Supplemental Figures and Data _ Process Design for Small-Scale Production of PEGDA-NVP Hydrogel Nanoparticle Emulsions for Therapeutic Applications

This data set supports the process development work described in the associated manuscript, capturing the transition from bench-scale synthesis to small-scale process development of hydrogel nanoparticles. It includes the raw and processed quantitative data for experimental variables tested during this scale-down effort. Additionally, the data set provides the original red-green channel versions of fluorescence microscopy images used in cell studies. In the manuscript, these images were adjusted to be accessible for readers with red-green color vision deficiency; here, the unmodified channel versions are made available to ensure scientific transparency and reproducibility

Resolvent Analysis of Turbulent Flow over Compliant Surfaces: Optimization Methods and Stability Considerations.

This thesis delves into the manipulation of turbulence properties through innovative compliant surface designs. Turbulence, known for its unpredictable fluid movements, presents substantial challenges across engineering disciplines, particularly in optimizing system efficiency and minimizing energy losses. This research explores the potential of compliant surfaces to control and mitigate the adverse effects of turbulent flow, thereby enhancing the performance and reliability of engineering systems.Employing the resolvent analysis method, this work investigates the interaction between turbulent flows and surfaces capable of dynamic adaptation. The study evaluates the impact of these surfaces on turbulence suppression through the application of both space-dependent and independent compliance models, where the compliance model is characterised by an admittance, which represents the relationship between the instantaneous surface pressure and surface velocity. This approach allows for a nuanced understanding of how different surface properties can influence the behavior of turbulent flows.A significant contribution of this thesis is the comprehensive stability analysis conducted to assess the implications of compliant surfaces on the linear stability of the dynamical system. By examining the eigenvalues of the mean-linearized system, the research identifies the conditions under which compliant surfaces may induce or mitigate instabilities within turbulent flows. This analysis is pivotal in developing compliant surface designs that not only reduce turbulence-induced energy losses but also ensure the stability of the flow, a critical consideration for practical engineering applications.The findings of this thesis offer valuable insights into the role of surface compliance in turbulence control, paving the way for further research and the development of advanced engineering solutions. Through a detailed investigation of the interactions between compliant surfaces and turbulent flows, this work contributes to the broader field of fluid dynamics and underscores the potential of innovative surface designs in achieving more efficient and sustainable engineering systems

Translational Research to Advance Remediation and Label-Free Detection

Translational research acts as a vital link between fundamental scientific discoveries and their real-world applications, especially within biotechnology and medical diagnostics. This interdisciplinary approach integrates knowledge from biology, chemistry, engineering, and medicine to create inventive solutions for urgent health challenges. Tissue remediation, essential in modern healthcare, seeks to restore the function and vitality of damaged tissues by imitating natural regenerative processes and employing biomaterials and scaffolds. Effective collaboration among academia, industry, and healthcare providers is essential for translating tissue remediation strategies into patient care, offering hope to those with injuries and chronic diseases.One project focuses on crafting biocompatible scaffolds resembling the body's extracellular matrix to facilitate tissue regeneration. This study focuses on enhancing collagen production, particularly for patients with pelvic organ prolapse (POP), using a combination of silk fibers functionalized with carbon nanotubes (SF-CNT) and electrical stimulation (ES). Key findings include superior alignment of SF-CNT fibers compared to pure silk fibers, with SF-CNT 0.1% showing optimal alignment. Higher CNT concentrations led to distorted fibers. SF-CNT 0.1% fibers displayed improved properties and minimal cytotoxicity, while ES increased collagen production, especially with SF-CNT 0.1% fibers. Customized ES conditions based on patient characteristics and tissue locations were crucial for optimal collagen enhancement. In vivo studies showed increased collagen content and improved fiber alignment with ES-treated fibroblast cells. Personalized ES conditions are essential for optimizing collagen enhancement, considering individual patient attributes and tissue-specific factors. The combination of SF-CNT fibers and ES offers promise for improving collagen production, particularly for POP patients. The study highlights the importance of personalized treatment strategies and tissue-specific considerations to maximize the effectiveness of electrical stimulation for collagen enhancement. Another promising avenue is the shift toward label-free detection methods in medical diagnostics, particularly for conditions like periodontitis. This project introduces an innovative method for rapid, label-free detection of bacterial species linked to periodontitis using Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning. Key advancements include optimizing saliva processing techniques, and establishing a reliable RT-qPCR method. SERS is utilized for bacterial detection with high accuracy, enhanced by incorporating a cell-free saliva matrix. These methods enable non-invasive, real-time characterization of biomolecular interactions and disease biomarkers, streamlining diagnostic processes and improving accessibility by eliminating the need for labeling agents. In the pursuit of translational research, collaboration and innovation are paramount. By translating scientific insights into practical solutions, researchers endeavor to advance tissue remediation and label-free detection, thereby contributing to a more sustainable, healthier, and safer future for all

Utilizing Concurrent Data Accesses for Data-Driven and AI Applications

In the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical, especially as these applications increasingly underpin modern daily life. Traditionally, architectural optimizations in computing systems have concentrated on data locality, utilizing temporal and spatial locality to enhance data access performance by maximizing data and data block reuse. However, as poor locality is a common characteristic of data-driven and AI applications, utilizing data access concurrency emerges as a promising avenue to optimize the performance of evolving data-driven and AI application workloads.This dissertation advocates utilizing concurrent data accesses to enhance performance in data-driven and AI applications, addressing a significant research gap in the integration of data concurrency for performance improvement. It introduces a suite of innovative case studies, including a prefetching framework that dynamically adjusts aggressiveness based on data concurrency, a cache partitioning framework that balances application demands with concurrency, a concurrency-aware cache management framework to reduce costly cache misses, a holistic cache management framework that considers both data locality and concurrency to fine-tune decisions, and an accelerator design for sparse matrix multiplication that optimizes adaptive execution flow and incorporates concurrency-aware cache optimizations.Our comprehensive evaluations demonstrate that the implemented concurrency-aware frameworks significantly enhance the performance of data-driven and AI applications by leveraging data access concurrency.Specifically, our prefetch framework boosts performance by 17.3%, our cache partitioning framework surpasses locality-based approaches by 15.5%, and our cache management framework achieves a 10.3% performance increase over prior works. Furthermore, our holistic cache management framework enhances performance further, achieving a 13.7% speedup. Additionally, our sparse matrix multiplication accelerator outperforms existing accelerators by a factor of 2.1.As optimizing data locality in data-driven and AI applications becomes increasingly challenging, this dissertation demonstrates that utilizing concurrency can still yield significant performance enhancements, offering new insights and actionable examples for the field. This dissertation not only bridges the identified research gap but also establishes a foundation for further exploration of the full potential of concurrency in data-driven and AI applications and architectures, aiming at fulfilling the evolving performance demands of modern and future computing systems

Large Language Model Based Machine Learning Techniques for Fake News Detection

With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation

Development of data assimilation for analysis of ion drifts during geomagnetic storms

The primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity. Geomagnetic storms affect our everyday lives because they give rise to transient signal loss, data transmission errors, negatively impacting users of satellite navigation systems. The Nighttime Localized Ionospheric Enhancement (NILE) is a localized plasma enhancement that because it is not well understood, drives the design of satellite-based augmentationsystems. To better secure operation of technological infrastructure, it is essential to build a comprehensive understanding of the atmospheric drivers, especially during solar active periods. Instrument measurements and climate models serve as valuable tools in obtaining information regarding the occurrence of space weather events; nonetheless, both sources exhibit quantitative and qualitative limitations. Data assimilation, an evolving technique, integrates measurements and model information to optimize the state estimations. This dissertation presents developments in a data assimilation algorithm known as Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), and its applications in investigating the atmospheric behaviors under varying solar conditions. EMPIRE is a data assimilation algorithm specifically designed for upper atmospheric driver estimation of neutral wind and ion drifts at user-defined spatial and temporal scales. The EMPIRE application in this work aims to contribute to a more comprehensive understanding of the effects of the NILE. EMPIRE utilizes the Kalman filter to optimize state calculations primarily based on electron density rates, provided by other data assimilation algorithms. Earlier runs of the algorithm used pre-defined values for the background state covariance cross time. To address model limitations under changing geomagnetic conditions, the algorithm is enhanced by concurrently updating the background state covariance during assimilation processes. Additionally, representation error is incor- porated as a component of the observation error, and error analysis is performed through a synthetic-data study. Previously, EMPIRE fused Fabry-Perot Interferometer (FPI) neutral wind measurements, demonstrating increased agreement with validation neutral wind data. In this work, this approach is extended to augment Coherent Scatter Radar (CSR) ion drift measurements from Super Dual Auroral Radar Network (SuperDARN), providing additional insights into EMPIRE’s estimated field-perpendicular ion motion. For an in-depth exploration of storm-related NILE, both EMPIRE and another data assimilation method, the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension coupled with Data Assimilation Research Testbed (WACCM-X + DART), is implemented for a storm event to test the proposed NILE driving mechanism. Furthermore, this dissertation introduces a Kalman smoother technique into the EMPIRE to enhance its ability to assess past storm events, and to explore the potential for algorithm improvements

The Voderettes: Gender, Labor, and Techno-Utopia at the 1939 New York World's Fair

This thesis explores the labor demands of the Voder, the electrical speech synthesis machine developed by Bell Labs to be a major component of AT&T's 1939 New York World's Fair exhibit. With the United States emerging from the Great Depression, and with political tensions escalating across the globe, the paper situates the Voder's labor demands within the historical context of the fair. Specifically, I explore the decision to have young women operate the Voder, the intricacies of the machine cloaked by the warm presence of its highly-skilled female operator. Using archival records from Bell Labs engineers, the paper exposes the previously unacknowledged engineering contributions of Voder operators in the years before the fair. These young women not only influenced major decisions about the Voder's mechanics but also gave early credence to the notion that developing a performance with the machine could make for a thrilling fair exhibit. Moreover, the paper argues that at the fair itself, AT&T and Bell Labs executives used the Voder operators to normalize a new vision of a technological utopia that relied heavily and conspicuously on the infrastructural labor of women. Given the Voder's legacy, as a tool that laid critical groundwork for voice encryption technology, the paper adds important context to the historical record, highlighting the young women at the heart of the machine

Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells

The performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the platinum catalyst. The production of platinum oxide is a major cause of the degradation of the fuel cell system, negatively affecting its performance and durability. In order to predict and prevent this degradation, this research examines a novel method to estimate degradation due to platinum oxide formation and predict the level of platinum oxide coverage over time. Mechanisms of platinum oxide formation are outlined and two methods are compared for platinum oxide estimation. Linear regression and two Artificial Neural Network (ANN) models, including a Recurrent Neural Network (RNN) and Feed-forward Back Propagation Neural Network (FFBPNN), are compared for estimation. The estimation model takes into account the influence of cell temperature and relative humidity.Evaluation of relative errors (RE) and root mean square error (RMSE) illustrates the superior performance of RNN in contrast to GT-Suite and FFBPNN. However, both RNN and GT-Suite showcase an average error rate below 5% while the FFBPNN had a higher error rate of approximately 7%. The RMSE of RNN shows mostly less compared to FFBPNN and GT-Suite, however, at 50% training data, GT-Suite shows lowest RMSE. These findings indicate that GT-Suite can be a valuable tool for estimating platinum oxide in fuel cells with a relatively low RE, but the RNN model may be more suitable for real-time estimation of platinum oxide degradation in PEM fuel cells, due to its accurate predictions and shorter computational time. This comprehensive approach provides crucial insights for optimizing fuel cell efficiency and implementing effective maintenance strategies

Cultivating Narrative Change in Collective Sensemaking

The complexity of social and ecological crises and the need for collective action to tackle them brought forth collective inquiry as an essential capacity for societies to combine their knowledge and creativity, navigate complexity, and respond with adaptive solutions. However, current approaches to sensemaking often remain anchored in the same mindsets and worldviews that underpin these crises, constraining their ability to account for the fundamental shifts needed. This research addresses this gap by exploring how collective inquiry, when framed as a narrative practice, can open space for alternative perspectives and pathways in systems transformation. This study positions narratives as dynamic meaning systems that shape how groups interpret issues, determine relevance, and envision new possibilities. The dissertation is structured as three studies, situated in the context of food systems, each exploring ways to engage and mobilize these meaning systems across different contexts and scales of collective inquiry. A central contribution of this research is the framework of narrative infrastructures—the social and material contexts through which narratives of change are constructed, circulated, and sustained within systems change efforts. This framework supports designers in navigating and shaping the spaces where narratives are articulated, contested, and maintained, to foster more critical, pluralistic, and transformative approaches to collective inquiry. Ultimately, this work enhances design’s capacity to foster the shifts in mindsets through which societies envision their collective futures, using its narrative agency to disrupt harmful paradigms and open space for radical possibilities in the making

Rapid Ex Vivo Detection of Cancer in Excised Lymph Nodes: Development of a Tissue Model and Initial Results

There are millions of new head and neck cancers diagnosed each year, and it is one of the most aggressive cancers. The typical first line of therapy for head and neck cancers is surgery; however, if the cancer has spread (metastasized) from the primary tumor, more advanced surgery and/or adjuvant therapy (chemotherapy and or radiation therapy) can be indicated. Clinically, metastasis is diagnosed by surgically removing one or more lymph nodes draining the primary tumor during the primary tumor resection. Each lymph node located and removed adds to the morbidity of the procedure, so many clinics are moving toward a “sentinel” lymph node biopsy strategy, where only the first lymph node draining the tumor is removed and sent to pathology. Assessment of the node for cancer can take up to a week. If this lymph node is found to have cancer, the patient is then asked to return for a secondary surgery where a complete neck dissection is carried out (removal of all the lymph nodes in the side of the neck ipsilateral to the tumor). This delay in diagnosis is stressful on patients, adds health care costs, and considering the invasiveness of some primary tumor resections, some patients opt not to return for callback surgeries even though it would improve their chances of survival. This thesis presents efforts to test the ability for a fluorescence molecular imaging system called “agent-dependent enhanced photon tomography” (ADEPT) to be able to detect cancer in an excised sentinel lymph node while the patient is still on the operating table. This would allow a significant reduction in the number of patients requiring callback surgeries. Specifically, this thesis explores (in chapter 1) the development of a porcine lymph node fresh tissue model using implanted human cancer spheroids to act as realistic models of a freshly excised sentinel lymph node; (in chapter 2) the advancement of this tissue model to include a range of cancer burden levels and cancer cells strains; (and in chapter 3) a first demonstration of the ADEPT system applied to these realistic tissue models to detect clinically relevant levels of cancer. The ADEPT is a prototype designed specifically for the purpose of being faster in terms of processing and eliminates the need for patient to come back surgeries. We were able to validate ADEPT by incorporating a metastatic model mimicking a human lymph node and verifying the presence of cancer tumor that was manually injected into the lymph node followed by infusion of imaging agents