ALBERT

Description: The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.

Description: The growing energy demand around the world has increased the usage of renewable energy sources (RES) such as photovoltaic and wind energies. The combination of traditional power systems and RESs has generated diverse problems due especially to the stochastic nature of RESs. Microgrids (MG) arise to address these types of problems and to increase the penetration of RES to the utility network. A microgrid includes an energy management system (EMS) to operate its components and energy sources efficiently. The objectives pursued by the EMS are usually economically related to minimizing the operating costs of the MG or maximizing its income. However, due to new regulations of the network operators, a new objective related to the minimization of power peaks and fluctuations in the power profile exchanged with the utility network has taken great interest in recent years. In this regard, EMSs based on off-line trained fuzzy logic control (FLC) have been proposed as an alternative approach to those based on on-line optimization mixed-integer linear (or nonlinear) programming to reduce computational efforts. However, the procedure to adjust the FLC parameters has been barely addressed. This parameter adjustment is an optimization problem itself that can be formulated in terms of a cost/objective function and is susceptible to being solved by metaheuristic nature-inspired algorithms. In particular, this paper evaluates a methodology for adjusting the FLC parameters of the EMS of a residential microgrid that aims to minimize the power peaks and fluctuations on the power profile exchanged with the utility network through two nature-inspired algorithms, namely particle swarm optimization and differential evolution. The methodology is based on the definition of a cost function to be optimized. Numerical simulations on a specific microgrid example are presented to compare and evaluate the performances of these algorithms, also including a comparison with other ones addressed in previous works such as the Cuckoo search approach. These simulations are further used to extract useful conclusions for the FLC parameters adjustment for off-line-trained EMS based designs.

Description: Ubiquitin is a small protein that is highly conserved throughout eukaryotes. It operates as a reversible post-translational modifier through a process known as ubiquitination, which involves the addition of one or several ubiquitin moieties to a substrate protein. These modifications mark proteins for proteasome-dependent degradation or alter their localization or activity in a variety of cellular processes. In most eukaryotes, ubiquitin is generated by the proteolytic cleavage of precursor proteins in which it is fused either to itself, constituting a polyubiquitin precursor, or as a single N-terminal moiety to ribosomal proteins, which are practically invariably eL40 and eS31. Herein, we summarize the contribution of the ubiquitin moiety within precursors of ribosomal proteins to ribosome biogenesis and function and discuss the biological relevance of having maintained the explicit fusion to eL40 and eS31 during evolution. There are other ubiquitin-like proteins, which also work as post-translational modifiers, among them the small ubiquitin-like modifier (SUMO). Both ubiquitin and SUMO are able to modify ribosome assembly factors and ribosomal proteins to regulate ribosome biogenesis and function. Strikingly, ubiquitin-like domains are also found within two ribosome assembly factors; hence, the functional role of these proteins will also be highlighted.