Modeling of an Elastocaloric Cooling System for Determining Efficiency

When it comes to covering the growing demand for cooling power worldwide, elastocalorics offer an environmentally friendly alternative to compressor-based cooling technology. The absence of harmful and flammable coolants makes elastocalorics suitable for energy applications such as battery cooling. Initial prototypes of elastocaloric systems, which transport heat by means of thermal conduction or convection, have already been developed. A particularly promising solution is the active elastocaloric heat pipe (AEH), which works with latent heat transfer by the evaporation and condensation of a fluid. This enables a fast and efficient heat transfer in a compression-based elastocaloric cooling system. In this publication, we present a simulation model of the AEH based on MATLAB-Simulink. The model showed very good agreement with the experimental data pertaining to the maximum temperature span and maximum cooling power. Hereby, non-measurable variables such as efficiency and heat fluxes in the cooling system are accessible, which allows the analysis of individual losses including the dissipation effects of the material, non-ideal isolation, losses in heat transfer from the elastocaloric material to the fluid, and other parasitic heat flux losses. In total, it can be shown that using this AEH-approach, an optimized system can achieve up to 67% of the material efficiency

Phenomenological model for first-order elastocaloric materials [Modèle phénoménologique pour les matériaux élastocaloriques de premier ordre]

Elastocaloric cooling systems may offer a potentially more efficient as well as environmentally friendly alternative to compressor-based cooling technology. These cooling systems use stress-induced phase transformation in elastocaloric materials to pump heat. Thermodynamically consistent material models can be used to design and quantify the efficiency of these cooling systems. In this paper, we present a phenomenological material model that depicts the behavior of first-order materials during stress-induced phase transformation. This model is based on a phenomenological heat capacity equation, from which the parameters adiabatic temperature change and isothermal entropy can be derived. Hysteresis of the materials, which determines it dissipative effects, is also taken into account. Based on this model, these parameters can be calculated as a function of stress and temperature. The performance coefficients derived from the model can be used to evaluate the materials efficiency. Furthermore, the data obtained using this model coincided very closely with experimental data

Characterization of Treponema denticola pyrF encoding orotidine-5′-monophosphate decarboxylase

The Treponema denticola ATCC 35405 genome annotation contains most of the genes for de novo pyrimidine biosynthesis. To initiate characterization of pyrimidine synthesis in Treponema , we focused on TDE2110 (the putative pyrF , encoding orotidine-5′-monophosphate decarboxlyase). Unlike the parent strain, an isogenic pyrF mutant was resistant to 5-fluoroorotic acid. In complex medium, growth of the pyrF mutant was independent of added uracil, indicating activity of a uracil uptake/salvage pathway. Transcription of pyrF was greatly reduced in T. denticola grown in excess uracil, demonstrating that de novo pyrimidine synthesis is regulated and suggesting a feedback mechanism. Treponema denticola PyrF complemented uracil auxotrophy in an Escherichia coli pyrF mutant. This study provides biochemical confirmation of T. denticola genome predictions of de novo and salvage pyrimidine pathways and provides proof of concept that pyrF has potential as a selectable marker in T. denticola .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75261/1/j.1574-6968.2006.00589.x.pd

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

<p>Abstract</p> <p>Background</p> <p>The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published <it>q</it>-Norm MKL algorithm.</p> <p>Results</p> <p>We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities<it> ab initio</it> along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.</p> <p>Conclusions</p> <p>We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.</p

Gaia Data Release 1. Astrometry: one billion positions, two million proper motions and parallaxes

Context. Gaia Data Release 1 (DR1) contains astrometric results for more than 1 billion stars brighter than magnitude 20.7 based on observations collected by the Gaia satellite during the first 14 months of its operational phase. Aims. We give a brief overview of the astrometric content of the data release and of the model assumptions, data processing, and validation of the results. Methods. For stars in common with the Hipparcos and Tycho-2 catalogues, complete astrometric single-star solutions are obtained by incorporating positional information from the earlier catalogues. For other stars only their positions are obtained, essentially by neglecting their proper motions and parallaxes. The results are validated by an analysis of the residuals, through special validation runs, and by comparison with external data. Results. For about two million of the brighter stars (down to magnitude ∼11.5) we obtain positions, parallaxes, and proper motions to Hipparcos-type precision or better. For these stars, systematic errors depending for example on position and colour are at a level of ±0.3 milliarcsecond (mas). For the remaining stars we obtain positions at epoch J2015.0 accurate to ∼10 mas. Positions and proper motions are given in a reference frame that is aligned with the International Celestial Reference Frame (ICRF) to better than 0.1 mas at epoch J2015.0, and non-rotating with respect to ICRF to within 0.03 mas yr−1 . The Hipparcos reference frame is found to rotate with respect to the Gaia DR1 frame at a rate of 0.24 mas yr−1 . Conclusions. Based on less than a quarter of the nominal mission length and on very provisional and incomplete calibrations, the quality and completeness of the astrometric data in Gaia DR1 are far from what is expected for the final mission products. The present results nevertheless represent a huge improvement in the available fundamental stellar data and practical definition of the optical reference frame

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe