Collaboration in scientific digital ecosystems: A socio-technical network analysis

This dissertation seeks to understand the formation, operation, organizational (collaboration) and the effect of scientific digital ecosystems that connect several online community networks in a single platform. The formation, mechanism and processes of online networks that influence members output is limited and contradictory. The dissertation is comprised of three papers that are guided by the following research questions: How does online community member’s productivity (or success) depend upon their ‘position’ in the digital networks? What are the network formation mechanism, structures and characteristics of an online community? How do scientific innovations traverse (diffuse) amongst users in online communities? A combination of exploratory, inductive and deductive research designs is applied sequentially but in a non-linear manner to address research question. The dissertation contributes to the literature on scientific collaboration, digital communities of creation, social network modelling and diffusion of innovation. The first paper applies network theory and spatial probit autocorrelative modelling technique to evaluate how member developer’s positioning in digital community correlate with his/her productivity. The second paper looks at the dynamics of developer’s participation in online developers’ network for a period spanning 7-years using exponential random graph models (ERGM). This paper applies theory of network (network science) to model network formation patterns in developer community. The third paper, like the first, applies network theory and to understand user network characteristics and communication channels which influence diffusion of scientific innovations. Bass and spatial probit autocorrelative models are applied for this analysis. Data from this study was mined from developers, authors and user communities of nanoHUB.org cyberinfrastructure platform. NanoHUB.org is a science and engineering online ecosystem comprising self-organized researchers, educators, and professional communities in eight member institutions that collaborate, share resources and solve nanotechnology related problems including development and usage of tools (scientific innovation). Data from collaboration and information sharing activities was used to create the developers, authors and user networks that were used for analysis. Results of the first paper show that the spatial autocorrelation parameter of the spatial probit model is negative and statistically different from zero. The negative spatial spillover effect in the developer network imply that developers that are embedded in the network have a lower probability of getting more output. The structural network characteristics of eigen vector centrality had statistically significant effects on probability of being more productive. Developers who are also authors were found to be more productive than those in one network. The implications of these findings is that developers will benefit from being in multiple network spaces and by associating with more accomplished developers. The autocorrelative and interaction models also reveal various new modelling approach of accounting for network autocorrelation effects to online member. Results of the second paper show that developers form in a manner that follow a pure uniform random distribution. Results also show that developer’s collaborative mechanisms are characterized by low tendencies to reciprocate and form homophiles (tendency of developers to associate with similar peers) but high tendency to form clusters. The implications of network formation mechanism and processes are that developers are forming in a purely random and self-organized manner and minimum efforts should be applied in trying to organize and influence the community organization. The results also reveal that a simple link to link ERGM and stochastic dominance criteria can be combined to characterize the network formation characteristics just like the ERG(p*) model but have an advantage of overcoming degeneracy challenges associated with ERG(p*) models. Results of the third paper show that bass model is a good predictor for diffusion of scientific innovations (tools) in online community setting. Results also show different innovations have varying levels and rates of adoption and these were influenced by both external and internal factors. Results of the micro-based model found degrees and betweeness centrality as some of the internal variables that have positive influence on the adoption of innovation while centrality measures of power or leadership were found to have negative influence of adoption process. The relative time taken to run a simulation (measured as job usage time) was also found to be negatively influencing diffusion. The implication of the study results is that bass model is a good fit for evaluating and forecasting adoption of innovation in online communities. Moreover, network structural characteristics are responsible for adoption of innovation adoption and policy making should consider tool adoption enhancing ones. Additionally, researchers could further explore the network structural characteristics that are driving diffusion of innovation

Fluidized bed gas-solid heat transfer using a CFD-DEM coarse-graining technique

Computational Fluid Dynamics - Discrete Element Method (CFD-DEM) is extensively used for modeling heat transfer in gas-solid fluidized beds. However, CFD-DEM is computationally expensive, leading to a restriction regarding the number of simulated particles. Coarse-grained CFD-DEM is a technique to circumvent this constraint, allowing one to simulate larger fluidized beds. In this work, a scaling law used for coarse-graining hydrodynamics is generalized to gas-solid heat-transfer. This approach for coarse-graining heat transfer is tested using three different superficial gas velocities where the coarse-grained particle temperatures and Nusselt numbers are obtained. The particle temperature shows good correspondence with the original system for all cases and the Nusselt number is accurately predicted by the coarse-graining scaling law

Geography matters: spatial dimensions of trade, migration and growth

Diese Arbeit wurde von Thomas Steinwachs während seiner Tätigkeit am ifo Zentrum für Außenwirtschaft angefertigt. Sie wurde im September 2018 fertiggestellt und von der Volkswirtschaftlichen Fakultät der Ludwig-Maximilians-Universität München als Dissertation angenommen. Es handelt sich um eine Sammlung aus vier eigenständigen Aufsätzen in separaten Kapiteln. Jedes Kapitel betrachtet die räumliche Dimension ökonomischer Prozesse in angewandter empirischer Forschung. Hierbei spielen die Definition von Messgrößen, die Verarbeitung von Geodaten und die Auswahl der ökonometrischen Methodik eine tragende Rolle. Kapitel 1 wendet eine Gravitätsanalyse auf bilaterale Handelsströme an, um die Handelseffekte des Schengener Abkommens zu quantifizieren. Hierfür wird eine neue Indikatorvariable eingeführt, welche die räumliche Ausdehnung des plurilateralen Abkommens berücksichtigt. Kapitel 2 umfasst eine Gravitätsanalyse von Migrationsströmen zur Untersuchung der internationalen Migrationswirkungen von Naturkatastrophen. Hierbei kommen räumlich kartierte Geoinformationen zur physischen Intensität von Naturkatastrophen zum Einsatz. Kapitel 3 setzt den Fokus auf die lokalen Wachstumseffekte von Naturkatastrophen und untersucht die damit verbundenen räumlichen Umlenkungseffekte ökonomischer Aktivität. Zu diesem Zweck wird eine neue Datenbank vorgestellt, welche physische Intensitäten geologischer und meteorologischer Ereignisse mit Nachtlichtemissionsdaten auf räumlich desaggregierten Rasterzellen verknüpft. Kapitel 4 verwertet geografische Informationen zu Landesgrenzen und Straßennetzwerken, um die Rolle räumlicher Konnektivität bei der Übertragung externer Effekte zu erforschen.This volume was prepared by Thomas Steinwachs while he was working at the ifo Center for International Economics. It was completed in September 2018 and accepted as a doctoral dissertation by the Department of Economics at the University of Munich (LMU). It is a collection of four self-contained essays which are included as separate chapters. Each chapter considers the spatial dimension of economic processes in applied empirical work, concerning measurement, data handling and econometric methodology. Chapter 1 applies an econometric gravity analysis to bilateral trade flows to assess how successful the European Schengen Agreement has been in boosting international trade. It proposes a new indicator variable to account for the plurilateral agreement's spatial extent. Chapter 2 provides a gravity analysis of international migration flows to investigate the impact of natural disasters on the movement of people between countries. It employs spatially mapped geographic data on the physical intensity of natural disasters. Chapter 3 zooms in on the local growth effects of natural disasters and assesses the associated diversion of economic activity across space. For this purpose, it introduces a new database combining physical intensities of geological and meteorological events with night-light emissions at spatially disaggregated grid cells. Chapter 4 further investigates the role of spatial connectivity for spillover transmission, exploiting geographic information on country borders and road networks

Aufsätze zur historischen politischen Ökonomie

This dissertation centralizes the concept that the significance of "empires" persists long after their collapse, exerting influence both overtly and subtly, a phenomenon referred to as persistent post-imperial syndrome. This syndrome fosters insular ideologies, xenophobia, and a yearning for past grandeur. However, delving into the origins of these ideologies requires an exploration of historical context and factors that led to the actual decline of empires. Consequently, my research centers on the region of Eastern Europe and Russia, which witnessed the downfall of two empires—the Russian Empire and the Soviet Union. This region also stands out due to its involvement in significant social experiments with far-reaching effects on the populace. These experiments encompass the eradication of serfdom, partial liberalization efforts, the ascent of the Bolsheviks during the 1917 Revolution, the enforced industrialization that propelled the Soviet Union into global superpower status, albeit at a tremendous human cost, and the dramatic disintegration of the Soviet Empire (Zhuravskaya et al. 2021, p. 1). This dissertation is structured into three distinct chapters, consisting of two empirical sections and one theoretical portion. The initial two empirical chapters scrutinize the political economy's impact on the labor market within the domains of the Russian Empire and the Soviet Union. However, precise demarcations are challenging to establish due to fluid territorial boundaries. The theoretical chapter furnishes a more intricate grasp of the mechanisms facilitating transmission and persistence. This is achieved through a comprehensive theoretical exposition that centers on the shift in regimes from Nazi Germany to the German Democratic Republic—a state closely aligned with the Soviet Union. In essence, this research endeavors to assess the efficacy of state and strategic decision-making mechanisms in exerting control over specific populations via methods such as forced deportations, state surveillance, and targeted indoctrination. The ultimate objective is to furnish a holistic comprehension of the enduring consequences of empires and the contributing factors to their decline, employing Eastern Europe and Russia as illustrative examples. Throughout my analysis, the figure of Joseph Vissarionovich Dzhugashvili, known as Stalin, recurs consistently, assuming a pivotal role in each chapter. He emerges as a left-wing extremist and possible informant within the archives of the tsarist secret police, a dictator for whom ethnically motivated violence constituted a rehearsed aspect of governance, and as the mastermind behind the division between East and West Germany

Aeronautical engineering: A continuing bibliography with indexes (supplement 214)

This bibliography lists 422 reports, articles and other documents introduced into the NASA scientific and technical information system in May, l987

Clay smear: Review of mechanisms and applications

AbstractClay smear is a collection of fault processes and resulting fault structures that form when normal faults deform layered sedimentary sections. These elusive structures have attracted deep interest from researchers interested in subsurface fluid flow, particularly in the oil and gas industry. In the four decades since the association between clay-smear structures and oil and gas accumulations was introduced, there has been extensive research into the fault processes that create clay smear and the resulting effects of that clay smear on fluid flow. We undertake a critical review of the literature associated with outcrop studies, laboratory and numerical modeling, and subsurface field studies of clay smear and propose a comprehensive summary that encompasses all of these elements. Important fault processes that contribute to clay smear are defined in the context of the ratio of rock strength and in situ effective stresses, the geometric evolution of fault systems, and the composition of the faulted section. We find that although there has been progress in all avenues pursued, progress has been uneven, and the processes that disrupt clay smears are mostly overlooked. We highlight those research areas that we think will yield the greatest benefit and suggest that taking these emerging results within a more process-based framework presented here will lead to a new generation of clay smear models

Self-propelled particles with inhomogeneous activity

Movement is an essential feature of life. It allows organisms to move towards a more favorable environment and to search for food. There are many biological systems that fall under the category active matter, from molecular motors walking on microtubules inside cells to flocks of birds. What these systems have in common is that each of its constituents converts energy into directed motion, that is, they propel themselves forward. Besides the many biological examples, there is also synthetic active matter, these are self-propelled particles made in a laboratory. These are typically colloidal sized particles that can propel themselves forward by self-phoresis. In this work the focus is on the low Reynolds number regime, meaning that the typical size of the constituents is less than a few micrometers. The models that are used to describe such active matter are can be viewed as nonequilibrium extensions to Brownian motion (the thermal motion of small particles dissolved in a fluid). In many systems the self-propulsion speed (activity) is not homogeneous in space: the particles swim faster in some areas than in others. The main topic of this dissertation is how a single active particle, or a few active particles tied together by a potential, behave in such systems. It is known that a single active particle without any steering mechanism spends most time in the regions where it moves slowly, or in other words, they spend most time in regions where they are less active. However, here it is shown that, even though they spend most time in the less active regions, dynamical properties, such as the probability to move towards the more active regions is higher than moving towards the less active regions. Furthermore, when the active particles are connected to a passive Brownian 'cargo' particle, chained together to form a colloidal sized polymer, or fixed to another active particle, the resulting active dimers or polymers either accumulate in the high activity regions or the low activity regions, depending on the friction of the cargo particle, the number of monomers in the polymer, or the relative orientation of active particles. Lastly, when the activity is both time- and space-dependent, a steady drift of active particles can be induced, without any coupling between the self-propulsion direction and the gradient in the activity. This phenomenon can be used to position the particles depending on their size.:1. Brownian Motion 2. Active Matter 3. Modeling Active Matter 4. Introduction: Inhomogeneous activity 5. Pseudochemotaxis 6. Cargo-Carrying Particles 7. Active Colloidal Molecules 8. Time-Varying Activity Fields Appendix: Hydrodynamic