Formació de coalicions en contextos de cooperació restringida; aplicació a la formació de coalicions postelectorals

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Mikel Álvarez-Mozos i Josep i Vives Santa Eulàlia[en] The main idea of this project is to find a way to represent mathematically different situations where various agents are involved to take each of them a decision. The decision taken by each agent will affect the others results. This is the reason why we need to consider the others strategies by the time we choose our own game strategy. In this project we will study what is a cooperative game in game theory, and how we can restrict this cooperation by introducing some affinities and incompatibilities between the players. This restrictions are represented by communication graphs where the vertices represent the players and the edges show the connexions between them. Finally, we will apply the graph-restricted cooperation games to a real case, in order to do a research of the possible post-election coalitions that can be formed between the players, after some political elections and by the initial situation where none of the electoral parties have got an absolute majority of votes

A Reinforcement Learning Control in Hot Stamping for Cycle Time Optimization

Hot stamping is a hot metal forming technology increasingly in demand that produces ultra-high strength parts with complex shapes. A major concern in these systems is how to shorten production times to improve production Key Performance Indicators. In this work, we present a Reinforcement Learning approach that can obtain an optimal behavior strategy for dynamically managing the cycle time in hot stamping to optimize manufacturing production while maintaining the quality of the final product. Results are compared with the business-as-usual cycle time control approach and the optimal solution obtained by the execution of a dynamic programming algorithm. Reinforcement Learning control outperforms the business-as-usual behavior by reducing the cycle time and the total batch time in non-stable temperature phases

Competencias Lingüísticas de los estudiantes de la Universidad de Granada

El trabajo versa sobre las competencias lingüísticas de estudiantes de la Universidad de Granada, no solo teniendo en cuenta su titulación de origen, sino también sus características demográficas. Poder tener estos perfiles identificados facilitará la población objetivo a la que la Incubadora de Talento debe dirigirse para fomentar el liderazgo y emprendimiento

Joint Observation of the Galactic Center with MAGIC and CTA-LST-1

MAGIC is a system of two Imaging Atmospheric Cherenkov Telescopes (IACTs), designed to detect very-high-energy gamma rays, and is operating in stereoscopic mode since 2009 at the Observatorio del Roque de Los Muchachos in La Palma, Spain. In 2018, the prototype IACT of the Large-Sized Telescope (LST-1) for the Cherenkov Telescope Array, a next-generation ground-based gamma-ray observatory, was inaugurated at the same site, at a distance of approximately 100 meters from the MAGIC telescopes. Using joint observations between MAGIC and LST-1, we developed a dedicated analysis pipeline and established the threefold telescope system via software, achieving the highest sensitivity in the northern hemisphere. Based on this enhanced performance, MAGIC and LST-1 have been jointly and regularly observing the Galactic Center, a region of paramount importance and complexity for IACTs. In particular, the gamma-ray emission from the dynamical center of the Milky Way is under debate. Although previous measurements suggested that a supermassive black hole Sagittarius A* plays a primary role, its radiation mechanism remains unclear, mainly due to limited angular resolution and sensitivity. The enhanced sensitivity in our novel approach is thus expected to provide new insights into the question. We here present the current status of the data analysis for the Galactic Center joint MAGIC and LST-1 observations

A Reinforcement Learning Control in Hot Stamping for Cycle Time Optimization

Hot stamping is a hot metal forming technology increasingly in demand that produces ultra-high strength parts with complex shapes. A major concern in these systems is how to shorten production times to improve production Key Performance Indicators. In this work, we present a Reinforcement Learning approach that can obtain an optimal behavior strategy for dynamically managing the cycle time in hot stamping to optimize manufacturing production while maintaining the quality of the final product. Results are compared with the business-as-usual cycle time control approach and the optimal solution obtained by the execution of a dynamic programming algorithm. Reinforcement Learning control outperforms the business-as-usual behavior by reducing the cycle time and the total batch time in non-stable temperature phases

Previous Incubation of Bradyrhizobium japonicum E109 and Azospirillum argentinense Az39 (formerly A. brasilense Az39) Improves the Bradyrhizobium-Soybean Symbiosis

The aim of this work was to evaluate under diverse plant growth conditions the previous incubation between B. japonicum E109 (BjE109) and A. argentinense Az39 (AaAz39) and elucidate their impact on the Bradyrhizobium-soybean symbiosis and plant growth. Five treatments were performed: (i) uninoculated seeds; (ii) seeds inoculated with BjE109; (iii) seeds inoculated with AaAz39; (iv) seeds co-inoculated at the seeds sowing with equal volume (1:1) of BjE109 and AaAz39; and (v) seeds inoculated with equal volume (1:1) of BjE109 and AaAz39 24 h before seed sowing. Each treatment was assessed through a seed recovery assay, glasshouse assays, and field assays. The single plant level differences were achieved under greenhouse conditions while differences at population level (crop) were achieved by a field assay. The previous incubation between BjE109 and AaAz39 improved the ability of BjE109 to survive on soybean seeds with 25% and 10% of cell recovery at 4 h and 6 days post-inoculation respectively. As a result of the greater bacterial survival, the symbiosis parameters like nodule number, size, and biomass and nodulation percentage also significantly increased. In agreement with these observations, the grain yield under field conditions showed 13.3 and 17.3% greater than immediate combination or single BjE109 inoculation respectively. The previous incubation between BjE109 and AaAz39 24 h before their inoculation improves the Bradyrhizobium-soybean symbiosis and increases both plant growth under culture controlled and crop productivity under field conditions, in comparison with the single inoculation with BjE109 or the immediate inoculation using both strains.Fil: Torres, Daniela Soledad. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Donadío, Evelyn Florencia. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Lopez, Gaston Alberto. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Molina, Romina Micaela. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Obando Castellanos, Dolly Melissa. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Nievas, Sofia Mariela. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Rosas, Susana Beatriz. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Biotecnología Ambiental y Salud - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Biotecnología Ambiental y Salud; ArgentinaFil: Zeljković, Sanja Ćavar. Palacky University; República ChecaFil: Diaz Zorita, Martin. Universidad Nacional de la Pampa. Facultad de Agronomia. Area de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: De Diego, Nuria. Palacky University; República ChecaFil: Cassan, Fabricio Dario. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; Argentin

A fine bioinformatical analysis of lymphocyte distribution predicts the diagnosis of primary Sjögren's syndrome among other systemic autoimmune diseases.

International audienc

A fine bioinformatical analysis of lymphocyte distribution predicts the diagnosis of primary Sjögren's syndrome among other systemic autoimmune diseases.

International audienc

The blazar TXS 0506+056 associated with a high-energy neutrino: insights into extragalactic jets and cosmic ray acceleration

International audienceA neutrino with energy ∼290 TeV, IceCube-170922A, was detected in coincidence with the BL Lac object TXS 0506+056 during enhanced gamma-ray activity, with chance coincidence being rejected at ∼3σ level. We monitored the object in the very-high-energy (VHE) band with the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes for ∼41 hr from 1.3 to 40.4 days after the neutrino detection. Day-timescale variability is clearly resolved. We interpret the quasi-simultaneous neutrino and broadband electromagnetic observations with a novel one-zone lepto-hadronic model, based on interactions of electrons and protons co-accelerated in the jet with external photons originating from a slow-moving plasma sheath surrounding the faster jet spine. We can reproduce the multiwavelength spectra of TXS 0506+056 with neutrino rate and energy compatible with IceCube-170922A, and with plausible values for the jet power of . The steep spectrum observed by MAGIC is concordant with internal γγ absorption above ∼100 GeV entailed by photohadronic production of a ∼290 TeV neutrino, corroborating a genuine connection between the multi-messenger signals. In contrast to previous predictions of predominantly hadronic emission from neutrino sources, the gamma-rays can be mostly ascribed to inverse Compton upscattering of external photons by accelerated electrons. The X-ray and VHE bands provide crucial constraints on the emission from both accelerated electrons and protons. We infer that the maximum energy of protons in the jet comoving frame can be in the range ∼1014 – 1018 eV

Performance of the Large-Sized Telescope prototype of the Cherenkov Telescope Array

The next-generation ground-based gamma-ray Cherenkov Telescope Array Observatory (CTAO) will consist of imaging atmospheric Cherenkov telescopes (IACTs) of three different sizes distributed in two sites. The Large-Sized Telescopes will cover the low-energy end of the CTA energy range, starting at about 20 GeV. After its first years of operation at the CTA northern site, the Large-Sized Telescope prototype (LST-1) is in the final stage of its commissioning phase, having collected a significant amount of scientific data to date.In this contribution, we present the physics performance of the telescope using low-zenith Crab Nebula observations and Monte Carlo simulations fine-tuned accordingly. We show performance figures of merit such as the energy threshold, effective area, energy and angular resolution, and sensitivity based on the standard Hillas-parameters approach and following the source-independent and dependent analysis methods. The analysis threshold is estimated at 30 GeV. The energy resolution is around 30%, and the angular resolution is 0.3 degrees at 100 GeV.The best integral sensitivity of LST-1 is about 1.1% of the Crab Nebula flux above 250 GeV for 50 hours of observations. We also show the spectral energy distribution and light curve from Crab Nebula observations, which agree with results from other IACTs and link smoothly with Fermi-LAT when considering statistical and systematic uncertainties near the energy threshold