The problem we study in this thesis asks under which conditions an r-factorization of Kₘʰ can be embedded into an s-factorization of Kₙʰ. This problem is a generalization of a problem posed by Peter Cameron which asks under which conditions a 1-factorization of Kₘʰ can be embedded into a 1-factorization of Kₙʰ. This was solved by Häggkvist and Hellgren. We study sufficient conditions in the case where s = h and m divides n. To that end, we take inspiration from a paper by Amin Bahmanian and Mike Newman and simplify the problem to the construction of an "acceptable" partition. We introduce the notion of irreducible sums and link them to the main obstacles in constructing acceptable partitions before providing different methods for circumventing these obstacles. Finally, we discuss a series of open problems related to this case.

Edge-based network disease models, in comparison to classic compartmental epidemiological models, better capture social factors affecting disease spread such as contact duration and social heterogeneity. We reason that there should exist infinitely many equilibria rather than only an endemic equilibrium and a disease-free equilibrium for the edge-based network disease model commonly used in the literature, as there do not exist any changes in demographic in the model. We modify the commonly used network model by relaxing some assumed conditions and factor in a dependency on initial conditions. We find that this modification still accounts for realistic dynamics of disease spread (such as the probability of contracting a disease based off your neighbors' susceptibility to the disease) based on the basic reproduction number. Specifically, if the basic reproduction number is below 1, then the infection dies out; while if the basic reproduction number is above 1, then there is possibility of an epidemic.

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The automation industry interest in Internet of Things (IoT) and Microsoft Azure IoT Hub has increased, as it provides improvements in terms of reducing costs, increases revenue and provides efficient management of devices and machines. Prevas is a company working in this field and in this project the assignment was to look at the possibilities of improving an already existing solution they have.The aim was to set up a connection between a Programmable Logic Controller (PLC) and a Azure IoT hub and get the two components to communicate with each other. Through a simple application the PLC and the Azure IoT hub will exchange data with each other. To establish communication of results, the IoT Edge runtime has been installed to transform the Raspberry Pi into an IoT Edge device. The application was created in Node Red, where it was possible to create a connection and communication between the PLC and the Azure IoT hub. Through this application, notifications and messages can be sent between the PLC and the cloud. In the application, it is checked that a machine is working during specific times and if the machine is off when it should be running, an error message will be sent to the IoT hub. / Inom automationsindustrin har intresset för Internet of Things (IoT) och Microsoft Azure IoT-hubben ökat då det ger förbättringar i form av minskade kostnader och ökade intäkter. IoT tillhandahåller en effektiv hantering av enheter och maskiner då allt kan styras från en och samma plats. Prevas är ett företag som arbetar inom detta, och i detta projekt var uppdraget att se på möjligheterna att förbättra en befintlig lösning som de har.Syftet var att sätta upp en anslutning mellan en Programmable Logic Controller (PLC) och Azure IoT-hubben och få de två komponenterna att kommunicera. Genom en enkel applikation ska PLC:n och Azure IoT-hubben utbyta data med varandra. För att upprätthålla kommunikation för resultat har IoT Edge runtime installerats för att omvandla Raspberry Pi:n till en edge-enhet. Applikationen skapades i Node Red, där det var möjligt att skapa en anslutning och kommunikation mellan PLC:n och Azure IoT-hubben. Genom denna applikation kan meddelanden skickas mellan molnet och enheten. I applikationen så kontrolleras det att en maskin arbetar under specifika tider och är maskinen avstängd när den ska vara igång kommer ett felmeddelande att skickas till IoT-hubben.

This Thesis is a case study on the design and development of a medical center comprised of three buildings to be located on the Blacksburg Campus of Virginia Tech. The three buildings will form a space unlike any other space on campus -- it will be an Urban Precinct that will introduce a new and inspiring space where students can experience urban life on their own traditionally rural campus. / Master of Architecture

The demand and interest in serving Computer Vision applications at the Edge, where Edge Devices generate vast quantities of data, clashes with the reality that many Devices are largely unable to process their data in real time. While computational offloading, not to the Cloud but to nearby Edge Nodes, offers convenient acceleration for these applications, such systems are not without their constraints. As Edge networks may be unreliable or wireless, offloading quality is sensitive to communication bottlenecks. Unlike seemingly unlimited Cloud resources, an Edge Node, serving multiple clients, may incur delays due to resource contention. This project describes relevant Computer Vision workloads and how an effective offloading framework must adapt to the constraints that impact the Quality of Service yet have not been effectively nor properly addressed by previous literature. We design an offloading controller, based on closed-loop control theory, that enables Devices to maximize their throughput by appropriately offloading under variable conditions. This approach ensures a Device can utilize the maximum available offloading bandwidth. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques. / Master of Science / Devices like security cameras and some Internet of Things gadgets produce valuable real-time video for AI applications. A field within AI research called Computer Vision aims to use this visual data to compute a variety of useful workloads in a way that mimics the human visual system. However, many workloads, such as classifying objects displayed in a video, have large computational demands, especially when we want to keep up with the frame rate of a real-time video. Unfortunately, these devices, called Edge Devices because they are located far from Cloud datacenters at the edge of the network, are notoriously weak for Computer Vision algorithms, and, if running on a battery, will drain it quickly. In order to keep up, we can offload the computation of these algorithms to nearby servers, but we need to keep in mind that the bandwidth of the network might be variable and that too many clients connected to a single server will overload it. A slow network or an overloaded server will incur delays which slow processing throughput. This project describes relevant Computer Vision workloads and how an effective offloading framework that effectively adapts to these constraints has not yet been addressed by previous literature. We designed an offloading controller that measures feedback from the system and adapts how a Device offloads computation, in order to achieve the best possible throughput despite variable conditions. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques.

This study addressed two aspects of ecological theory developed primarily in North America and examined these theories using an Australian passerine as a model species. The first theory concerns the mechanisms by which habitat fragmentation affects avian populations. I investigated the mechanisms causing the decline of the Brown Treecreeper (Climacteris picumnus) in fragmented habitat, and specifically considered the effects of isolation and habitat degradation, which are potentially important in Australian woodlands, and edge (patch size), which are important in North America. Brown Treecreeper groups were as productive in isolated patches as in connected patches of habitat regardless of patch size, yet unpaired males were common in isolated fragments of habitat. I conducted a field experiment that confirmed that female dispersal was disrupted among isolated fragments. Thus, my results suggested Brown Treecreepers were declining due to disruption of dispersal by habitat fragmentation rather than degradation or edge effects. I compared the results of an individual-based, spatially explicit simulation model to field observations and concluded that territory spatial arrangement and matrix composition altered dispersal success, recruitment, and subsequent population growth. With the aid of a geographic information system, I determined that both landscape factors (fragmentation patterns within 4.5-km) and habitat characteristics (cavity density) explained Brown Treecreeper presence and absence from random locations in woodland habitat. The birds appear to be absent from suitable habitat in unsuitable landscapes. The second theory I addressed concerns the maintenance of avian cooperative breeding. The most widely accepted models to explain cooperative breeding suggest that individuals that delay dispersal obtain a payoff under conditions in which the quality of breeding positions varies greatly. These models arose chiefly from a few long-term studies in North American. This is an unfortunate bias because the occurrence of cooperative breeding among birds of Gondwanan origin is 22%, whereas the worldwide incidence is only 3%. I used demographic and habitat data to examine the influence of habitat and cooperative breeding on Brown Treecreeper fitness. Group size affected one component of fitness and habitat variables affected another. High cavity density may be favorable due to intense inter-specific competition for suitable cavities, which Brown Treecreepers require for roosting and nesting. Low tree density may be advantageous by favoring ground foraging, in which Brown Treecreepers frequently engage. Experimental manipulations of important habitat variables are needed to determine whether variability in these ecological factors is critical in maintaining group formation in this species. / Ph. D.

This research performs an experimental study of a trailing edge blowing system that can adapt to variations in flow parameters and reduce the unsteady stator-rotor interaction at all engine operating conditions. The fan rotor of a 1/14 scale turbofan propulsion simulator is subjected to spatially periodic, circumferential inlet flow distortions. The distortions are generated by four struts that support a centerbody in the inlet mounted onto the simulator. To reduce the unsteady effects of the strut wakes on the rotor blades, the wake is re-energized by injecting mass from the trailing edge of the strut. Each strut is provided with discrete blowing holes that open out through the strut trailing edge. Each blowing hole is connected to a MEMS based microvalve, which controls the blowing rate of the hole. The microvalve is actuated by a signal voltage, generated by a PID controller that accepts free stream and wake axial flow velocities as inputs and minimizes their difference. To quantify the effectiveness of trailing edge blowing the far-field noise is measured in an anechoic chamber. The experiments are performed for two simulator test speeds, 29,500 rpm and 40,000 rpm, with and without trailing edge blowing. The maximum reduction recorded at 29,500 rpm is 8.2 dB, and at 40,000 rpm is 7.3 dB. Reductions of 2.9 dB and greater are observed at the first five harmonics of the blade passing frequency. The sound power level at the blade passing frequency, calculated from measured far-field directivity, is reduced by 4.4 dB at 29,500 rpm and by 2.9 dB at 40,000 rpm. The feasibility and advantage of active control is demonstrated by the ability of the system to respond to a step change in the inlet flow velocity, and achieve optimum wake filling in approximately 8 seconds. / Master of Science

Boundary layer characteristics at the trailing edge of a tiltrotor blade model were measured using a flattened pitot probe and a single hot wire. The blade was mounted in Virginia Tech Stability Wind tunnel stationary on a turntable on the wind tunnel's upper wall with the tip pointing down. The measurement point was located at 1 mm behind the trailing edge to make it possible to measure the flow near the blade surface and measure the boundary layer on both sides of the trailing edge in a same run. Mean velocity profiles were measured for a variety of Reynolds numbers and angles of attack. Turbulence intensity and spectral measurements were performed using a single hot wire at the highest Reynolds number. Conclusion was reached that both of the flattened pitot probe and single hot wire are good for boundary layer thickness measurements. Displacement thickness, which is important in trailing edge noise prediction, was calculated from the profile data and fit using an algebra expression against the tip angle of attack. Once the relationship between tip angle of attack and local effective angle of attack is obtained by lifting line theory, the results can be used in the trailing edge noise prediction code. / Master of Science