Four important ways of religious education in the field of beliefs are inferred in this article through the nature of religion in the Holy Quran. We have examined two categories of “nature of religion” and “methods of religious education” for this purpose. All verses including the root of “د ـ ی ـ ن” (religion) have been investigated in order to properly understand the nature of religion in the Holy Quran. Then, we have benefited from the inference method to find the ways of religious education in the Holy Quran. In the inferential method, we have reached the “conclusion”, which is the method of religious education by using two premises, “normative” and “realistic”. The solutions of the Holy Quran to achieve the methods of religious education are explained in the following. For example, using the Scriptures directly to know the right actions is one of the solutions of the Holy Quran to reach the way of believing in the punishment of deeds. The achievement of religious education methods through discovering the nature of religion and explaining the purpose of education on the one hand and using the inferential method as a research method on the other hand is one of the innovations of this research. The achievements of this paper can be used to provide an educational theoretical model for formal and public education system.

In this study the possibility of using fuzzy inference system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, seven models for estimating daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbagh et al. (R2/t) criteria. An experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data, 75 percent was used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the fuzzy models with different input parameters as compared with AQUACROP model showed that fuzzy systems were very well able to estimate the daily Yield. Fuzzy model so that the highest correlation with the 9 input variables (r=0. 98) had in mind and evaluate other parameters, the model with 2 parameters, match very well with the AQUACROP model had stage training. In the test phase, training phase was very similar results and the model with the second phase of harvest index and canopy cover will get the best match. According to the results of this study it can be concluded that fuzzy model approach is an appropriate method to estimate the daily yield.

‘, Approved opinions’,and the method of inferring them are one of the topics that have been briefly discussed in Avicenna’, s works. According to him, the intellect alone cannot infer ‘, approved opinions’, , and other components, such as theoretical opinions, reputation, induction, experience, and expediency, must also play positive roles in inferring them. But the method of inferring ‘, approved opinions’,from these different components is a matter of debate: while ‘, approved opinions’,fall under the category of conventional premises, and Avicenna talks about substantiating these opinions, there are other places in his work that he speaks of the indemonstrable character of the conventional premises. However, with an analytical look at Avicenna’, s discussions in the section on the conventional premises in logic and the functions of practical reason and examining his views on ethics, a new explanation can be provided that clarifies the incompatibility of the aforementioned components with the indemonstrability of conventional premises. This explanation originates from this philosophical point in Avicenna’, s work that there is a fundamental difference between substantiation and its premise for inferring the ‘, approved opinions’,and demonstration and its premises in classical logic and theoretical philosophy. When he talks about substantiating ‘, approved opinions’, , Avicenna does not mean to demonstrate them, which is incompatible with his view about the indemonstrability of conventional premises. Contrary to demonstration, substantiation can be based on analogy and induction, that is, ‘, approved opinions’,can be inferred analogically and inductively. The analogy can also have different premises, from primary and universal premises to experimental and individual ones. Matters such as manners, passions, and human expediencies can also be included in these premises.

To estimate actual evapotranspiration of grass, an experiment was a weather stationat, the Faculty of Agriculture, Ferdowsi University of Mashhad in 1389 year. In this experiment, actual evapotranspiration grass Deficit irrigation at different levels (5 levels) with a single branch sprinkler system, at two-day period was measure the water balance method. Also was estimated the reference crop evapotranspiration with FAO Penman, Hargreaves-Samani and pan evaporation methods. Coefficients calculated for each plant with water level and Five Fuzzy model was provided for estimating actual daily evapotranspiration. In these models was considered FAO Penman evapotranspiration as the output model. Performance models were compared using RMES, MAE, MBE, t and R2/t. The results showed that evapotranspiration values calculated in terms of standard methods of FAO Penman and Hargreaves - Samani, compared with the water balance method, respectively, 17 and 14 percent more than had been estimated. With analysis of evapotranspiration values in non-standard conditions were found to reduce grass Deficit irrigation is the actual evapotranspiration, The difference amounts to 20 percent evapotranspiration Deficit irrigation conditions was not a significant effect on evapotranspiration. Fuzzy model output results also showed that the fuzzy models developed using the combined model (PMF56) had a high Match and the ability to estimate actual evapotranspiration are included in the daily scale.

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

This article is an attempt to analyze Mualana’s approach to the concept of inference or reasoning in Mathnavi. Primarily, the definition of inference is provided and its various types are distinguished, then, the application of each type is studied in Mathnavi and a number of examples are given to illustrate Maulana’s approach to the act of reasoning. Moreover, the relation between Maulana, the author, and his addressee’s conception is discussed in details. It is demonstrated that Maulana applied reasoning to enhance addressee’s conception rather than to prove a claim. He particularly applied logical reasoning, a common concept in ethics, which is comprised of diverse types such as, rational, traditional, or jointly. As a pulpit preacher, he employed parables and similes for rational argument, and cited Qur’an and Hadith for traditional argument.

In recent years, using fuzzy sets theory in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for river flow forecasting, we have used models of FIS and ANFIS which are based on fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that ANFIS was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The ANFIS was also more precise in peak discharges simulation than FIS.