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We review the quantum nonadiabatic dynamics of atom + diatom collisions due to the Renner-Teller (RT) effect, i.e., to the Hamiltonian operators that contain the total spinless electronic angular momentum L. As is well-known, this rovibronic effect is large near collinear geometries when at least one of the interacting states is doubly degenerate. In general, this occurs in insertion reactions and at short-range, where the potential wells exhibit deep minima and support metastable complexes. Initial-state-resolved reaction probabilities, integral cross sections, and thermal rate constants are calculated via the real wavepacket method, solving the equation of motion with an approximated or with an exact spinless RT Hamiltonian. We present the dynamics of 10 single-channel or multichannel reactions showing how RT effects depend on the product channels and comparing with the Born-Oppenheimer (BO) approximation or coexisting conical-intersection (CI) interactions. RT effects not only can significantly modify the adiabatic dynamics or correct purely CI results, but also they can be very important in opening collision channels which are closed at the BO or CI level, as in electronic-quenching reactions. In the OH(A2Σ+) + Kr electronic quenching, where both nonadiabatic effects (CI and RT) coexist, they are in competition because CI dominates the reactivity but RT couplings reduce the large CI cross section and open a CI-forbidden evolution toward products, so that CI + RT quantum results are in good agreement with experimental or semiclassical findings. The different roles of these couplings are due to the unlike nuclear geometries where they are large: rather far from or near to linearity for CI or RT, respectively. The OH(A2Σ+) + Kr electronic quenching was investigated with the exact RT Hamiltonian, validating the approximated one, which was employed for all other collisions.

Nowadays, the incorporation of nanoparticles into thermal fluids has become one of the most suitable strategies for developing high-performance fluids. An unconventional improvement of thermo–physical properties was observed with the addition of 1% wt. of nanoparticles in different types of fluids, such as molten salts, allowing for the design of more thermally efficient systems using nanofluids. Despite this, there is a lack of knowledge about the effect that nanoparticles produce on the thermal stability and the decomposition kinetics of the base fluid. The present study performs IR- and UV-vis spectroscopy along with thermogravimetric analysis (TGA) of pure nitrate and nitrate based nanofluids with the presence of SiO2 and Al2O3 nanoparticles (1% wt.). The results obtained support that nanoparticles accelerate the nitrate to nitrite decomposition at temperatures below 500 °C (up to 4%), thus confirming the catalytic role of nanoparticles in nanofluids.

This article belongs to the Special Issue Journalism and Politics: New Influences and Dynamics in the Social Media Era [Abstract] Political bots, through astroturfing and other strategies, have become important players in recent elections in several countries. This study aims to provide researchers and the citizenry with the necessary knowledge to design strategies to identify bots and counteract what international organizations have deemed bots’ harmful effects on democracy and, simultaneously, improve automatic detection of them. This study is based on two innovative methodological approaches: (1) dealing with bots using hybrid intelligence (HI), a multidisciplinary perspective that combines artificial intelligence (AI), natural language processing, political science, and communication science, and (2) applying framing theory to political bots. This paper contributes to the literature in the field by (a) applying framing to the analysis of political bots, (b) defining characteristics to identify signs of automation in Spanish, (c) building a Spanish-language bot database, (d) developing a specific classifier for Spanish-language accounts, (e) using HI to detect bots, and (f) developing tools that enable the everyday citizen to identify political bots through framing. This article has been developed within the research project “Digital Native Media in Spain: Storytelling Formats and Mobile Strategy” (RTI2018–093346-B-C33) funded by the Ministry of Science, Innovation, and Universities and co-funded by the European Regional Development Fund (ERDF) and has received financial support from DOMINO project (PGC2018-102041-B-I00, MCIU/AEI/FEDER, UE), eRisk project (RTI2018-093336-B-C21), the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08, Groups of Reference: ED431C 2020/21, and ERDF 2014-2020: Call ED431G 2019/04) and the European Regional Development Fund (ERDF) Xunta de Galicia; ED431G/08 Xunta de Galicia; ED431C 2020/21 Xunta de Galicia; ED431G 2019/04

UIDB/04097/2020 UIDP/04097/2020 PGC2018-102041-B-I00 MCIU/AEI/FEDER,UE RTI2018-093336-B-C21 O trabalho apresentado neste artigo tem dois objectivos. Por um lado, descreve a adaptação de duas ferramentas de processamento da língua natural ao galego-português medieval, nomeadamente um analisador morfossintático e um reconhededor de variedades medievais, e por outro, visa testar hipóteses linguísticas sobre a evolução de sufixos medievais mediante o uso dessas ferramentas em corpora históricos. Apesar de o desempenho das ferramentas ser inferior do que quando utilizadas para variedades modernas mais estandardizadas e com menos variabilidade formal, mostramos que é possível usá-las com grande fiabilidade para estudos quantitativos baseados em corpus. O estudo linguístico baseado em corpus permite-nos conferir que, pela sua distribuição de frequências, a presença dos sufixos-CION e-BLE nos textos medievais da Galiza foi provavelmente influenciada pelo castelhano baixo medieval. The work presented in this paper has two objetives. On the one hand, it describes how to adapt two natural language processing tools to medieval Galician-Portuguese, namely a morphosyntactic analyzer and a medieval language recognizer, and on the other hand, it verifies linguistic hypotheses about the evolution of medieval suffixes by using these tools by using historical corpora. Although the performance of the tools is inferior to those used for more standardized modern varieties with less formal variability, we show that it is possible to use them with great reliability for quantitative corpus-based studies. The corpus-based linguistic study allows us to verify that, on the basis of their frequency distribution, the presence of the suffixes -CION and -BLE in medieval Galician texts is probably influenced by medieval Castilian publishersversion published

Zeolite-supported single-atom catalysts (SACs) have emerged as a novel class of cheap and tuneable catalysts that can exhibit high activity, selectivity and stability. In this work, we conduct an extensive screening by means of density functional theory calculations to determine the usefulness of 3d, 4d and 5d transition metal (TM) SACs-supported in MFI-type Silicalite-1 zeolite for CO2 conversion. Two reaction mechanisms are considered, namely the redox - direct CO2 dissociation - and associative - hydrogen-assisted CO2 dissociation - mechanisms. Early TM SACs exhibit the lowest energy barriers, which follow the redox mechanism. These energy barriers raise when going right in the periodic table up to group 10, where they become prohibitive and the associative mechanism should dominate. By also considering their resistance to aggregation, we support the use of Sc, Y, La, Ru, Rh, Ni, Pd and Pt as potentially active and stable catalysts for CO2 conversion, given their low energy barriers and strong interaction with the zeolite framework.

The discovery of graphene has catalyzed the search for other 2D carbon allotropes, such as graphynes, graphdiynes, and 2D π-conjugated polymers, which have been theoretically predicted or experimentally synthesized during the past decade. These materials exhibit a conductive nature bound to their π-conjugated sp2 electronic system. Some cases include sp-hybridized moieties in their nanostructure, such as acetylenes in graphynes; however, these act merely as electronic couplers between the conducting π-orbitals of sp2 centers. Herein, via first-principles calculations and quantum transport simulations, we demonstrate the existence of an acetylene-meditated transport mechanism entirely hosted by sp-hybridized orbitals. For that we propose a series of nanostructured 2D materials featuring linear arrangements of closely packed acetylene units which function as sp-nanowires. Because of the very distinct nature of this unique transport mechanism, it appears to be highly complementary with π-conjugation, thus potentially becoming a key tool for future carbon nanoelectronics., I.A. is grateful for a Humboldt postdoctoral fellowship from the Alexander von Humboldt Foundation and a Juan de la Cierva postdoctoral grant (FJC2019-038971-I) from the Ministerio de Ciencia e Investigación. Financial support by Villum Fonden (00013340) is gratefully acknowledged. ICN2 is funded by the CERCA Programme from Generalitat de Catalunya and is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706). The Center for Nanostructured Graphene (CNG) is sponsored by the Danish National Research Foundation (DNRF103). This work has also been supported by the Spanish MICIUN/FEDER RTI2018-095460-B-I00, RTI2018-094757-B-I00, and María de Maeztu MDM-2017-0767 grants and, in part, by Generalitat de Catalunya 2017SGR13 grant.

Single-atom catalysts (SACs) have emerged as a novel class of catalysts that show unique catalytic properties thanks to their easy tuneability. In this context, zeolites are widely used as support materials for SACs, since the steric restrictions from their channels can effectively suppress the aggregation of the metal particles and provide encapsulated metal atoms with exceptional stability. Although significant efforts have been made to study zeolite-supported SACs, only a few transition metal atoms have been tested, and the determination of precise locations of single metal atoms within the zeolite matrix by experiments remains a nontrivial task. Herein we provide a thorough dispersion corrected density functional theory study of the energetic, structural, magnetic and electronic properties for the full sets of 3d, 4d and 5d transition metal atoms encapsulated on MFI-type Silicalite-1 zeolite, namely TM@S-1. Most TM atoms prefer to adsorb in the center of the S-1 channels, weakly interacting with the zeolite, with the exception of Group 3 − Sc, Y, La − , Group 10 − Ni, Pd, Pt − , Ru and Rh atoms, making them good candidates as stable SACs with superior catalytic performance. The optimized geometry files for the full set of TM@S-1 have been uploaded to a public online repository, paving the road for further catalytic studies of these systems. The behavioral trends gained from this study provide a solid theoretical background that can be used as the basis for interpreting, understanding, and discussing already observed experimental features, and future findings as well.

We present the dynamics of the electronic quenching OH(A2Σ+) + Kr(1S) → OH(X2Π) + Kr(1S), with OH(A2Σ+) in the ground ro-vibrational state. This study relies on a new non-adiabatic quantum theory that uses three diabatic electronic states Σ+, Π', and Π'', coupled by one conical-intersection and nine Renner-Teller matrix elements, all of which are explicitly considered in the equation of the motion. The time-dependent mechanism and initial-state-resolved quenching probabilities, integral cross sections, thermal rate constants, and thermally-averaged cross sections are calculated via the real wavepacket method. The results point out a competition among three non-adiabatic pathways: Σ+ ↔ Π', Σ+ ↔ Π'', and Π' ↔ Π''. In particular, the conical-intersection effects Σ+-Π' are more important than the Renner-Teller couplings Σ+-Π', Σ+-Π'', and Π'-Π''. Therefore, Π' is the preferred product channel. The quenching occurs via an indirect insertion mechanism, opening many collision complexes, and the probabilities thus present many oscillations. Some resonances are still observable in the cross sections, which are in good agreement with previous experimental and quasi-classical findings. We also discuss the validity of more approximate quantum methods.

Mutations in the kelch propeller domain (K13 propeller) of Plasmodium falciparum parasites from Southeast Asia are associated with reduced susceptibility to artemisinin. We exposed in vitro-cultured stage V gametocytes from Cambodian K13 propeller mutant parasites to dihydroartemisinin and evaluated the inhibition of male gamete formation in an in vitro exflagellation inhibition assay (EIA)., Mutations in the kelch propeller domain (K13 propeller) of Plasmodium falciparum parasites from Southeast Asia are associated with reduced susceptibility to artemisinin. We exposed in vitro-cultured stage V gametocytes from Cambodian K13 propeller mutant parasites to dihydroartemisinin and evaluated the inhibition of male gamete formation in an in vitro exflagellation inhibition assay (EIA). Gametocytes with the R539T and C580Y K13 propeller alleles were less susceptible to dihydroartemisinin and had significantly higher 50% inhibitory concentrations (IC50s) than did gametocytes with wild-type alleles.

This article proposes a syntactic parsing strategy based on a dependency grammar containing formal rules and a compression technique that reduces the complexity of those rules. Compression parsing is mainly driven by the ‘single-head’ constraint of Dependency Grammar, and can be seen as an alternative method to the well-known constructive strategy. The compression algorithm simplifies the input sentence by progressively removing from it the dependent tokens as soon as binary syntactic dependencies are recognized. This strategy is thus similar to that used in deterministic dependency parsing. A compression parser was implemented and released under General Public License, as well as a cross-lingual grammar with Universal Dependencies, containing only broad-coverage rules applied to Romance languages. The system is an almost delexicalized parser which does not need training data to analyze Romance languages. The rule-based cross-lingual parser was submitted to CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. The performance of our system was compared to the other supervised systems participating in the competition, paying special attention to the parsing of different treebanks of the same language. We also trained a supervised delexicalized parser for Romance languages in order to compare it to our rule-based system. The results show that the performance of our cross-lingual method does not change across related languages and across different treebanks, while most supervised methods turn out to be very dependent on the text domain used to train the system.

While considerable attention has been given to the analysis of texts written by depressed individuals, few studies were interested in evaluating and improving lexical resources for supporting the detection of signs of depression in text. In this paper, we present a search-based methodology to evaluate existing depression lexica. To meet this aim, we exploit existing resources for depression and language use and we analyze which elements of the lexicon are the most effective at revealing depression symptoms. Furthermore, we propose innovative expansion strategies able to further enhance the quality of the lexica.

This article describes a system aimed at searching for word similarity over different time periods. The strategy is based on distributional models obtained from a chronologically structured language resource, namely Google Books Syntactic Ngrams. The models were created using dependency-based contexts and a strategy for reducing the vector space, which consists of selecting the more informative and relevant word contexts. A quantitative evaluation of the distributional models was performed. The linguistic data are stored in a NoSQL DB, which is provided with a Web interface allowing linguists to analyze the meaning change of Spanish words in written texts across time.

In this paper, we define two quantitative distances to measure how far apart two languages are. The distance measure that we have identified as more accurate is based on the perplexity of n -gram models extracted from text corpora. An experiment to compare forty-four European languages has been performed. For this purpose, we computed the distances for all the possible language pairs and built a network whose nodes are languages and edges are distances. The network we have built on the basis of linguistic distances represents the current map of similarities and divergences among the main languages of Europe.

This article provides a preliminary semantic framework for Dependency Grammar in which lexical words are semantically defined as contextual distributions (sets of contexts) while syntactic dependencies are compositional operations on word distributions. More precisely, any syntactic dependency uses the contextual distribution of the dependent word to restrict the distribution of the head, and makes use of the contextual distribution of the head to restrict that of the dependent word. The interpretation of composite expressions and sentences, which are analyzed as a tree of binary dependencies, is performed by restricting the contexts of words dependency by dependency in a left-to-right incremental way. Consequently, the meaning of the whole composite expression or sentence is not a single representation, but a list of contextualized senses, namely the restricted distributions of its constituent (lexical) words. We report the results of two large-scale corpus-based experiments on two different natural language processing applications: paraphrasing and compositional translation This work is funded by Project TELPARES, Ministry of Economy and Competitiveness (FFI2014-51978-C2-1-R), and the program “Ayuda Fundación BBVA a Investigadores y Creadores Culturales 2016” SI

The effects of N and B substitutional doping on α-, β- and γ-graphyne structures have been systematically considered by means of spin-polarized DFT calculations. The study is focused on describing the main changes that doping of sp2-and sp-C atoms produces on structural, elastic and electronic properties. Thus, the presence of N impurities contracts the carbon lattice increasing the in-plane stiffness whereas B impurities expand the lattice reducing the in-plane stiffness with respect to pure graphynes. This fact leads to harder materials or softer materials depending on doping with N or B atoms, respectively. Moreover, N and B atoms are n- and p-type dopants, respectively, that modify significantly the electronic structure of these materials. The different concentration of impurities in the lattice may lead to semiconductor-to-metal transitions, to the opening of a band gap, or to the appearance of new Dirac points (DPs) in the electronic structure, depending on the different parity of electrons or holes introduced per unit cell. The intrinsic physical properties of graphynes along with the possibility of tuning them by substitutional doping could extend their applicability as separation membranes, in transistor electronics, optoelectronics, among others.

Highlighted experimental studies on nanofluids reveal an anomalous increment in the specific heat capacity (Cp) of these ionic systems when nanoparticles are added. This fact is really important due the applicability of nanofluids in concentrating solar power plants as heat transfer fluid and storage media. These are promising results for the development of high-temperature heat storage applications by enhanced storage capacity materials. The present work focuses on the study of this effect in NaNO3 molten salt doped with SiO2 nanoparticles by molecular dynamics (MD) simulations and Differential Scanning Calorimetry (DSC) experiments. The study shows that for nanoparticles’ concentrations around 1% wt. the Cp increases by 26% compared to pure NaNO3, whereas at higher concentrations the effect disappears. The results approach high agreement between experimental and simulation results and MD simulations reveal that the increase of Cp at low concentrations is explained by the formation of a semi ordered layer of ionic fluid. This layer is rich in Na+ cations, around the nanoparticles whereas the reduction of Cp at concentrations higher than 2% wt. is related to the aggregation of nanoparticles as revealed by Scanning Electron Microscopy (SEM). However, deep experimental results with other materials will be required in order to validate the layering effect.

Phylogenetics is a sub-field of historical linguistics whose aim is to classify a group of languages by considering their distances within a rooted tree that stands for their historical evolution. A few European languages do not belong to the Indo-European family or are otherwise isolated in the European rooted tree. Although it is not possible to establish phylogenetic links using basic strategies, it is possible to calculate the distances between these isolated languages and the rest using simple corpus-based techniques and natural language processing methods. The objective of this article is to select some isolated languages and measure the distance between them and from the other European languages, so as to shed light on the linguistic distances and proximities of these controversial languages without considering phylogenetic issues. The experiments were carried out with 40 European languages including six languages that are isolated in their corresponding families: Albanian, Armenian, Basque, Georgian, Greek, and Hungarian This work received financial support from DOMINO project (PGC2018-102041-B-I00, MCIU/AEI/FEDER, UE), eRisk project (RTI2018-093336-B-C21), the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08, Consolidation and structuring of Groups with Growth Potential: ED431B 2017/39), and the European Regional Development Fund (ERDF) SI

Graphyne two-dimensional materials have been envisaged as potential useful membranes for applications such as gases separation and water desalination with very high salt rejection rates. The graphyne acetylenic linkages length defines its acetylenic holes size, thus offering a way of tuning their membrane permeability. The present study evaluates, based on density functional theory simulations, the possible use of γ-graphyne, the graphyne with the smallest acetylenic hole, to sieve Transition Metal (TM) atoms. The systematic study comprises obtaining adsorption minima for the 30 3d, 4d, and 5d TMs along with transition states for the diffusion across the γ-graphyne layer. The study reveals very small penetration barriers, below 0.5 eV, for late 3d, Pt-group, and coinage metals, whereas water molecules are found to display high penetration energy barriers above 5 eV, even when accounting for possible γ-graphyne out-of-plane deformations facilitating the water trespassing. The adsorption energy distribution of the adsorbed TMs shows that Pd and Au sieving by γ-graphyne would be specially enhanced versus their anchoring on the γ-graphyne carbon framework, thus pointing γ-graphyne as a possible effective membrane to sieve, particularly, late transition metals.

The correct description of crude oil/water interfaces is a very complex and an important task, particularly to the oil industry, whose main difficulty relies on understanding how the interfacial properties (i.e., interfacial tension and interfacial accumulation) of the system are affected by a very large number of components. To give some additional insight to the oil/water interfacial behavior, eleven oil/water mixtures (i.e., six binary, four ternary and a quaternary mixture) have been modeled through atomistic molecular dynamics simulations at laboratory conditions. All mixtures were built with a model oil based on dodecane, toluene, quinoline and a naphthenic acid, to represent the saturated, aromatic, basic resin and acid resin fractions, respectively. The results from this contribution show that interfacial tensions can be correlated to interfacial accumulation, which can be used as good starting point in predicting interfacial properties of oil mixtures. Additionally, the interfacial properties of mixtures behave similarly to the most polar pure oil/water interface, while all other compounds stay in the oil bulk as spectators. This behavior raises the question of whether using common n-alkane oils is a good enough approximation for modeling the interfacial properties of crude oils. (C) 2020 Elsevier B.V. All rights reserved.

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization., A wealth of gene expression data is publicly available, yet is little use without additional human curation. Ma'ayan and colleagues report a crowdsourcing project involving over 70 participants to annotate and analyse thousands of human disease-related gene expression datasets.

In this article, we introduce an explicit count-based strategy to build word space models with syntactic contexts (dependencies). A filtering method is defined to reduce explicit word-context vectors. This traditional strategy is compared with a neural embedding (predictive) model also based on syntactic dependencies. The comparison was performed using the same parsed corpus for both models. Besides, the dependency-based methods are also compared with bag-of-words strategies, both count-based and predictive ones. The results show that our traditional count-based model with syntactic dependencies outperforms other strategies, including dependency-based embeddings, but just for the tasks focused on discovering similarity between words with the same function (i.e. near-synonyms).

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinations This research was funded by project TelePares (MINECO, ref:FFI2014-51978-C2-1-R), and the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08) and the European Regional Development Fund (ERDF) SI

In this article, we define the outlier detection task and use it to compare neural-based word embeddings with transparent count-based distributional representations. Using the English Wikipedia as a text source to train the models, we observed that embeddings outperform count-based representations when their contexts are made up of bag-of-words. However, there are no sharp differences between the two models if the word contexts are defined as syntactic dependencies. In general, syntax-based models tend to perform better than those based on bag-of-words for this specific task. Similar experiments were carried out for Portuguese with similar results. The test datasets we have created for the outlier detection task in English and Portuguese are freely available This work was supported by a 2016 BBVA Foundation Grant for Researchers and Cultural Creators and by Project TELEPARES, Ministry of Economy and Competitiveness (FFI2014-51978-C2-1-R). It has received financial support from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF) SI

Consistently with social and political concern about hatred and harassment through social media, in recent years, automatic hate-speech detection and offensive behavior in social media are gaining a lot of attention. In this paper, we examine the performance of several supervised classifiers in the process of identifying hate speech on Twitter. More precisely, we do an empirical study that analyzes the influence of two types of linguistic features (n-grams, word embeddings) when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Complement Naive Bayes (CNB), Decision Tree (DT), Nearest Neighbors (KN), Random Forest (RF) and Neural Network (NN). The experiments we have carried out show that CNB, SVM, and RF are better than the rest classifiers in English and Spanish languages by taking into account all features.

This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classi- fication where the system predicting whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguis- tic features to improve the classifier’s perfor- mance. More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency- Inverse Document Frequency (TF-IDF) repre- sentation. The system performance reaches about 81% F1 when it is applied to the training dataset, but its F1 drops to 36% on the official test dataset for the English and 64% for the Spanish language concerning the hate speech class

In the recent years, many efforts have been carried out trying to comprehend how surfactants and salts interact among each other at the oil/brine interface to reduce the interfacial tension (IFT). To that end, the interfacial properties of several combinations of surfactants, salts and oils have been measured experimentally confirming the existence of a synergistic effect. Unfortunately, many of the proposed mechanisms for that effect arise from experimental observations, so this work, based on molecular dynamics simulations, intends to reproduce and explain this kind of phenomenon from a molecular point of view. The correct understanding of these phenomena can have application in many fields, especially in Enhanced Oil Recovery, where reducing IFT can potentially increase oil production. In this article we evaluate the effect of adding three different salts (i.e., NaCl, CaCl2 and MgCl2) on the IFT of a water/oil system with different non-ionic surfactants. We have evaluated the effect that the ions of salt produce to surfactants, as well as the perturbation that surfactants produce on the ions. From our results, we can assess that salts (especially NaCl) and surfactants are able to interact with each other, being both active species in reducing the IFT of the system.

Studies in sentiment analysis and opinion mining have examined how different features are effective in polarity classification by making use of positive, negative or neutral values. However, the identification of extreme opinions (most negative and most positive opinions) have overlooked in spite of their wide significance in many applications. In our study, we will combine empirical features (e.g. bag of words, word embeddings, polarity lexicons, and set of textual features) so as to identify extreme opinions and provide a comprehensive analysis of the relative importance of each set of features using hotel reviews.

This paper presents LinguaKit, a multilingual suite of tools for analysis, extraction, annotation and linguistic correction, as well as its integration into a Big Data infrastructure. LinguaKit allows the user to perform different tasks such as PoS-tagging, syntactic parsing, coreference resolution (among others), including applications for relation extraction, sentiment analysis, summarization, extraction of multiword expressions, or entity linking to DBpedia. Most modules work in four languages: Portuguese, Spanish, English, and Galician. The system is programmed in Perl and is freely available under a GPLv3 license.

This paper comparing a method to automatically build a sentiment lexicon, with four well-known sentiment lexicons. For this purpose, an indirect evaluation is carried out. The lexicons are integrated into supervised sentiment classifiers and their performance is evaluated in two sentiment classification tasks in order to identify i) the most negative vs. not most negative opinions, and ii) the most positive vs. not most positive. Moreover, a set of textual features is integrated into the classifiers so as to analyze how these textual features improve the lexicon performance.

INTRODUCTION. The present paper addresses a 2.0 implementation of a practical classroom strategy to increase university students’ performance, with emphasis given to STEM subjects (Science, Technology, Engineering and Mathematics). METHOD. The starting point is a scheme based on the flipped classroom (FC) concept. The work, however, starts in the classroom, using a synchronous FC, and modifications are introduced to increase students’ ability to work autonomously. The practical methodology is known as SABER (after the Spanish Supervision del Aprendizaje Basico con Ejercicios y autoReflexion). RESULTS. The paper describes a 2.0 version that incorporates (a) rewarded mistake correction as a key part in students’ consolidation of concepts; and (b) substantial changes in how subject content is introduced to students. In the latter case, comparison experiments and compared macroscopic physical properties have been used to introduce difficult concepts. DISCUSSION. This approach presents content from an experimental perspective that is much closer to students’ existing knowledge. The paper also provides some specific examples and practical tips to demonstrate how easily the methodology can be implemented.

We present a systematic computational study of Mg-MOF-74, CuBTC and zeolite 13X for CO2 separation from multi-component flue gas mixtures. The impurities’ impact was evaluated at the molecular level and process conditions. Adsorption isotherms and isosteric heats of adsorption of pure (CO2, N2, O2, H2O, SO2 and NO2) components, binary and ternary mixtures were obtained from Grand Canonical Monte Carlo simulations. Working capacities, purities, recoveries and exergetic performances were evaluated for VSA/PSA/TSA processes. Results show that NO2 has a negligible effect in the studied range. For H2O and SO2 the energy requirements are reduced as the impurity content increases and recovery and purity increase, up to an “optimal” point where a competition for CO2 preferred adsorption sites produces a sharp drop in purity and the energetic index grows exponentially. The minimum energy requirement were obtained for TSA at a desorbing temperature of 443 K in the three materials, with impurities of 1% H2O for CuBTC, 0.5% H2O for Mg-MOF-74 and 0.02% H2O for 13X, obtaining values of 1.13, 0.55 and 0.58 GJ/tCO2, respectively. Hybrid VTSA processes with impurities content in the feed mixture and CCS specifications achieve energy performances of 0.36 GJ/tCO2 and 0.46 GJ/tCO2 with Mg-MOF-74 and 13X, respectively. Mg-MOF-74 stands up as an attractive material for VTSA processes, presenting higher working capacities, purities and second-law efficiencies, with lower energy consumptions, also showing a better “buffer” behavior than zeolite 13X when trace impurities are present. This work represents the first quantitative assessment of the process performance of MOFs adsorbents in swing adsorption process for CO2 capture considering impurities effects. Results reinforce the validity of molecular simulations for guiding the optimization of these processes.

We present different properties of 2D carbon allotropes: α-, β- and γ-graphynes based on Density Functional Theory calculations. Among the set of properties studied there are structural (e.g., cohesive energies, optimum lattice parameters, interatomic distances, pore sizes, planar packing densities and specific surface areas), mechanical (e.g., uniaxial along with homogeneous and heterogeneous biaxial strains, in-plane stiffness and Poisson's ratio) and electronic properties (e.g., band structures, total and projected density of states, Dirac points or band gap location, Fermi velocity associated to the formers and the effective masses of the carriers associated to the latter). This study contributes towards remediating the lack of studies on the mechanical properties for the case of β-graphyne and allows the discussion of several properties from an atomic point of view, and their comparison with those of graphene. Basically, graphynes present lower values than graphene for cohesive energy, planar packing density, in-plane stiffness and Fermi velocities. Contrarily, graphynes posses a higher variety of pore sizes and a higher Poisson's ratio. These facts make graphynes potential candidates for a wide variety of membrane separations and for applications that need softer materials but maintaining, in a lower extent, some of the electronic properties considered exceptional in graphene.

The quantum dynamics of three CH(X(2)Π) + D((2)S) reactions is studied by means of the coupled-channel time-dependent real-wavepacket (WP) and flux methods at collision energy Ecol ≤ 0.6 eV and on three potential energy surfaces (PESs): the Born-Oppenheimer (BO) ground PES X̃(3)A″ and the excited ones ã(1)A' and b̃(1)A″, coupled by nonadiabatic (NA) Renner-Teller (RT) effects. This three-state model is suitable for obtaining initial-state-resolved observables, is based on a complete analysis of the correlation diagram of the lowest electronic states of the CHD intermediate and of their NA interactions, and neglects the smaller coupling effects due to the asymptotic electronic angular momenta that become important in state-to-state dynamics. WPs are propagated on each PES at total angular momentum values J ≤ 70, with CH in the two lowest vibrational states v0 and in the ground rotational state j0 = 1. Reaction probabilities are obtained for three possible final products (f): (dP) CH decay and C((3)P) + HD(X(1)Σ(+)) formation that occurs on the uncoupled ground PES, (dD) CH decay and C((1)D) + HD(X(1)Σ(+)) formation that depends on the RT-coupled singlet species, and (ex) exchange to CD(X(2)Π) + H((2)S) available adiabatically from the X̃(3)A″ PES and nonadiabatically from ã(1)A' and b̃(1)A″. Observable cross sections σf,v0j0 and rate constants kf,v0j0 in the temperature range T = 100-2000 K are obtained for (dP), (dD), and (ex) channels. Comparing BO with RT probabilities, we show that NA effects are important at high J values for the (ex) channel at v0 = 1. Real time mechanisms on the three PESs show that RT couplings are opened after some time and clearly point out the formation of the product channels. Both cross sections and rate constants present the same sequence, for example σex,11σdP,01 ∼ σex,01σdP,11 ≫ σdD,11 ≫ σdD,01, and the CH vibrational excitation enhances the total removal CH+D reactivity by a factor of ∼1.7, mainly due to the increase of the (ex) channel contribution from ∼47% at v0 = 0 to ∼76% at v0 = 1. This fact implies a considerable vibrational enhancement of combustion processes at high temperature. In agreement with the probability results, the ã(1)A'/b̃(1)A″ RT coupling increases both σex,11 and kex,11 up to ∼30%. Moreover, including the three PESs in the dynamics simulation of CH+D increase by far the (ex)/(dP) branching ratio with respect to the CH + H' reaction. Thus, at room temperature, kdP,01 changes from 10.8 × 10(-11) to 3.4 × 10(-11) cm(3) s(-1) substituting H atom by D.

Language identification, as the task of determining the language a given text is written in, has progressed substantially in recent decades. However, three main issues remain still unresolved: (1) distinction of similar languages, (2) detection of multilingualism in a single document, and (3) identifying the language of short texts. In this paper, we describe our work on the development of a benchmark to encourage further research in these three directions, set forth an evaluation framework suitable for the task, and make a dataset of annotated tweets publicly available for research purposes. We also describe the shared task we organized to validate and assess the evaluation framework and dataset with systems submitted by seven different participants, and analyze the performance of these systems. The evaluation of the results submitted by the participants of the shared task helped us shed some light on the shortcomings of state-of-the-art language identification systems, and gives insight into the extent to which the brevity, multilingualism, and language similarity found in texts exacerbate the performance of language identifiers. Our dataset with nearly 35,000 tweets and the evaluation framework provide researchers and practitioners with suitable resources to further study the aforementioned issues on language identification within a common setting that enables to compare results with one another.

This paper presents a comparative evaluation of several Portuguese parsers. Our objective is to use dependency parsers in a specific information extraction task, namely Open Information Extraction (OIE), and measure the impact of each parser in this task. The experiments show that the scores obtained by the evaluated parsers are quite similar even though they allow to extract different (and then complementary) itens of information.

Neste artigo propomo-nos a definir e desenvolver uma estratégia automática para procurar especificidades lexicais dentro de conjuntos de textos utilizando unidades lexicais simples e expressões com várias palavras, ou termos multipalavra (MWE, a sua sigla em inglês). Propomos uma metodologia para o cálculo da divergência de distribuições de lemas e de MWE que permitirá encontrar, automaticamente, diferenças e semelhanças entre textos não anotados. Esta metodologia poderá ser utilizada para posteriormente identificar grupos de textos sobre os quais se procederá a análises quantitativas e qualitativas semiautomáticas e/ou com intervenção humana. Num primeiro teste, utilizamos dois textos de especialidade (da área da pediatria) e um texto literário, presumindo que os textos de especialidade deveriam apresentar maiores divergências relativamente ao texto literário do que entre eles próprios. Como os testes feitos mostraram a tendência esperada, decidimos aplicar a mesma metodologia a um segundo grupo de textos (três conjuntos de entrevistas a visitantes da cidade de Santiago de Compostela)., Este trabalho é apoiado pelo projeto Narrativas, usos e consumos de visitantes como aliados ou amena¸cas para o bem-estar da comunidade local: o caso de Santiago de Compostela. Ref: FFI2017-88196-R, parcialmente subsidiado pelo Ministerio de Industria, Econom´ıa y Competitividad espanhol no quadro do Programa Estatal de I+D+i Orientada a los Retos de la Sociedad (2018-2021).

A simplified model is applied for the prediction of gas/solid adsorption isotherms of pure gases (i.e., CO2, N2, SO2) on the metal–organic framework Mg-MOF-74 and then applied to flue gas mixtures with small amounts of SO2 in the range 0.001–1% in weight. The model is based on periodic density functional theory (DFT) calculations and a dual-site Langmuir approach (DFT/DSL), using a mean-field approximation for the inclusion of the lateral interactions. This model not only provides reliable adsorption isotherms (P ≤ 1 atm, 293 ≤ T ≤ 373 K) but also isosteric heats of adsorption in good agreement with both available experimental data and more refined previous models. Moreover, the effect of SO2 in the efficiency of adsorption of the other components of the mixture has been evaluated by showing that a very low presence of SO2 is enough to poison the Mg-MOF-74 structure. Finally, several swing adsorption techniques have been analyzed at different operative conditions to quantify the impact of SO2 poisoning in...