The Thermodynamics of Cosmic String densities in U(1) Scalar Field Theory

We present a full characterization of the phase transition in U(1) scalar field theory and of the associated vortex string thermodynamics in 3D. We show that phase transitions in the string densities exist and measure their critical exponents, both for the long string and the short loops. Evidence for a natural separation between these two string populations is presented. In particular our results strongly indicate that an infinite string population will only exist above the critical temperature. Canonical initial conditions for cosmic string evolution are show to correspond to the infinite temperature limit of the theory.Comment: 4 pages, 4 figures, RevTe

Crystal Structure Generation with Autoregressive Large Language Modeling

The generation of plausible crystal structures is often an important step in the computational prediction of crystal structures from composition. Here, we introduce a methodology for crystal structure generation involving autoregressive large language modeling of the Crystallographic Information File (CIF) format. Our model, CrystaLLM, is trained on a comprehensive dataset of millions of CIF files, and is capable of reliably generating correct CIF syntax and plausible crystal structures for many classes of inorganic compounds. Moreover, we provide general and open access to the model by deploying it as a web application, available to anyone over the internet. Our results indicate that the model promises to be a reliable and efficient tool for both crystallography and materials informatics

Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning

Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. $\textit{Ab initio}$ models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from $\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and $\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available

Optimising mRNA vaccines manufacturing by using Machine learning approaches

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Maximizing mRNA vaccine production with Bayesian optimization

Messenger RNA (mRNA) vaccines are a new alternative to conventional vaccines with a prominent role in infectious disease control. These vaccines are produced in in vitro transcription (IVT) reactions, catalyzed by RNA polymerase in cascade reactions. To ensure an efficient and cost-effective manufacturing process, essential for a large-scale production and effective vaccine supply chain, the IVT reaction needs to be optimized. IVT is a complex reaction that contains a large number of variables that can affect its outcome. Traditional optimization methods rely on classic Design of Experiments methods, which are time-consuming and can present human bias or based on simplified assumptions. In this contribution, we propose the use of Machine Learning approaches to perform a data-driven optimization of an mRNA IVT reaction. A Bayesian optimization method and model interpretability techniques were used to automate experiment design, providing a feedback loop. IVT reaction conditions were found under 60 optimization runs that produced 12 g · L−1 in solely 2 h. The results obtained outperform published industry standards and data reported in literature in terms of both achievable reaction yield and reduction of production time. Furthermore, this shows the potential of Bayesian optimization as a cost-effective optimization tool within (bio)chemical applications

Distributed representations of atoms and materials for machine learning

Abstract: The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We derive distributed representations of compounds from their chemical formulas only, via pooling operations of distributed representations of atoms. These compound representations are evaluated on ten different tasks, such as the prediction of formation energy and band gap, and are found to be competitive with existing benchmarks that make use of structure, and even superior in cases where only composition is available. Finally, we introduce an approach for learning distributed representations of atoms, named SkipAtom, which makes use of the growing information in materials structure databases

The effects of different concentrations of the α2-Adrenoceptor Agonist Medetomidine on basal excitatory synaptic transmission and synaptic plasticity in hippocampal slices of adult mice

α2-Adrenoceptor agonists are used frequently in human and veterinary anesthesia as sedative/analgesic drugs. However, they can impair cognition. Little is known about the concentration-dependent effects of α2-adrenoceptor agonists on synaptic plasticity, the neurophysiological basis of learning and memory. Therefore, we investigated the effects of different concentrations of medetomidine, an α2-adrenoceptor agonist, on basal excitatory synaptic transmission and on 2 forms of synaptic plasticity: paired-pulse facilitation (PPF) and long-term potentiation (LTP).Funding: This work was supported by FCT (Lisbon, Portugal) and cofunded by COMPETE: 01-0124-FEDER-009497 (Lisbon, Portugal), through the project grants PTDC/CVT/099022/2008 and PTDC/SAU-NSC/122254/2010 and through a personal PhD grant (SFRH /BD/48883/2008) to Patrícia do Céu Oliveira Ribeiro and by QREN (09-68-ESR-FP-010).info:eu-repo/semantics/publishedVersio

Combining FTIR-ATR and OPLS-DA methods for magic mushrooms discrimination

Magic mushrooms are naturally occurring fungi that are considered hallucinogenic drugs because they contain psilocybin and psilocin. These substances are controlled in almost every country in the world, so the use, possession, cultivation, and sale of magic mushrooms are prohibited in whole or in part. Despite this, the abuse of magic mushrooms continues and can put at risk the life of the consumer and society in general if the consumer behaves in an unsafe manner. The number of mushroom species is very high, making it difficult to correctly identify them based only on physical and morphological characteristics. Therefore, there is a need to develop non-destructive mushrooms analysis methods that have less response time and higher discrimination ability. The present work used Attenuated Total Reflectance Fourier Transform Infrared (FTIR-ATR) Spectroscopy to study 64 mushroom samples from different genera including hallucinogenic, edible, and toxic species. In addition, this study used Orthogonal Partial Least Squares - Discriminant Analysis (OPLS-DA), using SIMCA chemometric software to analyse the obtained infrared (IR) spectra. The main molecular vibrations of the components of the fungus were successfully identified by IR spectroscopy. Although the specific bands corresponding to psilocybin or psilocin could not be assigned in the spectra, the regression method was able to discriminate the various species. Hallucinogenic mushrooms were well separated from other species, allowing the method to be used as an initial screening technique to determine whether or not the seized mushrooms are of forensic interest

Method and device for live-streaming with opportunistic mobile edge cloud offloading

A novel, pervasive approach to disseminating live streaming content combines secure distributed systems, WiFi multicast, erasure coding, source coding and opportunistic offloading using hyperlocal mobile edge clouds. The solution disclosed to the technical problem of disseminating live streaming content without requiring a substantial equipment, planning and deployment of appropriate network infrastructure points offers an 11 fold reduction on the infrastructural WiFi bandwidth usage without having to modify any existing software or firmware stacks while ensuring stream integrity, authorization and authentication