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Institution

University of Alcalá

EducationAlcalá de Henares, Spain
About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Receptor. The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors summarized the most representative research activities conducted to prevent membrane fouling and highlighted alternative routes to discarded end-of-life-membranes in order to prevent the uncontrolled disposal of fouled membranes in landfills.

116 citations

Journal ArticleDOI
TL;DR: Quality of life in elderly patients improved as much as in younger patients, thereby fully justifying the use of EPO for the elderly, and final hematocrit was positively related to global SIP improvement.

116 citations

Book ChapterDOI
01 Jan 2006
TL;DR: A taxonomy for classifying ontologies in SET is offered, in which two main categories are distinguished: (1) SET domain ontologies, created to represent and communicate agreed knowledge within some subdomain of SET, and (2) ontologies as software artifacts, with proposals in which ontologies play the role of an additional type of artifact in software processes.
Abstract: In this chapter, the state of the art on the use of ontologies in software engineering and technology (SET) is presented. The chapter is organized into four parts. In the second and third sections, serving as a supplement to Chap. 1,29 a wide review of the distinct kinds of ontologies and their proposed uses is presented respectively. In the fourth section, we offer a taxonomy for classifying ontologies in SET, in which two main categories are distinguished: (1) SET domain ontologies, created to represent and communicate agreed knowledge within some subdomain of SET, and (2) ontologies as software artifacts, with proposals in which ontologies play the role of an additional type of artifact in software processes. On the one hand, the former category is subdivided into those ontologies included in software engineering and those referring to other software technologies.

116 citations

Journal ArticleDOI
Sally E. Koerner1, Melinda D. Smith2, Deron E. Burkepile3, Niall P. Hanan4, Meghan L. Avolio5, Scott L. Collins6, Alan K. Knapp2, Nathan P. Lemoine2, Elisabeth J. Forrestel7, Stephanie Eby8, Dave I. Thompson9, Gerardo A. Aguado-Santacruz, John P. Anderson4, T. Michael Anderson10, Ayana Angassa11, Ayana Angassa12, Sumanta Bagchi13, Elisabeth S. Bakker, Gary Bastin, Lauren E. Baur6, Karen H. Beard14, Erik A. Beever15, Erik A. Beever16, Patrick J. Bohlen17, Elizabeth H. Boughton18, Don Canestro3, Ariela Cesa19, Enrique J. Chaneton20, Jimin Cheng21, Carla M. D'Antonio3, Claire Deléglise22, Fadiala Dembélé, Josh Dorrough23, David J. Eldridge24, Barbara Fernandez-Going25, Silvia Fernández-Lugo26, Lauchlan H. Fraser27, Bill Freedman28, Gonzalo García-Salgado28, Jacob R. Goheen29, Liang Guo21, Sean W. Husheer, Moussa Karembé, Johannes M. H. Knops30, Tineke Kraaij31, Andrew Kulmatiski14, Minna-Maarit Kytöviita32, Felipe Lezama33, Grégory Loucougaray22, Alejandro Loydi34, Dan G. Milchunas2, Suzanne J. Milton, John W. Morgan35, Claire Moxham, Kyle C. Nehring14, Han Olff36, Todd M. Palmer37, Salvador Rebollo38, Corinna Riginos39, Anita C. Risch40, Marta Rueda41, Mahesh Sankaran42, Mahesh Sankaran43, Takehiro Sasaki44, Kathryn A. Schoenecker2, Nick L. Schultz45, Martin Schütz40, Angelika Schwabe46, F. Siebert47, Christian Smit36, Karen A. Stahlheber48, Christian Storm46, Dustin J. Strong49, Jishuai Su21, Yadugiri V. Tiruvaimozhi43, Claudia M. Tyler3, James Val23, Martijn L. Vandegehuchte50, Martijn L. Vandegehuchte40, Kari E. Veblen14, Lance T. Vermeire49, David Ward51, Jianshuang Wu52, Truman P. Young7, Qiang Yu, Tamara J. Zelikova29 
University of North Carolina at Greensboro1, Colorado State University2, University of California, Santa Barbara3, New Mexico State University4, Johns Hopkins University5, University of New Mexico6, University of California, Davis7, Northeastern University8, University of the Witwatersrand9, Wake Forest University10, Hawassa University11, University of Agriculture, Faisalabad12, Indian Institute of Science13, Utah State University14, Montana State University15, United States Geological Survey16, University of Central Florida17, Archbold Biological Station18, International Trademark Association19, University of Buenos Aires20, Northwest A&F University21, University of Grenoble22, Office of Environment and Heritage23, University of New South Wales24, University of Texas at Austin25, University of La Laguna26, Thompson Rivers University27, Dalhousie University28, University of Wyoming29, University of Nebraska–Lincoln30, Nelson Mandela Metropolitan University31, University of Jyväskylä32, University of the Republic33, National Scientific and Technical Research Council34, La Trobe University35, University of Groningen36, University of Florida37, University of Alcalá38, The Nature Conservancy39, Swiss Federal Institute for Forest, Snow and Landscape Research40, Spanish National Research Council41, University of Leeds42, National Centre for Biological Sciences43, Yokohama National University44, Federation University Australia45, Technische Universität Darmstadt46, North-West University47, University of Wisconsin–Green Bay48, Agricultural Research Service49, Ghent University50, Kent State University51, Chinese Academy of Sciences52
TL;DR: It is shown that herbivore-induced change in dominance, independent of site productivity or precipitation (a proxy for productivity), is the best predictor of Herbivore effects on biodiversity in grassland and savannah sites.
Abstract: Herbivores alter plant biodiversity (species richness) in many of the world's ecosystems, but the magnitude and the direction of herbivore effects on biodiversity vary widely within and among ecosystems. One current theory predicts that herbivores enhance plant biodiversity at high productivity but have the opposite effect at low productivity. Yet, empirical support for the importance of site productivity as a mediator of these herbivore impacts is equivocal. Here, we synthesize data from 252 large-herbivore exclusion studies, spanning a 20-fold range in site productivity, to test an alternative hypothesis-that herbivore-induced changes in the competitive environment determine the response of plant biodiversity to herbivory irrespective of productivity. Under this hypothesis, when herbivores reduce the abundance (biomass, cover) of dominant species (for example, because the dominant plant is palatable), additional resources become available to support new species, thereby increasing biodiversity. By contrast, if herbivores promote high dominance by increasing the abundance of herbivory-resistant, unpalatable species, then resource availability for other species decreases reducing biodiversity. We show that herbivore-induced change in dominance, independent of site productivity or precipitation (a proxy for productivity), is the best predictor of herbivore effects on biodiversity in grassland and savannah sites. Given that most herbaceous ecosystems are dominated by one or a few species, altering the competitive environment via herbivores or by other means may be an effective strategy for conserving biodiversity in grasslands and savannahs globally.

116 citations

Proceedings ArticleDOI
13 May 2014
TL;DR: This paper compares different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently, using the well-known NASA datasets curated by Shepperd et al.
Abstract: Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.Further Results and replication package: http://www.cc.uah.es/drg/ease14

116 citations


Authors

Showing all 10907 results

NameH-indexPapersCitations
José Luis Zamorano105695133396
Jesús F. San Miguel9752744918
Sebastián F. Sánchez9662932496
Javier P. Gisbert9599033726
Luis M. Ruilope9484197778
Luis M. Garcia-Segura8848427077
Alberto Orfao8559737670
Amadeo R. Fernández-Alba8331821458
Rafael Luque8069328395
Francisco Rodríguez7974824992
Andrea Negri7924235311
Rafael Cantón7857529702
David J. Grignon7830123119
Christophe Baudouin7455322068
Josep M. Argilés7331019675
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20251
20243
202375
2022166
20211,660
20201,532