A finite-volume computational mechanics framework for multi-physics coupled fluid-stress problems

Abstract not available

Efficient algorithms to solve scheduling problems with a variety of optimization criteria

La programmation par contraintes est une technique puissante pour résoudre, entre autres, des problèmes d’ordonnancement de grande envergure. L’ordonnancement vise à allouer dans le temps des tâches à des ressources. Lors de son exécution, une tâche consomme une ressource à un taux constant. Généralement, on cherche à optimiser une fonction objectif telle la durée totale d’un ordonnancement. Résoudre un problème d’ordonnancement signifie trouver quand chaque tâche doit débuter et quelle ressource doit l’exécuter. La plupart des problèmes d’ordonnancement sont NP-Difficiles. Conséquemment, il n’existe aucun algorithme connu capable de les résoudre en temps polynomial. Cependant, il existe des spécialisations aux problèmes d’ordonnancement qui ne sont pas NP-Complet. Ces problèmes peuvent être résolus en temps polynomial en utilisant des algorithmes qui leur sont propres. Notre objectif est d’explorer ces algorithmes d’ordonnancement dans plusieurs contextes variés. Les techniques de filtrage ont beaucoup évolué dans les dernières années en ordonnancement basé sur les contraintes. La proéminence des algorithmes de filtrage repose sur leur habilité à réduire l’arbre de recherche en excluant les valeurs des domaines qui ne participent pas à des solutions au problème. Nous proposons des améliorations et présentons des algorithmes de filtrage plus efficaces pour résoudre des problèmes classiques d’ordonnancement. De plus, nous présentons des adaptations de techniques de filtrage pour le cas où les tâches peuvent être retardées. Nous considérons aussi différentes propriétés de problèmes industriels et résolvons plus efficacement des problèmes où le critère d’optimisation n’est pas nécessairement le moment où la dernière tâche se termine. Par exemple, nous présentons des algorithmes à temps polynomial pour le cas où la quantité de ressources fluctue dans le temps, ou quand le coût d’exécuter une tâche au temps t dépend de t.

A Multi-Objective Approach for VNE: Problems using multiple ILP formulations

Network Virtualization is a key technology for the Future Internet, allowing the deployment of multiple independent virtual networks that use resources of the same basic infrastructure. An important challenge in the dynamic provision of virtual networks resides in the optimal allocation of physical resources (nodes and links) to requirements of virtual networks. This problem is known as Virtual Network Embedding (VNE). For the resolution of this problem, previous research has focused on designing algorithms based on the optimization of a single objective. On the contrary, in this work we present a multi-objective algorithm, called VNE-MO-ILP, for solving dynamic VNE problem, which calculates an approximation of the Pareto Front considering simultaneously resource utilization and load balancing. Experimental results show evidences that the proposed algorithm is better or at least comparable to a state-of-the-art algorithm. Two performance metrics were simultaneously evaluated: (i) Virtual Network Request Acceptance Ratio and (ii) Revenue/Cost Relation. The size of test networks used in the experiments shows that the proposed algorithm scales well in execution times, for networks of 84 nodes

Performance of the Equivalent Reservoir Modelling Technique for Multi-Reservoir Hydropower Systems

This paper investigates the validity of a simplified equivalent reservoir representation of a multi-reservoir hydroelectric system for modelling its optimal operation for power maximization. This simplification, proposed by Arvanitidis and Rosing (IEEE Trans Power Appar Syst 89(2):319-325, 1970), imputes a potential energy equivalent reservoir with energy inflows and outflows. The hydroelectric system is also modelled for power maximization considering individual reservoir characteristics without simplifications. Both optimization models employed MINOS package for solution of the non-linear programming problems. A comparison between total optimized power generation over the planning horizon by the two methods shows that the equivalent reservoir is capable of producing satisfactory power estimates with less than 6% underestimation. The generation and total reservoir storage trajectories along the planning horizon obtained by equivalent reservoir method, however, presented significant discrepancies as compared to those found in the detailed modelling. This study is motivated by the fact that Brazilian generation system operations are based on the equivalent reservoir method as part of the power dispatch procedures. The potential energy equivalent reservoir is an alternative which eliminates problems with the dimensionality of state variables in a dynamic programming model.

Jointly multi-user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multiuser channel estimation (MuChE) and detection problems at its maximum-likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi-user detection (MuD) show that the proposed genetic algorithm multi-user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi-user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near-optimum multi-user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi-user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE-GAMuD scheme can be regarded as a promising alternative for implementing third-generation (3G) and fourth-generation (4G) wireless systems in the near future. Copyright (C) 2010 John Wiley & Sons, Ltd.

A generalized multi-period mean-variance portfolio optimization with Markov switching parameters

In this paper, we deal with a generalized multi-period mean-variance portfolio selection problem with market parameters Subject to Markov random regime switchings. Problems of this kind have been recently considered in the literature for control over bankruptcy, for cases in which there are no jumps in market parameters (see [Zhu, S. S., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean variance formulation. IEEE Transactions on Automatic Control, 49, 447-457]). We present necessary and Sufficient conditions for obtaining an optimal control policy for this Markovian generalized multi-period meal-variance problem, based on a set of interconnected Riccati difference equations, and oil a set of other recursive equations. Some closed formulas are also derived for two special cases, extending some previous results in the literature. We apply the results to a numerical example with real data for Fisk control over bankruptcy Ill a dynamic portfolio selection problem with Markov jumps selection problem. (C) 2008 Elsevier Ltd. All rights reserved.

Prevalence of Alcohol-Related Problems in an Elderly Population and Their Association With Cognitive Impairment and Dementia

Background: Studies investigating the association between alcohol use and cognitive disorders in the elderly population have produced divergent results. Moreover, the role of alcohol in cognitive dysfunction is not clear. The aims of this study were to estimate the prevalence of alcohol-related problems in an elderly population from Brazil and to investigate their association with cognitive and functional impairment (CFI) and dementia. Methods: A community-based cross-sectional study was performed. A sample of 1,145 elderly people was examined in 2 phases. Several instruments were utilized in the first phase: the CAGE questionnaire was used to identify potential cases of alcohol-related problems, and a screening test for dementia was used to estimate CFI. The CAMDEX interview (Cambridge Examination) and DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) criteria were used for the clinical diagnosis of dementia in the second phase. Results: ""Heavy alcohol use"" (CAGE >= 2) was found in 92 subjects (prevalence: 8.2%). It was associated with gender (males, p < 0.001), low education (only in females, p = 0.002), and low socioeconomic level (p = 0.001, in females; p = 0.002, in males). The Mini Mental State Examination exhibited a nonlinear relationship with alcohol-related problems in females; ""mild-moderate alcohol use"" (CAGE < 2) presented the highest score. A significant association between alcohol-related problems and cognitive dysfunction was found only in females. ""Heavy alcohol use"" was associated with higher CFI and dementia rates compared to ""mild-moderate alcohol use"" (p = 0.003 and p < 0.001, respectively). ""Mild-moderate alcohol use"" had a tendency of association with lower CFI and dementia rates when compared to ""no alcohol use"" (p = 0.063 and 0.050, respectively). Conclusion: Our findings suggest that alcohol use does not have a linear relationship with cognitive decline.

Plant transformation: Problems and strategies for practical application

Plant transformation is now a core research tool in plant biology and a practical tool for cultivar improvement. There are verified methods for stable introduction of novel genes into the nuclear genomes of over 120 diverse plant species. This review examines the criteria to verify plant transformation; the biological and practical requirements for transformation systems; the integration of tissue culture, gene transfer, selection, and transgene expression strategies to achieve transformation in recalcitrant species; and other constraints to plant transformation including regulatory environment, public perceptions, intellectual property, and economics. Because the costs of screening populations showing diverse genetic changes can far exceed the costs of transformation, it is important to distinguish absolute and useful transformation efficiencies. The major technical challenge facing plant transformation biology is the development of methods and constructs to produce a high proportion of plants showing predictable transgene expression without collateral genetic damage. This will require answers to a series of biological and technical questions, some of which are defined.

Clinical Validity of the Protocol for Multi-Professional Centers for the Determination of Signs and Symptoms of Temporomandibular Disorders. Part II

The aims of this study were to analyze the criterion and construct validity of Part II of the protocol for multi-professional centers for the determination of signs and symptoms of temporomandibular disorders (ProTMDMulti) as a measure of TMD severity. The study was conducted on eight asymptomatic subjects (CG) and 30 subjects with articular TMD (TMDG), according to the Research Diagnostic Criteria for TMD (RDC/TMD). The ProTMDMulti-Part II was validated using the Helkimo Clinical Dysfunction Index (Di). The construct validity was tested using the analysis of the ability of ProTMDMulti-part II to differentiate the CG from the TMDG and to measure the changes that occurred in the TMDG between the period before and after TMD treatment. Correlations between the Di and the ProTMDMulti-Part II scores were calculated using the Spearman test. Inter- and intragroup comparisons were made (p<0.05). There was a statistically significant correlation between the Helkimo Clinical Dysfunction Index (Di) and the severity scores of the ProTMDMulti-Part II. There was a significant difference between TMDG and CG regarding the severity of signs and symptoms. The present study provides statistical evidence of the clinical validity of the ProTMDmulti-Part II as a measure of the severity of TMD symptoms.

Xmds: eXtensible multi-dimensional simulator

A software package that efficiently solves a comprehensive range of problems based on coupled complex nonlinear stochastic ODEs and PDEs is outlined. Its input and output syntax is formulated as a subset of XML, thus making a step towards a standard for specifying numerical simulations.

Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces

Combinatorial optimization problems share an interesting property with spin glass systems in that their state spaces can exhibit ultrametric structure. We use sampling methods to analyse the error surfaces of feedforward multi-layer perceptron neural networks learning encoder problems. The third order statistics of these points of attraction are examined and found to be arranged in a highly ultrametric way. This is a unique result for a finite, continuous parameter space. The implications of this result are discussed.

Unknown Radial Distortion Centers in Multiple View Geometry Problems

The radial undistortion model proposed by Fitzgibbon and the radial fundamental matrix were early steps to extend classical epipolar geometry to distorted cameras. Later minimal solvers have been proposed to find relative pose and radial distortion, given point correspondences between images. However, a big drawback of all these approaches is that they require the distortion center to be exactly known. In this paper we show how the distortion center can be absorbed into a new radial fundamental matrix. This new formulation is much more practical in reality as it allows also digital zoom, cropped images and camera-lens systems where the distortion center does not exactly coincide with the image center. In particular we start from the setting where only one of the two images contains radial distortion, analyze the structure of the particular radial fundamental matrix and show that the technique also generalizes to other linear multi-view relationships like trifocal tensor and homography. For the new radial fundamental matrix we propose different estimation algorithms from 9,10 and 11 points. We show how to extract the epipoles and prove the practical applicability on several epipolar geometry image pairs with strong distortion that - to the best of our knowledge - no other existing algorithm can handle properly.

Multi-attribute classification of housing conservation status in urban regeneration actions

Urban regeneration is more and more a “universal issue” and a crucial factor in the new trends of urban planning. It is no longer only an area of study and research; it became part of new urban and housing policies. Urban regeneration involves complex decisions as a consequence of the multiple dimensions of the problems that include special technical requirements, safety concerns, socio-economic, environmental, aesthetic, and political impacts, among others. This multi-dimensional nature of urban regeneration projects and their large capital investments justify the development and use of state-of-the-art decision support methodologies to assist decision makers. This research focuses on the development of a multi-attribute approach for the evaluation of building conservation status in urban regeneration projects, thus supporting decision makers in their analysis of the problem and in the definition of strategies and priorities of intervention. The methods presented can be embedded into a Geographical Information System for visualization of results. A real-world case study was used to test the methodology, whose results are also presented.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.