Purpose: The science of interpretation, as the most important science responsible for understanding the Qur'an, needs to be methodized and updated so that the teachings of the Qur'an can be used easily for all classes of people. In the present study, the meaning of "future research in interpretation" is the ability to create desirable models for more efficient interpretation in the future, by using scientific tools, creativity and relying on the foundations and rules of interpretation and compensating the damages of interpretation in the past.Method: This research, with descriptive analytical method, answers the question, what are the fields and requirements of future research in interpretation? Findings: The results of the current research are based on the fact that special attention to the Qur'an, as the most important source of interpretation, rationality and methodical consideration in interpretation, attention to the prerequisites of interpretation, interpretation based on the requirements of the time, the need to pay attention to thematic interpretation, studying on the history of interpretation and observing the manners and conditions of interpretation are one of the most important requirements of interpretation in the future. Results: Considering the developments that will take place in the field of knowledge, methods and expectations from science in the future, the interpretation will also change. For this reason, in order not to cause various damages to the interpretation of the Qur'an, it is necessary to pay attention to the various contexts, principles and requirements of the interpretation of the Qur'anic commentators and scholars.

In order to predict the behavior of soil-related phenomena, it is necessary to have knowledge about unsaturated flow and using models that provide Optimal estimates of the retention curve and hydraulic conductivity of soils. Despite the widespread use of the classic van Genuchten-Mualem model (VGM), this model usually performs poorly in predicting hydraulic conductivity and modification of some of its parameters seems necessary. In this research, 283 soils from different textures of the UNSODA bank were selected and divided into two sections of calibration and validation and their soil parameters were exported and categorized. Then, by defining the modified unsaturated hydraulic conductivity (Ksc) instead of the saturated hydraulic conductivity (Ks) and determining the limits for l and n parameters, the hydraulic conductivity-moisture function of VGM were solved using 24600 pairs of points li and nj for each soil of the three main soil texture classes. In the following, the Optimal l value (l̂) of each texture class was selected based on the minimum value of the hydraulic conductivity estimation error using the root mean square error (RMSE) index and the n values that had created the minimum errors, were selected as the Optimal pore size distribution coefficients of the hydraulic conductivity-moisture function (n̂opt). In order to create pedotransfer functions for estimating n̂opt, we ran stepwise regression in MATLAB software considering the condition of statistical significance (P-value=0.05) for independent variables and functions for each soil texture class. After creating pedotransfer functions, the results of the proposed method of this research (MVGM) were compared with the VGM results using RMSE and Nash-Sutcliffe (NSE) indices. The results showed that in both sections of creation and validation functions, the MVGM performed better in estimating hydraulic conductivity and had a higher efficiency index in all textural classes of soil.

In this paper, a novel risk-based, two-objective (technical and economical) Optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

In this paper, we developed robust optimization approach that departs from the randomness assumption used in other methods of optimization under uncertainty and describe uncertainty in parameters through uncertainty sets; for portfolio selection problem. The model can control the conservativeness of investor for portfolio selection by a defined parameter. We used 50 active company of Tehran exchange stock in 3 first months of 1392 to study the performance of model. The results of paired comparisons in out of sample experiments shows that Markowitz portfolio which has same expected return by robust portfolio, has lower Sharpe ratio.

Objective Flexible Manufacturing Systems (FMS) are integrated workstations that utilize computer-controlled equipment components for production. These systems are managed by a central computer, which significantly enhances the efficiency and productivity of the production process. Accordingly, a case study is conducted on an FMS electrical manufacturing system with complex manufacturing processes, where automation on the production line is crucial. Selecting the Optimal number of advanced equipment is a challenging and vital issue for managers aiming to boost productivity and efficiently fulfill customer orders. It is a hard-to-change model, and replacing equipment incurs substantial costs.   Methods This study employs the two-stage D-Optimal method to optimize the combination of FMS elements and equipment. The D-Optimal response level input is derived from discrete-event simulation results. Depending on the conditions, various FMS equipment is allocated to each process. Each simulation result for element combinations serves as input for the experimental design. Additionally, the response level (y) of experiments from various FMS indexes is calculated using a weighting method. To reduce the number of experiments and increase data accuracy in a case study with hard-to-change parameters, all combinations are categorized based on the number of automated and manual equipment. The two stages of the D-Optimal design are defined as follows: In the first stage, all combinations within these categories are investigated. In the second stage, the optimized combination from the first stage is analyzed to determine the best configuration. Experiments in the top category from the first stage are simulated and further evaluated in the second stage of the D-Optimal method.   Results In the first phase, all advanced production equipment and FMS elements were considered. After selecting the best-calculated “y” value, which was 147,133.09 in this category, another D-Optimal design was optimized in the second phase to determine the best combination. This combination yielded a “y” value of 151,317.88, representing an improvement over the best category in the first phase of the D-Optimal design. Consequently, the optimized combination from the first phase was further refined. The results from the developed D-Optimal method and the second phase indicate that the Optimal combination of equipment involves using automated and FMS equipment for approximately 92.8% of the total components. Finally, a list of recommended FMS equipment is provided, and its productivity is compared with the productivity at the current level and a higher degree of automation for this production line.   Conclusion In summary, the results of the experimental design show that using advanced production systems does not necessarily improve system efficiency, and determining Optimal combinations requires accurate calculations.

In order to study the spatial structure of oriental beech (Fagus orientalis Lipsky) stand at Optimal stage, this research was carried out in the Caspian forests of Iran. The studied site consisted of a natural stand located at Sangdeh district (Mers-e-se) with three hectare area. It is located between 1900 and1950 m.a.s.l. Six sample plots each with 0.36 ha area, were systematically selected and all trees (1244 stem) within the plots were measured and recorded. Using increment borer, the mean age of the stand was estimated as 137 years; the age difference of trees was 40 years. Results showed that the stand has a closed canopy cover and distribution of stem number per diameter class was more or less homogenous (Bell shape) with a semi even-aged structure. Using Ripley's K function, the distribution of trees within the stand was random.

Services are an important major element of the economy in today's societies, and banks as one of the most important service organizations direct and support many of the community's economic activities. The purpose of this study was to develop an Optimal model for East Azarbaijan banks' performance based on organization risk management using the standardized questionnaire of Kosovo 2017. To achieve this purpose, the director or assistant director, head or deputy’, s head, bank managers, and experts of banks were selected for statistical sampling, and a struc-tural equation modeling approach was used for estimating the model and tests. Organizational risk management factors including "written job descriptions and resources to describe personnel duties, fraud risk assessment concerning how management and other employees participate" was assessed as factors affecting bank performance. Therefore, the structural problems of the banking system should be resolved so that this system can function and develop in the future, and consequently, to resolve the crisis of the banking system, it is necessary to reform the banking system.

As the main source of government revenue, the tax has always faced numerous challenges on the taxpayers’,part, such as tax evasion, late tax payment, and falsification of tax records. The problems with the calculation of taxable income cause these challenges. Therefore, a change in the calculation basis seems to serve as a solution to these challenges. This study aims to develop an Optimal model for tax calculation, where the confirmable income act as the basis for tax calculation. Taxable income results from the subtraction of confirmable revenues and costs from extra-organizational financial resources. The study population consists of financial information of firms listed on the Tehran Stock Exchange from 1997 to 2016. Initially, instead of the taxable income, confirmable income is introduced as an Optimal model,afterward, for the sake of accuracy, the tax rates for every firm and year are re-calculated using median, mean, regression, and artificial bee colony algorithm. Then the calculated taxes and real taxes were compared using paired t-tests. Finally, the rate that showed the slightest difference with the real rates, at the confidence level of 95%, is selected as the rate of the Industry/Firm-Year Group. The results show that this new method entails the use of the industry/firm group special rate every year,in other words, the tax rate of each group changes according to the inflation rate and economic conditions. This new method can also assist the government to gain more benefits from tax resources.

One of the key issues must be considered in management of reservoirs is Optimal well placement. Optimal well placement is defined as a mathematical problem which maximizes an objective function such as net present value (NPV) by adjusting the well locations in an optimum place while the physical and economic constraints are considered at the same time. modeling and simulation of reservoir is an important step in the well placement problem. The more the reservoir model is closer to the real one, the more precise the optimum well locations can be found. In most of prior suggested methods, the reservoir is modeled in Cartesian coordinate that makes the model complicated. In the present paper, a simpler reservoir model based on streamline technique is presented. Then, with the aid of the natural movement of flow in the streamline based reservoir model, we are trying to reach an effective and facile approach to solve Optimal well placement problem. First, the unique and valuable information from streamlined based model is introduced. Then, this information is added to the well-known genetic optimization algorithm. With the aid of this combination, the stochastic search strategy of genetic algorithm is changed to a semi-stochastic search or based on knowledge. It is shown that the number of reservoir simulation runs is decreased and the convergence of the proposed hybrid genetic is faster than the simple genetic.