and PROFITs, serves as a suitable tool for improving financial decision-making in infrastructure projects.

The agency theory from the economic perspective indicates that a rational economic man is after self-interest maximization. Therefore, to control the opportunistic behavior of the agent, independent directors should be appointed to the board. However, stewardship theory by considering the high-level needs of Maslow’, s hierarchy of needs and organizational psychology suggests that trust in managers leads to better performance. Since human behavior is influenced by variety of needs. The purpose of this study is to investigate the effect of board independence on financial performance by developing a theory of a combination of the above two theories. Balanced panel data of 124 corporations from the population of companies listed on the Tehran Stock Exchange during the years 2011-2019 is collected. The test of hypotheses is conducted by nonlinear feasible generalized least squares regression method with the help of software Stata done. According to the findings of the study, no significant effect is found between board independence and operational cash flows. However, there is a quadratic significant effect with a downward concussion between the number of non-executive directors and NET income. It can be stated that combining the economic perspective of agency theory and the psychological perspective of stewardship theory on board independence has a positive effect on firm PROFITability. In other words, the research findings confirm that the effectiveness of board consists of executive managers and the majority of non-executive members. Therefore, this study suggests a level of board independence that improves corporate PROFITability.

Reliability and efficacy of accruals and cash flows which are among the most important factors affecting dividend deviation have always been in question and subject to anomalies. The presence of these anomalies in accruals and cash flows and its effect on future returns and the consequences that they can have in country’s investments are the main motives to choose this issue for the current study.The statistical population includes all the accepted companies in Tehran Securities Exchange in the time domain of 2005 to 2012 that were studied after the systematic elimination of 153 companies from the original sample and 45 companies from the dividend continuity sample. The research is descriptive and correlational and the research assumptions are tested using statistical techniques. The results indicate that the first assumption based on rational pricing related to cash flows and total accruals in the companies in Tehran Securities Exchange is rejected in the study period; this indicates anomalies and regarding the significance of the coefficient related to the total accruals among the group of companies under study, the companies with low accruals have a higher abnormal return compared to companies with high accruals.

The present study compared the predictive performance of machine-learning models and statistical models for forecasting PROFIT and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting NET PROFIT and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting NET PROFIT and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models.Keywords: Classification, Data Mining, Machine Learning, NET PROFIT Forecasting, Operating Cash Flow Forecasting. IntroductionIn the current intensely competitive business environment, precise prediction of financial outcomes has emerged as a pivotal element in organizational triumph. Projecting crucial financial indicators, such as NET PROFIT and operating cash flows, equips businesses with the insight needed to make well-informed choices regarding investment strategies, resource distribution, and comprehensive financial strategizing. The capacity to anticipate future financial performance enables organizations to streamline operations and mitigate risks. Consequently, there is an escalating need for effective forecasting models.This study had two primary objectives: firstly, assessing the predictive capability of accrual and cash variables for forecasting PROFIT and future cash flows and secondly, comparing the efficacy of statistical models and machine-learning models in predicting NET PROFIT and operating cash flows. Statistical models seek to scrutinize historical data patterns and underlying relationships to anticipate future financial outcomes. Conversely, machine-learning models have emerged as a potent alternative, employing advanced computational techniques to glean insights from data and make predictions without explicit programming. This research was guided by four hypotheses:First hypothesis: The predictive capability of accrual ‎variables for future NET PROFIT significantly exceeds that of cash variables.‎Second hypothesis: The predictive capacity of accrual ‎variables for future operational cash flow significantly surpasses that of cash variables.‎Third hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting NET PROFIT.‎Fourth hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting operational cash flows.‎ Materials & MethodsThis study utilized the Bourseview software database, Rahavard Novin, and the Codal website for analyzing and drawing conclusions regarding the hypotheses. Additionally, data-mining software, such as Weka, SPM, RapidMiner, SPSS Modeler, and Eureqa, were employed for modeling, while Stata econometric and statistical software was used for the Vuong test, EViews for descriptive statistics, SPSS for mean comparison test, and Excel for data sorting and categorization. Following the application of these specified tools, 184 companies listed on the Tehran Stock Exchange (TSE) were examined. Initially, the study investigated the ability to explain each category of cash and accrual variables for NET PROFIT and future operating cash flow through special regression estimation of panel data and the Vuong test. Subsequently, the superior model was utilized for modeling and the average performance of the machine-learning models was compared with that of statistical models. FindingsThe significance of Vuong statistic in predicting NET PROFIT at a 1% significance level suggested a notable difference in the explanatory power of the two models with the model of accrual variables demonstrating higher explanatory power than that of the cash flow statement variables. Conversely, the non-significance of the Vuong statistic at the 5% significance level for predicting operational cash flow indicated no significant difference in the explanatory power of the two models. The performance results of both statistical and machine-learning models indicated that the symbolic regression classifier, utilizing the geNETic algorithm to predict NET PROFIT, exhibited the best overall performance and provided valuable results in the longitudinal test sample. Following symbolic regression, the linear support vector machine and MARS ranked second and third, respectively, in overall performance. Similarly, the symbolic regression classifier, employing the geNETic algorithm to predict operating cash flow, demonstrated the best overall performance in the longitudinal test samples. After symbolic regression, the deep learning classifier and MARS ranked second and third, respectively, in overall performance. Discussion & ConclusionsIn accordance with testing of the first and second hypotheses of the research, which posited that accrual variables have a greater explanatory capacity for NET PROFIT and future operating cash flow compared to cash variables, the coefficients of determination of the models were compared after estimating the appropriate panel data approach. The investigation results indicated that accrual variables indeed possessed greater explanatory power for NET PROFIT, thus providing no grounds for rejecting the first hypothesis of the study. However, in the case of operating cash flow, while the explanatory value of accrual variables surpassed that of cash variables, there was no statistically significant difference in the explanation between accrual and cash variables. Consequently, the second hypothesis of the research was rejected. In accordance with testing of the third and fourth hypotheses of the current study, which posited that machine-learning models outperform statistical models in predicting NET PROFIT and operating cash flow, the AUC criterion was derived through the implementation of both statistical and machine-learning models. By comparing the success rates of the statistical and machine-learning models, it was observed that the machine-learning models significantly outperformed statistical models in predicting NET PROFIT and operational cash flow. Therefore, there was no basis for rejecting the third and fourth hypotheses of the study.

Due to extensive agricultural activities and the need for significant irrigation, water consumption in the agricultural sector has increased. Therefore, providing solutions to reduce water consumption is an important issue. For this purpose, the optimization model was calculated and presented separately using the multi-objective geNETic algorithm (NSGA-II) with two functions of NET PROFIT and agricultural water consumption in the Shahriar plan (Tehran, Iran). Also, the optimal values of NET PROFIT, cultivation pattern, irrigation uses and gross irrigation requirement were estimated in the optimization model. The results showed that the highest NET PROFIT, cultivation area and the volume of optimal water demand are related to Islamshahr area, then Shahriyar area and finally Robat Karim have the most desired values. Shahriar area has the highest optimal water consumption in the agricultural sector, then Islamshahr and finally Robat Karim has the lowest optimal amount of agricultural water consumption. The highest ratio of NET PROFIT to cultivated area in the optimal condition was related to Robat Karim area, then Shahriyar area and finally Islamshahr area. Also, according to the cultivation area and the optimal water consumption of grape, pomegranate, onion, vegetables in each area, their NET PROFIT is appropriate and ideal. And grape is the best possible crop among them. The policy of optimal utilization of water resources has led to a reduction in the area under cultivation, the volume of optimal water consumption and the volume of water needs in the whole area compared to the current situation by 20. 44, 49. 71 and 20. 35 percent.

The purpose of this study was to investigate the effect of employer premium rate on operating PROFIT per share and change in NET sales income of companies listed on the Tehran Stock Exchange. This study, in terms of the type of research; Correlation is cross-sectional and is considered as a practical goal. The statistical population of the study included 666 companies listed on the Tehran Stock Exchange from the beginning of 2013 to the end of 2019, of which 111 companies were selected as a sample by systematic elimination method. In descriptive statistics, population parameters including central indices and population dispersion were measured and data analysis was performed using regression method based on composite data and Hausman's tests, F (Limer), by Oveys software. The results showed that there is a significant relationship between the employer's share insurance rate, operating PROFIT per share and the change in NET sales income of companies listed on the Tehran Stock Exchange. Therefore, it is better for the Social Security Organization to consider the premium rate according to the inflationary conditions and labor income in order to support the industries in attracting labor and the high premium rate should not cause the managers of the companies to increase the total price. Goods and services are driven.