Hydrological cycle is directed into thermodynamic non-equilibrium state and maximum entropy production through thermodynamic processes. Therefore, it is possible to understand and describe hydrological cycle through computation of entropy production rate and the maximum entropy production principle (MEP). Based on MEP, if there are sufficient degrees of freedom within a system, it will adopt a steady state at which entropy production is maximized under existing restrictions. This principle can help to estimate the hydrological components and parameters without need for detailed understanding of watershed characteristics. Due to the importance of this issue in the science of hydrological modeling and the lack of Persian resources in this field, the present study examines the hydrological cycle from a thermodynamic point of view using valid scientific sources. In this study, the hydrological cycle is considered as a thermodynamic system and the method of calculating entropy production rate by components of water balance is presented. Also, how to estimate hydrological components using the MEP principle is explained in a simple example. Overall, the MEP principle holds great promise for equipping us with a better understanding of the organization of hydrological processes within the Earth system and development of the models based on non-equilibrium thermodynamic.

The notion of entropy was introduced by Clausius in 1850, and some of the main steps towards the consolidation of the concept were taken by Boltzmann and Gibbs.Since then several extensions and reformulations have been developed in various disciplines with motivations and applications in different subjects, such as statistical mechanics, information theory, and dynamical systems. Fujii and Kamei introduced the notion of the relative operator entropy and Furuta introduced the notion of the parametric relative operator entropy as a generalization of the notion of the relative operator entropy. These concepts can be described by the notion of perspective of some elementary functions and this description makes simplifier their verification.The relative operator entropy is concave and the parametric relative operator entropy is also concave for some suitable parameters. In this paper, we study some properties of the parametric relative operator entropy including its bounds. These bounds generalize the calculated bounds of the relative operator entropy by other authors and in particular the previous results will achieve.

There are many methods for feature extraction from texture images. Local Binary Pattern (LBP) is one of the most important of these methods. It is a simple method for implementation and can extracttexture features efficiently. LBP can be combined with local variance (VAR) to provide higher classification rate. In this paper, a new method is proposedwhich is named Local Entropy Pattern (LEP). The equation of this method is similar to Entropy literally, butit is differ from Entropy in some issues. The proposed method is more robust to noise than LBP and VAR. In addition, by combiningit's features with LBP features the classification rate increases significantly and it provides higher accuracy than LBP/VAR. Local Entropy Pattern shows dissimilarity of a local neighborhood. This approach has all positive points of LBP and some state-of-art similar methods. It is not only rotation and grayscale invariant but also noise robust.

In this paper, entropy generated during a continuous two-dimensional boundary layer flow on a porous flat plate surrounded by a variable heat flux is studied through a similarity solution. This study is presented for modeling of the cooling process of metal surfaces with porous coating that is widely used in industry. Using similarity variables, the continuity, momentum and energy equations are transformed into ordinary differential equations. To validate the proposed solution, results are compared to the existing studies. The effects of various parameters such as heat flux (q􀭵 ), suction parameter (S), Prandtl number (Pr), Stretch parameter on the plate velocity (U), temperature distribution (T) and dimensionless entropy (Ns) are shown and discussed in detail. Bijan number is used as an important parameter for qualitative study in cooling process on porous surfaces. The increase of Bijan number is introduced as a measure for the increase of cooling rate. The results of the study show that when Prandtl number increases, the entropy will decrease which leads to a decrease in Bijan number. However, an increase in permeability of surface causes to increasing suction parameter and Bijan number.