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Girls’ Culture across Historical Divides: Negotiating Girlhood in Girls’ Magazines and Girls’ Comics in Mid-Twentieth Century Japan (1937-1973)
No full textIn this thesis, I examine the history of girls’ culture in Japan between 1937 and 1973. In particular, I address the lack of research in existing studies of women’s media on the intervening periods between early twentieth century and the post-1970s period Japan. To this end, I explore how agents of girls’ culture, such as illustrations artists, magazines editors, female students and female comic artists negotiated gender expressions of girlhood in relation to state expectations. I specifically focus on discourse of girlhood and womanhood in girls’ magazines of the Asia-Pacific War (1937-1945) and girls’ comics of the early 1970s. By analysing wartime girls’ magazines such as Girls’ Companion (jp. Shо̄jo no tomo, 1908-1955) and Girls’ Club (jp. Shо̄jo kurabu, 1923-1962), as well as postwar girls’ comics like Ikeda Riyoko’s The Rose of Versailles (jp. Berusaiyu no bara, 1972-1973), I argue that these two forms of mass media allowed girls and women to assert agencies over gender roles through imaginations of girlhood that offered alternatives to dominant gender ideologies without directly opposing it. Through my analysis, I propose a new historical approach to studying girls’ culture by highlighting its role as a discursive space where marginalized individuals like girls and women were free to create and consume alternative gender expressions from within the framework of dominant ideologies in modern Japan
Designing a Neurodiversity-Affirming School-Based Music Therapy Program to Support Autistic Students with Auditory Sensitivities
No full textThis thesis presents a neurodiversity-affirming music therapy program framework designed for autistic students experiencing auditory sensitivities in a specialized school environment. Guided by the Intervention Mapping (IM) framework, this project integrates theory, empirical research, and published accounts of lived experiences of autism to address a gap in practice and create a flexible and inclusive program that values sensory diversity. Drawing from autistic first-voice narratives, as well as literature on music therapy practice and other health-related fields, the program addresses the limitations of traditional desensitization approaches. This is achieved by emphasizing autonomy, predictable sound environments, and ethically grounded assent processes for speaking and non-speaking autistic students who may communicate in a variety of ways. Organized across structural, content and delivery components, the program may offer music therapists concrete strategies which may support sensory regulation through adaptive, student-led engagement with sound. By centering autistic perspectives and integrating cross-disciplinary perspectives, it seeks to contribute to advancing music therapy practice by calling for a refinement of sensory supports in specialized school contexts and informing the development of guidelines that explicitly integrate auditory considerations into therapeutic planning
Estimating Coronary Perfusion Pressure during Cardiopulmonary Resuscitation using Physiological Parameters and Machine Learning
No full textSudden cardiac arrest remains a critical global health issue with survival rates stagnating below 10%. Current resuscitation strategies often employ a “one-size-fits-all” approach, neglecting individual patient variations. Coronary perfusion pressure (CPP), the most reliable indicator of cardiopulmonary resuscitation (CPR) effectiveness, requires invasive catheterization for real-time measurement, limiting its utility in emergency and out-of-hospital scenarios. This study introduces a novel framework for real-time, continuous, non-invasive, and calibration-free CPP estimation, leveraging photoplethysmography (PPG) and electrocardiography (ECG) signals integrated with machine learning. Our method employs an animal-based data-split strategy to enhance generalization and eliminate the need for calibration to new animals, making it highly applicable to real-world situations. A dataset of 13 swine models was collected, each subjected to ventricular fibrillation and resuscitated using mechanical chest compressions aligned with American Heart Association guidelines. During these procedures, solid-state pressure catheters in the aortic arch and right atrium recorded CPP, while PPG and ECG signals were gathered simultaneously. The data was transformed into three different input modalities: single-cycle, rolling-window multi-cycle, and stacked multi-cycle. Among these input modalities, the stacked multi-cycle modality, paired with a transformer model trained using scheduled sampling and utilizing both PPG and ECG signals, achieved the best performance. This configuration yielded a mean absolute error of 6.410 mmHg on an animal-based data split, outperforming previous models. This work highlights the transformative potential of PPG and ECG-based models for non-invasive CPP prediction, enabling personalized and data-driven CPR adjustments. By providing subject-based, real-time feedback, this approach promises to optimize myocardial blood flow, improve resuscitation outcomes and advance the standard of care in cardiac arrest management
Exploring Brain Dynamics of Design Creativity: EEG-Based Analysis of Nonlinearity, Recurrence, and Functional Connectivity
No full textDesign creativity—the ability to generate novel, useful, and unexpected ideas—is a complex, nonlinear, and recursive cognitive process, as shown by theoretical and experimental studies. Unlike well-defined problem-solving, it involves iterative, open-ended exploration, making its neurocognitive mechanisms difficult to capture. While many models describe creativity as nonlinear, recursive, and co-evolutionary, neuroscientific empirical validation remains limited.
This thesis addresses this gap by applying advanced electroencephalography (EEG) analyses to investigate brain dynamics across four cognitive states: idea generation, idea evolution, idea rating, and rest. The brain, as a complex system, exhibits nonlinear and recursive dynamics across interacting lobes that support design and creativity cognition, dynamics often overlooked by traditional deterministic approaches.
A multi-phase methodology was introduced. First, nonlinear EEG features (entropy, fractal dimensions, Lyapunov and correlation dimensions) were employed to assess nonlinear theoretical models. Second, recurrence quantification analysis (RQA) quantified EEG recursive patterns and validated the recursive nature of design and creativity cognition. Third, functional connectivity based on weighted phase lag index (wPLI) and mutual information (MI) revealed inter-channel and inter-lobe interactions unique to each state.
Statistical analyses, feature selection, and classification were performed in each phase. Topographic mapping localized significant EEG markers, and machine learning models confirmed their discriminative power, yielding high classification accuracy.
Results confirm the nonlinear and recursive nature of design creativity and reveal complex neural interactions underlying distinct cognitive states. This thesis provides one of the first comprehensive EEG-based validations of theoretical models of design creativity, offering a robust framework for future research
Dataset for "Dopamine inhibits excitatory synaptic transmission in layer I of the rat parasubiculum.”
No full textOptimizing and Validating the Performance of a Low-cost Potentiostat for In-Situ Flow Battery Testing
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Optimizing and Validating the Performance of a Low-cost Potentiostat for In-Situ Flow Battery Testing
Dharik Purohit
This thesis relates to the design, optimization, and validation of a low-cost potentiostat tailored specifically for the requirements of flow battery testing. The primary objective is to deliver a reliable solution that maintains high-performance standards necessary for precise electrochemical measurements, achieved through simplified circuit design, affordable components, and open-source software.
The potentiostat design incorporates an expanded compliance voltage of ±2.5V, dynamic current range selection using a multiplexer, and enhanced noise filtering for accurate operation in aqueous systems and microelectrode setups. The system’s functionality was further extended with the addition of features like two-way pulse testing and battery charge-discharge testing over multiple cycles. Comprehensive performance validation was conducted to ensure the potentiostat meets the requirements for electrochemical applications, with tests focused on accuracy, stability, and flexibility across a range of experimental conditions.
The CellStat’s design specifications were derived through simulations and theoretical calculations, including the assessment of compliance voltage, DAC/ADC resolution, and projected measurement errors. These theoretical benchmarks were validated through cyclic voltammetry experiments under varying scan rates, showcasing the potentiostat’s ability to reliably measure and control redox reactions in flow batteries. The work highlights the potential for low-cost, open source potentiostats to meet the growing demand for accessible electrochemical testing tools, supporting advancements in renewable energy technologies
Identifying Socio-Technical Risks in Open-Source Software for Scholarly Communications: Tools, Metrics, and Opportunities for Libraries to Support Sustainable Development
Get PDFObjective –In the interest of helping libraries make evidence based decisions about open-source software (OSS), the objective of this research is to establish whether tools that automate the evaluation of OSS project communities could be used specifically on scholarly communications OSS (SC-OSS) projects to provide actionable insights for libraries to guide strategic decision making and corrective interventions.
Methods –Seven OSS project communities were selected for evaluation, chosen from widely used scholarly communicationssoftware applications used in Canada for repositories, journal hosting, and archives. While all aspects of OSS projects may be evaluated at the project or network/ecosystem level, addressing the actors, software, or orchestration (Linåker et al., 2022), community evaluation that looks at the interaction patterns between project contributors is the practical focus of this research paper since there are multiple human factors that librarians who may not be software developers can impact. We identified a community analysis tool called csDetector (Almarimi et al., 2021) from the software engineering literature. This tool was chosen based on two main criteria: 1) ability to analyze data from GitHub repositories (the code sharing platform used by all selected SC-OSS projects) and 2) capacity to automatically produce results without manual intervention. Since some of the seven OSS projects were spread across multiple GitHub repositories, a total of 11 datasets from GitHub, each containing three months’ worth of data, were analyzed using csDetector.
Results –The results produced by csDetector are interesting though not without limitations. The tool is complex and requires the user to have software development skills to use it effectively. It lacked sufficient documentation, which made interpreting the results challenging. The analysis from csDetector, which identifies community smells (i.e., types of organizational and social dysfunction within software projects [Tamburri et al, 2015, 2021a]), suggests that these SC-OSS project communities are experiencing knowledge sharing difficulties, weak collaboration practices, or other member interaction dysfunctions that can eventually permanently affect community health. Having a software tool that can take metrics from GitHub and detect community smells is a valuable way to illustrate problems in the project’s community and point the way to remedying dysfunction.
Conclusion–While the OSS community analysis tool csDetector currently presents several hurdles before it can be used, and results generated come with caveats, it can be part of an approach to support evidence based decision-making pertaining to SC-OSS in libraries. The information provided can be worth monitoring (especially social network metrics such as centrality) and their results, particularly for community smells, identify problems that may be addressed by non-developers. Awareness of community smells in OSS can provide a deeper understanding of OSS sustainability as it provides a language to identify suboptimal social dynamics
I Love (Monster) Girls
No full text“I Love (Monster) Girls” is a hybrid-poetic exploration of monstrosity as an escape from queer trauma, disability, and grief. The project has two primary components; (1) autofiction as ‘Syd’ seeks therapy for their slew of tumultuous relations and suicidal ideation; and (2) prose, lyric, and film script that explore Syd’s explosive and occasionally violent relationship with the shapeshifting Monster Girl that they find in their apartment wall. These two components shift between real world experiences and a highly fantastical look at self-loathing, anxiety, anger, hypersexuality and isolation. The project is inspired by
other contemporary texts such as Ariana Reines’ Coeur de Lion and Carmen Maria Machado’s In the Dream House, that also seek to comprehend heartbreak and violence through an author-forward perspective, and by monstrously queer media such as Psycho Nymph Exile by Porpentine Charity Heartscape, the film Ex Machina directed by Alex Garland or the graphic novel Layers of White by solopipb, all of which explore sexual and often violent relationships with monsters
A theoretical analysis of Nigerian politicians and their godfathers
Get PDFPolitical decision-making in Nigeria is often influenced by a system of patron-client relationships, commonly referred to as Godfatherism. This study examines how political Godfathers shape the policy choices of politicians and, in turn, affect democratic accountability. By extending the Maskin and Tirole (2004) model, this paper introduces the role of campaign contributions from Godfathers as a critical
factor in affecting political choices.
The theoretical framework considers a two-period model where elected politicians choose between policies that align with public welfare or the preferences of their Godfathers. The model accounts for re-election incentives, voter awareness, and institutional strength, revealing that politicians are likelier to prioritize the Godfather’s interests over public needs when electoral accountability is weak. The study also compares three governance structures which are Direct Democracy, Judicial Power, and Representative Democracy, to evaluate which system minimizes the distortive effects of this elite influence
Deep Learning Approximation of Matrix Functions: From Feedforward Neural Networks to Transformers
Get PDFDeep Neural Networks (DNNs) have been at the forefront of Artificial Intelligence (AI) over the last decade. Transformers, a type of DNN, have revolutionized Natural Language Processing (NLP) through models like ChatGPT, Llama and more recently, Deepseek. While transformers are used mostly in NLP tasks, their potential for advanced numerical computations remains largely unexplored. This presents opportunities in areas like surrogate modeling and raises fundamental questions about AI's mathematical capabilities.
We investigate the use of transformers for approximating matrix functions, which are mappings that extend scalar functions to matrices. These functions are ubiquitous in scientific applications, from continuous-time Markov chains (matrix exponential) to stability analysis of dynamical systems (matrix sign function). Our work makes two main contributions. First, we prove theoretical bounds on the depth and width requirements for ReLU DNNs to approximate the matrix exponential. Second, we use transformers with encoded matrix data to approximate general matrix functions and compare their performance to feedforward DNNs. Through extensive numerical experiments, we demonstrate that the choice of matrix encoding scheme significantly impacts transformer performance. Our results show strong accuracy in approximating the matrix sign function, suggesting transformers' potential for advanced mathematical computations