toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links (up)
Author ANTARES Collaboration (Tamburini, C. et al); Aguilar, J.A.; Bigongiari, C.; Dornic, D.; Emanuele, U.; Gomez-Gonzalez, J.P.; Hernandez-Rey, J.J.; Mangano, S.; Ruiz-Rivas, J.; Salesa, F.; Sanchez-Losa, A.; Toscano, S.; Yepes, H.; Zornoza, J.D.; Zuñiga, J. doi  openurl
  Title Deep-Sea Bioluminescence Blooms after Dense Water Formation at the Ocean Surface Type Journal Article
  Year 2013 Publication Plos One Abbreviated Journal PLoS One  
  Volume 8 Issue 7 Pages e67523 - 10pp  
  Keywords  
  Abstract The deep ocean is the largest and least known ecosystem on Earth. It hosts numerous pelagic organisms, most of which are able to emit light. Here we present a unique data set consisting of a 2.5-year long record of light emission by deep-sea pelagic organisms, measured from December 2007 to June 2010 at the ANTARES underwater neutrino telescope in the deep NW Mediterranean Sea, jointly with synchronous hydrological records. This is the longest continuous time-series of deep-sea bioluminescence ever recorded. Our record reveals several weeks long, seasonal bioluminescence blooms with light intensity up to two orders of magnitude higher than background values, which correlate to changes in the properties of deep waters. Such changes are triggered by the winter cooling and evaporation experienced by the upper ocean layer in the Gulf of Lion that leads to the formation and subsequent sinking of dense water through a process known as “open-sea convection”. It episodically renews the deep water of the study area and conveys fresh organic matter that fuels the deep ecosystems. Luminous bacteria most likely are the main contributors to the observed deep-sea bioluminescence blooms. Our observations demonstrate a consistent and rapid connection between deep open-sea convection and bathypelagic biological activity, as expressed by bioluminescence. In a setting where dense water formation events are likely to decline under global warming scenarios enhancing ocean stratification, in situ observatories become essential as environmental sentinels for the monitoring and understanding of deep-sea ecosystem shifts.  
  Address [Tamburini, Christian; Lefevre, Dominique; Martini, Verine; Robert, Anne; Dekeyser, Ivan; Fuda, Jean-Luc] Aix Marseille Univ, CNRS INSU, IRD, MIO,U110, Marseille, France, Email: christian.tamburini@univ-amu.fr;  
  Corporate Author Thesis  
  Publisher Public Library Science Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1932-6203 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000321765300012 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 1496  
Permanent link to this record
 

 
Author n_TOF Collaboration (Gawlik, A. et al); Domingo-Pardo, C.; Tain, J.L.; Tarifeño-Saldivia, A. doi  openurl
  Title Radiative Neutron Capture Cross-Section Measurement of Ge Isotopes at n_TOF CERN Facility and Its Importance for Stellar Nucleosynthesis Type Journal Article
  Year 2021 Publication Acta Physica Polonica A Abbreviated Journal Acta Phys. Pol. A  
  Volume 139 Issue 4 Pages 383-388  
  Keywords n_TOF; neutron time-of-flight; neutron capture cross-section; MACS  
  Abstract This manuscript summarizes the results of radiative neutron capture cross-section measurements on two stable germanium isotopes, Ge-70 and Ge-73. Experiments were performed at the n_TOF facility at CERN via the time-of-flight technique, over a wide neutron energy range, for all stable germanium isotopes (70,72,73,74, and 76). Results for Ge-70 [Phys. Rev. C 100, 045804 (2019)] and Ge-73 [Phys. Lett. B 790, 458 (2019)] are already published. In the field of nuclear structure, such measurements allow to study excited levels close to the neutron binding energy and to obtain information on nuclear properties. In stellar nucleosynthesis research, neutron induced reactions on germanium are of importance for nucleosynthesis in the weak component of the slow neutron capture processes.  
  Address [Gawlik, A.; Andrzejewski, J.; Perkowski, J.] Univ Lodz, Lodz, Poland, Email: aleksandra.gawlik@uni.lodz.pl  
  Corporate Author Thesis  
  Publisher Polish Acad Sciences Inst Physics Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0587-4246 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000653413700004 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 4840  
Permanent link to this record
 

 
Author Albiol, A.; Albiol, F.; Paredes, R.; Plasencia-Martinez, J.M.; Blanco Barrio, A.; Garcia Santos, J.M.; Tortajada, S.; Gonzalez Montano, V.M.; Rodriguez Godoy, C.E.; Fernandez Gomez, S.; Oliver-Garcia, E.; de la Iglesia Vaya, M.; Marquez Perez, F.L.; Rayo Madrid, J.I. doi  openurl
  Title A comparison of Covid-19 early detection between convolutional neural networks and radiologists Type Journal Article
  Year 2022 Publication Insights into Imaging Abbreviated Journal Insights Imaging  
  Volume 13 Issue 1 Pages 122 - 12pp  
  Keywords Deep learning; Covid-19; Radiology  
  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.  
  Address [Albiol, Alberto] Univ Politecn Valencia, iTeam Inst, ETSI Telecomunicac, Camino Vera S-N, Valencia 46022, Spain, Email: alalbiol@iteam.upv.es  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1869-4101 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000832727200003 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 5302  
Permanent link to this record
 

 
Author Fernandez Casani, A.; Garcia Montoro, C.; Gonzalez de la Hoz, S.; Salt, J.; Sanchez, J.; Villaplana Perez, M. doi  openurl
  Title Big Data Analytics for the ATLAS EventIndex Project with Apache Spark Type Journal Article
  Year 2023 Publication Computational and Mathematical Methods Abbreviated Journal Comput. Math. Methods  
  Volume 2023 Issue Pages 6900908 - 19pp  
  Keywords  
  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.  
  Address [Casani, Alvaro Fernandez; Montoro, Carlos Garcia; de la Hoz, Santiago Gonzalez; Salt, Jose; Sanchez, Javier; Perez, Miguel Villaplana] CSIC UV, Inst Corpuscular Phys IFIC, E-46980 Paterna, Spain, Email: alvaro.fernandez@ific.uv.es;  
  Corporate Author Thesis  
  Publisher Wiley-Hindawi Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001079548500001 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 5706  
Permanent link to this record
 

 
Author Sepehri, A.; Pincak, R.; Olmo, G.J. doi  openurl
  Title M-theory, graphene-branes and superconducting wormholes Type Journal Article
  Year 2017 Publication International Journal of Geometric Methods in Modern Physics Abbreviated Journal Int. J. Geom. Methods Mod. Phys.  
  Volume 14 Issue 11 Pages 1750167 - 32pp  
  Keywords M-theory; wormholes; graphene; superconductors  
  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.  
  Address [Sepehri, Alireza] Shahid Bahonar Univ, Fac Phys, POB 76175, Kerman, Iran, Email: alireza.sepehri@uk.ac.ir;  
  Corporate Author Thesis  
  Publisher World Scientific Publ Co Pte Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0219-8878 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000413442500018 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 3334  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records:
ific federMinisterio de Ciencia e InnovaciĆ³nAgencia Estatal de Investigaciongva