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.

A Model-Based Direct Power Control for Three-Phase Power Converters

Abstract: The direct power control (DPC) technique has been widely used as a control strategy for three-phase power rectifiers due to its simplicity and good performance. DPC uses the instantaneous active and reactive power to control the power converter. The controller design has been proposed as a direct control with a lookup table and, in recent works, as an indirect control with an inner control loop with proportional-plus-integral controllers for the instantaneous active and reactive power errors. In this paper, a model-based DPC for three-phase power converters is designed, obtaining expressions for the input control signal, which allow the design of an adaptive control law that minimizes the errors introduced by parameter uncertainties as the smoothing inductor value or the grid frequency. A controller design process, a stability study of the system, and experimental results for a synchronous three-phase power rectifier prototype are presented to validate the proposed controller.

Current-mode techniques for the implementation of continuous- and discrete-time cellular neural networks

Abstract: A unified, comprehensive approach to the design of continuous-time (CT) and discrete-time (DT) cellular neural networks (CNNs) using CMOS current-mode analog techniques is presented. The net input signals are currents instead of voltages, which avoids the need for current-to-voltage dedicated interfaces in image processing tasks with photosensor devices. Outputs may be either currents or voltages. Cell design relies on exploiting current mirror properties for the efficient implementation of both linear and nonlinear analog operators. Basic design issues, the influence of nonidealities and advanced circuit design issues, and design for manufacturability considerations associated with statistical analysis are discussed. Experimental results are given for three prototypes designed for 1.6- mu m n-well CMOS technologies. One is discrete-time and can be reconfigured via local logic for noise removal, feature extraction (borders and edges), shadow detection, hole filling, and connected component detection (CCD) on a rectangular grid with unity neighborhood radius. The other two prototypes are continuous-time and fixed template: one for CCD and other for noise removal. >

Fungal photoreceptors: sensory molecules for fungal development and behaviour.

Abstract: Light regulates fungal development and behaviour and activates metabolic pathways. In addition, light is one of the many signals that fungi use to perceive and interact with the environment. In the ascomycete Neurospora crassa blue light is perceived by the white collar (WC) complex, a protein complex formed by WC-1 and WC-2. WC-1 is a protein with a flavin-binding domain and a zinc-finger domain, and interacts with WC-2, another zinc-finger domain protein. The WC complex operates as a photoreceptor and a transcription factor for blue-light responses in Neurospora. Proteins similar to WC-1 and WC-2 have been described in other fungi, suggesting a general role for the WC complex as a fungal receptor for blue light. The ascomycete Aspergillus nidulans uses red light perceived by a fungal phytochrome as a signal to regulate sexual and asexual development. In addition, other photoreceptors, rhodopsins and cryptochromes, have been identified in fungi, but their functional relevance has not been elucidated. The investigation of fungal light responses provides an opportunity to understand how fungi perceive the environment and to identify the mechanisms involved in the regulation by light of cellular development and metabolism.

Impact of an Evidence-Based Bundle Intervention in the Quality-of-Care Management and Outcome of Staphylococcus aureus Bacteremia

Abstract: Background Staphylococcus aureus bacteremia (SAB) is associated with significant morbidity and mortality. Several aspects of clinical management have been shown to have significant impact on prognosis. The objective of the study was to identify evidence-based quality-of-care indicators (QCIs) for the management of SAB, and to evaluate the impact of a QCI-based bundle on the management and prognosis of SAB. Methods A systematic review of the literature to identify QCIs in the management of SAB was performed. Then, the impact of a bundle including selected QCIs was evaluated in a quasi-experimental study in 12 tertiary Spanish hospitals. The main and secondary outcome variables were adherence to QCIs and mortality. Specific structured individualized written recommendations on 6 selected evidence-based QCIs for the management of SAB were provided. Results A total of 287 and 221 patients were included in the preintervention and intervention periods, respectively. After controlling for potential confounders, the intervention was independently associated with improved adherence to follow-up blood cultures (odds ratio [OR], 2.83; 95% confidence interval [CI], 1.78-4.49), early source control (OR, 4.56; 95% CI, 2.12-9.79), early intravenous cloxacillin for methicillin-susceptible isolates (OR, 1.79; 95% CI, 1.15-2.78), and appropriate duration of therapy (OR, 2.13; 95% CI, 1.24-3.64). The intervention was independently associated with a decrease in 14-day and 30-day mortality (OR, 0.47; 95% CI, .26-.85 and OR, 0.56; 95% CI, .34-.93, respectively). Conclusions A bundle orientated to improving adherence to evidence-based QCIs improved the management of patients with SAB and was associated with reduced mortality.

Randomized Strategies for Probabilistic Solutions of Uncertain Feasibility and Optimization Problems

Abstract: In this paper, we study two general semi-infinite programming problems by means of a randomized strategy based on statistical learning theory. The sample size results obtained with this approach are generally considered to be very conservative by the control community. The first main contribution of this paper is to demonstrate that this is not necessarily the case. Utilizing as a starting point one-sided results from statistical learning theory, we obtain bounds on the number of required samples that are manageable for ldquoreasonablerdquo values of probabilistic confidence and accuracy. In particular, we show that the number of required samples grows with the accuracy parameter epsiv as 1/epsivln 1/epsiv , and this is a significant improvement when compared to the existing bounds which depend on 1/epsiv2ln 1/epsiv2. Secondly, we present new results for optimization and feasibility problems involving Boolean expressions consisting of polynomials. In this case, when the accuracy parameter is sufficiently small, an explicit bound that only depends on the number of decision variables, and on the confidence and accuracy parameters is presented. For convex optimization problems, we also prove that the required sample size is inversely proportional to the accuracy for fixed confidence. Thirdly, we propose a randomized algorithm that provides a probabilistic solution circumventing the potential conservatism of the bounds previously derived.