Yes / As a global strategic reserve resource, rare earth has been widely used in important industries, such as military equipment and biomedicine. However, through existing analyses based on the total volume of rare earth trade, the competition and dependency behind the trade cannot be revealed. In this paper, based on the principle of trade preference and import similarity, we construct dependency and competition networks and use complex network analysis to study the evolution of the global rare earth trade network from 2002 to 2018. The main conclusions are as follows: the global rare earth trade follows the Pareto principle, and the trade network shows a scale-free distribution. China has become the largest country in both import and export of rare earth trade in the world since 2017. In the dependency network, China has become the most dependent country since 2006. The result of community division shows that China has separated from the American community and formed new communities with the Association of Southeast Asian Nations (ASEAN) countries. The United States of America has formed a super-strong community with European and Asian countries. In the competition network, the distribution of competition intensity follows a scale-free distribution. Most countries are faced with low-intensity competition, but competing countries are relatively numerous. The competition related to China has increased significantly. The competition source of the United States of America has shifted from Mexico to China. China, the USA, and Japan have been the cores of the competition network. / This work was supported by the Ministry of Education of the People’s Republic of China Humanities and Social Sciences Youth Foundation (Grant No. 22YJC910014), the Social Sciences Planning Youth Project of Anhui Province (Grant No. AHSKQ2022D138), and the Innovation Development Research Project of Anhui Province (Grant No. 2021CX053).

The study of complex brain networks, where the brain can be viewed as a system with various interacting regions that produce complex behaviors, has grown tremendously over the past decade. With both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for dynamic brain network analysis are needed. We first propose a paradigm for longitudinal brain network analysis over patient cohorts where we adapt the Stochastic Actor Oriented Model (SAOM) framework and model a subject's network over time as observations of a continuous time Markov chain. Network dynamics are represented as being driven by various factors, both endogenous (i.e., network effects) and exogenous, where the latter include mechanisms and relationships conjectured in the literature. We outline an application to the resting-state fMRI network setting, where we draw conclusions at the subject level and then perform a meta-analysis on the model output. As an extension of the models, we next propose an approach based on Hidden Markov Models to incorporate and estimate type I and type II error (i.e., of edge status) in our observed networks. Our model consists of two components: 1) the latent model, which assumes that the true networks evolve according to a Markov process as they did in the original SAOM framework; and 2) the measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for estimation. Lastly, we focus on the study of percolation - the sudden emergence of a giant connected component in a network. This has become an active area of research, with relevance in clinical neuroscience, and it is of interest to distinguish between different percolation regimes in practice. We propose a method for estimating a percolation model from a given sequence of observed networks with single edge transitions. We outline a Hidden Markov Model approach and EM algorithm for the estimation of the birth and death rates for the edges, as well as the type I and type II error rates. / 2018-07-25T00:00:00Z

O presente trabalho tem como meta auxiliar na resposta a um dos principais problemas estudados no campo das ciências econômicas: o quanto e como intervenções regulatórias afetam a dinâmica dos mercados. Para isso será feita uma análise dos dados contratuais de compra e venda de energia elétrica no ambiente livre de comercialização de energia brasileiro por meio de uma metodologia que utiliza métricas de análise de redes complexas para avaliação da competitividade. Os dados abordam a atividade dos agentes comercializadores de energia nesse mercado durante o período de 2006 a 2015. É estabelecido então um ranking mensal desses agentes e criada a rede por meio da verificação das trocas de posições nesses rankings. Os resultados da análise indicam em quais anos houve maior variação na competitividade no mercado e pela análise das redes resultantes verifica-se a formação de estruturas de mercado. Posteriormente os resultados são comparados com métricas tradicionais de avaliação de competitividade e concentração de mercado e, por fim, é feita uma avaliação qualitativa dos índices sob a luz das principais alterações regulatórias ocorridas no período / The main goal of this project is to assist in the answer to one of the main issues in the study of Economics: how regulatory interventions affect the dynamics of the markets, in this case specifically, electricity markets. This will be achieved through an analysis of the contractual data of electric energy in the free Brazilian energy market environment through a methodology that uses complex network analysis for the evaluation of competitiveness. The data covers the contracts of all energy traders of this market in the period from 2006 to 2015. A monthly ranking of these agents is established and a network is created through the verification of position changes in these rankings. The results of the analysis indicates in which years there was greater variation in competitiveness and the analysis of the resulting networks indicates market structures formation. The results are then compared with traditional metrics for competitiveness and market concentration. Finally, a qualitative assessment of the results is made considering the major regulatory changes that have occurred in the study period

O presente trabalho tem como meta auxiliar na resposta a um dos principais problemas estudados no campo das ciências econômicas: o quanto e como intervenções regulatórias afetam a dinâmica dos mercados. Para isso será feita uma análise dos dados contratuais de compra e venda de energia elétrica no ambiente livre de comercialização de energia brasileiro por meio de uma metodologia que utiliza métricas de análise de redes complexas para avaliação da competitividade. Os dados abordam a atividade dos agentes comercializadores de energia nesse mercado durante o período de 2006 a 2015. É estabelecido então um ranking mensal desses agentes e criada a rede por meio da verificação das trocas de posições nesses rankings. Os resultados da análise indicam em quais anos houve maior variação na competitividade no mercado e pela análise das redes resultantes verifica-se a formação de estruturas de mercado. Posteriormente os resultados são comparados com métricas tradicionais de avaliação de competitividade e concentração de mercado e, por fim, é feita uma avaliação qualitativa dos índices sob a luz das principais alterações regulatórias ocorridas no período / The main goal of this project is to assist in the answer to one of the main issues in the study of Economics: how regulatory interventions affect the dynamics of the markets, in this case specifically, electricity markets. This will be achieved through an analysis of the contractual data of electric energy in the free Brazilian energy market environment through a methodology that uses complex network analysis for the evaluation of competitiveness. The data covers the contracts of all energy traders of this market in the period from 2006 to 2015. A monthly ranking of these agents is established and a network is created through the verification of position changes in these rankings. The results of the analysis indicates in which years there was greater variation in competitiveness and the analysis of the resulting networks indicates market structures formation. The results are then compared with traditional metrics for competitiveness and market concentration. Finally, a qualitative assessment of the results is made considering the major regulatory changes that have occurred in the study period

Avui en dia, la música està esbiaixada cap al consum d'alguns artistes molt populars. Per exemple, el 2007 només l'1% de totes les cançons en format digital va representar el 80% de les vendes. De la mateixa manera, només 1.000 àlbums varen representar el 50% de totes les vendes, i el 80% de tots els àlbums venuts es varen comprar menys de 100 vegades. Es clar que hi ha una necessitat per tal d'ajudar a les persones a filtrar, descobrir, personalitzar i recomanar música, a partir de l'enorme quantitat de contingut musical disponible. Els algorismes de recomanació de música actuals intenten predir amb precisió el que els usuaris demanen escoltar. Tanmateix, molt sovint aquests algoritmes tendeixen a recomanar artistes famosos, o coneguts d'avantmà per l'usuari. Això fa que disminueixi l'eficàcia i utilitat de les recomanacions, ja que aquests algorismes es centren bàsicament en millorar la precisió de les recomanacions. És a dir, tracten de fer prediccions exactes sobre el que un usuari pugui escoltar o comprar, independentment de quant útils siguin les recomanacions generades. En aquesta tesi destaquem la importància que l'usuari valori les recomanacions rebudes. Per aquesta raó modelem la corba de popularitat dels artistes, per tal de poder recomanar música interessant i desconeguda per l'usuari. Les principals contribucions d'aquesta tesi són: (i) un nou enfocament basat en l'anàlisi de xarxes complexes i la popularitat dels productes, aplicada als sistemes de recomanació, (ii) una avaluació centrada en l'usuari, que mesura la importància i la desconeixença de les recomanacions, i (iii) dos prototips que implementen la idees derivades de la tasca teòrica. Els resultats obtinguts tenen una clara implicació per aquells sistemes de recomanació que ajuden a l'usuari a explorar i descobrir continguts que els pugui agradar. / Actualmente, el consumo de música está sesgada hacia algunos artistas muy populares. Por ejemplo, en el año 2007 sólo el 1% de todas las canciones en formato digital representaron el 80% de las ventas. De igual modo, únicamente 1.000 álbumes representaron el 50% de todas las ventas, y el 80% de todos los álbumes vendidos se compraron menos de 100 veces. Existe, pues, una necesidad de ayudar a los usuarios a filtrar, descubrir, personalizar y recomendar música a partir de la enorme cantidad de contenido musical existente. Los algoritmos de recomendación musical existentes intentan predecir con precisión lo que la gente quiere escuchar. Sin embargo, muy a menudo estos algoritmos tienden a recomendar o bien artistas famosos, o bien artistas ya conocidos de antemano por el usuario.Esto disminuye la eficacia y la utilidad de las recomendaciones, ya que estos algoritmos se centran en mejorar la precisión de las recomendaciones. Con lo cuál, tratan de predecir lo que un usuario pudiera escuchar o comprar, independientemente de lo útiles que sean las recomendaciones generadas. En este sentido, la tesis destaca la importancia de que el usuario valore las recomendaciones propuestas. Para ello, modelamos la curva de popularidad de los artistas con el fin de recomendar música interesante y, a la vez, desconocida para el usuario.Las principales contribuciones de esta tesis son: (i) un nuevo enfoque basado en el análisis de redes complejas y la popularidad de los productos, aplicada a los sistemas de recomendación,(ii) una evaluación centrada en el usuario que mide la calidad y la novedad de las recomendaciones, y (iii) dos prototipos que implementan las ideas derivadas de la labor teórica. Los resultados obtenidos tienen importantes implicaciones para los sistemas de recomendación que ayudan al usuario a explorar y descubrir contenidos que le puedan gustar. / Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.

The present thesis deals with the reign of Nyuserre, one of great Old Kingdom rulers who ruled in the mid-Fifth Dynasty (2402-2374+25 BC). A transformation of whole society of ancient Egypt came to pass during his reign as a consequence of the events in the late Fourth and early Fifth Dynasties, when the highest posts in the administrative system had passed over from members of the royal family to dignitaries of non-royal origin. This fact had been reflected in whole society and started numerous rivulets of change, which merged in a single river in Nyuserre's reign. The gradual transformation of Egyptian society from a kingdom to a state took place during this crucial period, and a number of innovations came about in various spheres (religion, society, administration, tomb architecture, etc.), mirroring a change in the participation in power. This situation was reflected primarily in dignitaries' tombs dated to the given period, which became indicators of the transformation of society. The research is focused on an analysis more than 100 tombs of high-ranking individuals and their family members, and particularly of their titulary, offering formulae, false doors (the central point of the funerary cult), etc. The individual chapters of the present thesis exemplify the most noticeable changes in...

Made available in DSpace on 2014-12-17T14:56:17Z (GMT). No. of bitstreams: 1 EdsonAMRB_DISSERT.pdf: 7502344 bytes, checksum: 78d70443ae2fd9033fe78b23c5cbd811 (MD5) Previous issue date: 2013-07-30 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Complex network analysis is a powerful tool into research of complex systems like brain networks. This work aims to describe the topological changes in neural functional connectivity networks of neocortex and hippocampus during slow-wave sleep (SWS) in animals submited to a novel experience exposure. Slow-wave sleep is an important sleep stage where occurs reverberations of electrical activities patterns of wakeness, playing a fundamental role in memory consolidation. Although its importance there s a lack of studies that characterize the topological dynamical of functional connectivity networks during that sleep stage. There s no studies that describe the topological modifications that novel exposure leads to this networks. We have observed that several topological properties have been modified after novel exposure and this modification remains for a long time. Major part of this changes in topological properties by novel exposure are related to fault tolerance / A an?lise da topologia de redes ? uma poderosa ferramenta no estudo de sistemas complexos tal como as redes cerebrais. Este trabalho procura descrever as mudan?as na topologia de redes de conex?o funcional em neur?nios do c?rtex sensorial e do hipocampo durante o sono de ondas lentas (SWS) em animais expostos ? novidade. O sono de ondas lentas ? um importante estado do sono onde h? reverbera??o de padr?es de atividade el?trica ocorridos na vig?lia, tendo com isso papel fundamental na consolida??o de mem?ria. Apesar de sua import?ncia ainda n?o h? estudos que caracterizam a din?mica da topologia de redes de conex?o funcional durante este estado. Tampouco h? estudos que descrevem as modifica??es topol?gicas que a exposi??o ? novidade traz a essas redes. Observamos que v?rias propriedades topol?gicas s?o modificadas ap?s a exposi??o ? novidade e que tais modifica??es se mant?m por um longo per?odo de tempo. A maior parte das propriedades modificadas pela exposi??o ? novidade est? relacionada ? toler?ncia ? falha

The study of how social organizations work, change and develop is central to sociology and to our understanding of the social world and its transformations. At the same time, the underlying principles of organizational dynamics are extremely difficult to investigate. This is partly due to the difficulties of tracking organizations, individuals and their interactions over relatively long periods of time. But it is also due to limitations in the kinds of quantitative methods used to tackle these questions, which are for the most part based on regression analysis. This thesis seeks to improve our understanding of social organizing by using models to explore and describe the logics of the structures and mechanisms underlying organizational change. Particular emphasis is given to the modeling process, the use of new concepts and analogies, and the application of interdisciplinary methods to get new insights into classical sociological questions. The thesis consists of an introductory part and five studies (I-V). Using Swedish longitudinal data on employment in the Stockholm Region, the studies tackle different dimensions of organizational dynamics, from organizational structures and growth processes to labor mobility and employment trajectories. The introductory chapters contextualize the studies by providing an overview of theories, concepts and quantitative methods that are relevant for the modeling of organizational dynamics.  The five studies look into various aspects of organizational dynamics with the help of complementary data representations and non-traditional quantitative methods. Study I analyzes organizational growth statistics for different sectors and industries. The typically observed heavy-tailed statistical patterns for the size and growth rate distributions are broken down into a superposition of interorganizational movements. Study II models interorganizational movements as a labor flow network. Organizations tend to be more tightly linked if they belong to the same ownership sector. Additionally, public organizations have a more stable connection structure. Study III uses a similarity-based method called homogeneity analysis to map out the social space of large organizations in the Stockholm Region. A social distance is then derived within this space, and we find that the interorganizational movements analyzed in Studies I and II take place more often between organizations that are closer in social space and in the same network community. Study IV presents an approach to organizational dynamics based on sequences of employment states. Evidence for a positive feedback mechanism is found for large and highly sequence-diverse public organizations. Finally, Study V features an agent-based model where we simulate a social influence mechanism for organizational membership dynamics. We introduce a parameter analogous to a physical temperature to model contextual influence, and the familiar growth distributions are recovered as an intermediate case between extreme parameter values. The thesis as a whole provides suggestions for a more process-oriented modeling approach to social organizing that gives a more prominent role to the logics of organizational change. Finally, the series of methodological tools discussed can be useful for the analysis of many other social processes and more broadly for the development of quantitative sociological methods. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.</p><p> </p>

A prominent challenge in the healthcare system today is the limitation of resources in combi- nation with an increasing need for healthcare services. The pressure on healthcare is already extremely high and increasing due to a larger number of people seeking care as well as an aging population with an increased need for care. Therefore, it becomes more important to distribute resources effectively within healthcare to ensure high-quality care for everyone. Still, research shows that overcrowding of emergency departments and hospital wards is increasing affecting patient safety negatively with several negative implications including higher rates of medical errors and higher mortality. The problem is that healthcare is a complex system with many components that are interrelated and therefore hard to study with traditional approaches. Despite the huge quantity of studies on the overcrowding problem, there is yet to find a solution that could solve the problem. Thus, this thesis aims to design a data-driven pipeline to explore the clinical and logistical complexity of Emergency medicine patients admitted to hospital wards adopting a complex graph approach. Complex network theory provides a suitable tool to investigate complex networks by breaking complex systems down into smaller graphs with objects (nodes) and studying the relationship between these through various analysis tools. In this thesis, five complex networks were constructed representing co-morbidities in the car- diac, medicine, surgery, stroke, and orthopedic wards of the Academic Hospital of Uppsala, a hospital suffering from overcrowding. These networks were analyzed using degree distribution, centrality metrics, clustering coefficient, and community detection to reveal structural and clin- ical patterns. A comprehensive network of all hospital co-morbidities was also created and an- alyzed to compare it with the ward structures. Additionally, a network mapping patient flow from the emergency department based on chief complaints and ICD codes to wards was created and analyzed to identify admission patterns. The analysis of the co-morbidity networks revealed that there was an indication of structure between the wards. This was based on the visualization of nodes and edges of the networks, identified communities, and community comparisons between the wards. Further, it showed that there was a big overlap of common co-morbidities which could indicate the contrary. But it was also revealed that in terms of community structure, the wards were considerably different from each other indicating a good separation of diseases. The results of this research show that complex network theory could be used to increase the understanding of the complexity of healthcare wards in terms of the structure of diseases as well as clinical variability and allow for a discussion regarding if this is related to clinical or logistical factors. It also shows the potential of using complex network theory to increase the understanding of the path patients take from the emergency department to the wards based on the community detection analysis showing that there is a structure of where patient ends up based on the assigned ICD code and chief complaint in the emergency department. Previous studies have typically focused on specific diseases or patient flow within a single ward or the emergency department. This approach offers a tool to examine patient logistics across multiple wards alongside their clinical characteristics. The insights gained could help improve hospital structure by more efficiently distributing patients between wards, thereby enhancing resource use and hospital operations. Further research using complex network theory could deepen understanding of overcrowding issues and identify potential solutions. / En stor utmaning inom sjukvårdssystemet idag är begräsningen av resurser i kombination med ett ökat vårdbehov. Trycket på sjukvården är redan högt och ökar till följd av ett ökat antal personer som söker vård samt en åldrande befolkning med ett ökat vårdbehov. Därav blir det viktigare att fördela resurser inom sjukvården på ett effektivt sätt för att säkerställa en högkva- litativ vård till alla. Forskning visar dock att överbeläggningar på akutvårdsavdelningar och sjukvårdsavdelningar ökar vilket påverkar patientsäkerheten negativt med flera negativa kon- sekvenser däribland en högre andel medicinska misstag och en högre mortalitet. Problemet är att sjukvården är ett komplext system med många komponenter som samverkar och det är därav svårt att studera med traditionella tillvägagångssätt. Trots det höga antalet studier på överbeläggningar inom sjukvården behöver man fortfarande hitta en lösning på problemet. Därav är målet med denna avhandling att designa en datadriven pipeline för att undersöka den kliniska och logistiska komplexiteten hos patienter inlagda från akutvårdsavdelningen med hjälp av en komplex grafmetodik. Komplex nätverksteori är ett lämpligt verktyg för att studera komplexa nätverk genom att bryta ned det i mindre komponen- ter och undersöka sambanden mellan dem med hjälp av olika analysverktyg. I denna avhandling skapades 5 komplexa nätverk som representerade komorbiditeter utifrån tilldelad ICD-10-kod på hjärt-, medicin-, kirurgi-, stroke- och ortopediska avdelningen vid det akademiska sjukhuset i Uppsala, ett sjukhus som för närvarande lider av överbeläggningar. Nätverken analyserades med hjälp av gradfördelning, olika centralitetsmått, klusterkoefficient och samhällsdetektering för att identifiera skillnader eller likheter när det gäller struktur och klinisk variation. Ett heltäckande komplext nätverk skapades där alla komorbiditeter på hela sjukhuset inkluderades för att möjliggöra en jämförelse med strukturen på avdelningarna. Utö- ver detta, skapades och analyserades ett nätverk för att kartlägga patientflödet från akuten till sjukvårdsavdelningarna baserat på huvudorsak till patientens akutbesök och ICD kod. Analysen av samhällsstrukturen visade att det fanns en indikation av struktur mellan avdelning- arna. Detta baserat på visualisering av noder och kopplingar i nätverken, identifierade sam- hällen samt jämförelser av samhällen mellan avdelningarna. Vidare visade det dock att det fanns ett stort överlapp av vanliga komorbiditeter vilket kunde indikera motsatsen. Det visades dock att även när det gäller samhällsstruktur var avdelningarna väldigt olika vilket indikerade en god separering av sjukdomar. Resultaten av denna forskning visar att komplex nätverksteori kan användas för att öka förstå- elsen för komplexiteten på sjukvårdsavdelningarna gällande strukturen mellan sjukdomar såväl som klinisk variationen och öppnar upp för en diskussion om dessa är relaterade till kliniska eller logistiska faktorer. Det visar också potentialen att använda komplex nätverksteori för att öka förståelsen för den väg som patienterna tar från akutvårdsavdelningen till avdelningarna baserat på samhällsdetekteringsanalysen som visar att det finns en struktur av var patienten hamnar baserat på den tilldelade ICD-koden och huvudklagomål från akutvårdsavdelningen. Tidigare studier som har använt detta tillvägagångssätt har i huvudsak undersökt specifika sjuk- domar eller flöden på en specifik avdelning eller akutvårdsavdelning. Det här tillvägagångssät- tet ger ett verktyg för att utforska logistiken för patienters rutter till olika avdelningar samtidigt som deras kliniska egenskaper beaktas. Resultaten genom denna pipeline kan ge en grund för att öka förståelsen för hur man bättre kan strukturera sjukhuset genom att dela patienter mellanvavdelningar och genom detta effektivisera användningen av resurser och potentiellt förbättra rutiner på sjukhuset. Genom vidare studier, kan komplex nätverksteori användas för att öka förståelsen kring faktorer relaterade till problemet med överbeläggningar och hitta potentiella lösningar på problemet.