58 resultados para Hand-drawn concept map
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Proceedings IGLC-19, July 2011, Lima, Perú
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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Sustainable Construction, Materials and Practice, p. 426-432
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This article proposes a methodology to address the urban evolutionary process, demonstrating how it is reflected in literature. It focuses on “literary space,” presented as a territory defined by the period setting or as evoked by the characters, which can be georeferenced and drawn on a map. It identifies the different locations of literary space in relation to urban development and the economic, political, and social context of the city. We suggest a new approach for mapping a relatively comprehensive body of literature by combining literary criticism, urban history, and geographic information systems (GIS). The home-range concept, used in animal ecology, has been adapted to reveal the size and location of literary space. This interdisciplinary methodology is applied in a case study to nineteenth- and twentieth-century novels involving the city of Lisbon. The developing concepts of cumulative literary space and common literary space introduce size calculations in addition to location and structure, previously developed by other researchers. Sequential and overlapping analyses of literary space throughout time have the advantage of presenting comparable and repeatable results for other researchers using a different body of literary works or studying another city. Results show how city changes shaped perceptions of the urban space as it was lived and experienced. A small core area, correspondent to a part of the city center, persists as literary space in all the novels analyzed. Furthermore, the literary space does not match the urban evolution. There is a time lag for embedding new urbanized areas in the imagined literary scenario.
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Tese de doutoramento em Filosofia
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The foresight and scenario building methods can be an interesting reference for social sciences, especially in terms of innovative methods for labour process analysis. A scenario – as a central concept for the prospective analysis – can be considered as a rich and detailed portrait of a plausible future world. It can be a useful tool for policy-makers to grasp problems clearly and comprehensively, and to better pinpoint challenges as well as opportunities in an overall framework. The features of the foresight methods are being used in some labour policy making experiences. Case studies developed in Portugal will be presented, and some conclusions will be drawn in order to organise a set of principles for foresight analysis applied to the European project WORKS on the work organisation re-structuring in the knowledge society, and on the work design methods for new management structures of virtual organisations.
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Banks that has introduced CRM system, had to make some difficult changes in their organization in order to become more customer oriented. Beside the pure CRM banks try to adopt other innovative tools related with the core CRM. Some of these solutions are constructed in such a way so that ensured could be also access to the information beside to bank’s organization.
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This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.
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Emergent architectures and paradigms targeting reconfigurable manufacturing systems increasingly rely on intelligent modules to maximize the robustness and responsiveness of modern installations. Although intelligent behaviour significantly minimizes the occurrence of faults and breakdowns it does not exclude them nor can prevent equipment’s normal wear. Adequate maintenance is fundamental to extend equipments’ life cycle. It is of major importance the ability of each intelligent device to take an active role in maintenance support. Further this paradigm shift towards “embedded intelligence”, supported by cross platform technologies, induces relevant organizational and functional changes on local maintenance teams. On the one hand, the possibility of outsourcing maintenance activities, with the warranty of a timely response, through the use of pervasive networking technologies and, on the other hand, the optimization of local maintenance staff are some examples of how IT is changing the scenario in maintenance. The concept of e-maintenance is, in this context, emerging as a new discipline with defined socio-economic challenges. This paper proposes a high level maintenance architecture supporting maintenance teams’ management and offering contextualized operational support. All the functionalities hosted by the architecture are offered to the remaining system as network services. Any intelligent module, implementing the services’ interface, can report diagnostic, prognostic and maintenance recommendations that enable the core of the platform to decide on the best course of action.
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MSC Dissertation in Computer Engineering
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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This paper appears in International Journal of Information and Communication Technology Education edited by Lawrence A. Tomei (Ed.) Copyright 2007, IGI Global, www.igi-global.com. Posted by permission of the publisher. URL:http://www.idea-group.com/journals/details.asp?id=4287.