7 resultados para Combinatorial enumeration problems

em Universidade do Minho


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The Closest Vector Problem (CVP) and the Shortest Vector Problem (SVP) are prime problems in lattice-based cryptanalysis, since they underpin the security of many lattice-based cryptosystems. Despite the importance of these problems, there are only a few CVP-solvers publicly available, and their scalability was never studied. This paper presents a scalable implementation of an enumeration-based CVP-solver for multi-cores, which can be easily adapted to solve the SVP. In particular, it achieves super-linear speedups in some instances on up to 8 cores and almost linear speedups on 16 cores when solving the CVP on a 50-dimensional lattice. Our results show that enumeration-based CVP-solvers can be parallelized as effectively as enumeration-based solvers for the SVP, based on a comparison with a state of the art SVP-solver. In addition, we show that we can optimize the SVP variant of our solver in such a way that it becomes 35%-60% faster than the fastest enumeration-based SVP-solver to date.

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This paper presents a critical and quantitative analysis of the influence of the Power Quality in grid connected solar photovoltaic microgeneration installations. First are introduced the main regulations and legislation related with the solar photovoltaic microgeneration, in Portugal and Europe. Next are presented Power Quality monitoring results obtained from two residential solar photovoltaic installations located in the north of Portugal, and is explained how the Power Quality events affect the operation of these installations. Afterwards, it is described a methodology to estimate the energy production losses and the impact in the revenue caused by the abnormal operation of the electrical installation. This is done by comparing the amount of energy that was injected into the power grid with the theoretical value of energy that could be injected in normal conditions. The performed analysis shows that Power Quality severally affects the solar photovoltaic installations operation. The losses of revenue in the two monitored installations M1 and M2 are estimated in about 27% and 22%, respectively.

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During recent decades it has been possible to identify several problems in construction industry project management, related with to systematic failures in terms of fulfilling its schedule, cost and quality targets, which highlight a need for an evaluation of the factors that may cause these failures. Therefore, it is important to understand how project managers plan the projects, so that the performance and the results can be improved. However, it is important to understand if other areas beyond cost and time management that are mentioned on several studies as the most critical areas, receive the necessary attention from construction project managers. Despite the cost and time are the most sensitive areas/fields, there are several other factors that may lead to project failure. This study aims at understand the reasons that may cause the deviation in terms of cost, time and quality, from the project management point of view, looking at the knowledge areas mentioned by PMI (Project Management Institute).

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The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

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Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

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We propose a novel hanging spherical drop system for anchoring arrays of droplets of cell suspension based on the use of biomimetic superhydrophobic flat substrates, with controlled positional adhesion and minimum contact with a solid substrate. By facing down the platform, it was possible to generate independent spheroid bodies in a high throughput manner, in order to mimic in vivo tumour models on the lab-on-chip scale. To validate this system for drug screening purposes, the toxicity of the anti-cancer drug doxorubicin in cell spheroids was tested and compared to cells in 2D culture. The advantages presented by this platform, such as feasibility of the system and the ability to control the size uniformity of the spheroid, emphasize its potential to be used as a new low cost toolbox for high-throughput drug screening and in cell or tissue engineering.

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Tese de Doutoramento em Psicologia Aplicada.