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: A review of the recent contributions related to the environmental evaluation of building refurbishment and renovation using the lifecycle assessment (LCA) methodology is presented in this article, where the main barriers found for disseminations are discussed: system boundaries interpretation of EN 15978, functional unit, LCI methods, operational stage and the end-of-life stage definition.
275 citations
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01 Jan 1994TL;DR: A major theme for future research is how information about the N status of the cell is sensed and transduced to the protein(s) effecting regulation of gene expression, as paths of N assimilation in cyanobacteria are induced upon ammonium deprivation.
Abstract: The element nitrogen (N) constitutes about 5–10% of the dry weight of a cyanobacterial cell. The purpose of this chapter is to review the assimilatory pathways which in free-living cyanobacteria lead from different extracellular N-sources to cellular N-containing components. Inorganic nitrogen in the form of ammonium is incorporated into glutamine and glutamate via the glutamine synthetase/glutamate synthase cycle. The glnA gene, encoding glutamine synthetase, has been characterized in a number of cyanobacteria. Glutamate (and glutamine) distribute N to other organic compounds by means of transaminases, and glutamate is itself a precursor of some other nitrogenous metabolites. Ammonium can be taken up from the external medium by the cyanobacterial cell, but it can also be derived from other nutrients, essentially N2, nitrate and urea. Many cyanobacteria are able to fix N2 under aerobic conditions. Strategies for protecting nitrogenase from O2 in cyanobacteria include the temporal separation of nitrogenase activity and photosynthetic O2 evolution, and in some filamentous cyanobacteria, the differentiation of heterocysts (cells specialized in N2 fixation). A detailed characterization of nif genes has only been performed in a heterocyst-forming cyanobacterium. Nitrate reduction has been found to use photosynthetically reduced ferredoxin as an electron donor, and genes encoding nitrate transport and reduction proteins have been identified and shown to constitute an operon. Some amino acids like arginine and glutamine can also contribute N to some cyanobacteria; however, urea and amino acid utilization have been poorly investigated thus far. Pathways of N assimilation in cyanobacteria are induced upon ammonium deprivation, ammonium being the preferred N source. A gene, ntcA, encoding a transcriptional regulator required for expression of proteins subjected to nitrogen control has been identified. A major theme for future research is how information about the N status of the cell is sensed and transduced to the protein(s) effecting regulation of gene expression.
274 citations
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TL;DR: Evidence that Dam methylation regulates virulence genes in Escherichia coli, Salmonella, and Yersinia at the posttranscriptional level is found, especially intriguing is the evidence that dams play roles in host-pathogen interactions.
Abstract: The DNA adenine methyltransferase (Dam methylase) of Gammaproteobacteria and the cell cycle-regulated methyltransferase (CcrM) methylase of Alphaproteobacteria catalyze an identical reaction (methylation of adenosine moieties using S-adenosyl-methionine as a methyl donor) at similar DNA targets (GATC and GANTC, respectively). Dam and CcrM are of independent evolutionary origin. Each may have evolved from an ancestral restriction-modification system that lost its restriction component, leaving an 'orphan' methylase devoted solely to epigenetic genome modification. The formation of 6-methyladenine reduces the thermodynamic stability of DNA and changes DNA curvature. As a consequence, the methylation state of specific adenosine moieties can affect DNA-protein interactions. Well-known examples include binding of the replication initiation complex to the methylated oriC, recognition of hemimethylated GATCs in newly replicated DNA by the MutHLS mismatch repair complex, and discrimination of methylation states in promoters and regulatory DNA motifs by RNA polymerase and transcription factors. In recent years, Dam and CcrM have been shown to play roles in host-pathogen interactions. These roles are diverse and have only partially been understood. Especially intriguing is the evidence that Dam methylation regulates virulence genes in Escherichia coli, Salmonella, and Yersinia at the posttranscriptional level.
273 citations
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TL;DR: In this paper, the authors introduce a way of selecting the stepsizes such that the implementation of the CQ algorithm does not need any prior information about the operator norm, which is the most popular iterative method for the split feasibility problem.
Abstract: The split feasibility problem (SFP) consists in finding a point in a given closed convex subset of a Hilbert space such that its image under a bounded linear operator belongs to a given closed convex subset of another Hilbert space. Iterative methods can be employed to solve the SFP. The most popular iterative method is Byrne’s CQ algorithm. However, to employ Byrne’s CQ algorithm, one needs to know a priori the norm (or at least an estimate of the norm) of the bounded linear operator (matrix in the finite-dimensional framework). It is the purpose of this paper to introduce a way of selecting the stepsizes such that the implementation of the CQ algorithm does not need any prior information about the operator norm. We also practise this way of selecting stepsizes for variants of the CQ algorithm, including a relaxed CQ algorithm where the two closed convex sets are both level sets of convex functions, and a Halpern-type algorithm. Both weak and strong convergence are investigated. Numerical experiments are included to illustrate the applications in signal processing of the CQ algorithm with stepsizes selected in an adaptive way.
273 citations
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TL;DR: In this paper, the authors build on studies from the literature on market orientation and internationalization to develop a model and a set of hypotheses regarding the relationships among MO, knowledge acquisition (KA), and market commitment (MC), and the direct and indirect effects of these variables on the performance of SMEs in foreign markets.
Abstract: This article builds on studies from the literature on market orientation (MO) and internationalization to develop a model and a set of hypotheses regarding the relationships among MO, knowledge acquisition (KA), and market commitment (MC), and the direct and indirect effects of these variables on the performance of small and medium-sized enterprises (SMEs) in foreign markets. The model and its hypotheses are tested by means of an empirical study of a multi-industry sample of Spanish SMEs operating in foreign markets. The results, obtained by structural equation modeling, indicate that a direct positive relationship exists between MO and a strategy of internationalization, and that the effect of MO on performance in foreign markets is moderated by KA and MC.
273 citations
Authors
Showing all 20465 results
Name | H-index | Papers | Citations |
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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 |