Permeability of porous materials determined from the Euler characteristic

We study the permeability of quasi two-dimensional porous structures of randomly placed overlapping monodisperse circular and elliptical grains. Measurements in microfluidic devices and lattice Boltzmann simulations demonstrate that the permeability is determined by the Euler characteristic of the conducting phase. We obtain an expression for the permeability that is independent of the percolation threshold and shows agreement with experimental and simulated data over a wide range of porosities. Our approach suggests that the permeability explicitly depends on the overlapping probability of grains rather than their shape

Transcriptional down-regulation of suppressor of cytokine signaling (SOCS)-3 in chronic obstructive pulmonary disease

Background: Tobacco is a leading environmental factor in the initiation of respiratory diseases and causes chronic obstructive pulmonary disease (COPD). Suppressor of cytokine signaling (SOCS) family members are involved in the pathogenesis of many inflammatory diseases and SOCS-3 has been shown to play an important role in the regulation, onset and maintenance of airway allergic inflammation indicating that SOCS-3 displays a potential therapeutic target for anti-inflammatory respiratory drugs development. Since chronic obstructive pulmonary disease (COPD) is also characterized by inflammatory changes and airflow limitation, the present study assessed the transcriptional expression of SOCS-3 in COPD. Methods: Real-time PCR was performed to assess quantitative changes in bronchial biopsies of COPD patients in comparison to unaffected controls. Results: SOCS-3 was significantly down-regulated in COPD at the transcriptional level while SOCS-4 and SOCS-5 displayed no change. Conclusions: It can be concluded that the presently observed inhibition of SOCS-3 mRNA expression may be related to the dysbalance of cytokine signaling observed in COPD

Optimized network structure and routing metric in wireless multihop ad hoc communication

Inspired by the Statistical Physics of complex networks, wireless multihop ad hoc communication networks are considered in abstracted form. Since such engineered networks are able to modify their structure via topology control, we search for optimized network structures, which maximize the end-to-end throughput performance. A modified version of betweenness centrality is introduced and shown to be very relevant for the respective modeling. The calculated optimized network structures lead to a significant increase of the end-to-end throughput. The discussion of the resulting structural properties reveals that it will be almost impossible to construct these optimized topologies in a technologically efficient distributive manner. However, the modified betweenness centrality also allows to propose a new routing metric for the end-to-end communication traffic. This approach leads to an even larger increase of throughput capacity and is easily implementable in a technologically relevant manner.Comment: 25 pages, v2: fixed one small typo in the 'authors' fiel

Relevance of Structural Brain Connectivity to Learning and Recovery from Stroke

The physical structure of white matter fiber bundles constrains their function. Any behavior that relies on transmission of signals along a particular pathway will therefore be influenced by the structural condition of that pathway. Diffusion-weighted magnetic resonance imaging provides localized measures that are sensitive to white matter microstructure. In this review, we discuss imaging evidence on the relevance of white matter microstructure to behavior. We focus in particular on motor behavior and learning in healthy individuals and in individuals who have suffered a stroke. We provide examples of ways in which imaging measures of structural brain connectivity can inform our study of motor behavior and effects of motor training in three different domains: (1) to assess network degeneration or damage with healthy aging and following stroke, (2) to identify a structural basis for individual differences in behavioral responses, and (3) to test for dynamic changes in structural connectivity with learning or recovery

Guided Machine Learning for Business Users

Advanced and predictive analytics are playing an increasingly important role in all industries. However, the productive use of new analytic methods and applications seems to stagnate. One reason for this is a lack of people with the necessary data science skills, especially for small and medium sized businesses. This paper proposes design principles that are important for enhancing the usage and adoption of applications for advanced and predictive analytics. The identified principles are implemented in a prototype application for predictive maintenance which can be used by employees without knowledge of data mining and machine learning

Selbstorganisierende Strukturen und Metriken komplexer Netzwerke

In nature, society and technology many disordered systems exist, that show emergent behaviour, where the interactions of numerous microscopic agents result in macroscopic, systemic properties, that may not be present on the microscopic scale. Examples include phase transitions in magnetism and percolation, for example in porous unordered media, biological, and social systems. Also technological systems that are explicitly designed to function without central control instances, like their prime example the Internet, or virtual networks, like the World Wide Web, which is defined by the hyperlinks from one web page to another, exhibit emergent properties. The study of the common network characteristics found in previously seemingly unrelated fields of science and the urge to explain their emergence, form a scientific field in its own right, the science of complex networks. In this field, methodologies from physics, leading to simplification and generalization by abstraction, help to shift the focus from the implementation's details on the microscopic level to the macroscopic, coarse grained system level. By describing the macroscopic properties that emerge from microscopic interactions, statistical physics, in particular stochastic and computational methods, has proven to be a valuable tool in the investigation of such systems. The mathematical framework for the description of networks is graph theory, in hindsight founded by Euler in 1736 and an active area of research since then. In recent years, applied graph theory flourished through the advent of large scale data sets, made accessible by the use of computers. A paradigm for microscopic interactions among entities that locally optimize their behaviour to increase their own benefit is game theory, the mathematical framework of decision finding. With first applications in economics e.g. Neumann (1944), game theory is an approved field of mathematics. However, game theoretic behaviour is also found in natural systems, e.g. populations of the bacterium Escherichia coli, as described by Kerr (2002). In the present work, a combination of graph theory and game theory is used to model the interactions of selfish agents that form networks. Following brief introductions to graph theory and game theory, the present work approaches the interplay of local self-organizing rules with network properties and topology from three perspectives. To investigate the dynamics of topology reshaping, coupling of the so called iterated prisoners' dilemma (IPD) to the network structure is proposed and studied in Chapter 4. In dependence of a free parameter in the payoff matrix, the reorganization dynamics result in various emergent network structures. The resulting topologies exhibit an increase in performance, measured by a variance of closeness, of a factor 1.2 to 1.9, depending in the chosen free parameter. Presented in Chapter 5, the second approach puts the focus on a static network structure and studies the cooperativity of the system, measured by the fixation probability. Heterogeneous strategies to distribute incentives for cooperation among the players are proposed. These strategies allow to enhance the cooperative behaviour, while requiring fewer total investments. Putting the emphasis on communication networks in Chapters 6 and 7, the third approach investigates the use of routing metrics to increase the performance of data packet transport networks. Algorithms for the iterative determination of such metrics are demonstrated and investigated. The most successful of these algorithms, the hybrid metric, is able to increase the throughput capacity of a network by a factor of 7. During the investigation of the iterative weight assignments a simple, static weight assignment, the so called logKiKj metric, is found. In contrast to the algorithmic metrics, it results in vanishing computational costs, yet it is able to increase the performance by a factor of 5.In Natur, Gesellschaft und Technik existiert eine Vielzahl ungeordneter Systeme, für die die Emergenz makroskopischer Eigenschaften aus mikroskopischen Wechselwirkungen charakteristisch sind. Beispiele für emergente Eigenschaften in physikalischen Systemen sind Phasenübergänge, wie sie etwa in der Perkolation auftreten. Weitere bedeutende Beispiele sind komplexe technologische Systeme, insbesondere solche, bei deren Entwicklung eine hohe Ausfallsicherheit ohne zentrale Kontrollinstanz eine wichtige Rolle spielt. Ein archetypisches Beispiel eines komplexen, selbstorganisierten Systems, gesteuert durch eigennützig handelnde Einheiten, sind Kommunikationsnetzwerke, insbesondere das Internet. Motiviert durch die immense Bedeutung solcher Kommunikationsnetze für die heutige moderne Gesellschaft untersucht die vorliegende Arbeit Wege zur Optimierung dieser Netze. Hier helfen Methoden der Physik, wie Generalisierung und Reduktion auf grundlegende Eigenschaften, die Aufmerksamkeit von implementationsspezifischen Details der mikroskopischen Dynamik auf makroskopische Folgen zu lenken. Insbesondere die Konzepte und Methoden der Statistischen Physik erweisen sich im Umgang mit komplexen Netzwerken als nützlich. Der mathematische Rahmen zur Beschreibung von Netzwerken ist die Graphentheorie, in deren Formalismus vernetzte Strukturen als Menge von Knoten dargestellt werden, welche durch Kanten miteinander verbunden sind. Ein mathematischer Formalismus zur Beschreibung von eigenständig handelnden Entitäten und deren Wechselwirkung ist die Spieltheorie. Diese beschreibt das Verhalten und die Entscheidungsfindung von Agenten bzw. Spielern, die eigenständig und eigennützig ihr Verhalten, charakterisiert durch ihre Strategie, optimieren. Die vorliegende Arbeit nutzt die Verknüpfung dieser beiden Formalismen um Interaktionen vernetzter eigenständiger Entitäten zu modellieren, und die daraus resultierenden emergenten Eigenschaften der Netzstruktur zu untersuchen. Das Konzept der Reorganisation von Netzwerken wird durch Kopplung der Netzstruktur an die Spieldynamik des Iterated Prisoners' Dilemma untersucht. Dies erlaubt eine Beeinflussung der Reorganisationsdynamik durch kontinuierliche Änderung der Spielparameter und ermöglicht damit eine Optimierung der Spieldynamik in Bezug auf Eigenschaften der emergenten Netzstruktur. Die vorgestellte Art der selbstorganisierenden Netzwerkoptimierung wird exemplarisch anhand einer Quantifizierung der Netzwerkperformanz demonstriert. In Abhängigkeit des gewählten Spielparameters wird im Vergleich zu Zufallsgraphen eine Erhöhung der Performanz um den Faktor 1.2 bis 1.9 erreicht. Eine weitere Herangehensweise zur Untersuchung von Spielen auf Netzwerken wird verfolgt, indem die Kooperativität von Prisoners' Dilemma Spielern auf dem Netz, quantifiziert durch die sogenannte Fixation Probability, untersucht wird. Hier werden Strategien zur Verteilung individueller Kooperationsanreize untersucht. Die vorgeschlagenen Strategien resultieren relativ zur globalen homogenen Verteilung derselben Summe der Anreize zu einer um den Faktor 5 erhöhten Kooperativität. An die spieltheoretischen Betrachtungen anschließend liegt das Hauptaugenmerk der folgenden Betrachtungen auf Kommunikationsnetzwerken. Zusätzlich zu durch vorangegangene Arbeiten vorgeschlagenen Metriken werden drei weitere Metriken eingeführt, von denen sich zwei, die Hybrid Metrik und die logKiKj Metrik, als äußerst erfolgreich im Sinne einer Optimierung der Throughput Capacity erweisen, was durch ausführliche numerische Simulationen belegt wird. Die Vorteile der hier eingeführten Metriken liegen im Fall der Hybrid Metrik in der unter den verglichenen Metriken besten resultierenden Performanz für Netze mit mehr als 3000 Knoten, mit einer mittleren Steigerung um den Faktor 7 im Vergleich zur Performanz ohne Metrik. Für Netze mit bis zu 3000 Knoten erreicht die sogenannte Extremal Metrik zwar eine leicht höhere Performanz, sie ist jedoch wegen ihrer sehr hohen numerischen Anforderungen für größere Netze nicht anwendbar. Im Falle der logKiKj Metrik ist die numerische Komplexität vernachlässigbar, dieser Vorteil an vermindertem numerischen Aufwand wird jedoch durch eine leichte Reduktion des Performanzgewinns erkauft, nichtsdestotrotz bewirkt auch diese Metrik eine mittlere Performanzsteigerung um den Faktor 5 und erreicht damit die Größenordnung der Hybrid Metrik