Entropy production in the early-cosmology pionic phase

We point out that in the early universe, for temperatures in the approximate interval 175-80 MeV (after the quark-gluon plasma), pions carried a large share of the entropy and supported the largest inhomogeneities. Thus, we examine the production of entropy in a pion gas, particularizing to inhomogeneities of the temperature, for which we benefit from the known thermal conductivity. We finally put that entropy produced in relaxing such thermal inhomogeneities in the broad context of this relatively unexplored phase of early-universe cosmology.Comment: 10 pages, 10 figures

Breaking the sound barrier in holography

It has been conjectured that the speed of sound in holographic models with UV fixed points has an upper bound set by the value of the quantity in conformal field theory. If true, this would set stringent constraints for the presence of strongly coupled quark matter in the cores of physical neutron stars, as the existence of two-solar-mass stars appears to demand a very stiff equation of state. In this article, we present a family of counterexamples to the speed of sound conjecture, consisting of strongly coupled theories at finite density. The theories we consider include N = 4 super Yang-Mills at finite R-charge density and nonzero gaugino masses, while the holographic duals are Einstein-Maxwell theories with a minimally coupled scalar in a charged black hole geometry. We show that for a small breaking of conformal invariance, the speed of sound approaches the conformal value from above at large chemical potentials.Peer reviewe

DeepVATS : Deep Visual Analytics for time series

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with an interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://github.com/vrodriguezf/deepvats

Three-phase DC/AC power converter with power quality optimization

Introduction— The DC/AC power conversion systems currently used in multiple applications such as alternative energy sources involve power quality problems due to voltage regulations and distortions such as those caused by harmonic distortion. Objective— Develop a power converter prototype that, through a multi-objective genetic algorithm, allows optimizing the energy quality of three-phase inversion systems through the use of multilevel converters. Methodology— The prototype to be used in the power conversion is defined, the output voltage is modeled mathematically, the optimization multi-objective genetic algorithms are developed, the prototype is implemented and its operation is validated. Results— The algorithm developed and implemented in the developed prototype mitigates power quality phenomena associated with short and long duration variations such as swell, sag, undervoltage and overvoltage, avoids the presence of voltage fluctuations and presents a lower harmonic content in all 1% cases. Conclusions— The implemented prototype allows optimizing the power quality of three-phase power supply systems through the use of multilevel inverters, avoiding the presence of power quality phenomena.Introducción— Los sistemas de conversión DC/AC utilizados actualmente en múltiples aplicaciones como las fuentes alternativas de energía involucra problemas de la calidad de la energía debido regulaciones de tensión y deformaciones como las causadas por la distorsión armónica. Objetivo— Desarrollar un prototipo que a través de un algoritmo genético multiobjetivo permita optimizar la calidad de la energía de los sistemas de inversión trifásicos a través del uso de convertidores multinivel. Metodología— Se define el prototipo a utilizar en la conversión de potencia, se modela matemáticamente la tensión de salida, se desarrollan los algoritmos genéticos multiobjetivo de optimización, se implementa el prototipo y se valida su funcionamiento. Resultados— El algoritmo desarrollado e implementado en el prototipo desarrollado mitiga los fenómenos de calidad de la energía asociados a las variaciones de corta y larga duración como swell, sag, undervoltaje y overvoltaje, evita la presencia de fluctuaciones de tensión y presenta un contenido armónico menor en todos los casos del 1% Conclusiones— El prototipo implementado optimizar la calidad de la energía de los sistemas trifásicos de suministro de energía a través del uso de un inversor multinivel, evitando la presencia de fenómenos de calidad de la energía.&nbsp

Controllable production of Janus ligaments by AC fields in a flow-focusing junction

We report the production of bicomponent Janus filaments of miscible aqueous fluids in a micro- fluidic electro-flow focusing device under the action of an AC electric field. The production of liquid filaments can lead to the generation of microfibers by adding a subsequent process of polymerization. Janus microfi- bers are of paramount importance in biomedical appli- cations such as tissue production on crimped scaffolds. We show that the filament length is a function of the frequency signal, voltage amplitude and of the visco- sity and conductivity of the dispersed phase. In par- ticular, Janus filaments with diameters ∼ 10μm and longer than 1 mm are produced by AC voltages with frequencies below 150 kHz and a viscosity of the dis- persed phase ∼ 10 cP

A mechatronic leg replica to benchmark human-exoskeleton physical interactions

: Evaluating human-exoskeleton interaction typically requires experiments with human subjects, which raises safety issues and entails time-consuming testing procedures. This paper presents a mechatronic replica of a human leg, which was designed to quantify physical interaction dynamics between exoskeletons and human limbs without the need for human testing. In the first part of this work, we present the mechanical, electronic, sensory system and software solutions integrated in our leg replica prototype. In the second part, we used the leg replica to test its interaction with two types of commercially available wearable devices, i.e. an active full leg exoskeleton and a passive knee orthosis. We ran basic test examples to demonstrate the functioning and benchmarking potential of the leg replica to assess the effects of joint misalignments on force transmission. The integrated force sensors embedded in the leg replica detected higher interaction forces in the misaligned scenario in comparison to the aligned one, in both active and passive modalities. The small standard deviation of force measurements across cycles demonstrates the potential of the leg replica as a standard test method for reproducible studies of human-exoskeleton physical interaction

Holographic Quark Matter and Neutron Stars

We use a top-down holographic model for strongly interacting quark matter to study the properties of neutron stars. When the corresponding equation of state (EOS) is matched with state-of-the-art results for dense nuclear matter, we consistently observe a first-order phase transition at densities between 2 and 7 times the nuclear saturation density. Solving the Tolman-Oppenheimer-Volkov equations with the resulting hybrid EOSs, we find maximal stellar masses in excess of two solar masses, albeit somewhat smaller than those obtained with simple extrapolations of the nuclear matter EOSs. Our calculation predicts that no quark matter exists inside neutron stars.Peer reviewe

Turbulent hydrodynamics in strongly correlated Kagome metals

A current challenge in condensed matter physics is the realization of strongly correlated, viscous electron fluids. These fluids are not amenable to the perturbative methods of Fermi liquid theory, but can be described by holography, that is, by mapping them onto a weakly curved gravitational theory via gauge/gravity duality. The canonical system considered for realizations has been graphene, which possesses Dirac dispersions at low energies as well as significant Coulomb interactions between the electrons. In this work, we show that Kagome systems with electron fillings adjusted to the Dirac nodes of their band structure provide a much more compelling platform for realizations of viscous electron fluids, including non-linear effects such as turbulence. In particular, we find that in stoichiometric Scandium (Sc) Herbertsmithite, the fine-structure constant, which measures the effective Coulomb interaction and hence reflects the strength of the correlations, is enhanced by a factor of about 3.2 as compared to graphene, due to orbital hybridization. We employ holography to estimate the ratio of the shear viscosity over the entropy density in Sc-Herbertsmithite, and find it about three times smaller than in graphene. These findings put, for the first time, the turbulent flow regime described by holography within the reach of experiments.Comment: 4 pages plus supplement, 2 figures, 1 tabl