Autonomously monitoring and analyzing the behavior of the crowd is an open research topic in the transportation field because of its criticality to the safety of people. Real-time identification, tracking, and prediction of crowd behavior are primordial to ensure smooth crowd management operations and the welfare of the public in many public areas, such as public transport stations and streets. This being said, enabling such systems is not a straightforward procedure. First, the complexity brought by the interaction and fusion from individual to group needs to be assessed and analyzed. Second, the classification of these actions might be useful in identifying danger and avoiding any undesirable consequences. The adoption of the Internet-of-things (IoT) in such systems has made it possible to gather a large amount of data. However, it raises diverse compatibility and trustworthiness challenges, among others, hindering the use of conventional service discovery and network navigability processes for enabling crowd management systems. In fact, as the IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as availability, battery capacity, and memory usage), traditional methods fail to learn and understand the evolving behavior of the network so as to enable real-time and context-aware service discovery to assign and select relevant IoT devices for monitoring and managing the crowd. In large-scale IoT networks, crowd management systems usually collect large data streams of images from different heterogeneous sources (e.g., CCTVs, IoT devices, or people with their smartphones) in an inadvertent way. Due to the limitations and challenges related to communication bandwidth, storage, and processing capabilities, it is unwise to transfer unselectively all the collected images since some of these images either contain duplicate information, are inaccurate, or might be falsely submitted by end-users; hence, a filtering and quality check mechanism must be put in place. As images can only provide limited information about the crowd by capturing only a snapshot of the scene at a specific point in time with limited context, an extension to deal with videos to enable efficient analysis such as crowd tracking and identification is essential for the success of crowd management systems. In this thesis, we propose to design a smart image enhancement and quality control system for resource pooling and allocation in the Internet-of-Things applied to crowd management systems. We first rely on the Social IoT (SIoT) concept, which defines the relationships among the connected objects, to extract accurate information about the network and enable trustworthy and context-aware service exchange and resource allocation. We investigate the service discovery process in SIoT networks and essentially focus on graph-based techniques while overviewing their utilization in SIoT and discussing their advantages. We also propose an alternative to these scalable methods by introducing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery in a large-scale heterogeneous IoT network to enable efficient crowd management systems. Secondly, we propose to design a smart image selection procedure using an asymmetric multi-modal neural network autoencoder to select a subset of photos with high utility coverage for multiple incoming streams in the IoT network. The proposed architecture enables the selection of high-context data from an evolving picture stream and ensures relevance while discarding images that are irrelevant or falsely submitted by smartphones, for example. The approach uses the photo's metadata, such as geolocation and timestamps, along with the pictures' semantics to decide which photos can be submitted and which ones must be discarded. To extend our framework beyond just images and deal with real-time videos, we propose a transformer-based crowd management monitoring framework called V3Trans-Crowd that captures information from video data and extracts meaningful output to categorize the crowd's behavior. The proposed 3D Video Transformer is inspired from Video Swin-Transformer/VIVIT and provides an improved hierarchical transformer for multi-modal tasks with spatial and temporal fusion layers. Our simulations show that due to its ability to embed the devices' features and relations, the GNN is capable of providing more concise clusters compared to traditional techniques, allowing for better IoT network learning and understanding. Moreover, we show that the GNN approach speeds up the service lookup search space and outperforms the traditional graph-based techniques to select suitable IoT devices for reporting and monitoring. Simulation results for three different multi-modal autoencoder architectures indicate that a hierarchical asymmetric autoencoder approach can yield better results, outperforming the mixed asymmetric autoencoder and a concatenated input autoencoder, while leveraging user-side rendering to reduce bandwidth consumption and computational overhead. Also, performance evaluation for the proposed V3Trans-Crowd model has shown great results in terms of accuracy for crowd behavior classification compared to state-of-the-art methods such as C3D pre-trained, I3D pre-trained, and ResNet 3D pre-trained on the Crowd-11 and MED datasets.

Enterprise networks are traditionally mapped via layers two or three, providing a view of what devices are connected to different parts of the network infrastructure. A method was developed to map connections at layer four, providing a view of interconnected systems and services instead of network infrastructure. This data was graphed and displayed in a web application. The information proved beneficial in identifying connections between systems or imbalanced clusters when troubleshooting problems with enterprise applications.

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Trichomonas vaginalis is a sexually-transmitted parasite that is the causative agent in the disease trichomoniasis. Resistance to the only FDA-approved medication to this disease, metronidazole, has been on the increase giving rise to the need for finding targets for new inhibitors to exploit. New inhibitors can target enzymes such as 4-coumarate:CoA ligase and S-adenosylhomocysteine hydrolase. Another potential target is a cathepsin D-like protease found in T. vaginalis . This aspartic protease in humans is responsible for degrading proteins in the lysosome, and degrading hemoglobin in P. falciparum as the homologue plasmepsin. Searching the gene database, only one cathepsin-D like protease was discovered throughout the organism's genome. Utilizing RT-PCR, this gene is found to be expressed in two different strains of the organism. Transfection of an epitope-tagged version of this cathepsin D-like protease into T. vaginalis was accomplished, and subsequent immunofluorescence of this tagged version shows it to be localized in intracellular compartments, which can be colocalized using the SNARE and VAMP proteins found in T. vaginalis .

The purpose of this study was to evaluate a period in the history of the region surrounding Mount Shasta, a sentinel on the surface of the earth and in the minds of ancient as well as modern man. The study presents the area's geologic history, its pre-history and discovery by the white man in order to provide the reader with an understanding of the later exploration and early settlement of the region. The emergence of the U.S. Forest Service and the creation of Shasta Forest as well as the evolution of Federal regulatory policy and control is presented along with a discussion of the area's transition and recent wilderness legislation. It is the writer's hope that this information might be utilized by others who are concerned with the preservation of the Shasta Peak Wilderness Area.

Recent technological advances in fields like medicine and genomics have produced high-dimensional data sets and a challenge to correctly interpret experimental results. The Optimal Discovery Procedure (ODP) (Storey 2005) builds on the framework of Neyman-Pearson hypothesis testing to optimally test thousands of hypotheses simultaneously. The method relies on the assumption of normally distributed data; however, many applications of this method will violate this assumption. This thesis investigates the sensitivity of this method to detection of significant but nonnormal data. Overall, estimation of the ODP with the method described in this thesis is satisfactory, except when the nonnormal alternative distribution has high variance and expectation only one standard deviation away from the null distribution.

The exploration of Utah Valley and the history of Fort Utah is the story of the conquest and colonization of the American frontier. Discovered in the days of Western expansion, the Valley was identified with the principal factors in the development of the Intermountain West. It heard the chant of the gray robed Franciscan priests, became a favorite haunt of the trail blazing fur trapper and trader, was the site of the ancient rendezvous of the Indian, saw the gold seekers trudge wearily on to California, and with the founding of Fort Utah served as the springboard of Southern Utah Mormon colonization. Today this Fort is the foundation of the modern and beautiful city of Provo, Utah.During its heyday, Fort Utah was identified with the redman as well as the white; it was the guardian and outpost to the south of Salt Lake of Brigham Young's colonization plan; it was the scene of extensive bartering with the Indian; it was the setting of major peace councils, and it was a base of military operations in protecting those courageous Mormon pioneers as they built an empire from the heart of the American desert.In narrating the story of the exploration of this Valley and the establishment of this pioneer outpost, it is intended not to portray an isolated fragment of history, although the main setting will be centered here, but to build another link in the development and colonization of the Intermountain West, one of America's last frontiers.

In 1849, President Brigham Young commissioned a fifty man company, headed by Parley P. Pratt, to explore Southern Utah for possible colonization. The four month trek spanned the coldest months of the winter, and afforded some very harrowing and hazardous experiences. These events weave a heroic story filled with excitement and adventure, while simultaneously revealing the tremendous dedication and fortitude on the part of the explorers to successfully complete their mission.Many of the Southern Utah colonies that were initiated in the subsequent years following the expedition were based on information gathered during this seven-hundred mile expedition. In addition, their findings provided a crucial link in Brigham Young's imminent decision to colonize southern Utah.

Through the SARS-CoV-2 pandemic, it has become clear that the development of antivirals is essential for the health and wellbeing of the population. In this study, novel active site protease inhibitors against SARS-CoV-2 were tested for their inhibitory activity against the viral 3-Chymotrypsin like protease through the means of FRET based enzymatic assays. Additionally, Compound 104 targeting the NS2B-NS3 protease was tested against Zika virus through yield reduction assays as a means to assess whether these assays are suitable for the assessment of peptide hybrid compounds in Zika virus.Novel compounds against SARS-CoV-2 were screened and five of the selected six active compounds were found to inhibit the viral protease at a half-maximal inhibitory concentration (IC50) of below 0.075 µM.In Zika virus, the yield reduction assay was assessed and it was found that under the conditions tested, this assay is not suitable for the assessment of peptide hybrid compounds in Zika virus.The active novel compounds against SARS-CoV-2 should be taken for further assessment in cell based assays as the next step of development. Compound 104 should be assessed under different experimental conditions to identify whether different conditions can make this assay suitable for the intended use.

This thesis investigates the price discovery process between the stock and the credit default swap market (CDS). We link the financial theory of efficient markets and the underlying models and conditions involved in CDSs, the stock market and financial crashes. This study uses publicly listed firms and the European market CDS series to construct a matched stock portfolio and uses financial data collected between the years 2019 to 2021. The purpose is to better understand the price discovery process during a potential new type of crisis in modern financial history. It could potentially allow portfolio managers, traders, arbitrageurs and stakeholders who monitor systematic indices to gauge the level of risk in the overall economy. It can also better inform regulators about how the CDS and the stock market reacted to each other during the COVID-19 pandemic. This deductive and quantitative research is based on secondary data gathered from the Eikon financial database. It uses a vector autoregressive model to test a hypothesis regarding the price discovery process between the stock and CDS portfolios.  Our results show that when using only the variables for the CDS and stock market, both variables cause each other, which is to say a feedback effect is present between the CDS Europe index and the matched portfolio of stocks. When adding the three control variables, the stock variable no longer causes the CDS variable, while the CDS variable still causes the stock variable. We conclude that the European credit default swap index leads the matched portfolio of stocks in the price discovery process with our chosen variables.