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Enhancing DeFi by Improving ERC-20 Token Security and Addressing Leveraged Token Shortcomings
Get PDFERC-20 tokens have become widely adopted as tools for representing real-world assets on the blockchain. They function as code, running through smart contracts. However, the development of smart contracts has proven to be error-prone, often leading to security vulnerabilities. This study addresses these issues by systematizing 82 known vulnerabilities and best practices. We then introduce a new ERC-20 implementation, TokenHook, which considers all these security aspects. This improved model outperforms widely used ERC-20 templates in terms of security and reliability.
As the blockchain ecosystem evolves, the rise of leveraged tokens (LVTs) presents additional challenges. They extend the features of ERC-20 by adding decentralized finance (DeFi) functionality. Users can buy and sell LVTs like cryptocurrencies but with amplified returns. However, an analysis of over 1,600 LVTs from 10 issuers reveals critical deficiencies due to the absence of a standardized framework, compromising their return on investment. To protect investors, we introduce LeverEdge, a fully decentralized model designed for deploying LVTs on the blockchain. Unlike existing implementations, LeverEdge operates entirely on-chain, overcoming limitations such as transparency, latency, and gas fees through a hybrid L1-L2 approach. With its security carefully tested, LeverEdge provides a potential reference framework for future decentralized LVT deployments.
This progression, from enhancing the security of ERC-20 with TokenHook to developing LeverEdge as a decentralized LVT, contributes to a more secure, transparent, and decentralized approach to DeFi ecosystem
Collective intelligence in the digital age: facilitating dialogic education on climate science with GPTs
Get PDFMy research aims to investigate to what extent Artificial Intelligence (AI), like Generative pre-trained transformers (GPTs) can be used to facilitate or enhance dialogic education (DE) on complex topics like climate science, particularly in university settings. Discussions involving climate science are often polarizing because they concern not only the sciences, but a multitude of interconnected socio-economic concerns. Consequently, the likelihood of reaching consensus or compromise is complicated by the diversity of perspectives involved. The ability to have constructive conversations on these topics is therefore essential to this process because environmental action and policy decisions are collaborative problem-solving endeavors among individuals from multiple backgrounds and perspectives. My research involved developing a custom GPT (Agora) based on dialogic principles and testing its performance while collecting data on user interactions. Agora was used as part of a focus group study that included 11 participants who engaged with Agora on topics related to climate science. The study also included discussion periods before and after the Agora interactions. Data from surveys, reflection exercises, and chat logs were analyzed to determine the extent to which Agora facilitated DE, and to better understand user experiences and preferences when conversing with Agora. The results of the study showed that Agora was able to facilitate DE by asking questions that encouraged participants to reflect on their beliefs and assumptions. Participants also expressed increased interest in using GPTs for personal applications in the future, despite their initial reservations about AI. The study also revealed that the quality of interactions with Agora depended largely on the participants' expectations, perceptions, motivations, and approaches. Additionally, reflection exercises indicated that many participants currently struggle with having conversations about climate change, including with those in their personal networks. Some of these struggles included barriers to perspective-taking and perspective-getting, emotional regulation, and conflict resolution. As such, participants expressed a strong interest in learning how to better communicate with others on climate topics, and in receiving personalized guidance on how to navigate interpersonal challenges without alienating others. These insights suggest that there is a need for skills development in climate communication, and that a DE approach in combination with AI could be a promising avenue for addressing this need
Enhancing Automated Testing With GUI Rendering Inference for Mobile Applications
Get PDFIncreasingly complex mobile applications require more accurate methods of automated GUI testing.
Traditional testing frameworks relying on fixed delays or pixel-based image comparison methods
have a lot of limitations. Most of the time, these methods misclassify GUI rendering states, which
leads to false positives. This thesis proposes a new approach to these issues by the inference of the
rendering state of GUIs. Drawing on large-scale pre-trained image classification models like Vision
Mamba, it enables the accurate classification between rendered GUIs. It does this through a fine-
tuning process of a large model. It also involves more sophisticated model-training techniques to
ensure that the best is obtained. Instead, this system architecture’s semantic examination of GUI
elements goes deep into more meaningful matches of context and visual information well beyond
anything at the level of pixels.
The efficiency and accuracy of GUI testing will increase significantly with the proposed approach.
That is different from fixed throttles that introduce unnecessary delays: it guarantees automated
tests execute on fully rendered GUIs, which, in turn, reduces false positives. With this enhancement,
development teams will save time and resources. Deep learning-based classification introduces a
dynamic system that changes according to various GUI rendering scenarios; therefore, it is also
more robust than what is already available.
The contributions of this thesis are three-fold: it proposes a new deep learning-based approach
for inferring GUI rendering states, develops a high-quality dataset to support fine-tuning models,
and performs an in-depth comparative analysis of large image classification models for GUI testing
Hub-line location problems with elastic demands and their application in the design of urban mobility hubs
Get PDFNowadays, urban transportation networks face challenges such as a rapid increase in urban population, city sprawl, and the use of private vehicles. To guarantee adequate mobility, it is crucial to develop efficient public transportation networks. In this thesis we focus our attention in the design of hub-line location problems (HLLP) to address the problems of designing efficient public transit networks.
First, we present an extension of the HLLP where we integrate gravity models to incorporate demand elasticity into an optimization model. This extension is denoted as the profit-oriented hub line location problem with elastic demand (ED-HLLP). We propose mixed-integer formulations, including a nonlinear mathematical model, and a path-based linear model. The linear formulations assign variables to each possible in the hub-line. A smart enumeration mechanism is provided to create all possible candidate paths. Finally, we also present a computational experience that evaluates the strengths and limits of these formulations.
Second, we introduce a column generation-based algorithm and a hybrid matheuristic that combines column generation with local search to address the ED-HLLP for large-sized problems, to better cope with the combinatorial nature of the number variable of the linear model. Computational experiments show that the proposed approaches are more robust and provide optimal and near-optimal solutions for all problems in our study when compared to the method introduced in ED-HLLP. Furthermore, we conduct a case study in the metropolitan area of Montreal to show the applicability and relevance of the proposed heuristic in a real-world context.
Third, we further extend the ED-HLLP to incorporate additional decisions on the services provided at the hub-nodes. We assume that the demand model is sensitive to both travel times, and quality of service, and our optimization model aims at maximizing the total profit derived from time savings while offering enhanced mobility services. A mixed-integer programming formulation is proposed, and a case study in Montreal is conducted to show the effectiveness of the proposed model
Impossible Nation de Ray Conlogue : anatomie d’une non-traduction
Get PDFLe présent mémoire de maîtrise se compose d’une traduction de passages choisis d’Impossible Nation : Longing for Homeland in Canada and Quebec de Ray Conlogue et d’une analyse critique approfondie. L’essai, qui explore la fracture culturelle et linguistique entre le Québec et le Canada anglais, présente les réflexions de l’auteur sur le nationalisme, l’identité culturelle et la reconnaissance mutuelle. L’analyse critique aborde la question de la non-traduction de cet essai en français, en sondant les résistances (idéologiques, économiques et esthétiques), le contexte des années 1990 et la réception tiède qui a été réservée à l’ouvrage au Canada anglais. On y postule que, malgré la pertinence manifeste de l’essai pour le lectorat québécois dans un contexte de réflexion sur l’avenir du biculturalisme, le triomphe du paradigme néolibéral et le désintérêt croissant pour la question nationale qui en découle auraient défavorisé sa traduction
Exploring the Dynamics of Occupants’ Thermal and Visual Perception, Physiological Responses, and Performance in Office Environments
Get PDFUnderstanding occupant comfort in indoor environments is critical for designing spaces that promote well-being and efficiency. However, traditional assumptions regarding occupants’ thermal and visual preferences often result in energy inefficiency or discomfort. This thesis examines different approaches for acquiring occupant information—ranging from subjective feedback and physiological measurements—to better understand comfort preferences in varying environments.
Additionally, this work addresses the gaps in comfort research, which is predominantly focused on the Global North, by conducting experimental studies in contrasting climatic regions (Montreal, Canada - ASHRAE Climate Zone 6, and Cairo, Egypt - ASHRAE Climate Zone 2B). These studies investigate the interplay between thermal and visual comfort domains under varied lighting and temperature conditions and their impact on physiological responses such as heart rate variability (HRV) and skin temperature (ST). Furthermore, thermal comfort analyses were conducted using wearable sensing technologies to monitor physiological signals, including electroencephalography (EEG), HRV, and ST. These analyses assess how thermal conditions influence comfort perceptions and task performance across different genders and locations, revealing significant variations in physiological responses to temperature and lighting conditions. The experiments were conducted in controlled office environments to simulate real-world conditions, and the data collected aimed to evaluate location-specific and gender-related differences in comfort and performance.
Comparative analyses from experimental trials in Montreal and Cairo show notable differences in thermal comfort perception and task performance, with males being more sensitive to thermal conditions and location-specific variations affecting heart rate variability and skin temperature. These findings provide a foundation for developing adaptive building environments that can dynamically adjust indoor conditions to improve occupant well-being and energy efficiency.
These findings offer valuable insights into the relationship between physiological responses, thermal comfort perceptions, and occupant performance in office environments, offering a pathway toward the integration of Occupant-Centric Control (OCC) strategies in future smart building environment
Designing Molybdenum Disulfide Nanocomposite Electrodes for Post-Lithium-Ion Batteries
Get PDFExpanding electrical energy storage beyond lithium-ion batteries (LIBs) is crucial due to their limitations in material availability, cost, and performance. LIBs rely on lithium, which has a limited supply, uneven global distribution, and increasing demand, leading to concerns about long-term sustainability and price volatility. Additionally, LIBs face intrinsic challenges such as safety risks from thermal runaway, capacity fading over extended cycles, and performance limitations at extreme temperatures. These constraints drive the search for alternative battery chemistries, such as sodium ion, potassium-ion, magnesium-ion, zincion, and aluminum-ion batteries. However, these alternatives introduce new challenges due to differences in ionic size, charge density, and electrochemical behavior, necessitating the development of advanced electrode materials. Molybdenum disulfide (MoS2), a 2D nanomaterial with larger interlayer spacing than graphene, shows promise for storing such ions but faces challenges from its semiconducting nature and side reactions.
This thesis begins with a comprehensive review of recent literature on strategies for modifying the structure of MoS2 and its nanocomposites to enhance ion storage capabilities for Na+, K+, Mg2+, Zn2+, and Al3+. Next, four novel composites were designed and synthesized, with their potential as active materials for post LIBs comprehensively investigated. A range of material characterization techniques, including Brunauer-Emmett-Teller (BET) surface area analysis, thermogravimetric analysis (TGA), X ray diffraction (XRD), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS), were employed to study their structural and chemical properties. To evaluate their electrochemical performance as active battery materials, various methods such as cyclic voltammetry (CV), galvanostatic charge discharge (GCD), and electrochemical impedance spectroscopy (EIS) were utilized.
Two anode composites for sodium-ion batteries (SIBs), MoS2@HPC (crystalline MoS2 in hierarchically porous carbon) and a-MoSx@HPC (amorphous MoSx in HPC), were designed and evaluated. The hybridization with HPC was found to enhance Na+ storage by improving capacity and cycling stability. At 0.5 A g−1, capacities of 550 mAh g−1 and 301 mAh g−1 were achieved by a-MoSx@HPC and MoS2@HPC, respectively, both outperforming pure MoS2, which delivered 253 mAh g−1. After 100 cycles, capacity retentions of 75% and 89% were maintained by a-MoSx@HPC and MoS2@HPC, respectively, in contrast to the 53% retention observed for pure MoS2.
Following this, a 1T/2H mixed-phase MoS2 (MP-MoS2) modified with a polyethylene ionomer (I@MP-MoS2) was investigated for Mg2+ storage in magnesium-ion batteries (MIBs) and Mg2+/Li+ storage in dual-salt magnesium-lithium-ion batteries (MLIBs). With 53% of metallic 1T phase, increased interlayer spacing (1.11 nm vs. 0.62 nm in MoS2), and enhanced electrolyte interaction, I@MP-MoS2 achieved 144 mAh g−1 at 20 mA g−1 in MIBs and 270 mAh g−1 in MLIBs, with 87% of capacity retention after 200 cycles.
Finally, a novel cathode design was investigated for MLIBs using a 2D/2D nanocomposite of 1T/2H-MoS2 and delaminated Ti3C2Tx MXene (1T/2H-MoS2@MXene). This structure improves Mg2+ kinetics, structural integrity, and reversible Mg2+/Li+ co-intercalation, achieving 253 mAh g−1 at 50 mA g−1 and retaining 36% of capacity at 1,000 mA g−1.
Overall, this thesis presents innovative MoS2-based materials for post-LIBs, addressing ion storage, conductivity, and stability challenges, contributing to next-generation energy storage solutions
Power-hardware-in-the-loop (PHIL) based Emulation of Faults in Induction Machines
No full textInduction motors (IMs) are commonly used motors because they are very rugged, require low maintenance, and are mechanically simple. The IM is the workhorse as a traction motor in rolling stock applications due to the inherent advantages in terms of cost, controllability, reliability, and maximum speed. An induction machine has three common faults: stator inter-turn faults, rotor broken bars, and bearing faults. These faults can affect the industry's productivity and cause outages in the transportation industry. For example, an induction motor failure in a freight locomotive can cause stalling and subsequent line congestion. Similarly, a wound rotor induction machine (WRIM) is used as a doubly fed induction generator (DFIG), the most commonly used topology for wind power applications. The most common electrical faults in DFIG are bearing faults and inter-turn winding faults in the stator and rotor. If these faults are not detected and rectified in time, they can proliferate quickly and become catastrophic, leading to unexpected outages and contributing to high operating and maintenance costs. Hence, it is essential to study the behavior of WRIM’s in the event of these faults and what corrective action can be taken in the event of a fault. Also, it is necessary to investigate the faulted IM’s behavior with accurate models and replace expensive test benches and equipment with safer and economical test procedures.
There are a lot of practical difficulties in creating the fault with machines. Even if the fault is implemented, full voltage cannot always be applied, and motors with higher loads cannot be tested since it may damage the machine faster. However, a virtual machine with power electronic converters can be tested for different fault conditions using emulation. Power-hardware-in-loop (PHIL) based emulation is increasingly used as an economical test bench for testing the drive system. With PHIL-based emulation, the electrical machine is emulated in a laboratory environment with real-time power flow using power electronics converters. With PHIL-based emulation, the faulted machine behavior can be studied with direct online start or machines fed from variable frequency drives (VFD) without damaging the motor or VFD. With emulation, the risk, time, and cost associated with a physical machine subjected to faults can be reduced. Also, the emulation test setup is designed to return the power flow from the inverter to the mains supply. This requires an active front-end converter connected to the DC link of the emulator converter. Hence, only power losses are drawn from the power supply. Thus, the laboratory power supply requirements are reduced.
The machine's mathematical model must be very accurate for better emulation accuracy. So, there is a need to develop detailed mathematical models which can account for the nonlinearities in the machine. The machine parameters vary when temperature changes. For an accurate emulation, it is necessary to consider the effect of temperature on the machine's behavior. For this, the thermal behavior of the machine is crucial. Lumped parameter thermal network (LPTN) models can be used for emulation purposes since finite element models (FEM) require long execution times which are not suitable for real-time emulation though it has higher accuracy.
This thesis investigates PHIL emulation of stator inter-turn faults and rotor inter-turn faults. Detailed and improved mathematical models for these faults are developed. An alternative mathematical model has been proposed for stator inter-turn faults, i.e., the Voltage-behind-reactance (VBR) model, which has some advantages compared to the conventional modeling approach of the voltage-in & current-out (VICO) model. Fault conditions are emulated with experimental hardware in real time. Emulation results are validated with a practical machine subjected to the faults. Thermal analysis of an induction machine with stator inter-turn fault is carried out. An analytical model is developed to estimate the temperature rise caused by the inter-turn fault, and the model is validated with experimental results
Navigating Collaboration Between MedTech Startups and Incubators: Enhancing MedTech Entrepreneurship Ecosystem
Get PDFThis thesis investigates the challenges and opportunities within the MedTech entrepreneurship ecosystem, focusing on the collaborative dynamics between startups and incubators. The research identifies the specific needs of MedTech startups, including regulatory guidance, resource access, and specialized mentorship, which are often unmet in general incubation settings. Through a comprehensive literature review, thematic analysis of expert interviews, and validation via survey data, this study uncovers prevalent ecosystem challenges, such as resource fragmentation and regulatory complexity. A model is proposed to enhance collaboration and facilitate efficient resource-sharing across ecosystem actors. This approach provides a structured framework for incubators and startups to address funding, mentorship, and regulatory compliance gaps. Future research should explore real-world applications of this model to validate its effectiveness in diverse settings, ultimately advancing the MedTech ecosystem and improving support structures for startups
Producing Petroculture: Ads, Automobility, and the “American Way of Life”, 1929-1939
Get PDFThis study focuses on the cultural construction of oil in the United States that occurred during the Great Depression to reexamine accepted historical narratives on race, class, and gender. Though it is present in thousands of products and represented in countless visual forms that we buy and see today, oil has paradoxically become a difficult thing to grasp and concept to represent historically; oil is both physical substance and social material that has defied adequate materialization in scholarship. Of particular interest to this study are the intersections between the symbolic economy surrounding oil use and automobile culture, and how both reflected a particular discourse that defined “American ways of life” and who was deserving of it. Critically, this study examines how automobile ownership and automobility reconfigured particular middle-class imaginaries during the 1930s. A discursive analysis of print advertisements from this time, therefore, provides unique historical insight into the peculiarities of middle-class American lifestyles developed by and premised on the combustion of oil (petroculture), and why today many Americans are loathe to disentangle themselves and their definitions of life from it. Chapter one explores the development of American consumer culture during the early twentieth century that normalized the centrality of oil in human life. Chapter two discusses how and why New Deal policies created societal institutions to provide Americans with “modern” standards of living premised on the mass consumption of oil. Chapter three analyzes how automobile advertisements became the single largest factor promoting petro-capital life in which social, gendered, and racial division were (re)produced and legitimated via acts of oil consumption and the exercise of class power. In so doing, automobile advertisements re-envisioned the aesthetics of life through representations of mobility, freedom, distinction, and modernity; the most prominent fantasies within white, middle-class consumer society mediated and enabled by oil energy