Institution
University of Seville
Education•Seville, Andalucía, Spain•
About: University of Seville is a education organization based out in Seville, Andalucía, Spain. It is known for research contribution in the topics: Population & Model predictive control. The organization has 20098 authors who have published 47317 publications receiving 947007 citations. The organization is also known as: Universidad de Sevilla.
Topics: Population, Model predictive control, Control theory, Nonlinear system, Context (language use)
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors conducted an in-depth study of the state of the art in service operations management research and found that only 7.5% of service operations research is focused on service operations.
165 citations
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13 Jun 2016TL;DR: The Campus Virtual dela Universidad Complutense de Madrid as discussed by the authors describes the factores claves that han impulsado its desarrollo, i.e., the use of sistemas de gestion of cursos in las universidades, where sirven de soporte alo que podriamos denominar campus virtuales.
Abstract: Hoy en dia el uso de las tecnologias de la informacion y la comunicacion se encuentra plenamente integrado en muchos procesos docentes. Uno de estos usos se concreta en la utilizacion de sistemas de gestion de cursos en las universidades, donde sirven de soporte alo que podriamos denominar campus virtuales. Este articulo describe el Campus Virtual dela Universidad Complutense de Madrid, incidiendo en los factores claves que han impulsado su desarrollo.
165 citations
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TL;DR: Investigation of TAS14, an mRNA that is induced in tomato upon osmotic stress or abscisic acid (ABA) treatment and that shares expression and sequence characteristics with other dehydrin genes in different species, found that it accumulated in adventitious root primordia and associated to the provascular and vascular tissues in stems and leaves.
Abstract: We previously isolated and characterized TAS14, and mRNA that is induced in tomato upon osmotic stress or abscisic acid (ABA) treatment and that shares expression and sequence characteristics with other dehydrin genes in different species. Affinity-purified antibodies against TAS14 protein were used to study the expression of TAS14 protein, both in seedlings and mature plants, its tissue distribution and its subcellular localization. TAS14 protein was not detected in 4-day-old seedlings but accumulated after ABA, NaCl or mannitol treatments. In NaCl-treated seedlings, some protein was detectable after 6 h of treatment and reached maximal levels between 24 and 48 h. Concentrations ranging from 5 to 12.5 g/l NaCl induced the protein to similar levels. In salt-stressed mature plants, TAS14 was expressed abundantly and continuously in aerial parts, but only slightly and transiently in roots. Immunocytochemical analysis of salt-treated plants showed TAS14 accumulated in adventitious root primordia and associated to the provascular and vascular tissues in stems and leaves. Immunogold electron microscopy localized TAS14 protein both in the cytosol and in the nucleus, associated to the nucleolus and euchromatin. Since TAS14 is a phosphoprotein in vivo, the classes of protein kinases potentially responsible for its in vivo phosphorylation were tested in in vitro phosphorylation assays. TAS14 protein was phosphorylated in vitro by both casein kinase II and cAMP-dependent protein kinase.
165 citations
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TL;DR: P porous 3D composite scaffolds, optimised with respect to the BG filler content capable of inducing angiogenic response, show potential for the regeneration of hard-soft tissue defects and increased bone formation arising from enhanced vascularisation of the construct.
164 citations
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TL;DR: A methodology for non-technical loss detection using supervised learning that uses all the information the smart meters record to obtain an in-depth analysis of the customer’s consumption behavior and shows that extreme gradient boosted trees outperform the rest of the classifiers.
Abstract: Non-technical electricity losses due to anomalies or frauds are accountable for important revenue losses in power utilities. Recent advances have been made in this area, fostered by the roll-out of smart meters. In this paper, we propose a methodology for non-technical loss detection using supervised learning. The methodology has been developed and tested on real smart meter data of all the industrial and commercial customers of Endesa. This methodology uses all the information the smart meters record (energy consumption, alarms and electrical magnitudes) to obtain an in-depth analysis of the customer’s consumption behavior. It also uses auxiliary databases to provide additional information regarding the geographical location and technological characteristics of each smart meter. The model has been trained, validated and tested on the results of approximately 57 000 on-field inspections. It is currently in use in a non-technical loss detection campaign for big customers. Several state-of-the-art classifiers have been tested. The results show that extreme gradient boosted trees outperform the rest of the classifiers.
164 citations
Authors
Showing all 20465 results
Name | H-index | Papers | Citations |
---|---|---|---|
Russel J. Reiter | 169 | 1646 | 121010 |
Aaron Dominguez | 147 | 1968 | 113224 |
Jose M. Ordovas | 123 | 1024 | 70978 |
Detlef Lohse | 104 | 1075 | 42787 |
Miroslav Krstic | 95 | 955 | 42886 |
María Vallet-Regí | 95 | 711 | 41641 |
John S. Sperry | 93 | 160 | 35602 |
Jose Rodriguez | 93 | 803 | 58176 |
Shun-ichi Amari | 90 | 495 | 40383 |
Michael Ortiz | 87 | 467 | 31582 |
Bruce J. Paster | 84 | 261 | 28661 |
Floyd E. Dewhirst | 81 | 229 | 42613 |
Joan Montaner | 80 | 489 | 22413 |
Francisco B. Ortega | 79 | 503 | 26069 |
Luis Paz-Ares | 77 | 592 | 31496 |