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Frequency-dependent dielectric properties of 3-D-printed structured dielectrics (dielectric crystals) with engineered effective permittivity for micro- and mmWave applications are studied. Different modeling and measurement techniques for broadband dielectric properties of such 3-D-printed crystals are reviewed, their tradeoffs discussed, and individual results compared. Numerically obtained results from the plane wave expansion method (PWEM) and Floquet port scattering are compared with traveling-wave measurements in both guided and free-space setups. Furthermore, the shortcomings of effective media theories (EMTs) and resonance measurement methods are addressed and contrasted against broadband methods. Individual simulation and measurement setups are reviewed with respect to dielectric crystals with simple cubic (SC) and face-centered cubic (FCC) symmetry, with different unit cell sizes and volumetric infill fractions. Extracted effective permittivity values from PWEM and Floquet port simulations show excellent agreement with traveling-wave measurements in both guided and free-space scenarios. Furthermore, the discussed broadband methods predict and measure frequency-dependent effects that are not covered by EMTs and resonance measurement setups, highlighting the necessity to adopt more sophisticated simulation tools for the design of graded-index devices. It is shown that the effective media bandwidth of dielectric crystals depends on the respective unit cell symmetry and that FCC symmetry obtains a significantly increased bandwidth compared with SC symmetry.

In this paper, an exploratory mixed-method study is presented examining the video-related behavior of participants of a massive open online course (MOOC; N=1.238). Firstly, detailed log-file analysis of six videos has been carried out to compare clickstreams of videos with and without integrated H5P quiz integration. It shows quite different seeking and watching pattern behavior in video with H5P quizzes. In a second step, learners participated in an online questionnaire (N=707): Most of them see the quizzes in videos as always or mostly helpful (67%). The survey also shows that for many, taking notes, turning on subtitles, using the transcripts or even increasing the speed are important activities when learning with the videos in the MOOC. In a third step, interviews with ten MOOC participants are a source for qualitative insights in how the learners use the learning videos.

This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. The sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is part of a growing trend to use comparative historical data on a large scale and contributes as such to a growing consilience between the humanities and social sciences. Seshat is underpinned by a robust and transparent workflow to ensure the ever growing dataset is of high quality.

Systemic risk research is gaining traction across diverse disciplinary research communities, but has as yet not been strongly linked to traditional, well-established risk analysis research. This is due in part to the fact that systemic risk research focuses on the connection of elements within a system, while risk analysis research focuses more on individual risk to single elements. We therefore investigate how current systemic risk research can be related to traditional risk analysis approaches from a conceptual as well as an empirical point of view. Based on Sklar’s Theorem, which provides a one-to-one relationship between multivariate distributions and copulas, we suggest a reframing of the concept of copulas based on a network perspective. This provides a promising way forward for integrating individual risk (in the form of probability distributions) and systemic risk (in the form of copulas describing the dependencies among such distributions) across research domains. Copulas can link continuous node states, characterizing individual risks, with a gradual dependency of the coupling strength between nodes on their states, characterizing systemic risk. When copulas are used for describing such refined coupling between nodes, they can provide a more accurate quantification of a system’s network structure. This enables more realistic systemic risk assessments, and is especially useful when extreme events (that occur at low probabilities, but have high impacts) affect a system’s nodes. In this way, copulas can be informative in measuring and quantifying changes in systemic risk and therefore be helpful in its management. We discuss the advantages and limitations of copulas for integrative risk analyses from the perspectives of modeling, measurement, and management.

Social interactions take place simultaneously through different interaction types, such as communication, friendship, trade, exchange, enmity, revenge, etc. These interactions can be conveniently described with time-dependent multi-layer networks. Little is known about the dynamics of social network formation on single layers. How the dynamics on one layer is coupled to and influences the dynamics on another layer is a completely unexplored territory. This is mainly due to the lack of comprehensive microscopic interaction data on time-dependent multi-layer networks. In this work, we study a unique dataset of 350,000 odd players in a massive multi-player online game, for which we know practically every social interaction event. We focus on the dynamics of friendship interactions and how they are coupled to the dynamics of enmity interactions. We are able to identify the driving processes behind the joint network formation of friendship and enmity links. The essential mechanisms turn out to be specific triadic closure rules. We propose a simple dynamical model that allows us to predict not only the correct levels of social balance but also the detailed simultaneous structural properties of the friendship and enmity networks, including their degree distributions, clustering coefficients and nearest neighbor degrees. While the formation of new friendship links can be largely understood on the basis of structural features of the friendship network alone, this is not true for enmity networks. The formation of enmity links is driven by the need to socially balance triadic relations that contain negative and positive interactions. Networks of enmity relations can only be understood structurally in the context of the positive social ties they are embedded in.

As anthropologists study complex societies and large databases, we have to ask whether using demographic divisions within a population help or hinder our understanding of people’s cognition, particularly political cognition. Using a database of 1,283 interviews gathered in Mongolia in 1998 and 2003, the authors divide the sample population by the demographic categories of location, gender, education level attained, and age. We use network analysis to note differences in network structure and textual analysis to learn how Mongolians characterize democracy as they emerge from Soviet-style socialism. We find that even though Mongolians are a rather homogeneous society, people’s concept of democracy varies by demographic category. We conclude that because experience often varies with a person’s position in society, demography correlates with people’s perception of democracy and therefore remains a valid and helpful way to study the political culture of a population.

Auf Grund der stetig steigenden Anforderungen an die Produkte des Maschinen-, Werkzeug- und Formenbaus hinsichtlich ökonomischer und ökologischer Gesichtspunkte ist ein Umdenken in der Anwendung von Beschichtungstechnologien notwendig. Die steigende Komplexität der hoch beanspruchten Baugruppen und Werkzeuge bei gleichzeitig gestiegenen Anforderungen an die Standzeit machen die Anwendung von verschleiß- und temperaturfesten Werkstoffe bei gleichzeitig neuen Bearbeitungsstrategien unumgänglich. Die Grenzen der bislang dafür zum Einsatz gebrachten Wärmebehandlungsverfahren sind erreicht, sodass die Verwendung partiell aufgetragener Verschleißschutzschichten eine Alternativlösung bietet. Dabei zeigt die Beschichtung kompletter Werkzeuge genau wie die Regeneration durch auftraggeschweißte Schichten enorme ökonomische Potenziale. Da die aufgetragenen Schichtsysteme jedoch die Anforderungen an Maß, Form, Lage und Oberflächenqualität nur begrenzt erfüllen, ist eine spanende Endbearbeitung essentiell. Zu diesem Thema werden die Prozesskette, die Anforderungen und die Versuchsergebnisse zur Fräsbearbeitung verschleißfester Schichten auf Kobaltbasis vorgestellt.

This study presents a potentially novel method of screening for pathogenetic factors in diabetic audiopathy by comparing the absolute plasma concentration of a microangiopathy biomarker, stromal cell-derived factor 1a (SDF-1a), with frequency-specific audiometric results.

Kinetic studies of cell proliferation rates shed light on the growth dynamics of cancer. Most such studies are based on measurements of cell numbers that were evaluated in time intervals of about 12 h. Studies of the initial tumour growth with short measuring intervals are rare. This study was therefore designed with 1 h measuring intervals over a 24 h period. Human breast cancer cell lines (ZR‐75‐1, SK‐BR‐3, MCF‐7) and a benign cell line (HBL‐100) were used to study the hourly thymidine uptake as a measure of cells in synthesis. In parallel experiments, the same cell lines were also exposed to tumour necrosis factor alpha (TNF‐α) to explore the effect of an apoptosis‐inducing substance on initial tumour growth kinetics. In time‐evolution plots, there was an oscillation of the labelling index of thymidine uptake for all investigated cell lines, with and without TNF‐α. Based on the results obtained, a mathematical model was developed mimicking the real experiment. To describe the system dynamically a cellular automaton model was studied. The growth kinetics revealed by the simulation were in accordance with our experimental data. Two‐ and three‐dimensional growth simulations of this computer model yielded objects morphologically similar to real images of human breast cancer. Almost identical fractal dimensions of the virtual and real tumours further supported this visual similarity. The cellular automata models could, therefore, be seen as a bridge towards realistic in vivoscenarios. From a clinical point of view, the results obtained may be applicable not only to primary tumours, but even to tumour cell microfoci and small metastases, which are a major concern in early metastasizing tumours such as breast cancer. Copyright © 2002 John Wiley & Sons, Ltd.

Human posture is a prototypical example of a complex control system. The joint output of several physiological – most likely nonlinearly interacting – processes leads to correctional movements which enable us to stand upright. These correctional body movements reflect some features of the underlying control mechanisms. We analyze the movements of quietly standing persons by means of various types of fractal measures, which are designed to capture ‘degrees of complexity’. We observe changes of these fractal measures as a function of age and show that aging goes hand in hand with a decrease of complexity in movement patterns towards more regular movements. We try to explain these results in a stochastic resonance framework. We conjecture that the reduction of posture complexity is linked to deteriorated balance performance and argue that clinical treatment of age-related balance problems should focus on regaining this complexity therapeutically. We line out two possible starting points for actual therapy.

Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches from network science, machine learning and computational phenotyping to assess interactions between two or more diseases in a transparent way across the full diagnostic spectrum. We demonstrate that health states of hospitalized patients can be better characterized by including higher-order features capturing interactions between morethan two diseases. We identify a meaningful set of higher-order diagnosis features that account for synergistic disease interactions in a population-wide (N= 9 M) medical claims dataset. We construct a generalized disease networkwhere (higher-order) diagnosis features are linked if they predict similar diagnoses across the whole diagnostic spectrum. The fact that specific diagnoses are generally represented multiple times in the network allows for the identification of putatively different disease phenotypes that may reflect different disease aetiologies. At the example of obesity, we demonstrate the purely data-driven detection of two complex phenotypes of obesity. As indicated by a matched comparison between patients having these phenotypes, we show that these phenotypes show specific characteristics of what has been controversially discussed in the medical literature as metabolically healthy and unhealthy obesity, respectively. The findings also suggest that metabolically healthy patients show some progression towards more unhealthy obesity over time, a finding that is consistent with longitudinal studies indicating a transient nature of metabolically healthy obesity. The disease network is available for exploration at https://disease.network/.

Urban scaling laws relate socio-economic, behavioural and physical variables to the population size of cities. They allow for a new paradigm of city planning and for an understanding of urban resilience and economics. The emergence of these power-law relations is still unclear. Improving our understanding of their origin will help us to better apply them in practical applications and further research their properties. In this work, we derive the basic exponents for spatially distributed variables from fundamental fractal geometric relations in cities. Sub-linear scaling arises as the ratio of the fractal dimension of the road network and of the distribution of the population embedded in three dimensions. Super-linear scaling emerges from human interactions that are constrained by the geometry of a city. We demonstrate the validity of the framework with data from 4750 European cities. We make several testable predictions, including the relation of average height of cities and population size, and the existence of a critical density above which growth changes from horizontal densification to three-dimensional growth.

With the availability of internet, social media, etc., the interconnectedness of people within most societies has increased tremendously over the past decades. Across the same timespan, an increasing level of fragmentation of society into small isolated groups has been observed. With a simple model of a society, in which the dynamics of individual opinion formation is integrated with social balance, we show that these two phenomena might be tightly related. We identify a critical level of interconnectedness, above which society fragments into sub-communities that are internally cohesive and hostile towards other groups. This critical communication density necessarily exists in the presence of social balance, and arises from the underlying mathematical structure of a phase transition known from the theory of disordered magnets called spin glasses. We discuss the consequences of this phase transition for social fragmentation in society.

We analyze the vacuum structure with respect to magnetic monopoles of quenched QCD in the presence of static color sources. Distributions of the monopole density around static quarks and mesons are computed in both phases of QCD. We observe a suppression of the monopole density in the vicinity of external sources. In the confinement phase the density of color magnetic monopoles is reduced along the flux tube between a static quark-antiquark pair.

The formation of sentences is a highly structured and history-dependent process. The probability of using a specific word in a sentence strongly depends on the ‘history’ of word usage earlier in that sentence. We study a simple history-dependent model of text generation assuming that the sample-space of word usage reduces along sentence formation, on average. We first show that the model explains the approximate Zipf law found in word frequencies as a direct consequence of sample-space reduction. We then empirically quantify the amount of sample-space reduction in the sentences of 10 famous English books, by analysis of corresponding word-transition tables that capture which words can follow any given word in a text. We find a highly nested structure in these transition tables and show that this ‘nestedness’ is tightly related to the power law exponents of the observed word frequency distributions. With the proposed model, it is possible to understand that the nestedness of a text can be the origin of the actual scaling exponent and that deviations from the exact Zipf law can be understood by variations of the degree of nestedness on a book-by-book basis. On a theoretical level, we are able to show that in the case of weak nesting, Zipf's law breaks down in a fast transition. Unlike previous attempts to understand Zipf's law in language the sample-space reducing model is not based on assumptions of multiplicative, preferential or self-organized critical mechanisms behind language formation, but simply uses the empirically quantifiable parameter ‘nestedness’ to understand the statistics of word frequencies.

We report on new analyses of the topological and chiral vacuum structure of four-dimensional QCD on the lattice. Correlation functions as well as visualization of monopole currents in the maximally Abelian gauge emphasize their topological origin and gauge invariant characterization. The (anti)selfdual character of strong vacuum fluctuations is revealed by smoothing. In full QCD, (anti)instanton positions are also centers of the local chiral condensate and quark charge density. Most results turn out generically independent of the action and the cooling/smoothing method.

We report on new analyses of the topological and chiral vacuum structure of four-dimensional QCD on the lattice. Correlation functions as well as visualization of monopole currents in the maximally Abelian gauge emphasize their topological origin and gauge invariant characterization. The (anti)selfdual character of strong vacuum fluctuations is revealed by smoothing. In full QCD, (anti)instanton positions are also centers of the local chiral condensate and quark charge density. Most results turn out generically independent of the action and the cooling/smoothing method.