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 review of nature-based solutions for urban water management in European circular cities: a critical assessment based on case studies and literature

Abstract: Nature-based solutions (NBS) can protect, manage and restore natural or modified ecosystems. They are a multidisciplinary, integrated approach to address societal challenges and some natural hazards effectively and adaptively, simultaneously providing human well-being and biodiversity benefits. NBS applications can be easily noticed in circular cities, establishing an urban system that is regenerative and accessible. This paper aims to offer a review on NBS for urban water management from the literature and some relevant projects running within the COST Action ‘Implementing nature-based solutions for creating a resourceful circular city’. The method used in the study is based on a detailed tracking of specific keywords in the literature using Google Scholar, ResearchGate, Academia.edu, ScienceDirect and Scopus. Based on this review, three main applications were identified: (i) flood and drought protection; (ii) the water-food-energy nexus; and (iii) water purification. The paper shows that NBS provide additional benefits, such as improving water quality, increasing biodiversity, obtaining social co-benefits, improving urban microclimate, and the reduction of energy consumption by improving indoor climate. The paper concludes that a systemic change to NBS should be given a higher priority and be preferred over conventional water infrastructure.

Benchmarking nuclear models of FLUKA and GEANT4 for carbon ion therapy.

Abstract: As carbon ions, at therapeutic energies, penetrate tissue, they undergo inelastic nuclear reactions and give rise to significant yields of secondary fragment fluences. Therefore, an accurate prediction of these fluences resulting from the primary carbon interactions is necessary in the patient's body in order to precisely simulate the spatial dose distribution and the resulting biological effect. In this paper, the performance of nuclear fragmentation models of the Monte Carlo transport codes, FLUKA and GEANT4, in tissue-like media and for an energy regime relevant for therapeutic carbon ions is investigated. The ability of these Monte Carlo codes to reproduce experimental data of charge-changing cross sections and integral and differential yields of secondary charged fragments is evaluated. For the fragment yields, the main focus is on the consideration of experimental approximations and uncertainties such as the energy measurement by time-of-flight. For GEANT4, the hadronic models G4BinaryLightIonReaction and G4QMD are benchmarked together with some recently enhanced de-excitation models. For non-differential quantities, discrepancies of some tens of percent are found for both codes. For differential quantities, even larger deviations are found. Implications of these findings for the therapeutic use of carbon ions are discussed.

Complete n-hexane oxidation over supported Mn–Co catalysts

Abstract: Two series of Co–Mn samples were prepared by impregnation of silica with aqueous solutions of Co(NO3)2·6H2O and/or Mn(NO3)2·6H2O. Cobalt oxide was the predominant phase in one of the series and manganese was used as the promoter. The major component in the second series was manganese oxide and Co was the promoter. The prepared samples were characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), temperature-programmed reduction (TPR), and X-ray photoelectron spectroscopy (XPS) and tested in the reaction of complete n-hexane oxidation. The catalytic activity of both single component cobalt and manganese samples was similar, however, a combination between the two elements changed significantly the activity and this depended on the method of preparation. Catalysts prepared by a common solution of Co- and Mn nitrates manifested a considerable increase in activity as a result of very low crystallinity of the supported metal oxide phases, partial enrichment of the surface with cobalt and uniform distribution of oxide agglomerates on the support.

Nonlinear Model Predictive Control: An Introductory Review

Abstract: Model Predictive Control (MPC) originated in the late seventies and has developed considerably since then. The term Model Predictive Control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control signal by minimizing an objective function. The ideas, appearing in greater or lesser degree in the predictive control family, are basically the explicit use of a model to predict the process output at future time instants (horizon), the calculation of a control sequence minimizing an objective function and the use of a receding strategy, so that at each instant the horizon is displaced towards the future, which involves the application of the first control signal of the sequence calculated at each step.