Assembly of Two CCDD Rice Genomes, Oryza grandiglumis and Oryza latifolia, and the Study of Their Evolutionary Changes(2021-01) [Thesis]
Advisor: Wing, Rod Anthony
Committee members: Gojobori, Takashi; Zuccolo, AndreaEvery day more than half of the world consumes rice as a primary dietary resource. Thus, rice is one of the most important food crops in the world. Rice and its wild relatives are part of the genus Oryza. Studying the genome structure, function, and evolution of Oryza species in a comparative genomics framework is a useful approach to provide a wealth of knowledge that can significantly improve valuable agronomic traits. The Oryza genus includes 27 species, with 11 different genome types as identified by genetic and cytogenetic analyses. Six genome types, including that of domesticated rice - O. sativa and O. glaberrima, are diploid, and the remaining 5 are tetraploids. Three of the tetraploid species contain the CCDD genome types (O. grandiglumis, O. latifolia, and O. alta), which arose less than 2 million years ago. Polyploidization is one of the major contributors to evolutionary divergence and can thereby lead to adaptation to new environmental niches. An important first step in the characterization of the polyploid Oryza species is the generation of a high-quality reference genome sequence. Unfortunately, up until recently, the generation of such an important and fundamental resource from polyploid species has been challenging, primarily due to their genome complexity and repetitive sequence content. In this project, I assembled two high-quality genomes assemblies for O. grandiglumis and O. latifolia using PacBio long-read sequencing technology and an assembly pipeline that employed 3 genome assemblers (i.e., Canu/2.0, Mecat2, and Flye/2.5) and multiple rounds of sequence polishing with 5 both Arrow and Pilon/1.23. After the primary assembly, sequence contigs were arranged into pseudomolecules, and homeologous chromosomes were assigned to their respective genome types (i.e., CC or DD). Finally, the assemblies were extensively edited manually to close as many gaps as possible. Both assemblies were then analyzed for transposable element and structural variant content between species and homoeologous chromosomes. This enabled us to study the evolutionary divergence of those two genomes, and to explore the possibility of neo-domesticating either species in future research for my PhD dissertation.
Modeling Human Learning in Games(2020-12) [Thesis]
Advisor: Shamma, Jeff S.
Committee members: Feron, Eric; Laleg-Kirati, Taous-MeriemHuman-robot interaction is an important and broad area of study. To achieve success- ful interaction, we have to study human decision making rules. This work investigates human learning rules in games with the presence of intelligent decision makers. Par- ticularly, we analyze human behavior in a congestion game. The game models traffic in a simple scenario where multiple vehicles share two roads. Ten vehicles are con- trolled by the human player, where they decide on how to distribute their vehicles on the two roads. There are hundred simulated players each controlling one vehicle. The game is repeated for many rounds, allowing the players to adapt and formulate a strategy, and after each round, the cost of the roads and visual assistance is shown to the human player. The goal of all players is to minimize the total congestion experienced by the vehicles they control. In order to demonstrate our results, we first built a human player simulator using Fictitious play and Regret Matching algorithms. Then, we showed the passivity property of these algorithms after adjusting the passivity condition to suit discrete time formulation. Next, we conducted the experiment online to allow players to participate. A similar analysis was done on the data collected, to study the passivity of the human decision making rule. We observe different performances with different types of virtual players. However, in all cases, the human decision rule satisfied the passivity condition. This result implies that human behavior can be modeled as passive, and systems can be designed to use these results to influence human behavior and reach desirable outcomes.
On Using D2D Collaboration and a DF-CF Relaying Scheme to Mitigate Channel Interference(2020-12) [Thesis]
Advisor: Alouini, Mohamed-Slim
Committee members: Shihada, Basem; Park, KihongGiven the exponentially increasing number of connected devices to the network which will lead to a larger number of installed celluar towers and base stations that are in closer proximity to one another when compared to the current cellular network setup, and the increasing demand of higher data rates by end users, it becomes essential to investigate new methods that will more effectively mitigate the larger interference introduced by the more packed celluar grid and that result in higher data rates. This paper investigates using Device-to-Device communication where neighboring users can cooperate to mitigate the correlated interference they both receive, where one user acts as a relay and the other as the intended destination of a broadcast message sent by the source base station. The setup studied utalizes a non-orthogonal multiple access (NOMA) scheme and a combined decode-forward and compress-forward relaying scheme. We show that this combined scheme outperforms the individual schemes for some channels and network setups, or reduces to either scheme when the combination does not offer any achievable rate gains. The performance of each scheme is measured with respect to the locations of the base station and the two devices, and to the capacity of the digital link between the users.
BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data(2020-11-25) [Thesis]
Advisor: Ombao, Hernando
Committee members: Sun, Ying; Laleg-Kirati, Taous-Meriem; Ting, Chee-MingIntrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.
Deep GCNs with Random Partition and Generalized Aggregator(2020-11-25) [Thesis]
Advisor: Ghanem, Bernard
Committee members: Thabet, Ali Kassem; Zhang, XiangliangGraph Convolutional Networks (GCNs) draws significant attention due to its power of representation learning on graphs. Recent works developed frameworks to train deep GCNs. Such works show impressive results in tasks like point cloud classification and segmentation, and protein interaction prediction. While for large-scale graphs, doing full-batch training by GCNs is still challenging especially when GCNs go deeper. By fully analyzing a clustering-based mini-batch training algorithm ClusterGCN, we propose random partition which is a more efficient and effective method to implement mini-batch training. Besides, selecting different permutation invariance function (such as max, mean or add) for neighbors’ information aggregation will result in every different results. Therefore, we propose to alleviate it by introducing a novel Generalized Aggregation Function. In this thesis, I analyze the drawbacks caused by ClusterGCN and discuss about its limits. I further compare the performance of ClusterGCN with random partition and the final experimental results show that simple random partition outperforms ClusterGCN with very obvious advantageous for node property prediction task. For the techniques which are commonly used to make GCNs go deeper, I demonstrate a better way of applying residual connections (pre-activation) to stack more layers for GCNs. Last, I show the complete work of training deeper GCNs with generalized aggregators and display the promising results over several datasets from the Open Graph Benchmark (OGB).
Imitation Learning based on Generative Adversarial Networks for Robot Path Planning(2020-11-24) [Thesis]
Advisor: Michels, Dominik L.
Committee members: Wonka, Peter; Moshkov, MikhailRobot path planning and dynamic obstacle avoidance are defined as a problem that robots plan a feasible path from a given starting point to a destination point in a nonlinear dynamic environment, and safely bypass dynamic obstacles to the destination with minimal deviation from the trajectory. Path planning is a typical sequential decision-making problem. Dynamic local observable environment requires real-time and adaptive decision-making systems. It is an innovation for the robot to learn the policy directly from demonstration trajectories to adapt to similar state spaces that may appear in the future. We aim to develop a method for directly learning navigation behavior from demonstration trajectories without defining the environment and attention models, by using the concepts of Generative Adversarial Imitation Learning (GAIL) and Sequence Generative Adversarial Network (SeqGAN). The proposed SeqGAIL model in this thesis allows the robot to reproduce the desired behavior in different situations. In which, an adversarial net is established, and the Feature Counts Errors reduction is utilized as the forcing objective for the Generator. The refinement measure is taken to solve the instability problem. In addition, we proposed to use the Rapidly-exploring Random Tree* (RRT*) with pre-trained weights to generate adequate demonstration trajectories in dynamic environment as the training data, and this idea can effectively overcome the difficulty of acquiring huge training data.
Leveraging Graph Convolutional Networks for Point Cloud Upsampling(2020-11-16) [Thesis]
Advisor: Ghanem, Bernard
Committee members: Wonka, Peter; Pottmann, HelmutDue to hardware limitations, 3D sensors like LiDAR often produce sparse and noisy point clouds. Point cloud upsampling is the task of converting such point clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling using deep neural networks. The effectiveness of a point cloud upsampling neural network heavily relies on the upsampling module and the feature extractor used therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle. NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves the performance of previous upsampling methods. I also propose a new GCN-based multi-scale feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, Inception DenseGCN learns a hierarchical feature representation and enables further performance gains. I combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling network called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.
Prediction of Novel Virus–Host Protein Protein Interactions From Sequences and Infectious Disease Phenotypes(2020-11-11) [Thesis]
Advisor: Tegner, Jesper
Committee members: Hoehndorf, Robert; Ombao, HernandoInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. We developed DeepViral, a deep learning based method that predicts protein– protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.
Spatio-Temporal Prediction and Stochastic Simulation for Large-Scale Nonstationary Processes(2020-11-04) [Thesis]
Advisor: Sun, Ying
Committee members: McCabe, Matthew; Wikle, Christopher K.; Zhang, XiangliangThere has been an increasing demand for describing, predicting, and drawing inferences for various environmental processes, such as air pollution and precipitation. Environmental statistics plays an important role in many related applications, such as weather-related risk assessment for urban design and crop growth. However, modeling the spatio-temporal dynamics of environmental data is challenging due to their inherent high variability and nonstationarity. This dissertation is composed of four signi cant contributions to the modeling, simulation, and prediction of spatiotemporal processes using statistical techniques and machine learning algorithms. This dissertation rstly focuses on the Gaussian process emulators of the numerical climate models over a large spatial region, where the spatial process exhibits nonstationarity. The proposed method allows for estimating a rich class of nonstationary Mat ern covariance functions with spatially varying parameters. The e cient estimation is achieved by local-polynomial tting of the covariance parameters. To extend the applicability of this method to large-scale computations, the proposed method is implemented by developing software with high-performance computing architectures for nonstationary Gaussian process estimation and simulation. The developed software outperforms existing ones in both computational time and accuracy by a large margin. The method and software are applied to the statistical emulation of high-resolution climate models. The second focus of this dissertation is the development of spatio-temporal stochastic weather generators for non-Gaussian and nonstationary processes. The proposed multi-site generator uses a left-censored non-Gaussian vector autoregression model, where the random error follows a skew-symmetric distribution. It not only drives the occurrence and intensity simultaneously but also possesses nice interpretations both physically and statistically. The generator is applied to 30-second precipitation data collected at the University of Lausanne. Finally, this dissertation investigates the spatial prediction with scalable deep learning algorithms to overcome the limitations of the classical Kriging predictor in geostatistics. A novel neural network structure is proposed for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. The proposed method, called DeepKriging, has multiple advantages over Kriging and classical neural networks with spatial coordinates as features. The method is applied to the prediction of ne particulate matter (PM2:5) concentrations in the United States.
Competitive Drone Racing Using Game Theory(2020-11) [Thesis]
Advisor: Shamma, Jeff S.
Committee members: Feron, Eric; Laleg-Kirati, Taous-MeriemDrone racing has recently became a topic of interest in research especially with the increase of power of mobile processors. There are many approaches of localizing (perception), planning, and strategizing against an adversarial agent online, with varying degrees of computational complexity and success. This thesis presents a game theoretic approach to solve this problem in the context of drone racing. The game theory planner strategizes against an opponent by using the “iterated best response” learning method from game theory, to attempt to reach a Nash equilibrium, where neither players can improve their strategy. Furthermore, to complement the functionality of the game theory planner, a polynomial trajectory generation algorithm is used to generate a reference track. Lastly, a model predictive controller is used to execute the strategic path generated by the game theory planner. The game theory planner performed better than the pure MPC by holding the lead position significantly longer, even though it had lower maximum velocity. On the other hand, the pure MPC held the lead position for a shorter time when the roles were switched. Furthermore, the game theory planner also performed better against the policy improvement racer. However, the policy improvement racer fared better against the game theory planner compared to the pure MPC. Lastly, some intuitive competitive behaviors such as blocking and overtaking came naturally as a result of the algorithm.
Increasing the Top Brine Temperature of Multi-Effects Distillation-MED to Boost Its Performance through Controlling the Formation of Scale by Nanofilteration and Antiscalants(2020-11) [Thesis]
Advisor: Ng, Kim Choon
Committee members: Ghaffour, NorEddine; Lai, ZhipingThermal desalination technology especially, multi-effect distillation MED is of great importance to oil producing countries such as those in the gulf region owing to its efficacy in processing seawater with the minimum pre-treatment of the feed and cheap energy input available from waste heat. One of the main drawback of the current MED processes is the susceptibility of scaling when operate above 70 ºC. This limitation deprives the technology to be energy efficient and reduce its optimal productivity. An optimized pre-treatment of the seawater feed by NF membranes can enhance its efficiency significantly. In this work, the possibility of applying a tailored feed quality using thermodynamic speciation chemistry of the feed water and prediction of the scale propensity based on the Saturation Index of the scale minerals was investigated. Different NF membranes with different properties were used and compared experimentally with each other and theoretically with predictions that are based the saturation index. Moreover, new generation of the polymeric antiscalants promoted by the main manufacturers of the inhibitors industry to control scale formation has been investigated. In addition, as part of planned work for future studies, design and construction of a pilot scale based on NF membrane process was carried out and meant to be a potential extension of this work.
Theoretical Kinetic Study of Gas Phase Oxidation of Nicotine by Hydroxyl Radical(2020-11) [Thesis]
Advisor: Sarathy, Mani
Committee members: Mishra, Himanshu; Castaño, PedroCigarette smoke is suspected to cause diverse illnesses in smokers and people breathing second- and third-hand smoke. Although diﬀerent studies have been done to elucidate the impact on health due to smoking, there is a lack of kinetic information regarding the degradation of nicotine under diﬀerent environmental conditions. As a consequence, currently it is not possible to determine thoroughly the risk due to exposure to nicotine and the compounds derived from its decomposition. With the aim of contributing to clarify the diﬀerent degradation paths followed by nicotine during and after the consumption of cigarettes, this work presents a theoretical study of the hydrogen atom abstraction reaction by hydroxyl radical at four sites in the nicotine molecule in a broad range of temperature, speciﬁcally be-tween 200-3000 K. The site-speciﬁc kinetic rate constants were computed by means of the multi-structural torsional variational transition state theory with small curvature tunneling contribution, performing ab initio calculations at the level M06-2X/aug-cc-pVQZ//M06-2X/cc-pVTZ. According to our computations, the dependence on temperature of the studied rate constants exhibited a non-Arrhenius fashion, with a minimum at 873 K. A negative temperature dependence was observed at temperatures lower than 873 K, indicating more prolonged exposure to nicotine in warmer environments. On the other hand, the opposite behavior was observed at higher temperatures; this non-Arrhenius be-havior results of interest in tobacco cigarette combustion, inducing diﬀerent reaction mechanisms depending on the burning conditions of the diﬀerent smoking devices. The results indicate that multi-structural and torsional anharmonicity is an im-portant factor in the computation of accurate rate constants, especially at high tem-peratures where the higher-energy conformers of the diﬀerent species exert a larger inﬂuence. The anharmonicity factors suggest that disregarding the anharmonic de-viations leads to overestimation of the rate constant coeﬃcients, by a factor between four and six. Our computed overall kinetic rate constant at 298 K exhibited very good agreement with the only experimental value meausred by Borduas et al. , af-fording certainty about our calculated site-speciﬁc rate constants, which are currently inaccessible to experiments. However, further experimental studies are necessary to validate our kinetic studies at other temperatures.
The Harrat volcanic Fields on the Arabian Peninsula: their geologic setting, petrology, and suitability for carbon disposal(2020-11) [Thesis]
Advisor: Hoteit, Hussein
Committee members: Van der Zwan, Froukje M.; Afifi, Abdulkader M.; Arkadakskiy, SergueyThis thesis evaluates the suitability of the Late Miocene-Recent basalts on the Harrat volcanic elds of Saudi Arabia for the disposal of CO2 emitted from industrial sources. For this evaluation, topography, geological setting, hydrology, and petrology of the Harrat basalts are the most important parameters. The basalts must have su cient thickness of at least 500 m of which at least 400 m must be saturated with groundwater in order to completely dissolve CO2 at the injection depth. The basalts must be reactive with dissolved CO2 and must have su cient matrix or fracture permeability. In addition, the basalts must be located near a xed industrial source of CO2, and there must be su cient supply of water for injection. All the volcanic elds in western and northern Saudi Arabia are evaluated in this study, amounting to 17 individual elds. The basalt elds were grouped in an older and younger generation, each with speci c geological characteristics. The basalts are reactive with CO2, because they are relatively unaltered. Field observations con rm that the basalts are su ciently permeable, particularly in tu s, agglomerates near vents, in distal lava ows along natural shrinkage joints and along vesicular margins of individual ows. The total thickness of basalt within lava elds was mapped using the digital elevation model by subtracting the base elevation from the surface elevation. The level of the groundwater table was estimated from Google Earth observations of the local topography and well data. Most elds did not have su cient basalt thickness and/or groundwater for the process. Harrat Rahat meets most of the requirements for 5 the CarbFix process, having su ciently thick basalts in three areas and an extensive groundwater aquifer. However, the maximum height of the groundwater aquifer in basalts is estimated to be 225 meters, which is less than optimal. This study concludes that 16 out of the 17 basalt elds in Saudi Arabia are not suitable for carbon mineralization by the CarbFix process, mainly because they are too thin and located higher than the local groundwater table. However, this pioneering study establishes a baseline for additional research in new technologies using CarbFix or other processes.
The effect of aging on the spatial distribution of glycogen in layer I of the somatosensory cortex in mice(2020-11) [Thesis]
Advisor: Magistretti, Pierre J.
Committee members: Cali, Corrado; Hadwiger, Markus; Falqui, AndreaAstrocytes are the most abundant type of glial cell in the brain, required to ensure optimal neuronal functioning, neurogenesis, and brain vascular tone. Moreover, they play a crucial role in support of neuronal metabolism. The human brain utilizes around 20% of the energy consumed to ensure its proper function. Glucose, an important energy source for the brain, access the neuropil across the blood-brain barrier (BBB), and then is transported into astrocytes through their perivascular end-feet, where it can be stored as glycogen. Furthermore, lactate can be synthesized through glycogenolysis and then shuttled via monocarboxylate transporters (MCTs) to neurons to fuel their tricarboxylic acid (TCA) cycle. This mechanism is known as astrocyte-neuron lactate shuttle (ANLS) and is involved in learning and memory formation. Aging is associated with a decline of faculties such as memory, motor skills, and sensory perception. These deficits are not thought to be due to a substantial loss of neurons but rather changes at the level of connectivity, morphological changes, and white matter structure. In the present study, we aim to compare the glycogen distribution in layer I of the somatosensory cortex between adult (4 months old) and geriatric mice (24 months old). We carried out the visual analysis using Connectome Explorer, which allows us to explore, in real-time, brain reconstructions at the nanometric-level. Using the computational tool GLAM (Glycogen-derived Lactate Absorption Map), we can infer a probability map of the locations where astrocytic glycogen-derived lactate is most likely accessing the surrounding neurites. We analyzed and compared the probability maps on axons, dendrites, boutons, and spines to make a functional hypothesis about single compartments’ energy consumption. Our results indicate that aging brains have a more glycolytic metabolism, with fewer peaks facing mitochondria, and smaller glycogen granules.
Multi-Threshold Bidirectional MEMS Inertial Switches(2020-11) [Thesis]
Advisor: Younis, Mohammad I.
Committee members: Farooq, Aamir; Salama, Khaled N.In this work, MEMS inertial switches intended to be triggered at multiple acceleration thresholds in two directions were implemented and proven effective. The switches consume virtually no power in their open switching state. Multiple acceleration thresholds can be beneficial in triggering different actions for different acceleration events. Low power consumption can aid in their use for portable applications such as in cycling helmets. The developed designs rely mainly on a suspended shuttle mass, which is used to implement one of two methods of actuation. The first relies on simple contact between the moving shuttle mass and a flexible electrode. In the second, the pull-in instability is induced by applying a voltage between a cantilever and an electrode, and then having the shuttle mass force the cantilever moving towards the electrode as it moves under the applied acceleration. Ten designs varying in their actuation method, suspension design, intended acceleration thresholds, and dimensions were modeled using a finite element model, fabricated, through the SOIMUMPs process, and then electrically and mechanically tested. Mechanical testing has been conducted using Drop-table tests and mechanical shakers. The simple contact devices were proven effective through shock test results showing triggering at two acceleration thresholds in two directions. Initial results also were promising for the pull-in based devices showing switching by moving their shuttle mass with a probe while applying appropriate voltage and observing under a microscope.
Arsenic Removal via Defect-Free Interfacially-Polymerized Thin-Film Composite Membranes(2020-11) [Thesis]
Advisor: Pinnau, Ingo
Committee members: Han, Yu; Lai, ZhipingBillions of people rely solely on groundwater for drinking and daily use. In the last few decades, groundwater was shown to be contaminated with arsenic in high concentrations, especially in Asian countries such as Bangladesh. Arsenic (As) is ranked the first among 20 toxic substances by the Agency for Toxic Substances and Disease Registry (ATSDR) and United States Environmental Protection Agency (USEPA). Because many diseases and deaths were linked to consumption of arsenic-contaminated groundwater, the world health organization (WHO) reduced the arsenic standard level for drinking water from 50 to 10 µg L-1. Urgent demands for safe drinking water lead to developing potential technologies for removal of arsenic from groundwater. Arsenic is mainly present as uncharged As(III) in groundwater, which makes it difficult to be efficiently removed by conventional treatment methods. Therefore, membrane technology could be a promising potential solution. Because membrane technology has not been widely tested for arsenic removal, a novel in-house defect-free interfacially-polymerized (IP) cross-linked polyamide thin-film composite (TFC) nanofiltration membrane, namely, PIP-KRO1, was tested in this research. Two commercial TFC membranes, namely Dow NF270 and Sepro RO4, were also tested and compared to PIP-KRO1. The membranes were tested at four different pH conditions (4, 6, 8, and 10) in a cross-flow flat sheet membrane unit. The experiments were divided into two parts: (i) the membranes were tested for water permeance and salt (NaCl) removal and (ii) tested for As(III) removal in the presence of 250 ppm NaCl. The results in this study showed strong size sieving rejection for RO4 and a combination of size sieving and charge exclusion mechanisms for PIP-KRO1 and NF270. In general, the rejection trend was RO4 > PIP-KRO1 > NF270 for both NaCl and As(III). In contrast, the trend for water permeance was NF270 > PIP-KRO1 > RO4. The minimum and maximum salt rejection at pH 4 and pH 10, respectively, were 85 and 98.8% for RO4, 57 and 89% for PIP-KRO1, and 34 and 76.8% for NF270. In addition, the TFC membranes demonstrated a maximum As(III) rejection of 98.7, 69.5, and 46.3% for RO4, PIP-KRO1, and NF270, respectively. Based on the characterizations of the membranes, PIP-KRO1 had the highest cross-linking (N/O ratio) followed by RO4 and NF270, respectively. The same trend was observed for the thickness of the polyamide selective layer (PIP-KRO1 > RO4 > NF270). The zeta potential for NF270 was slightly higher than that for PIP-KRO1; RO4 had much lower membrane surface charge. In terms of surface roughness, the following trend was observed: RO4 > PIP-KRO1 > NF270.
A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks(2020-11) [Thesis]
Advisor: Ghanem, Bernard
Committee members: Ghanem, Bernard; Al-Naffouri, Tareq Y.; Wonka, Peter; Thabet, Ali K.Large scale semantic segmentation is considered as one of the fundamental tasks in 3D scene understanding. Point clouds provide a basic and rich geometric rep- resentation of scenes and tangible objects. Convolutional Neural Networks (CNNs) have demonstrated an impressive success in processing regular discrete data such as 2D images and 1D audio. However, CNNs do not directly generalize to point cloud processing due to their irregular and un-ordered nature. One way to extend CNNs to point cloud understanding is to derive an intermediate euclidean representation of a point cloud by projecting onto image domain, voxelizing, or treating points as vertices of an un-directed graph. Graph-CNNs (GCNs) have demonstrated to be a very promising solution for deep learning on irregular data such as social networks, bi- ological systems, and recently point clouds. Early works in literature for graph based point networks relied on constructing dynamic graphs in the node feature space to define a convolution kernel. Later works constructed hierarchical static graphs in 3D space for an encoder-decoder framework inspired from image segmentation. This thesis takes a closer look at both dynamic and static graph neighborhoods of graph- based point networks for the task of semantic segmentation in order to: 1) discuss a potential cause for why going deep in dynamic GCNs does not necessarily lead to an improved performance, and 2) propose a new approach in treating points in a static graph neighborhood for an improved information aggregation. The proposed method leads to an efficient graph based 3D semantic segmentation network that is on par with current state-of-the-art methods on both indoor and outdoor scene semantic segmentation benchmarks such as S3DIS and Semantic3D.
A Novel HVDC Architecture for Offshore Wind Farm Applications(2020-11) [Thesis]
Advisor: Ahmed, Shehab
Committee members: Salama, Khaled N.; Lima, RicardoThe increasing global participation of wind power in the overall generation ca- pacity makes it one of the most promising renewable resources. Advances in power electronics have enabled this market growth and penetration. Through a literature review, this work explores the challenges and opportunities presented by offshore wind farms, as well as the different solutions proposed concerning power electron- ics converters, collection and transmission schemes, as well as control and protection techniques. A novel power converter solution for the parallel connection of high power offshore wind turbines, suitable for HVDC collection and transmission, is presented. For the parallel operation of energy sources in an HVDC grid, DC link voltage con- trol is required. The proposed system is based on a full-power rated uncontrolled diode bridge rectifier in series with a partially-rated fully-controlled thyristor bridge rectifier. The thyristor bridge acts as a voltage regulator to ensure the flow of the desired current through each branch, where a reactor is placed in series for filtering of the DC current. AC filters are installed on the machine side to mitigate harmonic content. The mathematical modeling of the system is derived and the control design procedure is discussed. Guidelines for equipment and device specifications are pre- sented. Different setups for an experimental framework are suggested and discussed, including a conceptual application for hardware-in-the-loop real-time simulation and testing.
The Influence of pH and Temperature on the Encapsulation of Quinine by Alpha, Beta, and Gamma Cyclodextrins as Explored by NMR Spectroscopy(2020-11) [Thesis]
Advisor: Jaremko, Mariusz
Committee members: Arold, Stefan T.; Saikaly, Pascal; Gao, XinCyclodextrins are well known for their ability to encapsulate molecules and have captured the attention of scientists for many years. This ability alone makes cyclodextrins attractive for study, research, and applications in many fields including food, cosmetics, textiles, and the pharmaceutical industry. In this thesis, we specifically look at the ability of the three native cyclodextrins, alpha, beta, and gamma cyclodextrin (α-CD, β-CD, and γ-CD, respectively), to encapsulate the drug molecule, quinine, a small hydrophobic, lipophilic molecule used to treat malaria, leg cramps, and other similar conditions. This encapsulation process is driven by the molecular interactions, which have been studied by NMR techniques at different temperatures (288 K, 293 K, 298 K, 303 K, 308 K) and pH values (7.4, 11.5). These factors (temperature and pH) influence these molecular interactions, which in turn significantly affects the entire encapsulation process. Detailed studies of the influences of temperature and pH on the interactions that drive the encapsulation may suggest some new directions into designing controlled drug release processes. Results obtained throughout the course of this work indicate that β-CD is the best native cyclodextrin to bind quinine, and that binding is best at pH = 11.5. It was found that temperature does not significantly affect the binding affinity of quinine to either α-CD, β-CD, or γ-CD.
An Unexplored Genome Insulating Mechanism in Caenorhabditis Elegans(2020-11) [Thesis]
Advisor: Frøkjær-Jensen, Christian
Committee members: Krattinger, Simon G.; Tegner, JesperCaenorhabditis Elegans genome maintains active H3K36me3 chromatin domains interspersed with repressive H3K27me3 domains on the autosomes’ distal ends. The mechanisms stabilizing these domains and the prevention of position-effect variegation remains unknown as no insulator elements have been identified in C. elegans. De-novo motif discovery applied on mes-4 binding sites links the H3K36me3-specific methyltransferase to a class of non-coding DNA known as Periodic An/Tn Clusters (PATCs). PATCs display characteristics of insulator elements such as local nucleosome depletion and their restriction to genes with specific expression profiles and chromatin marks. Finally, I describe a set of experiments to further investigate the role of PATCs and mes-4 in the maintenance of stable chromatin domains using a synthetic biology approach.