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Sorted Bucket Hash and Combinatorial Game Algorithms: Two Efficient Solvers for the Game of NoGo
Get PDFNoGo is a variant of the popular game of Go. NoGo shares the same mechanisms as Go, but it requires stones, once played, to be not removed from the board. Strong computer players have been created for NoGo, yet the game properties and optimal play strategies are not well studied. This thesis describes our recent contributions to solving and understanding NoGo. Two solvers were developed for this purpose: SBHSolver and CGTSolver. SBHSolver uses a newly proposed Sorted Bucket Hash hashing method and its general data structure to build the transposition table. It is capable of weakly solving games on memory-limited machines efficiently. CGTSolver equips Negamax search algorithm with enhancements derived from combinatorial game theory to solve Linear NoGo which is NoGo played on a one-dimensional board. SBHSolver weakly solved all NoGo positions of sizes up to 27 points, and CGTSolver pushed beyond and ultra-weakly solved Linear NoGo up to 39 points. Those achievements were made possible by the improved efficiency of these solvers. Statistical observations of game-playing strategies, rigorous proofs of combinatorial game properties, and detailed experimental results are provided in this thesis
Women Wrestlers' Experiences of their Menstrual Cycle
No full textDeveloping a shared understanding of athlete experiences with their menstrual cycles is critical for creating sport environments that are more inclusive to women. The purpose of this research was to explore women wrestlers’ experiences of their menstrual cycles in training and competition. Purposeful sampling was used to recruit seven women wrestlers between 16 and 20 years old who experienced a menstrual cycle and competed locally, nationally, or internationally within the last year. Data was generated via talking circles, individual one-on-one semi-structured interviews, and a reflexive research journal. All interviews were audio-recorded and transcribed verbatim with Otter AI. Elo and Kyngäs’ (2008) three phase process of inductive content analysis was utilized to analyze the data, and findings are represented by six themes: (a) “I’ll try and hide it”: Feelings of inconvenience; (b) “I feel like he takes it as a joke”: Lack of coach knowledge; (c) “It changes the way I participate”: Impact on mental and emotional states; (d) “It’s not because we’re weak”: A culture of no excuses; (e) “It really feels like a family”: Teammate support; and (f) “Is this sweat, or am I bleeding through?”: Fear of the unexpected. The detailed stories shared by women wrestlers provide unique insights into how their menstrual cycle experiences impact their training and competition, and how coaches can better support their athletes as they navigate the menstrual cycle
Using Serum Biomarkers to Evaluate Disease Course in Relapsing-Remitting Multiple Sclerosis: Evidence from Real-World Clinical Practice
Get PDFMultiple Sclerosis (MS) is a chronic neurological condition characterized by inflammation in the central nervous system, leading to demyelination and neurodegeneration. A hallmark of MS is its unpredictable clinical course, with most patients experiencing a Relapsing-Remitting MS (RRMS) pattern characterized by symptom relapses followed by variable periods of remission. Current prognostic standards primarily rely on MRI scans to monitor its progression, but these are often constrained by high costs, long wait times and lack of access in lower-income settings. While cerebrospinal fluid analysis may provide another venue to track disease evolution, the invasiveness of the test makes it impractical for routine assessments in clinical practice. Therefore, there is an urgent need for alternative biomarkers that can be routinely implemented.
Recent advancements in MS biomarker research have highlighted the neurofilament light (NfL) and Glial Fibrillary Acid Protein (GFAP) protein as a promising indicator of disease progression and severity. NfL is a subunit of neurofilaments, which are structural components found in neurons and are released extracellularly into the serum upon neuroaxonal injury, as in MS, making it detectable as a valuable biomarker. Meanwhile, GFAP is a cytoskeletal protein found inside astrocytes that are responsible for structural integrity and are also released extracellularly into the serum upon neuroinflammation, as MS is a chronic inflammatory disease. A significant advantage of these two biomarkers is that they can be measured in the serum component of blood using the highly sensitive single-molecule array (Simoa) machine, proposing a potential non-invasive prognostic tool.
Despite their potential, many of these studies are limited to controlled research conditions with precise MRI schedules, same-day blood and MRI collection, timely follow-up intervals and the use of either clinically-impractical measures (e.g. complex volumetric MRI analyses) or clinically-restricted measures (e.g. only using EDSS, an exam that is heavily biased to capture motor and sensory symptoms, but not cognitive or other domains). In clinical practice, these parameters are often very difficult to control and exhibit significant variability, raising concerns about the generalizability of these results beyond the constraints of research settings. These studies involving NfL and GFAP also often look at these biomarkers in isolation, overlooking the potential of using these biomarkers in combination to better enhance predictive potential or stratify MS populations. In addition, the current gold-standard Simoa immunoassay presents several barriers that further hinder the clinical implementation of NfL measurements. These challenges include high costs, time-consuming operation (2-3 hours per run), and being optimized for batch processing rather than individual patient tests. In contrast, newer developments, such as the Elecsys NfL assay, offer a more practical solution with lower costs, shorter assay duration of 18 minutes and flexible on-demand testing, although its utility in MS patient care has yet to be fully explored.
In this study, our primary objective is to assess whether serum NfL and GFAP levels can associate with standard clinical measures in a real-world cohort of patients with RRMS, and whether the joint use of these biomarkers can provide additional prognostic information beyond using them in isolation. Our secondary aim is to evaluate how the Elecsys assay, with its added clinical practicality, performs in relation to the Simoa assay. To answer these study objectives, we conducted a cross-sectional study that recruited 97 patients with RRMS and collected a comprehensive set of demographical, clinical and MRI variables from their medical records. Additionally, their sNfL and sGFAP levels were measured using both the Simoa and Elecsys immunoassays, and their associations with outcomes were evaluated using various linear and logistic regression models, with covariate adjustments reflective of clinical conditions.
Our results suggest that serum NfL and GFAP are robust biomarkers for MS disease activity and functional disability measures, even within the real-world complexities of clinical care. By using NfL and GFAP in combination, we observed that it stratified MS patients into benign, inflammatory and progressive subgroups, which is crucial in informing healthcare and treatment decision tailored to their disease course. Furthermore, we found that alternative assays, like Elecsys, had comparable performance to the gold standard Simoa, presenting cost-effective and scalable alternatives to integrate these biomarkers in clinical practice. Ultimately, our study strongly recommends the advancement of NfL and GFAP biomarkers from research to clinical use to enhance MS care
Fourth Sound Helmholtz Resonator as a Probe for Confined Superfluid 3He
No full textInvestigation of the low-temperature properties of both stable isotopes of helium (He and He) has been of major importance, not only to the development of cryogenics as a field but to our modern understanding of phase transitions in condensed matter systems. Both isotopes exhibit superfluid phase transitions, but due to differences in quantum statistics, they are qualitatively different kinds of superfluid. In fact, it is misleading to speak of a singular superfluid phase in He, since there are several distinct phases of He that exhibit superfluid properties. Since the discovery of superfluid He in 1972, significant experimental and theoretical effort has been devoted to developing a framework capable of describing the rich phenomenology of this system. In doing so, mathematical analogies have been drawn both to superconducting systems and also cosmology. Because experimental samples of He can be made extremely pure, it is in many respects an ideal system for studying topics such as phase transitions, finite size effects, collective modes, topological defects, and bound state excitations. For this reason, superfluid He is of interest not only as a specialist topic but as a paradigm system for general concepts in condensed matter physics.
A great deal is known about the bulk properties of the system. It is also well-established that the surfaces of the sample play a pivotal role in determining the thermodynamically stable phase, and can result in inhomogeneous features known as textures or defects. When He samples are confined to small geometries, phases not seen in the bulk system may be realized, and surface-bound states may play a decisive role in the transport properties. Many aspects of these highly confined systems remain under-explored, primarily due to experimental limitations. To remedy this, we have developed a new experimental probe capable of confining superfluid He to well-defined channels with thicknesses on the order of hundreds of nanometers, created using modern nanofabrication techniques. These nanofluidic devices, dubbed Helmholtz resonators, are also capable of driving acoustic modes in the superfluid to probe its fluid dynamic properties.
Here we make use of Helmholtz resonators with a variety of channel thicknesses to explore the role of confinement in modifying the physics of superfluid He. In Chapter 5, the frequency response of these devices is used to identify phase transitions while varying the temperature at many different pressures. In this way, a phase diagram is mapped out for differing degrees of confinement, and the results are compared to the predictions of Ginzburg-Landau theory. In addition to the expected phases, we see evidence of an intermediate phase and discuss possible interpretations. In Chapter 6 the dissipation of the Helmholtz resonators is investigated, and the temperature dependence is found to be well described by a vortex mutual friction model. We use our measurements to estimate a vortex density, which we find to be large compared to the expected remnant vortex density predicted by Kibble-Zurek theory. We offer an interpretation of this result in which the small size of the channels stabilizes a greater number of vortices. In Chapter 7 we investigate the critical velocity of the A-phase. We observe a clear threshold in the force-velocity curves of the Helmholtz resonator which implies the onset of dissipation at a critical velocity. Dissipation in the bulk A-phase is typically dominated by textural effects that exist at arbitrarily low velocities, however, the high confinement of the channels in our device is expected to stabilize a uniform A-phase texture. We argue, in analogy to observations of the B-phase, that the onset of dissipation should then be due to the escaping of surface-bound state excitation into the bulk
Wildfire Evacuation Choice-Making among Underserved Groups in Alberta and British Columbia
No full textWildfire events continue to threaten multiple regions in Canada, and evacuations are often the primary means of ensuring life safety. Understanding how people make decisions before and during wildfire evacuations is thus important in informing future preparedness and planning. This research collected online survey data between May and July 2023, from residents living in high to moderate fire risk areas of Alberta and British Columbia (n=2868) to understand their intended evacuation behavior and choice-making during a future wildfire event. Our analysis focuses on underserved groups (people with disabilities, older adults, lower-income households, visible minorities, and carless residents) who are often neglected in disaster management and evacuation planning processes.
We contribute to the literature by uniquely focusing on decision-making within distinct underserved groups, rather than simply using these identities as variables within a broader model. Estimated logit models offer insight into factors affecting evacuation departure timing, destination and route choices, transportation mode choices, and preferred shelter types. Key results suggest that factors such as perceived wildfire risk, previous evacuation experiences, and intersecting vulnerabilities have a significant influence on choices among the different groups. Based on our findings, we provide several policy recommendations for local agencies, including: 1) ensuring multimodal evacuation plans with transit and shared mobility considerations, 2) equipping public shelters with amenities to accommodate diverse needs, and 3) providing targeted support for those with intersecting vulnerabilities
Non-Invasive Assessment of Cobb Angle Severity and Progression Using Markerless Surface Topography and Machine Learning in Adolescent Idiopathic Scoliosis
No full textScoliosis is a complex three-dimensional (3D) spinal deformity that impacts physical and mental health. Adolescent idiopathic scoliosis (AIS), which occurs between the ages of 10 and 18, can significantly affect physical growth.
Implementing surface topography (ST) assessment in scoliosis clinics presents a promising opportunity to reduce reliance on X-ray radiographs, thereby minimizing the associated risk of radiation-induced cancer. The primary challenge lies in accurately quantifying the relationship between torso ST scans and the underlying spinal structure. While several machine learning (ML) techniques have been proposed in the literature, limitations persist in predicting traditional radiographic measures, such as the Cobb angle (CA), particularly regarding accuracy and practical applicability in clinical settings. This thesis addresses these limitations by leveraging advanced artificial intelligence (AI) techniques to predict the CA and its longitudinal progression from ST scans.
Firstly, we introduce two models for estimating the maximum CA from an ST scan of AIS patients. The first model employs repeated stratified cross-validation, demonstrating its applicability in biomedical data analysis to estimate the CA. The second model combines transfer learning with an ensemble learning approach, achieving a mean absolute error (MAE) of 3.63° on unseen data, marking significant improvements over previously reported work for CA estimation from ST data.
Secondly, we present a progression model quantifying changes in CA between two scans for the same patient temporally separated by at least 6 months. This model integrates transfer learning with regression and classification techniques, achieving an MAE of 2.97° for ΔCA on unseen data. This study presents a novel approach to quantifying progression in AIS cases, demonstrating promising preliminary results on a limited dataset, including accurate identification of 100% of non-progressed cases when progression is classified.
These results establish a robust framework for estimating CA values and changes, demonstrating high performance suitable for clinical implementation. The application of transfer learning has significantly enhanced the ability to extract spine-related parameters from ST scans. This approach supports patient-centered care, offering a reliable and non-invasive method for use in the clinical management of AIS
The Role of Political Identity, Emotion, and Efficacy in Shaping Pro-Climate Action
No full textClimate change stands as a critical crisis of our time, yet responses remain insufficient to counteract the severe impacts of a warming planet. While numerous studies have explored strategies to encourage pro-climate action, few have examined the pivotal role of social identity in this behavior. This thesis applies a social identity framework in analyzing survey data, investigating how political identity, emotion, and efficacy shape pro-climate actions. Analysis revealed that political identity significantly moderates the effects of both emotion and efficacy on climate behavior, providing evidence that social identity is critical in shaping the performance of pro-climate action. However, my research also demonstrates that intense emotional experiences can attenuate the effects of political identity, providing a pathway to mitigate identity-based barriers to climate action
The Nature of Change in Emotional Dysregulation Symptoms in PTSD During Multi- modal, Motion-assisted, Memory Desensitization and Reconsolidation (3MDR) Therapy
No full textBackground: Emotional regulation has emerged as a significant factor in the treatment of post-traumatic stress disorder (PTSD). Current evidence suggests that emotional regulation and dysregulation may be key factors correlated with symptom severity and maintenance of PTSD. 3MDR (Motion-Assisted, Multi-Modal Memory Desensitization and Reconsolidation)
therapy is an innovative virtual reality supported, motion-assisted psychotherapeutic intervention developed to address treatment-resistant PTSD. While previous research on 3MDR has demonstrated promising results for combat-related, treatment-resistant PTSD among military members and veterans in randomized controlled trials, limited research has examined the nature of emotional regulation changes during the intervention itself. This study explores both the
immediate and long-term impacts of 3MDR on emotional regulation trajectories and investigates the underlying mechanisms of change.
Methods: The studies involved military personnel and veterans with combat-related PTSD who were nonresponsive to prior evidence-based treatments. Participants underwent six
weekly 3MDR sessions with comprehensive assessments conducted at baseline (T1), during weekly therapy sessions (T2.1-T2.6), and at multiple post-intervention 1-week, and 1-, 3-, 6-, and 12-month follow-up sessions (T3, T4, T5, T6, T7). Emotional dysregulation was measured using various clinician-administered and self-report questionnaires, including the PCL-5, GAD-
7, DERS-18, SUDS, PHQ-9, PAB-Q, PDEQ, CD-RISC-25, and OQ-45.2. Outcome measure scores were analyzed to compare baseline, intervention, and post-intervention timepoints (up to
3-month post-intervention). Quantitative data analysis employed Friedman ANOVA and post-hoc Wilcoxon Signed-rank tests, with Benjamini-Hochberg corrections for multiple comparisons. Additionally, qualitative data from sessions, debriefs, and follow-up interviews were transcribed and analyzed descriptively.
Results: Initial analyses showed statistically significant improvements in DERS-18 subscores Impulse (p = 0.013) and Goals (p = 0.038), PCL-5 (p = 0.014), PEDQ (p = 0.015), and
SUDS (p = 0.028) from baseline through intervention up to 1-week post-intervention. While these improvements did not maintain statistical significance after FDR correction, the results
comparing baseline to 1-week, 1-, and 3-month post-intervention demonstrated statistically significant decreases in DERS-18 scores. Qualitative findings indicated perceived improvements
in emotional regulation within specified DERS-18 domains.
Discussion: The studies suggest that 3MDR's impact on emotional regulation occurs through multiple mechanisms, including cognitive-motor stimulation, narration, divergent
thinking, reappraisal of aversive stimuli, dual-task processing, and reconsolidation of traumatic memories. Although not statistically significant, changes were most pronounced between early and late intervention sessions. Statistically significant changes between baseline and post-intervention timepoints suggested that improvements may continue beyond the course of intervention. The neurobiological mechanisms, such as N-methyl-D-aspartate receptor activation responsible for memory reconsolidation, may contribute to delayed emotional regulation
improvement. While sample size limitations and missing data affected statistical robustness, the findings provide stronger evidence for 3MDR's effectiveness in addressing emotional
dysregulation in treatment-resistant PTSD. This underscores the need for future research with larger sample sizes, extended follow-up periods, and more refined quantitative and qualitative
measures targeting emotional regulation.
Conclusions: The collective evidence suggests that 3MDR therapy shows promise in remediating difficulties in emotional regulation among military members and veterans with
treatment-resistant PTSD, with effects potentially extending well beyond the brief 6-week intervention period. Further research is needed to better understand the underlying
neurobiological mechanisms and to validate these findings with larger sample sizes and extended follow-up periods