The Mercedes water Cherenkov detector

The concept of a small, single-layer water Cherenkov detector,with three photomultiplier tubes (PMTs), placed at its bottom in a 120◦ star configuration (Mercedes Water Cherenkov Detector) is presented. The PMTs are placed near the lateral walls of the stations with an adjustable inclination and may be installed inside or outside the water volume. To illustrate the technical viability of this concept and obtain a first-order estimation of its cost, an engineering designwas elaborated. The sensitivity of these stations to low energy Extensive Air Shower (EAS) electrons, photons and muons is discussed, both in compact and sparse array configurations. It is shown that the analysis of the intensity and time patterns of the PMT signals, using machine learning techniques, enables the tagging of muons, achieving an excellent gamma/hadron discrimination for TeV showers. This concept minimises the station production and maintenance costs, allowing for a highly flexible and fast installation. Mercedes Water Cherenkov Detectors (WCDs) are thus well-suited for use in high-altitude large gamma-ray observatories covering an extended energy range from the low energies, closing the gap between satellite and ground-based measurements, to very high energy regions, beyond the PeV scale.Portuguese Foundation for Science and Technology PTDC/FISPAR/4300/2020 DL57/2016/cP1330/cT0002MEYS of the Czech Republic LTT 20002Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) 211.342/2021Portuguese Foundation for Science and TechnologyEuropean Commission PRT/BD/151553/202

Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning

We would like to thank to A. Bueno for all the support and useful discussions during the development of this work. The authors thank also for the financial support by OE - Portugal, FCT, I. P., under project PTDC/FIS-PAR/29158/2017. R. C. is grateful for the financial support by OE-Portugal, FCT, I. P., under DL57 /2016/cP1330/cT0002. A. G. is grateful for the financial support by the projects MINECO FPA2017-85197-P and PID2019-104676GB-C32. B.S.G. is grateful for the financial support by grant LIP/BI - 14/2020, under project IC&DT, POCI-01-0145-FEDER-029158.This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used, and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art machine learning analysis techniques and water Cherenkov detectors with low water depth can be used to efficiently identify muons, which may lead to huge investment savings due to the reduction of the amount of water needed at high altitudes. This achievement can be used in further research to be able to discriminate between gamma and hadron-induced showers using muons as discriminant.OE - Portugal, FCT, I. P. PTDC/FIS-PAR/29158/2017 DL57 /2016/cP1330/cT0002Spanish Government FPA2017-85197-P PID2019-104676GB-C32 LIP/BI - 14/2020 POCI-01-0145-FEDER-02915

Composition Classification of Ultra-High Energy Cosmic Rays

The study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.Spanish Ministry of Science, Innovation and Universities FPA2017-85197-P RTI2018-101674-B-I00European Union (EU)CENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em Sao Paulo) UNICAMP/FINEP - MCTFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)National Council for Scientific and Technological Development (CNPq) 2016/19764-9404993/2016-

Chatbots and messaging platforms in the classroom: An analysis from the teacher’s perspective

Funding for open access charge: Universidad de Granada/CBUA. This work has been supported by EDUBOTS project, funded under the scheme Erasmus + KA2: Cooperation for innovation and the exchange of good practices - Knowledge Alliances (grant agreement no: 612446).Messaging platforms are applications, generally mediated by an app, desktop program or the web, mainly used for synchronous communication among users. As such, they have been widely adopted officially by higher education establishments, after little or no study of their impact and perception by the teachers. We think that the introduction of these new tools and the opportunities and challenges they have needs to be studied carefully in order to adopt the model, as well as the tool, that is the most adequate for all parties involved. We already studied the perception of these tools by students, in this paper we examine the teachers' experiences and perceptions through a survey that we validated with peers, and what they think these tools should make or serve so that it enhances students learning and helps them achieve their learning objectives. The survey has been distributed among tertiary education teachers, both in universitary and other kind of tertiary establishments, based in Spain (mainly) and Spanish-speaking countries. We have focused on collecting teachers' preferences and opinions on the introduction of messaging platforms in their day-to-day work, as well as other services attached to them, such as chatbots. What we intend with this survey is to understand their needs and to gather information about the various educational use cases where these tools could be valuable. In addition, an analysis of how and when teachers' opinions towards the use of these tools varies across gender, experience, and their discipline of specialization is presented. The key findings of this study highlight the factors that can contribute to the advancement of the adoption of messaging platforms and chatbots in higher education institutions to achieve the desired learning outcomes.Universidad de Granada/CBUAErasmus + KA2, EDUBOTS 61244

Variable Selection in a GPU Cluster Using Delta Test

The work presented in this paper consists in an adaptation of a Genetic Algorithm (GA) to perform variable selection in an heterogeneous cluster where the nodes are themselves clusters of GPUs. Due to this heterogeneity, several mechanisms to perform a load balance will be discussed as well as the optimization of the fitness function to take advantage of the GPUs available. The algorithm will be compared with previous parallel implementations analysing the advantages and disadvantages of the approach, showing that for large data sets, the proposed approach is the only one that can provide a solution.Spanish CICYT Project TIN2007-60587 and TEC2008-04920Junta Andalucia Projects P08-TIC-03674 and P08-TIC03928 and PYR-2010-17 of CEI BioTIC GENIL (CEB09-0010) of the MICIN

Multiobjective RBFNNs Designer for Function Approximation: An Application for Mineral Reduction

Radial Basis Function Neural Networks (RBFNNs) are well known because, among other applications, they present a good perfor- mance when approximating functions. The function approximation prob- lem arises in the construction of a control system to optimize the process of the mineral reduction. In order to regulate the temperature of the ovens and other parameters, it is necessary a module to predict the ¯nal concentration of mineral that will be obtained from the source materials. This module can be formed by an RBFNN that predicts the output and by the algorithm that designs the RBFNN dynamically as more data is obtained. The design of RBFNNs is a very complex task where many parameters have to be determined, therefore, a genetic algorithm that determines all of them has been developed. This algorithm provides sat- isfactory results since the networks it generates are able to predict quite precisely the ¯nal concentration of mineral.Spanish CICYT Project TIN2004-01419European Commission's Research Infrastructures RII3-CT-2003-506079 (HPC-Europa

Montaje de los componentes de un servidor para la asignatura del nuevo grado en Ingeniería en Informática: Ingeniería de Servidores

En este trabajo, se presenta una visión general de la nueva asignatura Ingeniería de Servidores, del nuevo plan de estudios del Grado en Ingeniería Informática de la Universidad de Granada, así como una nueva metodología interactiva para que el alumno aprenda a montar un servidor de gama baja. A través de este aprendizaje práctico, que el Espacio Europeo de Educación Superior promueve activamente, tratamos de que el alumno descubra cómo asociar la arquitectura de un servidor con los componentes de los computadores con los que ellos trabajan a diario

Bolonia 'for dummies'

El objeto de este trabajo es describir de forma sencilla y clara cómo impartir una asignatura nueva o transformar una asignatura existente, y la metodología docente para su impartición en el marco del Espacio Europeo de Educación Superior (EEES). Hacemos especial hincapié en aquellos aspectos afines a las materias impartidas tradicionalmente en Ingenierías como Informática, Telecomunicaciones o Electrónica, titulaciones relevantes en el área de Arquitectura y Tecnología de Computadores. Enunciamos los principios básicos y el enfoque que motiva y dirige la reforma educativa de Bolonia. Consecuencia de ello, describimos métodos sencillos para transformarnos en unos “nuevos docentes,” alineados con la reforma educativa, tratando a la vez de minimizar el impacto que pueda tener en el profesor y de incluir nuevos enfoques de realización de nuestras tareas docentes.Departamento de Arquitectura y Tecnología de Computadores (Universidad de Granada

Intelligent system based on genetic programming for atrial fibrillation classification

This article focuses on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will study the ECG, and the treatment of the ECG in order to work with it with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers who obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of atrial fibrillation (AF) that they showed, was realized. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction previously mentioned. A new method based on evolutionary algorithms will be used to realize a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology

Efectividad de fluxapyroxad + pyraclostrobin en el control de (Oidium mangiferae Berthet) en mango (Mangifera indica L.) en el estado de Morelos, México

El mango (Mangifera indica L.) es una de las frutas más demandadas en México, por su amplio valor comercial y su uso en la industria alimenticia, siendo susceptible a varias enfermedades en todas las etapas de su desarrollo. La cenicilla causada por el hongo (Oidium mangiferae Berthet) es una de las más importantes en este frutal, llegando a causar pérdidas hasta del 90% de la producción cuando incide en floración. El objetivo de este trabajo fue determinar la efectividad biológica de (fluxapyroxad + pyraclostrobin) en el control de este agente patógeno en panículas de mango, variedad “Ataulfo”. El ensayo se realizó durante el año 2019, en el estado de Morelos, México. Se efectuaron tres aplicaciones sobre el cultivo a intervalos de 7 días, comparando tres tratamientos de la mezcla fluxapyroxad + pyraclostrobin (300, 350 y 400 mL ha-1), contra un testigo regional (benomilo) 60 g 100-1 L de agua y un control sin aplicación. La severidad y efectividad biológica del producto fueron registradas durante tres evaluaciones semanales. Como resultado, todas las dosis empleadas, lograron prevenir el desarrollo de la enfermedad en la etapa de floración. La dosis de 350 mL ha-1 tuvo un control absoluto del 100%, mientras que el testigo sin aplicación llegó a alcanzar 77,64% de infección. En todos los tratamientos se alcanzaron eficiencias biológicas superiores al 96%, lo que representa una combinación eficaz para la prevención del patógeno en este frutal