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Dextran-Gold Nanoparticle-Based Tablets and Swabs for Colorimetric Detection of Urinary H2O2
Get PDFDiagnosis of oxidative stress is essential to avoiding serious life-threatening situations. Hydrogen peroxide (H2O2) is a potential biomarker of oxidative stress. Herein, we introduce a reagent-free, nanoscale approach for the colorimetric detection of urinary H2O2 utilizing dextran-gold nanoparticles (dAuNPs). The plasmonic properties of these nanoparticles are central to their function, leveraging their high surface area and tunable optical characteristics for sensitive detection. We transformed the colloidal dAuNPs solution into two formats: as a tablet (dAuNPs-Tablet) or impregnated on a cotton swab (dAuNPs-Swab). The assay generates hydroxyl radicle (•OH) from H2O2 via the Fenton reaction, followed by nanoscale-driven detection of H2O2 using a plasmonic tablet and swab sensors. In the presence of H2O2 in a sample, the red color of the tablet solution or plasmonic swab turns to blue color due to salt-induced nanoparticles aggregation. The transition in color is observed due to •OH-assisted degradation of the dextran layer around dAuNPs, leading to the loss of colloidal stability and subsequent aggregation of dAuNPs. Sodium chloride acts as the aggregating agent, enhancing the nanoscale interactions. The detection limit in artificial urine is found to be 50 µM for the tablet sensor and 100 µM for the swab sensor. The plasmonic tablet is more stable as compared to a plasmonic swab which gradually loses stability, after one month, with approximately 40% degradation within three months. Interference studies demonstrate the high selectivity of both platforms for H2O2 detection. Notably, we investigated the H2O2 levels in human urine samples from healthy volunteers (both female and male) before and after green tea consumption. The observed decrease in H2O2 level in urine post-green tea consumption suggests a potential role of green tea antioxidants in mitigating oxidative stress. The utilization of nanoprobes in our research not only enhances our understanding of oxidative stress dynamics but also drives advancements in point-of-care detection platforms, offering enhanced portability and ease of use of nanoprobes. These platforms open exciting avenues in healthcare diagnosis
An Exploratory Approach to Implementing a Shortened Mentalization-Based Workshop for Parents: Considerations for Reflective Functioning and Self-Efficacy
No full textThe current exploratory study involves the implementation of a shortened Mentalization-Based Training (MBT) for parents of preschool children without prior diagnoses related to cognitive, intellectual and/or behavioural challenges. It explores the triadic link between parenting self-efficacy, parenting stress and mentalization (i.e., parental reflective functioning). The research questions are: (1) To what extent does participation in a MBT program affect parents’ reflective thinking and mentalization capacity? (2) To what extent does participation in a MBT program affect parental self-efficacy? (3) To what extent does participation in a MBT program impact parental stress levels? (4) To what extent does participation in a MBT program impact the ways in which parents make attributions about their children’s behaviours and misbehaviours and (5) To what extent does participation in a MBT program affect parents’ attitudes about discipline? A total of five parents took part in the study and took part in two workshops. The Tool to Measure Parenting Self-Efficacy (TOPSE), the Parenting Stress Scale (PSS), the Parental Reflective Functioning Questionnaire (PRFQ), as well as semi-structured interviews were conducted before and after the MBT workshops. The results showed that parents demonstrated emerging mentalizing behaviours following the MBT workshops. However, the MBT workshops’ effect on parenting stress and parenting self-efficacy varied according to: (1) parent’s gender; (2) marital status and spousal support; (3) having a support network; (4) parents’ childhood experiences; and (5) parents’ perceptions of their children’s temperament and needs
Scaling up Machine Learning Models for fMRI Brain Encoding
Get PDFThis thesis investigates techniques for optimizing brain encoding models, emphasizing computational efficiency and the scalability of both data and models within the framework of large-scale functional magnetic resonance imaging (fMRI) datasets. Brain encoding aims to predict neural responses to complex stimuli, such as video frames, by utilizing latent feature representations from artificial neural networks. The first study explores the acceleration of
ridge regression, a widely used predictive model in brain encoding, particularly when applied to large fMRI datasets like the CNeuroMod Friends dataset. By implementing a novel batch-parallelization strategy using Dask, we achieved significant computational speedups of up to 33× with 8 compute nodes and 32 threads compared to a single-threaded scikit-learn.
The second study investigates how dataset size and model scaling affect brain encoding performance using vision Transformers. To do so, the VideoGPT model was trained end-to-end to extract spatiotemporal features from the Shinobi video game dataset with varying sample sizes (10K, 100K, 1M, and 6M) and model size (number of training parameters). Ridge regression is then used to predict brain activity based on fMRI data and the extracted features from video games. Our results show that larger datasets lead to significantly improved
encoding accuracy, with the 6M-sample dataset producing the highest Pearson correlation coefficients across subjects. Additionally, while increasing hidden layer dimensions in the transformer model greatly enhances performance, the number of attention heads appears to have a minimal effect. These findings emphasize the importance of data scaling for improving brain encoding, offering practical insights for optimizing neural network architectures in
the context of large-scale stimuli data.
This research advances the field of computationally efficient brain encoding, which is crucial for enhancing both computational speed and accuracy. These advancements are essential not only for improving our understanding of brain function but also for enabling scalable machine learning models on high-dimensional data and sophisticated stimuli, including applications
in neuroprosthetics and clinical neuroscience
Prediction of In-Plane and Out-of-Plane Defects in Steered Prepreg Tape during Automated Fiber Placement: Experimental and Analytical Modeling
Get PDFAutomated Fiber Placement (AFP) is a promising technology for manufacturing high-quality, large-scale structural components with complex geometries. However, a major challenge with AFP is the formation of manufacturing-induced defects, such as wrinkles, waviness, and tape folding, that occur during the steering process. These defects arise from the mismatch in length between the inner and outer edges of the prepreg tape when it is placed along a curved path. Such defects can degrade the mechanical properties of the part, leading to a reduction in its quality. Therefore, minimizing or eliminating these defects is crucial to improve the final product's overall quality. This thesis aims to analytically predict the in-plane and out-of-plane defects occurring at a steered tape in the AFP process, and the ultimate goal is to propose some solutions for reducing and eliminating the steering-induced defects.
A thorough experimental investigation was conducted using a variety of process parameters and steering radii to enhance our understanding of the defect formation during the steering of thermoset prepreg tows. Based on the experimental observations, two micro and macro models were presented to predict planar and non-planar deformations at steered tapes. According to the analytical results, it was then shown that interlayer bonding plays a significant role in the generation of defects in the AFP process. Consequently, a systematic series of experiments and finite element analysis was performed to enhance the interlaminar bondings at the AFP process. At the end according to all experimental and analytical analysis, a novel compaction roller is designed and manufactured to provide variable pressure distributions and contact length based on the geometry of the part, unlike traditional rollers, to reduce and minimize the defect formation during steering
Securing Control of Clustered DC Microgrids with Multiple Interlinking Converters
Get PDFThe integration of multiple direct current (DC) microgrids offers a resilient and efficient solution for modern energy demands, particularly with the increasing adoption of intermittent renewable energy sources. However, the reliance on communication networks for coordinating multiple interlinking converters (MICs) introduces vulnerabilities, particularly to False Data Injection Attacks (FDIAs), which can significantly disrupt system stability and operation.
This thesis presents AI-driven cyber-defense strategies to protect clustered DC microgrids interconnected via MICs against FDIAs.
At the primary control level, a Support Vector Machine (SVM)-based anomaly detection framework is developed to identify FDIAs in real time. Once an attack is detected, the system autonomously transitions to a localized power-balancing control to maintain operational stability.
At the secondary control level, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based signal estimation strategy is proposed to detect injected FDIAs and subsequently reconstruct compromised control signals, thereby maintaining MIC coordination.
Extensive simulation studies validate the effectiveness of the proposed methods, demonstrating their ability to enhance microgrid resilience against various FDIA scenarios, including time-varying and unbounded attacks. The results confirm the efficacy of both the SVM and ANFIS frameworks in safeguarding clustered DC microgrids interconnected via Multiple Interlinking Converters against cyber threats, ensuring stable and secure operation
AI-Enabled Uncrewed Aircraft System Traffic Management Methods: Hybrid Intelligence for Autonomous Navigation and Swarm Control
Get PDFThe rising global urban population has led to increased vehicles on the road and the demand for faster and more efficient transportation. As a result, the need for low-altitude spaces to support safe Unmanned Aerial Vehicles (UAV) operations has become increasingly urgent. To address this need, countries are constructing systems and implementing relevant regulations and techniques to ensure the safety of people and properties on the ground and in the air while enabling aerial vehicles to navigate and complete tasks autonomously amidst uncertainties.
In recent years, the rapid advancement in Artificial Intelligence (AI) and a corresponding exponential increase in computing power have unlocked new possibilities. This synergy enables UAVs equipped with AI abilities to improve over time and perform complex tasks more adeptly than traditional models. This innovative direction not only expands the range of tasks that UAVs can undertake but also enhances their safety, opening up a new dimension in various sectors of human activity by leveraging the evolving capabilities of intelligent systems.
This research advances the field of safe and intelligent UAV development through a systematic, four-stage process. In the initial stage, the exploitation and improvement of advanced sensors facilitated the integration of their outputs with control systems, leading to the creation of an emergency landing system that enhances reliability. The second stage involved the design of several UAV autonomous navigation and obstacle avoidance algorithms based on diverse Reinforcement Learning (RL) algorithms. This phase also explored the performance of multiple UAV units operating in adversarial and cooperative modes, laying the groundwork for subsequent studies.
Building on the aforementioned algorithms, the third stage saw the establishment of a hybrid intelligent SAC-FIS controller. This system combines an enhanced Soft Actor-Critic (SAC) method with a Fuzzy Inference System (FIS), integrating universal expert experience to streamline the learning process. It ensures real-time path planning in three-dimensional spaces, enabling UAVs to dodge obstacles and intercept multiple dynamic targets. It addresses two significant challenges in RL: dynamic-environment problems and multi-target dilemmas.
In the final stage, a multi-layered control framework was constructed to integrate all previously developed algorithms and functionalities. This structure enables decentralized swarm control among a custom number of UAVs for dynamic target interception and includes a distributed communication strategy for effective and dynamic target allocation. The system enhances robustness and maintains low risk by activating a failsafe mode for an emergency landing whenever an individual unit in the swarm fails, allowing other units to continue and complete the mission.
The effectiveness and superiority of all proposed algorithms and the control framework have been validated by simulations and experiments. Comparisons with previously published research work highlight the enhanced efficiency and higher success rate of these approaches
Navigating Difference in the Shadow of War: A Local History of Identity Formation after Political Mass Violence in Zvornik County
No full textAs was the case in numerous localities in the Independent State of Croatia, the civilians of Zvornik County in Bosnia and Herzegovina were subjected to collective mass violence committed by various military actors during the Second World War. This dissertation uses testimonies gathered by the Communist Party of Yugoslavia (Komunistička partija Jugoslavije – KPJ) in their “Territorial Commission for Determining the Crimes of the Occupiers and their Collaborators of Bosnia and Herzegovina” to dissect the nature of political mass violence in Zvornik. Local Ustaša began arresting Serbian political and religious elites as early as the summer of 1941, escalating further in August during a country-wide campaign to capture and expel Serbs living in the country to German-occupied Serbia. Often motivated by revenge for crimes against Serbian civilians, Četnik units responded with violence against Muslim communities, a group that was collectively identified with the Ustaša regime and their policies.
Using monthly reports submitted by the County Committee for the KPJ stationed in Zvornik, this project analyzes the impact of the conflict on how the residents of Zvornik (Zvorničani) defined their relationships to the group categories of “Serbian” and “Muslim” in the immediate postwar period. It is argued that the grievances from crimes committed during the Second World War exacerbated the perceived separateness of these identities.
Moreover, while the national Party platform of the KPJ celebrated the diversity of Yugoslavia and the Republic of Bosnia and Herzegovina, the execution of several policies, particularly those related to the policing of Islam, fueled greater disharmony between groups. In the case of Zvornik County, this problem was made worse by the antagonistic behavior and incompetence of local KPJ officials. This not only affected the legitimacy of Party institutions and policies in Zvornik, but also generated more distrust between Muslims and Serbs. It is only through this kind of micro-level data and analysis that one can begin to understand how ordinary people internalize their own sense of national identity, and how they define those categories in relation to others
Religiopolitical Echo Chambers on Social Media in Shaping Pakistan’s Violent Extremism Discourse
Get PDFSocial media have emerged as a potent instrument for disseminating religiopolitical disinformation, inciting violent radicalism and extremism among youth. Young individuals are spending considerable time online, rendering them increasingly susceptible to radical propaganda aimed at political mobilization. This risk is heightened in societies with fragile political systems, where mainstream media lack credibility, and social media serves as the principal news source. This dissertation investigates the role of religiopolitical narratives on platforms such as Facebook in propagating violent radicalism among Pakistani youth and analyze how critical social media literacy (CSML) can mitigate or counteract online radical narratives.
This dissertation employs a manuscript-based approach with three interrelated sub-studies. The first sub-study systematically reviews how social media drives extremist discourse in Pakistan, setting the study’s context. The second sub-study examines CSML as a tool to counter radical propaganda, identifying key skills for prevention. Both systematic reviews use the PRISMA framework with peer-reviewed articles from Web of Science and Scopus databases. Finally, the third sub-study undertakes a critical ethnographic analysis in relation to the Pakistani youth’s perceptions and lived experiences about online discourses of radicalism and extremism. In-depth interviews were conducted with university students from diverse regions in Pakistan. Together, these studies use a thematic analysis approach and offer a comprehensive look at social media’s role in radicalism and how CSML can help combat it.
The sub-study 1 reveals that scholarship on social media’s role in violent extremism in Pakistan generally focuses on individual factors behind violent extremism, overlooking situational or political influences. The sub-study 2 highlights two key insights: Limited scholarship connects CSML to radicalism or extremism, yet CSML emerges as a promising educational countermeasure to prevent toxic narratives on social media. Pedagogies and curriculums should encourage and promote critical thinking abilities, as youth equipped with CSML skills are better positioned to safely navigate social media amid political disinformation. Ethnographic findings from sub-study 3 identify political interest groups and the military elite as major players who propagate radical religiopolitical narratives on Facebook, manipulating young users’ perceptions, electioneering, and political power. Such misuse of Facebook has fueled political violence, sectarianism, blasphemy cases, and distrust in institutions. Sub-study 3 also contextualizes how CSML could be a counter to prevent violent radicalism among Pakistani youth and mitigate the misuse of platforms like Facebook.
Overall, this dissertation argues that the political use and misuse of social media, particularly the widely popular Facebook, have complicated the already complex dynamics of radicalism and violent extremism in Pakistan. While these issues predate social media (e.g., anti-India and pro-Afghan jihad narratives), platforms like Facebook have amplified their reach and impact. The study shows how young people, often unwillingly, engage with competing radical narratives online, shaping their political ideologies, and sometimes justifying political violence. To address this, the study advocates for equipping Pakistani youth with CSML skills, enabling them to navigate social media safely and critically amidst the surge of radical religiopolitical narratives competing for attention
Charles G. Finney and the Second Great Awakening
Get PDFThis thesis explores Charles G. Finney’s theological and philosophical perspectives and their impact on the Second Great Awakening. The work examines how Finney's ministry influenced this pivotal movement and established a lasting legacy on the religious landscape of the United States. His notable contributions include significant advancements in various social reforms, such as the abolitionist movement, the temperance movement, women’s rights, education, and the democratization of religion in the country. The research uses historical and theological sources will, drawing on a range of primary and secondary sources. Finney’s writings, including his autobiography and sermons, will be analyzed. Secondary sources will include scholarly studies of the Second Great Awakening, Finney’s life and work, and the period’s broader religious and cultural context
My Story of a Global Art Educator: Exploring Creative Encounters with Ukrainian Vernacular Art in Postmigrant Refugee Worldmaking
Get PDFThe destruction in my native Ukraine intensified my need to critically and meaningfully locate the sense of self in relation to the world and the groundbreaking events surrounding the outbreak of the Russia-Ukraine war in 2022, alongside my role as an art educator in fostering peaceful and inclusive educational environments. I turned to Global Citizenship theory and explored the globality of forced migration and the issue of war by considering the migrant identity of a global citizen from my local perspective as a Ukrainian immigrant, as well as that of Ukrainian refugees in Montreal, Canada. My main question was how selected Ukrainian refugees experience identity, belonging, and difference beyond the traditional binary and exclusionary perspectives on migration. To understand their experiences, I investigated the notion of worldmaking denizen proposed by the postmigrant analytical perspective while applying narrative inquiry methods coupled with the artistic lens of Ukrainian vernacular art, particularly Petrykivka painting. This approach facilitated a nuanced and meaningful understanding of the participants’ experiences as a transversal dialogue across differences and antagonisms. The understanding guided me in conceptualizing my globally oriented professional position through a hybrid confabulation of motif-motive—an inclusive and relational representational system that encompasses art, citizenship, and the significance of individual and community action in shaping the future