We present results of numerical simulation of thermodynamics for array of Classical Heisenberg spins placed on 2D square lattice, which effectively represents the behaviour of single layer. Using Metropolis algorithm, we show the temperature behaviour of system with competing Heisenberg and Dzyaloshinskii-Moriya interaction (DMI) in contrast with classical Heisenberg system. We show the process of nucleating of skyrmion depending on the value of external magnetic field. We proposed the controlling method for movement of skyrmions.

Yang and Qiu proposed and reframed an expected utility-entropy (EU-E) based decision model, later on similar numerical representation for a risky choice was axiomatically developed by Luce et al. under the condition of segregation. Recently, we established a fund rating approach based on the EU-E decision model and Morningstar ratings. In this paper, we apply the approach to US mutual funds, and construct portfolios using the best rating funds. Furthermore, we evaluate the performance of the fund ratings based on EU-E decision model against Morningstar ratings by examining the performance of the three models in portfolio selection. The conclusions show that portfolios constructed using the ratings based on the EU-E models with moderate tradeoff coefficients perform better than those constructed using Morningstar. The conclusion is robust to different rebalancing intervals.

We analyze existing models for material transport in non-isothermal non-electrolyte liquid mixtures that utilize non-equilibrium thermodynamics. Many different sets of equations for material have been derived that, while based on the same fundamental expression of entropy production, utilize different terms of the temperature- and concentration-induced gradients in the chemical potential to express the material flux. We reason that only by establishing a system of transport equations that satisfies the following three requirements can we obtain a valid thermodynamic model of thermodiffusion based on entropy production and understand the underlying physical mechanism: (1) maintenance of mechanical equilibrium in a closed steady-state system, expressed by a form of the Gibbs-Duhem equation that accounts for all the relevant gradients in concentration, temperature, and pressure and respective thermodynamic forces; (2) thermodiffusion (thermophoresis) is zero in pure unbounded liquids (i.e., in the absence of wall effects); (3) invariance in the derived concentrations of components in a mixture, regardless of which concentration or material flux is considered to be the dependent versus independent variable in an overdetermined system of material transport equations. The analysis shows that thermodiffusion in liquids is based on the entropic mechanism.

Software clustering is usually used for program comprehension. Since it is considered to be
the most crucial NP-complete problem, therefore, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness function) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) apply on another software graph such as artifact feature dependency graph. To overcome the existing objective functions limitations, this paper presents a new objective function. A new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

There are various approaches to the problem of how one is supposed to conduct a statistical analysis. Different analyses can lead to contradictory conclusions in some problems so this is not a satisfactory state of affairs. It seems that all approaches make reference to the evidence in the data concerning questions of interest as a justification for the methodology employed. It is fair to say, however, that none of the most commonly used methodologies is absolutely explicit about how statistical evidence is to be characterized and measured. We will discuss the general problem of statistical reasoning and the development of a theory for this that is based on being precise about statistical evidence. This will be shown to lead to the resolution of a number of problems.

In the present work we applied the non equilibrium thermodynamic theory in the analysis of the Dielectric Breakdown (DB) process. As the tree channel front moves, the intense field near the front moves electrons and ions irreversibly in the region beyond the tree channel tips where electromechanical, thermal and chemical effects cause irreversible damage and from the non equilibrium thermodynamic viewpoint: entropy production. From the non equilibrium thermodynamics analysis the entropy production are due to the product of fluxes J_i and conjugated forces X_i: σ = ∑_i J_i X_i ≥ 0 We consider that the coupling between fluxes can describe the dielectric breakdown in solids as a phenomenon of transport of heat, mass and electric charge.

The new development in Artificial Intelligence, has significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake is so realistic that it is difficult to distinguish the authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial ones, a novel model has been developed based on deep learning and ELA detection, which is related to entropy and information theory, such as cross-entropy loss function in the final Softmax layer, normalized mutual information in image preprocessing and some applications of encoder based on information theory. Due to the limitations of computing resources and production time, DeepFake algorithm can only generate limited resolutions, resulting in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive artifacts. By using the error level analysis detection method, we can detect the presence or absence of different image compression ratios, and then use CNN to detect whether the image is fake. Experiments show that the training efficiency of CNN model can be significantly improved by using the ELA method. And the detection accuracy rate can reach more than 97% based on CNN architecture of this method. Compared to the state-of-the-art models, the proposed model has the advantages such as fewer layers, shorter training time and higher efficiency.

In recent years geologists have put a lot of effort trying to study the evolution of earth using different techniques studying rocks, gases, and water at different channels like mantle, lithosphere and atmosphere. Some of the ways are Estimation of heat flux between the atmosphere and sea ice, Modelling global temperature changes, Groundwater monitoring networks, etc. But algorithms involving the study of earth’s evolution have been a debated topic for decades. Also, there is distinct research on studying the mantle, lithosphere, and atmosphere using isotopic fractionation which this paper will take into consideration to form genes at the former stage. This factor of isotopic fractionation could be molded in QGA to study the earth’s evolution. We combined these factors because the gases containing these isotopes move from mantle to lithosphere or atmosphere through gaps or volcanic eruptions contributing to it. We are likely to use the Rb/Sr and Sm/Nd ratios to study the evolution of these channels. This paper, in general, provides the idea on gathering some information of temperature changes by using isotopic ratios as chromosomes, in QGA the chromosomes depict the characteristic of a generation. Here these ratios depict the temperature characteristic and other steps of QGA would be molded to study these ratios in the form of temperature changes, which would further signify the evolution of earth based on the study that temperature changes with the change in isotopic ratios. This paper will collect these distinct studies and embed them into an upgraded quantum genetic algorithm called Quantum Genetic Terrain Algorithm or Quantum GTA.

The Cosmic Microwave Background (CMB) temperature and polarization anisotropy measurements from the Planck mission have provided a strong confirmation of the Lambda Cold Dark Matter (LCDM) model of structure formation. However, there are a few interesting tensions with other cosmological probes that leave the door open to possible extensions to LCDM. I will review some interesting extended cosmological scenarios, in order to find a new concordance model that could explain and relieve tensions in current cosmological data.

Entropy feature analysis is an important tool for classification and identification of different types of ships. In order to improve the limitations of traditional feature extraction of shipradiation noise in complex marine environments, we proposed a novel feature extraction method for ship-radiated noise based on improved intrinsic time-scale decomposition (IITD) and multiscale dispersion entropy (MDE). The proposed feature extraction technique, named IITD-MDE. IITD as an improved algorithm has more reliable performance than instrinsic time-scale decomposition(ITD). Firstly, five types of ship-radiated noise signals are decomposed into a series of intrinsic scale component (ISCs) by IITD. Then, we select the ISC with main information through the correlation analysis, and calculate the MDE value as feature vector. Finally, input the feature vector into the support vector machine (SVM) classifier to analysis and get classification. The experimental results demonstrate that the recognition rate of the proposed technique reaches 86% of accuracy. Therefore, compare with the other feature extraction methods, the proposed method is able to classify the different types of ships effectively.