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

RouteMe - Uma Aplicação para Dispositivos Móveis Android para resolução de problemas de rotas de veículos

Os Problemas de Rotas de Veículos (PRV) são estudados há dezenas de anos, existindo nos dias de hoje um grande número de algoritmos que lhes dão resposta de forma muito satisfatória. Paralelamente à evolução dos métodos de resolução desses mesmos problemas, tem-se igualmente assistido a uma enorme evolução dos dispositivos móveis que estão cada vez mais presentes no nosso quotidiano, tanto a nível pessoal como profissional. O presente documento descreve o trabalho realizado no âmbito do Mestrado em Computação Móvel do Instituto Politécnico da Guarda, que visou aliar ambos os pontos referidos, numa aplicação para dispositivos móveis Android de modo a permitir a qualquer utilizador proceder à resolução de PRV através desse tipo de dispositivos. A aplicação desenvolvida tira partido das características e serviços da plataforma Android, em especial o serviço Google Maps, para aquisição de dados georreferenciados usados na definição dos PRV, assim como as características de interatividade com o utilizador que permitiram o desenvolvimento de ferramentas interativas para o utilizador poder construir e resolver PRV com base no conhecimento existente em cada uma das situações. Para além da aplicação móvel, foi também desenvolvida uma plataforma web que serve de intermediária entre a aplicação e o serviço Google Maps, que ficou responsável por processar todos os pedidos entre estes. Acredita-se que a aplicação desenvolvida permite ao utilizador uma maior mobilidade, possibilitando a que em qualquer local possa proceder à construção de um novo PRV, bastando para isso a existência de uma ligação à internet. Acredita-se igualmente nas potencialidades da mesma para a resolução desses problemas da forma o mais otimizada possível, bem como no potencial crescimento e adaptabilidade para casos mais específicos nesta vasta área

Bateria de provas de raciocínio (BPR-5): estudo de validação em contexto escolar

[Resumo] A Bateria de Provas de Raciocínio (BPR-S; Almeida & Primi, 1996) é formada por cinco provas, todas avaliando o raciocínio (opera<;ao comum) através de itens de conteúdo diverso (abstracto, verbal, numérico, espacial, e mecanico). Desta forma a BPR-S pretende conciliar a teoria do factor g e as teorias das múltiplas inteligencias na descri<;ao e avalia<;ao da inteligencia. A bateria subdivide-se em duas versoes: (i) Versao A - destinada aos alunos do 7° ao 9° ano de escolaridade; e a Versao B - destinada aos alunos do 10° ao 12° ano. O estudo de construçao e validaçao da bateria tem sido conduzido conjuntamente em Portugal e no Estado de Sao Paulo (Brasil). Este artigo apresenta as análises da consistencia interna dos itens e da validade dos resultados. Os valores obtidos adequam-se aos objectivos das provas, mesmo com urna redu<;ao no número de itens face a outras provas similares. Dada a maior utiliza<;ao destas provas no contexto escolar, sobretudo em orientaçao vocacional, reflectem-se algumas quest5es em tomo da utiliza<;ao da BPR-S na consulta psicológica nas escola

Lidocaine supplementation in clove-oil and 2-phenoxyethanol anesthesia for gilthead seabream (Sparus aurata)

This study had the support of Fundaçao ˜ para a Ciˆencia e a Tecnologia (FCT) through the Strategic Project granted to MARE - Marine and Environmental Sciences Centre (https://doi.org/10.54499/UIDB/04 292/2020 and https://doi.org/10.54499/UIDP/04292/2020), the project granted to the Associated Laboratory ARNET (https://doi.org/10.54499/LA/P/0069/2020), the project MAR02.05.01-FEAMP-0013 and CITAB (https://doi.org/10.54499/UIDP/04033/2020.)Com Acordo B-On-Politécnico de Leiria - Elsevier, da lista das Revistas GOA (Aquaculture Reports) na taxa de APC.Animal welfare and reducing stress during procedures are key objectives for success in animal production. Anesthesia has been used for procedures to reduce animal stress and its negative impact on welfare. This study aimed first to refine the concentrations of the anesthetic clove-oil (CO) and lidocaine (L) in gilthead seabream (Sparus aurata) juveniles (56.0 ± 15.09 g) and then combine clove-oil and 2-phenoxyethanol (2PHE) with the refined concentration of lidocaine. The concentrations of clove-oil (30, 45, and 60 mg L− 1), and the concentrations of lidocaine (2.5, 5, and 7.5 mg L− 1), were evaluated in the refinement trial. Based on these results, a second trial was performed with 45 mg L− 1 CO or 0.4 mL L− 1 2PHE as anesthetics alone or combined with 2.5 mg L− 1 of lidocaine. Results from this work showed an improvement in induction times for 2-phenoxyethanol when lidocaine was added (2PHE 179.53 ± 63.21 s; 2PHE + L 130.65 ± 40.16 s). Recovery time also showed a reduction for clove-oil when lidocaine was used (CO 349.90 ± 123.69 s; CO + L 250.11 ± 51.99 s). The use of lidocaine showed better results, reducing lactate and histological progressive alterations. Lidocaine showed stress-induced oxidative alterations when it was combined with 2-phenoxyethanol. Lidocaine exposure increased ALT, AST, histological regressive alterations for both anesthetics, and gene expression of hsp70 in the gills when clove-oil was used. Further studies are necessary to comprehend the synergistic effects of lidocaine when combined with synthetic and natural anesthetics and to discern potential acute or chronic toxic responses in fish. These insights will be crucial for refining anesthesia protocols and ensuring the well-being of aquatic species in aquaculture practices and research settings.info:eu-repo/semantics/publishedVersio

Effect of ultrasound transducer design on the acoustically-assisted supercritical fluid extraction of antioxidants from oregano

[EN] Power ultrasound is applied in food technology to intensify extraction processes, due to the phenomena ultrasonic energy induces in the medium, enhancing mass transfer. The purpose of this work was the acoustic characterization of four transducers of different geometries and the evaluation of their performance in the ultrasonically assisted supercritical fluid extraction of antioxidants from oregano. The transducers differed in the amount of energy transmitted into the medium. Designs varied from the base model (T1), a larger cylindrical headmass (T2), a stepped circular section sonotrode (T3) and a multiplate configuration (T4). The highest nominal power density provided according to the calorimetric method was for T4 (151.6 +/- 7.1 W/L). The T2 produced a more uniform acoustic field and a higher acoustic pressure (150.6 +/- 20.5 kPa). Both parameters had an impact on total phenolics and antioxidants extraction with CO2 under supercritical conditions (35 MPa, 35 degrees C, 2.3% ethanol as co-solvent). T4 and T2 were equally efficient (4.0 +/- 0.2 and 4.2 +/- 0.2 mg GA/g) for phenolic extraction, and with respect to antioxidant capacity, the best performance was that of T4 (26.4 +/- 1.1 mu mol TE/g). Of the antioxidant compounds extracted, flavones and flavanones were identified. Therefore, transducer geometry influenced the amount and distribution of energy transmitted into the medium, thus determining the efficiency of the extraction process.This work was supported by the PROMETEOII\2014\005 project financed by the Generalitat Valenciana (Conselleria d'Educacio, Cultura i Esport, Valencia, Spain).Santos-Zea, L.; Antunes-Ricardo, M.; Gutierrez-Uribe, J.; Garcia-Perez, J.; Benedito Fort, JJ. (2018). Effect of ultrasound transducer design on the acoustically-assisted supercritical fluid extraction of antioxidants from oregano. Ultrasonics Sonochemistry. 47:47-56. https://doi.org/10.1016/j.ultsonch.2018.04.019S47564

Systematic reviews and meta-analysis published in indexed Portuguese medical journals: time trends and critical appraisal

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Introduction: Over the last years, the number of systematic reviews published is steadily increasing due to the global interest in this type of evidence synthesis. However, little is known about the characteristics of this research published in Portuguese medical journals. This study aims to evaluate the publication trends and overall quality of these systematic reviews. Material and methods: This was a methodological study. We aimed the most visible Portuguese medical journals indexed in MEDLINE. Systematic reviews were identified through an electronic search (through PUBMED). We included systematic reviews published up to August 2020. Systematic reviews selection and data extraction were done independently by three authors. The overall quality critical appraisal using the A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) was independently assessed by three authors. Disagreements were solved by consensus. Results: Sixty-six systematic reviews published in 5 Portuguese medical journals were included. Most (n = 53; 80.3%) were systematic reviews without meta-analysis. Up to 2010 there was a steady increase in the number of systematic reviews published, followed by a period of great variability of publication, ranging from 1 to 10 in a given year. According to the systematic reviews' typology, most have been predominantly conducted to assess the effectiveness/efficacy of health interventions (n = 27; 40.9%). General and Internal Medicine (n = 20; 30.3%) was the most addressed field. Most systematic reviews (n = 46; 69.7%) were rated as being of "critically low-quality". Conclusions: There were consistent flaws in the methodological quality report of the systematic reviews included, particularly in establishing a prior protocol and not assessing the potential impact of the risk of bias on the results. Through the years, the number of systematic reviews published increased, yet their quality is suboptimal. There is a need to improve the reporting of systematic reviews in Portuguese medical journals, which can be achieved by better adherence to quality checklists/tools.info:eu-repo/semantics/publishedVersio

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