infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

Introduction: Embryonic Stem (ES) cells as pluripotent cells derived from the inner cell mass of blastula can differentiate to neural cells in vitro and this property is valuable in studies of neurogenesis and in the generation of donor cells for transplantation. In this regard, the propose of this research, was the study of the role of two important factors in the development of neural syst...

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...

human umbilical cord matrix (hucm) is considered as a promising source of mesenchymal stem cells (mscs) due to several advantages over other tissues. the potential of neural differentiation of hucm-mscs is of great interest in the context of treating neurodegenerative diseases. in recent years, considerable efforts have been made to establish in vitro conditions for improving the differentiatio...

Background and Aim: Recently, several studies have indicated that the central nervous system has the capacity for endogenous repair. But, the proliferation capacity of endogenous neural stem cells (NSCs) isn’t sufficient for the treatment of neurodegenerative diseases. So, it sounds that stimulation of endogenous NSC proliferation is essential for neuroregeneration. The aim of this study ...