An efficient air transport system is an important part of social and economic development of Southern African Development Community (SADC). Efficient intra-SADC air service connections enhance regional integration, access to the global economy, international tourism and contribute towards the vision to establish the African Economic Community by 2034. SADC, in July 1998, embarked on liberalisation of the regional civil aviation sector in order to enhance the efficiency of air transport services. In the United States of America and European Union, the liberalisation of air transport has transformed civil aviation networks. The fragmentation of air service connections on the intra-SADC network in the midst of the liberalisation process is symptomatic of a poor implementation strategy coupled with air transport market imperfections. The purpose of this thesis is to examine, understand and explain the factors that influence the disintegration of the intra-SADC air transport network .The aim is to identify how regional air transport services can be transformed to meet the social and economic demands of the region. This research adopts network theory, as the conceptual framework of the investigation. Assuming a graph approaching maximal connection as the sought after state of affairs for SADC; this study benchmarked the post liberalisation network structure to the regional economic communities of ASEAN and MERCOSUR. The aim of the benchmarking is to identify the extend of the differences in air transport network in those two regions, resulting from the policies adopted and to establish how the SADC policies may be improved and implemented more efficiently. The findings of the study are that, in comparison to the two developing regions, SADC’s liberalisation measures have failed. The study developed and evaluated an econometric model which analysed demand patterns on the intra-SADC passenger air transport network. Although low levels of passenger demand seem to characterise the majority of SADC city-pairs, the study identified nodes with sufficient demand to justify direct connections which would in turn reduce network fragmentation. This research also establishes that the absence of a realistic detailed roadmap, an ill-defined programme of action and inadequate resources contributed to the failure of SADC’s liberalisation strategy. In its final sections, this study proposes an ideal demand-driven network configuration and offers specific recommendations to SADC member states for that network to be functional. The proposed network improves network connectivity from the current poor levels, where a connectivity measure of 15% suggests underdevelopment, to levels over 40%. The study however, acknowledges that air transport liberalisation does not necessarily guarantee equitable distribution of network efficiency in developing regions. There are communities that cannot sustain commercially viable air service connections without economic subvention, probably in the form of the Public Service Obligation (PSO) programme adopted in the EU.

Thesis (M.S. in electrical engineering)--Washington State University. / Includes bibliographical references.

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user- defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open- source code for academic purposes.

Magisterská práce se zabývá stochastickou optimalizací síťových úloh. Teoretická část pokrývá tři témata - teorii grafů, optimalizaci a progressive hedging algoritmus. V rámci optimalizace je hlavní část věnována stochastickému programování a dvoustupňovému programování. Progressing hedging algoritmus zahrnuje také metodu přiřazování scénářů a modifikaci obecného algoritmu na dvou stupňové úlohy. Praktická část je věnována modelům na reálných datech z oblasti svozu odpadu v rámci České republiky. Data poskytl Ústav procesního inženýrství.

Das Studium von Transportmechanismen in komplexen Organismen stellt eine zentrale Herausforderung dar, nicht nur in medizinischen und biologischen Disziplinen, sondern auch zunehmend in der Physik und Netzwerktheorie. Insbesondere sind bionisch inspirierte Designprinzipien zunehmend relevant, da sie zuverlässige Lösungsansätze zu verschiedenen theoretischen und technischen Problemen bieten. Herausstechend sind dabei vaskuläre Netzwerke in Säugetieren, deren Entwicklung auffällig stark auf Selbstorganisation beruhen und die korrekte Verteilung von Sauerstoff, Wasser, Blut oder Ähnlichem erlaubt. Dies wird erreicht durch ein komplexes biochemisches Signalsystem, welches an makroskopische Stimulationen, wie z. B. Reibung und Stress, gekoppelt ist. Die Morphogenese solcher Flussnetzwerke ist allerdings noch anderen Restriktionen unterworfen, da diese räumlich eingebettete Objekte darstellen. Sie sind als solche signifikant beschränkter in ihrer Skalierbarkeitund Dynamik. Diese Dissertation addressiert daher relevante Fragestellungen zur Charakterisierung von Netzwerken und der Morphogenesesimulationen von drei-dimensional eingebetteten Netzwerken Die Schlüsselmechanismen auf die wir uns hier konzentrieren sind Flussfluktuationen, Interaktionen zwischen Paarstrukturen und die Aufnahme von Nährstoffen. Zu Beginn zeigen wir, wie sich konventionelle Ansätze zu Flussfluktuationen als allgemeine Einparametermodelle darstellen lassen. Wir demonstrieren damit den kontinuierlichen Übergang zu zunehmend vernetzten Strukturen und indizieren Topologieabhängigkeiten der Plexus in Anbetracht dieses Übergangs. Darauf aufbauend formulieren wir ein neues Adaptationsmodell für ineinander verwobene Gefäßnetzwerke wie sie auch in der Leber, Bauchspeicheldrüse oder Niere vorkommen. Wir diskutieren anhand dieser Strukturen lokale Wechselwirkungen von dreidimensionalen Netzwerken. Dadurch können wir zeigen, dass repulsiv gekoppelte Netzwerke fluktuationsinduzierte Vernetzungen auflösen und attraktive Kopplungen einen neuen Mechanismus zur Erzeugung eben jener darstellen. Als nächstes verallgemeinern wir die Murray Regel für solch komplexe Wechselwirkungen und Fluktuationen. Die daraus abgeleiteten Relationen nutzen wir zur Regression der Modellparameter und testen diese an den Gefäßnetzwerken der Leber. Weiterhin verallgemeinern wir konventionelle Transportmodelle für die Nährstoffaufnahme in beliebigem Gewebe und testen diese in Morphogenesemodellen gegen die bekannten Ansätze zur Dissipationsminimierung. Hier zeigen sich komplexe Übergänge zwischen vernetzten Strukturen und unkonventionelles Phasenverhalten. Allerdings indizieren die Ergebnisse Widersprüche zu echten Kapillargefäßen und wir vermuten Adaptationsmethoden ohne Gefäßgrößenänderung als wahrscheinlicheren Mechanismus. Im Ausblick schlagen wir auf unseren Ergebnissen aufbauende Folgemodelle vor, welche die Modellierung komplexer Transportprozesse zwischen verschränkten Gefäßnetzwerken zum Ziel haben.:Introduction 1 1.1 Complex networks in biology . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Flow networks in mammals . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Network morphogenesis . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1 Modelling flow network adaptation . . . . . . . . . . . . . . . . 8 1.2.2 Metrics for biological flow networks . . . . . . . . . . . . . . . . 11 Scaling in spatial networks . . . . . . . . . . . . . . . . . . . . . 12 Redundancy of flow networks . . . . . . . . . . . . . . . . . . . 13 1.3 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.1 Spatial embedding in metabolic costs models . . . . . . . . . . . 16 1.3.2 Characterizing three-dimensional reticulated networks . . . . . . 17 1.3.3 Optimal design for metabolite uptake . . . . . . . . . . . . . . . 20 2 Theory and Methods 23 2.1 Basic principles and mathematics . . . . . . . . . . . . . . . . . . . . 23 2.1.1 Mathematical basics . . . . . . . . . . . . . . . . . . . . . . . . 23 Linear equation systems . . . . . . . . . . . . . . . . . . . . . 23 Dynamical systems and optimization . . . . . . . . . . . . . . 25 Graph theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1.2 Basic hydrodynamics . . . . . . . . . . . . . . . . . . . . . . . . 30 Momentum and mass balance . . . . . . . . . . . . . . . . . . . 30 Diffusion-Advection . . . . . . . . . . . . . . . . . . . . . . . . . 31 Flow in a thin channel . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.3 Kirchhoff networks . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Complex transport problems . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 Taylor dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Flow-driven pruning . . . . . . . . . . . . . . . . . . . . . . . . 38 Metabolic cost functions . . . . . . . . . . . . . . . . . . . . . . 38 Adaptation and topological transitions . . . . . . . . . . . . . . 40 3 Results 43 3.1 On single network adaptation with fluctuating flow patterns . . . . . . 43 3.1.1 Incorporating flow fluctuations: Noisy, uncorrelated sink patterns 44 3.1.2 Fluctuation induced nullity transitions . . . . . . . . . . . . . . 48 3.1.3 Finite size effects and topological saturation limits . . . . . . . 52 3.2 On geometric coupling between intertwined networks . . . . . . . . . . 55 3.2.1 Power law model of interacting multilayer networks . . . . . . . 55 3.2.2 Adaptation dynamics of intertwined vessel systems . . . . . . . 57 x 3.2.3 Repulsive coupling induced nullity breakdown . . . . . . . . . . 59 3.2.4 Attractive coupling induced nullity onset . . . . . . . . . . . . 66 3.3 On generalizing and applying geometric laws to complex transport networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.1 Generalizing Murray’s law for complex flow networks . . . . . . 73 Murray’s law for fluctuating flows . . . . . . . . . . . . . . . . . 74 Murray’s Law for extended metabolic costs models . . . . . . . 77 3.3.2 Interpolating model parameters for intertwined networks . . . . 78 Testing ideal Kirchhoff networks . . . . . . . . . . . . . . . . . . 79 3.3.3 Identifying geometrical fingerprints in the liver lobule . . . . . . 85 3.4 On the optimization of metabolite uptake in complex flow networks . . 91 3.4.1 Metabolite transport in thin channel systems . . . . . . . . . . . 91 On single channel solutions . . . . . . . . . . . . . . . . . . . . 91 On detailed absorption rate models . . . . . . . . . . . . . . . . 93 On linear network solutions . . . . . . . . . . . . . . . . . . . . 96 On the uptake in spanning tree and reticulated networks . . . . 97 3.4.2 Optimizing metabolite uptake in shear-stress driven systems . . 100 Link-wise supply-demand model . . . . . . . . . . . . . . . . . . 101 Volume-wise supply-demand model . . . . . . . . . . . . . . . . 110 4 Discussion and Outlook 119 4.1 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 4.3.1 Metabolite transport in the liver lobule . . . . . . . . . . . . . . 124 Expansion of the Ostrenko model . . . . . . . . . . . . . . . . . 124 Complex multi transport probems in biology . . . . . . . . . . . 127 4.3.2 Absorption rate optimization and microscopic elimination models 128 Appendix A More on coupled intertwined networks 131 A.1 Coupling of Diamond lattices . . . . . . . . . . . . . . . . . . . . . . . 131 A.1.1 Repulsive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 131 A.1.2 Attractive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 133 A.2 Coupling of Laves Graphs . . . . . . . . . . . . . . . . . . . . . . . . . 134 A.2.1 Repulsive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 134 A.2.2 Attractive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 136 B More on metabolite uptake adaptation 139 B.1 Deriving dynamical systems from demand-supply relationships . . . . . 139 B.2 Microscopic uptake models . . . . . . . . . . . . . . . . . . . . . . . . . 142 B.2.1 Detailed uptake estimation in single layer systems . . . . . . . . 142 B.2.2 Detailed uptake estimation in liver sinusoids . . . . . . . . . . . 143 B.3 Metabolite uptake in three-dimensional plexi . . . . . . . . . . . . . . . 145 B.3.1 Link-wise demand adaptation . . . . . . . . . . . . . . . . . . . 145 B.3.2 Volume-wise demand adaptation . . . . . . . . . . . . . . . . . . 150 Bibliography 155 / Understanding the transport of fluid in complex organisms has proven to be a key challenge not only in the medical and biological sciences, but in physics and network theory as well. This is even more so as biologically-inspired design principles have been increasing in popularity, reliably generating solutions to common theoretical and technical problems. On that note, vascular networks in mammalian organs display a magnificent level of self-organization, allowing them to develop and mature, yet miraculously orchestrate the correct transport of oxygen, water, blood etc. This is achieved by a dedicated biochemical feedback system, which is coupled to macroscopic stimuli, such as mechanical stresses. Another important constraint for the morphogenesis of flow networks is their environment, as these networks are spatially embedded. They are therefore exposed to significant constraints with regards to their scalability and dynamical behavior, which are not yet well understood. This thesis addresses the current challenges of network characterization and morphogenesis modeling for three-dimensional embedded networks. In order to derive proper maturation mechanisms, we propose a set of toy models for the creation of non-planar, entangled and reticulated networks. The key mechanisms we focus on in this thesis are flow fluctuation, coupling of pairing structures and metabolite uptake. We show that in accordance with previous theoretical approaches, fluctuation induced nullity can be formulated as a single parameter problem. We demonstrate that the reticulation transition follows a logarithmic law and find plexi with certain topologies to have limited nullity transitions, rendering such plexi intrinsically wasteful in terms of fluctuation generated reticulation. Moreover, we formulate a new coupling model for entangled adapting networks as an approach for vasculature found in the liver lobules, pancreas, kidneys etc. We discuss a model based on local, distance-dependent interactions between pairs of three-dimensional network skeletons. In doing so we find unprecedented delay and breakdown of the fluctuation induced nullity transition for repulsive interactions. In addition we find a new nullity transition emerging for attractive coupling. Next, we study how flow fluctuations and complex metabolic costs can be incorporated into Murray’s Law. Utilizing this law for interpolation, we are able to derive order of magnitude estimation for the parameters in liver networks, suggesting fluctuation driven adaptation to be the dominant factor. We also conclude that attractive coupling is a reasonable mechanism to account for the maintenance of entangled structures. We test optimal metabolite uptake in Kirchhoff networks by evaluating the impact of solute uptake driven dynamics relative to wall-shear stress driven adaptation. Here, we find that a nullity transition emerges in case of a dominant metabolite uptake machinery. In addition to that, we find re-entrant behavior in case of high absorption rates and discover a complex interaction between shear-stress generation and feedback. Nevertheless, we conclude that metabolite uptake optimization is not likely to occur due to radial adaptation alone. We suggest areas for further studies, which should consider absorption rate variation in order to account for realistic uptake profiles. In our outlook, we suggest a complex morphogenesis model for intertwined networks based on the results of this thesis.:Introduction 1 1.1 Complex networks in biology . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Flow networks in mammals . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Network morphogenesis . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1 Modelling flow network adaptation . . . . . . . . . . . . . . . . 8 1.2.2 Metrics for biological flow networks . . . . . . . . . . . . . . . . 11 Scaling in spatial networks . . . . . . . . . . . . . . . . . . . . . 12 Redundancy of flow networks . . . . . . . . . . . . . . . . . . . 13 1.3 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.1 Spatial embedding in metabolic costs models . . . . . . . . . . . 16 1.3.2 Characterizing three-dimensional reticulated networks . . . . . . 17 1.3.3 Optimal design for metabolite uptake . . . . . . . . . . . . . . . 20 2 Theory and Methods 23 2.1 Basic principles and mathematics . . . . . . . . . . . . . . . . . . . . 23 2.1.1 Mathematical basics . . . . . . . . . . . . . . . . . . . . . . . . 23 Linear equation systems . . . . . . . . . . . . . . . . . . . . . 23 Dynamical systems and optimization . . . . . . . . . . . . . . 25 Graph theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1.2 Basic hydrodynamics . . . . . . . . . . . . . . . . . . . . . . . . 30 Momentum and mass balance . . . . . . . . . . . . . . . . . . . 30 Diffusion-Advection . . . . . . . . . . . . . . . . . . . . . . . . . 31 Flow in a thin channel . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.3 Kirchhoff networks . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Complex transport problems . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 Taylor dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Flow-driven pruning . . . . . . . . . . . . . . . . . . . . . . . . 38 Metabolic cost functions . . . . . . . . . . . . . . . . . . . . . . 38 Adaptation and topological transitions . . . . . . . . . . . . . . 40 3 Results 43 3.1 On single network adaptation with fluctuating flow patterns . . . . . . 43 3.1.1 Incorporating flow fluctuations: Noisy, uncorrelated sink patterns 44 3.1.2 Fluctuation induced nullity transitions . . . . . . . . . . . . . . 48 3.1.3 Finite size effects and topological saturation limits . . . . . . . 52 3.2 On geometric coupling between intertwined networks . . . . . . . . . . 55 3.2.1 Power law model of interacting multilayer networks . . . . . . . 55 3.2.2 Adaptation dynamics of intertwined vessel systems . . . . . . . 57 x 3.2.3 Repulsive coupling induced nullity breakdown . . . . . . . . . . 59 3.2.4 Attractive coupling induced nullity onset . . . . . . . . . . . . 66 3.3 On generalizing and applying geometric laws to complex transport networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.1 Generalizing Murray’s law for complex flow networks . . . . . . 73 Murray’s law for fluctuating flows . . . . . . . . . . . . . . . . . 74 Murray’s Law for extended metabolic costs models . . . . . . . 77 3.3.2 Interpolating model parameters for intertwined networks . . . . 78 Testing ideal Kirchhoff networks . . . . . . . . . . . . . . . . . . 79 3.3.3 Identifying geometrical fingerprints in the liver lobule . . . . . . 85 3.4 On the optimization of metabolite uptake in complex flow networks . . 91 3.4.1 Metabolite transport in thin channel systems . . . . . . . . . . . 91 On single channel solutions . . . . . . . . . . . . . . . . . . . . 91 On detailed absorption rate models . . . . . . . . . . . . . . . . 93 On linear network solutions . . . . . . . . . . . . . . . . . . . . 96 On the uptake in spanning tree and reticulated networks . . . . 97 3.4.2 Optimizing metabolite uptake in shear-stress driven systems . . 100 Link-wise supply-demand model . . . . . . . . . . . . . . . . . . 101 Volume-wise supply-demand model . . . . . . . . . . . . . . . . 110 4 Discussion and Outlook 119 4.1 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 4.3.1 Metabolite transport in the liver lobule . . . . . . . . . . . . . . 124 Expansion of the Ostrenko model . . . . . . . . . . . . . . . . . 124 Complex multi transport probems in biology . . . . . . . . . . . 127 4.3.2 Absorption rate optimization and microscopic elimination models 128 Appendix A More on coupled intertwined networks 131 A.1 Coupling of Diamond lattices . . . . . . . . . . . . . . . . . . . . . . . 131 A.1.1 Repulsive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 131 A.1.2 Attractive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 133 A.2 Coupling of Laves Graphs . . . . . . . . . . . . . . . . . . . . . . . . . 134 A.2.1 Repulsive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 134 A.2.2 Attractive coupling . . . . . . . . . . . . . . . . . . . . . . . . . 136 B More on metabolite uptake adaptation 139 B.1 Deriving dynamical systems from demand-supply relationships . . . . . 139 B.2 Microscopic uptake models . . . . . . . . . . . . . . . . . . . . . . . . . 142 B.2.1 Detailed uptake estimation in single layer systems . . . . . . . . 142 B.2.2 Detailed uptake estimation in liver sinusoids . . . . . . . . . . . 143 B.3 Metabolite uptake in three-dimensional plexi . . . . . . . . . . . . . . . 145 B.3.1 Link-wise demand adaptation . . . . . . . . . . . . . . . . . . . 145 B.3.2 Volume-wise demand adaptation . . . . . . . . . . . . . . . . . . 150 Bibliography 155