Aesthetic Quality Properties of Carbonate Breccias Associated with Textural and Compositional Factors: Marrón Emperador Ornamental Stone (Upper Cretaceous, Southeast Spain)

The aesthetic properties of ornamental stones, including colour, texture, and the presence or absence of discontinuities, are influential in their use and marketing. This is particularly critical in brecciated rocks such as the Marrón Emperador (ME) ornamental stone, a dark brown breccia dolostone (Upper Cretaceous, southeast Spain). ME shows a high chromatic and textural variability, which is one of its most appreciated commercial features. Through a petrographic, mineralogical, geochemical and colourimetric study of samples obtained from quarries, outcrops and/or drilling cores, several quality categories have been established, as well as the relationship between the aesthetic properties of ME ornamental stone with its compositional and textural factors. Three main types of breccia constitute the ME exploitable lithotect: crackle and mosaic packbreccias, and rubble floatbreccias. Breccia clasts are mainly composed of hypidiotopic-idiotopic medium- to coarsely-crystalline dolosparites, microcrystalline dolosparites and dolomicrites. Results show that diagenetic processes are mainly responsible for the colour of ME dolostones, revealing that the Sr content is a key factor. The darker brown dolomites always show a higher Sr content, where other typical chromophore elements in dolomites, such as Fe and Mn, do not present this correlation. This study provides evidence for the complexity of processes and factors that are responsible for aesthetic quality features in ornamental stones

Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas

The increasing competition among scientific organizations for limited resources requires researchers to publish quality papers, causing the development of tools to establish the most influential institutions. This bibliometric analysis characterizes research activity of Spanish universities and their academic staff in the area of computer sciences, identifying both their strengths and weaknesses nationwide. The analysis is also performed by autonomous regions, public universities, subject areas and professional standing. Thanks to this analysis a comprehensive overview of the current situation in the area of computer sciences is achieved.La creciente competencia entre organismos científicos por los recursos limitados exige que los investigadores tengan que publicar con calidad y en cantidad. Ello ha provocado la aparición de herramientas a diferentes niveles para establecer qué instituciones son más influyentes en el mundo científico. Este análisis bibliométrico caracteriza la producción científica de las universidades españolas y sus profesores funcionarios en el área de las tecnologías informáticas, detectando tanto las fortalezas como las debilidades de los mismos a nivel nacional. Dicho análisis se realiza también por comunidades autónomas, universidades públicas, áreas de conocimiento y categoría profesional. Gracias a este análisis se consigue una visión global y detallada de la situación actual en el área de las tecnologías informáticas

Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas

The increasing competition among scientific organizations for limited resources requires researchers to publish quality papers, causing the development of tools to establish the most influential institutions. This bibliometric analysis characterizes research activity of Spanish universities and their academic staff in the area of computer sciences, identifying both their strengths and weaknesses nationwide. The analysis is also performed by autonomous regions, public universities, subject areas and professional standing. Thanks to this analysis a comprehensive overview of the current situation in the area of computer sciences is achieved.<br><br>La creciente competencia entre organismos científicos por los recursos limitados exige que los investigadores tengan que publicar con calidad y en cantidad. Ello ha provocado la aparición de herramientas a diferentes niveles para establecer qué instituciones son más influyentes en el mundo científico. Este análisis bibliométrico caracteriza la producción científica de las universidades españolas y sus profesores funcionarios en el área de las tecnologías informáticas, detectando tanto las fortalezas como las debilidades de los mismos a nivel nacional. Dicho análisis se realiza también por comunidades autónomas, universidades públicas, áreas de conocimiento y categoría profesional. Gracias a este análisis se consigue una visión global y detallada de la situación actual en el área de las tecnologías informáticas

Decision support systems (DSS) for wastewater treatment plants: a review of the state of the art

The use of decision support systems (DSS) allows integrating all the issues related with sustainable developmentin view of providing a useful support to solve multi-scenario problems. In this work an extensive review on theDSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide anupdated compendium on DSSs in view of supporting researchers and engineers on the selection of the mostsuitable method to address their management/operation/design problems. Results showed that DSSs weremostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective inorder to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based onquality and operational issues are very often applied to site-specific conditions. Finally, it would be important toencourage the development of more user-friendly DSSs to increase general interest and usability.This work is part of a research project supported by grant of the Italian Ministry of Education, University and Research (MIUR) through the Research project of national interest PRIN2012 (D.M. 28 December 2012 n. 957/Ric – Prot. 2012PTZAMC) entitled “Energy consumption and Greenhouse Gas (GHG) emissions in the wastewater treatment plants: a decision support system for planning and management – http://ghgfromwwtp.unipa.it” in which the first author is the Principal Investigator. In addition, some coauthors acknowledge the partial support of the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

Tutorització Intel·ligent de Comunitats Virtuals d'Aprenentatge

L’evolució de la tecnologia ha produït canvis profunds en els paradigmes de l'ensenyament i, particularment, en l'aplicació d'aquests a l’aprenentatge en línia (e-learning). De fet va ser la pròpia revolució tecnològica la que va fer néixer aquest nou model d'aprenentatge virtual i, actualment, poques són les institucions que no compten amb alguna aplicació de l'e-learning, ja sigui com a alternativa al model educatiu tradicional o com a complement (blended learning). La introducció de l'e-learning, i en general de les Tecnologies de la Informació i Comunicació (TIC), al món educatiu ha fet que la teoria instructivista de l'educació tradicional s'hagi desplaçat cap a un paradigma constructivista, generant un model molt més centrat en l'alumne. Les eines educatives han anat evolucionant cap aquest nou paradigma, on la personalització i l’adaptació són fils conductors, i els Sistemes Tutors Intel·ligents (STI) en són un bon exemple. Tanmateix, l'arribada de la Web 2.0 ha desencadenat un moment social que ha acabat marcant de nou el món educatiu. El desplegament de la teoria connectivista, sorgida de l'aplicació de la Web Social en l’àmbit educatiu, i la implantació de múltiples iniciatives d'e-learning han afavorit la proliferació d'Entorns Virtuals d'Aprenentatge (EVA) i de diferents tecnologies educatives basades en Web. Atès que la tecnologia associada a Internet està en constant evolució, però, tot fa pensar que els entorns d’aprenentatge hauran d’evolucionar en els propers anys de manera paral·lela a com ho està fent la pròpia Web. Així, és probable que les següents generacions d'e-learning implementin característiques pròpies de la Web 3.0 (semàntica) i de la Web 4.0 (simbiòtica) i esdevinguin entorns on els agents intel·ligents hi tinguin un paper significatiu. En aquesta tesi s’analitza en primer lloc quina ha estat la trajectòria que ha seguit l’educació al llarg de la història i quina influència ha tingut en la implantació dels sistemes d’aprenentatge en línia, des dels més senzills i poc adaptatius, fins als més moderns i pensats per millorar l’experiència en l’aprenentatge. A més, en vistes de la trajectòria tecnològica que es divisa, es proposa una nova arquitectura que permeti incloure, d’una banda, les capacitats dels entorns ja existents d’aprenentatge en línia, i, de l’altra, els agents intel·ligents que convertiran l’experiència de l’ensenyament a distància en una experiència adaptativa i social, on el concepte de grup tindrà cabdal importància. Els sistemes educatius intel·ligents futurs, per tant, hauran de disposar d'una part complexa de computació avançada, aspecte abordat des del camp de la Intel·ligència Artificial, que permeti reconèixer quina és l’evolució de l’alumne en el seu aprenentatge i com aquest està interactuant i rendint amb els companys de la seva classe virtual. A més, la quantitat d'interaccions produïdes en aquests entorns generarà un gran volum de dades educatives, la Big Learning Data, amb informació vital que caldrà processar per millorar i adaptar el sistema a l’alumne a mesura que el curs avança, i per recollir informació valuosa per a la seva tutorització. Així, la darrera part d’aquesta tesi mostra les contribucions realitzades en Intel·ligència Artificial i els resultats de la seva implementació per crear la part intel·ligent d’aquesta arquitectura, podent extreure d'aquesta manera el màxim rendiment d’aquests nous entorns d’aprenentatge col·laboratiu que seran realitat d’aquí a pocs anys.La evolución de la tecnología ha producido cambios profundos en los paradigmas de la enseñanza y, particularmente, en la aplicación de éstos en el aprendizaje en línea (e-learning). De hecho fue la propia revolución tecnológica la que hizo nacer este nuevo modelo de aprendizaje virtual y, actualmente, pocas son las instituciones que no cuentan con alguna aplicación del e-learning, ya sea como alternativa al modelo educativo tradicional o como complemento (blended learning). La introducción del e-learning, y en general de las Tecnologías de la Información y Comunicación (TIC), en el mundo educativo ha hecho que la teoría instructivista de la educación tradicional se haya desplazado hacia un paradigma constructivista, generando un modelo mucho más centrado en el alumno. Las herramientas educativas han ido evolucionando hacia este nuevo paradigma, donde la personalización y la adaptación son hilos conductores, y los Sistemas Tutores Inteligentes (STI) son un buen ejemplo. Sin embargo, la llegada de la Web 2.0 ha desencadenado un momento social que ha marcado de nuevo el mundo educativo. El despliegue de la teoría conectivista, surgida de la aplicación de la Web Social en el ámbito educativo, y la implantación de varias iniciativas de e-learning han favorecido la proliferación de entornos virtuales de aprendizaje y de diferentes tecnologías educativas basadas en Web. Dado que la tecnología asociada a Internet está en constante evolución, todo hace pensar que los entornos de aprendizaje deberán evolucionar en los próximos años de manera paralela a como lo está haciendo la propia Web. Así, es probable que las siguientes generaciones de e-learning implementen características propias de la Web 3.0 (semántica) y de la Web 4.0 (simbiótica) y se conviertan en entornos donde los agentes inteligentes tengan un papel significativo. En esta tesis se analiza en primer lugar cuál ha sido la trayectoria que ha seguido la educación a lo largo de la historia y qué influencia ha tenido en la implantación del e-learning, desde los más sencillos y poco adaptativos, hasta los más modernos y pensados para mejorar la experiencia en el aprendizaje. Además, en vistas de la trayectoria tecnológica que se divisa, se propone una nueva arquitectura que permita incluir, por un lado, las capacidades de los entornos ya existentes de aprendizaje en línea, y, por otro, los agentes inteligentes que convertirán la experiencia de la enseñanza a distancia en una experiencia adaptativa y social, donde el concepto de grupo tendrá capital importancia. Los sistemas educativos inteligentes futuros, por tanto, deberán disponer de una parte compleja de computación avanzada, aspecto abordado desde el campo de la Inteligencia Artificial, que permita reconocer cuál es la evolución del alumno en su aprendizaje y como éste está interactuando y rindiendo con los compañeros de su clase virtual. Además, la cantidad de interacciones producidas en estos entornos generará un gran volumen de datos educativos, la Big Learning Data, con información vital que habrá que procesar para mejorar y adaptar el sistema al alumno a medida que el curso avanza, y para recoger información valiosa para su tutorización. Así, la última parte de esta tesis muestra las contribuciones realizadas en Inteligencia Artificial y los resultados de su implementación para crear la parte inteligente de esta arquitectura, pudiendo extraer de este modo el máximo rendimiento de estos nuevos entornos de aprendizaje colaborativo que serán realidad dentro de pocos años.The evolution of technology has produced profound changes in the paradigms of teaching and, particularly, in their application in online learning (e-learning). In fact it was the technological revolution itself that gave birth to this new model of virtual learning and there are currently few institutions that do not have an e-learning application, either as an alternative to traditional methods or to complement them (blended learning). The introduction of e-learning, and in general of the Information Technology and Communication (ICT) in the educational world has made the instructivist traditional education theory move to a constructivist paradigm, creating a more focused learning model. Educational tools have evolved towards this new paradigm, where customization and adaptation are the backbone of the model. Intelligent Tutoring Systems (ITS) provide a good example of this new methodology. However, the advent of Web 2.0 has created a social era which has rebranded the educational world. The deployment of the connectionist theory, arising from the implementation of the Social Web in education, and the implementation of various e-learning initiatives have led to the proliferation of virtual learning environments and different educational Web-based technologies. Since the technology associated with the Internet is constantly evolving, everything suggests that learning environments should evolve in the coming years in parallel with the Web itself. Thus it is likely that the next generation of e-learning implements own Web 3.0 (semantic) and Web 4.0 (symbiotic) characteristics and create environments where intelligent agents have a significant role. In this thesis we first analyze the path of education throughout history and discuss the influence it has had on the implementation of e-learning, from the simplest and less adaptive measures to the most modern, designed methods to enhance the learning experience. Furthermore, in view of the visible technological background, we propose a new architecture to include, on the one hand, the capabilities of existing online learning environments, and secondly, intelligent agents which can convert the experiences acquired in distance learning into an adaptive and social experience, where the group concept is of paramount importance. Future intelligent educational systems must therefore have an intricate part of advanced computing, an aspect from the field of Artificial Intelligence, which recognizes the evolution of students in their learning and how they interact and perform with their virtual class mates. In addition, the number of interactions produced in these environments will generate a large volume of educational data, the Big Data Learning with vital information that must be processed to improve and adapt the system to the student as the course progresses, and to collect valuable information for tutorship. So, the last part of this thesis shows the contributions made in Artificial Intelligence and the results of their implementation to create the intelligent part of this architecture. The benefits of these new collaborative learning environments will enable us to optimize performance in coming years

Proyecto Docente e Investigador

PROYECTO DOCENTE E INVESTIGADOR Catedráticos de Universidad Área de Ciencia de la Computación e Inteligencia Artificial Universidad de Valladolid 19 de Mayo de 2023 David Escudero Manceb

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Financiado para publicación en acceso aberto: Universidad de Granada / CBUA.[Abstract]: Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovacón y Universidades”), P18-RT-1624, UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”. The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”. The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019. Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00. José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2022/33, and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). In [247], the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain). In [248], the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4, 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies”BG05M2OP001-1.002-0023. The work reported here has been partially funded by the support of MICIN project PID2020-116346GB-I00. The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III; by MinCiencias project 1222-852-69927, contract 495-2020. The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058. S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)).Junta de Andalucía; CV20-45250Junta de Andalucía; A-TIC-080-UGR18Junta de Andalucía; B-TIC-586-UGR20Junta de Andalucía; P20-00525Junta de Andalucía; P18-RT-1624Junta de Andalucía; UMA20-FEDERJA-086Portugal. Fundação para a Ciência e a Tecnologia; DSAIPA/AI/0099/2019Xunta de Galicia; ED431G 2019/01Xunta de Galicia; GPC ED431B 2022/33Chile. Agencia Nacional de Investigación y Desarrollo; 1201572Generalitat Valenciana; PROMETEO/2019/119Bulgarian National Science Fund; KP-06-N42/4Bulgaria. Operational Programme Science and Education for Smart Growth; BG05M2OP001-1.002-0023Colombia. Ministerio de Ciencia, Tecnología e Innovación; 1222-852-69927Junta de Andalucía; UMA18-FEDERJA-084Suíza. State Secretariat for Education, Research and Innovation; 22 00058Institute of Information & Communications Technology Planning & Evaluation (Corea del Sur); 2020-0-0136

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications