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In this work, digital holography (DH) is extensively utilized to characterize microparticles. Here, “characterization” refers to the determination of a particle’s shape, size, and, in some cases, its surface structure. A variety of microparticles, such as environmental dust, pollen, volcanic ash, clay, and biological samples, are thoroughly analyzed. In this technique, the microscopically fine interference pattern generated by the coherent superposition of an object and a reference wave fields is digitally recorded using an optoelectronic sensor, in the form of a hologram, and the desired particle property is then computationally extracted by performing a numerical reconstruction to form an image of the particle. The objective of this work is to explore, develop, and demonstrate the feasibility of different experimental arrangements to reconstruct the image of various arbitrary-shaped particles. Both forward- and backward-scattering experimental arrangements are constructed and calibrated to quantify the size of several micron-sized particles. The performance and implications of the technique are validated using the National Institute of Standards and Technology (NIST)-traceable borosilicate glass microspheres of various diameters and a Thorlabs resolution plate. After successful validation and calibration of the system, the resolution limit of the experimental setup is estimated, which is ~10 microns. Particles smaller than 10 microns in size could not be imaged well enough to ensure that what appeared like a single particle was not in fact a cluster. The forward- and backward-scattering holograms of different samples are recorded simultaneously and images of the particles are then computationally reconstructed from these recorded holograms. Our results show that the forward- and backward-scattering images yield different information on the particle surface structure and edge roughness, and thus, reveal more information about a particle profile. This suggests that the two image perspectives reveal aspects of the particle structure not available from a more commonly used forward-scattering based image alone. The results of this work could be supportive to insight more on the particles’ morphology and subsequently important for the advancement of contactree particle characterization technique.

This study aims to assess the risk from oil spills in the NEOM region based on marine traffic, with the aid of Modelo Hidrodinâmico (MOHID), oil spill model driven by the outputs of a validated regional met-ocean data set. The region is classified into two sub-regions: the immediate region, extending 50 km in both directions (north and south) along the coastline from the pinpoint location of NEOM; and the extended region, covering as additional 50 km coastal segments in both directions. A total of 15 spill locations are selected in the regions of high marine traffic density, and for each location a total of 48 instantaneous spill events are considered, triggered at the beginning of each month during the period 2013-2016. An independent simulation is conducted for each event, tracking the evolution of the spill over a 30 day period. Simulation results are analyzed to estimate three hazard metrics, namely the volume beached at the end of the month, arrival time to the coast, and the rise time of the beached volume profile. Based on these metrics and historical data on the oceanic-atmosphericconditions, oil spill risk maps are generated, signaling hot spots. Also, an analysis of the seasoned circulation effects on the fate of the oil spills is conducted. The results of this study provide useful information for assessing the impact of an oil spill contamination, and designing monitoring and mitigation measures.

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With the advent of wireless sensor technologies becoming more and more common-place in wearable devices and smartphones, indoor localization is becoming a heavily researched topic. One such application for this topic is in the medical field where wireless sensor devices that are capable of monitoring patient vitals and giving accurate location estimations allow for a less intrusive environment for nursing home patients. This project explores the usage of using received signal strength indication (RSSI) in conjunction with an inertial navigation system (INS) to provide location estimations without the use of GPS in a Particle Filter with a small development microcontroller and base station. The paper goes over the topics used in this thesis and the results.

Microfluidics is the science of processing microliters or less of fluid at a time in a channel with dimensions on the order of microns. The small size of the channels allows fluid properties to be studied in a world dominated by viscosity, surface tension, and diffusion rather than gravity and inertia. Microfluidic droplet generation is a well studied and understood phenomena, which has attracted attention due to its potential applications in biology, medicine, chemistry and a wide range of industries. This dissertation adds to the field of microfluidic droplet studies by studying individual droplet deformation and the process of scaling-up microfluidic devices for industrial use. The study of droplet deformation under extensional and mixed shear and extensional flows was performed within a microfluidic device. Droplets were generated using a flow-focusing device and then sent through a hyperbolic contraction downstream of the droplet generator. The hyperbolic contraction allowed the smallest droplets to be deformed by purely extensional flows and for the larger droplets to experience mixed extensional and shear flows. The shear resulted from the proximity of the droplet to the walls of the microfluidic channel. The continuous phase in all of these devices was oil and the dispersed phase was water, an aqueous surfactant solution, or an aqueous suspension of colloidal particles. Droplet deformation dynamics are affected by the use of surfactants and colloidal particles, which are commonly used to stabilize emulsion droplets again coalescence. Microfluidic droplet generating devices have many potential industrial applications, however, due to the low output of product from a single droplet generating device, their potential has not been realized. Using six parallel flow-focusing droplet generators on a single chip, the process of microfluidic droplet formation can be scaled up, thus resulting in a higher output of droplets. The tuning of droplet size and production frequency can be achieved on chip by varying the outlet tubing lengths, thus allowing for a single device to be used to generate a range of droplet sizes.

The Standard Model of particle physics is a phenomenologically successful description of the strong, weak, and electromagnetic interactions at all currently accessible energy scales with few exceptions \cite{Agashe:2014kda}. The notable deficiencies of the Standard Model are its inability to explain the matter anti-matter asymmetry, the existence of neutrino oscillations \cite{Fukuda:1998mi,Ahmad:2002jz}, the anomalous magnetic moment of the muon \cite{Bennett:2006fi,Hagiwara:2011af}, and its failure to provide a suitable candidate for the gravitationally observed dark matter \citep{Dolgov:1995np}. We explore an extension of the Standard Model that introduces a new gauge symmetry $L_\mu-L_\tau$ along with three right-handed neutrinos, and a symmetry breaking scalar field. The inclusion of right-handed neutrinos are motivated by the aforementioned neutrino oscillation data while the scalar field is motivated by cosmological bounds on a new $Z'$. We attempt to fit our model to the observed neutrino mass textures in the see-saw limit. Despite having a Lagrangian density with three Yukawa couplings, and four right-handed mass parameters we found the left handed neutrino mass matrix was controlled by only four independent quantities. We were attempting to fit to a set of five measured parameters $\{ \Delta m_{12}^2,\Delta m_{13}^2,\theta_{12},\theta_{23},\theta_{13} \}$. This was found to be impossible with our proposed model. Higher dimensional operators were introduced to allow the model to generate neutrino textures that agree with experiment. Our first minimal model was able to reproduce the correct neutrino textures with the exception of one of either $\theta_{13}$ or $\theta_{12}$ the disagreements was at the level of $25\%$. We found that our model was able to fit to the central value of neutrino data after the introduction of various combinations of dimension-five operators. The parametric dependence of these solutions were found to be incompatible with the $Z'$ as a progenitor of dark matter scenario proposed by Shuve and Yavin \cite{Shuve:2014doa}. The $Z'$ progenitor scenario and the see-saw mechanism seem to be distinct entities in the sense that for the former to be viable the dark matter candidate cannot play a significant role in the generation of neutrino textures. / Thesis / Master of Science (MSc)

This research explores optimal sequencing of aircraft engine maintenance events. Due to the high ongoing maintenance costs and large capital investments required for supporting an aircraft engine fleet, the timing and associated costs of maintenance events are key to minimizing overall costs for an airline. This paper examines a novel application of particle swarm optimization techniques in order to create a decision tool which may be easily implemented by the practitioner. Numerical experiments demonstrate the quality of this solution method under multiple maintenance pricing structures and operational constraints.

This thesis compares the performance of heterogeneous and homogenous swarms. Swarms are defined as particles or agents which react to their environment and fellow particles or agents according to social rules. The weights of three attributes of an individual agent were varied for these experiments: Collision Avoidance with individual agents in the swarm, Center of Mass of the swarm and the parameter that controls Velocity Matching in the swarm. In homogenous swarms, all individuals had the same attribute weights while in heterogeneous swarms weights for one attribute were taken from a normal distribution for the population. These swarms were then given goals on a map to pursue. The maps were two-dimensional grid-surfaces with terrains of open, mountain and swamps. Performance was defined as the number of steps it took for 90% of the swarm to reach its final goal. The results show that heterogeneous swarms outperformed homogenous swarms if the weights for the Center of Mass Weight attribute were heterogeneous in the population. The Collision Avoidance and Matched Velocity attributes showed little performance difference for heterogeneous and homogenous swarms for the parameter weights tested. However, swarms heterogeneous in the Matched Velocity parameter showed substantial performance improvements for the most difficult map.

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