identifier: Sharifi Rad, Seyedeh Maryam  (2020) PrFA: a framework for image-based counterfeit coin detection using Pruned Fuzzy Associative Classifier.  PhD thesis, Concordia University.

abstract:
The numismatic industry has been dealing with the problem of counterfeit coins for centuries. With the growth of technology in counterfeiting, untrained users cannot distinguish fake from genuine coins, especially for rare and precious coins. Using coin experts is also very expensive, and even with them, in some cases, detecting counterfeit coin is not guaranteed. Consequently, the demand for a computer-aided system that can detect counterfeit coins and be robust and reliable has increased. This thesis proposes a new fake coin detection method that focuses on image mining approaches instead of extracting only statistical features from the coin image, as suggested by other researchers.
In this research, a novel framework called PrFA is proposed for counterfeit coin detection that shows the effectiveness of image mining techniques. We develop a new image mining system on top of the fuzzy concept that helps us to discover the implicit information from the images in the way closer to the human’s viewpoint. The advantage of the fuzzy set concept is that it can deal with uncertain objects, and we take this advantage for the decision-making problem by implementing an associative classifier model.
Our proposed framework is developed in two modules, and the principle of least privilege of it is a compressed system that can be considered as a knowledge attainment tool. In the first module, a method to detect the region of interests (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining.
Image data are generally high dimensional due to a wide range of resolution levels. According to state of the art, the rule-based association methods demonstrated their efficiency by generating defensible solutions at an acceptable level of accuracy when dealing with small and medium-size samples. Regrettably, to cope with a large amount of data such as the image database, these methods were not robust enough. To tackle the above
iv
challenge, we propose a new algorithm for feature selection to reduce the dimensions of features via analyzing the relationships among different features.
Image classification is imperative to search for more available and appropriate information. In recent years, various methods based on image mining approaches for classification tasks have been explored. Apart from their usefulness, the available classifiers are often vulnerable to low accuracy. Accordingly, we present a pruned based fuzzy associative classifier algorithm to create a robust counterfeit coin detector system. This classifier is a mixture of the association rules method and the fuzzy set concept.
In this research, we preserve the full power of fuzzy association rule mining to reduce the amount of redundant and insignificant rules by focusing on pruning methods. By comparing the achieved results with some other methods obtained from the same dataset, we demonstrate that our framework surpasses in terms of lower feature dimensions, and smoother boundaries while maintaining satisfactory accuracy. Besides that, we show that our proposed classifier is more accurate compared to other associative classifiers.
In this research, the problem with a general form will be described to provide a common framework for issues appearing in other domains.


identifier: Huang, Linxiang  (2021) Quantum Transport in Strained Single-wall Carbon Nanotube Transistors.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract: Can we control quantum interferences and many-body interactions mechanically, i.e. by pulling on a nano-system? While many idealized theoretical proposals address this question, very few have been realized experimentally. To bridge this gap with single-wall carbon nanotubes (SWCNTs), we are developing simultaneously an experimental platform and an applied theoretical model. I have nanofabricated several high quality strain-tunable suspended SWCNT transistors. I first located and characterized SWCNTs (diameters ≤ 2 nm) grown by our former group member Andrew McRae, using scanning electron microscopy (SEM) and atomic force microscopy (AFM). I patterned nanoscale bowtie-shaped gold break junctions (≈ 300 nm wide) on top of SWCNTs, using electron beam lithography (EBL). Finally, I suspended these break junctions by removing the supporting SiO2 beneath them, using a buffered oxide etch (BOE). After opening nanogaps in gold break junctions via electromigration, it will allow straining of ultra-short SWCNT channels (≈ 20 nm) with our custom-built quantum transport strain engineering (QTSE) platform. Besides the fabrication, I have also extended and modified the previous applied theory from describing strain transport behaviors in graphene to those in SWCNTs. This theoretical model considers dominant uniaxial strain effects on the band structure and all relevant experimental parameters. In quasi-metallic SWCNTs, I predicted that the uniaxial strain can widely tune conductance, leading to outstanding quantum transistors. In metallic ones, I observed a valley filter behaviour where electrons are only allowed to flow through certain valleys of the band structure. In semiconducting ones, I predicted the strong tunability of electron-hole asymmetry via uniaxial strain, which would permit us to engineer two vastly different transport behaviors into a single device.


identifier: Wei, Guoqing  (2021) Quantum Transport Strain Engineering in Graphene.  Masters thesis, Concordia University.

abstract: We measure ballistic charge conductivity in strained suspended graphene and observe theoretically predicted strain-induced scalar and vector potentials. To do so, we built an experimental platform for quantum transport strain engineering in 2D materials. This instrumentation permits low temperature (0.3 K- 70 K) transport and a tunable uniaxial strain (up to 3%) which is independent from the gate-tunable charge density. We show slippage-free clamping of high aspect-ratio graphene crystals where atomically ordered edges are unnecessary for quantitative straintronics. We perform in-depth study of transport in a graphene channel with length 90 nm and width 600 nm. We observe ballistic transport in both the naked graphene channel and metal-film-coated graphene contacts. Through the strain-induced scalar potential, we can down shift the low energy band structure of the graphene channel by 20 meV. We also clearly observe the effect of a gauge vector potential, which reversibly suppresses the conductance by up to 13.6%.


identifier: Mohammadi Khomjani, Majid  (2020) Effect of Digital Storytelling on Learning Motivation and Reducing Anxiety of Learners to Talk to Others in a Foreign Language.  Masters thesis, Concordia University.

abstract:
The world of technology has had a significant impact on the development of education. One of these powerful technologies used in education is digital storytelling which integrates computer technologies and telling stories together. By combining texts, images, and audios, DST becomes a powerful multimedia tool in the domain of language teaching and learning where teachers can enhance their teaching creativity and students are encouraged to actively involve in their learning process to improve their speaking skills and tell stories in their own words and voice. While much research has emphasized the effectiveness of digital storytelling in motivation and listening anxiety, the purpose of this study was to investigate the effect of digital storytelling on learning motivation and reducing anxiety of learners to talk to others in a foreign language. Our intervention group participants were taught online by the researcher and they met regularly for two sessions of one hour and a half per week. The online course lasted 10 sessions including the final test session. The course took about a month with 15 hours of teaching in total. At the beginning, students had to take pre-test to identify their speaking motivation and anxiety before digital storytelling (DST) intervention. Afterwards, the post-test was used to compare for finding out how different students’ anxiety and motivation were before and after digital storytelling (DST) intervention.
The findings of the present study based on pre-tests and post-tests in the form of questionnaires support that digital storytelling (DST) reduces anxiety for English foreign language speakers to talk to others, but DST intervention does not affect the motivation of English foreign language speakers to talk to others.
Keywords: digital storytelling, speaking anxiety, speaking motivation, teaching speaking, computer technologies


identifier: Melhoff, Craig  (2020) Space, Form, and Cognitive Mapping in the Literature of the London Underground.  PhD thesis, Concordia University.

abstract: This dissertation considers literary representations of the London Underground. It synthesizes theories of literary space and recent research in the cognitive sciences on spatial reasoning. My central claim is that by means of structural relationships of mapping between spaces and their representations—the encoding and decoding of diegetic spaces in writing, the individual’s cognitive mapping of the environment, and the spatial forms of literary texts more generally—the coordinates of spatial structures such as the London Underground serve as formal organizing principles for literary expression. In making this case, the dissertation aims to articulate what we mean by the term organizing principle in the context of literary formalism in general and spatial forms in particular. If a spatial pattern “organizes” a literary text, how does such organization work, and what textual elements are so organized? The dissertation addresses this question in connection with both fiction and poetry. Chapter 1 focuses on the diegetic spaces of Underground-related narratives, and demonstrates in the examples of three novels—Geoffrey Household’s Rogue Male, Julian Barnes’s Metroland and Rose Macaulay’s Told by an Idiot—how narrative sensemaking involves mapping spaces onto Underground structures with which they correspond schematically. Chapter 2 turns to the narrative “total pattern” to read the overall spatial structure of Iris Murdoch’s A Word Child, Zadie Smith’s NW, and Virginia Woolf’s The Waves. Chapter 3 focuses on Tube poetry, and on the body as a means of mapping the Underground environment under the blinding and disorienting subterranean conditions that I describe as “space-confusion”; in readings of several poems, I argue that the sensations and movements of the body, organized according to the coordinates of what cognitive scientists call the spatial body framework, provide a means of cognitively mapping the environment that compensates for visual deprivations and distortions belowground. In Chapter 4, I examine the role of Harry Beck’s famous schematic Tube diagram in the overall conceptualization of the Underground environment, and demonstrate how poets have resisted the visual dominance of this iconic image and instead mapped the Tube environment and experience with reference to the body of the passenger in transit.


identifier: Desjardins, Gabriel A.  (2020) Crossing the Presuppositional Divide: A Problematization and Comparative Analysis of the Inerrancy Debate in Evangelicalism.  Masters thesis, Concordia University.

abstract: The inerrancy debate in evangelicalism is highly contentious, and the growing political power of evangelicals makes understanding their hermeneutics a crucial issue. Inerrantists approach the Bible deductively, firmly upholding Scripture’s veracity. By contrast, their critics approach the Bible inductively, arguing that hermeneutics should adapt based on modern findings in history, archaeology, textual studies, and science. The operative lenses on both sides—deductivism and inductivism—create an imposing presuppositional divide between inerrantists and their critics. Both sides fail to represent their opponents’ views, constructing strawmen of their positions. In many cases, critics present solid arguments against inerrancy. But their arguments are only solid from an inductivist perspective, making it difficult for inerrantists to hear critiques. Moreover, there are many forms of inerrancy, some of which are easier to dialogue with than others. In my thesis, I present three case studies of the inerrancy debate, involving critics from philosophy, sociology, and biblical studies. I demonstrate the presuppositional divide in the inerrancy debate through these cases. Critics should argue from within deductivist presuppositional frameworks by being both non-confrontational and familiar with primary sources.


identifier: Dermardiros, Vasken  (2020) Data-Driven Optimized Operation of Buildings with Intermittent Renewables and Application to a Net-Zero Energy Library.  PhD thesis, Concordia University.

abstract:
We are at the intersection of three major trends in the built environment where: (i) occupants' comfort, health and safety requirements are needed to support a productive workplace while maintaining a low operating cost, (ii) economic and environmental advantages are favouring an increased use of renewable energy generation and to reduce our reliance on fossil fuels, and (iii) major utilities will require regulation and are gradually shifting towards a more dynamic energy market. This thesis contributes a modelling and control framework that unifies and addresses these three points together.

This thesis contributes a methodology for the development of a bootstrapped ensemble-based low-order data-driven grey-box thermal models for supervisory-level optimal controls. The model is integral to a robust sampling-based predictive control (MPC) framework. This approach is directly applicable to most commercial buildings operating on a schedule and can be extended to consider occupant-driven spaces.

The methodology is applied to the Varennes Net-Zero Energy Library: Canada's first institutional net-zero energy building. Exogenous inputs are modelled to consider likely probabilistic outcomes for ambient temperature, cloudiness and interior plug loads. Bounding cases are simulated to contrast the proposed approach against conventional methods. MPC is applied to minimize various cost functions and emphasis is placed on a flexible profile-tracking cost function. The profile to track can be an open-market electrical price or a demand response signal thus improving the grid's flexibility while satisfying the building constraints and better utilizing its systems and storage. In a morning peak demand reduction case, given at least a 4-hour notice, our method is able to pre-heat the building, use minimal energy on-peak and yield the full benefits. Considering a profile tracking case to reduce grid interaction, a 10-12% total energy reduction was achieved for winter where the space was gradually heated in the morning and evening while maximizing HVAC utilization during periods of large photovoltaic generation promoting self-consumption. A similar strategy would be near-impossible to handcraft without optimization-based approaches.

This proposed methodology can guide later implementations in the development of the next generation of low-cost cloud-connected controllers that are easy to deploy and can be adapted dynamically.


identifier: mousa, afaf  (2020) Trust Management for Context-Aware Composite Services.  PhD thesis, Concordia University.

abstract:
In the areas of cloud computing, big data and internet of things, composite services are designed to effectively address complex levels of user requirements. A major challenge for composite services management is the dynamic and continuously changing run-time environments that could raise several exceptional situations such as service execution time that may have greatly increased or a service that may become unavailable. Composite services in this environmental context have difficulty securing an acceptable quality of service (QoS). The need for dynamic adaptations to be triggered becomes then urgent for service-based systems. These systems also require trust management to ensure service level agreement (SLA) compliance. To face this dynamism and volatility, context-aware composite services (i.e., run-time self-adaptable services) are designed to continue offering their functionalities without compromising their operational efficiency to boost the added value of the composition.
The literature on adaptation management for context-aware composite services mainly focuses on the closed world assumption that the boundary between the service and its run-time environment is known, which is impractical for dynamic services in the open world where environmental contexts are unexpected. Besides, the literature relies on centralized architectures that suffer from management overhead or distributed architectures that suffer from communication overhead to manage service adaptation. Moreover, the problem of encountering malicious constituent services at run-time still needs further investigation toward a more efficient solution. Such services take advantage of the environmental contexts for their benefit by providing unsatisfying QoS values or maliciously collaborate with other services. Furthermore, the literature overlooks the fact that composite services data is relational and relies on propositional data (i.e., flattened data containing the information without the structure). This contradicts with the fact that services are statistically dependent since QoS values of service are correlated with those of other services.
This thesis aims to address these gaps by capitalizing on different methods from software engineering, computational intelligence and machine learning. To support context-aware composite services in the open world, dynamic adaptation mechanisms are carried out at design-time to guide the running services. To this end, this thesis proposes an adaptation solution based on a feature model that captures the variability of the composite service and deliberates the inter-dependency relations among QoS constraints. We apply the master-slaves adaptation pattern to enable coordination of the self-adaptation process based on the MAPE loop (Monitor-Analysis-Plan-Execute) at run time. We model the adaptation process as a multi-objective optimization problem and solve it using a meta-heuristic search technique constrained by SLA and feature model constraints. This enables the master to resolve conflicting QoS goals of the service adaptation. In the slave side, we propose an adaptation solution that immediately substitutes failed constituent services with no need for complex and costly global adaptation. To support the decision making at different levels of adaptation, we first propose an online SLA violation prediction model that requires small amounts of end-to-end QoS data. We then extend the model to comprehensively consider service dependency that exists in the real business world at run time by leveraging the relational dependency network, thus enhancing the prediction accuracy. In addition, we propose a trust management model for services based on the dependency network. Particularly, we predict the probability of delivering a satisfactory QoS under changing environmental contexts by leveraging the cyclic dependency relations among QoS metrics and environmental context variables. Moreover, we develop a service reputation evaluation technique based on the power of mass collaboration where we explicitly detect collusion attacks. As another contribution of this thesis, we introduce for the newcomer services a trust bootstrapping mechanism resilient to the white-washing attack using the concept of social adoption. The thesis reports simulation results using real datasets showing the efficiency of the proposed solutions.


identifier: Javadian, Sahar  (2020) Application of machine learning and deep learning methods for load prediction in institutional buildings.  Masters thesis, Concordia University.

abstract:
Worldwide, the building sector consumes a significant amount of energy in different stages such
as construction and operation. Depending on the type of energy source used, buildings have a
considerable impact on air pollution and greenhouse gas emissions. To reduce the amount of emissions
from the building sector and manage energy consumption, many tools and incentives are used
around the world. One of the most recent and successful approaches in this regard is the application
of machine learning techniques in building engineering. The increasing availability of real-time
data measured by sensors and building automation systems enable the owner and energy planner
to analyze the collected information and explore the hidden useful knowledge and use it to answer
specific questions such as which parts need retrofit, how much energy can be saved and what would
be the cost. At the building level, machine learning has different applications, such as pattern extraction
and load prediction. Amongst those, load analysis and energy demand prediction are of
specific importance for the building energy managers, as it can lead to a more efficient operation
schedule of energy systems in the building. The analysis of load profiles can give a good overview
of the energy use and user behavior in the building. Detailed load analysis and understanding is an
essential step before the predictive analysis.

In this study, electrical load data from three transformers installed in EV building, Concordia
University and weather data collected from the weather station installed in EV building were used
for load analysis and load prediction. EV building includes two main parts, which are Engineering (ENCS) and visual arts departments (VA). The three transformers considered in this study measure
heating, ventilation, and air conditioning (HVAC) load from a mechanical room (located in 17th
floor of the EV building) in addition to the plug and miscellaneous loads from ENCS and VA departments.
In the load analysis part, the representative daily loads of these three transformers of
the building are studied. The magnitude and trend of daily loads are extracted and discussed. The
average load from 17th floor’s transformer is found to be 1,441 kW during office hours of weekdays
in summer, whereas this load during office time in winter is 991 kW. Note that, this load does not
include the gas consumption, used for meeting the heating load during the winter. Regarding the
plug load from ENCS and VA department, the average load during office hours of weekdays in
summer is 512 kW, and 453 kW, respectively. Moreover, the load reduction during the COVID19
pandemic is studied by comparing the two months (April and May) of 2019 and 2020 for all three
transformers. There was a significant reduction of 42 % for the load of 17th floor between April
2019 and April 2020 (weekdays), while 24% and 40% load reduction was observed for ENCS and
VA transformers, respectively. Based on the results during COVID 19 period, we see that the existence
of people in the building affects the load, but a great part of the load is related to the schedule
and policy of the building. That is why there is a good potential to save energy just by changing the
schedule and plans that systems are running based on.

The second part of the work deals with load prediction using regression analysis and long shortterm
memory (LSTM) model. The importance of input variables for load prediction is evaluated
in the regression section. In linear regression, twenty scenarios are considered. Each scenario is
a different combination of input features. It was found from the results that the best scenario is
when all calendar and weather data are considered as input attributes. The best scenario in winter
has R2=0.29 and MAPE=24.46, while in summer, R2=0.64 and MAPE=10.47. The results are
confirmed with correlation analysis. For this case study, adding meteorological data did not improve
prediction in winter significantly because in winter, gas is used for heating and the considered
data does not reflect it, but in summer, weather variables were of great importance. Also, specific
and unusual events in consumption could be detected with polynomial regression. Regarding load
forecasting, LSTM is used as a deep learning model, which considers the sequential load data and predicts future load for different time horizons. Regarding the size of the dataset and LSTM parameters,
the best performance was obtained for one-year ahead forecasting with R2= 0.75, and MAPE=
10.97. Another result was that the type of load influences the performance of the LSTM model.
Considering different load types, the plug and lighting loads from the ENCS and VA departments
could be better predicted than the 17th floor HVAC load, since HVAC load is affected by weather
variables that are fluctuating and not easy to predict, but plug loads are more related to the schedule
of building. The other influencing factor on prediction performance is the choice of train-set and
test-set. The lowest R-squared belongs to the model that has the year 2019 as test-set. The results
of this project could be useful for building facility managers to adapt and optimize the schedule of
the energy systems and give recommendations to the users to improve energy efficiency.


identifier: Hotte-Kilburn, Alexis  (2021) Topological photonic crystal ring resonators.  Masters thesis, Concordia University.

abstract:
Concepts of topology arise in condensed matter physics when considering states at the boundary of a periodic structure. Recently, there has been interest in exploring topology in optical contexts. Optics is an excellent platform to study these effects, due to the high degree of control over the geometry and material. The simplest example of a model exhibiting topological properties is the Su-Schrieffer-Heeger (SSH) chain, where an extra periodicity (also known as dimerization) is added to a photonic crystal waveguide. By creating sites where the dimerization changes abruptly, it is possible to create topological edge states, which confines resonant modes that are protected from imperfections.
Here, we study the SSH model in a ring resonator, where a dimerized photonic crystal waveguide loops in on itself. The dispersion relations of various waveguides are first calculated using a frequency domain approach. By using those waveguide unit cells as the elements to construct phase transitions, the properties of those resonant modes and the extent of topological protection will be studied by adding random variations to the holes of the unit cells. Simulations show that topological protection of the quality factors hold for 7.8% variation in the hole radii. The loss mechanisms of these optical cavities will also be analyzed to confirm topological protection of the lattice. At last, we then discuss how these ring resonators can be fabricated and experimentally studied on a silicon chip.


identifier: Scheiding, Ryan  (2020) Zombies, Vaults & Violence: Collective Memory and the Representation of Atomic Fears in Video Games.  PhD thesis, Concordia University.

abstract:
This dissertation examines the representation of atomic/nuclear weapons and fears within video games through the lens of collective/cultural memory, historiography, game studies and critical theory. These diverse fields are combined into a research typology labelled as discourses of the past which argues that the past is mediated based upon the needs of entrenched power for use in, and the organization of, the present. The study focuses specifically on the representation of the atomic bombings of Hiroshima and Nagasaki in August 1945 in both their American and Japanese contexts. The dissertation first engages with key works in the relevant fields. This literature review grounds the work in established theory while also defining and outlining the discourses of the past research typology. The second major section engages in a discussion of the author’s positionality and describes the methodological considerations of the typology.

Through a historiographical analysis, the dissertation argues that the dominant discourses of the past of the atomic bombings in the United States/West is to justify the use of the bombs while erasing Japanese victims. This is further examined in three case studies of American games: Fallout 4, Far Cry 5, and Far Cry New Dawn. A historiographical analysis of Japanese sources finds that there is no one dominant collective/cultural memory and, instead, atomic bomb and war memory is diffused into three discourses of the past (a hero/victim/perpetrator memory triad) where no one discourse is entirely dominant or marginalized. Instead, the relative power of each is dictated by person, place, space, and/or temporality. This is further explored in a case study of three Japanese games: Resident Evil 3, Yakuza 6: The Song of Life, and Valkyria Chronicles 4.

Through the combination of theory, historiography, and case studies the dissertation ultimately argues that the selected games mimic the dominant discourses of the past while also adhering to established video game genre conventions and expectations. These findings are then put into contemporary context to argue for the continued relevance of Hiroshima and Nagasaki.


identifier: Guimaraes Almeida, Lucas  (2020) Cardiorespiratory fitness, muscle strength, and body composition responsiveness to weight loss in adults with different ages of obesity onset.  Masters thesis, Concordia University.

abstract: Research shows that those who have had obesity from childhood have greater risk of cardiometabolic disease compared to those who only develop obesity in adulthood. A main way of mitigating the risk of cardiometabolic disease in obesity is with weight loss. Weight loss has been shown to positively affect cardiorespiratory fitness (CRF), muscle strength, and body composition of adults. However, it is unclear whether the response of the aforementioned outcomes to weight loss may be influenced by age of obesity onset. The current study investigated how CRF, muscle strength, and body composition of adults with childhood- versus adulthood-onset obesity are affected by weight loss. Measurements were conducted before, at 12 weeks, and after ~10% weight loss. In total, 37 adults completed the 12-weeks assessments (age=30.5±3.2 y; BMI=32.9±3.0 kg/m2) and 23 participants with adulthood-onset (n=11) and childhood-onset obesity (n=12) reached 8.8±3.2% weight loss. The YMCA cycle ergometer test (YMCA) and the 20-m shuttle run test (20MSR) were used to measure CRF (ml.kg-1.min-1). A handgrip dynamometer was used to measure muscle strength. Body composition was assessed by DEXA and CT-scan. Overall, body composition and CRF improved (time effect: p<0.05) after 12 weeks and moderate weight loss and there was no group-by-time interaction for YMCA, 20MSR, muscle strength, and body composition variables. Age of onset of obesity does not affect the responsiveness of CRF, handgrip strength, and body composition at 12 weeks and after moderate weight loss in adults with obesity.


identifier: Namvarrechi, Saman  (2021) Fabrication, Characterization and Modelling of Piezoelectric PVDF-TrFE polymer as a Force Sensor Using Spin Coating Method.  Masters thesis, Concordia University.

abstract:
Since last decades, needs of new valuable material and reliable sensing technologies were among the researcher's focus for different industry’s application. Piezoelectric Polyvinylidene Fluoride (PVDF) polymer and its copolymer, Trifluoroethylene (TrFE), are one of these materials that can be a strong candidate for new transducers and sensors due to its electromechanical properties for force/pressure sensing in different industries such as biomedical device companies. Since PVDF is Biocompatible, a thin film of it can be integrated with biomedical devices using adhesive materials. However, it is not advisable for biomedical devices/tools to use those materials due to potential toxicity of gluing different objects inside human body. An example of such devices is laparoscopic tools used in Minimally Invasive Surgery (MIS), where PVDF film is deployed at the tip of an endoscopic grasper for force and softness sensing. To avoid toxicity problem, it is possible to deposit PVDF film directly on a potential device using a spin coating approach and fabricate it from scratch. However, it has its own challenges.
In this research, PVDF-TrFE polymer is deposited via spin coating method and treated with various post-deposition processes to investigate its piezoelectricity and amount of electroactive β phase. These processes include different post thermal annealing, the effect of spin coating speed, different layer of deposition and presence of additional hydrate salt. Using FTIR spectroscopy and SEM images, the amount of the β phase and porosity of each sample is determined. In addition, the optimum experimental study is established by considering every aspect of the fabrication process.
Finally, sample sensors are tested, their output voltage signals are verified, and the results are compared to the Comsol simulation software. This study clearly shows the effective way of deposition and fabrication of a tactile PVDF-TrFE based sensor and an enhancement methodology to have a higher β phase, piezoelectric constant and higher output voltage in order to have a better force/pressure sensitivity for different applications such as MIS’s end effector.


identifier: Jabagi, Nura ORCID: https://orcid.org/0000-0003-2218-1802 <https://orcid.org/0000-0003-2218-1802>  (2020) Four Essays on the Impacts of Platform-Mediated Work in the Gig-Economy.  PhD thesis, Concordia University.

abstract:
This dissertation consists of four essays which address the fast-growing gig-economy – a global phenomenon which has been of concern for scholars, policymakers, and practitioners over the last decade. The gig-economy is a labour-market characterized by the prevalence of part-time and/or temporary positions in which organizations engage independent workers for short-term contracts by connecting workers to customers via a digital marketplace. The technological foundations underlying these digital marketplaces are known as digital labour platforms. Despite being lauded as the future of work (Shapiro, 2018), a deeper understanding of digital labour platform architectures is needed to provide “a more realistic picture of how platforms are shaping the future of work in the online gig-economy” (Corporaal & Lehdonvirta, 2017, p. 2). Moreover, our understanding of the impacts of advanced technologies in interaction with work contexts on gig-workers’ attitudes and behaviours remains under-developed (Deci, et al., 2017; Kuhn, 2016; Kuhn & Maleki, 2017; Pichault, et al., 2017; de Reuver, et al., 2018; Spreitzer, et al., 2017; Sutherland & Jarrahi, 2018). This collection of essays aims to bridge these research gaps.

The first essay in this dissertation explores how the architecture of the digital labour platforms underlying the gig-economy, and the characteristics of jobs mediated through these IT artefacts, can impact key antecedents of self-motivation. Drawing on self-determination theory (Deci & Ryan, 2000), job-characteristic theory (e.g. Hackman & Oldham, 1980), and enterprise social media research, this essay develops a mid-range theory demonstrating how organizations can engender gig-workers’ intrinsic motivation through the thoughtful design of their digital labour platforms and the integration of two social media tools, namely: social networking and social badging. In doing so, it advances the notion that the operational choices embodied in a gig-organization’s digital labour platform will act as critical determining factors of a gig-worker’s intrinsic motivation.

The second essay in this dissertation explores how gig-workers perceive organizational support in the context of algorithmically-managed platform work. Organizational support theory proposes that employees develop global beliefs concerning the degree to which an organization values their contributions and cares about their well-being. These beliefs, known as perceived organizational support (POS), are related to a number of positive employee outcomes, including: job satisfaction, work effort, performance, etc. Three categories of POS antecedents have been recognized in the literature: perceived supervisor support; the fairness of organizational procedures; and organizational rewards and job conditions. In this conceptual paper, we explore these antecedent categories in the gig-work context where organizations replace human managers with algorithmic management practices and data-driven procedures. In doing so, we develop a conceptual model that centres on the role that a gig-organization’s algorithm plays in engendering POS by promoting perceptions of algorithmic fairness (PAF) and perceptions of autonomy support (PAAS) – two newly proposed theoretical constructs.

The third essay in this dissertation is an instrument development piece which describes the process of developing and validating a theoretically-based measure of perceived algorithmic autonomy support (PAAS). To develop our instrument, we adopt Mackenzie  et al.’s (2011) well-cited scale development process. As part of the scale development process, interviews were conducted with Uber drivers to support item generation. This was followed by content-validation with subject matter experts to develop and validate our instrument, and finally statistical and nomological validation were conducted using data collected from a total sample of 435 Uber drivers. The results of the survey confirm that: (i) PAAS is a second-order formative measure with four first-order reflective constructs; (ii) our resulting 13-item scale demonstrates adequate psychometric properties; and finally (iii) PAAS is positively, and significantly, related to perceived organizational support (POS) and job satisfaction.

The fourth essay in this dissertation aims to advance our understanding of how workers perceive managerial algorithms. On digital labour platforms, algorithms are responsible for making a wide range of decisions including performance evaluation, matching, and reward assignments, to name a few. Although algorithmic decision‑making is a central feature of digital labour platforms, our understanding of how people perceive decisions made by algorithms, particularly in terms of fairness, remains underdeveloped. Drawing on the Theory of Organizational Justice (Colquitt, 2001; Greenberg, 1987), this paper explores the impacts of workers’ perceived fairness of managerial algorithms on perceived organizational support (POS) and job satisfaction. Through a survey of 435 Uber drivers, this paper finds that workers’ perceptions of the fairness of performance evaluation decisions taken by the Uber app’s algorithm play a central and significant role in promoting perceptions of organizational support and job satisfaction. On the other hand, workers’ perceptions of the fairness of matching (work allocation) decisions were found to play a less important, albeit positive, role. Moreover, our analyses also find POS to be a partial mediator between workers’ perceptions of fairness and job satisfaction.


identifier: Badu-Marfo, Godwin  (2020) Privacy Preserved Model Based Approaches for Generating Open Travel Behavioural Data.  PhD thesis, Concordia University.

abstract: Location-aware technologies and smart phones are fast growing in usage and adoption as a medium of service request and delivery of daily activities. However, the increasing usage of these technologies has birthed new challenges that needs to be addressed. Privacy protection and the need for disaggregate mobility data for transportation modelling needs to be balanced for applications and academic research. This dissertation focuses on developing modern privacy mechanisms that seek to satisfy requirements on privacy and data utility for fine-grained travel behavioural modelling applications using large-scale mobility data. To accomplish this, we review the challenges and opportunities that are needed to be solved in order to harness the full potential of “Big Transportation Data”. Also, we perform a quantitative evaluation on the degree of privacy that are provided by popular location anonymization techniques when undertaken on sensitive location data (i.e. homes, offices) of a travel survey. As a step to solve the trade-off between privacy and utility, we develop a differentially-private generative model for simultaneously synthesizing both socio-economic attributes and sequences of activity diary. Adversarial attack models are proposed and tested to evaluate the effectiveness of the proposed system against privacy attacks. The results show that datasets from the developed privacy enhancing system can be used for travel behavioural modelling with satisfactory results while ensuring an acceptable level of privacy.


identifier: Mohammadi Bandari, Naghmeh ORCID: https://orcid.org/0000-0002-6284-6402 <https://orcid.org/0000-0002-6284-6402>  (2020) OPTICAL-BASED TACTILE SENSORS FOR MINIMALLY INVASIVE SURGERIES: DESIGN, MODELING, FABRICATION AND VALIDATION.  PhD thesis, Concordia University.

abstract: Loss of tactile perception is the most challenging limitation of state-of-the-art technology for minimally invasive surgery. In conventional open surgery, surgeons rely on their tactile sensation to perceive the tissue type, anatomical landmarks, and instrument-tissue interaction in the patient’s body. To compensate for the loss of tactile feedback in minimally invasive surgery, researchers have proposed various tactile sensors based on electrical and optical sensing principles. Optical-based sensors have shown the most compatibility with the functional and physical requirements of minimally invasive surgery applications. However, the proposed tactile sensors in the literature are typically bulky, expensive, cumbersome to integrate with surgical instruments and show nonlinearity in interaction with biological tissues. In this doctoral study, different optical tactile sensing principles were proposed, modeled, validated and various tactile sensors were fabricated, and experimentally studied to address the limitations of the state-of-the-art. The present thesis first provides a critical review of the proposed tactile sensors in the literature with a comparison of their advantages and limitations for surgical applications. Afterward, it compiles the results of the design, modeling, and validation of a hybrid optical-piezoresistive sensor, a distributed Bragg reflecting sensor, and two sensors based on the variable bending radius light intensity modulation principle. The performance of each sensor was verified experimentally for the required criteria of accuracy, resolution, range, repeatability, and hysteresis. Also, a novel image-based intensity estimation technique was proposed and its applicability for being used in surgical applications was verified experimentally. In the end, concluding remarks and recommendations for future studies are provided.


identifier: Brooks, Jane  (1977) Immigration Policy and the Railways: The Formation of the Railway Agreement.  Masters thesis, Concordia University.

abstract: The use of Canada's immigration policy to promote its agricultural development and to provide traffic for its railways is examined through the "Railway Agreement between the Dominion Department of Immigration and Colonization and the country's two railways. By this agreement the railways gained the authority to select, transport, and the responsibility to place immigrants from eleven central and eastern European countries as agriculturists, agricultural workers, and domestic servants. Officials of their colonization departments became immigration agents for Canada on the continent of Europe. The agreement exemplified the nature of the relationship between government and railways in Canada's immigration policy and activities in the 1920's. It is best understood within the context of Canada’s British, continental European, and United States immigration policy in the years following World War I.


identifier: Marcoux-Fortier, Iphigénie  (2020) HOMING: A Process of Co-Creating Documentary and Re-Making Home With Rural Women of Diverse Backgrounds.  Masters thesis, Concordia University.

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abstract: HOMING is an act of documentary co-creation, of creating new connections, re-imagining the land and the people who inhabit it. It’s an act of re-making home and rewriting the future. This research-creation project explores the processes of documentary co-creation as a tool for valuing the plurality of knowledges of women from various backgrounds living in rural areas. Working with the concept of home, HOMING is inspired by a range of methods of inquiry and concepts, such as “digital storybridging” inspired from “video bridging” (Frise and Cizek 2009), “conversation” (Kovach 2009), “small places” (Zimmermann and De Michiel 2018) and “poetic inquiry” (Galvin and Prendergast 2016). These acts of co-creation, which are open, flexible, mobile and multiscalar (Zimmermann and De Michiel 2018; Auguiste et al. 2020) are grounded in an approach of “sensitive engagement” (Nash 2009). Emerging from these co-creative processes is a plurality of microhistories of women and ruralities. Together we cultivate and co-create “spaces of home” in a time when we are witnessing waves of individualism, nationalism and othering wash across the world. HOMING goes beyond the act of storytelling (Juhasz and Lebow 2018), inasmuch as it uses documentary co-creation to generate new social configurations on a rural territory.


identifier: MacKenzie, Stephanie  (2020) Protective Effects of Zinc Supplementation for Diabetes-Induced Vascular Damage.  Masters thesis, Concordia University.

abstract:
ABSTRACT

Protective Effects of Zinc Supplementation for Diabetes-Induced Vascular Damage Diabetes

Stephanie MacKenzie

The leading cause of mortality in diabetic patients is cardiovascular disease developed through the accumulation of damage done by oxidative stress. Hyperglycemic conditions activate pathogenic pathways, including mitochondrial dysfunction, which generate reactive oxygen species. The endogenous antioxidant defense system is unable to adequately mitigate this damage as diabetes is linked with zinc deficiency, and zinc plays a crucial role in the functionality of the antioxidant system. The effects of zinc supplementation in the prevention of diabetic cardiomyopathy have been documented in previous research. However, the protective capacity of zinc supplementation in the vasculature has yet to be established. We hypothesized that a four-week zinc supplementation intervention would enhance mitochondrial respiration, reduce protein oxidation, and increase the expression of several important antioxidants (superoxide dismutase (SOD), zinc-induced metallothionein (MT), catalase, and peroxiredoxin-2 (Prx2)) in the vasculature of streptozotocin-induced diabetic mice when compared to diabetic controls. Results revealed that mitochondrial respiration was significantly more efficient in the zinc-supplemented group. Additionally, it was found that the intervention group experienced significantly reduced levels of protein oxidation as well as a significant upregulation in both SOD and MT expression. No differences were observed among levels of catalase and Prx2. These findings suggest that zinc supplementation may have the potential to prevent deleterious changes in mitochondrial health and act in a protective capacity against oxidative damage in the vasculature.


identifier: Shabanijafroudi, Nima  (2020) Buckling and Postbuckling response of laminated composite plates with interlaminar flaws.  PhD thesis, Concordia University.

abstract:
High stiffness and strength, corrosion resistance, and ease of manufacturing have made laminated composites an excellent replacement for isotropic materials. The outstanding properties of composites especially appeal to industries such as the aerospace industry where lightweighting is of great importance. Despite the mentioned advantages, susceptibility to defects is a major drawback in using these materials. Being composed of several layers bonded together using an adhesive material, delamination or debonding of layers is one of the most common flaws in composite laminates. Delamination may drastically impact the mechanical behavior of laminates, especially in unstable conditions. Larger deflections and higher levels of stresses that a laminate experiences in the postbuckling state may, in turn, lead to the growth of a delamination. Given the complications that are caused by delamination, keeping delaminated plates in service requires an in-depth understanding of their postbuckling behavior including the possibility of the growth of the delamination.  The objective of this thesis is to develop a comprehensive mathematical and mechanical methodology for accurately predicting the buckling and postbuckling behavior of delaminated composite plates including the fracture mechanical phenomena involved in the postbuckling state.
In all the formulations derived in the framework of the new methodology, the deformations of laminates are approximated using the first-order shear deformation theory and the equilibrium equations were derived using the principle of stationary total potential energy and the Ritz method. In both linear buckling and nonlinear postbuckling analyses, the effect of large rotations is incorporated by adopting Von Kármán’s approximations of the Green strain tensor.
The present thesis work approaches the buckling and postbuckling behavior of delaminated plates through nonlinear analyses. However, conducting nonlinear analyses requires a preliminary knowledge of the potential buckling modes (local, mixed or global) and the loading level at which they may emerge. This preliminary information is used for determining the optimal configuration of imperfections to be incorporated in the nonlinear analyses. In the present work, a novel eigenvalue buckling solution was developed for the required preliminary information. The developed formulation in order to account for the prebuckling stress field nonuniformity caused by in-plane constraints, bases the eigenvalue analysis on a calculated stress field obtained using a prebuckling stress analysis.
For interrogating the details of the postbuckling behavior of delaminated plates, a state of the art postbuckling solution is proposed. The proposed methodology uses a new partitioning scheme that splits the delaminated plate using the plane of the delamination. Outside the delaminated regions the bond between the sublaminates is modeled using a penalty function method. The penalty functions model the effect of an extremely thin layer of elastic adhesive gluing the sublaminates together. The use of the penalty function method offers the advantage of providing the distribution of interlaminar traction in the plane of the delamination. Given the availability of interlaminar tractions in the vicinity of the borders of the delaminated region, Irwin’s crack closure integrals were integrated into the solution for calculating strain energy release rates corresponding to the three fracture modes separately.
Given the importance and the abundant industrial use of curved composite panels especially in the aerospace industries, in an attempt to extend the applicability of the developed methodology to delaminated curved panels, an eigenvalue buckling solution for curved plates subjected to rotational edge restraints is developed.
The validity of the deliverable results of each of the pieces of the developed methodology was verified by comparing the results with experimental data and numerical results obtained using finite element analyses.


identifier: Igwe, Charles Nnaemeka ORCID: https://orcid.org/0000-0002-7989-7468 <https://orcid.org/0000-0002-7989-7468>  (2021) A Process-Based Approach for Integrating the Last Planner System In 4D Modeling for Equipment Workspace Planning in Elevated Urban Highway.  PhD thesis, Concordia University.

abstract:
Transportation developments are shifting from the construction of new highways to the reconstruction of existing ones, especially in urban areas. The reconstruction of elevated urban highways typically requires substantial capital investments and long durations. The prevalence of non-value adding activities otherwise referred to as non-physical wastes according to the Lean Construction (LC) paradigm is one attributable reason for this. Another feature of urban highway projects is the use of heavy construction equipment.  Planning the equipment workspace becomes very important to facilitate the reduction/elimination of non-physical wastes and ensure no delays to the project completion arising from spatio-temporal conflicts. Four-dimensional (4D) modelling techniques have proven benefits to effective construction planning. Still, some limitations exist in the lack of a practical approach to support construction planning and incorporate workspace modelling in the 4D model development process. Several studies with different perspectives have been carried out to describe the gains of using 4D models in workspace management. However, none of them considered the effects of the limited usable space in the reconstruction of elevated urban highways. Moreover, the requirements for multiple levels of detail (LOD) in scheduling large and complex projects present a new challenge. To counter these challenges, a considerable amount of time is required to ensure that the LOD of the 4D model is sufficient to account for the following: (1) micro-scheduling of heavy equipment typically used in these types of operations, and (2) producing a 4D model with a sufficient LOD to accommodate daily work plans.
The purpose of this study is to categorize and prioritize factors contributing to non-physical wastes using empirical data obtained from a questionnaire survey. The survey results identified "planning" as an important factor in promoting non-physical wastes in elevated urban highway projects. A hybrid Multi-Criteria Decision Making (MCDM) approach was proposed to formalize selecting project planning/scheduling methods applicable to elevated urban highway projects where micro-scheduling short duration activities involving heavy construction equipment is critical to project success. Equipment workspace planning was considered a vital aspect in the planning process as conventional planning methods fail to consider spatial planning for short duration activities, especially in highway projects. To facilitate the equipment workspace planning, a research initiative that involved developing a detailed 4D model by integrating the Last Planner System (LPS), a LC planning and scheduling technique in a 4D model with multiple LOD's was proposed. The development of this 4D model can help facilitate the reduction of non-physical wastes during the construction phase of elevated urban highways, improve the reliability of the planning process, and reduce the time waste associated with planning and scheduling urban highway projects subject to space constraints. The research method is described, and a case study is developed to demonstrate the proposed method's feasibility.


identifier: Saberi Derakhtenjani, Ali  (2020) A Methodology for Modeling of Hydronic Radiant Slab Heating Systems for Predictive Control and Energy Flexibility.  PhD thesis, Concordia University.

abstract:
Radiant slab heating systems are receiving considerable attention due to the multiple advantages they offer such as improved thermal comfort in buildings and suitability for other related applications in cold climates, particularly snow melting and de-icing of pavements and infrastructure. Hydronic radiant heating can utilize low temperature renewable energy heat sources. The operation of these systems can be optimized by applying predictive control and further the energy costs can be reduced by optimizing their interaction with smart grids by utilizing the flexibility in their demand profiles. However, compared to conventional air heating systems, radiant systems have several added complexities such as the slow transient heat conduction within the slab. Efficient design and operation of radiant slabs require several critical decisions on design and control variables to maintain comfortable thermal conditions in the space and achieve slab surface temperatures within the recommended range depending on the application.
This thesis presents a methodology for modelling of hydronic radiant slab heating systems with significant thermal mass (a concrete layer with embedded tubes) for predictive control to utilize the energy flexibility of the building and/or infrastructure in interaction with smart grids and dynamic pricing of electricity. The modelling approaches include low-order grey box models as well as frequency domain techniques. Each approach has its own specific advantages and unique information can be obtained from each one that complement each other when optimizing system operation and designing the control strategies.
Using the developed frequency domain model of the zone, key transfer functions are calculated for a case study. By means of transfer functions, the effect of different levels of thermal mass on the zone thermal response and quantification of the energy flexibility in response to grid signals is studied. The model is used to evaluate different design and operation options on a relative basis. It is shown how transfer function analysis provides insight into the building thermal dynamics without the need for simulations. A new transfer function that relates the radiant floor heat source at the bottom of the slab to the zone air temperature is introduced and its derivation is demonstrated. By means of the transfer function, the delay between the heat input of the radiant slab and zone air temperature is determined. Experiments with a full scale test room in an environmental chamber are used to validate the key design parameters obtained from the frequency domain transfer function regarding the operational strategies for energy flexibility of the thermal zone. Frequency domain techniques may also be utilized to establish the appropriate order for low-order RC models for different applications.
A low-order thermal network RC model for a case study, validated with experimental measurements, is utilized to study several predictive control strategies in response to changes in the grid price signal, including short term, more reactive changes of the order of 10-15 minutes notice. An index is utilized to quantify the energy flexibility with the focus on the peak demand reduction for specific periods of time when the electricity prices are higher than usual. It is shown that the developed predictive control strategies can aid greatly in minimizing the electricity cost of the building and up to 100% reduction in peak power demand and energy consumption is achieved during the high price periods.
Low-order thermal models are also utilized to study radiant slab heating systems of infrastructure that have much higher thermal mass than the radiant slabs in buildings due to structural reasons. Hydronic heating of roads and pavements surfaces to avoid ice formation has several advantages compared to traditional surface salting and other anti-skid methods which have a lot of limitations. However, the traditional method of designing such systems does not consider the thermal mass of the system and its potential for predictive control, energy flexibility and optimized performance. Finally, predictive control strategies are presented and studied to take advantage of the hydronic slab thermal mass and minimize the energy consumption and peak power demand and are validated in a full scale experiment in an environmental chamber.


identifier: Carvajal Gonczi, Catalina Marysol  (2020) Modulation of Helper T Cells by β2 Adrenergic Receptor Ligands in a PKA-Dependent Manner.  PhD thesis, Concordia University.

abstract:
Abstract

Modulation of Helper T Cells by β2 Adrenergic Receptor Ligands in a PKA-Dependent Manner

Catalina Marysol Carvajal Gonczi, Ph.D
Concordia University, 2020

Background: T helper (Th) cells may attack self-tissues in susceptible people resulting in chronic autoimmune disease. A subtype of Th cells called Th17 cells are considered to be pro-inflammatory by secreting IL-17A cytokines. Discovering new drugs to suppress Th17 cells is a major goal for researchers. Ligands for the beta2-adrenergic receptor (β2AR, encoded by ADRB2) are known to suppress pro-inflammatory Th1 cells, but their effects on Th17 cells have not been widely studied. I studied the effect of β2AR ligands on Th1 and Th17 cells and determined the influence of common polymorphisms ADRB2. The goal was to discover a potential new immunomodulatory drug to explore as autoimmune disease treatment.
Methods: Human immune cells from healthy participants obtained after informed consent were tested in vitro. The samples were activated with T cell-specific activator, and cytokines were measured. The in vitro drug treatments included a β2AR specific agonist (terbutaline), an inverse-agonist (nebivolol), a β2AR specific antagonist (ICI 118-551), cAMP analogues that promote or inhibit the pathway, PKA inhibitor and phosphodiesterase inhibitor (which raises cAMP levels). Known polymorphisms were determined by sequencing ADRB2 from samples.
Results: Primary human Th17 cells expressed the β2AR. Terbutaline augmented IL-17A in activated peripheral blood mononuclear cells and Th17 cells, while IFNγ was concurrently inhibited. Proliferation was not inhibited, rather, an increase was observed in the presence of terbutaline. Using PKA inhibitors and cAMP analogues, it was shown that IL-17A was augmented by terbutaline in a cAMP and PKA-dependent manner. Terbutaline promoted phosphorylation of CREB. Nebivolol inhibited both IL-17A and IFNγ in activated peripheral blood mononuclear cells and Th cells. In samples where ADRB2 was homozygous for Arg16, terbutaline inhibited IFNγ but did not augment IL-17A. Nebivolol inhibited both cytokines regardless of the polymorphism of the ADRB2.
Relevance: These results are novel because the inverse-agonist of β2AR has not been widely studied as an immunomodulator. The cell signalling results demonstrated that Th17 cells respond differently than other Th cells to cAMP-PKA pathway, which suggests the use of other drugs with inverse-agonist properties. Discovering new immunomodulatory drugs will give doctors more options to treat autoimmune patients.


identifier: Elliott, Heather  (2020) A Moment of Reckoning: Reconciliation Through Decolonial Prefiguration in a Food Movement Organization.  Masters thesis, Concordia University.

abstract: There is an increasing recognition that settler colonialism is a root cause of food insecurity for Indigenous Peoples, and that it is also a contributor to the food insecurity of Black people and people of colour. Recent research reveals stark racial disparities, with food insecurity 4.3 times higher in Indigenous households and 2.6 times higher in Black households compared with white households (First Nations Food, Nutrition and Environment Study, 2019; Statistics Canada, 2017). Food movements are a forum through which multiple groups seek to address the lived experience of inequity. However, as predominantly white/settler-led, food movement organizations fail to adequately address the unequal impacts of food injustice and may even be complicit in perpetuating colonial and racist structures and processes. In this research, I examine a specific “moment of reckoning” at Food Secure Canada’s 2018 Assembly, arguably the largest food movement event in Canada, and its aftermath through the analysis of 124 qualitative questionnaires, ten interviews and participant observation. Using two foundational treaties as a conceptual framework, this case study demonstrates how by refusing settler processes and structures to make space for resurgence, Indigenous Peoples, Black people and people of colour are creating the conditions needed for reconciliation as transformation, rather than assimilation.  This study also shows the importance of white/settlers responding by taking on the work of personal (un)learning and making concrete organizational change to governance and procedures in order to enact their distinct responsibilities to decolonize in order to reconcile with Indigenous Peoples, Black people and people of colour. The lessons learned apply widely across community organizations, advocacy groups and social movement spaces as well as public and private institutions working towards reconciliation and decolonization.


identifier: Porter, Keith  (2021) The Weighted Average Cost of Capital and Investment; new insights from Emerging Markets.  Masters thesis, Concordia University.

abstract: The cost of capital is one of the fundamental concepts of modern finance. The theoretical value of a firm is the sum of its future cash flows discounted back to present value by this cost of capital. In classical theory, companies should only accept projects that have returns higher than their cost of capital. There has been considerable research into the determination of the cost of capital, but less into the practical side. Is there a measurable relationship between cost of capital and investment? Do firms that generate sufficient cash flow from their investments enjoy a lower cost of capital than those that do not? This paper looks at these questions from an Emerging Markets perspective, given the increase in both available information and interest in these markets, focusing on the Information Technology sector. In addition to the cost of capital, we include other variables such as corruption perceptions, cash flows and institutional ownership which can theoretically impact both the level of investment and the cost of capital. We find evidence of a significant relationship between both cash flow and corruption to the rate of investment. However, the relationship between investment and cost of capital is less clear.


identifier: Shirani, Paria ORCID: https://orcid.org/0000-0001-5592-1518 <https://orcid.org/0000-0001-5592-1518>  (2020) Binary Code Fingerprinting with Application to Automated Vulnerability Detection.  PhD thesis, Concordia University.

abstract:
With the growing popularity of emerging technologies, the prevalence of digital systems is more than ever. Security, however, has still lagged behind, as evidenced by the increasing rate of recent attacks. Oftentimes, cyber-attacks are initiated by running a malicious code or by exploiting vulnerabilities in the underlying software. To mitigate such alarming threats, analyzing software binary code (a.k.a. binary analysis) has been known as a promising
solution. This thesis answers the following research question: how to automatically fingerprint a cross-architecture code with optimization and obfuscation by attributing compiler provenance, identifying library functions, and detecting vulnerable functions? Specifically, it first analyzes the syntax, structure, and semantic of functions to extract compiler provenance in cross-complied binaries. Second, it introduces a single robust function signature
based on heterogeneous features to solve library function identification problem. Third, it overcomes vulnerable function detection problem through a multi-stage fuzzy matching approach on firmware images. Finally, it addresses vulnerability detection problem in cross-architecture obfuscated binaries and firmware images through a neural machine translation-based approach. This thesis advances the state-of-the-art by improving the accuracy, scalability, and efficiency of binary code analysis. All of the proposed approaches are implemented as a prototype system and their performance are evaluated with real data.


identifier: Belhaj, Muhsan Milad  (2020) Theoretical and Experimental Investigation of out-of-plane Wrinkle Formation during Steering in Automated Fiber Placement.  PhD thesis, Concordia University.

abstract:
Automated fiber placement is being widely applied in the aerospace industry due to its advantages. This technology has the capability to improve the efficiency of composite structures by steering where properties such as stiffness can vary within the same part. However, steering is limited by process-induced defects such as out-of-plane wrinkles, which occur when the steering radius exceeds its critical limit. This thesis proposes a theoretical model for wrinkle formation during steering of the autoclave thermosetting prepreg. The Rayleigh-Ritz approach is used to model wrinkle formation based on the critical buckling load. The prepreg tape is considered an orthotropic plate resting on a Pasternak elastic foundation, which consists of one elastic spring layer connected to an elastic shear layer. Closed-form solutions for predicting both critical steering radius and buckling wavelength is presented. The two foundation parameters and the required mechanical properties of the prepreg are experimentally characterized. The model-predicted results are validated by the experimental results. The results reveal good agreement between the predicted and experimental values. It is also found that a significant improvement in the model was achieved by adding the shear layer to the foundation.
In this context, Normal stiffness of the foundation (prepreg tackiness) is demonstrated an important role in the AFP process and steering limitations. The second stage of this work aims to measure the prepreg tackiness at different AFP processing conditions to study the effect of the process parameters on prepreg tackiness. Finally, this experimental work is used to predict the optimum parameters for high tackiness levels. Two in-house setups were developed for layup and measuring tackiness. The first setup was designed to simulate the AFP process and precisely control layup speed, compaction force, and temperature. It is used to layup the prepreg under different conditions and with different rollers. The second setup performs a peel-off test to measure the sample’s tackiness. Taguchi method is applied to optimize the layup process parameters and find the optimal combination for high resultant tackiness. It is also applied to study the effect of the placing roller. Analysis of results shows that prepreg tack is affected by the interaction among the process parameters rather than the individual effect of these parameters. The study shows that the Taguchi method is suitable to solve the stated problem with a minimum number of trials as its results are experimentally validated.


identifier: Vargas-Mejía, Susana  (2020) A Message of Hope: Fragmentos, Melted Guns, and Art Education in Post-Conflict Colombia.  Masters thesis, Concordia University.

abstract: This research is guided by an analysis of Fragmentos: Espacio de Arte y Memoria (2018) by the Colombian artist Doris Salcedo. Located in Bogotá, Colombia, this counter-monument with a floor made of melted weapons constitutes an innovative pedagogical space where it is possible to create dialogues and reflect on the effects and fractures caused by the Colombian armed conflict. By adopting key tenets of peace education and museum education, I explore how Fragmentos offers the means to address the trauma that has become a part of the everyday lives of Colombia’s citizens, and by extension, how bringing the experiences of war together provides a better understanding of how art can inspire creativity to help people heal, with the potential to significantly change society. Given the challenging times Colombia is experiencing, it is valuable to highlight the benefits that art education can bring to a society that urgently needs change and innovation, and moving towards peace education is a way to delve into the deeply traumatized consciousness of the nation. There is a need for a more complete set of analytical tools that capture the complexities of counter-monuments and shed light on the relationship between art education and peace education. This investigation was conducted through case study and arts-based research methodology, which allowed me to use my own artmaking to inform my research. In this regard, I have organized the chapters with a visual prelude to initiate the discussion, with the goal of connecting the form with the content. This has allowed me to sequence my thesis in a way that makes it possible to connect my story, through art expressions, to a broader narrative of my homeland.


identifier: Soroko, Maxim  (2021) High Throughput Screening Tools to Probe and Inhibit Core Fucosylation.  Masters thesis, Concordia University.

abstract: We report a chemo-enzymatic transglycosylation synthesis of the 4-methylumbelliferyl glycoside of a complex-type oligosaccharide substrate for core fucosylation. We demonstrate the use of the glycoconjugate in a newly developed enzyme assay to probe the activity and inhibition of fucosyltransferase VIII, which catalyzes the core fucosylation of N-glycans found on eukaryotic glycoproteins. In our assay, the fucosyltransferase VIII reaction is coupled to a specific glycosidase enzyme, allowing to distinguish an unmodified 4-methylumbelliferyl oligosaccharide probe from the fucosylated probe. Our results demonstrate that the assay is very sensitive and specific to the detection of enzymatic activity, while also enabling the identification of potential fucosyltransferase VIII inhibitors.


identifier: Akbari, Saba ORCID: https://orcid.org/0000-0001-5298-2614 <https://orcid.org/0000-0001-5298-2614>  (2021) Investigating the impact of households’ occupancy patterns and activity routines on daily load profiles: a data-driven approach.  Masters thesis, Concordia University.

abstract: Examining individual households' load profiles and discovering contextual and temporal factors of energy usage (e.g., occupancy, time of use, and occupant activity) gain lots of popularity in recent studies. Given the proliferation of Home and Building Energy Management Systems (HEMS and BEMS) and the availability of high-resolution data of households’ energy usage, it is possible to gain a deeper understanding of temporal factors of load profiles and take advantage of the services offered by these systems. The incorporation of smart meters in the grid has several economic and environmental benefits. These technologies (1) provide the opportunity for appliance scheduling, which can reduce electricity costs on the customer-side, and (2) optimize the integration of intermittent renewable energy sources to the electricity grid. Despite the importance of temporal determinants, previous works mainly focused on determinants of the annual end-use load. Additionally, studies on residential energy mainly address district and city scales, while small-scale analyses are highly overlooked. Based on the identified limitations, in this study, two time-series analysis methods (k-shape clustering and change point detection) are implemented on historical, sensor-collected data of three residential units to discover the frequent occupancy schedule patterns of each household and identify the high- and low- consumption periods within each occupancy pattern. Then LASSO regression is utilized to find the comparative contribution of various activity factors on households’ energy usage (e.g., kitchen-, living room-, bathroom-, or bedroom-related activities indicated by plug loads recorded in specific rooms of the apartments) during the identified energy consumption periods. The results suggest that occupancy patterns are able to explain temporal variations in daily load profiles, and the shape of daily load profiles can be characterized by the occupancy schedule pattern of a day. Furthermore, the analysis of this study can make households aware of the most influencing activities during high-consumption periods. And as a result, households can reduce their energy bills by shifting the energy-consuming activities from high-consumption periods to off-peak periods.


identifier: Patel, Ashutosh  (2020) Modeling and Detection of Stator Winding Insulation Degradation in VSI-fed Electric Machines Under High dv/dt.  Masters thesis, Concordia University.

abstract:
Nowadays, in many advanced applications, for example electric vehicles and aircrafts, electric motors are being fed by voltage source inverters (VSIs). These inverters generally utilize fast-switching power electronics devices to provide the modulated pulse voltages for motor operation. Motor’s winding insulation undergoes additional electrical stress due to high voltage slew rate (dv/dt) resulted from the use of VSI, which may cause accelerated insulation degradation. To investigate and quantify this electrical stress, various modeling techniques have been proposed over the years with a goal to optimize the motor stator design for safer and more reliable operation. In this thesis, various winding insulation modeling techniques are investigated, in which a simple case study along with comparative analysis is performed to analyze the electrical stress for different dv/dt levels. Based on this investigation, finite element (FE) based approach is selected for detailed stator winding insulation modeling, parameter determination and analysis.
Degradation of winding insulation is inevitable due to various internal and external factors. Therefore, monitoring of the insulation condition becomes necessary to ensure a safe and reliable operation of the VSI-fed electric machines. Detecting degradation in insulation in an early stage can prevent major failure in the machine and the drive system. To this end, an online insulation monitoring technique is required. Based on the existing literature, the high frequency transient current could be a good indicator for online insulation monitoring. In this thesis, how winding insulation degradation affects the machine transient current is investigated. To do so, complex random wound stator winding is modeled at first using the FE based approach, in which detailed arrangement of each wire inside the stator slot and different insulation layers are considered. Thereafter, insulation degradations of various types and severity are emulated within the FE based model, including the turn to ground (TG) degradations and turn to turn (TT) degradations. With the emulated winding insulation degradations, the effectiveness of the detection techniques is investigated. In this work, a new insulation condition detection technique based on current response and wavelet packet decomposition (WPD) is proposed, which has the capability to determine the overall state of health (SOH) of insulation and classify types of insulation degradation. Therefore, the stator winding insulation is modeled and the SOH indicator for insulation degradation detection is also developed through this thesis research.


identifier: Umezude, Nkemdilim Okechukwu  (2021) Design of a Variable Stiffness Passive Layer Jamming Structure for Anthropomorphic Robotic Finger Applications.  Masters thesis, Concordia University.

abstract:
Soft robots can effectively mimic human hand interface characteristics and facilitate collaborative operations with humans in a safe manner. This dissertation research concerns the design and fabrication of a low cost variable stiffness structure for applications in compliant robotic fingers. A conceptual design of a compact multi-layer structure is proposed for realizing variable stiffness, when applied to underactuated fingers of an
anthropomorphic robotic hand. The proposed design comprises thin material layers with clearance that permits a progressive hardening feature while grasping and added design flexibility and tuning of the fingers’ compliance. The design permits stiffness variations in a passive manner in the soft contact regions. The design is realized to ensure ease of scalability and cost-effective fabrication by the ’Additive Manufacturing (AM)’/3D-printing technology. Both the multi-layer structures and the fingers could be fabricated as a single entity, and from a single base material with relatively low elastic modulus. The proposed design also exhibits finite degrees-of-freedom representative of the human finger - The feasibility of the design and its manufacturability are verified through prototype fabrication using a readily available 3D-printing material, namely; 'Thermoplastic PolyUrethane (TPU)' with Young’s Modulus of 25MPa. The chosen material permitted low stiffness of the multi-layer structure in the contact interface under relatively small deformations, while ensuring sufficient rigidity on the non-contact regions of the finger.

A finite element (FE) model is formulated considering 3D tetrahedral elements and a nodal-normal contact detection method together with the augmented Lagrange formulation. The model is analyzed to determine the force-displacement characteristics of the structure subject to linearly increasing compressive load, under the assumption of low interface friction. A simplified analytical model of the multi-layer structure is also formulated considering essential boundary and support conditions for each individual layer. The model revealed progressive hardening characteristics of the multilayer structure during compression due to sequential jamming of individual layers. The force-displacement characteristics of the design could thus be varied by varying the multi-layer structure parameters, such as number of layers, thickness of individual layers, material properties, and clearance between the successive layers. It is shown that the simplified analytical model could provide reasonably good estimate of the force-deflection properties of the structure in a computationally efficient manner. The analytical model is subsequently used to investigate the influences of variations in the multilayer structure parameters in a computationally efficient manner. It is shown that the proposed design offers superior tuning flexibility to realize desired force-displacement characteristics of the structure for developing scalable anthropomorphic robotic fingers of a compliant robotic hand, in addition to the cost-effective manufacturability.


identifier: Krishna, Bivi  (2021) Maintaining the consistencies in electropolishing results by characterizing the polishing bath state as a function of its instant key properties.  Masters thesis, Concordia University.

abstract:
Electropolishing is an advanced industrial metal finishing in practice commercially since the mid-20th century, to treat the metals with electricity and industrial chemicals. The process has grown remarkably in the last 50 years; the medical and pharmaceutical industry's growth is a strong driving force for the electropolishing industry now.
The work detailed in this thesis focuses on maintaining the uniformities in electropolishing qualities by specifying the polishing bath state as an approximation of its fundamental properties. In light of the scarcity of precise information regarding the techniques to keep the electropolishing process in control as the polishing bath ages, this research will present the organized data for an ageing bath. A mathematical model constructed from the vital polishing bath properties measured on-the-spot is used to quantify the polishing deliverables concerning surface roughness as a function of its immediate critical bath properties. The work results demonstrate that the model can anticipate the polishing capabilities under selected polishing conditions for a given polishing bath state, fresh, aged or regenerated. This model-based technique reduces the trial and error-based efforts the polishing industry takes to figure out the suitable operating parameters to deliver the polishing results when the aged bath is no longer efficient.


identifier: Kibria, Md Golam  (2021) Ultrasound Elastography: Deep Learning Approach.  Masters thesis, Concordia University.

abstract: Ultrasound elastography images the elasticity of a biological tissue. Conventional algorithms for ultrasound elastography suffer from different noises severely compromising the quality of time-delay estimation. Calculation of time-delay estimation is a key component of strain estimation. However, time-delay estimation is analogous to optical flow estimation, a classical computer vision problem. Deep learning networks have reported recent success in optical flow estimation compared to the conventional techniques. Classical ultrasound elastography algorithms have been unable to provide a single solution to both commonly known issues of noise and computation time. Deep learning techniques have a bright prospect in addressing both issues. The goal of this thesis is to investigate whether optical flow estimation is translatable to ultrasound elastography as the core nature of both of these problems are analogous. In this thesis we aim to develop and train a robust deep neural network for ultrasound elastography. First, an efficient deep learning network trained for optical flow estimation is used for time-delay estimation. The initial time-delay estimation is further fine-tuned by optimizing a global cost function for generating high quality strain images. Simulation, phantom and clinical experiments show the robustness of the deep learning approach both quantitatively and qualitatively. Next, the weights of the deep learning network are fine-tuned using transfer learning technique for transferring the efficacy of optical flow estimation to time-delay estimation. The objective is to retain the robustness introduced by the deep learning network while enhancing the overall performance of the time-delay estimation in ultrasound elastography. Simulation and experimental phantom results show that the time-delay estimation has improved slightly after fine-tuning the weights using transfer learning.


identifier: Evdokias, Gregory  (2020) Characterization and regulation of biosynthetic gene clusters in Aspergillus niger.  Masters thesis, Concordia University.

abstract:
Fungi produce a broad range of secondary metabolites with various bioactivities that have allowed them to be used as antibiotics and pharmaceutical drugs. The genes encoding secondary metabolites are commonly organized contiguously into biosynthetic gene clusters. The close spacing of functionally related genes facilitates the identification of multiple parts of a secondary metabolite pathway, its regulation, and provides the possibility of discovering novel bioactive compounds.
However, (i) the function of each genes within biosynthetic gene cluster is often not well defined, (ii) the regulation of gene clusters is very complex and, so far, remains mostly uncharacterized.
Herein, three Aspergillus niger gene clusters were investigated. The transcriptional regulation of two clusters thought to be involved in malformins production and one unknown cluster not associated with any metabolites, was studied. The overexpression of transcription factors and knocked out of backbone enzymes were done by gene replacement. The production of malformins and others secondary metabolites was assessed by comparative metabolomics and transcriptomics using mass spectrometry and RNA-sequencing, respectively.


identifier: Ni, Shuang ORCID: https://orcid.org/0000-0002-4854-1397 <https://orcid.org/0000-0002-4854-1397>  (2020) Quantitative Measurement of Muscle Oxygen Saturation Using 5-Wavelength Near-Infrared Spectroscopy with Fault Diagnostic.  Masters thesis, Concordia University.

abstract:
Regular physical activity can help people improve both physical and mental health. As people pay more attention on their health, fitness has become a popular activity. For individuals who have specific training goals, such as losing fat, gaining weight, and preparing to participate in competitions, it is important to avoid injury during exercise and improve the efficiency of training. Physiological monitoring during exercise, such as heart rate, blood lactate, oxygen uptake, and tissue oxygenation, is helpful for improve the effectiveness and safety of training. Many researchers are devoting their efforts to propose methodology and invent instruments of measuring these metrics. It is well known that heart rate is a commonly used measurement indicator, which can be measured on many fitness equipment and wearable devices. However, heart rate is a global parameter of the trainer's body and cannot represent the training intensity of a specific muscle. Because the muscles are directly affected by the exercise, it is necessary to measure the metrics of muscles to determine whether a specific muscle can tolerate the exercise load or not. If the muscles are overworked, there is a high probability of injury, which must be avoided. Hence, it is imperative to measure local muscles.

Muscle oxygen saturation (SmO2) is an indicator of the altering between oxygen delivery and consumption in the muscles. The more intense the exercise, the more oxygen is consumed by the muscles. So SmO2 is a good indicator to assess how fatigued a specific muscle is. In sports science, it is usually measured non-invasively by near-infrared spectroscopy (NIRS). Many instruments were developed by researchers previously based on different NIRS techniques and algorithms. In this thesis, a methodology of measuring absolue value of SmO2 was proposed for a wearable measurement device with one source and two detectors using 5-wavelength NIRS. The algorithm of fitting the light attenuation to the Taylor expansion model by bound-constrained non-linear least squares fitting was evaluated with simulated tissues. For in vivo measurement, an orthogonalization technique was introduced to reduce the effect of the absorption and scattering of overlying tissues. With comparison and analysis, the measuring SmO2 values of two designed running procedures were reasonable.

During exercise, the trainer may not always wear the device correctly and the device may move or fall off. And some of the individuals may not exercise as the designed training procedures. So the measured data will be unreliable in these cases. In order to remind users to wear the device properly and to train as the designed procedures, a fault diagnostic method was proposed by machine learning approach in this thesis. With labelling data by its reliability and splitting data into different training status, a support vector machines (SVM) model with Gaussian radial basis function kernel was trained. According to two evaluation curves, ROC curve and cross-validation learning curve, the SVM classifiers in both training states can achieve an accuracy of over 97%. These trained models can be applied as a fault diagnostic for the measurement device.

There is no screen on the device, so the results need to be displayed on a computer or mobile phone. In this thesis, an application that integrated the SmO2 calculation and fault diagnostic was developed in Matlab App Designer. With this application, after three clicks by users, the SmO2 curve during training and the absolute values could be displayed in the interface. Since the device didn't have the feature of real-time wireless transmission, a simulation of real-time mode was done to show the possibility of real-time measurement in the future.


identifier: Wang, Liang  (2020) Three Essays on Online Economic Experiments and Experimental Data Analysis.  PhD thesis, Concordia University.

abstract:
This dissertation consists of three chapters on economic experiments and experimental data analysis. The first two chapters are online experiments and surveys, which explore the two topics of the health state valuation and the voluntary provision of public goods, respectively. The third chapter is a strategy analysis of trust behavior.
In the first chapter, to explore how people value the state of health and what socio-economic factors they might consider, I conducted a survey experiment to elicit individuals’ decisions under hypothetical health states. The main task for the subjects was a valuation task (standard gamble) under given health states, in which the subjects were required to make decisions on whether to take a risky medical treatment when facing various success probabilities. After this procedure, the subjects filled a survey about what factors they might have considered when making the previous decisions. The subjects were from two separate online pools of the United States (Amazon MTurk) and Canada (AskingCanadians).
My results show that in those who choose the risky medical treatment under the same health states, the Canadian participants are willing to accept a lower success probability. Among the socio-economic factors that are significant to this health valuation, several factors are considered by both samples such as “employer-purchased insurance plans”, “personal financial situations”, and “waiting times for treatment”. Some factors are only significant in one country’s participants. For the American sample, it is “access to health insurance”, while for the Canadian sample, it is “disturbances in everyday family life”.
The second chapter is an online experiment of a public goods game, which has a particular feature of polarized preference. From the 2020 U.S. election to the oil pipeline development in Canada, these types of situations may be modeled by a public goods game in which two groups of individuals have polarized preferences. The outcome of the election or debate will affect the utility of individuals in both groups but in an opposite direction. Meanwhile, individuals from each group can make costly efforts (in their favor) trying to affect the outcome.
We study a public goods game with polarized preferences by using a generalized voluntary contribution mechanism (GVCM). The strategy method was applied to the design of an online experiment. There are two groups of players, a majority group and a minority group, who have polarized preferences for a public good. Each player decides whether to contribute to their group's public account or keep the token in their private account. The experiment consists of a 2×2 design, which allows us to examine the effect of different MPCRs and frameworks in the conditional contribution. The subjects were recruited from Amazon MTurk and the experiment was implemented using o-Tree.
The main results that we found are that the MPCR effect and framework effect are mixed and only significant in some treatments. The results vary depending on the role of the participants (the majority and the minority). Overall, the individual contribution frequency in the majority group is significantly larger than in the minority group. Furthermore, players' contribution significantly increases with the contribution of others in their own group but is not dependent on the contribution from the other group.
The third chapter is an experimental data analysis, which seeks to reveal the strategy behind trust behavior among the encountering of strangers. The data set is from a trust game experiment reported by Duffy, Xie, and Lee (2013). If people never met again and people would not be punished for dishonesty (at least not directly from the person they cheated), the actions would be different. This scenario could be simulated in a trust game where there are two roles (Investor and Trustee) and the subjects are randomly and anonymously matched.
The method of finite automata is applied to infer the strategies subjects used in the experiment. In the strategy fitting procedure, I define for Investor 16 strategies and 6 strategy sets, and for the second player (Player B: Trustee) 24 strategies and 11 strategy sets. Then I match the data through a fitting procedure with these defined strategies. I report that the top three strategies in order are “grim trigger”, “systematically Send”, and “forgiving”; for Trustee, the most used strategies are “systematically Return”, “grim trigger”, and “tit-for-tat”. By taking the probability of the participant’s error into account, more observations are classified into the strategy, and the strategy pattern and proportions are still maintained.


identifier: Ou, Guangjun  (2021) Lateral Force Resistance for Shell Foundation.  Masters thesis, Concordia University.

abstract:
Shell foundation has been increasingly used around the world as an economic alternative to the traditional flat foundation. It provides higher bearing capacity and experience less settlement as compared to its counterpart flat foundation, these beside its high resistance to lateral pressure. The objective of this thesis is to highlight shell foundation superiorities in resisting horizontal loading and accordingly seismic condition.
A comprehensive analytical and numerical investigation were conducted. Analytically, a MATLAB code was generated to calculate the lateral resistance for shell and flat foundations.  Numerically, a 2-D finite-element model was developed to examine the governing parameters affecting soil-shell structure interaction, using the commercial software “PLAXIS”. In this analysis the soil was modeled by the Mohr-Coulomb failure criteria, and the elastic perfectly plastic model. Mesh deformations, displacement vectors and failure points diagrams are presented.
The results of this study indicated that shell foundations are superior to resist higher lateral forces than the flat foundations, which may advance its use in seismic zones.