942 resultados para Ancestral selection graph


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The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.

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The problem of scheduling a parallel program presented by a weighted directed acyclic graph (DAG) to the set of homogeneous processors for minimizing the completion time of the program has been extensively studied as academic optimization problem which occurs in optimizing the execution time of parallel algorithm with parallel computer.In this paper, we propose an application of the Ant Colony Optimization (ACO) to a multiprocessor scheduling problem (MPSP). In the MPSP, no preemption is allowed and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time.We therefore rely on heuristics to find solutions since solution methods are not feasible for most problems as such. This novel heuristic searching approach to the multiprocessor based on the ACO algorithm a collection of agents cooperate to effectively explore the search space.A computational experiment is conducted on a suit of benchmark application. By comparing our algorithm result obtained to that of previous heuristic algorithm, it is evince that the ACO algorithm exhibits competitive performance with small error ratio.

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The traveling salesman problem is although looking very simple problem but it is an important combinatorial problem. In this thesis I have tried to find the shortest distance tour in which each city is visited exactly one time and return to the starting city. I have tried to solve traveling salesman problem using multilevel graph partitioning approach.Although traveling salesman problem itself very difficult as this problem is belong to the NP-Complete problems but I have tried my best to solve this problem using multilevel graph partitioning it also belong to the NP-Complete problems. I have solved this thesis by using the k-mean partitioning algorithm which divides the problem into multiple partitions and solving each partition separately and its solution is used to improve the overall tour by applying Lin Kernighan algorithm on it. Through all this I got optimal solution which proofs that solving traveling salesman problem through graph partition scheme is good for this NP-Problem and through this we can solved this intractable problem within few minutes.Keywords: Graph Partitioning Scheme, Traveling Salesman Problem.

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The purpose of this thesis is to identify the destination site selection criteria for internationalconferences from the perspectives of the three main players of the conference industry,conference buyers (organizers and delegates) and suppliers. Additionally, the researchidentifies the strengths and weaknesses of the congress cities of Stockholm and Vienna.Through a comparison with Vienna, the top city for hosting international conferences, a roadmap for Stockholm has been designed, to strengthen its congress tourism opportunities, thus,obtaining a higher status as an international congress city. This qualitative research hascombined both primary and secondary data methods, through semi-standardized expertinterviews and secondary studies respectively, to fulfil the study’s aim. The data have beenanalysed by applying the techniques of qualitative content analysis; the secondary dataadopting an inductive approach according to Mayring (2003) while the expert interviewsusing a deductive approach according to Meuser & Nagel (2009). The conclusions of thesecondary data have been further compared and contrasted with the outcomes of the primarydata, to propose fresh discoveries, clarifications, and concepts related to the site selectioncriteria for international conferences, and for the congress tourism industry of Stockholm. Theresearch discusses the discoveries of the site selection criteria, the implications of thestrengths and weaknesses of Stockholm in comparison to Vienna, recommendations forStockholm via a road map, and future research areas in detail. The findings andrecommendation, not only provide specific steps and inceptions that Stockholm as aninternational conference city can apply, but also propose findings, which can aid conferencebuyers and suppliers to cooperate, to strengthen their marketing strategies and developsuccessful international conferences and destinations to help achieve a greater competitiveadvantage.

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The problems of finding best facility locations require complete and accurate road network with the corresponding population data in a specific area. However the data obtained in road network databases usually do not fit in this usage. In this paper we propose our procedure of converting the road network database to a road graph which could be used in localization problems. The road network data come from the National road data base in Sweden. The graph derived is cleaned, and reduced to a suitable level for localization problems. The population points are also processed in ordered to match with that graph. The reduction of the graph is done maintaining most of the accuracy for distance measures in the network.

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We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

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Agent-oriented software engineering (AOSE) is a promising approach to developing applications for dynamic open systems. If well developed, these applications can be opportunistic, taking advantage of services implemented by other developers at appropriate times. However, methodologies are needed to aid the development of systems that are both flexible enough to be opportunistic and tightly defined by the application requirements. In this paper, we investigate how developers can choose the coordination mechanisms of agents so that the agents will best fulfil application requirements in an open system.