Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

Network reconfiguration and loss allocation for distribution systems with distributed generation

Distribution systems with distributed generation require new analysis methods since networks are not longer passive. Two of the main problems in this new scenario are the network reconfiguration and the loss allocation. This work presents a distribution systems graphic simulator, developed with reconfiguration functions and a special focus on loss allocation, both considering the presence of distributed generation. This simulator uses a fast and robust power flow algorithm based on the current summation backward-forward technique. Reconfiguration problem is solved through a heuristic methodology and the losses allocation function, based on the Zbus method, is presented as an attached result for each obtained configuration. Results are presented and discussed, remarking the easiness of analysis through the graphic simulator as an excellent tool for planning and operation engineers, and very useful for training. © 2004 IEEE.

Methodology analysis of a computational tool used in electronic circuit design

This paper considers the importance of using a top-down methodology and suitable CAD tools in the development of electronic circuits. The paper presents an evaluation of the methodology used in a computational tool created to support the synthesis of digital to analog converter models by translating between different tools used in a wide variety of applications. This tool is named MS 2SV and works directly with the following two commercial tools: MATLAB/Simulink and SystemVision. Model translation of an electronic circuit is achieved by translating a mixed-signal block diagram developed in Simulink into a lower level of abstraction in VHDL-AMS and the simulation project support structure in SystemVision. The method validation was performed by analyzing the power spectral of the signal obtained by the discrete Fourier transform of a digital to analog converter simulation model. © 2011 IEEE.

A multi-agent architecture for intelligent data analysis

The present study introduces a multi-agent architecture designed for doing automation process of data integration and intelligent data analysis. Different from other approaches the multi-agent architecture was designed using a multi-agent based methodology. Tropos, an agent based methodology was used for design. Based on the proposed architecture, we describe a Web based application where the agents are responsible to analyse petroleum well drilling data to identify possible abnormalities occurrence. The intelligent data analysis methods used was the Neural Network.

Scientific collaboration network among Brazilian universities: an analysis in Dentistry area

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

Comparative analysis of different meat traceability systems using multiple criteria and a social network approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analyzing online communication from a social network point of view: questions, problems, perspectives

This programmatic paper investigates the possibilities, chances, and risks of analyzing personal and professional online communication from the point of view of interactional sociolinguistics combined with modern social network analysis (SNA). Thus, it has two complementing goals: One is the exploration of adequate, innovative concepts and methods for analyzing online communication, the other is to use online communication and its ontological and functional specificities to enrich the conceptual and methodological background of SNA. The paper is organized in two parts. It begins with an introduction to recent developments in sociolinguistic social network analysis. Here, three interesting new concepts and tools are discussed: latent versus emergent networks (Watts 1991), coalitions (Fitzmaurice 2000a, Fitzmaurice 2000b), and communities of practice (Wenger 1998

Repeatability analysis of global and local metrics of brain structural networks

Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric and neurodegenerative disorders. In order to ensure the validity of results in clinical settings the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, nineteen healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in networks comparisons.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

First results from a Swiss level I trauma centre participating in the UK Trauma Audit and Research Network (TARN): Prospective cohort study

QUESTIONS UNDER STUDY: Patient characteristics and risk factors for death of Swiss trauma patients in the Trauma Audit and Research Network (TARN). METHODS: Descriptive analysis of trauma patients (≥16 years) admitted to a level I trauma centre in Switzerland (September 1, 2009 to August 31, 2010) and entered into TARN. Multivariable logistic regression analysis was used to identify predictors of 30-day mortality. RESULTS: Of 458 patients 71% were male. The median age was 50.5 years (inter-quartile range [IQR] 32.2-67.7), median Injury Severity Score (ISS) was 14 (IQR 9-20) and median Glasgow Coma Score (GCS) was 15 (IQR 14-15). The ISS was >15 for 47%, and 14% had an ISS >25. A total of 17 patients (3.7%) died within 30 days of trauma. All deaths were in patients with ISS >15. Most injuries were due to falls <2 m (35%) or road traffic accidents (29%). Injuries to the head (39%) were followed by injuries to the lower limbs (33%), spine (28%) and chest (27%). The time of admission peaked between 12:00 and 22:00, with a second peak between 00:00 and 02:00. A total of 64% of patients were admitted directly to our trauma centre. The median time to CT was 30 min (IQR 18-54 min). Using multivariable regression analysis, the predictors of mortality were older age, higher ISS and lower GCS. CONCLUSIONS: Characteristics of Swiss trauma patients derived from TARN were described for the first time, providing a detailed overview of the institutional trauma population. Based on these results, patient management and hospital resources (e.g. triage of patients, time to CT, staffing during night shifts) could be evaluated as a further step.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Methods for automatic error analysis of numerical algorithms /

Vita.

Methods of mathematical analysis and computation.

Mode of access: Internet.

Social Support Networks and Self-Efficacy of Peruvian Women Diagnosed with Cancer: A Biocultural Analysis of Health Behavior Constructs

Thesis (Ph.D.)--University of Washington, 2016-06

Globalisation and connectedness: A network approach to international business

There have been recent calls for the field of International Business to retool its routines by becoming genuinely interdisciplinary. This paper takes such an approach by using recent advances in the fields of evolutionary economics and applying them to IB. Evolutionary economists are now viewing the economy as an actual network. Consequently, one the key analytical tools in this approach is network analysis. Some of the basic methods in network analysis are reviewed. The paper then looks at how using these tools might be of use in IB studies. In particular, it outlines fruitful research paths in the areas of globalisation and regionalisation, and the measurement of performance in multi-national firms and alliances. In each case, propositions are put forward which can be analytically tested with the use of network analysis. The paper concludes with a brief outline of a research agenda which utilises this approach in International Business studies.