Fluorescence of nitrobenzoxadiazole (NBD)-labeled lipids in model membranes is connected not to lipid mobility but to probe location

Nitrobenzoxadiazole (NBD)-labeled lipids are popular fluorescent membrane probes. However, the understanding of important aspects of the photophysics of NBD remains incomplete, including the observed shift in the emission spectrum of NBD-lipids to longer wavelengths following excitation at the red edge of the absorption spectrum (red-edge excitation shift or REES). REES of NBD-lipids in membrane environments has been previously interpreted as reflecting restricted mobility of solvent surrounding the fluorophore. However, this requires a large change in the dipole moment (Dm) of NBD upon excitation. Previous calculations of the value of Dm of NBD in the literature have been carried out using outdated semi-empirical methods, leading to conflicting values. Using up-to-date density functional theory methods, we recalculated the value of Dm and verified that it is rather small (B2 D). Fluorescence measurements confirmed that the value of REES is B16 nm for 1,2-dioleoyl-sn-glycero-3- phospho-L-serine-N-(NBD) (NBD-PS) in dioleoylphosphatidylcholine vesicles. However, the observed shift is independent of both the temperature and the presence of cholesterol and is therefore insensitive to the mobility and hydration of the membrane. Moreover, red-edge excitation leads to an increased contribution of the decay component with a shorter lifetime, whereas time-resolved emission spectra of NBD-PS displayed an atypical blue shift following excitation. This excludes restrictions to solvent relaxation as the cause of the measured REES and TRES of NBD, pointing instead to the heterogeneous transverse location of probes as the origin of these effects. The latter hypothesis was confirmed by molecular dynamics simulations, from which the calculated heterogeneity of the hydration and location of NBD correlated with the measured fluorescence lifetimes/REES. Globally, our combination of theoretical and experiment-based techniques has led to a considerably improved understanding of the photophysics of NBD and a reinterpretation of its REES in particular.

Offshore Wind Energy Conversion System Connected to the Electric Grid: Modeling and Simulation

This paper is on modeling and simulation for an offshore wind system equipped with a semi-submersible floating platform, a wind turbine, a permanent magnet synchronous generator, a multiple point clamped four level or five level full-power converter, a submarine cable and a second order filter. The drive train is modeled by three mass model considering the resistant stiffness torque, structure and tower in deep water due to the moving surface elevation. The system control uses PWM by space vector modulation associated with sliding mode and proportional integral controllers. The electric energy is injected into the electric grid either by an alternated current link or by a direct current link. The model is intend to be a useful tool for unveil the behavior and performance of the offshore wind system, especially for the multiple point clamped full-power converter, under normal operation or under malfunctions.

Evolutionary algorithms for the modeling of bioreactors

This work aims to study the application of Genetic Algorithms in anaerobic digestion modeling, in particular when using dynamical models. Along the work, different types of bioreactors are shown, such as batch, semi-batch and continuous, as well as their mathematical modeling. The work intendeds to estimate the parameter values of two biological reaction model. For that, simulated results, where only one output variable, the produced biogas, is known, are fitted to the model results. For this reason, the problems associated with reverse optimization are studied, using some graphics that provide clues to the sensitivity and identifiability associated with the problem. Particular solutions obtained by the identifiability analysis using GENSSI and DAISY softwares are also presented. Finally, the optimization is performed using genetic algorithms. During this optimization the need to improve the convergence of genetic algorithms was felt. This need has led to the development of an adaptation of the genetic algorithms, which we called Neighbored Genetic Algorithms (NGA1 and NGA2). In order to understand if this new approach overcomes the Basic Genetic Algorithms (BGA) and achieves the proposed goals, a study of 100 full optimization runs for each situation was further developed. Results show that NGA1 and NGA2 are statistically better than BGA. However, because it was not possible to obtain consistent results, the Nealder-Mead method was used, where the initial guesses were the estimated results from GA; Algoritmos Evolucionários para a Modelação de Bioreactores Resumo: Neste trabalho procura-se estudar os algoritmos genéticos com aplicação na modelação da digestão anaeróbia e, em particular, quando se utilizam modelos dinâmicos. Ao longo do mesmo, são apresentados diferentes tipos de bioreactores, como os batch, semi-batch e contínuos, bem como a modelação matemática dos mesmos. Neste trabalho procurou-se estimar o valor dos parâmetros que constam num modelo de digestão anaeróbia para o ajustar a uma situação simulada onde apenas se conhece uma variável de output, o biogas produzido. São ainda estudados os problemas associados à optimização inversa com recurso a alguns gráficos que fornecem pistas sobre a sensibilidade e identifiacabilidade associadas ao problema da modelação da digestão anaeróbia. São ainda apresentadas soluções particulares de idenficabilidade obtidas através dos softwares GENSSI e DAISY. Finalmente é realizada a optimização do modelo com recurso aos algoritmos genéticos. No decorrer dessa optimização sentiu-se a necessidade de melhorar a convergência e, portanto, desenvolveu-se ainda uma adaptação dos algoritmos genéticos a que se deu o nome de Neighboured Genetic Algorithms (NGA1 e NGA2). No sentido de se compreender se as adaptações permitiam superar os algoritmos genéticos básicos e atingir as metas propostas, foi ainda desenvolvido um estudo em que o processo de optimização foi realizado 100 vezes para cada um dos métodos, o que permitiu concluir, estatisticamente, que os BGA foram superados pelos NGA1 e NGA2. Ainda assim, porque não foi possivel obter consistência nos resultados, foi usado o método de Nealder-Mead utilizado como estimativa inicial os resultados obtidos pelos algoritmos genéticos.