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Indiana University-Purdue University Indianapolis (IUPUI) / Recent preclinical reports have provided evidence that endothelial colony forming cells (ECFCs), a subset of endothelial progenitor cells, significantly improve vessel formation, largely due to their robust vasculogenic potential. While it has been known that the Rho family GTPase Cdc42 is involved in this ECFC-driven vessel formation process, the effect of extracellular matrix (ECM) stiffness on its activity during vessel formation is largely unknown. Using a fluorescence resonance energy transfer (FRET)-based Cdc42 biosensor, we examined the spatio-temporal activity of Cdc42 of ECFCs in three-dimensional (3D) collagen matrices with varying stiffness. The result revealed that ECFCs exhibited an increase in Cdc42 activity in a soft (150 Pa) matrix, while they were much less responsive in a rigid (1 kPa) matrix. In both soft and rigid matrices, Cdc42 was highly activated near vacuoles. However, its activity is higher in a soft matrix than that in a rigid matrix. The observed Cdc42 activity was closely associated with vacuole formation. Soft matrices induced higher Cdc42 activity and faster vacuole formation than rigid matrices. However, vacuole area is not dependent on the stiffness of matrices. Time courses of Cdc42 activity and vacuole formation data revealed that Cdc42 activity proceeds vacuole formation. Collectively, these results suggest that matrix stiffness is critical in regulating Cdc42 activity in ECFCs and its activation is an important step in early vacuole formation.

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A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

Die vorliegende Arbeit untersucht den Anregungsenergie-Transfer in Nano-hybrid-Systemen, welche zweidimensionale Übergangsmetall-Dichalkonid-Schichten (TMDCs) enthalten. Heterostrukturen, welche TMDC-Schichten mit sogenannten nulldimensionalen Systemen kombinieren, werden als wesentlich für die nächste Generation von elektronischen und photonischen Bauelementen angesehen. Trotz dieser großen Bedeutung existieren wenige theoretische Untersuchungen. Insbesondere ist der Anregungsenergie-Transfer in diesen Hybridsystemen nicht umfassend erklärt, und die Behandlung von TMDC-Schichten bezieht sich auf sehr kleine oder periodische Systeme. Daher wird in der Arbeit der Versuch unternommen, existierende Theorien zu verbessern, und es werden Transferprozesse in zwei Typen von Heterostrukturen simuliert. Die berechneten Systeme enthalten tausende von Atomen und kommen damit in den Bereich experimentell untersuchter Strukturen. In dem einen Nanohybrid-System ist eine MoS2-Monoschicht mit einem einzelnen Para-Sexiphenyl-Molekül kombiniert, wogegen im zweiten System ein CdSe-Nanokristall an der MoS2-Mono-schicht plaziert ist. Dabei ermöglicht die Coulomb-Wechselwirkung zwischen Monoschicht und Molekül bzw. Nanokristall den Anregungsenergie-Transfer. In allen untersuchten Heterostrukturen ist die Stärke der Anregungsenergie-Transfer-Kopplung auf den sub-meV-Bereich beschränkt. In diesem Bereich ist der Anregungsenergie-Transfer inkohärent und bestimmt durch Raten, die aus Fermi's Goldener Regel folgen. Auch wird eine Abhängigkeit der Transferrate von der relativen Position des para-Sexiphenyl-Moleküls gefunden. Durch die Analyse der Übergangsladungsdichte des CdSe-Nanokristalls kann aufgezeigt werden, dass die energetisch tiefliegenden Exziton-Niveaus mit ausgeprägtem Dipolcharakter zu einer stärkeren Transferkopplung führen. Die resultierenden Transferzeiten erstrecken sich vom Piko- zum Nanosekunden-Bereich und decken sich mit entsprechend gemessenen Werten. / This thesis explores the excitation energy transfer in two-dimensional transition metal dichalcogenides (TMDCs) based nanohybrid systems. Such heterostructures combining TMDC layers with zero-dimensional materials are considered in next-generation electronics and photonics. However, there exists a shortage of current theoretical work, because the general process of excitation energy transfer in these hybrid systems has rarely been explored and the treatment of TMDCs is limited to a small size. We therefore improve the existing theories and investigate the transfer phenomena in two types of heterostructures. The considered systems contain thousands of atoms close to the experimental system size. In the first nanohybrid system, a MoS2 monolayer is combined with a single para-sexiphenyl molecule. In the second hybrid, a CdSe semiconductor spherical nanocrystal is placed close to the MoS2 monolayer. The MoS2 monolayer is coupled to the para-sexiphenyl molecule or the CdSe spherical nanocrystal via Coulomb interaction, which makes the excitation energy transfer mechanism possible. In our heterostructures, all excitation energy transfer coupling strengths lie in the meV-range or below. Within this limitation, the non-coherent excitation transfer is determined by rate expressions derived from Fermi’s Golden Rule. An effective transfer rate dependency on the relative positions of the para-sexiphenyl molecule is found. For the case of the CdSe spherical nanocrystal , by visualizing the shape of transition charge densities of CdSe excitons, we find that the low-lying exciton levels with more obvious dipole character lead to a stronger transfer coupling. The resultant transfer times range from picoseconds to nanoseconds and coincide with experimental data.

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Wireless sensor networks (WSN) are one of the most rapidly evolving technical areas right now. They can be used in a lot of different applications, environmental monitoring and health applications being two examples. The sensors can be placed in hazardous and remote environments since there is no need for cabling or manual maintenance. One of the biggest problems and constraints of today's WSNs is the limited energy capacity of the sensor nodes. Eventually they will be power-depleted, and the network will be dead. A solution to this can be wireless energy transfer technology which makes it possible to recharge sensor nodes with the help of a mobile charger and prolong the lifetime of networks.  This thesis aims to investigate how the charging completion time can be reduced by considering that the charger can charge while moving and by controlling its velocity. The charging completion time is the time from when the charger starts charging the first node until it returns to that starting point.  For this, a two-dimensional WSN was defined that contains sensor nodes and a mobile charger. Anchor nodes, which the charger travels between were defined by choosing the nodes with most neighbors within a defined charging radius. The traveling salesman problem were used to find a path that the charger travels along. A simulation environment was developed in Python to execute tests.  The results show that the charging while moving approach with controlled velocity could reduce the charging completion time with up to 20%. It was also ascertained that this approach works better in dense networks than in sparse. / Trådlösa sensornätverk är ett av de snabbast växande tekniska områdena just nu. De har många olika användningsområden, miljöövervakning och olika hälsotillämpningar är två exempel. Sensorerna kan placeras i farliga och avlägsna miljöer eftersom det inte finns något behov av kablar eller manuellt underhåll. Ett av de största problemen och begränsningarna på dagens trådlösa nätverk är den begränsade energikapaciteten hos sensornoderna. Slutligen kommer de att bli tömda på ström och nätverket kommer att dö. En lösning på detta kan vara trådlös strömöverföring vilket gör det möjligt att ladda sensorerna med hjälp av en mobil laddare och därmed förlänga livstiden på nätverket.  Syftet med detta examensarbete är att undersöka hur slutförandetiden för laddningen kan reduceras i betraktande av att laddaren kan ladda när den rör sig samt att reglera laddaren hastighet. Slutförandetiden för laddningen är den tid det tar från att laddaren börjar ladda den första sensor-noden tills att den kommer tillbaka till punkten den startade på.  För att göra detta definierades ett två-dimensionellt trådlöst sensornätverk som bestod av sensornoder och en mobil laddare. Ankarnoder, vilka laddaren rörde sig emellan, definierades genom att hitta de noder med flest antal grannar inom en bestämd laddningsradie. Handelsresandeproblemet användes för att bestämma rutten som laddaren färdas längs. En simuleringsmiljö utvecklades i Python för att utföra testerna i.  Resultaten visar att med laddaren som laddade när den rörde på sig samt hade reglerad hastighet kunde slutförande-tiden för laddning reduceras med upp till 20%. Det kunde även konstateras att detta tillvägagångssätt fungerar bättre i täta nätverk än i glesa.