OpenAIRE: Open Access Infrastructure for Research in Europe

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Archivo Abierto Institucional de la Universidad Carlos III de Madrid: OpenAIRE: Open Access Infrastructure for Research in Europe

OpenAIRE promueve el acceso abierto a los resultados de proyectos de investigación financiados por la Unión Europea (7PM, H2020, etc.), en virtud del cuál, los beneficiarios de dichos proyectos han de depositar los artículos producidos revisados por pares.

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Now showing 1 - 20 of 1468
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    An early warning dropout model in higher education degree programs: A case study in Ecuador
    (CEUR-WS.org, 2020-09-14) Heredia-Jimenez, Vanessa; Jiménez Macías, Alberto Alejandro; Ortiz Rojas, Margarita; Imaz Marín, Jon; Moreno-Marcos, Pedro Manuel; Muñoz Merino, Pedro José; Delgado Kloos, Carlos; Ministerio de Economía, Comercio y Empresa; Comunidad de Madrid. Consejería de Educación e Investigación; European Commission
    Worldwide, a significant concern of universities is to reduceacademic dropout rate. Several initiatives have been made to avoid thisproblem; however, it is essential to recognize at-risk students as soon aspossible. In this paper, we propose a new predictive model that can iden-tify the earliest moment of dropping out of a student of any semester inany undergraduate course. Unlike most available models, our solution isbased on academic information alone, and our evidence suggests that byignoring socio-demographics or pre-college entry information, we obtainmore reliable predictions, even when a student has only one academicsemester finished. Therefore, our prediction can be used as part of anacademic counseling tool providing the performance factors that couldinfluence a student to leave the institution. With this, the counselorscan identify those students and take better decisions to guide them andfinally, minimize the dropout in the institution. As a case study, we usedthe students¿ data of all undergraduate programs from 2000 until 2019from a public high education university in Ecuador.
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    Role of porous microstructure on dynamic shear localization and ductile failure
    (2024-01) Ambikadevi Rajasekharan Nair, Vishnu; Rodríguez-Martínez, José A.; UC3M. Departamento de Mecánica de Medios Continuos y Teoría de Estructuras; European Commission; Rordríguez-Martínez, José A.
    This doctoral thesis aims to provide a comprehensive analysis of the role of porous microstructure on dynamic shear localization and ductile failure. For this purpose, a computational approach was developed that includes large-scale 3D finite element models incorporated with actual porous microstructures that are derived from different additively manufactured materials. This is the distinctive feature of the numerical simulations conducted in this study, in which a large population of voids that are statistically representative of the actual porosity in 3D-printed metals has been included. Additional microstructures varying the void volume fraction, the mean and standard deviation of the size distribution of voids were generated, enabling a systematic parametric analysis of the microstructural features. Finite element simulations of multiple shear bands formation in radially collapsing thick-walled cylinders and shear band formation in thin-tubes subjected to dynamic torsion were performed to investigate the effect of spatial and size distribution of voids on dynamic shear localization. The numerical results demonstrate that microstructural porosity promotes dynamic shear localization, determining preferential sites for the nucleation of the shear bands, accelerating their development, and tailoring their direction of propagation. Specifically, collapsing thick-walled cylinder calculations bring out that, for a given void volume fraction more shear bands are nucleated as the number of voids increases, while the shear bands are triggered earlier and develop faster as the size of the pores increases. Moreover, the numerical results for porous thin-tubes subjected to dynamic torsion quantitatively show that the size of the largest pore is a main microstructural feature controlling the specimen ductility. Furthermore, this numerical methodology was extended to perform unit-cell calculations to investigate the void growth in porous ductile materials under monotonic loading conditions by prescribing constant triaxiality and Lode parameter throughout the loading. The simulations were carried out with random spatial distributions of pores and with void clusters. The calculations with random spatial distribution of voids revealed that the interaction between neighboring pores dictates the volume evolution of individual voids, especially at higher macroscopic triaxiality. The calculations with clusters demonstrated that pores clustering promotes localization/coalescence due to increased interaction between the voids, resulting in an increased growth rate of voids in clusters with large number of pores. Moreover, the results for the evolution of the distribution of plastic strains in the unit-cell have provided some quantitative indications of the role of porous microstructure on the development of heterogeneous plastic strain fields which promote macroscopic strain softening.
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    PLA-PCL textile reinforced composites for connective tissues applications
    (2024-01) Pereira Lobato Costa, Carolina; Campos Gómez, Mónica; González, Carlos Daniel; Llorca, Javier; UC3M. Departamento de Ciencia e Ingeniería de Materiales e Ingeniería Química; European Commission; Campos Gómez, Mónica
    Tissue engineering presents a promising frontier in addressing injuries and degenerative conditions within the human body. This doctoral thesis focuses on developing biodegradable textile polymer composite materials, specifically woven from commingling yarns, to gain insights into their long-term performance for connective tissue engineering applications. This interdisciplinary project aims to advance the creation of robust materials for connective tissues, utilizing hybrid PLA/PCL and PLA/PLA commingled yarns to manufacture woven materials and composites. The unique combination of different grades of PLA and PCL characteristics in hybrid yarns enables the production of fabrics and composites with high strength and ductility. The mechanical, thermal, and biological performance of these materials is analyzed in the thesis, exploring the advantages of commingled hybrid yarns. Different degradation rates of PLA and PCL allow for tailoring this property. Materials undergo degradation in phosphate-buffered saline solution for up to 160 days at 37◦C and accelerated degradation at 50◦C. Observations reveal different degradation patterns of the materials. The PLAPCL woven textile shows minimal changes in thermal and mechanical properties after 80 days at 37◦C, with slight degradation observed after 160 days, which is attributed to chain scission in PLA fibres. This trend is also observed in the PLA-PCL composite materials. Conversely, PLA-PLA weaves experience a notable decrease in elastic modulus after 40 days. Upon immersion at 50°C, the PLA-PCL weave undergoes a rapid strength reduction after 40 days, primarily due to PLA hydrolysis, and significant degradation after 160 days, attributed to PCL chain scission. The PLA-PLA composite experiences the fastest deterioration, rendering it impossible to test samples after 40 days of degradation. The study concludes that all materials exhibit potential for connective tissue implants, assuming a six-month average regeneration time. Despite indirect tests did not ensured optimal biocompatibility, direct tests indicated a good cell/material interaction, with the PLA-PLA composite showcasing superior performance. These findings underscore the potential of hybrid commingled yarns in manufacturing textile scaffolds and composites with tailored mechanical properties and good ductility for connective tissue engineering applications.
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    Analysis of the wave-plasma interaction in electrodeless plasma thrusters
    (2024-03) Jiménez Jiménez, Pedro José; Merino Martínez, Mario; Ahedo Galilea, Eduardo Antonio; UC3M. Departamento de Ingeniería Aeroespacial; European Commission; Comunidad de Madrid; Ministerio de Ciencia, Innovación y Universidades (España); Merino Martínez, Mario
    La tesis presentada contribuye a la comprensión y el modelado numérico de propulsores de plasma sin electrodos (EPTs). Con un enfoque dual que combina herramientas prácticas de diseño y modelos de investigación fundamental, este trabajo ofrece un conjunto de herramientas versátil y completo para avanzar el estado del arte en física de plasmas de baja temperatura aplicada a propulsión eléctrica. El núcleo de la investigación lo constituyen los avances en el estudio de EPTs y tecnologías para su modelado y simulación. Estos se enfocadan principalmente en la interacción de ondas electromagnéticas y su relación con fenómenos de transporte en el plasma. El estudio comienza con un análisis detallado del modelo de plasma frío, aplicado a problemas de propagación de ondas en propulsores de plasma de clase Helicón (HPT). Cabe destacar la introducción de PWHISTLER, una herramienta de simulación de ondas que emplea el método de elementos finitos (FE). Este modelo destaca por su mayor velocidad, precisión y capacidad para simular geometrías complejas, mejorando significativamente el estudio de fenómenos electromagnéticos en plasmas magnetizados. Una serie de análisis utilizando tanto un modelo de diferencias finitas (FD) como PWHISTLER demuestran su efectividad en la caracterización de la propagación y absorción de ondas en HPTs, siendo una observación clave la absorción de potencia concentrada en la superficie de resonancia electrónica-ciclotrónica (ECR). La integración de PWHISTLER con el código de simulación para el transporte de plasma HYPHEN facilita un estudio exhaustivo de una nueva topología de campo magnético con cúspide en HPT. Las simulaciones, verificadas con datos experimentales, ofrecen conclusiones sobre las pérdidas de rendimiento y la eficiencia de empuje, destacando el papel de las corrientes de plasma a pared, la temperatura de electrones y la influencia de la topología magnética. Finalmente se presenta una nueva formulación de un algoritmo implícito de partículas en celda (PIC), diseñado específicamente para toberas magnéticas. El método PIC implícito mejora la eficiencia computacional frente a métodos bien establecidos, y constituye un avance sustancial en la simulación y optimización de toberas magnéticas para EPTs.
  • Publication
    Multiple localization and fracture in metallic rings and plates subjected to dynamics expansion
    (2023-12) Murlidhar, Anil Kumar; Rodríguez Martínez, José Antonio; UC3M. Departamento de Mecánica de Medios Continuos y Teoría de Estructuras; European Commission; Rodríguez Martínez, José Antonio
    Ductile materials are commonly used in high-strain rate applications involving impact or blast loads due to their notable capacity to absorb energy and undergo plastic deformation before fracture. Over the last two decades, studies on dynamic strain rates have evolved dramatically, leading to a better knowledge of material behavior under high-speed loading situations and generating advances in a variety of industries. Imperfections in ductile metals, such as cracks, inclusions, and voids, are significant, and these imperfections can significantly impact the material’s mechanical properties and structural integrity, thereby affecting its suitability for various industrial applications. Hence, further research is necessary to understand the mechanical behavior of ductile metals with imperfections. This doctoral thesis investigates the effect of porosity, anisotropy, and tensioncompression asymmetry on the mechanical response of metallic materials under dynamic loading conditions. In the first part of the thesis, we used linear stability analysis and unit-cell finite element calculations to investigate the onset of necking instabilities in porous ductile plates under biaxial loading. In the next part of the work, we used three techniques-linear stability analysis, a nonlinear two-zone model, and unit-cell finite element calculations-to examine the necking formability of anisotropic and tension-compression asymmetric metallic sheets subjected to in-plane loading paths spanning plane strain tension to near equal-biaxial tension. The last part of the study focused on examining the fragmentation process of 3D-printed AlSi10Mg porous ring specimens. This was achieved by implementing two experimental ring expansion test setups and subjecting the aluminum alloy to electromagnetic and mechanical loadings. The objective was to gain insights into the behavior of these alloys when exposed to high strain rates.
  • Publication
    Clustering and forecasting of day-ahead electricity supply curves using a market-based distance
    (2024) Li, Zehang; Alonso Fernández, Andrés Modesto; Elías, Antonio; Morales, Juan M.; Universidad Carlos III de Madrid. Departamento de Estadística; European Commission; Ministerio de Ciencia e Innovación (España); European Commission
    Gathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market’s performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market’s specific features. Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered.
  • Publication
    Baseline pupil size does not correlate with fluid intelligence: a laboratory study with children and adults
    (2024-03) Lorente Labrado, Patricia; Ruuskanen, Veera; Mathôt, Sebastiaan; Crespo, Antonio; Radl, Jonas; European Commission
    Recent studies have investigated resting-state, or baseline, pupil size as a general measure of cognitive abilities, based on the previous finding that larger pupils might be predictive of higher general intelligence or working memory capacity. However, evidence for such relationships has been mixed, and all previous studies thus far have focused on adult samples. The present study adds to this debate by examining the correlation between fluid intelligence and baseline pupil size in a sample of both children (10 years old) and adults (their parents). Importantly, our sample is representative in terms of socioeconomic background, which was not the case in previous studies, thus addressing concerns about sample selection and variability. We did not find evidence for a relationship of fluid intelligence with baseline pupil size or with pupil-size variability, neither for children nor adults. Therefore, our results do not replicate the relationship between cognitive abilities and baseline pupil size as reported in previous research.
  • Publication
    HEFactory: A symbolic execution compiler for privacy-preserving Deep Learning with Homomorphic Encryption
    (Elsevier, 2023-05-01) Cabrero Holgueras, José; Pastrana Portillo, Sergio; Comunidad de Madrid; European Commission; Ministerio de Economía y Competitividad (España)
    Homomorphic Encryption (HE) allows computing operations on encrypted data, and it is a potential solution to enable Deep Learning (DL) in privacy-enforcing scenarios (e.g., sending private data to cloud services). However, HE remains a complex technology with multiple challenges that prevent successful application by non-experts. In this work, we present HEFactory, a program compiler that effectively assists in building HE applications in Python for both general-purpose and Deep Learning applications, focusing on non-expert data scientists. HEFactory relies on a layered architecture that deals with challenges such as automatic parameter selection and specific data representation of HE applications. Our benchmarks show that HEFactory substantially lowers the programming complexity (i.e., a reduction of 80% in the number of lines of code) with negligible performance overhead over programs written by experts using native HE frameworks.
  • Publication
    On Futuring Body Perception Transformation Technologies: Roles, Goals and Values
    (Association For Computing Machinery (ACM), 2023-10-03) Turmo Vidal, Laia; Vega Cebrián, José Manuel; D'Adamo, Amar; Roel Lesur, Marte Ernesto; Dehshibi, Mohammad Mahdi; Díaz Durán, Joaquín Roberto; Tajadura Jiménez, Ana; European Commission
    Body perception transformation technologies augment or alter our own body perception outside of our usual bodily experience. As emerging technologies, research on these technologies is limited to proofs-of-concept and lab studies. Consequently, their potential impact on the way we perceive and experience our bodies in everyday contexts is not yet well understood. Through a speculative design inquiry, our multidisciplinary team envisioned utopian and dystopian technology visions. We surfaced potential roles, goals and values that current and future body perception transformation technologies could incorporate, including non-utilitarian purposes. We contribute insights on such roles, goals and values to inspire current and future work. We also present three provocations to stimulate discussions. Finally, we contribute methodologically with insights into the value of speculative design as a fruitful approach for articulating and bridging diverse perspectives in multidisciplinary teams.
  • Publication
    Sustainable conditions for waste tires recycling through gasification in a bubbling fluidized bed
    (Elsevier, 2023-08-20) Batuecas Fernández, Esperanza; Serrano García, Daniel; Horvat, Alen; Abelha, Pedro; European Commission
    Gasification in a bubbling fluidized bed reactor was introduced as sustainable technology to treat waste such as tires. However, uncertainty arises when defining the most sustainable gasification process conditions. In this paper, eight experimental conditions were analyzed. The experiments were carried out at 700 and 850 °C with different equivalence ratios (ER) while using air as a gasifying agent. At 850 °C the effect of steam addition was also studied. A Life Cycle Assessment (LCA) was assessed to the product gas and its energy content by the following environmental impacts: Climate Change (CC), Ozone Depletion (OD), and Particulate Matter (PM). LCA methodology revealed that optimal gasification conditions provide 74, 69 and 66% environmental impact reductions in CC, PM, and OD, respectively by comparing the best and the worst-case scenarios. The combination of high temperature, low ER, and steam addition offered the most environmentally sustainable process, achieving 8.3·10−2 kg CO2 eq/MJ in CC, 1.3·10−8 kg CFC-11eq/MJ in OD and 4.9·10−5 kg PM2.5eq/MJ in PM. Despite the great savings in environmental impacts, additional efforts are still needed to reduce the energy consumption of the preheating system to ensure the product gas is levelled to conventional natural gas.
  • Publication
    Exascale programing models for extreme data processing
    (IEEE, 2023-05-09) García Blas, Francisco Javier; Carretero Pérez, Jesús; European Commission
    Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analysed in (near) real-time by using a very large number of memory/storage elements and Exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analysed, the millions of images per day that must be mined/analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage cannot handle nowadays the extreme scale of such application data. Following the need of improvement of current concepts and technologies, ASPIDE's activities focus on data-intensive applications running on systems composed of up to millions of computing elements (Exascale systems). Practical results will include the methodology and software prototypes that will be designed and used to implement Exascale applications. The ASPIDE project contributed with the definition of a new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems, which can pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real-time. This work is continued now in the ADMIRE EuroHPC project.
  • Publication
    Deep Neural Network-Based QoT Estimation for SMF and FMF Links
    (IEEE, 2023-03-15) Amirabadi, Mohammad Ali; Kahaei, Mohammad Hossein; Nezamalhosseini, S. Alireza; Arpanaei, Farhad; Carena, Andrea; European Commission
    Quality of transmission (QoT) estimation tools for fiber links are the enabler for the deployment of reconfigurable optical networks. To dynamically set up lightpaths based on traffic request, a centralized controller must base decisions on reliable performance predictions. QoT estimation methods can be categorised in three classes: exact analytical models which provide accurate results with heavy computations, approximate formulas that require less computations but deliver a reduced accuracy, and machine learning (ML)-based methods which potentially have high accuracy with low complexity. To operate an optical network in real-time, beside accurate QoT estimation, the speed in delivering results is a strict requirement. Based on this, only the last two categories are candidates for this application. In this paper, we present a deep neural network (DNN) structure for QoT estimation considering both regular single-mode fiber (SMF) and future few-mode fiber (FMF) proposed to increase the overall network capacity. We comprehensively explore ML-based regression methods for estimating generalized signal-to-noise ratio (GSNR) in partial-load SMF and FMF links. Synthetic datasets have been generated using the enhanced Gaussian noise (EGN) model. Results indicate that the proposed DNN-based regressor can provide better accuracy along with less computation complexity, compared with other state-of-the-art ML methods as well as closed-form-EGN and closed-form-GN models
  • Publication
    Ultra-Wideband Multi-Octave Planar Interconnect for Multi-Band THz Communications
    (Springer, 2023-08-01) Iwamatsu, Shuya; Ali, Muhsin; Fernandez Estevez, Jose Luis; Tebart, Jonas; Kumar, Ashish; Makhlouf, Sumer; Carpintero del Barrio, Guillermo; Stohr, Andreas; European Commission
    An ultra-wideband (UWB) interconnect technology using indium phosphide (InP)-based transitions for coupling the output signals from terahertz (THz) photodiodes featuring coplanar waveguide (CPW) outputs to low-loss dielectric rod waveguides (DRWs) is presented. The motivation is to exploit the full bandwidth offered by THz photodiodes without limitations due to standard rectangular waveguide interfaces, e.g., for future high data rate THz communications. Full electromagnetic wave simulations are carried out to optimize the electrical performance of the proposed InP transitions in terms of operational bandwidth and coupling efficiency. The transitions are fabricated on 100-µm-thin InP and integrated with silicon (Si) DRWs. Experimental frequency domain characterizations demonstrate efficient THz signal coupling with a maximum coupling efficiency better than - 2 dB. The measured 3-dB and 6-dB operational bandwidths of 185 GHz and 280 GHz, respectively, prove the multi-octave ultra-wideband features of the developed interconnect technology. The 6-dB operational bandwidth covers all waveguide bands between WR-12 to WR-3, i.e., a frequency range between 60 and 340 GHz. In addition, the multi-octave performances of the fabricated interconnects were successfully exploited in proof-of-concept THz communication experiments. Using intermediate frequency orthogonal frequency division multiplexing (OFDM), THz communications are demonstrated for several frequency bands using the same interconnect. Considering soft-decision forward error correction, error-free transmission with data rates of 24 Gbps at 80 GHz and 8 Gbps at 310 GHz is achieved
  • Publication
    On-Chip Terahertz antenna array based on amalgamation of metasurface-inspired and artificial magnetic conductor technologies for next generation of wireless electronic devices
    (Published by Elsevier GmbH., 2023-07-01) Alibakhshikenari, Mohammad; Virdee, Bal Singh; Salekzamankhani, Shahram; Babaeian, Fatemeh; Ali, Syed Mansoor; Iqbal, Amjad; Al-Hasan, Muath; European Commission
    The paper presents a feasibility study on an innovative terahertz (THz) on-chip antenna array designed to reliably meet the high-performance connectivity requirements for next generation of wireless devices to enable bandwidth intensive applications, superfast fast streaming, bulk data exchange between internet of things (IoT) devices/smartphones and the development of holographic video conferencing. The significantly smaller wavelength of the THz-band and metasurface-inspired and artificial magnetic conductor (AMC) technologies are exploited here to realize an on-chip antenna. Several experimental on-chip antenna arrays of various matrix sizes were investigated for application at millimeter-wave/Terahertz RF front-end transceivers. The technique proposed here is shown to enhance the antennas impedance bandwidth, gain and radiation efficiency. Purely for experimental purposes a 2 × 24 radiation element array was fabricated. It exhibits an average measured gain of 20.36 dBi and radiation efficiency of 37.5% across 0.3-0.314 THz. For proof of the concept purposes a THz receiver incorporating the proposed on-chip antenna was modelled. The results show that with the proposed antenna array a THz receiver can provide a gain of 25 dB when the antenna is directly matched to low-noise amplifier stage.
  • Publication
    All-solid-state sodium-ion batteries operating at room temperature based on NASICON-type NaTi2(PO4)3 cathode and ceramic NASICON solid electrolyte: A complete in situ synchrotron X-ray study
    (Elsevier, 2023-09-15) Pandit, Bidhan; Johansen, Morten; Andersen, Bettina P.; Martínez Cisneros, Cynthia Susana; Levenfeld Laredo, Belén; Ravnbaek, Dorthe B.; Varez, Alejandro; Agencia Estatal de Investigación (España); Comunidad de Madrid; European Commission
    All-solid-state sodium-ion batteries that work at ambient temperature are a potential approach for large-scale energy storage systems. Nowadays, ceramic solid electrolytes are gaining attention because of their good ionic conductivity and excellent mechanical and chemical stabilities. Furthermore, a good interface between electrode and solid electrolyte is also required to achieve successful cell performances. In this work, sintered ceramic layer electrolyte Na3.16Zr1.84Y0.16Si2PO12, with high ionic conductivity (0.202 mS/cm at room temperature), are prepared by using uniaxial pressing followed by a sintering process. The conductive carbon coated NASICON material (NaTi2(PO4)3/C) exhibits, as cathode material, enhanced rate capability and stability for sodium ion batteries for high carbon (18.95 %) coated sample. At C/10, the optimized cathode (with higher carbon content) achieves a remarkable initial discharge capacity of 107.3 mAh/g (reversible capacity of 101.4 mAh/g), a sufficient rate capability up to a rate of 10C, and a long cycle life (capacity retention of 58% after 950 cycles). The one-stage reversible biphasic reaction mechanism and potential-dependent structure–property of NaTi2(PO4)3 can be explained by employing in situ X-ray synchrotron method. Sequential Rietveld refinements of the in situ data show the evolution of the Na-poor NaTi2(PO4)3 and Na-rich Na3Ti2(PO4)3 phase fractions (wt%), unit cell characteristics, and unit cell volume. The design of an all-solid-state sodium ion half-cell with a NaTi2(PO4)3/C cathode and a Na3.16Zr1.84Y0.16Si2PO12 solid-state electrolyte interface results in stable capacity of 83.6 mAh/g at C/10 and excellent reversible capacity at high C-rate. The results show that sintered NASICON-based electrolytes can significantly contribute for the fabrication of all-solid-state sodium-ion battery due to the superior conductivity and stability.
  • Publication
    Artificial Intelligence in Government: Risks and Challenges of Algorithmic Governance in the Administrative State
    (Indiana University Press, 2023-01-01) Vida Fernández, José; European Commission; Ministerio de Ciencia e Innovación (España)
    This article analyzes the legal implications of using artificial intelligence in government and how it is challenging the foundations of the administrative state. It begins by demonstrating that a new model of government is emerging, based on information and intelligence (i-Gov). To understand the nature and scope of this new i-Gov model, this article will explain what artificial intelligence really is and analyze the applications that are currently being carried out in the US and the EU. Next, it will review the regulatory framework that is emerging that regulates government use of artificial intelligence in both the US and the EU. Finally, the article concludes by identifying and analyzing the main legal and policy problems involved in the use of artificial intelligence in government. It challenges values, principles, and institutions of the traditional administrative state and also requires us to think of new frameworks for constitutional and administrative law to guarantee citizens' rights and public interest.
  • Publication
    Enhanced confinement induced by pellet injection in the stellarator TJ-II
    (2023-07-01) Garcia Cortes, Maria Isabel; Mccarthy, K. J.; Estrada, T.; Tribaldos Macía, Víctor; Medina-Roque, D.; Van Milligen, B.; Ascasibar, E.; Carrasco, R.; Chmyga, A. A.; Garcia, R.; Hernandez Sanchez, J.; Hidalgo, C.; Kozachek, A. S.; Medina, F.; Ochando, M.A.; De Pablos, J.L.; Panadero, N.; Pastor, I.; European Commission; Ministerio de Economía y Competitividad (España)
    Enhanced confinement is observed in neutral beam injector (NBI)-heated hydrogen discharges made in the stellarator TJ-II after the injection of a single cryogenic fuel pellet into the plasma core. In addition to the expected increase in electron density, ne, in the core after pellet injection (PI), the plasma diamagnetic energy content is seen to rise, with respect to similar discharges without PI, by up to 40%. Furthermore, the energy confinement time, sE diag, as determined using a diamagnetic loop, is enhanced when compared to predictions obtained using the International Stellarator Scaling law [H. Yamada et al., Nucl. Fusion 45, 1684 (2005)] and the triple product, ne _ Ti _ sE diag, exhibits a clear bifurcation point toward an improved confinement branch as compared to the branch product predicted by this scaling law. In general, once such a pellet-induced enhanced confinement (PiEC) phase has been established, it is characterized by steepened radial density gradients, by more negative plasma potential in the core, more negative radial electric fields, Er, across a broad plasma region, as well as by reductions in density and plasma potential fluctuations in the density gradient region. In addition, experimental observations show increased peaking of core radiation losses, this pointing to edge/core plasma decoupling. In parallel, neoclassical simulations of reference and PiEC plasmas predict increased particle and energy confinement times during a PiEC phase together with a more negative Er profile. Qualitative rather than quantitative agreement with experimental parameters is found, indicating that turbulence seems to play a significant role here. In summary, single cryogenic pellet injection facilitates the achievement of an enhanced operational regime that was previously not observed in NBIheated discharges of the TJ-II.
  • Publication
    Online verification in Cyber-physical Systems: Practical bounds for meaningful temporal costs
    (John Wiley and Sons, 2018-03-01) Bersani, Marcello M.; García Valls, María Soledad; European Commission; Ministerio de Economía y Competitividad (España); Ministerio de Educación (España)
    Cyber-physical systems (CPS) are highly dynamic and large scale systems integrated with the physical environment that they monitor and actuate on. Given the changing nature of physical environments, CPS have to adapt on-line to new situations while preserving their correct operation. This means that the system model may have to change or, at least, will have to be modified during its operation life, preserving correctness. Correctness by construction relies on using formal tools, which suffer from a considerable computational overhead. As the current system model of a CPS may adapt to the environment, the new system model must be verified before its execution to ensure that the properties are preserved. However, CPS development has mainly concentrated on the design-time aspects, existing only few contributions that address their on-line adaptation. We design a framework for managing dynamic changes of a system with a core entity that is an autonomic manager; we investigate the pros and cons of using formal tools within this framework to guarantee that the system properties are met at all times and across changes.We formalize the semantics of the adaptation logic of an autonomic manager (OLIVE) that performs on-line verification for a specific application, a dynamic virtualized server system. The on-line verification manager services requests from mobile clients that might require a change in both the running software components and services executed by the server. We explore the use of formal tools based on CLTLoc to express functional and non-functional properties of the system. In this scenario, we provide empirical results showing the temporal costs of our approach.
  • Publication
    Fast Marching Techniques for Teaming UAV's Applications in Complex Terrain
    (MDPI, 2023-02-01) Garrido Bullón, Luis Santiago; Muñoz Mendi, Javier; López Palomino, Blanca; Quevedo Vallejo, Fernando; Monje Micharet, Concepción Alicia; Moreno Lorente, Luis Enrique; European Commission
    In this paper, we present a study on coverage missions carried out by UAV formations in 3D environments. These missions are designed to be applied in tracking and search and rescue missions, especially in the case of accidents. In this manner, the presented method focuses on the path planning stage, the objective of which is to compute a convenient trajectory to completely cover a certain area in a determined environment. The methodology followed uses a Gaussian mixture to approximate a probability of containment distribution along with the Fast Marching Square (FM2) as path planner. The Gaussians permit to define a zigzag trajectory that optimizes the path. Next, a first 2D geometric path perpendicular to the Voronoi diagram of the Gaussian distribution is calculated, obtained by skeletonization. To this path, the height above the ground is added plus the desired flight height to make it 3D. Finally, the FM2 method for formations is applied to make the path smooth and safe enough to be followed by UAVs. The simulation experiments show that the proposed method achieves good results for the zigzag path in terms of smoothness, safety and distance to cover the desired area through the formation of UAVs.
  • Publication
    Towards Unsupervised Knowledge Extraction
    (Ceur-Ws.Org Team, 2021-03-22) Tsatsou, Dorothea; Karangeorgos, Konstantinos; Dimou, Anastasios; Carbó Rubiera, Javier Ignacio; Molina López, José Manuel; Daras, Petros; European Commission
    Integration of symbolic and sub-symbolic approaches is rapidly emerging as an Artificial Intelligence (AI) paradigm. This paper presents a proof-of-concept approach towards an unsupervised learning method, based on Restricted Boltzmann Machines (RBMs), for extracting semantic associations among prominent entities within data. Validation of the approach is performed in two datasets that connect language and vision, namely Visual Genome and GQA. A methodology to formally structure the extracted knowledge for subsequent use through reasoning engines is also offered.