Now showing items 41-60 of 2297

    • Effects of Zinc and Vitamin Supplementation on the Coral Acropora hemprichii Health and Growth.

      Alabyadh, Ali (2023-07) [Thesis]
      Advisor: Peixoto, Raquel S
      Committee members: Vahrenkamp., Volker; Carvalho, Susana
      Coral reefs are complex ecosystems that provide several ecological, environmental, and economic services. However, climate change has heavily threatened these ecosystems, particularly due to increasing sea surface temperature. Innovative solutions to improve coral tolerance to climate change are therefore urgently needed. Vitamin and trace element supplements can improve the fitness of several animals (e.g., fish and crustaceans) in aquaculture systems, and could represent an alternative treatment to improve coral health and growth in coral nurseries. Here, we tested whether the supplementation of vitamins B6, B12, and zinc could boost coral growth, and health. For this purpose, fragments (n=10) of colonies of five Acropora hemprichii were collected from the central Red Sea were treated with B6, B12, zinc, and a combination of these supplements for 21 days. Coral fragments were collected before and after the experiment. Calcification and oxygen metabolism (respiration, photosynthesis) were measured, while symbiont density, chlorophyll, total protein, and carbohydrate were quantified in the lab. Our data showed that corals’ symbionts density, chlorophyll c2, net productivity, and total protein were significantly increased due to zinc supplementation when compared to control colonies. In addition, the multi-treatment also increased the corals’ total proteins. In contrast, none of the other treatments showed a significant effect on the tested coral’s physiological traits. The results of this study may provide data to support alternative approaches to improve coral growth for restoration efforts.
    • Axisymmetric flows with swirl for Euler and Navier-Stokes equations

      Mousikou, Ioanna (2023-07) [Dissertation]
      Advisor: Tzavaras, Athanasios
      Committee members: Parsani, Matteo; Schmid, Peter J.; Malek-Madani, Reza; Katsaounis, Theodoros
      We consider the incompressible axisymmetric Navier-Stokes equations as an idealized model of tornado-like flows. Assuming that an infinite vortex line that interacts with a boundary surface resembles the tornado core, we look for stationary self-similar solutions of the axisymmetric Euler and the axisymmetric Navier-Stokes equations emphasizing the connection among them as the viscosity goes to zero. We construct a class of explicit stationary solutions for the axisymmetric Euler equations and prove that solutions of self-similar Euler equations with slip discontinuity at a finite number of points do not exist. The nonexistence result is then extended to a class of flows with mass input or loss through the vortex line, as well as to conical flows. In addition, a study of the system of self-similar axisymmetric Navier-Stokes equations provides a-priori bounds and information about the configuration of their solutions which are then verified by solving it numerically. Using techniques that are motivated by the theory of viscosity approximation for Riemann problems in conservation laws, we also prove that, under certain assumptions, solutions of the axisymmetric Navier-Stokes equations converge to solutions of axisymmetric Euler equations as the viscosity tends to zero. This also allows us to characterize the type of Euler solutions that exist. Furthermore, a new approach is proposed for solving the one-dimensional system of elastodynamics. The main idea of this approach involves the method of gradient descent to solve an implicit scheme with a constrained variational formulation and the discontinuous Galerkin finite element methods to discretize in space. The resulting optimization scheme performed well, it has an advantage on how it handles oscillations near shocks, and a disadvantage in computational cost, which can be partly alleviated by using techniques on step selection from optimization methods.
    • Production of Electrical Current by Glucose-Utilizing Shewanella chilikensis JC5 and its Coexistence with Geobacter sulfurreducens

      Alkurdi, Marya (2023-07) [Thesis]
      Advisor: Saikaly, Pascal
      Committee members: Arold, Stefan T.; Ghaffour, NorEddine
      Shewanella spp. are model electroactive bacteria (EAB) and well-known for their broad metabolic capabilities and extracellular electron transfer (EET) properties, which allow them to utilize a diverse range of carbon substrates, such as formate, lactate, pyruvate, and amino acids. However, the majority of Shewanella spp. cannot metabolize glucose, a naturally occuring carbon and energy source. Here, we examine the electrode respiring potential of Shewanella chilikensis JC5T- and its coexistence with acetoclastic EAB, Geobacter sulfurreducens in glucose-fed microbial electrolysis cells (MECs) operated at a set anode potential condition of 0 V vs. Ag/AgCl. Chronoamperometry analysis revealed that the maximum current density was 0.04523 mA/cm2 (CE: 14.55%) in S-MEC (only S. chilikensis JC5T) and 0.299 mA/cm2 (CE: 53.85%) in CC-MEC (S. chilikensis JC5T and G. sulfurreducens) which is 6.6-folds higher in current density and 3.7-folds higher in coulombic efficiency than S-MEC. Cyclic voltammetry analysis demonstrates presence of biofilm and redox mediator for EET mechanisms. Metabolic analysis showed that S. chilikensis as a monoculture and co-culture with G. sulfurreducens can metabolize glucose and produce intermediates such as acetate, formate, and lactate. These intermediates are likely used to generate electrical current. Collectively, these results provide novel insights on electrode respiring properties of S. chilikensis and its coexistence with acetoclastic EAB, G. sulfurreducens to enhance the current density and coulombic efficiency from glucose-fed MEC.
    • Evaluation under Real-world Distribution Shifts

      Alhamoud, Kumail (2023-07) [Thesis]
      Advisor: Ghanem, Bernard
      Committee members: Gao, Xin; Elhoseiny, Mohamed
      Recent advancements in empirical and certified robustness have shown promising results in developing reliable and deployable Deep Neural Networks (DNNs). However, most evaluations of DNN robustness have focused on testing models on images from the same distribution they were trained on. In real-world scenarios, DNNs may encounter dynamic environments with significant distribution shifts. This thesis aims to investigate the interplay between empirical and certified adversarial robustness and domain generalization. We take the first step by training robust models on multiple domains and evaluating their accuracy and robustness on an unseen domain. Our findings reveal that: (1) both empirical and certified robustness exhibit generalization to unseen domains, and (2) the level of generalizability does not correlate strongly with the visual similarity of inputs, as measured by the Fréchet Inception Distance (FID) between source and target domains. Furthermore, we extend our study to a real-world medical application, where we demonstrate that adversarial augmentation significantly enhances robustness generalization while minimally affecting accuracy on clean data. This research sheds light on the importance of evaluating DNNs under real-world distribution shifts and highlights the potential of adversarial augmentation in improving robustness in practical applications.
    • Micronanobubbles as cleaning strategies for SWRO biofouling

      Alvarez Sosa, Damaris (2023-07) [Thesis]
      Advisor: Vrouwenvelder, Johannes S.
      Committee members: Farhat, Nadia; Witkamp, Geert Jan; Johnson, Maggi
      Water desalination has the potential to alleviate a significant part of the world’s thirst, with a majority of desalinated water capacity coming from seawater reverse osmosis (SWRO). However, SWRO membrane systems suffer from the loss of performance due to biofouling leading to economic costs. There is no control or preventive strategy for SWRO biofouling and current industry practices recommend chemical treatments to restore membrane performance. Chemical cleaning results in high economic costs due to chemical acquisition, storage, transportation, long plant downtimes and ultimately shorter membrane lifetime and early replacement; in addition to the environmental impact associated with disposing of chemicals. Therefore, there is a need for novel effective green cleaning strategies for SWRO to meet the increasing demand for desalinated water while taking care of the environment. Micronanobubbles (MNBs) consist of small gas cavities formed in aqueous solutions. This study evaluates the efficiency of both air-filled micronanobubbles (AMNBs) and CO2 nucleated MNBs as: i) curative cleaning-in-place (CIP) treatments and ii) preventive daily treatments for biofouling over long-term studies. Experiments were performed using the membrane fouling simulator (MFS) under conditions that are representative of SWRO membrane systems. Pressure drop was implemented as the main biofilm growth monitoring parameter as used by standard industry practices. Curative studies showed that both MNBs CIP treatments had high cleaning efficiencies of 49-56% pressure drop recovery. MNBs pressure drop recovery values were close to the conventional chemical cleaning (NaOH/HCl) at 51% and were significantly higher than the hydraulic flush (HF) physical cleaning control at 24%. The pressure drop recovery results were supported by the optical coherence tomography (OCT) images before and after CIP and biomass autopsy results. Similarly, preventive MNBs daily treatments showed a significant delay in the system’s performance decline. This delay was 5.1 days for the CO2 MNBs experiments, 4 days for the AMNBs, and only 0.6 days for the hydraulic flushing treatments compared to the control. Compared to the control the duration of the operation was doubled in time before the cleaning criteria was met. OCT images confirmed biofilm growth delay with lower biomass occurrence.
    • Optimized Escape Path Planning for Commercial Aircraft Formations

      Saber, Safa I. (2023-07) [Dissertation]
      Advisor: Feron, Eric
      Committee members: Lacoste, Deanna; Park, Shinkyu; Baillieul, John
      There is growing interest in commercial aircraft formation flight as a means of reducing both airspace congestion and the carbon footprint of air transportation. Wake vortex surfing has been researched extensively and proven to have significant fuel-saving benefits, however, commercial air transportation has yet to take advantage of these formation benefits due to understandable safety concerns. The realization of these formations requires serious consideration of formation contingencies and safety during closer-in maneuvering of large commercial aircraft. Formation contingency scenarios are much more complex than those of individual aircraft and have not yet been studied in depth. This thesis investigates the utility of optimization modeling in providing insight into generation of aircraft escape paths for formation contingency planning. Three high-altitude commercial aircraft formation scenarios are presented; formation join, formation emergency exit, and formation escape. The model-generated paths are compared with pilot-generated escape plans using the author’s pilot expertise. The model results compare well with pilot intuition and are useful in presenting solutions not previously considered, in evaluating separation requirements for improvement of escape path planning and in confirming the viability of the pilot-generated plans. The novel optimization model formulation presented in this thesis is the first model shown to be capable of generating escape paths comparable to pilot- generated escape plans and is also the first to incorporate avoidance of persistent and drifting wake turbulence within the formation.
    • Optical Properties of Wide Bandgap Perovskites

      Al Nasser, Hamza (2023-07) [Thesis]
      Advisor: Laquai, Frédéric
      Committee members: De Wolf, Stefaan; Schwingenschlögl, Udo
      Wide bandgap perovskites are emerging as suitable candidates for the technology of tandem solar cells. Understanding their optical properties is a prerequisite for improving the corresponding solar cells’ efficiencies. In this thesis, we employ various steady-state spectroscopies to reveal the optical properties of two wide bandgap perovskites: FA0.83Cs0.17Pb(I0.7Br0.3)3 or PVK1 and FA0.83Cs0.17Pb(I0.5Br0.5)3 or PVK2. The optical properties of interest are the semiconductors’ absorption spectra, the sub-bandgap absorption features, the bandgap energy, the Urbach energy, and the excitonic binding energy. We find that the sub-bandgap absorption can be characterized by a single exponential function. We also find that the Urbach energies and the excitonic binding energies are below the thermal energy at room temperature, which signals that PVK1 and PVK2 are excellent nominees for photovoltaic absorbers. Finally, the bandgap energy is red shifted due to excitonic effects as revealed by the Elliot model.
    • Well On/Off Time Classification Using RNNs and a Developed Well Simulator to Generate Realistic Well Production Data

      AlHammad, Yousef (2023-07) [Thesis]
      Advisor: Hoteit, Hussein
      Committee members: Yan, Bicheng; Hoteit, Hussein; Ravasi, Matteo
      Supervised machine learning (ML) projects require data for model training, validation, and testing. However, the confidential nature of field and well production data often hinders the progress of ML projects. To address this issue, we developed a well simulator that generates realistic well production data based on physical, governing differential equations. The simulation models the reservoir, wellbore, flowline, and choke coupled using transient nodal analysis to solve for transient flow rate, pressure, and temperature as a function of variable choke opening over time in addition to a wide range of static parameters for each component. The simulator’s output is then perturbed using the gauge transfer function to introduce systematic and random errors, creating a dataset for ML projects without the need for confidential production data. We then generated a simulated dataset to train a recurrent neural network (RNN) on the task of classifying well on/off times. This task typically requires a significant number of manhours to manually filter and verify data for hundreds or thousands of wells. Our RNN model achieves high accuracy in classifying the correct on/off labels, representing a promising step towards a fully-automated rate allocation process. Our simulator for well production data can be used for other ML projects, circumventing the need for confidential data, and enabling the study and development of different ML models to streamline and automate various oil and gas work processes. Overall, the success of our RNN model demonstrates the potential of ML to improve the operational efficiency of various oil and gas work processes.
    • Unraveling the Molecular Impact of Missense Variants: Insights into Protein Structure and Disease Associations

      Alvarez, Ana C. Gonzalez (2023-07) [Thesis]
      Advisor: Arold, Stefan T.
      Committee members: Henao, Ricardo; Hoehndorf, Robert
      One of the primary challenges in clinical genetics is the interpretation of the numerous genetic variants identified through sequencing applications. Assessing the impact of missense variants where only one amino acid is substituted is particularly difficult. In this study, we examined the structural characteristics of amino acids affected by missense substitutions in 26,690 pathogenic variants and compared them to 11,302 common variants found in the general population. This analysis was conducted across 6,747 protein structures. The residues were annotated using 7 protein features with a total of 35 feature subtypes. Subsequently, we assessed the burden of both common and pathogenic missense variants across these features. Additionally, we carried out separate analyses relative to protein function (with variants grouped in 24 protein functional classes) and relative to diseases (with variants grouped in 86 diseases). Through a comprehensive analysis of the entire dataset, we identified 25 pathogenic features that play a crucial role in the overall fitness and stability of proteins. Additionally, when we conducted individual analyses for 24 protein functional classes, we discovered specific features that are relevant to each function. For the disease analysis we identified 3 main clusters. Type I diseases primarily result from ordered mutations and are mainly affected by charge loss. This cluster is dominated by transporter protein class and includes diseases linked to X-chromosome. Type II diseases involve hydrolases and are characterized by enriched variants at the protein core, resulting in protein destabilization. Type III diseases involve extracellular matrix proteins (mainly collagen), are predominantly found in disordered regions, and are affected by charge gain and introduction of polar residues. Gly variants are particularly relevant in this cluster, as collagen proteins require Gly in every third residue in the collagen triple-helix. Considering the structural aspects when interpreting mutations associated with diseases offers valuable insights into their underlying mechanisms. Our work can serve as resource to delineate and understand variant pathogenicity by mapping a genetic variant into its structural context.
    • Characterizing the BIRD Protein Network Interactions and Phase Separation Abilities

      Alashoor, Kawthar (2023-07) [Thesis]
      Advisor: Arold, Stefan T.
      Committee members: Blilou, Ikram; Jaremko, Lukasz
      In plants, growth and defense pathways are regulated by hormone cross-talk and a set of proteins responsible for the activation or repression of these pathways. Transcription factors JACKDAW (JKD), SHOORTROOT (SHR), and SCARECROW (SCR) form a complex to regulate the patterning of the roots. JASMONATE-ZIM domain (JAZ) proteins repress the Jasmonic Acid (JA) defense response through inhibiting transcription factor MYC2. TCP14, a member of the Teosinte branched1/Cycloidea/Proliferaitng cell factor1&2 (TCP) family of transcription factors, promotes Gibberellin (GA) growth pathway activation and is a repressor of the JA defense pathway. Previous data shows that JKD binds to JAZ3 and TCP14, and that JAZ3 and TCP14 may interact. Whether these interactions occur in vitro, and if other protein complexes also form, has not been confirmed yet. In this project, I aimed to study these protein interactions, as they may play a role in the growth-defense trade-offs affecting plant fitness. I used pull-down assays to confirm direct protein-protein binding, and utilized Alphafold to identify the interaction interfaces. I also developed an assay to assess liquid-liquid phase separation (LLPS) and found that TCP14 phase separates. These studies provide insight to how these proteins may regulate and connect growth and defense.
    • Generation of iPSCs isogenic control from GLP1-R -/- patient iPSCs using CRISPR Cas9 system

      Alqahtani, Hussain (2023-07) [Thesis]
      Advisor: Li, Mo
      Committee members: Adamo, Antonio; Schmidt, Fabian
      In this study, we employed CRISPR-Cas9 technology to generate isogenic induced pluripotent stem cells (iPSCs) with a corrected GLP1-R gene, serving as a control to patient-derived iPSCs harboring GLP1-R mutations. We tested these isogenic iPSCs and their pluripotency potential by measuring the expression of two common pluripotency markers Tra-1-81 and Tra-1-60. Moreover, we performed quantitative PCR (qPCR) experiments to measure the transcription levels of the GLP1-R gene in isogenic and mutant iPSC clones. Our findings revealed significant differences in GLP1-R transcription levels and mRNA structures between the mutant and isogenic clones. These insights contribute to a better understanding of the mosaic nature of mutant iPSCs and the functional consequences of GLP1-R mutations.
    • Exploring the addition of complex B-vitamins and Zinc, in the Red Sea coral, Acropora hemprichii

      Beenham, Laura (2023-07) [Thesis]
      Advisor: Peixoto, Raquel S
      Committee members: Jaremko, Lukasz; Berumen, Michael L.
      A diversity of human-assisted approaches to rehabilitate and boost coral health have been suggested and investigated throughout the past years. Vitamins and trace-metal supplementation is a well-known strategy in human medicine and aquaculture, but vitamin addition is not currently actively tested for coral growth and recovery. These molecules are essential cofactors that have been correlated with coral thermal resistance and upregulated in corals treated with beneficial microorganisms (i.e., probiotics). To assess the effects of B12, B6 and zinc supplementation on coral health, we conducted a 2-month experiment in an open-closed-loop system mesocosm joined to a peristaltic pump continuously dosing the vitamins and/or zinc to individual 250 L tanks. Fragments of five different colonies of Acropora hemprichii were randomly distributed into the respective treatment tanks (B12, B6, zinc, multi-treatment and control). After 21 days, the corals were exposed to a pulse (1 day) of thermal stress, followed by three weeks of recovery. Substantial mortality (55%) in the control treatment was observed during the stress and recovery, with B12, B6, zinc and multi treatments exhibiting significantly less mortality (<20%). Coral health data combined with analysis of microbiome and metabolomic approaches suggest that both vitamins and zinc have a positive effect on coral health recovery. This study is the first to provide evidence that complex B-vitamins accompanied by zinc supplementation, can be a valuable tool for coral reef rehabilitation, and paves the way to further understanding specific mechanisms by which these nutrients promote coral health will be needed.
    • Fabrication of 3D Multicellular Acute Lymphoblastic Leukemia Disease Models Using Biofunctionalized Peptide-Based Scaffolds

      Baldelamar Juarez, Cynthia Olivia (2023-07) [Thesis]
      Advisor: Hauser, Charlotte
      Committee members: Mahfouz, Magdy M.; Habuchi, Satoshi
      Acute Lymphoblastic Leukemia (ALL) is one of the most common type of hematologic malignancy in children, characterized by an excessive proliferation of unfunctional immature lymphoblasts in the blood and the bone marrow, which leads to a range of severe blood-related complications. Given the remarkable increase in the prevalence of leukemia in the past 20 years, there has been a particular interest in the development of in vitro experimental models for cancer research. Ultra-short self-assembling peptides have shown to be a promising class of synthetic biomaterials due to their biocompatibility, tunable mechanical properties, and the possibility of controlling the scaffold composition. The objective of this study was to create a bioactive but well-defined synthetic 3D model of the bone marrow (BM) microenvironment for the simulation of ALL using biofunctionalized ultrashort self-assembling peptide scaffolds. Different bioactive motifs derived from integral extracellular matrix (ECM) constituents that are known to enhance cell-matrix adhesion, including RGDS from fibronectin, YIGSR from laminin, and GFOGER from collagen, were incorporated into the parent peptide IIZK. These peptides demonstrated to be capable of generating stable hydrogel structures composed of fibrous porous networks, each with unique nanofiber morphology and mechanical properties. All the peptide scaffolds that were investigated in this study exhibited optimal characteristics concerning the cytocompatibility of multiple BM niche cells, including human bone marrow mesenchymal stem cells (MSCs), human umbilical vein endothelial cells (HUVECs), and patient derived ALL cells. The suitability of the scaffolds as drug screening platforms was evaluated, demonstrating their potential as versatile tools for the assessment of drug efficacy.
    • Hardware-Aware Distributed Pipelined Neural Network Models Inference

      Alshams, Mojtaba (2023-07) [Thesis]
      Advisor: Eltawil, Ahmed
      Committee members: Salam, Khaled N.; Fahmy, Suhaib A.
      Neural Network models got the attention of the scientific community for their increasing accuracy in predictions and good emulation of some human tasks. This led to extensive enhancements in their architecture, resulting in models with fast-growing memory and computation requirements. Due to hardware constraints such as memory and computing capabilities, the inference of a large neural network model can be distributed across multiple devices by a partitioning algorithm. The proposed framework finds the optimal model splits and chooses which device shall compute a corresponding split to minimize inference time and energy. The framework is based on PipeEdge algorithm and extends it by not only increasing inference throughput but also simultaneously minimizing inference energy consumption. Another thesis contribution is the augmentation of the emerging technology Compute-in-memory (CIM) devices to the system. To the best of my knowledge, no one studied the effect of including CIM, specifically DNN+NeuroSim simulator, devices in a distributed inference. My proposed framework could partition VGG8 and ResNet152 on ImageNet and achieve a comparable trade-off between inference slowest stage increase and energy reduction when it tried to decrease inference energy (e.g. 19% energy reduction with 34% time increase) and when CIM devices were augmenting the system (e.g. 34% energy reduction with 45% time increase).
    • The Interplay of Human Serum Albumin and Green Tea vs Black Tea Flavonoids Regulating alpha-Synuclein Aggregation

      Lozano Sandoval, Cecilia Alexandra (2023-07) [Thesis]
      Advisor: Jaremko, Lukasz
      Committee members: Chodasiewicz, Monika; Liberale, Carlo
      Parkinson’s disease is a neurodegenerative disorder characterized by the loss of motor skills and cognitive impairment. The hallmark of this disease is the presence of Lewy bodies in the substantia nigra of the brain, where the accumulation of alpha-synuclein amyloid fibrils lead to the death of dopaminergic neurons. Understanding the factors influencing AS aggregation and developing effective strategies for its inhibition is of paramount importance for developing potential therapeutic interventions. Previous studies suggest that flavonoids in green and black tea have neuroprotective properties that decrease the fibrillization rate of AS. This thesis investigates the inhibitory effects of two flavonoids coming from green and black tea, namely: EGCG, TFDG, and HSA on AS aggregation, shedding light on their potential as therapeutic candidates. The study employed a combination of biochemical and biophysical experimental techniques to elucidate the inhibitory mechanisms of EGCG, TFDG, and HSA on AS aggregation. Initial experiments involved the characterization of AS fibrillation kinetics using ThT assays, TEM, and CD. Results revealed that flavonoids exhibited similar inhibitory effects on AS aggregation, with more than 90% inhibitory potency. Interestingly, when aggregated AS was exposed to EGCG or TFDG, the amyloid fibrils changed conformation and formed non-toxic amorphous oligomers. The 13C-detected NMR experiments, adapted to probe the AS dynamic conformation and interactions at the atomic level at physiologically relevant conditions, further provided insights into the binding interactions between flavonoids and AS, revealing interaction with hydrophobic residues involved in the inhibition process. Furthermore, the role of HSA, a major protein component of the blood plasma, in modulating α-synuclein aggregation in the presence of tea-derived flavonoids was investigated. The study demonstrated, in line with the previous reports, that HSA can significantly suppress AS fibrillation, and moreover, the presence of HSA further enhances the flavonoids’ inhibitory effect. My findings provide valuable atomic level mechanistic insights into the inhibitory effects of EGCG and TFDG on alpha-synuclein aggregation. The comprehensive spectroscopic and biophysical investigation provides a solid foundation for further developing flavonoid-based inhibitors, subsequently drug candidates blocking the AS toxic oligomers formation and aggregation.
    • Probing Surface Charge Densities of Common Dielectrics

      Alghonaim, Abdulmalik (2023-07) [Thesis]
      Advisor: Mishra, Himanshu
      Committee members: Fatayer, Shadi P.; Daniel, Dan
      The value of the surface charge density of polypropylene reported in literature has a three order of magnitude discrepancy. Nauruzbayeva et al report a 0.7nCcm−2 as the surface charge density of polypropylene as measured using the charge of electrified droplets[1]. Meagher and Craig reported result 111nCcm−2 as estimated by electric double layer theory from colloidal probe Atomic force microscopy (AFM) force spectroscopy [2]. We show that oxidation of hydrophobic surfaces as a potential mechanism in origin of these surface charges. Using colloidal probe AFM We measured the surface charge densities of Teflon AF, perfluorodecanethiol, Perfluorodecyltrichlorosilane(FDTS), Octadecyltrichlorosilane, polystyrene, and polypropylene. Also, The pH dependence of the surface charge density for FDTS was studied and it shows the behavior expected of a weak acid in response to pH. We suspect that the origin of the surface charges is mostly likely impurities or surface oxidation. We conclude that the electrometer and dispensed droplets approach cannot detect these charges because of the process of de-wetting all the surface be neutralized to maintain charge neutrality. This explanation supports Nauruzbayeva et al claims about surface bound charges[1].
    • Transportation Mode Recognition based on Cellular Network Data

      Zhagyparova, Kalamkas (2023-07) [Thesis]
      Advisor: Al-Naffouri, Tareq Y.
      Committee members: Shihada, Basem; Alouini, Mohamed-Slim
      A wide range of contemporary technologies leveraging ubiquitous mobile phones have addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. This problem has found applications in various areas, including smart city transportation, carbon footprint calculation, and context-aware mobile assistants. Previous research has primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones. However, these approaches often necessitate the installation of specialized mobile applications on users’ devices to collect sensor data, resulting in power inefficiency and privacy concerns. In this study, we tackle these issues by presenting a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. Our system was developed using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. The underlying concept is to correlate phone speed with features extracted from Channel State Information (CSI), which includes information about Physical Cell ID, received signal strength, and other relevant data. The feature extraction process involves utilizing sliding windows over both the time and frequency domains. By employing statistical classification and boosting techniques, we achieved remarkable F-scores of 85%, 95%, 88%, and 70% for the car, bus, walking, and train modes, respectively. Moreover, we conducted an analysis of the handover rate in a one-tier network and compared the analytical results with real data. This investigation provided novel insights into the influence of transportation modes on handover rate, revealing the correlation between different modes of mobility and network connectivity. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.
    • All-in-Focus Image Reconstruction Through AutoEncoder Methods

      Al Nasser, Ali (2023-07) [Thesis]
      Advisor: Wonka, Peter
      Committee members: Feron, Eric; Moshkov, Mikhail
      Focal stacking is a technique that allows us to create images with a large depth of field, where everything in the scene is sharp and clear. However, creating such images is not easy, as it requires taking multiple pictures at different focus settings and then blending them together. In this paper, we present a novel approach to blending a focal stack using a special type of autoencoder, which is a neural network that can learn to compress and reconstruct data. Our autoencoder consists of several parts, each of which processes one input image and passes its information to the final part, which fuses them into one output image. Unlike other methods, our approach is capable of inpainting and denoising resulting in sharp, clean all-in-focus images. Our approach does not require any prior training or a large dataset, which makes it fast and effective. We evaluate our method on various kinds of images and compare it with other widely used methods. We demonstrate that our method can produce superior focal stacked images with higher accuracy and quality. This paper reveals a new and promising way of using a neural network to aid in microphotography, microscopy, and visual computing, by enhancing the quality of focal stacked images.
    • Ontology design patterns and methods for integrating phenotype ontologies

      Alghamdi, Sarah M. (2023-07) [Dissertation]
      Advisor: Hoehndorf, Robert
      Committee members: Gojobori, Takashi; Moshkov, Mikhail
      Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. The combination of ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This process requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain. This thesis presents use cases where we applied ontology composition, integration, and alignment of phenotype ontologies, and evaluated the resulting ontologies and alignment. First, we analyzed a large aging dataset of inbred laboratory mice, using Mouse Anatomy and Mouse Pathology ontologies. Second, we integrated phenotype ontologies for human and model organism phenotypes to enable comparisons of phenotypes between and within individual species. We developed Pheno-e, an extension of PhenomeNet. We identified novel abnormal anatomical classes for fly phenotypes, allowing the annotation of fly genes that were not annotated before. We demonstrate the distinct contributions of each species' phenotypic data to detecting human diseases using Pheno-e, and show that mouse phenotypic data contributes the most to the discovery of gene--disease associations. This work could guide the selection of model organisms when building methods to find gene-disease associations. Additionally, we refined class definitions in phenotypic ontologies, specifically targeting cell cardinality phenotypes. This representation resolved incorrect inferences in the utilized ontologies, enabling accurate interpretation of phenotypic descriptions. Our findings reveal that this correction enhances gene-disease prediction for diseases associated with cardinality phenotypes. Third, we introduce a novel neural-symbolic method that combines logic fundamentals with machine learning for ontology alignment. This method begins with symbolic representation, followed by iterative neural learning for alignment and symbolic representation consistency checking and reasoning, and back to neural learning. We demonstrate that our system generates noncontroversial alignments first and these alignments are coherent with respect to OWL EL. This novel method can pave the way for more accurate and efficient ontology-based methods, which can have significant implications for various semantic web applications.
    • Solutions for Perishables Shelf-life Extension and Spoilage Detection Towards Food Waste Reduction

      Damdam, Asrar N. (2023-07) [Dissertation]
      Advisor: Salama, Khaled N.
      Committee members: Kurdahi, Fadi; Massoud, Yehia Mahmoud; Tester, Mark A.
      Food loss and waste represent a significant challenge to global sustainability. In a world where the number of people suffering from hunger has been rising, approximately 1.3 million tonnes of food are lost or wasted each year. When food is lost or wasted, all the resources used to produce it, including water, land, energy, labor, and capital, are also lost. In addition, it is estimated that the disposal of food in landfills generates 11% of all greenhouse gas emissions, thereby contributing to climate change. Food loss and waste can also have a negative impact on food security and prices. This dissertation introduces non-invasive and chemicals-free solutions for the shelf-life extension and quality monitoring of fresh foods. First, we propose the creation of a sterilized anaerobic storage environment using UV-C irradiation and vacuum sealing for increasing the shelf-life of perishables. The proposed combination was tested on fresh strawberries and quartered tomatoes and has successfully increased the shelf-life by 124.41% and 54.41%, respectively, while acceptable sensory characteristics were maintained throughout the storage period. Second, the proposed combination was tested on fresh beef, chicken and salmon fillets, where a shelf-life increase of 66% was achieved. The shelf-life of strawberries, tomatoes and meats were determined by monitoring the organoleptic qualities and counting the microbial populations of various bacteria, which includes aerobic bacteria, Lactic Acid Bacteria, Pseudomonas spp., yeast, mold, Salmonella and E-coli in addition to pH measurements. In the third part, we propose an IoT-enabled electronic nose system for rapid beef quality monitoring. The e-nose system includes carbon dioxide, ammonia, and ethylene sensors to measure the volatile organic compounds' (VOCs) concentrations. Microbial population quantifications of various bacteria were conducted to identify the concentrations of VOCs that are associated with raw beef spoilage. The production of VOCs was correlated with the proliferation of bacteria using linear regression, and it was discovered that aerobic bacteria and Pseudomonas spp. play a significant role in the production of VOCs in raw beef, as opposed to LAB. This system demonstrates how the IoT-enabled e-nose system can be an effective tool for monitoring the quality of perishables.