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From the Screen, with Love: Enhancing Social Presence through an AI afforded Bichronous Pedagogy
No full textCompanion document for the interactive poster From the Screen, with Love: Enhancing Social Presence through an AI afforded Bichronous Pedagogy presented at the Teaching and Learning Conference, 85th Academy of Management Annual Meeting on July 27, 2025. Version Ver20250703. This document provides expanded implementation guidance connected to the peer reviewed poster session.This project introduces an AI afforded bichronous pedagogy of care designed to enhance social presence among students in online management education. Grounded in compassion and integrating both synchronous and asynchronous modalities, the approach addresses the engagement gap between online and face to face learning through AI supported team coaching. The method reinforces collaborative learning across developmental milestones, including producing care focused team contracts and completing joint projects. Drawing on an online Organizational Behavior course, the project illustrates how a structured blend of AI coaching prompts, instructor led synchronous check ins, and empathy driven prompt design can strengthen community formation, improve team accountability, and support student well being. The project offers management educators concrete strategies for treating AI as an assistant coach within bichronous course structures to promote inclusive and humanized online learning. It also provides implementation guidance, sample prompts, and lessons learned.LITE Grant, Centre for Teaching Excellence, University of Waterloo || UW/SSHRC Exchange Grant
Exploring the importance of community freezers and a country food box distribution program in the Inuvialuit Settlement Region, Northwest Territories, Canada
No full textBackground: Consuming food harvested from the land, water, and sky (country food) is important for the physical and cultural wellbeing of Inuvialuit. Community freezers are infrastructure intended to support the safe storage of country foods. Further, community freezers have the potential to play a role in food-related programming, such as country food distribution programs. Despite the prevalence of community freezers across Inuit Nunangat, there are few published studies evaluating the implementation and impact of these spaces.
Objectives: The specific objectives of this thesis are to describe the use, importance, strengths and barriers, and overall outcomes of the implementation of community freezers in the context of the Inuvialuit Settlement Region, and to describe the importance, strengths, and areas for improvement of the Inuvialuit Community Economic Development Organization country food box distribution program as a program run in connection to community freezers across the region.
Methods: Using a qualitative case study design, this project involved semi-structured interviews with community members (n=42) who use the community freezer(s) or could potentially use the community freezer(s) in Paulatuk and Tuktoyaktuk, as well as semi-structured interviews with individuals responsible for managing community freezers (n=7) across Inuvialuit Settlement Region communities. Data were analyzed using reflexive thematic analysis, drawing upon participatory analysis techniques with local community researchers to ensure that results reflect community contexts and realities.
Results: The findings describe the importance of freezer space and supplies for storing food (i.e., bins, Ziploc bags, and vacuum sealers) for access to country food, particularly given the number of factors such as cost of gas and equipment that impact community members’ ability to engage in harvesting practices. Despite different community freezer management practices used across the communities of Paulatuk and Tuktoyaktuk, community members in both communities experienced benefits from having and using a community freezer. Such benefits include increased access to freezer space and enjoying increased access to country foods via programs run through the community freezer. Across both communities, community freezers enable the sharing of country foods, enable the storage of larger food items and large quantities of food, and support Inuvialuit culture, way of life, and wellbeing. Challenges with community freezers include lack of communication and awareness about the freezer, reliability of the freezer, ensuring food safety and organization, and the cost and time required for managing and maintaining community freezers.
The findings also describe the importance of the Inuvialuit Community Economic Development Organization country food box distribution program. Community members expressed that the country food boxes increase their access to country food and provide support for harvesters who contribute foods to the program. Strengths of the program include the convenience of the foods included in the boxes, variety and the ability to try new foods, and that the program enables the sharing of country food. Suggested areas for improvement include increasing portion size and knowing where and who the harvested foods came from.
Conclusion: Reliable and sufficient storage for country foods is an important component of food security in the Inuvialuit Settlement Region. This project has made an important contribution to the literature on the importance of community freezers in the Western Arctic and provides valuable evidence for communities across the Inuvialuit Settlement Region seeking to advocate for additional investment into community freezers. This study also contributes valuable information to the ongoing improvement of the Inuvialuit Community Economic Development Organization country food box distribution program, highlighting both strengths and potential areas for improvement
Towards Safe Initialization of Scala Global Objects
No full textThis thesis focuses on safe initialization of global objects in Scala. Global objects encapsulate global information in Scala, and their initialization is susceptible to causing run-time errors. Moreover, global objects are initialized by demand (i.e. on their first access). The initialization safety of a global object is brittle if it depends on the initialization point of the object, because the initialization point is the first access in the entire program. This motivates the idea of automatically detecting potential initialization errors during compilation.
The main contribution of this thesis is designing and implementing a global object initialization checker in the Scala compiler. Theoretically, we identified run-time errors caused by unsafe initialization patterns of global objects and organized three static principles to enforce on Scala programs: Prohibiting accesses to uninitialized fields, which prevents null pointer exceptions; partial ordering of global object initialization order, which prevents deadlocks between locks that guard the initialization of global objects; and initialization-time irrelevance, which ensures that initialization safety of the global object is independent of the initialization point. We then designed the global object initialization checker by proposing the formal initialization semantics of a Scala initialization calculus, and the initialization checker is presented as an abstract interpreter of the initialization calculus. The initialization checker also checks the initialization process of each global object individually rather than conducting a whole-program analysis.
Practically, we have integrated the abstract interpreter into the Scala compiler after extending the initialization semantics with more Scala features. The initialization checker can be turned on when compiling Scala programs, and we evaluated the initialization checker during the compilation of many widely-used open-source Scala projects which form a test suite. The initialization checker reports warnings in several projects that are verified to be true positives. The result highlights the necessity of checking the initialization safety of Scala projects and the utility of the global object initialization checker in this thesis
Love, Resilience, and the Past: The Role of Positive Emotion Regulation in Overcoming Childhood Maltreatment and Building Strong Romantic Relationships
No full textExperiences of childhood maltreatment (CM) are associated with relationship and sexual difficulties in adulthood (Vaillancourt-Morel, et al., 2024). Research has shown that these issues are partially explained by difficulties regulating negative emotions (DiLillo et al., 2009). However, the effect CM has on the regulation of positive emotions has received considerably less attention. In this thesis I examined how CM is related to fear of positive and negative emotional states (Studies 1-3), using online-self report questionnaires, I tested if this anxiety mediates the association between CM and difficulties in relationships (Studies 2 & 3), and finally, I examined how CM is related to an individuals’ ability to regulate their positive and negative emotions in response to images that evoke positive and negative emotions. My results consistently showed that CM is related to fear of positive and negative emotional states (Studies 1-3). Consistently I found an association between the intensity of CM experienced and decreased satisfaction with communication in adult long-term relationships (Studies 2 & 3). This association was mediated by fear of emotions (Study 2), with Study 3 showing unique effects for both fear of positive and negative emotions. Finally, my results showed that CM was associated with difficulty enhancing positive emotions and improved performance when asked to decrease positive emotions (Study 3). These results provide evidence that CM affects individuals’ ability to tolerate and regulate their positive emotions. Furthermore, my results suggest that difficulties with positive emotions play a role in long-term relationship difficulties reported by survivors of CM. The present research suggests that clinicians should focus on improving tolerance for positive emotions and teaching tools for capitalizing on positive experiences when working with survivors of CM
Recovery and Reuse of Nanomaterials from Radically Polymerizable Thermoset Nanocomposites; Towards A Circular Economy
No full textThe widespread adoption of thermoset nanocomposites has created significant end-of-life management challenges due to their permanent crosslinked networks, which resist conventional recycling methods and trap valuable nanomaterials within non-degradable matrices. This work presents a proof-of-concept study to assess a new approach for achieving a circular economy for thermoset nanocomposites; recovering and reusing nanomaterials from thermoset nanocomposites through the incorporation of cleavable comonomers into the polymer matrix, enabling controlled matrix degradation and nanofiller recovery at end-of-life.
Carbon nanotubes (CNTs) were selected as the nanofiller for this study due to their widespread use in nanocomposites and growing industrial significance, and a styrene/divinylbenzene (DVB) thermoset matrix was chosen as a model matrix for its chemical compatibility with CNTs. To enable controlled degradation at end-of-life and nanofiller recovery, comonomer additives that can install cleavable bonds into the matrix’s polymer network were systematically evaluated. Several candidates were investigated, including cyclic ketene acetal (CKA) (specifically 2-methylene-1,3-dioxepane, MDO), which underwent hydrolysis too rapidly and an unwanted ring-retaining side reaction for practical application, and thionolactones (specifically dibenzo[c,e]-oxepine-5(7H)-thione, DOT and 2-(isopropylthio)dibenzo[c,e]oxepine-5(7H)-thione, 2SiPrDOT), which was limited by the monomers’ solubility in the styrene/DVB system. Through this careful screening process, 2SiPrDOT was selected as the most suitable option, offering both chemical stability during processing and sufficient solubility in the system.
Comprehensive characterization of the primary nanocomposites using thermal gravimetric analysis (TGA), differential scanning calorimetry (DSC), electrical resistivity measurements, and hardness testing confirmed that 2SiPrDOT incorporation did not significantly alter the thermal, electrical, or mechanical properties of the material, preserving the high-performance characteristics essential for practical applications.
The thermoset matrix was then deconstructed through nucleophilic degradation, allowing recovery of finely distributed CNTs from the crosslinked network. Analysis of recovered CNTs using energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), and Raman spectroscopy revealed no significant changes in the nanofiller’s structure or surface chemistry, demonstrating the gentle nature of the recovery process. The recovered CNTs (68.7% yield) were subsequently re-embedded into a fresh styrene/divinylbenzene matrix and polymerized. Characterization of these secondary nanocomposites using the same characterization techniques showed properties comparable to the primary nanocomposites, confirming successful retention of nanofiller functionality through the recovery and reuse cycle.
This research demonstrates that strategic incorporation of cleavable comonomers into thermoset matrices offers a viable pathway toward circularity for high-performance nanocomposites. By enabling controlled matrix deconstruction while preserving nanomaterial quality, this approach addresses both environmental concerns associated with nanocomposite waste and the economic imperative to reclaim valuable nanomaterials. The demonstrated success with the styrene/DVB system suggests broader applicability of this methodology. As a general radical ring-opening polymerization strategy, this approach has the potential to be extended to other vinyl-based thermosets and diverse nanofillers, offering a promising foundation for developing next-generation recyclable composites across multiple industrial sectors
Advancing Freezing of Gait Heterogeneity Modeling through Subtype-aware Detection, Generative Augmentation, and Adaptive Prediction
No full textFreezing of Gait (FOG) is a disabling symptom of Parkinson’s Disease (PD) that varies in manifestations and motion contexts. Its heterogeneity motivates subtype categorization such as manifestation-specific subtypes (akinesia, trembling, or shuffling) and motion-specific subtypes (gait-initiation, walking, or turning), with occurrence and frequency of subtype varying across patients. FOG detection and prediction have attracted significant research interest for their applications in daily monitoring, automated FOG dataset labeling, and on-demand activation of intervention devices.
With respect to FOG detection, despite numerous promising Deep Learning (DL) FOG detection studies, few consider FOG heterogeneity. It remains unclear whether different subtypes require distinct detection strategies, and whether tailoring subtype-specific models could enhance detection generalizability across subtypes. Additionally, training a DL detection model with robustness and generalization across subtypes is limited by data scarcity and imbalances between FOG/non-FOG classes and among subtypes, while FOG generative augmentation is considered a promising solution. However, subtype-conditioned FOG generative augmentation has not been developed, and its effectiveness and advantages compared to simpler, cheaper classical augmentation methods on detection model performance remain unknown. Regarding FOG prediction, one gap lies in the limited adaptability and complexity of available labeling approaches for pre-FOG (transition state leading to FOG), which exhibits heterogeneity across subjects and FOG episodes. Analyzing pre-FOG heterogeneity with respect to FOG subtypes may help better interpret it, but is currently underexplored. Another gap with respect to existing prediction model design is the lack of a multi-horizon prediction function, which could specify FOG onset while simultaneously enabling both short- and long-term alarms.
These gaps are addressed in this thesis through three projects, each detailed in a methodology chapter, focusing on subtype-aware FOG detection, subtype-conditioned FOG generative augmentation, and multi-horizon FOG prediction incorporating a soft, data-driven, adaptive pre-FOG labeling.
The FOG detection chapter first categorizes FOG data into manifestation- or motion-specific subtypes via classifier or clustering methods and then derives their corresponding detection strategies as interpretable feature masks. This chapter then proposes a feature-mask-based Convolutional Neural Network (CNN) that explicitly embeds the identified strategies. Using waist-mounted 3D accelerometer data, a general CNN and subtype-specific CNNs are trained. The results show that according to feature-mask analysis, motion-specific subtypes share a common detection strategy, whereas manifestation-specific subtypes require distinct strategies. Manifestation models exhibit enhanced generalizability across subtypes compared to the general model, boosting the overall average FOG detection sensitivity by 10.95% ± 9.24% and specificity by 32.08% ± 9.01%. Conversely, motion models reduce the overall FOG sensitivity by 1.89% ± 8.74% and specificity by 5.17% ± 10.76%. Consequently, the detection strategy is mainly driven by manifestation composition of the data. The general model favors the dominant manifestation-specific subtype group(s), a bias corrected by tailored manifestation-specific strategies. No comparable benefit arises from motion models due to their similar manifestation compositions. This chapter reveals the detection strategies required by different FOG subtypes and demonstrates the effectiveness of subtype-specific tailoring in improving FOG detection generalizability.
The FOG augmentation chapter proposes a subtype-aware FOG augmentation technique enabling training of DL models to perform consistently across subtypes. Specifically, it introduces Hi-CF cGAN, a two-stage model that generates subtype-conditioned FOG-like ankle accelerations that are realistic and diverse, as verified through visualization, UAMPs, and MMD comparison against real signals. This chapter evaluates Hi-CF cGAN’s effectiveness by training CNNs for FOG detection with both general (subtype-stratified) and personalized (subtype-variant, based on patient-specific subtype composition) augmentation via Hi-CF cGAN, benchmarking against classical augmentations and baseline (no augmentation). Compared to baseline, general augmentation with Hi-CF cGAN effectively improves average detection rates of FOG, trembling FOG, and especially the previously overlooked minor subtypes, shuffling FOG (from 66.8% to 81.6%) and akinesia FOG (from 58.7% to 77.9%). These improvements exceed those of classical augmentations, demonstrating superior realism, richness, and adaptability of Hi-CF cGAN -generated data in addressing FOG/non-FOG and subtype imbalances. Personalized augmentation further enhances accuracy on targeted subtype(s) compared to general augmentation, highlighting its potential for tailored model optimization.
The FOG prediction chapter first proposes a soft, data-driven, and adaptive pre-FOG labeling approach that identifies potential pre-FOG windows using statistical signal properties, including Shannon entropy and auto mutual information, and data-driven features via a CNN-predicted FOG probability. This adaptive labeling effectively captures intensifying pre-FOG characteristics while approaching a FOG episode and generalizes effectively across subjects. The labeling results reveal that for motion-specific subtypes, turning shows the strongest and most statistically reliable pre-FOG trends, while gait-initiation lacks a clear pre-FOG pattern. For manifestation-specific subtypes, trembling exhibits the most statistically consistent pre-FOG trend, while shuffling has the weakest trend. Some subjects display strong general pre-FOG trends, while others only show strong pre-FOG trend with specific subtype(s), highlighting the value of subtype-specific pre-FOG labeling and the interpretability of pre-FOG heterogeneity via subtypes. Additionally, this chapter also proposes a sequence-to-sequence, multi-horizon CNN-transformer that predicts the FOG state for each of the next six seconds. Combined with the proposed adaptive labeling, the model predicts both a discrete FOG state and a soft FOG Score representing FOG probability. It achieves a low mean error of 11.4% ± 4.1% and above-benchmark Prediction horizons of 3.19 ± 0.34 s. Comparisons across labeling methods show that the adaptive labeling improves both window- and sequence-wise prediction accuracy and stability relative to fixed labeling, confirming its higher clarity and flexibility in pre-FOG identification. However, compared to no-pre-FOG labeling, the adaptive labeling demonstrates improved Prediction horizons and prediction success rate on transition sequence but reduced accuracy on non-transition sequence due to increased false alarms, which is a trade-off to consider in practical application.
Collaboratively, these three chapters demonstrate the necessity and benefits of tailoring with respect to manifestation-specific subtypes for cross-subtype detection generalization, manifestation-conditioned FOG augmentation for data imbalance correction, and episode-adaptive pre-FOG labeling for reliable prediction, while also proposing innovative deep learning solutions for each specific FOG modeling problem
An Investigation of Overt Visual Attention and Gaze Behaviour in Social Human-Robot Interaction and Human-Computer Interaction Contexts
No full textIn human-human and human-robot interaction gaze has a consequential role as a type of non-verbal communication behaviour, affecting the social interaction depending on gaze behaviour's characteristics. As such, gaze behaviour has been a topic of major research throughout the past number of years since a better understanding of gaze behaviour could lead to design of robot behaviour for social interactions.
In the context of the human-human interaction (HHI) and human-robot interaction (HRI) studies, gaze behaviour has been seldom investigated while taking into consideration all social interaction elements including interaction partners' personalities and social roles in addition to the social context. There are a number of studies which investigate conversational roles and personality matching in relation to gaze behaviour in the context of HRI in separate studies. However, works which investigate gaze behaviour in tandem with these social interaction elements are needed since such a study will contextualize gaze behaviour in relation to variations in these social elements (e.g. gaze behaviour characteristics based on introverted and extroverted personalities) while taking into consideration the compounded effects of these social elements in combination.
What this thesis accomplishes is incorporation of all these social elements in tandem with gaze, all under the umbrella of one body of research. Utilization of this integrative approach was inspired by recent HRI literature, encouraging the investigation of verbal and non-verbal social interaction elements together with social interaction elements.
This thesis investigates gaze behaviour in the context of HRI while taking into account social role and designed personalities in robotic platforms. As the social context, this thesis explores dyadic human-robot interactions involving objects of discourse from a gaze-centric point of view while considering the robot's gaze-centric perspective and the participant's gaze-centric perspective. Four major studies are conducted in the context of this thesis to fulfill this exploration.
Tools for recording overt visual behaviour are vital in conducting human-computer interaction (HCI) research. However, specific tools enabling the recording of these metrics in online settings, facilitating video viewing were not available, therefore Study 1 created the FocalVid platform. This platform collects cursor location attentional data for the participants in online settings such as Amazon Mechanical Turk. The cursor metrics gathered through this platform were then compared to eye tracking data
and our rendition of another relevant platform (BubbleView). It was determined that human gaze and cursor movements are distinct but have similarities in relation to velocities and dwell timing. This platform allowed for large-scale data collection for HCI and HRI studies, which is not possible in the context of in-person studies.
Personality and social role are major elements of social interactions; however, perception of designed introverted/extroverted personalities for the humanoid iCub robot were not previously examined and additionally these two elements have not been explored simultaneously in the previous literature involving the iCub robot. In the second study, I explore the participants' perception of a robot in interactions between a robot and a human actor utilizing recorded online scenarios. In this study, the robot takes on different social roles while embodying different personalities. The robot is either a teacher, a student or a collaborator while either introverted or extroverted. To conduct this study, the Amazon Mechanical Turk platform and HRI video recordings were used. I discovered the presence of perceiver effects in participants’ assessment of the robot’s Ten-Item Personality Inventory (TIPI) dimensions perception vs. self TIPI dimensions, where participants' self-assessment of their personality correlated to their assessment of robot’s personality. TIPI questionnaire is a measure used to assess personality dimensions. It was also determined that the designed robot personality was perceived accordingly by the participants. These findings indicated that even though participants’ self-assessment of their personality dimensions affects their perception of the robot, they could still perceive the robot’s designed personality as intended.
Observation and analysis of people’s overt visual attention dynamics in HRI could allow for better understanding of these interactions however, such overt attention while considering social interaction elements have not been previously explored in detail. The third study investigated participants' overt visual attention in the context of dyadic social settings using the FocalVid platform. In this study, I was also interested in the efficacy of the use of the FocalVid platform to collect attention metrics relating to such social settings. This study, taking advantage of the HRI scenarios designed in Study 2 and using the FocalVid platform, recorded the cursor attentional data for participants while the robot was enabled with different social roles and personalities. It was determined that the robot’s social role and personality significantly affected the participants’ overt visual attention. It was also determined that the presence of the FocalVid platform did not adversely affect the perception of the robot.
Gaze studies in Human Robot Interaction should investigate both the human partner’s gaze behaviour’s effect on the social interaction, in addition to the robot’s gaze behaviour’s effect on the social interaction. A limited number of studies have explored the effects of gaze-architecture-enabled robots' behaviour on social interaction. In the fourth study, after the design of gaze-based interaction architectures based on Social Gaze Space taxonomy in dyadic interactions involving objects of discourse, the effects of using these gaze interaction architectures for robot gaze control were evaluated utilizing eye tracking data and Human Robot Interaction questionnaires. Through this study, it was determined that the SGS-IA architecture led to higher visual engagement by the participants towards the robot’s face and eye region compared to the TutorSpotter architecture, which was used for comparison purposes. One of the main contributions of this thesis is the design and evaluation of these gaze-based interaction architectures for anthropomorphic humanoid robots involved in human-robot interactions.
All four of these studies were geared towards gathering a better understanding of gaze behaviour in HRI and HCI. Studies 1 and 2 had a preparatory role to this end. Study 1 allowed us to design the FocalVid platform and to investigate the attention metrics gathered through this platform against gaze metrics in this Human Computer Interaction platform. Study 2 allowed us to design the Human Robot Interaction scenarios needed for Studies 3 and 4. Study 3 investigated gaze behaviour of the human interaction partner involved in Human Robot Interaction using the FocalVid platform, and in Study 4 we designed and evaluated a gaze interaction architecture for the iCub robot through an in-person Human Robot Interaction study. These studies allowed for better understanding of the role of gaze behaviour in social HRI settings. These studies also enabled us to design gaze-specific interaction architectures for the iCub robot
Bioptic Telescopic Spectacles and Driving Rehabilitation for People with Age-Related Macular Degeneration: A Pilot Project
No full textIntroduction
Driving is a multifactorial task of which vision is a component. A person’s ability to meet requirements to gain or maintain a driver’s license may be compromised by visual impairments (VI) caused by ocular diseases such as Age-related Macular Degeneration (AMD). However, vision is only one component of safe driving. Programs aimed at permitting and individually assessing a person’s ability to drive are important for improving the quality of life of Canadians living with VI whose ability to safely drive may be in question.
Purpose
In Ontario in October 2020, the Highway Traffic Act was amended to allow the use of [bioptic] telescopes to meet provincial visual acuity requirements for Class G licensure for the first time. pending successful completion of an on-road driving assessment while using the telescope (MTO Bioptic Telescope Program).
The George & Judy Woo Centre for Sight Enhancement (CSE) in Waterloo, Ontario has formulated a training protocol involving both fitting and training with a bioptic telescope for driving. The aim of this study aim was to determine whether the program sufficiently prepares individuals with AMD to effectively use their bioptic telescope, as determined by success in a simulated in-car evaluation.
Methods
The study aimed to recruit up to 10 participants with AMD who had lost their driver’s license within the last 5 years, met MTO Bioptic Telescope Program visual requirements and did not suffer from motion sickness or cognitive impairment (by self-report). Visual function and visual perceptual testing as well as bioptic telescope fitting were administered. Participants also proceeded into the CSE training program which included 3-5 training sessions and a counselling session. Participants’ driving skills and use of bioptic telescope(s) were then assessed in a virtual reality SUV driving simulator both with and without the telescope.
Results
Three participants (mean age of 74 years, male) were enrolled and completed the study. Visual parameters met the eligibility criteria (corrected visual acuity ranged from 0.35logMAR (20/50) to 0.77logMAR (20/126), contrast sensitivity ranged from 1.00-1.45logCS units and visual fields were full peripherally). All three participants had binocular central scotomas identified on Humphrey monocular full field tests and Nidek microperimetry. However, no central scotoma was evident on the Humphrey Estermann binocular field assessment. Each participant was fit with a bioptic telescope (2.2-3x magnification) and completed three training sessions. All participants passed the Scan Course but failed the other visual perceptual tests (Useful Field of View, Trail Making, Motor Free Visual Perceptual Test-4). Participant 3 failed the Montreal Cognitive Assessment for the Blind and Visually Impaired. In the final assessment, Participant 1 successfully completed the driving simulation with and without the telescope with no critical driving errors. Participant 2 passed only with the telescope which was used appropriately to identify traffic and speed signs. Participant 3, despite having the best visual acuity, failed under both conditions with critical errors.
Conclusion
Although further research is required, several important findings to date have been identified. First, the Esterman binocular visual field test cannot be solely used to determine the full field of those with a potential central deficit. Second, the visual perceptual tests conducted with the cut-offs provided, did not definitively identify those individuals that may not be suitable for driving with bioptics; a functional assessment appears to continue to be the gold standard. Third, full medical evaluation should be conducted prior to the visual function evaluation to help identify those that are not safe driving due to non-visual reasons. Finally, it appears that the CSE Bioptic Fitting and Training Protocol may effectively enable participants to learn to apply the fundamental skills required for using a bioptic telescope in a driving situation
Development and Evaluation of Hydrophobic Catalysts for Deuterium Enrichment in a Gas-Vapor Reactor
No full textDeuterium, an isotope of hydrogen, has found diverse applications in pharmaceutical, nuclear, and analytical fields owing to its chemical similarity to protium, but distinct mass which gives rise to the kinetic isotope effect. The water-hydrogen catalysis method offers a clean route for deuterium enrichment from its natural abundance (0.015 %). This process can achieve industrial-grade purity (> 99 %) in the presence of a hydrophobic catalyst. However, the industrially used Pt/C/PTFE catalyst is costly, making the initial capital requirement for deuterium enrichment high. Further, limited documentation on the synthesis and performance of Pt/C/PTFE hinders the development of viable alternatives. This thesis develops a co-current gas-phase reactor and tests its ability to directly study kinetics for catalysts that perform deuterium enrichment through isotopic exchange between H2/H2O. A protocol for a Pt/C/PTFE catalyst was developed and standardized. This catalyst was used to establish benchmark catalytic performance metrics for deuterium enrichment of H2O by catalytic exchange between a blended H2/D2 and H2O vapor stream. A series of Ni-Pt alloys were then explored as low-Pt alternatives to this industry standard catalyst. The reaction temperature and reaction time required for alloying of NiPt with stoichiometric ratios of 1Ni:3Pt, 1Ni:1Pt and 3Ni:1Pt were established. The resultant alloy nanoparticles were prepared into Ni-Pt/C/PTFE catalysts, analogous to the industry standard Pt catalyst, and their catalytic properties were tested. A reliable evaluation method for assessing catalytic activity was developed, through which mass-transfer coefficients and activation energies were determined. Comparative analysis showed the NiPt/C/PTFE alloy to successfully catalyze the isotopic exchange reactions but not outperform Pt/C/PTFE. Mechanistic analysis provides evidence for OH* oversaturating the surface at elevated temperatures in the reaction, which may be responsible for lower-than-anticipated catalytic performance. Further investigations into temperature-dependent kinetics may guide the rational design of cost-effective catalyst for deuterium enrichment
Broadband Linear Modulator Driver Design for High-data-rate Wireline Communications
No full textThis thesis presents the design, analysis, and implementation of a broadband optical modulator driver targeting high-speed wireline communication systems in a 22-nm FD-SOI CMOS technology. The driver is designed to meet stringent specifications for 15.7-dB gain, 90-GHz bandwidth, linear output swing of 4 Vppd, and minimum energy per bit, with a focus on compensating for high-frequency losses in optical modulators through the driver's 9-dB gain peaking.
The core of the design revolves around a two-stage broadband amplifier, incorporating a novel shunt/double-series interstage network to achieve amplitude peaking at 75 GHz and extended bandwidth. The interstage network is optimized to provide a 4-pole, 1-zero transfer function, enabling efficient inductive peaking and minimizing in-band ripple. The preamp stage features a PFET cascode amplifier with a shunt/double-series input matching network, designed to achieve broadband input matching up to 80 GHz. The postamp stage employs a parallel-path NFET differential cascode amplifier with an output combining network, optimized for more than 15.2-dBm output 1-dB compression point up to 75 GHz.
Experimental results demonstrate the driver's performance across a wide range of operating conditions. The prototype achieves a small-signal bandwidth of over 90 GHz, with a peak gain of 20.4 dB at 76 GHz for a 60-Ohm differential load. The driver demonstrates a low-frequency gain of 15 dB and maintains a flat group delay of 17 ps up to 60 GHz, peaking at 30 ps at 76 GHz. The driver's linearity is characterized using the 1-dB compression point (OP-1dB) and total harmonic distortion (THD). The OP-1dB remains above 13 dBm, equivalent to a differential output swing of 4 Vppd, up to 75 GHz across a 100-Ohm differential load. The THD is measured at 1.6% for a 1-GHz input signal with a 4-Vppd output swing.
Time-domain measurements demonstrate the driver's ability to transmit 140-GBaud NRZ signals with a 4-Vppd eye amplitude. For 100-GBaud PAM-4 signals, the driver achieves a 4-Vppd outer optical-modulation amplitude, but the top and bottom eyes begin to close due to the driver's frequency response and distortion with 100-Ohm differential loads. The use of a 4-tap feedforward equalizer (FFE) compensates for frequency response limitations, resulting in improved eye opening. The driver's performance is compared with other state-of-the-art broadband drivers, highlighting its competitive bandwidth, linearity, and energy efficiency. The prototype occupies a core area of 0.17 mm2 and a total area of 0.7 mm2, including pads. The results provide valuable insights into the design and optimization of broadband drivers for high-speed communication systems