Gene evolution is important in several areas, not least the understanding of thehuman body. Since the time of Darwin, researchers have visualized evolution withrooted binary trees. Hence, genes evolve constrained to how species have evolvedand that relationship is interesting to understand and explore. To solve this, onemethod is to infer the gene tree into the species tree, a so called reconciliation, butvisualizing reconciled trees with classical binary trees often results in clutteredvisualizations that quickly become difficult to understand. Therefore, in this thesisI present a new visualization method, Genvo, for reconciled phylogenies, and definea simple tree layout algorithm. I also present a problem characterization for thedomain-specific tasks, performed when working with gene evolution visualizations.A prototype of Genvo is then studied in a small pair analytics study, where fivepairs of master students tested the developed a prototype of Genvo. The results areanalyzed compared to a workshop with three participants, all with prior knowledgein the field of gene evolution. The analysis indicates a faster understanding of thegene data in Genvo, most likely through the pre-attentive features. / Att förstå geners evolution är relevant för flertalet områden, inte minst för attutforska den mänskliga kroppen. Ända sedan Darwin har forskare visualiseratevolution med hjälp av rotade binära träd. Dock utvecklas gener begränsat till hurdess arter utvecklats, och denna relation är av intresse att förstå och utforska. Attgöra en sammanslagning av ett gen-träd med sitt art-träd (reconciliation) är ett sättatt visualisera relationen, men lösningen blir ofta väldigt rörig och svårförståelig.Därför presenterar jag i detta examensarbete Genvo, ett reconciliation programsom använder en simpel träd-layout-algoritm för att visualisera geners evolution irelation till arters evolution i 3D. Jag presenterar även en gedigen problemkarakteriseringför de uppgifter forskarna utför när de jobbar med dagensvisualiseringsverktyg. En prototyp av Genvo testades sedan i en pairanalytics studie, med en testgrupp bestående av fem par. Studien är sedan jämfördmed en workshop där tre testpersoner, med tidigare erfarenhet, jobbade medsamma uppgifter. Analysen av studiernas resultat tyder på en snabbare förståelseav datan när användarna använde sig av Genvo.

There are many diverse applications that are mathematically modelled in terms of mixed discrete-continuous variables. The optimization of these models is typically difficult due to their combinatorial nature and potential existence of multiple local minima in the search space. Genetic algorithms (GAs) are powerful tools for solving such problems. GAs do not require gradient or Hessian information. However, to reach an optimal solution with a high degree of confidence, they typically require a large number of analyses during the optimization search. Performance of these methods is even more of an issue for problems that include continuous variables. The work here enhances the efficiency and accuracy of the GA with memory using multivariate approximations of the objective and constraint functions individually instead of direct approximations of the overall fitness function. The primary motivation for the proposed improvements is the nature of the fitness function in constrained engineering design optimization problems. Since GAs are algorithms for unconstrained optimization, constraints are typically incorporated into the problem formulation by augmenting the objective function of the original problem with penalty terms associated with individual constraint violations. The resulting fitness function is usually highly nonlinear and discontinuous, which makes the multivariate approximation highly inaccurate unless a large number of exact function evaluations are performed. Since the individual response functions in many engineering problems are mostly smooth functions of the continuous variables (although they can be highly nonlinear), high quality approximations to individual functions can be constructed without requiring a large number of function evaluations. The proposed modification improve the efficiency of the memory constructed in terms of the continuous variables. The dissertation presents the algorithmic implementation of the proposed memory scheme and demonstrates the efficiency of the proposed multivariate approximation procedure for the weight optimization of a segmented open cross section composite beam subjected to axial tension load. Results are generated to demonstrate the advantages of the proposed improvements to a standard genetic algorithm. / Ph. D.

The environment is made up of composition of small particles. Hence, particle simulation is an important tool in many scientific and engineering research fields to simulate the real life processes of the environment. Because of the enormous amount of data in such simulations, data management, storage and processing are very challenging tasks. Spatial Distance Histogram (SDH) is one of the most popular queries being used in this field. In this thesis, we are interested in investigating the performance of improvement of an existing algorithm for computing SDH. The algorithm already being used is using a conceptual data structure called density map which is implemented via a quad tree index. An algorithm having density maps implemented via binary tree is proposed in this thesis. After carrying out many experiments and analysis of the data, we figure out that although the binary tree approach seems efficient in earlier stage, it is same as the quad tree approach in terms of time complexity. However, it provides an improvement in computing time by a constant factor for some data inputs. The second part of this thesis is dedicated to an approach that can potentially reduce the computational time to a great extent by taking advantage of regions where data points are uniformly distributed.

A Temporal database is one that supports some aspect of time distinct from user defined time. Over the last two decades interest in the field of temporal databases has increased significantly, with contributions from many researchers. However, the lack of efficient access methods is perhaps one of the reasons why commercial RDBMS vendors have been reluctant to adopt the advances in temporal database research. Therefore, an obvious research question is: can we develop more robust and more efficient access methods for temporal databases than the existing ones? This thesis attempts to address this question, and the main contributions of this study are summarised as follows: We investigated different representations of 'now' and how the modelling of current time influences the efficiency of accessing 'now relative' temporal data. A new method, called the 'Point' approach, is proposed. Our approach not only elegantly models the current time but also significantly outperforms the existing methods. We proposed a new index structure, called a Virtual Binary tree (VB-tree), based on spatial representation of interval data and a regular triangular decomposition of this space. Further, we described a sound and complete query algorithm. The performance of the algorithm is then evaluated both asymptotically and experimentally with respect to the state-of-the-art in the field. We claim that the VB-tree requires less space and uses fewer disk accesses than the currently best known structure - the RI-tree.

In these few years, gradually Internet develops to wideband, multimedia is being used on video or music. In addition, the use of IP Multicast must be based on the deployment of routers, which is too difficult to arrange. Utilities of Application Layer Multicast is in the middle and just between IP Multicast and Unicast.Therefore, in this paper, Application Layer Multicast is still worth to study it. In this paper, is applied effectively build the Application Layer Multicast. Control through the IP Anycast Technique, we can lower the time for host join the Multicast Tree. Every host can join the nearest cluster. We use the hierarchical cluster-based Method in order to serve more hosts. This concept about cluster can substantially decrease control overhead. The Complete Binary Trees lower the cluster leader¡¦s burden, also phased RTT decided effectively the transit sequence. In Summary, associate techniques with methods, to make up the defects from NICE and I-Zigzag.

Despite being found in all presently sequenced genomes, the evolution of tandemly repeated sequences has only just begun to be understood. We can represent the duplication history of tandemly repeated sequences with duplication trees. Most phylogenetic techniques need to be modified to be used on duplication trees. Due to gene loss, it is not always possible to reconstruct the duplication history of a tandemly repeated sequence. This thesis addresses this problem by providing a polynomial-time locally optimal algorithm to reconstruct the duplication history of a tandemly repeated sequence in the presence of gene loss. Supertree methods cannot be directly applied to duplication trees. A polynomial-time algorithm that takes a forest of ordered phylogenies and looks for a super duplication tree is presented. If such a super duplication tree is found then the algorithm constructs the super duplication tree. However, the algorithm does not always find a super duplication tree when one exists. The SPR topological rearrangement in its current form cannot be used on duplication trees. The necessary modifications are made to an agreement forest so that the SPR operation can be used on duplication trees. This operation is called the duplication rooted subtree prune and regraft operation (DrSPR). The size of the DrSPR neighbourhood is calculated for simple duplication trees and the tree shapes that maximize and minimize this are given.

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The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.

A novel parallel block-based anisotropic adaptive mesh refinement (AMR) technique for multi-block body-fitted grids is proposed and described. Rather than adopting the more usual isotropic approach to mesh refinement, an anisotropic refinement procedure is proposed which allows refinement of grid blocks in each coordinate direction in an independent fashion. This allows for more efficient and accurate treatment of narrow layers and/or discontinuities which occur, for example, in the boundary and mixing layers of viscous flows, and in regions of strong non-linear wave interactions with shocks. The anisotropic AMR technique is implemented within an existing finite-volume framework, which encompasses both explicit and implicit solution methods, and is capable of performing calculations with second- and higher-order spatial accuracy. To clearly demonstrate the feasibility of the proposed technique, it is applied to the unsteady and steady-state solutions of both the advection diffusion equation, as well as the Euler equations, in two space dimensions.