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Albiol, A., Albiol, F., Paredes, R., Plasencia-Martinez, J. M., Blanco Barrio, A., Garcia Santos, J. M., et al. (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging, 13(1), 122–12pp.
Abstract: Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.
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Fernandez Casani, A., Garcia Montoro, C., Gonzalez de la Hoz, S., Salt, J., Sanchez, J., & Villaplana Perez, M. (2023). Big Data Analytics for the ATLAS EventIndex Project with Apache Spark. Comput. Math. Methods, 2023, 6900908–19pp.
Abstract: The ATLAS EventIndex was designed to provide a global event catalogue and limited event-level metadata for ATLAS experiment of the Large Hadron Collider (LHC) and their analysis groups and users during Run 2 (2015-2018) and has been running in production since. The LHC Run 3, started in 2022, has seen increased data-taking and simulation production rates, with which the current infrastructure would still cope but may be stretched to its limits by the end of Run 3. A new core storage service is being developed in HBase/Phoenix, and there is work in progress to provide at least the same functionality as the current one for increased data ingestion and search rates and with increasing volumes of stored data. In addition, new tools are being developed for solving the needed access cases within the new storage. This paper describes a new tool using Spark and implemented in Scala for accessing the big data quantities of the EventIndex project stored in HBase/Phoenix. With this tool, we can offer data discovery capabilities at different granularities, providing Spark Dataframes that can be used or refined within the same framework. Data analytic cases of the EventIndex project are implemented, like the search for duplicates of events from the same or different datasets. An algorithm and implementation for the calculation of overlap matrices of events across different datasets are presented. Our approach can be used by other higher-level tools and users, to ease access to the data in a performant and standard way using Spark abstractions. The provided tools decouple data access from the actual data schema, which makes it convenient to hide complexity and possible changes on the backed storage.
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Sepehri, A., Pincak, R., & Olmo, G. J. (2017). M-theory, graphene-branes and superconducting wormholes. Int. J. Geom. Methods Mod. Phys., 14(11), 1750167–32pp.
Abstract: Exploiting an M-brane system whose structure and symmetries are inspired by those of graphene (what we call a graphene-brane), we propose here a similitude between two layers of graphene joined by a nanotube and wormholes scenarios in the brane world. By using the symmetries and mathematical properties of the M-brane system, we show here how to possibly increase its conductivity, to the point of making it as a superconductor. The questions of whether and under which condition this might point to the corresponding real graphene structures becoming superconducting are briefly outlined.
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n_TOF Collaboration(Patronis, N. et al), Babiano-Suarez, V., Balibrea Correa, J., Domingo-Pardo, C., Ladarescu, I., & Lerendegui-Marco, J. (2023). Status report of the n_TOF facility after the 2nd CERN long shutdown period. EPJ Tech. Instrum., 10(1), 13–10pp.
Abstract: During the second long shutdown period of the CERN accelerator complex (LS2, 2019-2021), several upgrade activities took place at the nTOF facility. The most important have been the replacement of the spallation target with a next generation nitrogen-cooled lead target. Additionally, a new experimental area, at a very short distance from the target assembly (the NEAR Station) was established. In this paper, the core commissioning actions of the new installations are described. The improvement in the nTOF infrastructure was accompanied by several detector development projects. All these upgrade actions are discussed, focusing mostly on the future perspectives of the n_TOF facility. Furthermore, some indicative current and future measurements are briefly reported.
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FCC Collaboration(Abada, A. et al), Aguilera-Verdugo, J. J., Hernandez, P., Ramirez-Uribe, N. S., Renteria-Olivo, A. E., Rodrigo, G., et al. (2019). HE-LHC: The High-Energy Large Hadron Collider Future Circular Collider Conceptual Design Report Volume 4. Eur. Phys. J.-Spec. Top., 228(5), 1109–1382.
Abstract: In response to the 2013 Update of the European Strategy for Particle Physics (EPPSU), the Future Circular Collider (FCC) study was launched as a world-wide international collaboration hosted by CERN. The FCC study covered an energy-frontier hadron collider (FCC-hh), a highest-luminosity high-energy lepton collider (FCC-ee), the corresponding 100 km tunnel infrastructure, as well as the physics opportunities of these two colliders, and a high-energy LHC, based on FCC-hh technology. This document constitutes the third volume of the FCC Conceptual Design Report, devoted to the hadron collider FCC-hh. It summarizes the FCC-hh physics discovery opportunities, presents the FCC-hh accelerator design, performance reach, and staged operation plan, discusses the underlying technologies, the civil engineering and technical infrastructure, and also sketches a possible implementation. Combining ingredients from the Large Hadron Collider (LHC), the high-luminosity LHC upgrade and adding novel technologies and approaches, the FCC-hh design aims at significantly extending the energy frontier to 100 TeV. Its unprecedented centre-of-mass collision energy will make the FCC-hh a unique instrument to explore physics beyond the Standard Model, offering great direct sensitivity to new physics and discoveries.
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