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With rapidly growing global economies and world population, fossil fuel continues to play a major role in meeting current energy demands. Carbon capture and storage (CCS) technologies have, hence, been implemented to reduce CO2 emissions in the atmosphere. With the implementation of CCS technologies comes the need to utilize the captured CO2. This research, therefore, proposes CO2 gasification of biochar as a viable carbon utilization pathway. With captured CO2 coming at various concentrations, there is a need to understand how CO2 concentration affects biochar conversion to useful products. This study, therefore, evaluates the effects of CO2 concentration on carbon conversion and product yield and performs kinetic analysis, using the volume reaction model, to determine important parameters such as activation energy and pre-exponential factor. The operating conditions utilized in this study include gasification temperatures of 700, 800, and 900 , inlet CO2 concentrations of 15, 30, 45, and 60 % by volume (N2 balance), and an inlet CO2 flow rate of 5 L/min. Biochar was gasified with CO2 in a fixed bed reactor and the concentration of the output gas was analyzed. An increase in temperature and inlet CO2 concentration both resulted in an increase in carbon conversion with a maximum carbon conversion of 57.1% occurring at 900 and 60% inlet CO2 concentration. Results also showed that CO2 conversion increased against temperature but decreased with increasing inlet CO2 concentration. The maximum CO2 conversion reported, in this study, was 76%, occurring at 900 and 15% inlet CO2 concentration. An activation energy in the range of 109 to 117 kJ/mol and pre-exponential factor in the range of 63 to 253 s-1 were determined in this study. This research explains that while carbon (biochar) conversion increases with increasing inlet CO2 concentration, CO2 conversion decreases as inlet CO2 concentration increases. Flue gases captured at low concentrations can, therefore, be utilized for gasification, with only impurities needing to be removed, as opposed to carbon sequestration. Overall, this study describes how captured CO2 improves carbon conversion efficiency and suggests that CO2 gasification of biochar is a potential carbon utilization pathway for greenhouse gas emission reduction.

Animation of human motion is one of the most challenging topics in computer graphics. This is due to the large number of degrees of freedom of the body and to our ability to detect unnatural motion. Keyframing and interpolation remains the form of animation that is preferred by most animators because of the control and flexibility it provides. However this is a labour intensive process that requires skills that take years to acquire. Human motion capture techniques provide accurate measurement of the motion of a performer that can be mapped onto an animated character to provide strikingly natural animation. This raises the problem of how to allow an animator to modify captured movement to produce a desired animation whilst preserving the natural quality. This thesis introduces a new approach to the animation of human motion based on combining the flexibility of keyframing with the visual quality of motion capture data. In particular it addresses the problem of synthesising natural inbetween motion for sparse keyframes. This thesis proposes to obtain this motion by sampling high quality human motion capture data. The problem of keyframe interpolation is formulated as a search problem in a graph. This presents two difficulties: The complexity of the search makes it impractical for the large databases of motion capture required to model human motion. The second difficulty is that the global temporal structure in the data may not be preserved in the search. To address these difficulties this thesis introduces a layered framework that both reduces the complexity of the search and preserves the global temporal structure of the data. The first layer is a simplification of the graph obtained by clustering methods. This layer enables efficient planning of the search for a path between start and end keyframes. The second layer directly samples segments of the original motion data to synthesise realistic inbetween motion for the keyframes. A number of additional contributions are made including novel representations for human motion, pose similarity cost functions, dynamic programming algorithms for efficient search and quantitative evaluation methods. Results of realistic inbetween motion are presented with databases of up to 120 sequences (35000 frames). Key words: Human Motion Synthesis, Motion Capture, Character Animation, Graph Search, Clustering, Unsupervised Learning, Markov Models, Dynamic Programming.

Television and language are two imperfect forms of communication that are recursively explored in the works of David Foster Wallace. Many critics have analyzed these two motifs as they function in the author’s works, however a decisive link between these two devices has yet to be fully established critically. Critic David A. Kessler suggests that television’s dominance over its audience’s consciousness has created a disconnection with reality, as it did in Wallace’s own life. Kessler coins this disconnection, “capture”, and theorizes that it led to Wallace’s own disillusionment and eventual withdrawal from life. In the thesis, I take this idea further by arguing that the capture television caused in Wallace’s own life can be meta-reflexively identified in the characters Neal (“Good Old Neon”), Julie (“Little Expressionless Animals”), and Chris Fogle (The Pale King). By close reading these short and long fictions paired with Wallace’s own criticism, treatises, and interviews, I investigate how television and language are used as imperfect forms of mediation, then assess the capture that supervenes as a result. Citing popular Wallace critics, I explore the evolution of these concepts over Wallace’s career as he strives to create a “New Sincerity”. Wallace ultimately demonstrates that language and television create a capture in the lives of his characters that distorts their perception of reality, and thus an understanding and expression of the self. It is only by becoming aware of this capture – that is hidden in plain sight – that one can learn to transcend it.

Carbon, Capture, and Storage (CCS) is considered an essential technology that can contribute to reaching the IPCC’s target to limit global average temperature rise to no more than 2.0°C. The fundamental purpose of CCS is to reduce anthropogenic CO₂ emissions by capturing gas from large point sources and injecting it into deep geologic formations. In the offshore Texas State Waters (10.3 miles; 16.6 kilometers), the potential to develop CO₂ storage projects is viable, but the size of storage opportunity at the project level is poorly constrained. This research characterizes the High Island 24L Field, a relatively large historic hydrocarbon field, that has produced mainly natural gas (0.5 Tcf). The primary motivation for this study is to demonstrate that depleted gas fields can serve as volumetrically significant CO₂ storage sites. The stratigraphy of the inner continental shelf in the Gulf of Mexico has been extensively explored for hydrocarbon for over 50 years, and this area is well suited for CCS. Lower Miocene sandstones beneath the regional transgressive Amphistegina B shale have appropriate geologic properties (porosity, thickness, extent) and can be characterized utilizing 3D seismic and well logs in this study. Identifying key stratigraphic surfaces, faults, and mapping structural closure footprints illustrates the field’s geologic structure. The interpreted stratigraphic framework can then be used to model three different lithologic facies and effective porosity to calculate CO₂ storage capacity for both the ~200-ft (60-m) thick HC Sand (most productive gas reservoir) and the overlying thicker 1700 ft (520 m), but non-productive, Storage Interval of Interest. Four different methodologies are utilized to achieve confidence in the CO₂ storage capacity estimates. A storage capacity of 15 – 23 MT is calculated for the HC Sand and 108 – 179 MT for the Storage Interval of Interest by applying interpreted efficiency factors. This study evaluates the accuracy of these storage capacity methodologies to better understand the key geologic factors that influence CO₂ storage in a depleted hydrocarbon field for CCS

In this technology review, the process of turning an average biogas into methanol will be presented. The purpose is to find a better method of transporting the energy contained within biogas so that it may be used in industry as a value added product. The first step is removal of hydrogen sulfide, a corrosive chemical that can breakdown mechanical parts. The second step is carbon capture and conversion of the biogas into hydrogen. The final step is the hydrogenation of carbon dioxide into methanol using a copper catalyst and an alcohol co-catalyst.

I have been working on two projects through my master thesis. The first project described in Chapter 1 is focusing on studying the regulatory mechanisms of specific methylated DNA motifs using optimized CAPTURE technique. DNA motifs are the sequences that are presumed to have biological functions in gene regulatory, however, the regulatory compositions of most motifs remain unknown. CAPTURE is a perfect tool that we can optimize and apply on our interest methylated DNA motifs.Epigenetic mechanisms of gene regulation such as DNA methylation and chromatin modification are playing an important role in the development of pathology have been demonstrated, but the epigenetic regulation of gout disease is unclear. The second project in Chapter 2, we modified and applied Atac-seq and RNA-seq to study the difference of open chromatin pattern and gene expression respectively between gout patients and healthy individuals.

A key trade-off for planetary system exploration is the fuel cost required versus science data obtained. Historically, planetary systems have been explored utilizing multiple flybys, such as Galileo, Cassini's complex Saturnian tour, as well as the trajectory for the proposed Europa Clipper mission. While this approach eliminates the need for expensive capture maneuvers, it can require days to weeks between observations, limiting available science data. An alternative that seeks to maximize science return is to capture about each moon of interest. Investigations of low-energy dynamics have shown the existence of relatively inexpensive transfers between halo orbits at different moons. Chaining these transfers in a moon-hopping tour allows one spacecraft to visit multiple moons. The next step for a multi-moon mission is to connect the inter-moon transfers to science orbits at specific moons. Two capture orbit scenarios are considered for comparison: 1) traditional, tightly captured low-altitude orbits and 2) low-energy, loosely captured high-altitude orbits. Near-global grid search methods are developed to generate initial capture trajectories from staging halo orbits. To help determine which solutions are near optimal, an analytical expression for the predicted floor cost is derived. Low cost captures are identified and optimized using impulsive primer vector theory to determine the ideal number and location of impulses. The trajectory is then extended to include the last resonant-orbit of the inter-moon transfer, using the halo orbit as a patch point to connect the phases. A new three-dimensional periodic orbit that naturally transfers between the resonant and halo orbits is generated to facilitate the connection. The resulting resonant-to-capture transfers are again optimized with primer vector theory, resulting in several optimized options for comparison. As an additional mission design option, the possibilities of advanced exploration using an electrodynamic tether are investigated. An approximation to the tether-perturbed dynamics is derived that allows for an integral of motion, enabling useful analytical techniques. New periodic orbit families are generated as a function of tether length, using continuation from non-perturbed Lyapunov orbits. The new orbits are analyzed in terms of stability and utility for future use in mission design.

Autonomous virtual characters are important in a growing number of applications ranging from simulation-based training to computer games. In this dissertation I propose a new data-based mobile manipulation framework for achieving real-time autonomous characters able to perform full-body interactions with the environment. The overall approach relies on few example motions in order to guide the generation of complex movements that replicate human-like characteristics.The proposed framework is based on three major components. The first one consists of a fast locomotion module that generates controllable models from single motion examples, and is capable of independent control of direction, orientation and velocity, with known coverage and quality characteristics. The approach is computationally efficient and achieves high controllability of stepping behaviors, thus addressing key properties for supporting a variety of whole-body manipulation tasks.The second component relies on the proposed locomotion controller applied to multiple locomotion behaviors in order to plan multi-behavior paths around obstacles. A new locomotion planning approach is proposed where the behavioral capabilities of the character are considered during the path planning stage, in order to address trade-offs related to path length and preferred navigation behavior when selecting narrow passages to take. The approach relies on new types of operations with planar navigation meshes, reaching fast execution times suitable for real-time applications.The last component focuses on the coordination between locomotion and upper-body manipulation. The proposed approach is based on learning spatial coordination features from example motions and on associating body-environment proximity information to the body configurations of each example motion. Body configurations then become the input to a regression system which in turn is able to generate new interactions for different situations in similar environments. The regression model is capable of encoding and replicating key spatial strategies with respect to body coordination and management of environment constraints. Obtained results successfully synthesize complex full-body actions such as opening doors and drawing in a wide whiteboard.The models proposed in this dissertation achieve new interactive controllers able to synthesize coordinated full-body motions for a variety of complex interactions requiring body mobility and manipulation.

Abstract: Low dose rate (LDR) brachytherapy is a minimally invasive form of radiation therapy, used to treat prostate cancer, and it involves permanent implantation of radioactive sources (seeds) inside of the prostate gland. Treatment planning in brachytherapy involves a decision making process for the placement of radioactive sources in order to deliver an effective dose of radiation to cancerous tissue in the prostate while sparing the surrounding healthy tissue. Such a decision making process can be modeled as a mixed-integer linear programming (MILP) problem. In this thesis, we initially introduce a novel MILP optimization model for interstitial LDR prostate brachytherapy that attempts to mimic the qualities of treatment plans produced manually by expert planners. Our approach involves incorporating a unique set of clinically important constraints, called spatial constraints, into our models. These constraints are combined with several proposed data processing techniques, and collectively these methods enable us to capture the fundamental aspects of the planning style present at a local cancer clinic. While this represents the primary clinical challenge to overcome in this thesis, the engineering challenge involves producing clinically acceptable treatment plans in a matter of seconds to minutes with our automated planning system. Such a goal initially presents itself to be a difficult one to accomplish, as our large-scale models exhibit tendencies of intractability while using high-resolution data sets. We introduce pseudo high-resolution data sets and constraint-violating feasibility-based modelling in order to overcome these issues related to solution performance of our model. Through the synergistic effects of these two modelling techniques, we demonstrate the ability to produce treatment plans with solution times suitable for pre-operative and intra-operative planning, which constitute the two main forms of treatment planning in LDR brachytherapy. Specifically speaking, our solution times range from less than a minute to roughly five minutes for prostates of varying shapes and sizes (volume-wise, the tested prostates range from 20.4 cc to 63.1 cc). Finally, with the help of a radiation oncologist, we verify the clinical acceptability of our automated plans through a pilot study involving data from twenty patients. Therefore, through this study we also confirm the clinical suitability of the several concepts introduced in this thesis; namely, these are spatial constraints, pseudo high-resolution data sets and constraint-violating feasibility-based modelling. Following these results, we consequently identify three main areas of improvements for our automated system: avoiding placement of strands outside of the prostate and seeds in the bladder, as well as lowering dose to the organs-at-risk. Moving forward, we suggest branching out and examining the clinical performance of our automated planning system in intra-operative planning, as the results obtained in thesis reflect the performance of our approach within pre-operative planning. In summary, through this thesis we demonstrate the ability to uniquely capture the planning style of expert planners at a local clinic in a fraction of the time that it currently takes expert planners to produce treatment plans.