University of Seville

About: University of Seville is a education organization based out in Seville, Andalucía, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 20098 authors who have published 47317 publications receiving 947007 citations. The organization is also known as: Universidad de Sevilla.

Distributed model predictive control of nonlinear process systems

Abstract: This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre-existing control system and the new control system are designed via Lyapunov-based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based controller that stabilizes the nominal closed-loop system using only the pre-existing control loops, two separate Lyapunov-based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009

Signaling Pathways in Inflammation and Anti-inflammatory Therapies

Abstract: During the past decade, an abundance of new evidence highlighted the importance of inflammation in the development of chronic pathologies such as neurodegeneration, cancer, diabetes, cardiovascular disease and inflammatory bowel disease. However, most of the current therapies do not address the underlying problem and better therapies are urgently needed. A growing number of researchers have discovered various signaling pathways that are associated with the initiation and progression of inflammation. Among different pathways, we will focus on three classical inflammatory pathways: p38 MAPK, IL-6/JAK/STAT3 and PI3K; and a non-classical inflammatory pathway, the Hippo. Recently, the Hippo pathway has been linked to various inflammatory modulators such as FoxO1/3, TNFα, IL-6, COX2, HIF-1α, AP-1, JAK and STAT. In this review, the molecular mechanisms, associated pathologies and selected drugs (both preclinical and clinical) of these signaling pathways will be summarized. Finally, limitations and potential risks of anti-inflammatory drugs will also be discussed.

Can amphibians take the heat? Vulnerability to climate warming in subtropical and temperate larval amphibian communities

Abstract: Predicting the biodiversity impacts of global warming implies that we know where and with what magnitude these impacts will be encountered. Amphibians are currently the most threatened vertebrates, ...

Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation

Abstract: Overall system performance on a production line is one of the major concerns in modern industry where induction motors are present and their condition monitoring is mandatory. Periodic offline monitoring of the motor condition is usually performed in the industry, consuming production time and increasing cost. Broken rotor bars are among the most common failures in induction motors. Reported research projects give a broken-rotor-bar-detection methodology based on personal-computer implementation that is performed offline and requires an expert technician interpretation which is not a cost-effective solution. The novelty of this paper is the development of an automatic online diagnosis algorithm for broken-rotor-bar detection, optimized for single low-cost field-programmable gate-array (FPGA) implementation, which guarantees the development of economical self-operated equipment. The proposed algorithm requires less computation load than the previously reported algorithms, and it is mainly based on the discrete-wavelet-transform application to the start-up current transient; a further single mean-square computation determines a weighting function that, according to its value, clearly points the motor condition as either healthy or damaged. In order to validate the proposed algorithm, several tests were performed, and an FPGA implementation was developed to show the algorithm feasibility for automatic online diagnosis.