Open Theses and Dissertations

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Browsing Open Theses and Dissertations by Author "Abbasi, Zahra"

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    Enhancing the Efficiency of Subject-Specific Knee Joint Biomechanical Simulations With Applications to Osteoarthritis
    (2024-08-14) Kakavand, Reza; Komeili, Amin; Edwards, William Brent; Abbasi, Zahra; Souza, Roberto M.
    There are three challenges in conventional subject-specific modelling techniques. First, having an accurate material model is essential for studying biomechanical response of musculoskeletal systems. For instance, the stresses and strains in knee joint articular cartilage are influenced by site-specific variations in collagen fibril orientations that vary with aging, which is ignored in finite element (FE) analyses using a generic knee geometry. The other challenge is related to the manual geometry reconstruction from biomedical images, which is a time-consuming process and not practical for clinical applications. Finally, estimating joint forces and moments with conventional methods requires marker-based motion capture facilities to study the kinematics of human body motion and convoluted human body modeling to determine the kinetics of motions. Although marker-based motion capture offers the precise measurement of marker positions on the body and enables the calculation of kinematics and kinetics in a controlled setting, its data collection is labor-intensive and requires staff with expensive equipment and specialized technical experience. Subject-specific FE modeling provides a viable approach for the study of cartilage mechanics in normal and pathomorphological knee, thus providing insight into the mechanics of knee articular cartilage. Therefore, in this project we aimed to facilitate the development of subject-specific FE models of the knee. We developed and validated: 1) a 3D remodeling algorithm of collagen fibrils within knee joint cartilage under simulated gait, 2) a semi-automatic segmentation routine of knee joint geometry from magnetic resonance images (MRIs), and 3) a markerless motion capture to perform kinematics and kinetics analyses. To develop the cartilage fibril remodeling algorithm, a fibril-reinforced, biphasic cartilage model was integrated with 3D human knee joint geometry. For the MRI segmentation, we used 3D Swin UNETR, a statistical shape model (SSM) and automated filtering techniques to extract the distal femur, proximal tibia, femoral and tibial cartilages. To facilitate the estimation of joint forces and moments, OpenCap, a markerless motion capture software, was used during a cycling task. This technique is intended to expedite kinematics and kinetics analysis. The ultimate goal of this project is to develop an efficient pipeline for subject-specific FE modeling.
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    Exploration of Techniques for Working with Sparse Data when Applying Natural Language Processing to Assist a Qualitative Data Analysis of a COVID-19 Open Innovation Community
    (2024-04-17) Yamani, Shirin; Barcomb, Ann; Far, Behrouz; Abbasi, Zahra
    This thesis undertakes a novel integration of Natural Language Processing (NLP) with Qualitative Data Analysis (QDA) to investigate the dynamics of volunteer involvement within the TeamOSV community, a collective formed in response to the COVID-19 pandemic. Central to this study is the exploration of roles and interaction patterns among episodic and habitual volunteers, alongside an analysis of the factors influencing their engagement and disengagement within the community. A significant methodological contribution of this work lies in addressing the sparse data challenge, a common constraint in qualitative research, particularly within multi-class classification contexts. The study employs and critically evaluates a range of NLP techniques, with a focus on data augmentation strategies, to enhance the efficacy of various models, including Logistic Regression, Naive Bayes, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and particularly the Self-Attention model. The proposed framework, identified for its superior performance, demonstrates a noteworthy ability to process and interpret sparse qualitative data, surpassing both traditional approaches in its effectiveness. Furthermore, the thesis explores an in-depth analysis of model variations, assessing the impact of differing configurations of Self-Attention blocks and layers of feed-forward neural networks. It also explores the implications of pre-training on model performance, offering insights into the architectural complexities and training dynamics of NLP models. A crucial aspect of this exploration is the consideration of the trade-offs between model complexity and computational efficiency, highlighting the practical challenges and considerations in deploying these models in qualitative research contexts. Qualitatively, the study offers a detailed examination of volunteer roles within the TeamOSV community. It identifies the distinct contributions and challenges associated with episodic volunteers, characterized by their sporadic engagement patterns, and habitual volunteers, who provide stability and long-termvision. The research also sheds light on the reasons behind volunteer disengagement, such as lifestyle changes and diminishing interest, providing a holistic understanding of volunteer participation in open-source, community-driven projects. The thesis concludes by emphasizing the collaborative strengths of merging NLP with QDA, a union that significantly augments the depth of qualitative research. It proposes a roadmap for future investigations, concentrating on enhancing insights into volunteer coordination within open innovation settings and broadening the application range of NLP in qualitative data examination.
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    Improved Cavitation Monitoring and Detection Methods for Focused Ultrasound Blood-Brain Barrier Disruption
    (2022-01) Khan, Sonia; Curiel, Laura; Curiel, Laura; Smith, Michael; Fear, Elise; Abbasi, Zahra
    Transcranial focused ultrasound (FUS) in combination with microbubbles has demonstrated promising outcomes in treatment of brain disorders by stimulating transient BBB disruption, allowing therapeutics to enter into the brain. Currently, time signals from a hydrophone are transformed into frequency domain to monitor cavitation activity during BBB opening. The area under the curve (AUC) in a 300 Hz bandwidth around the subharmonic is used as a metric to determine cavitation activity. However, given the available frequency resolution, there are very few points within the 300 Hz bandwidth for precisely analysing the AUC. Also, many recorded signals show no detectable subharmonic above the noise as the A/D calibration is overwhelmed due to the strong fundamental. These issues can result in the subject being under treated or over treated. This research aims to better monitor and control the cavitation phenomenon for BBB disruption by developing methods to improve the cavitation spectra. The acoustic signals captured by a hydrophone from the excited microbubble phantom were filtered with a low pass analog filter to improve the system’s dynamics of self-calibration by suppressing the strong fundamental. The acoustic signals were further improved with two proposed signal processing techniques, namely, Fourier interpolation via zero-padding to increase the spectral frequency resolution, and windowing, which allowed us to uncover previously unreported subharmonic side lobes. Additionally, we propose the bandwidth to be wider than the current 300 Hz for AUC to include the useful information in these side bands. Our proposed improvements were validated on animal data. Finally, the performance of our proposed improvements was compared to traditional methods by evaluating metrics for cavitation detection. Previous studies reported a steady increase in the AUC with increase in pressure, whereas our work presented that the AUC would rise drastically at the stable cavitation threshold indicating that maximum energy is concentrated in stable cavitation regime. Then, beyond this threshold, the AUC will drop before rising again, signifying the energy shift towards initiating inertial cavitation. Our findings can be beneficial to enhance cavitation detection metrics and we can actually visualize the different cavitation regimes when the AUC and power are evaluated for subharmonic.
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    Pedersen Conductance as Measured by Swarm
    (2023-07) Pourkarim, Pouya; Knudsen, David; Burchill, Johnathan; Cully, Christopher; Abbasi, Zahra
    We estimate ionospheric Pedersen conductance using measurements from the European Space Agency’s Swarm A satellites of magnetic and electric fields. We provide long-term averages in the form of global maps, and case-by-case results using scatter plots of Pedersen conductance versus solar zenith angle. The long-term results display expected ionospheric features and show agreement with previous studies. However, we find that the case-by-case results display a high variability, and abnormally low Pedersen conductance values. We identify possible sources of the high variability and low magnitude, and we revisit the underlying assumptions conventionally used in such calculations. We find that variation of ionospheric parameters in the zonal direction, and contamination of zonal magnetic field by Hall currents are potentially significantly affecting our conductance estimates. Further, we identify non-quasi-static fields and presence of Alfvén waves as another significant source potentially affecting the results.
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    Self-healing of Direct Written Conductive Inks for Curvilinear Circuits
    (2023-04-18) Jeong, Chan Woo (Robin); Park, Simon; Du, Ke; Abbasi, Zahra; Komeili, Amin
    The increased electrification of vehicles in both automotive and aerospace industries has introduced new challenges in manufacturing complexities and weight management. Complex and heavy wirings are currently being utilized and conventional printed circuit board (PCB) manufacturing methods are limited in 2D geometries. Alternatively, a direct-writing approach presents weight and materials saving opportunities where planar substrates with circuits already printed are formed to a final shape. However, designing a circuit or a printed ink formula able to withstand the high strain of substrate forming is challenging. Instead, a circuit able to regain functionality after sustaining strain induced cracks presents a more versatile approach. In this study, a conductive ink with self-healing capabilities is developed. A copper-nanoparticle based ink compatible with existing lithographic methods is developed and printed on planar polymeric substrates. Intense pulsed light (IPL) is utilized to photothermally heat, reduce, and sinter copper nanoparticles within milliseconds. By utilizing light-matter energy absorption and the plasmonic effect, heat sensitive polymeric substrates are unaffected while conductive copper tracks are formed. After printing, drying, and IPL flashing, the substrate and printed tracks are subjected to cyclic bending and thermoforming. Afterwards flashing is performed once again to initiate the healing process through reflow of indium microparticles. These indium healing agents added to the ink bridges microcracks via capillary forces to recover severed electron pathways. Mechanisms of photothermal heating and sintering is simulated to better understand the underlying physical phenomena. Ultimately, a planarly written copper nanoparticle ink capable of surviving substrate deformation to produce curvilinear circuits is achieved. This direct writing method can provide drastic wiring weight reduction imparting fuel savings in the next generation of electronics in vehicles.
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    Sensitivity-enhanced Microwave Sensors for Real-time Detection and Monitoring
    (2024-05-27) Vestrum, Sarah Viola; Abbasi, Zahra; Abbasi, Zahra; Murari, Kartikeya; Badv, Maryam
    Planar microwave sensors have gained popularity due to their real-time, non-invasive sensing abilities. These structures have successfully enabled various range of applications in various applications, from small-volume liquid characterization in biomedical applications to sensing and detection in high-pressure and temperature environments. While planar resonator structures were introduced to the filter design domain first, they have transited into an ideal candidate for real-time sensing and monitoring to address different limitations that waveguide microwave sensing approaches suffer from, including bulky structures and requiring higher volumes of the sample under the test. This work focuses on enhancing the sensitivity of microwave structures using single-port reader-tag based structures. Unlike the popular two-port planar microwave sensor structures, single-port structure designs have the advantage of lowering the requirements and costs for measuring equipment, making them suitable for personalized sensing applications. Here, three single-port reader-tag based planar sensors have been introduced to enhance sensitivity and sensing distance for different rapid liquid characterization applications in the medical field. First, a patch antenna sensor for distant, small volume water-content detection. This structure detects water absorption with a resolution of 25 μL using a hydrogel-integrated sensing tag to improve sensitivity. Then, a patch antenna sensor for distant electrolyte concentration detection in urine for hydration monitoring was developed. The fabricated sensor was able to detect concentration changes of 0.5% at a distance of 24 mm from the reader, making it a well-fit candidate for wearable dehydration monitoring applications in older adults due to their increased susceptibility to dehydration.
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    Water Cut Measurement and Solvent Detection for Production Surveillance and Optimization using Microwave Sensors
    (2023-11-22) Kamal, Bushra; Hassanzadeh, Hassan; Abbasi, Zahra; Nassar, Nashaat N.; Murari, Kartikeya
    Monitoring of water-cut and solvent in multiphase flows is essential in petroleum production, processing, and transportation. Achieving precise online water cut measurements is highly desirable but challenging, especially when high accuracy is necessary. It holds great importance for maintaining production quality, assessing well conditions, reducing energy consumption, and automating oil production management. Solvent recovery in Enhanced Oil Recovery (EOR) methods such as Expanding Solvent-Steam Assisted Gravity Drainage (ES-SAGD) is an important key metric for assessing the solvent return that meets economic threshold. Online water-cut meters typically rely on sensing the dielectric permittivity, density, infrared, or gamma-ray spectral absorption characteristics of the oil and water mixture. The currently available methods lack an inexpensive, non-contact, real-time water cut (WC) measurement that is high in demand and technology. Microwave technology can introduce a practical solution to address these problems. Over the past two decades, planar microwave resonator-based sensors have become increasingly popular. This heightened popularity is due to several factors, including their uncomplicated fabrication process, ease of integration with experimental setups, and adaptability in design. A noteworthy and intriguing aspect of microwave sensors is their ability to perform non-contact sensing and real-time monitoring. This unique capability arises from the interaction between the electromagnetic field produced by the sensor and the characteristics of the sensing material in its immediate surroundings. This research work investigated water-cut measurement and solvent detection through the integration of unique planar microwave sensor designs within highly sophisticated experimental setups at various conditions. The sensors are designed such that they do not require any special facilities or equipment for their integration in the pipelines to perform efficiently.