In the past several decades, Next Generation Sequencing (NGS) methods have produced large amounts of genomic data at the exponentially increasing rate. It has also enabled tremendous advancements in the quest to understand the molecular mechanisms underlying human complex traits. Along with the development of the NGS technology, many genetic variation and genotype–phenotype databases and functional annotation tools have been developed to assist scientists to better understand the intricacy of the data. Together, the above findings bring us one step closer towards mechanistic understanding of the complex phenotypes. However, it has rarely been possible to translate such a massive amount of information on mutations and their associations with phenotypes into biological or therapeutic insights, and the mechanisms underlying genotype-phenotype relationships remain partially explained. Meanwhile, increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. Among them, protein- protein interaction (PPI) network studies have attracted perhaps most attention. Our overarching goal of this dissertation is to (i) perform a systematic study to investigate the role of pathogenic human genetic variant in the interactome; (ii) examine how common population-specific SNVs affect PPI network and how they contribute to population phenotypic variance and disease susceptibility; and (iii) develop a novel framework to incorporate the functional effect of mutations for disease module detection. In this dissertation, we first present a systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction- disrupting mutations in type 2 diabetes mellitus and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. Second, aided with our SNP-IN tool, we performed a systematic edgetic profiling of population specific non-synonymous SNVs and interrogate their role in the human interactome. Our results demonstrated that a considerable amount of normal nsSNVs can cause disruptive impact to the interactome. We also showed that genes enriched with disruptive mutations associated with diverse functions and have implications in various diseases. Further analysis indicates that distinct gene edgetic profiles among major populations can help explain the population phenotypic variance. Finally, network analysis reveals phenotype-associated modules are enriched with disruptive mutations and the difference of the accumulated damage in such modules may suggest population-specific disease susceptibility. Lastly, we propose and develop a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non- synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations. Our work shows the feasibility of a large- scale in-silico edgetic study and revealing insights into the orchestrated play of mutations inside a complex PPI network. We also expect for our module detection method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

The number of first-generation college students (FGCSs) attending four-year colleges/universities is on the rise. While numerous studies have examined descriptive characteristics of this growing population, few studies have examined why FGCSs choose to attend college. This study sought to tackle this question by conducting focus groups with thirty-five FGCSs. Participants were asked to identify sources of vocational anticipatory socialization (VAS) that were influential in their decision to pursue a college degree as well as the VAS messages they received from these sources. Focus group data revealed seven sources of VAS with parents being the number one source of VAS messages regarding higher education. Results also revealed five VAS message types, though messages referencing a perceived overall better quality of life were the most common. The findings show that FGCSs receive socializing messages from a variety of sources but parents maintain the greatest influence.

Solar energy has come to the forefront as a scalable and largely underutilized renewable energy resource. The current cost of solar electricity, namely from photovoltaics, along with other logistics factors, has prevented the widespread adaptation of the technology. A key determinant of efficiency and cost for a solar cell is the current collector grid. This work presents the Collimated Aerosol Beam Direct Write (CAB-DW) system as a non-contact printing method that can achieve current collector grid finger widths of less than 10 μm which are amenable to decreasing both resistive and optical losses. The ability to produce high aspect ratio grid fingers, and deposit optimized grid structures on high efficiency SHJ solar cells using silver nanoparticle inks is also demonstrated. A decrease in shadowing and via profile modification of the grid fingers is presented, along with a study of aging and degradation of electrical properties within silver nanoparticle inks.

Mobile generation platforms are very common among both military and civilian applications. However, in military applications getting fuel to the front lines can come at a very high cost. This cost is both financial, costing upwards of hundreds of dollars a gallon, and human, with resupply convoys being the leading cause of casualties in today's warfront. Diesel generators operate much more efficiently at higher loads, rather than the lower loads that the systems normally operate at. To improve fuel efficiency, a hybridized generator system is proposed. This system combines a standard generator with a large rechargeable battery pack. The addition of the battery pack allows for several unique power scenarios to occur through power generation. The battery pack functions to provide an efficient storage capability for the system. During times of excess load, the battery and generator work together. This allows for algorithms to manage the generator set to operate at peak efficiency while addressing load spikes. A system like this has been theoretically designed and a simulation has been developed to determine the impact over a standard system. Actual load cycle information from military sources has been used to evaluate the concept. The results of the simulation show increase efficiency, in the low load scenarios, to more than double the standard generator efficiency. / Master of Science

Yes / The classical generation scheduling problem defines on/off decisions (commitment) and dispatch level of all available generators in a power system for each scheduling period. In recent years researchers have focused on developing new approaches to solve nonclassical generation scheduling problems in the newly deregulated and decentralized electricity market place. In this paper a GA-based approach has been developed for a system operator to schedule generation in a market akin to that operating in England and Wales. A generation scheduling problem has been formulated and solved using available trading information at the time of dispatch. The solution is updated after information is obtained in a rolling fashion. The approach is tested for two IEEE network-based problems, and achieves comparable results with a branch and bound technique in reasonable CPU time.

Distributed generation (defined as the production of power in small quantities at the point of use) has recently gained significant interest due to its benefits over a centralized approach. This thesis investigates the integration of a natural gas fed solid-oxide fuel cell (SOFC) and compressed air energy storage (CAES) technologies for distributed generation at the building-level scale. The SOFC/CAES system is also integrated with multiple vital sub-systems (including on-site solar panels) for the building to provide the heat, through an in-floor heating system, hot water, and power demanded by the building. This thesis investigates the models for the SOFC/CAES system, and implements them in a generic analysis tool providing a means for rapid analysis of a wide variety of case studies. The analysis tool determines the ability of the SOFC/CAES system to follow the power and heat loads demanded by the building, and evaluates its performance with an assortment of metrics, including efficiencies, CO2 emissions and grid-independence. The SOFC/CAES system was investigated for the new ExCEL building at McMaster University. It was found that the system was able to produce upwards 75% of the heat and hot water demand, and upwards of 94% of the power demand of the building. When compared to the current state-of-the-art natural gas based power producing technology and high efficiency furnace, the SOFC/CAES system reduces the CO2 emissions associated with the building by a minimum of 8.7% and a maximum of 26.95%. The cost of electricity for the system is significantly (21% to 150%) more costly than current market prices; however the SOFC/CAES system is the least costly of all other distributed generation technologies investigated for the case of the ExCEL building. / Thesis / Master of Applied Science (MASc)

This dissertation introduces an innovative technique to extract ground elevation models using smallootprint LIDAR data. This technique consists of a preprocessing step, ground modeling, and interpolation. In the preprocessing step, much of the non-terrain points are eliminated using a histogram-based clustering technique. Then, in the ground modeling stage, the information such as elevation and slope between nearest neighbor points is extracted. This step corresponds to an outlier detection process. In this stage, residuals and gradient indices for elevation and slope, are introduced. These indices are investigated for a constructed 95% confidence interval to discard the remaining non-terrain points. Finally, using spline interpolation, a smooth ground surface is generated. Experimental results show that the presented technique is more robust and yields better results compared to existing techniques, such as linear prediction, modified linear prediction, and adaptive smoothing, in terms of the root mean squared error, absolute mean, and absolute standard deviation. Furthermore, the possibility of using a ground trend model developed from specific tree height measurements is investigated. For this analysis, a statistical regression analysis model is used. Performing this analysis, a 0.63 R-squared value is obtained. This result indicates that the LIDAR ground surface obtained from the presented algorithm is related to the true ground surface.

The Diagnostic Instrumentation and Analysis Laboratory (DIAL) at Mississippi State University was awarded a project to study the feasibility of measuring particulate matter downstream of a high efficiency particulate air (HEPA) filter by testing commercially available equipment used to monitor the downstream side of a HEPA filter. In order to perform the work required for this project, a test stand to accommodate a 12? x 12? x 11.5? HEPA filter with a rated flow of 250 standard cubic feet per minute (scfm) was constructed. The test stand required an aerosol generator capable of producing a mass loading rate of 30 mg/m3 of dry aerosol at the face of the HEPA filter. It was determined that there was not a commercially available aerosol generator that quite fit the needs of the project. Therefore, it was necessary to develop an aerosol generator with the capabilities required for the project.

No description available.

Thesis (D. Min.)--Trinity Evangelical Divinity School, 1995. / Abstract. Includes bibliographical references (leaves 223-231).