Made available in DSpace on 2014-06-12T23:15:47Z (GMT). No. of bitstreams: 2 arquivo9271_1.pdf: 3117344 bytes, checksum: 7e3891199e4672408f72d2f95dbc6a6c (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004 / Simulações computacionais com o método de Monte Carlo foram realizadas para a água, para o metanol e mistura água-metanol, em condições ambiente e algumas próximas da crítica, utilizando os modelos de quatro (TIP4P) e cinco (TIP5P) sítios para a água e um modelo de três sítios para o metanol. As estruturas geradas nas simulações, foram então utilizadas para análise das ligações de hidrogênio e presença de padrões de small-world. O comportamento de small-world foi caracterizado para água supercrítica, ao passo que para a água em condições ambiente um cluster gigante é formado e observa-se a percolação no sistema. De forma análoga a mistura água-metanol percola no sistema em condições ambiente e apresenta padrão de small-world na condição supercrítica (500 bar e 400oC). No caso do metanol verificamos uma estrutura que o distancia do comportamento randômico, entretanto, a presença ou não de padrões de small-world ainda é inconclusiva. Simulações computacionais com o método de Monte Carlo também foram realizadas para sais contendo o cátion BMI+ (1-Butil-3-Metilimidazólio) e os ânions Cl- e BF4 -. Os sais deste cátion e ânions são líquidos à temperatura ambiente, sendo então denominados de líquidos iônicos. Verificou-se que a flexibilidade da cadeia n-butil é provavelmente a responsável pela desordem dos sais que os mantém no estado líquido. As simulações de líquidos, geralmente, necessitam de um grande número de moléculas, de modo que o cálculo das interações pode tornar-se impraticável. Sendo assim, foi testado um método alternativo baseado em redes neurais artificiais (RNAs). Esta técnica foi testada para a água e extendida para descrever as interações entre as espécies iônicas formadoras dos líquidos iônicos. Superfícies de energia potencial (SEPs) foram então preditas por estes modelos baseados em RNAs, com pequenos erros randômicos, tanto para água líquida, como para o par [BMI+][BF4 -]. Contudo, estes erros ainda são suficientemente grandes para produzir simulações de Monte Carlo da água líquida inconsistentes com os resultados obtidos usando o modelo TIP5P. É importante citar que as simulações de Monte Carlo realizadas usando RNAs são 2 a 12 vezes mais rápidas do que aquelas usando os potenciais analíticos

Ziel dieser Arbeit ist es, Kollokationen auf Satzbasis aus dem Wortschatz-Lexikon Leipzig als Datenbasis nehmend, ein Verfahren zu entwickeln, welches die den Satzkollokationen immanenten Beziehungen zwischen den Wörtern erkennen und handhaben kann. Weiterhin ist es Ziel, diese Beziehungen für wortbedeutungsorientierte Klassifikationsverfahren zu erschliessen und deren unmittelbare Anwendung zu demonstrieren, indem Sachgebietszuweisungen über diese Beziehungen weitervererbt werden können. Es wird gezeigt, dass Cluster in den Satzkollokationen mit einer Approximation für die maximale Clustersuche mit rechnerisch geringem Aufwand gefunden werden können, wenn diese als ein Graph mit der seit kurzem untersuchten small-world Eigenschaft betrachtet werden. Es wird daraufhin ein Disambiguierungsverfahren konstruiert, welches Beziehungen zwischen einem Wort und seinen unmittelbar angrenzenden Clustern berechnet, wobei die verschiedenen Cluster den diversen Gebrauchskontexten und damit auch unter anderem den Bedeutungen des Wortes entsprechen. Dieses Disambiguierungsverfahren dient dann als Grundlage für den Entwurf eines Sachgebietsklassifizierungsverfahrens, welches zu einer inhaltlich homogenen Wortgruppe, zum Beispiel einem Sachgebiet, weitere passende Wörter finden kann. Die vorgeschlagenen Verfahren wurden prototypisch implementiert und Beispiele werden auch im Hinblick auf eine Praxisanwendung diskutiert.

Prisoner's Dilemma is a game theory model used to describe altruistic behavior seen in various populations. This theoretical game is important in understanding why a seemingly selfish strategy does persist and spread throughout a population that is mixing homogeneously at random. For a population with structure determined by social interactions, Prisoner's Dilemma brings to light certain requirements for the altruistic strategy to become established. Monte Carlo simulations of Prisoner's Dilemma are carried out using both simulated social networks and a dataset of a real social network. In both scenarios we confirm the requirements for the persistence of altruism in a population.

Prisoner's Dilemma is a game theory model used to describe altruistic behavior seen in various populations. This theoretical game is important in understanding why a seemingly selfish strategy does persist and spread throughout a population that is mixing homogeneously at random. For a population with structure determined by social interactions, Prisoner's Dilemma brings to light certain requirements for the altruistic strategy to become established. Monte Carlo simulations of Prisoner's Dilemma are carried out using both simulated social networks and a dataset of a real social network. In both scenarios we confirm the requirements for the persistence of altruism in a population.

With the transition from single-core to multi-core computing and CMOS technology reaching its physical limits, new computing architectures which are scalable, robust, and low-power are required. A promising alternative to conventional computing architectures are Cellular Automata (CA) networks and Random Boolean Networks (RBN), where simple computational nodes combine to form a network that is capable of performing a larger computational task. It has previously been shown that RBNs can offer superior characteristics over mesh networks in terms of robustness, information processing capabilities, and manufacturing costs while the locally connected computing elements of a CA network provide better scalability and low average interconnect length. This study presents a low level hardware analysis of these architectures using a framework which generates the HDL code and netlist of these networks for various network parameters. The HDL code and netlists are then used to simulate these new computing architectures to estimate the latency, area and power consumed when implemented on silicon and performing a pre-determined computation. We show that for RBNs, information processing is faster compared to a CA network, but CA networks are found to a have lower and better distribution of power dissipation than RBNs because of their regular structure. A well-established task to determine the latency of operation for these architectures is presented for a good understanding of the effect of non-local connections in a network. Programming the nodes for this purpose is done externally using a novel self-configuration algorithm requiring minimal hardware. Configuration for RBNs is done by sending in configuration packets through a randomly chosen node. Logic for identifying the topology for the network is implemented for the nodes in the RBN network to enable compilers to analyze and generate the configuration bit stream for that network. On the other hand, the configuration of the CA network is done by passing in configuration data through the inputs on one of the sides of the cell array and shifting it into the network. A study of the overhead of the network configuration and topology identification mechanisms are presented. An analysis of small-world networks in terms of interconnect power and information propagation capability has been presented. It has been shown that small-world networks, whose randomness lies between that of completely regular and completely irregular networks, are realistic while providing good information propagation capability. This study provides valuable information to help designers make decisions for various performance parameters for both RBN and CA networks, and thus to find the best design for the application under consideration.

Many real life complex networks consists of a tremendous amount of nodes and edges which make them difficult to extract and analyze. This thesis aims to examine what network prop- erties that can be deduced when considering small samples of a complex network and how well they correspond to the characteristics of the complete network. This is of importance as sampling will most likely be the de facto method when analyzing complex networks in the future. The study examine the scale-free property, the small-world property and the com- munity structure of the network spanned by the KTH domain. The method consisted of gathering data about the network through sampling it in a breadth-first manner using a web crawler. The samples was then compared with respect to each property. The results was that good approximations of the scale-free property could be made from small samples of the KTH network. However, no good approximation could be made about the small-world property using the sampling technique. Good approximations about a node’s community affiliation could be observed. However, general conclusions of the com- plete network’s community structures could not be made. To summarize, the result indi- cate that small samples can be used to approximate some properties of the complete KTH network. However, to determine if the result is true for the general case more research is necessary. / Komplexa nätverk i vår omvärld består av ett stort antal hörn och kanter vilket gör dem svå- ra att extrahera och analysera. Denna rapport undersöker vilka nätverksegenskaper som kan härledas vid undersökningen av små stickprov av ett nätverk och hur bra dessa representerar egenskaperna hos det fullständiga nätverket. Detta är av betydelse eftersom användandet av små stickprov kommer troligtvis att vara standarden vid undersökningar av nätverk i framtiden. Denna studie undersökte scale-free egenskapen, small-world egenskapen och community strukturen för nätverket som omfattas av KTH domaänen. Metoden innefattade att samla in data om nätverket genom stickprov baserat på en bredden-först sökning. Detta realiserades genom att använda en sökrobot. Sedan jämfördes de olika stickproven med avseende på de olika nätverksegenskaperna. Resultetat visade att nätverkets scale-free egenskap kunde approximaeras med små stickprov. Däremot var det inte möjligt att approximera nätverkets small-world egenskap genom användet av den givna stickprovsmetoden. Goda approximationer observerades för att avgöra ett hörns community tillhörighet men den allmäna community strukturen kunde inte approximeras. Sammanfattningsvis visade resultatet att stickprov kan användas för att approximera vissa egenskaper hos det fullständiga KTH nätverket men att mer forskning krävs för att avgöra om resultaten kan generaliseras.

It has been observed that real-world random networks like the WWW, Internet, social networks, citation networks, etc., organize themselves into closely-knit groups that are locally dense and globally sparse. These closely-knit groups are termed communities. Nodes within a community are similar in some aspect. For example in a WWW network, communities might consist of web pages that share similar contents. Mining these communities facilitates better understanding of their evolution and topology, and is of great theoretical and commercial significance. Community related research has focused on two main problems: community discovery and community identification. Community discovery is the problem of extracting all the communities in a given network, whereas community identification is the problem of identifying the community, to which, a given set of nodes belong. We make a comparative study of various existing community-discovery algorithms. We then propose a new algorithm based on bibliographic metrics, which addresses the drawbacks in existing approaches. Bibliographic metrics are used to study similarities between publications in a citation network. Our algorithm classifies nodes in the network based on the similarity of their neighborhoods. One of the drawbacks of the current community-discovery algorithms is their computational complexity. These algorithms do not scale up to the enormous size of the real-world networks. We propose a hash-table-based technique that helps us compute the bibliometric similarity between nodes in O(m ?) time. Here m is the number of edges in the graph and ?, the largest degree. Next, we investigate different centrality metrics. Centrality metrics are used to portray the importance of a node in the network. We propose an algorithm that utilizes centrality metrics of the nodes to compute the importance of the edges in the network. Removal of the edges in ascending order of their importance breaks the network into components, each of which represent a community. We compare the performance of the algorithm on synthetic networks with a known community structure using several centrality metrics. Performance was measured as the percentage of nodes that were correctly classified. As an illustration, we model the ucf.edu domain as a web graph and analyze the changes in its properties like densification power law, edge density, degree distribution, diameter, etc., over a five-year period. Our results show super-linear growth in the number of edges with time. We observe (and explain) that despite the increase in average degree of the nodes, the edge density decreases with time.

When it comes to hospice care, patients and their families residing in rural counties need competent rural community-based hospice social workers on their team. The study of information seeking behaviors of rural community-based hospice social workers during the global COVID-19 pandemic is significant as there is a need to fill gaps regarding how this specific medical discipline performs their job responsibilities when duties become more complicated due to evolving infection control protocols, decreased direct access to patients, and poor internet services. Community-based rural hospice social workers rely on up-to-date information and resources when providing support services to patients and their families. This has become particularly important during the global pandemic COVID-19. Utilizing Chatman's small world theory, this research lends itself to community-based hospice social workers identifying solutions to the challenges of finding timely and accurate COVID-19 information and resources for hospice patients and their families. Purposive sampling, semi-structured interviews, and qualitative social network analysis (SNA) with ATLAS.ti comprise the methodology for this research. The purpose of this study is to investigate the information seeking behaviors of community-based hospice rural social workers in order to understand how they select their COVID-19 information resources.

In this dissertation, the critical scaling behavior of magnetic Ising models with long range interactions is studied. These long range interactions, when imposed in addition to interactions on a regular lattice, lead to small-world graphs. By using large-scale Monte Carlo simulations, together with finite-size scaling, the critical behavior of a number of different models is obtained. The Ising models studied in this dissertation include the z-model introduced by Scalettar, standard small-world bonds superimposed on a square lattice, and physical small-world bonds superimposed on a square lattice. From the scaling results of the Binder 4th order cumulant, the order parameter, and the susceptibility, the long-range interaction is found to drive the systems behavior from Ising-like to mean field, and drive the critical point to a higher temperature. It is concluded that with a large amount of strong long-range connections (compared to the interactions on regular lattices), so the long-range connection density is non-vanishing, systems have mean field behavior. With a weak interaction that vanishes for an infinite system size or for vanishing density of long-range connections the systems have Ising-like critical behavior. The crossover from Ising-like to meanield behavior due to weak long-range interactions for systems with a large amount of long-range connections is also discussed. These results provide further evidence to support the existence of physical (quasi-) small-world nanomaterials.

No description available.