Computer simulations and in particular mesoscopic simulation techniques, such as dissipative particle dynamics (DPD) enable researchers to study the complexities of soft materials and polymeric systems by performing in silico experimentations alongside with in vivo experiments. In this work we describe a complete implementation of DPD running entirely on a graphics processing unit (GPU). The design of the algorithms and optimizations needed to fully take advantage of a GPU are discussed. The performance of the code is evaluated and shown to be up to more than 30 times faster than a conventional implementation running on a single CPU core. We illustrate the potential of using DPD on GPU’s with applications to the physical and biological sciences as well as in engineering areas, where it can be a novel and versatile tool. SIMES is free for academics and can be downloaded from our web site: http://www.simes.uaemex-labs.org.mx.

Stueckelberg-Horwitz-Piron (SHP) electrodynamics formalizes the distinction between coordinate time (measured by laboratory clocks) and chronology (temporal ordering) by defining 4D spacetime events x^μ as functions of an external evolution parameter τ.  The spacetime evolution of classical events x^μ(τ), as τ grows monotonically, trace out particle worldlines dynamically and induce the five U(1) gauge potentials through which events interact.  Since Lorentz invariance imposes time reversal symmetry on x⁰ but not τ, the formalism resolves grandfather paradoxes and related problems of irreversibility.  Nevertheless, the causal structure of the 5D Green's function introduces singularities in the τ-dependence of the induced fields that must be treated with care for classical interactions.  These singularities are regularized by generalizing the action to include a non-local kinetic term for the fields.  The resulting theory remains gauge and Lorentz invariant, and the related QFT becomes super-renormalizable.  The field equations are Maxwell-like but τ-dependent and sourced by a current that represents a statistical ensemble of events distributed along the worldline.  The width of the distribution defines a mass spectrum for the photons that carry the interaction.  As the width becomes very large, the photon mass goes to zero and the field equations become τ-independent Maxwell's equations.  Maxwell theory thus emerges as an equilibrium limit of SHP.  Particles and fields can exchange mass in the SHP theory, however on-shell particle mass is restored through self-interaction.  

Electrogastrograms (EGG) are electrical signals generated by the muscles of the stomach and the features of these signals can be used to diagnose several digestive disorders. Entropy is a measure of the disorder associated with a system and hence is a measure of complexity of the system. In medical diagnostics, entropy has proved to be an efficient feature for discriminating the normal and abnormal states of biological systems. In this work the EGG signals have been obtained from normal and abnormal subjects having different digestive abnormalities (diarrhea, vomiting and stomach ulcer), from a local hospital. Further, the Tsallis entropy associated with the collected signals has been analyzed. Results demonstrate that the Mean Tsallis Entropy (MTE) (with α=0.5) of the EGG signals obtained from normal individuals (MTE=313.861) is high when compared to the individuals having diarrhea (MTE=278.0259), vomiting (MTE=105.1278) and stomach ulcer (MTE=-839.201). Since, entropy is the complexity associated with the signal, it is found that the complexity of the normal EGG signals is high when compared to the abnormal EGG signals. This work appears to be of high clinical relevance, since feature extraction from EGG signals is highly useful for diagnosis of digestive abnormalities.

This paper investigates whether the dually flat geometries introduced by Amari are useful as a tool to study the manifold of thermodynamic equilibrium states. The mathematical setting is that of statistical models belonging to an exponential family. The metric tensor is derived from the relative entropy. Flat geometries are introduced and thermodynamic length is calculated. The ideal gas serves as an example.

The impetus for new tools for a comprehensive and accurate analysis of energy utilization and industrial systems comes from the need for sustainable development that could be impeded by exhausting energy sources and deteriorating environment. Exergy evaluation provides insight to achieve highest technological efficiency at the lowest cost while meeting the social and legal conditions. Desalination processes are known as major energy consumers and exergy destruction and entropy of these processes is essential for their sustainable development. This paper identifies important areas of exergy destruction and entropy generation in desalination processes including multi-effect distillation (MED), multistage flash (MSF) and reverse osmosis (RO) with case studies and offers recommendations for future efforts in research and development.

This paper provides an entropy inference method based on an objective Bayesian approach for upper record values having the two-parameter logistic distribution. We derive the entropy based on i-th upper record value and the joint entropy based on the upper record values, and examine their properties. For objective Bayesian analysis, we provide objective priors such as the Jeffreys and reference priors for unknown parameters of the logistic distribution based on upper record values. Then, an entropy inference method based on the objective priors is developed. In real data analysis, we assess the quality of the proposed models under the objective priors.

The aim of this study is to model shapes from complex systems using Information Geometry tools. It is well-known that the Fisher information endows the statistical manifold, defined by a family of probability distributions, with a Riemannian metric, called the Fisher-Rao metric. With respect to this, geodesic paths are determined, minimizing information in Fisher sense. Under the hypothesis that it is possible to extract from the shape a finite number of representing points, called landmarks, we propose to model each of them with a probability distribution, as for example a multivariate Gaussian distribution. Then using the geodesic distance, induced by the Fisher-Rao metric, we can define a shape metric which enables us to quantify differences between shapes. The discriminative power of the proposed shape metric is tested performing a cluster analysis on the shapes of three different groups of specimens corresponding to three species of flatfish. Results show a better ability in recovering the true cluster structure with respect to other existing shape distances.

Both the maximum entropy (MaxEnt) and Bayesian methods update a prior to a posterior probability density function (pdf) by the inclusion of new information in the form of constraints or data respectively. To find the posterior, the MaxEnt method maximizes an entropy function subject to constraints, using the method of Lagrange multipliers, whereas the Bayesian method finds its posterior by multiplying the prior with likelihood functions, in which the measured data are substituted into the appropriate terms. The purpose of this work is to develop a Bayesian method to analyze flow networks and compare it to the MaxEnt method. Flow networks include, among others, water and electrical distribution networks and transport networks. The purpose of using probabilistic methods to model these networks is to predict the flow rates (and other variables) when there is not enough information to model them deterministically, and also to incorporate the effects of uncertainty. After developing the Bayesian method, we show that the Bayesian and Maxent methods obtain the same posterior means but, when the prior is a normal distribution, their covariances are different. The Bayesian method incorporates interactions between variables through the likelihood function. It achieves this through second order or higher cross-terms within the posterior pdf. The MaxEnt method however, incorporates interactions between variables using Lagrange multipliers, avoiding second order correlation terms in the posterior covariance. Therefore, the mean value inferences made by the MaxEnt and Bayesian methods are similar, but the MaxEnt method has a numerical advantage in its integrations, as the correlation terms can be avoided.

This paper states the case for applying the conceptual and analytic tools associated with the study of entropy in physical systems to cognition, focusing on creative cognition. It is proposed that minds modify their contents and adapt to their environments to minimize psychological entropy: arousal-provoking uncertainty, which can be experienced negatively as anxiety, or positively as a wellspring for creativity (or both). Thus, intrinsically motivated creativity begins with detection of high psychological entropy material (e.g., a question or inconsistency), which provokes uncertainty and is arousal-inducing. This material is recursively considering from new contexts until it is sufficiently restructured that arousal dissipates and entropy reaches an acceptable level. Restructuring involves neural synchrony and dynamic binding, and may be facilitated by temporarily shifting to a more associative mode of thought. The creative outcome may similarly induce restructuring in others, and thereby contribute to the cultural evolution of more nuanced understandings. Thus, the concept of entropy could play a unifying role in cognitive science as a driver of thought and action, and in cultural studies as the driver of the creative innovations that fuel cultural evolution. The paper concludes with an invitation for cross-disciplinary exploration of this potential new arena of entropy studies.

Black holes are objects of significant interest in modern cosmology. From what initially looked like a superficial analogy between black hole mechanics and thermodynamics, a new epistemological framework has emerged according to which far-reaching conclusions about black hole can be reached through thermodynamic analysis. An example of this is the view that the temperature of a black hole is inversely proportional to its mass. This paper raises doubts about the currently accepted connection between black holes and entropy. It does so by first reviewing the principles of thermodynamics and the properties a system must have in order to admit of proper thermodynamic analysis. It is argued that the current view of black holes preclude their distinct classification either as closed or open systems, a fact which has a bearing on the formulation of the First and Second Laws. From a mechanistic view of temperature and heat, combined with my recent work on the physical meaning of classical entropy, I show that the generalized Second Law of black hole thermodynamics is probably in error. The notion of heat transfer (which is central to entropy definition) is not explicit in the black hole energy equation. To address the challenges raised, black hole mechanics must either commit to a phenomenological approach and therefore only invoke thermodynamics in the classical sense or accept a microscopic view of black hole matter in order to readily draw from established results of statistical mechanics. It is argued that a proper connection to classical  thermodynamics would lead to the view that the temperature of a black hole increases with its mass, as a result of which a positive specific heat capacity is to be expected, contrary to the prevailing doctrine.