Abstract

Abstract

Abstract
This paper considers the single machine scheduling problem with weighted quadratic tardiness costs. Three metaheuristics are presented, namely iterated local search, variable greedy and steady-state genetic algorithm procedures. These address a gap in the existing literature, which includes branch-and-bound algorithms (which can provide optimal solutions for small problems only) and dispatching rules (which are efficient and capable of providing adequate solutions for even quite large instances). A simple local search procedure which incorporates problem specific information is also proposed. The computational results show that the proposed metaheuristics clearly outperform the best of the existing procedures. Also, they provide an optimal solution for all (or nearly all, in the case of the variable greedy heuristic) the smaller size problems. The metaheuristics are quite close in what regards solution quality. Nevertheless, the iterated local search method provides the best solution, though at the expense of additional computational time. The exact opposite is true for the variable greedy procedure, while the genetic algorithm is a good all-around performer.

Abstract
The olive oil polyphenols 3,4-DHPEA-EA and 3,4-DHPEA-EDA displayed an endothelium dependent vasorelaxant effect in rat aorta, starting at similar to 1 mu M and abolished by N-G-nitro-L-arginine (L-NA) or N-acetylcysteine, and an endothelium-independent vasorelaxant effect, starting at similar to 10 mu M. Hydroxytyrosol only presented an endothelium-independent effect at 100 mu M. DHPEA-EA and 3,4-DHPEA-EDA, but not hydroxytyrosol, also increased NO generation within endothelial cells. At higher concentrations, the three compounds reduced argininevasopressin-induced increase of cytosolic Ca2+ concentration ([Ca2+](c)) in vascular myocytes. By UV-visible spectroscopy, we found that these polyphenols undergo autoxidative processes in organ-bath conditions. Thus, 3,4-DHPEA-EA and 3,4-DHPEA-EDA have an endothelium-dependent vasorelaxant effect caused by an enhanced NO production, probably through a redox mechanism within endothelial cells and an endothelium-independent vasorelaxant effect mediated by a reduction of agonist-induced [Ca2+](c) increase in vascular myocytes. Bearing in mind the plasmatic concentrations of these polyphenols following dietary intake of olive oil, these effects could modulate vascular tone in vivo.

Abstract
Administrative burden represents the costs to businesses, citizens and the administration itself of complying with government regulations and procedures. The burden tends to increase with new forms of public governance that rely less on direct decisions and actions undertaken by traditional government bureaucracies, and more on government creating and regulating the environment for other, non-state actors to jointly address public needs. Based on the reviews of research and policy literature, this paper explores administrative burden as a policy problem, presents how Digital Government (DG) could be applied to address this problem, and identifies societal adoption, organizational readiness and other conditions under which DG can be an effective tool for Administrative Burden Reduction (ABR). Finally, the paper tracks ABR to the latest Contextualization stage in the DG evolution, and discusses possible development approaches and technological potential of pursuing ABR through DG.

Abstract
The electricity markets environment has changed completely with the introduction of renewable energy sources in the energy distribution systems. With such alterations, preventing the system from collapsing required the development of tools to avoid system failure. In this new market environment competitiveness increases, new and different power producers have emerged, each of them with different characteristics, although some are shared for all of them, such as the unpredictability. In order to battle the unpredictability, the power supplies of this nature are supported by techniques of artificial intelligence that enables them crucial information for participation in the energy markets. In electricity markets any player aims to get the best profit, but is necessary have knowledge of the future with a degree of confidence leading to possible build successful actions. With optimization techniques based on artificial intelligence it is possible to achieve results in considerable time so that producers are able to optimize their profits from the sale of Electricity. Nowadays, there are many optimization problems where there are no that cannot be solved with exact methods, or where deterministic methods are computationally too complex to implement. Heuristic optimization methods have, thus, become a promising solution. In this paper, a simulated annealing based approach is used to solve the portfolio optimization problem for multiple electricity markets participation. A case study based on real electricity markets data is presented, and the results using the proposed approach are compared to those achieved by a previous implementation using particle swarm optimization.