Spatial and temporal patterns of urban vegetation have been widely studied since the mid-twentieth century, but these studies have focused mainly on northern hemisphere countries, and little research has been conducted in developing countries. Urban vegetation is characterized by the presence of species that are adapted to the particular environmental conditions of cities, and a high diversity of exotic species. This occurs due to a combination of factors: on one hand, it is possible to find wild vegetation (weeds) on abandoned lands or those with little intervention, as well as on walls and buildings. On the other hand, there is also an enormous variety of ornamental and mainly exotic species, which have been cultivated by humans. The processes of species selection performed individually or collectively are a major determinant of the diversity of urban vegetation and flora. Individual decisions relate to private spaces such as residential gardens whose owners manage the vegetation according to their preferences and interests. Collective decisions relate to public spaces, which, by their nature, are subject to the action of multiple stakeholders. At the collective level, decision-making occurs in the context of processes involving local governments, other state agencies, NGOs and various interest groups present in the city. Each of these actors has its own vision on the role of urban vegetation, their preferences and criteria for the selection and management. This study aims to investigate the processes of decision-making responsible for the current composition of the vegetation in public spaces of the Metropolitan Area of Santiago (MAS). Through this research is expected to identify the criteria for the selection of species to be planted in public spaces, the reasons that explain the predominance of certain species, and the difference between parks managed by different public agencies in MAS. The research assumes that the various public and private actors involved in the planting and management of vegetation in public spaces, act guided by criteria and preferences that are finally expressed in the observed patterns of urban vegetation. For this purpose, the conceptual framework of governance is used, understood as the process of decision-making concerning public affairs, which involves multiple agents or interests including government agencies, non-governmental organizations and civil society groups. The overarching objectives of this Thesis are: a) To contribute to the knowledge of interactions between governance system and urban vegetation patterns in metropolitan areas of developing countries, recognizing both social and environmental processes interacting. b) Contribute to urban planning and policies by generating knowledge relevant to decision- making regarding urban vegetation. A robust knowledge of the factors defining the composition and structure of urban vegetation is essential to design effective policies for increasing vegetation cover, with consequent environmental and social benefits.

Do Populist Radical Right Parties have an impact on the attitudes of other parties? Despite drawing much attention from the general public as well as academics, there is no clear answer to this conundrum. In this paper I examine how mainstream political parties change their positions in parliamentary debates on immigration and refugees after Populist Radical Right Parties enter parliament. In order to do this, I use theoretical concepts such as discourse coalitions and storylines in combination with network methodology to map out how parties in the Swedish parliament relate to one another through their attitudes towards key themes in the debate on immigration and refugees. This paper focuses on the relations between parties through language by applying Discourse Network Analysis on parliamentary debates. Thus, it contributes with a new relational aspect and methodological tool on a relatively underutilised material. The findings indicate that there is a change in other parties’ attitudes towards immigration and refugees, with two mainstream right parties moving closer to the Populist Radical Right Party. However, the datatype does not support causal language and the findings are limited due to small amounts of data.

No description available.

This thesis explores the Danish labor movement’s use of Twitter during the collective bargaining in spring 2018 from a mobilisation perspective. This is done to investigate 1) the form of contentious politics practiced by the Danish labor movement, and 2) the role of trade unions in the Danish labor movement. One specific hashtag, #ok18, is analyzed. This investigation mainly builds on framing theory as developed by Snow & Benford (1986; 2000) and its connection to the logic of collective action, and the logic of connective action developed by Bennet & Segerberg (2013). Three methods were used to analyze the labor movement on Twitter: a social network analysis of @mentions, semantic network analyses of Twitter streams, and a quantitative content analysis. This study finds that the most important and central actors within the labor movement on Twitter are trade unions. Nothing indicates that Danish public employees used Twitter to organize independently of trade unions. Furthermore, the labor movement used Twitter to articulate collective action frames that served as shared “schemata of interpretation” for the collective bargaining. In addition, several framing processes that changed the collective action frames were identified. These results all indicate that the labor movement’s mobilisation on Twitter during the collective bargaining of 2018 is best described by the logic of collective action. There were no indications of personalization of politics or of an increased symbolical inclusiveness. The successful mobilisation in Spring 2018 might therefore be interpreted, with the big proviso that that this study only investigates Twitter, as the first small steps towards a revitalization of conventional trade union politics in Denmark.

A diversidade de interações que ocorre dentro de sistemas biológicos, considerando desde as organelas de uma célula até toda a biosfera, pode ser modelada por meio da teoria de redes. A dinâmica das interações entre os elementos é uma propriedade intrínseca desses sistemas. Diversas ferramentas foram propostas para comparar redes, que representam os muitos estados assumidos por um sistema. Porém, nenhuma delas é capaz de comparar características estruturais de mais de duas redes simultaneamente. Devido à grande quantidade de estados que um sistema pode assumir, construímos uma ferramenta estatística para comparar duas ou mais redes e indicar variáveis chave no processo estudado. A principal proposta deste trabalho foi comparar redes de correlação usando medidas baseadas nos espectros dos grafos (conjunto de autovalores das matrizes de adjacência), como a distribuição espectral. Essa medida está associada a diversas características estruturais das redes como o número de caminhos, diâmetro e cliques. Além da distribuição espectral, também comparamos as redes por entropia espectral, distribuição dos graus e pelas centralidades dos nós. Usamos dois diferentes conjuntos de dados biológicos (expressão gênica de células tumorais e metabolismo vegetal) para realizar os testes de desempenho da ferramenta e para os estudos de caso. O método proposto está implementado em um pacote do programa R, chamado BioNetStat, com interface gráfica para o usuário leigo em programação. Constatamos que os testes são eficientes em diferenciar mais de duas redes. Além disso, o aumento do número de redes comparadas e a queda dos números de unidades amostrais, diminui o poder estatístico do teste. Mostramos ainda que ocorre uma economia de tempo significativa ao realizarmos uma única análise para comparar muitas redes ao invés de compará-las par-a-par. Além disto, o método apontou grupos de variáveis com papel central nos sistemas biológicos estudados que não foram encontrados nas análises onde apenas a expressão ou concentração dos elementos foi estudada. Foi possível assim diferenciar células de tipos cancerígenos ou órgãos de organismos vegetais através das centralidades das redes. As variáveis levantadas possibilitam ao usuário gerar hipóteses sobre seus papeis nos processos em estudo. O BioNetStat pode assim ajudar a detectar possíveis novas descobertas associadas a mecanismos de funcionamento de sistemas. / The diversity of interactions, which are among elements of the biological systems, can be studied based on the networks theory. Moreover, the dynamic of these interactions is an inherent trait of those systems. In this sense, several tools have been proposed to compare networks, in that each network represents a state assumed by the system. However, the biological systems generally can assume much more than two biological states and none of the tools are able to compare structural characteristics among more than two networks simultaneously. To solve this issue, we developed a statistical tool to compare two or more networks and highlight key variables of a system. Here we describe the new method, called BioNetStat, that is able to compare correlation networks using traits that are based on graph spectra (the group of eigenvalues of the adjacency matrix), such as the spectral distribution. This measure is associated with several structural characteristics of networks such as the number of walks, diameter, and cliques. In addition to the spectral distribution, BioNetStat can also compare networks to the node centralities. We used two different biological datasets, tumoral cells genes expressions and plant metabolism, to evaluate the performance of BioNetStat and as case studies. The tool is implemented in an R package, and it also has a user-friendly interface. We showed that BioNetStat is efficient in distinguishing more than two networks. In comparison with a similar tool (GSCA), the increase in the number of compared networks reduces less the statistical power of the BioNetStat than the GSCA. Furthermore, BioNetStat is able to find signaling pathways in a bigger proportion than the GSCA, complementing tools proposed in the literature. In the case studies, the method pointed out variables, and sets of variables, with a central role in biological systems, which were not highlighted when only gene expression pattern or metabolomics were studied. For instance, BioNetStat allowed us to differentiate among cancer types and plant organs. The BioNetStat results bring new findings on what differentiate the states, giving us a systemic view of our study subject and affording the proposition of new hypotheses about the studied processes.

A theoretical study has been carried out on different methods available to analyze complex mufflers. Segmentation methods have been discussed in detail. The latest two port segmentation method has been discussed and employed for a few common muffler configurations, describing its implications and limitations. A new transfer matrix based method has been developed in view of the lacunae of the available approaches. This Integrated Transfer Matrix (ITM) method has been developed particularly to analyze complex mufflers. An Integrated transfer matrix relates the state variables across the entire cross-section of the muffler shell, as one moves along the axis of the muffler, and can be partitioned appropriately in order to relate the state variables of different tubes constituting the cross-section. The method presents a 1-D approach, using transfer matrices of simple acoustic elements which are available in the literature. Results from the present approach have been validated through comparisons with the available experimental and three dimensional FEM based results. The total pressure drop across perforated muffler elements has been measured experimentally and generalized expressions have been developed for the pressure loss across cross-flow expansion, cross-flow contraction elements, etc. These have then been used to derive empirical expressions for flow-acoustic resistance for use in the Integrated Transfer Matrix Method in order to predict the flow-acoustic performance of commercial mufflers. A flow resistance model has been developed to analytically determine the flow distribution and thereby pressure drop of mufflers. Generalized expressions for resistance across the perforated elements have been derived by means of flow experiments as mentioned above. The derived expressions have been implemented in a flow resistance network that has been developed to determine the pressure drop across any given complex muffler. The results have been validated with experimental data.

Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: The successful execution of the Physical Asset Management Strategy (PAMS) is an important value driver for organisations, whose core business is highly dependent on the service delivery of physical assets. However, contemporary research demonstrates that scheduled targets are often not met and the means to detect the constraints that can undermine the strategy execution efforts are deficient. The purpose of this thesis is to investigate Social Network Analysis (SNA) as a method to map primary constraints in Physical Asset Management Strategy Execution (PAMSE). A comprehensive literature review addresses the domains of Physical Asset Management (PAM) and SNA. The review of the literature is supported by interviews with practitioners in the field of PAM. Consequently, the challenges experienced in PAM are contextualised along with the capabilities of SNA and the most important constraints in PAMSE are identified. As an interim result, the study found that dysfunctional information flow and poor decision making are the primary constraints that could hinder the execution of a PAMS. As a consequence thereof, a SNA application methodology was developed in order to scrutinise these areas of concern. The methodology was applied at two research sites in the South African mining industry. Network data for the study was collected by surveys conducted in June and July 2012. The case studies demonstrate that a SNA application in PAM requires a number of prerequisites that are crucial to its success. Nevertheless, a successful SNA application may yield valuable results identifying the problems encountered in PAMSE. Most importantly, the SNA highlights overloaded key employees, collaborative breakdowns and excessive intradepartmental collaboration that have the potential to hinder the PAMSE process. The results were validated by means of dialogue with the individuals involved in the study. This study found that SNA can be used as a method to map the primary constraints experienced by PAMSE. It also emphasises that there are important prerequisites that have to be established for SNA to be successful. Future research could be carried out based on the results of this thesis, in order to design improvement plans for the studied research site and possibly conduct a second SNA to investigate whether the constraints, identified in the study, had been resolved. / AFRIKAANSE OPSOMMING: Die suksesvolle uitvoering van die Fisiese Batebestuurstrategie is ’n belangrike genereerder van waarde vir organisasies waar die kernbesigheid tot ’n groot mate afhanklik is van die dienslewering van fisiese bates. Hedendaagse navorsing wys egter dat geskeduleerde mylpale meestal nie bereik word nie en dat die metodes wat gebruik word om vas te stel watter beperkings die strategie-uitvoering ondermyn, ontoereikend is. Die doel van hierdie tesis is om die aanwending van Sosiale Netwerkanalise as ’n metode te ondersoek, om die primêre beperkings ten opsigte van die uitvoering van Fisiese Batebestuurstrategie te identifiseer. ’n Omvattende literatuuroorsig is uitgevoer wat die gebied van Fisiese Batebestuur en Sosiale Netwerkanalise aanspreek. Die literatuuroorsig is aangevul deur onderhoude met kundiges op die gebied van Fisiese Batebestuur. Sodoende kon die uitdagings van Fisiese Batebestuur bestudeer word in die konteks en vermoëns van Sosiale Netwerkanalise, en gevolglik is die belangrikste beperkings in Fisiese Batebestuurstrategie-Uitvoering geïdentifiseer. Die voorlopige resultate van die studie het bevind dat gebrekkige inligtingsvloei en swak besluitneming die hoofbeperkings is wat die uitvoering van ’n Fisiese Batebestuurstrategie kan verhinder. Gevolglik is ’n Sosiale Netwerkanalise toepassingsmetodiek ontwikkel om dié probleemareas onder die mikroskoop te plaas. Dié metodiek is dan op twee teiken-aanlegte in die Suid-Afrikaanse Mynbou Industrie toegepas. Netwerkdata vir die studie is deur middel van meningsopnames in Junie en Julie 2012 ingesamel. Die gevallestudies demonstreer dat ’n Sosiale Netwerkanalisetoepassing in Fisiese Batebestuur waardevolle resultate kan lewer met betrekking tot die probleme wat ondervind word in die uitvoer van Fisiese Batebestuurstrategie. Onder die belangrikste bevindings van die Sosiale Netwerkanalise tel die oorbelasting van sleutelposwerknemers, mislukte samewerking, asook eksessiewe interdepartementele samewerking met die potensiaal om die uitvoering van die Fisiese Batebestuurstrategie te verhinder. Die bevindings is deur dialoog met die individuele deelnemers in die ondersoek gestaaf. Die studie het bevind dat Sosiale Netwerkanalise geskik is as ’n metode om die primêre beperkings, wat ondervind word in die uitvoering van Fisiese Batebestuurstrategie, te identifiseer. Dit moet egter ook beklemtoon word dat daar belangrike voorvereistes bestaan, wat vir die suksesvolle toepassing van Sosiale Netwerkanalise in plek moet wees. Toekomstige navorsing kan gebaseer word op die uitkoms van dié tesis met die doel om ontwerpverbeteringsplanne vir die teiken-aanlegte op te stel. Daarbenewens kan ’n moontlike opvolg Sosiale Netwerkanalise uitgevoer word om te meet of die beperkings wat deur die ondersoek geidentifiseer is, oorkom is.

Study of complex networks by researchers from many disciplines has provided penetrating insights on various complex systems. A study of the world wide web from a network theoretic perspective has led to the design of new search engines [65]. The spread of diseases is now better understood by analyzing the underlying social network [26]. The study of metabolic networks, protein-protein interaction networks and the transcriptional regulatory networks with graph theoretic rigor, has led to the growing importance of an interdisciplinary approach [71]. Network centrality measures, which has been of interest to the social scientists, from as long as 1950 [13], is today studied extensively in the framework of complex networks. The thesis is an investigation on understanding human navigation with a network analytic approach using the well established and widely used centrality measures. Experiments were conducted on human participants to observe how people navigate in a complex environment. We made human participants way-find a destination from a source on a complex network and analyzed the paths that were taken. Our analysis established a fact that the learning process involved in navigating better in an unknown network boils down to learning certain strategic locations on the network. The vertices in the paths taken by the participants, when analyzed using the available centrality measures, enabled us to conclude experimentally that humans are naturally inclined to learn superior ranked vertices to navigate better and reach their intended destination. Our experiments were based on a word game called the word-morph. A generalized version of the experiment was conducted on a 6x6 photo collage with an underlying network hidden from the participant. A detailed analysis of the above experiment established a fact that, when humans are asked to take a goal-directed path, they were prone to take a path that passed through landmark nodes in the network. We call such paths center-strategic. We then present an algorithm that simulates the navigational strategy adopted by humans. We show empirically that the algorithm performs better than naive random walk based navigational techniques. We observe that the algorithm produces rich center-strategic paths on scale-free networks. We note that the effectiveness of the algorithm is highly dependent on the topology of the network by comparing its functionality on Erdos-Renyi networks and Barabasi-Albert networks. Then we discuss a lookahead algorithm to compute betweenness centrality in networks under vertex deletion operations. We show that the widely used Brandes algorithm can be modified to a lookahead version. We show that our proposed algorithm performs better than recomputing the betweenness centrality values in the vertex deleted graph. We show that our method works 20% faster than the Brandes algorithm.

Indiana University-Purdue University Indianapolis (IUPUI) / Biological systems behave differently under different conditions. Advances in sequencing technology over the last decade have led to the generation of enormous amounts of condition-specific data. However, these measurements often fail to identify low abundance genes/proteins that can be biologically crucial. In this work, a novel text-mining system was first developed to extract condition-specific proteins from the biomedical literature. The literature-derived data was then combined with proteomics data to construct condition-specific protein interaction networks. Further, an innovative condition-specific differential analysis approach was designed to identify key differences, in the form of subnetworks, between any two given biological systems. The framework developed here was implemented to understand the differences between limb regeneration-competent Ambystoma mexicanum and –deficient Xenopus laevis. This study provides an exhaustive systems level analysis to compare regeneration competent and deficient subnetworks to show how different molecular entities inter-connect with each other and are rewired during the formation of an accumulation blastema in regenerating axolotl limbs. This study also demonstrates the importance of literature-derived knowledge, specific to limb regeneration, to augment the systems biology analysis. Our findings show that although the proteins might be common between the two given biological conditions, they can have a high dissimilarity based on their biological and topological properties in the subnetwork. The knowledge gained from the distinguishing features of limb regeneration in amphibians can be used in future to chemically induce regeneration in mammalian systems. The approach developed in this dissertation is scalable and adaptable to understand differential subnetworks between any two biological systems. This methodology will not only facilitate the understanding of biological processes and molecular functions which govern a given system but also provide novel intuitions about the pathophysiology of diseases/conditions.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / The idea of socializing with other people of different backgrounds and cultures excites the web surfers. Today, there are hundreds of Social Networking sites on the web with millions of users connected with relationships such as "friend", "follow", "fan", forming a huge graph structure. The amount of data associated with the users in these Social Networking sites has resulted in opportunities for interesting data mining problems including friendship link and interest predictions, tag recommendations among others. In this work, we consider the friendship link prediction problem and study a topic modeling approach to this problem. Topic models are among the most effective approaches to latent topic analysis and mining of text data. In particular, Probabilistic Topic models are based upon the idea that documents can be seen as mixtures of topics and topics can be seen as mixtures of words. Latent Dirichlet Allocation (LDA) is one such probabilistic model which is generative in nature and is used for collections of discrete data such as text corpora. For our link prediction problem, users in the dataset are treated as "documents" and their interests as the document contents. The topic probabilities obtained by modeling users and interests using LDA provide an explicit representation for each user. User pairs are treated as examples and are represented using a feature vector constructed from the topic probabilities obtained with LDA. This vector will only capture information contained in the interests expressed by the users. Another important source of information that is relevant to the link prediction task is given by the graph structure of the social network. Our assumption is that a user "A" might be a friend of user "B" if a) users "A" and "B" have common or similar interests b) users "A" and "B" have some common friends. While capturing similarity between interests is taken care by the topic modeling technique, we use the graph structure to find common friends. In the past, the graph structure underlying the network has proven to be a trustworthy source of information for predicting friendship links. We present a comparison of predictions from feature sets constructed using topic probabilities and the link graph separately, with a feature set constructed using both topic probabilities and link graph.