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Artificial intelligence affordances in self-directed learning: a case of ChatGPT
No full textThis study examines ChatGPT's role in supporting self-directed learning (SDL) among graduate students. SDL emphasizes key learner-driven attributes, including autonomy, self-management, self-monitoring, and motivation. This research gathered data through focus group discussions to explore participants' experiences with ChatGPT as a learning tool, using a qualitative method to ensure all aspects of participants' experiences. The findings indicate that ChatGPT significantly enhances self-management by allowing learners to customize their educational experiences. Participants reported improvements in planning, task prioritization, and organization, as they could tailor content and goals to their unique needs. Regarding self-monitoring, ChatGPT provided instant and adaptive feedback, helping learners continuously evaluate and refine their learning strategies. This process reinforced participants' sense of control and engagement by enabling them to make real-time adjustments to their study approaches. ChatGPT also promotes intrinsic motivation by fostering greater autonomy. Participants highlighted that the tool empowered them to make independent decisions regarding their learning paths, encouraging exploration and persistence in challenging areas. This sense of ownership over the learning process enhanced their motivation and productivity. However, participants raised concerns about over-reliance and the potential impact on critical thinking and creativity. The study emphasizes the need for balanced integration of AI tools in education to optimize SDL outcomes
Evolutionary equations concerning fractional differential operators and applications in signal processing
No full textIn this thesis, we investigate a class of partial differential equations known as evolutionary
equations, incorporating fractional-order derivatives, the fractional Laplacian,
and the fractional p-Laplacian. We establish the existence and uniqueness of weak
solutions to these equations and analyze their asymptotic behavior over time. Solutions
in a time-separated form are shown to be closely related to the eigenfunctions
of gradient operators. Leveraging Caffarelli-Silvestre's and del Teso-Gómez-Castro-
Vázquez's extension results for the fractional Laplacian and fractional p-Laplacian respectively,
we introduce weighted Laplace and weighted p-Laplace operators as their
counterparts to represent fractional gradients, serving as gradient operators in the
evolutionary equations under consideration. As these operators are local, numerical
solutions to the studied partial differential equations can be efficiently approximated
using forward difference schemes. Our research not only involves parabolic and wave
equations but also extends to equations with first- and second-order time derivatives in
a curved setting. Furthermore, our research is employed in the real world. By treating
an original signal as initial data, we accomplish its decomposition based on different
smoothness degrees through the solutions of those partial differential equations due
to the fractional fundamental theorem of Calculus
Economy of cod: trade, connection, and cultural resilience in the French Atlantic
No full textThe fall of Nouvelle-France (1763) is intrinsically linked with the reorganisation of European powers in the Atlantic World. It ushered in an era of political instability as nations fought over the rights to exploit economic drivers such as sugar and cod. France was not immune to the power struggle as they fought to continue participating in the Atlantic economy and sought to establish or maintain overseas territories. However, the efforts to maintain Caribbean colonies eclipsed the crucial role of cod fishing in the Northwestern Atlantic in the shaping and maintaining of the French Atlantic World. In the period following the collapse of Nouvelle-France, the reorganization of the French Atlantic created a mobile constellational network that distributed cod to support the sugar trade and connected colonies across the Atlantic. This generated an interdependency that, when studied from an agency and actor-network perspective, was key to the continuation of the French commercial network despite war, violence, and political uncertainty. Further, this network created lasting cultural exchange between colonies that continues to today. By framing the French Atlantic as an intercolonial constellational network, the French reorganization and the distribution of goods creating an interdependency between colonies and people was at the core of the French success.Includes bibliographical references (pages 150-159
Lagrangian back-trajectory dispersion and mass balance models for methane emission localization and quantification
No full textMethane (CH₄), a potent greenhouse gas with 86 times the global warming potential
of carbon dioxide over 20 years, contributes significantly to global temperature rise. As part
of the Global Methane Pledge (GMP), Canada aims to reduce CH₄ emissions from oil and gas
production by 75% and from the waste sector by 50% by 2030. This research develops and
applies advanced CH₄ quantification and localization methods, addressing critical gaps across
diverse spatial scales and emission source types.
Chapter 1 provides the basic explanation about Lagrangian back-trajectory model
(TERRAFEX) that was used in this study. In this chapter the concept of shape function and
footprint calculation based on the pre-calculated footprint tables is described in brief.
Chapter 2 focuses on localizing emissions within oil and gas facilities using a TERRAFEX and a
Gradient Indicator (GI) tool. Results indicate a 90% probability of detection within 25–75
meters of sources under favorable atmospheric conditions, providing valuable insights for
optimizing Continuous Emissions Monitoring (CEM) systems. Chapter 3 applies TERRAFEX to
mobile surveys at landfills, achieving R² values of 0.77–0.86 between measured and modeled
rates, with hotspots identified within ~50 meters of aerial detections. Chapter 4 scales
TERRAFEX to regional assessments, finding underestimations in oil and gas inventory values,
where TERRAFEX-derived inventories align more closely with field-measured values.
Wetlands were underestimated by a factor of 1.43, while emissions from agriculture and
waste were also significantly underestimated, emphasizing the need for improved spatial
datasets. Chapter 5 uses mass balance and Gaussian dispersion methods to quantify CH₄
emissions from offshore oil platforms and calculate production-weighted emission intensities. Measured emissions ranged between 860 and 8,500 m³ CH₄ day⁻¹, with key
contributors identified as venting, flaring, and fugitive emissions. This work bridges
methodological gaps in CH₄ quantification and localization, providing insights for
policymakers and helps to advance mitigation strategies across oil and gas, waste, and
offshore sectors
Lower limb amputation rates in adults living with diabetes: identifying COVID-19 pandemic trends and addressing methodological challenges
No full textThis thesis evaluates the impact of the COVID-19 pandemic on lower limb amputation (LLA) rates in adults with diabetes using a systematic review and meta-analysis. A search of PubMed/MEDLINE, Cochrane, Embase, and ProQuest identified ten retrospective cohort studies (n = 2,940,935). The meta-analysis found no significant change in total amputation, major amputation, or 30-day mortality rates. Although minor amputations initially appeared to decrease during the pandemic, sensitivity analysis revealed that this finding was not robust. High heterogeneity among studies highlights variability in methodologies, makes it difficult to draw conclusions emphasizing the need for more consistent approaches.
The review identified several methodological challenges in estimating LLA incidence in diabetes. Through analyzing current literature on LLAs, this thesis proposes ways to address these limitations and advance research in this field. LLA incidence studies face limitations due to inconsistent case definitions. Variations in counting methods (person, case, and procedure level) affect reported rates and hinder comparability across studies. The adoption of standardized case definitions, particularly through admission-based methods, can improve consistency in data reporting and support more accurate estimations of LLA burden in people living with diabetes. This thesis highlights the value of establishing clear and consistent criteria to enhance the quality of clinical epidemiological studies and better inform healthcare strategies aimed at reducing diabetes-related amputation rates
Employee well-being among remote workers
No full textUsing Conservation of Resources (COR) and Job Demands–Resources (JD-R) theories, this study explores how perceived organizational support relates to work engagement in a sample of remote workers and investigates three potential moderators of this relationship. The study explored whether techno-overload, work-family conflict, and psychological distress moderated the perceived organizational support–work engagement relationship. Data were collected from 242 full-time remote employees in Canada, the UK, and the USA using a cross-sectional survey design. Findings showed a significant positive link between perceived organizational support and work engagement, highlighting support as a crucial organizational resource. Techno-overload and work-family conflict showed no significant moderating effects. Psychological distress, however, changed the relationship between perceived organizational support and work engagement such that it reduced the positive effects of perceived organizational support when employees reported high distress. These findings highlight psychological distress as a boundary condition for resource efficacy in remote settings. This also provides practical insights for supporting employee well-being in remote work environments
Improved power quality in grid-connected inverters using robust sliding mode control techniques
No full textGrid-connected inverters (GCI) play a crucial role in injecting DC power from renewable resources into the utility grid; moreover, the quality of the power transferred to the grid largely depends on the effectiveness of the adopted control strategy. This research proposed a novel super-twisting double integral sliding mode control (SMC) algorithm for a three-phase grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. In this study, the control algorithm is first derived based on the system’s dynamic mathematical model. Then, extensive simulation studies are carried out in MATLAB and Simulink software to validate its performance. Furthermore, the controller effectiveness is rigorously assessed under challenging conditions, including a 400% grid impedance variation, a 66% system parametric variation, grid frequency variation, and higher-order grid harmonic components. In the worst-case scenario, the total harmonic distortion (THD) of the grid current stays below 2.6%, demonstrating the controller's effectiveness in enhancing the power quality supplied to the grid.
In the second control technique a novel integral terminal sliding mode control (IT-SMC) for a grid-connected three-phase inverter is presented to overcome the adverse effects of external disturbances on the power being injected into the grid. The integral terminal SMC guarantees finite-time convergence, minimal overshoot, and chattering-free operation. In the mathematical design of the controller, a derivative term is introduced in the capacitor voltages to achieve a better damping effect and prevent overshoot. Additionally, the integral of the grid and inverter current error is incorporated into the sliding surface to achieve precise tracking of the current reference. A 3-kW system is designed using the MATLAB/Simulink tool to analyze the controller’s performance. The grid-current quality and the stability of the controller are analyzed under
conditions of grid voltage distortions, impedance variations, and filter resonance frequency. Experimental results confirm that the designed controller ensures nearly zero steady-state error, low grid current harmonics, rapid dynamic response, and system stability under weak grid conditions, thereby significantly improving the power quality supplied to the grid.
The third control method is constructed while integrating the sliding mode control (SMC) algorithm with proportional resonant harmonic compensators (PR-HCs) for a three-phase GCI with an LCL filter, offering a promising solution for injecting power generated from DC sources into the utility grid. The existing controllers exhibit a notable tendency toward GCI system instability when facing system with simultaneous occurrence of parameter mismatches, distorted grid voltage, and resonance frequency events. The proposed SMC+PR-HC method inserts a loop of PR-HCs in parallel with the SMC control loop, significantly suppressing unwanted grid current harmonic components. Furthermore, a combined SMC and PR-HC control law is appropriately derived to analyze system stability using linear control theory approach. The performance of the proposed control method is validated using MATLAB/Simulink platform while considering the grid impedance variation, grid voltage fluctuation, filter parametric change, and the resonance frequency of ƒₛ ̸ 6.Finally, experimental results demonstrate that the proposed controller ensures minimal steady-state error, low grid current harmonics, rapid dynamic response, and system stability, even under ultra-weak grid conditions, leading to significant power quality improvement.Includes bibliographical reference
Not every story has two sides: the effect of false balance on perceived scientific consensus
No full textFalse balance arises when opposing viewpoints about a scientific issue are portrayed as more evenly matched than what the empirical evidence demonstrates, creating the impression of a fake debate. Across three experiments, using samples from the general population, the effect of false balance on perceived expert consensus was examined when weight-of-evidence (WOE) data were provided; focusing on the topics of interrogation tactics and non-verbal lie detection. Partition dependence was explored as a potential mechanism of the false balance effect, and forewarning was investigated as a possible intervention. In Experiment 1, participants (N = 259) read a statement about minimization tactics and WOE data showing either a high or low level of expert consensus about the interrogation tactic. The data was presented either without or with balanced expert comments about the factual nature of the tactic. In Experiment 2, participants (N = 371) read a statement about false evidence ploys and WOE data showing a high level of expert consensus about the interrogation tactic, accompanied by either no comments or balanced expert comments with varying levels of balance (i.e., 3:3, 5:1, 1:5). In Experiment 3, participants (N = 307) read one of five versions of a media report on non-verbal lie detection that included WOE data showing high level of expert consensus, along with either no comments, evenly balanced comments (3:3), evidentiary balanced comments (5:1), evenly balanced comments with forewarning, or evidentiary balanced comments with forewarning. Results indicated that the presence of balanced messages decreased perceived expert consensus, even when WOE data showed the actual level of consensus was high. Such distortion varied with the level of balance among the messages delivered by multiple sources—despite the general underestimation of expert consensus. Exposure to evidentiary balanced comments (5:1) led to more accurate perceptions than evenly balanced comments (3:3), followed by two-sided messages with more contrarian comments (1:5). Forewarning people about a “fake debate” was found to have minimal impact on reducing the effect of balanced comments. These findings replicated the false balance effect in the field of forensic psychology, suggesting that false balance may undermine the strength of evidence and produce a binary partition of expert opinions, shifting judgments toward a midpoint and overshadowing statistical data. Moreover, forewarning might help individuals when reading evidentiary balanced comments (5:1) with WOE data and might enhance support for science-based policies around using non-verbal cues to lie detection when presented alongside evenly balanced comments (3:3)
Inference on autoregressive moving average models for count data
No full textIn the analysis of count time series at equally spaced intervals with covariate information,
Poisson Autoregressive (AR) or Integer-Valued Autoregressive (INAR) models
have been widely discussed in the literature, with their fundamental properties and
estimation methods thoroughly explored. However, when time series data exhibits
both long-term dependencies (autocorrelation) and moving average effects, capturing
both of these elements is essential for more effective modeling and forecasting. To address
this, we introduce autoregressive moving average (ARMA) models of order (1,1)
for count time series. We first consider the case where the offspring random variable
follows a Bernoulli distribution, meaning that each individual in the population at
time t - 1 can produce only one or zero offspring at time t. Additionally, we extend
this model to incorporate the possibility of any individual producing multiple offspring
at a given time point, resulting in a binomial offspring random variable. We derive
the key properties of these models, present methods for parameter estimation and
forecasting function. The performance of the proposed methods are assessed through
simulation studies.Includes bibliographical references (pages 37-39
The feasibility of functional near-infrared spectroscopy (fNIRS) to measure rehabilitation-induced changes in upper limb movement related to motor learning interventions in chronic stroke
No full textBackground: Functional near-infrared spectroscopy (fNIRS) offers a non-invasive approach to monitoring rehabilitation-induced brain activity changes following motor learning interventions in stroke patients. This study aimed to explore the extent of brain activity and motor performance changes resulting from such interventions.
Methods: Seven participants with chronic stroke (63.6 ± 7.5 years old; six males, one female) underwent a ten-day intervention consisting of aerobic exercise priming combined with task-specific motor practice using the Kinesiological Instrument for Normal and Altered Reaching Movements (KINARM) End-Point robotic system. Motor performance was evaluated using the Wolf Motor Function Test (WMFT), and brain activity was measured with fNIRS, focusing on key Regions of Interest (ROIs) such as the motor cortex, somatosensory cortex, and prefrontal cortex.
Results: Clinical tests such as the WMFT showed moderate improvements in upper limb recovery, but these changes were not statistically significant (p < 0.05). Similarly, overall fNIRS analysis revealed that changes in brain activity before and after the intervention were not statistically significant (p < 0.05). However, significant changes were observed in certain ROIs regarding Oxyhemoglobin (HbO) concentrations and time-to-peak values in specific cases.
Conclusion: Although motor performance improvements were modest and not statistically significant, fNIRS detected significant changes in brain activity in certain brain areas before and after the intervention. These findings highlight the potential of fNIRS as a biomarker for rehabilitation-induced neuroplasticity and offer insights for enhancing stroke recovery interventions.Includes bibliographical references (pages 127-145