identifier: Juneau, Gabrielle  (2022) Introduction of Modifications to the Nucleic Acid Scaffold for the Preparation of Circular Structures and Applications in Biochemistry.  Masters thesis, Concordia University.

abstract: The use of oligonucleotides as therapeutic and diagnostic tools has grown significantly in recent decades. However, native oligonucleotides face numerous challenges such as poor cellular uptake and low resistance to nucleases hence necessitating the incorporation of modifications to their scaffold. Small circular oligonucleotides have shown promise in these fields; however, their formation is limited by their length and the proximity of the ligating ends. To address this challenge, we investigated a methodology to synthesize circular constructs containing a chemically stable butylene or base labile sulfonylethane interstrand cross-link to pre-organize the system to aid in the circularization via CNBr-assisted ligation or click chemistry. Moreover, nucleic acid crystallography has proven to be an asset in determining nucleic acid structures and complexes. Incorporation of a selenium atom to the scaffold of DNA has helped with phasing issues. Thus, we investigated a novel approach to introduce a 5’-methylphosphoroselenoate handle by the use of a methylphosphoramidite followed by treatment with potassium selenocyanate. Lastly, the O6-position of guanine bases can be methylated by various exo- and endogenous agents. This adduct can be repaired by the protein O6-Alkylguanine DNA Alkyltransferase (AGT). The activity of AGT can reduce the efficiency of some alkylating chemotherapeutic drugs. Thus, the discovery and evaluation of AGT inhibitors is desirable. Hence, we explored development of a method to evaluate the repair proficiency of AGT in the presence of inhibitors towards DNA duplexes which contain an O6-methyl-2’-deoxyguanosine adduct.


identifier: Alavez, José  (2022) If I die Far from You: Deep Mapping Grief and Loss in the Context of Migration.  PhD thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract: The death of a loved one is one of the most challenging episodes in a person’s life. This experience becomes even more complicated when someone dies in the context of migration. Beyond the emotional shock, family members and friends might have to hold posthumous ceremonies at a distance, organize the corpse’s repatriation, and deal with their own need to grieve from far away. In this research, I aim to shed light on the potential of mapping for revealing these intimate and heterogenous posthumous geographies. To do so, I have deployed three different cartographic strategies to map the stories of eight migrants. First, I designed a series of narrative maps to focus on postmortem mobilities. These maps reveal that the movement of bodies continues to be influenced by emotional and economic decisions after death. They also display the local and global networks of communication and support triggered by the demise of a migrant. Second, I mobilized two mapping approaches dedicated to charting the personal and the emotional (i.e., inductive visualization and sensibility mapping) to represent the very intimate moments associated with the experience of death in the context of migration. Finally, I introduce the concept of “mapping-ofrenda” as a form of mourning and remembering. This third project emphasizes the value of the mapping process and the opportunity it offers to turn memories into maps. It also illustrates the importance to reconnect with the past and with relatives from afar. As a whole, this thesis considers deep mapping as an intimate and non-replicable practice, as a desire and a never-ending task that calls for a diversification of mapping forms and practices to reflect and face the challenges of engaging with difficult stories. It also establishes postmortem cartographies of mobilities, grief, memory, emotions, and solidarity as essential components of the geographies of death in migratory contexts.


identifier: El-Masry, Magdalina  (2022) The Act of Suffering: Intense Performances in Contemporary American Cinema.  Masters thesis, Concordia University.

abstract: This thesis proposes a study of dramatic film performance in contemporary US cinema through the lens of intensity. Many famed performances in American film history are noted for their intensity achieved through the actor’s commitment to a level of real suffering. “The Act of Suffering” seeks to unpack the history of the association between suffering and dramatic performance. Each case study in this thesis concerns an actor whose legacy, through the media coverage of their career, has been defined by the intensity of their performances that is framed as resulting from their unique level of artistic commitment. The first chapter on Gena Rowlands in Opening Night (1977) calls for a renewed focus on the actor’s work and argues that her intense performance of a troubled actress emerges from her own working methods and is further developed in collaboration with the cast and crew. The second chapter compares Robert De Niro and Daniel Day-Lewis in Raging Bull (1980) and Gangs of New York (2002) respectively, and argues that their efforts to fully inhabit their historical characters are rooted in a desire to legitimize the labour of performance and distance themselves from their star image. The third chapter on Viola Davis in Fences (2016) and Widows (2018) argues that her balance of strength, vulnerability, and intensity contributes to the growth of opportunities for authentic Black female narratives in Hollywood. Through these analyses, this thesis aims to trace the history of how intensity has become a production value in contemporary US cinema.


identifier: Church, Matthew  (2022) Desired Light: A Study of Edmund Spenser’s Epithalamion.  Masters thesis, Concordia University.

abstract: Edmund Spenser’s Epithalamion has typically been read through the lens of number symbolism or as a poem enwrapped in the heights of joy. The former reading tends to locate the poem’s climax at the alter, around the twelfth and thirteenth stanza; the latter, at the conclusion, the final two stanzas. While seemingly contradictory readings, this essay argues that both are essentially right. Proceeding stanza by stanza through the poem, this argument demonstrates a creative ascent in the first half, which is characterized by sunlight, vernal imagery, and spiritual love and which climaxes at the poem’s middle point, and a creative descent in the second, which is characterized by night, autumnal or hibernal imagery and which climaxes in the final two stanzas. These double movements and double climaxes are best identified with each other, just as all the poems’ other cyclical imagery is to be joined in union. To unify the poem’s opposites—implicit in the poem’s structure and its central theme of matrimony—is to enable an understanding of its primary concern of sex and the dissolution of binary thought, above all the subject-object dichotomy. The Epithalamion’s total vision erodes the stable barriers of identity and, in so doing, points to the central concern of all metaphorical thought.


identifier: Arfaoui, Mohamed Amine  (2022) LiFi Transceiver Designs for 6G Wireless Networks.  PhD thesis, Concordia University.

abstract:
Due to the dramatic increase in high data rate services, and in order to meet the demands of the sixth-generation (6G) wireless networks, researchers from both academia and industry have been exploring advanced transmission techniques, new network archi-
tectures and new frequency bands, such as the millimeter wave (mmWave), the infrared, and the visible light bands. Light-fdelity (LiFi) particularly is an emerging, novel, bidirectional, high-speed and fully networked optical wireless communication (OWC) technology that has been introduced as a promising solution for 6G networks, especially for indoor connectivity, owing to the large unexploited spectrum that translates to signifcantly high data rates.
Although there has been a big leap in the maturity of the LiFi technology, there is still a considerable gap between the available LiFi technology and the required demands of 6G networks. Motivated by this, this dissertation aims to bridge between the current research literature of LiFi and the expected demands of 6G networks. Specifcally, the key goal of this dissertation is to fll some shortcomings in the LiFi technology, such as channel modeling, transceiver designs, channel state information (CSI) acquisition, localization, quality-of-service (QoS), and performance optimization. Our work is devoted to address and solve some of these limitations. Towards achieving this goal, this dissertation makes signifcant contributions to several areas of LiFi. First, it develops novel and measurements-based channel models for LiFi systems that are required for performance analysis and handover management. Second, it proposes a novel design for LiFi devices that is capable of alleviating the real behaviour of users and the impurities of indoor
propagation environments. Third, it proposes intelligent, accurate and fast joint position and orientation techniques for LiFi devices, which improve the CSI estimation process and boost the indoor location-based and navigation-based services. Then, it proposes novel proactive optimization technique that can provide near-optimal and real-time service for indoor mobile LiFi users that are running some services with high data rates, such as extended reality, video conferencing, and real-time video monitoring. Finally, it proposes advanced multiple access techniques that are capable of cancelling the efects of interference in indoor multi-user settings. The studied problems are tackled using various tools from probability and statistic theory, system design and integration theory, optimization theory, and deep learning. The Results demonstrate the efectiveness of the proposed designs, solutions, and techniques. Nevertheless, the fndings in this dissertation highlight key guidelines for the efective design of LiFi while considering their unique propagation
features.


identifier: Tourangeau, Jennifer  (2022) The Impact of Gender Inclusivity in Video Games Subscriptions Ads.  Masters thesis, Concordia University.

abstract: While the concept of inclusivity in advertisement is becoming more universal, male-dominated industries might see a counter effect when adapting their advertisement and including more diversity. This research examines how gender inclusivity in video game advertisements influences consumer responses. It empirically tested in 2x2 experiments whether gender inclusive (vs. gender non-inclusive) advertising impacted consumers’ (male vs females) likeability of the ad and purchase intentions. Study 1 found that male consumers reported a significantly lower liking of the gender inclusive (versus gender non-inclusive) ad compared to female consumers, however, this adverse effect was not replicated for purchase intention. Study 2 tested the mediating role of perception of endorser competence, as well as the role of perception of fit of the ad to the video game industry, but the results are not significant. This research provides insights for marketers that want to penetrate female markets in the video gaming industry. Additional implications and future research ideas are also discussed.


identifier: Gepner, Clara  (2022) “A Double-Edged Sword:” Revealing the COVID-19 Pandemic’s Disproportionate Impacts on the Productivity of Women Print Journalists through Mixed-Methods Research.  Masters thesis, Concordia University.

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abstract: In 2020, surveys revealed the COVID-19 pandemic was increasing gender inequalities among different professions, including journalists and academics. There was therefore a need to examine the first wave’s impact on Canadian reporters. The aim of this study was to determine whether women and precariously employed journalists were unequally affected and, if so, to help prevent negative effects on them in the event of a future crisis. This mixed methods project used an explanatory sequential design. Quantitative data on productivity was measured by comparing the number of by-lines published by journalists in three daily francophone publications between March 1 and May 31, 2020, to the same period in 2019. Six semi-directed qualitative interviews with journalists picked from the quantitative sample were then conducted. Analyzed through thematic analysis, they explored the hypotheses formulated to explain the changes in productivity discovered and served to contextualize the quantitative results. Overall, journalists in the sample were slightly more productive than the previous year. The increase was driven by women, whose productivity increased by 7% (men’s decreased by 3%), and by staff, whose publication rate increased by 9% (precarious journalists’ decreased by 33%). Women staff’s productivity increased more than men’s while that of women precarious journalists decreased more. Based on these findings, we argue that women were disproportionately affected by the pandemic. Staff women likely experienced more pressure to produce because of the gendered division of beats, and therefore published more, while precariously employed women lost more work than men and likely experienced more financial insecurity.


identifier: Dubé, Simon  (2022) Toward Erobotics: An Investigation of the Relationships Between Stigma, Personality, Sexual Arousal, and Willingness to Engage Erotically with Robots.  PhD thesis, Concordia University.

abstract: The rise of erobots (erôs + bots)—artificial erotic agents, such as sex robots—offers new opportunities for intimate experiences with machines. Their advent has also polarized academic and public debates: some denounce their risks, while others defend their benefits. Yet, the scientific study of human–machine erotic interaction and co-evolution remains limited: it lacks comprehensive theoretical models, and its empirical literature is scarce and fragmented. There is a need for a new, unified transdisciplinary field of research focusing on such phenomena, and guiding the development of beneficial technologies. We call this field erobotics. As a theoretical contribution to this new discipline, Chapter 2 defines erobotics and its related concepts, proposes a model of human-erobot interaction and co-evolution, and suggests a path to design beneficial erotic machines. As an empirical contribution to erobotics, this thesis examines some of the sociocultural, individual, and situational factors highlighted by this model. Specifically, it investigates the relationships between perceived stigma, personality traits, sexual arousal, and people’s willingness to engage erotically with robots. Chapter 3 shows that stigma related to erotic technology exists and increases as a function of products’ human-likeness. Chapter 4 shows that the willingness to engage with and perceived appropriateness of using sex robots more closely relate to erotophilia and sexual sensation seeking, rather than technophilia, non-sexual sensation seeking, and Big-Five traits. Chapter 5 shows that sexual arousal increases willingness to have sex with robots. In these three chapters, men were more interested in engaging erotically with robots than women. Together, these findings suggest that erotophilic sensation seekers—especially, men—may become the primary users of erobots, and that sexually aroused individuals may be more willing to engage erotically with such machines: potentially influencing their design and our relationship with them. Ultimately, this thesis founds erobotics and opens future directions for the study of human-machine erotic interaction and co-evolution.


identifier: Tayanov, Vitaliy  (2022) Nonlinear Classifier Stacking on Riemannian and Grassmann Manifolds with Application to Video Analysis.  PhD thesis, Concordia University.

abstract:
This research is devoted to the problem of overfitting in Machine Learning and Pattern Recognition. It should lead to improving the generalisation ability and accuracy boosting in the case of small and/or difficult classification datasets. The aforementioned two problems have been solved in two different ways: by splitting the entire datasets into functional groups depending on the classification difficulty using consensus of classifiers, and by embedding the data obtained during classifier stacking into nonlinear spaces i.e. Riemannian and Grassmann manifolds. These two techniques are the main contributions of the thesis. The insight behind the first approach is that we are not going to use the entire training subset to train our classifiers but some part of it in order to approximate the true geometry and properties of classes. In terms of Data Science, this process can also be understood as Data Cleaning. According to the first approach, instances with high positive (easy) and negative (misclassified) margins are not considered for training as those that do not improve (or even worsen) the evaluation of the true geometry of classes. The main goal of using Riemannian geometry consists of embedding our classes in nonlinear spaces where the geometry of classes in terms of easier classification has to be obtained. Before embedding our classes on Riemannian and Grassmann manifolds we do several Data Transformations using different variants of Classifier Stacking. Riemannian manifolds of Symmetric Positive Definite matrices are created using the classifier interactions while Grassmann manifolds are built based on Decision Profiles. The purpose of the two aforementioned approaches is Data Complexity reduction.  There is a consensus among researchers, that Data Complexity reduction should lead to an overfitting decrease as well as to classification accuracy enhancement.

We carried out our experiments on various datasets from the UCI Machine Learning repository. We also tested our approaches on two datasets related to the Video Analysis problem. The first dataset is a Phase Gesture Segmentation dataset taken from the UCI Machine Learning repository. The second one is the Deep Fake detection Challenge dataset. In order to apply our approach to solve the second problem, some image processing has been carried out. Numerous experiments on datasets of general character and those related to Video Analysis problems show the consistency and efficiency of the proposed techniques. We also compared our techniques with the state-of-the-art techniques. The obtained results show the superiority of our approaches for most of the cases. The significance of carried out research and obtained results manifests in better representation and evaluation of the geometry of classes which may overlap only in feature space due to some improper measurements, errors, noises, or by selecting features that do not represent well our classes. Carried out research is a pioneering in terms of Data Cleaning and Classifier Ensemble Learning in Riemannian geometry.


identifier: Nasr, Mohamed  (2022) Designer Biosensors as Tools for Optimizing Engineered Biosynthetic Pathways.  PhD thesis, Concordia University.

abstract:
Synthetic biology techniques aimed at constructing artificial metabolic pathways in genetically modified microorganisms are important to develop sustainable methods to produce biofuels, pharmaceuticals, and value-added chemicals. To reach industrially relevant scales, challenges related to pathway bottlenecks and system optimization must be addressed. Since these are typically complex multi-enzyme pathways, techniques such as enzyme and genome engineering offer solutions to these limitations. However, screening methods for most products are laborious and inefficient. In this work, we utilize and engineer transcription factor-based biosensors to develop high-throughput molecule detection tools.
Bacterial allosteric transcription factors (aTFs) bind a limited set of effectors, which restricts their utility as biosensors. Our aim is to expand the toolbox of available aTFs, which we achieve by using two methods. First, we use high-throughput in vivo biosensor assays to uncover novel ligands for an aTF that could be used towards detecting new chemistries. As well, we employ protein engineering and directed evolution methodologies to engineer another aTF over several generations towards multiple aromatic molecules of increasing complexity, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. In addition to their response in Escherichia coli, we demonstrate the functionality of our engineered biosensors in the model eukaryote Saccharomyces cerevisiae. Finally, we coupled our engineered biosensors with a genome-wide, multi-functional CRISPR system to identify genetic changes that contribute towards improving the productivity of an engineered cis,cis-muconic acid pathway in Saccharomyces cerevisiae.


identifier: Dragon, Marilyne  (2022) To copy or not to copy: Comparing selective social learning and overimitation in childhood.  Masters thesis, Concordia University.

abstract: Even young children use social learning, or the ability to learn from others’ actions, to make sense of the world. Two social learning strategies that both develop rapidly during the preschool period are selective social learning and overimitation. Previous research suggests that different mechanisms might underlie these skills, namely theory of mind and social affiliation, respectively. To our knowledge, no study has compared performance on these two abilities. We hypothesized that these abilities would be associated, but that this association would decrease with age; that performance on the selective social learning task would be more strongly related to theory of mind; and that overimitation would be more strongly related to social affiliation. To test these predictions, we conducted two experiments. In Experiment 1, we administered a selective word learning task and an overimitation task with puppets via video-chat to a sample of 3.5- and 5-year-old children. Parents filled out questionnaires measuring cognitive and social abilities. Correlation and regression analyses revealed no association between the two tasks, nor between task performance and scores on the questionnaires. As the overimitation rate was very low, we conducted a second experiment using the same procedures, but with human agents, with a sample of 5-year-old children. Results showed an association between selective social learning and theory of mind, in line with our second hypothesis. Most children also copied the irrelevant actions, suggesting that overimitation might indeed be related to social affiliation, as our participants more frequently imitated a human agent compared to a puppet agent.


identifier: Taylor, Kimberly  (2022) Exploring social attitudes toward second language speakers of English across Canada.  PhD thesis, Concordia University.

abstract:
Nearly a fifth of Canada’s population is represented by people from other countries, most of whom speak languages other than English or French. Though there has been some exploration of social attitudes toward these ethnolinguistic groups, there have been no multi-city investigations of social attitudes, particularly toward second language (L2) speakers, in major cities where immigrant populations are most concentrated. Furthermore, there have been few studies that present speech samples alongside images suggesting speaker ethnicity and/or religious affiliation. Therefore, this dissertation explores ratings of L2 speakers across multiple dimensions and considers the role of social attitudes and social network exposure in formation of those judgments.

Study 1 explored how residents of Calgary and Montreal judge the comprehensibility and accentedness of L2 speech in audio-only and audiovisual conditions and whether those judgments are associated with residents’ overall social attitudes toward immigrants. There were no context or image effects, but differences emerged among ratings of certain language groups, as well as raters’ general attitudes toward immigrants. Ultimately, raters’ attitudes were not associated with their ratings of L2 speech.

Study 2 explored how native-born residents of Canada judge L2 speakers’ intelligence, friendliness, and trustworthiness and investigated how those judgments might be related to residents’ general social attitudes toward immigrants. No significant differences emerged between speech samples presented as audio-only versus with a nonreligious or religious image. However, there was a clear hierarchy in how specific language groups were evaluated. Social attitudes questionnaire responses revealed generally positive attitudes toward immigrants. Ultimately, those attitudes had weak relationships with rater judgments of L2 speakers’ intelligence, friendliness, and trustworthiness.

Study 3 explored how native-born residents of Montreal and Calgary compared in judgments of L2 speaker citizenship and how those judgments might be related to raters’ L2 social networks. No differences based on image condition or context surfaced in judgments of L2 speaker citizenship. However, specific language groups differed in how their citizenship status was perceived. There were also between-context differences in native-born residents’ interactions with L2 speakers. Ultimately, social network characteristics had no influence on citizenship ratings in either context.


identifier: Redden, Rebeccah  (2022) Climate Futures: A 360 mapping experience of Montreal under Climate Change.  Masters thesis, Concordia University.

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abstract: The planet is facing massive climate change effects; Canada itself is warming at an unprecedented rate in comparison to the rest of the world. However as inland populations living in Montreal, we don’t often see these effects day-to-day. In addition, the media surrounding the future is often saturated with images of climate apocalypse that annihilate us. This makes it increasingly difficult to visualize a world that isn’t hopeless, and how we will survive in climate change. In this research-creation project, I question if there are hands-on methods to visualize a future world, where we thrive despite, and alongside, climate challenges. This research-creation project looks at the interacting aspects of climate change, critical mapping, speculative futurisms, and marginalized community voices through the creation of a ‘future map’ of Montreal. To explore this, I collect interviews from 6 community members/activists and render their visions into 360 images built into a digital map. I provide context with theories of world-building, mapping, and the creation process itself, bringing to life the participants’ visions of the future. I then describe how marginalized viewpoints and anti-oppressive frameworks build a better, tangible vision of tomorrow. I use the concept of remediation to explain the process of building futures in order to ‘fix’ society. To summarize, a critical mapping and 360 experience of Montreal under climate change will increase the body of work on climate change projection from a community perspective, and explore more embodied, hopeful, and grounded experience of climate futures.


identifier: Esrafilian Najafabadi, Mohammad  (2022) Self-learning building HVAC control system based on dynamic occupancy patterns: A predictive approach using deep Q-networks and transfer learning.  PhD thesis, Concordia University.

abstract: This dissertation reports the development of a self-learning control system that adjusts the building setpoint temperature to the dynamic occupancy schedules, aiming to maximize energy saving and thermal comfort. The controller interacts with the environment and learns the occupancy patterns and the lag time of heating, ventilation, and air-conditioning (HVAC) systems with no need for developing online models of occupancy and buildings. This process aims to minimize the runtime of HVAC systems during vacancy periods to save energy while providing thermal comfort conditions upon the occupants’ arrival. This control framework also leverages the knowledge of the pre-trained controllers to accelerate the training process in unseen new buildings. This transfer learning method is performed based on an inter-building similarity analysis using unsupervised learning of the occupancy profiles. This process intends to minimize the thermal discomfort caused by the trial-and-error nature of the self-learning algorithm. The proposed system takes advantage of a double deep Q-network (DDQN) algorithm to find the optimal control policy. Moreover, an optimal feature selection algorithm is integrated into this framework for identifying irrelevancy and redundancy in the feature sets to further improve the training process. The merit of the controller is quantified by comparing its performance with that of a model-based predictive control (MPC), as a well-practiced occupancy-based control method. The results demonstrate that the control system provides superior thermal comfort for occupants by taking the occupancy forecasting uncertainty into account in the decision-making process. This ability improves thermal comfort by 7.87% on average with MPC as the benchmark. The use of the transfer learning method enhances thermal comfort by 68% during the training process of the algorithm.


identifier: Sanches, Marlon  (2022) Critical Pedagogy in a Freirean Tradition to Promote Social Justice in Adult Education: A Qualitative Study.  PhD thesis, Concordia University.

abstract: The advancement of neoliberalism, the increase of disinformation, the rise of fascist ideals and the suppression of freedom are putting democracies in danger all over the world. Adult education is critical to keep learners informed. Critical pedagogy in a Freirean tradition has the potential to empower citizens with critical skills to respond to challenging issues. It revolves around social justice-oriented practices aiming at the development of critical awareness (conscientização) in individual and collective perspectives. Common aspects of critical pedagogy are centred around the humanization of teaching and learning, education as a political act, problem-posing strategies, banking education, pedagogical praxis, and hope, among others. This dissertation engaged adult educators in non-formal learning situations who self-identify as critical pedagogues in a Freirean tradition, to reflect on their daily practices. The overarching research question was: How do adult educators who self-identify as critical pedagogues in a Freirean tradition implement critical pedagogy principles into their daily practices in non-formal settings? The specific questions searched for definitions of critical pedagogy, reasons to practice it, key characteristics of critical pedagogues, principles of critical pedagogy that are primarily implemented, strategies and evaluations used, and challenges and barriers found on the way and how to overcome them. A qualitative research methodology was used to answer these questions. Twelve adult education practitioners generously shared their stories and their expertise on how they implement critical pedagogy in their own contexts. Participants also provided images that represented critical pedagogy for them, and artifacts were collected to corroborate participants’ responses. Findings revealed that critical pedagogy in a Freirean tradition is non-hierarchical and starts with learners’ experiences. Recurring themes that emerged were the educators’ orientation towards social justice and commitment to learners. This study amplified the voices of educators who have made sound contributions to adult education. Their voices have the potential to empower future practitioners with encouragement and curiosity to challenge current issues in education.


identifier: Kwakye-Nimo, Shadrach  (2022) Rheological characterization of polymer melts with bimodal distributions.  PhD thesis, Concordia University.

abstract:
Polymer melt exhibits complex relaxation and flow behaviors when subject to some
form of deformation. The ensued stress experienced by the melt depends on the entire deformation
history and not just the deformation rate or the magnitude. To better anticipate
the response of melts to deformations, an understanding of their molecular structure is
essential. This understanding helps not only in optimizing the process conditions, which
turn to be big cost savers, but also enables the production of end-products with controlled
and desirable properties.
The research presented here establishes correlations between the features of the molecular
weight distributions (MWD) and the rheological behavior of broadly distributed bimodal
HDPE and well tailored distributions of polybutadiene. We start by characterizing
a set of high-density polyethylene (HDPE) materials which possess very high levels of
polydispersity. Then, using statistical learning, the most important features influencing
the rheological behavior are identified. We show that by using just the shape features of
the MWD the relaxation behavior of the melt can be inferred. We proceed to investigate
the behavior of polymer melt at the interface. Using the simple shear flow experimental
setting, we eliminate the possibility of contributions from flow induced fractionation
and show for the first time that surface segregation could occur in the absence of bulk
shear gradient. We additionally show that apart from the surface enrichment being shearrate
dependent, the enrichment-depletion transition point also occurs at a much lower
molecular weight value than predicted in literature. We then proceed to show the key
role played by the short chain in surface fraction. By using bi-disperse polybutadiene
designed to vary in both short chain length and volume, an enhanced relaxation of the
melt was shown to positively improve the polymer surface fraction.


identifier: Wright, Emily  (2022) Sensitivity Testing Using Expectiles with Applications in Extremes.  Masters thesis, Concordia University.

abstract: Climate change is leading to an increase in the severity and prevalence of natural catastrophes. From a statistical and actuarial perspective, it is desirable to measure the potential impact of changes in different aspects of these extreme events. Sensitivity analysis is used to measure and characterize uncertainty of a model based on these changes, where a baseline model includes a number of covariates mapped to an output via an aggregation function. Given a defined stress on the baseline distribution, a type of sensitivity analysis used in actuarial mathematics, Reverse Sensitivity Testing, is suitable for several types of models (including black box models) and uses different risk measures along with the Kullback–Leibler divergence (KL divergence) as a measure of discrepancy between the baseline probability measure and the stressed probability measure. An expansion of Reverse Sensitivity Testing is provided to include both a coherent and elicitable risk measure; expectiles. Since the KL divergence is considered to be a pessimistic divergence for extreme values, the Renyi divergence, which is a broader divergence, is included as an extension ideal for extreme events given a user specified order parameter. Both the KL divergence and the Renyi divergence are implemented on a standard normal random variable, a numerical example, and then applied to extreme loss data from a natural catastrophe that hit Western Canada in 2020.


identifier: Kashefinishabouri, Farzad  (2022) A Technique for Real-Time Detection of Defects in Composite Structure using Carbon nanotubes, and Transfer Learning.  Masters thesis, Concordia University.

abstract:
ABSTRACT

A Technique for Real-Time Detection of Defects in Composite Structure using Carbon nanotubes, Machine Learning Including Transfer Learning

Farzad Kashefinishabouri

Fiber-reinforced polymer composites have garnered interest in a range of industrial applications due to their outstanding mechanical characteristics and lightweight. Monitoring the health of polymer composite structures in real-time is one of the most challenging issues in the practical use of composites due to their susceptibility to many types of damage. Conventional non-destructive tests (NDT) methods such as X-ray tomography and ultrasonic may be used to assess composite materials but the drawback of conventional NDT techniques is that they cannot be implemented when the composite part is in use.
Composite plates’ electrical characteristics can be to do real-time health monitoring. Carbon nanotubes (CNTs) because of their excellent conductivity characteristics, are used in composites to enhance the conductivity of resins within composites. It is shown that by monitoring the electrical behavior of composites with CNT embedded resins, defects can be detected. The issue with this approach is that numerous wires and connections are required. The weight of composite structures is greatly influenced by the number of wires and connections, which also makes the system more prone to errors because numerous connections must function well for the system to respond as intended.
To tackle this problem, in this study, the goal is to reduce the number of required probes and connections by limiting the probes to the edges of composite plates rather than throughout the plate. By having the probes on the edges of the plate, there may not be a direct correlation between defects at different locations within the plates to the measurements as it was in the previous cases. There are two approaches to tackle this problem:1. To develop a physics-based model that can precisely model the electrical behavior of composite with CNTs embedded in the resin. 2. To develop a data-driven model that can relate the measurements to the location of the defect. As the first approach is expensive and time-consuming the second approach is picked in this study. Neural network (NN) is used to find the pattern between measurements on the edges and the location of the defect. The problem with using neural network (NN) models is that they require numerous numbers of labeled examples. To tackle this problem Transfer Learning (TL) and data augmentation is used. TL is used to reduce the number of labeled data points required for the training process as in composites it is too expensive and time-consuming to generate a huge number of data points. In the TL method that is used, the training is done in two stages, first stage the training is done based on the data generated from a similar problem that the data can be abundant, and the second stage of training is done using experimental data of the exact problem. Data augmentation is used on experimental data to increase the data points for training NN. The performance of the trained neural network in locating defects by having the probes only on the edges of the samples is promising (accuracy of 78.57% on the test set).
Also, the performance of the neural network models for different precisions and sample sizes were studied. The precision is defined as the area in which the defects can be located within.

KEYWORDS
Real-time defect detection, Neural Network, Transfer learning, Data augmentation


identifier: Masoumi-Verki, Shahin  (2022) Analysis and Reduced-Order Modeling of Urban Airflow and Pollutant Dispersion under Thermal Stratification Conditions.  PhD thesis, Concordia University.

abstract:
Different thermal stratification conditions, namely, stable, isothermal (or neutral), and unstable, can locally occur in urban areas. Alteration in the thermal condition of an urban area may significantly change the airflow pattern and pollutant dispersion process by affecting both the mean and fluctuating components of the variables. The unstable effects can increase the vertical flow movement, while the stable ones can suppress it. Furthermore, unstable conditions increase turbulence kinetic energy (TKE), which increases the fluctuations in concentration. On the other hand, stable conditions lead to buoyant destruction. Due to frequent changes in the boundary conditions, a model is required for monitoring these situations, which can be used as a fast-response (near real-time) model. This thesis aims to propose a systematic approach for analysis and reduced-order modeling of the airflow and concentration fields under non-isothermal conditions.
The present study uses a high-fidelity computational fluid dynamics approach, i.e., embedded large eddy simulation (ELES), to simulate the impact of the aforementioned thermal conditions on the airflow and concentration fields. The model considers the pros of both the Reynolds-averaged Navier-Stokes, RANS, (i.e., high speed), and large eddy simulation, LES, (i.e., high accuracy) approaches. After thoroughly analyzing the results, the proper orthogonal decomposition (POD) and frequency analyses are performed to investigate the impact of thermal conditions on the turbulence structure of the flow field. Considering the most energetic POD modes can lead to a good approximation of the whole airflow field, which is an important finding in developing a reduced-order model (ROM). Due to the limitations arising from the linear nature of POD, convolutional autoencoder (CAE)-based methods are used for model order reduction, using the unstable dataset generated by ELES. In addition to the conventional CAE, multiscale CAE (MS-CAE) and self-attention CAE (SA-CAE) are developed to capture multiscale and long-range dependencies among the datapoints, respectively. Afterwards, a parallel long short-term memory (LSTM) network is used to compute the temporal dynamics of the low-dimensional subspaces. ROMs maintain prediction accuracy at an acceptable level compared to ELES, while reducing the data reconstruction time to the order of seconds.


identifier: Humadi, Khaled  (2022) Advanced User-centric Modeling for Future Wireless Communication Networks: Performance Analysis and Optimization.  PhD thesis, Concordia University.

abstract:
Due to the increasingly growing demand for high data rates and a massive number of connected devices, future wireless communication networks are required to provide much more resources than the current networks can do. As an emerging
solution for future cellular networks, dense deployment of small cell base stations (BSs) has received a great deal of attention both in academia and industry. A major challenge in dense cellular networks is the interference experienced by the user from
its neighboring active BSs. The effect of such interference is more deleterious at cell-edge users which limits the density of deployed BSs.
An effective promising solution is to move from a cell-centric to a user-centric paradigm which allows each user to be connected to a set (cluster) of BSs instead of being associated with a single one. This will mitigate the interference effect and remove the cell boundaries, i.e, no cell-edge users. In this thesis, we develop novel
BS clustering models to enable a user-centric BS cooperation for future wireless networks. Unlike the existing clustering models, where a user is served by a cluster of BSs with fixed size (either a fixed number of BSs or fixed cluster radius), our proposed models adapt the cluster of each user dynamically based on its channel condition and quality-of-service (QoS) requirements.
To design user-centric networks, we focus on several technologies introduced for future wireless wireless communication systems such as millimeter wave (mmWave) and terahertz (THz) networks, unmanned aerial vehicle (UAV)-assisted networks, hybrid multi-tier networks, and energy harvesting networks. We first investigate the performance of a user-centric mmWave network under the proposed dynamic BS clustering model using tools from stochastic geometry. To maximize the system spectral efficiency, an optimization framework for the user’s serving cluster is developed. Then, a user-centric THz system is designed to compensate for the
high pathloss and hence improve the coverage of THz networks. Both dynamic and static clustering approaches are considered, based on which we study the coverage probability of the user-centric THz network by using stochastic geometry. Then, to design an energy-efficient and reliable air-to-air connection in UAV networks, we design a 3D user-centric clustering model where a set of UAV transmitters spatially distributed in a 3D space in the sky are carefully selected to serve another UAV receiver. Analytical expressions for the spectral efficiency and energy efficiency of this
user-centric UAV network are provided and an efficient and tractable optimization framework to maximize its energy efficiency is developed.
In this thesis, we also implement a user-centric BS clustering for hybrid networks where THz, mmWave, and sub6-GHz BSs coexist. In this system, a user can be associated with the best BS cluster, from either a sub6-GHz, mmWave or THz tier based on either the maximum SINR criterion or the maximum rate criterion. Thus, with carefully planned networks, enabling hybrid user-centric wireless systems can provide ultra-high rates while maintaining sufficient coverage in future multitier networks. Furthermore, we adopt the proposed user-centric clustering model to enhance the joint rate and energy coverage of cellular networks with simultaneous
wireless information and power transfer (SWIPT). For this setup, we aim to insure that the user can harvest sufficient energy in a given time slot and receive the required minimum data from a given serving cluster. Then, a mathematical optimization model for the time switching coefficient is developed to maximize the system joint rate and energy coverage performance. All analytical results are validated by simulation with comparison to some of the existing works, demonstrating that the proposed analytical frameworks are accurate and efficient in the design and deployment of future user-centric wireless networks.


identifier: Huber, Sandra-Jo  (2022) Witchy Methodologies: Bewitchment, Shapeshifting, and Communication with More-Than-Human Kin.  PhD thesis, Concordia University.

abstract:
Shapeshifting, spellcasting, salt circles: who are witches and why should we, as scholars, care to know more about them? My dissertation, “Witchy Methodologies: Bewitchment, Shapeshifting, and Communication with More-Than-Human Kin” examines the media and techniques of contemporary North American witchcraft — such as tools, spells, and communication with spirits, ancestors, and more-than-human kin — against a theoretical background that reaches towards feminist and unsettled positionalities. How does the witch act in / on the world in a way that opens tricky questions surrounding the methods we use to craft knowledge as well as who is deemed able to do so?

My dissertation is broken down into three chapters, where each chapter involves a set of media or techniques that witches use as well as their surrounding questions — Chapter 1: “Mirrors”; Chapter 2: “Knots,” and Chapter 3: “Fluids.” In Chapter 1, I look at mirrors, concentrating especially on the witches’ black scrying mirror that does not reflect, but, rather, divines. In chapter 2, I open a discussion of what I call the methodology of bewitchment and how it is involved with tricks and traps, such as knot spells, cauldrons, and animal familiars. In chapter 3, I focus on fluids; in particular water, blood, and ectoplasm. What is it, finally, that the techniques and tools of witches produce, if anything?

Ultimately, my dissertation is about communication. The tools and techniques that I discuss throughout may seem extraordinary but are in fact quotidian and widespread. We all communicate and seek to connect with more-than-human companions and kin. Rather than learning or making, witches’ methods offer a way of unlearning and unmaking the structures that define and bind the perimeters of the enlightened and empirical. How do witches and their techniques teach us to attune to the subtle frequencies of our environments and underworlds — and open our ability to listen.


identifier: Selber-Hnatiw, Susannah  (2022) Methyltransferase Specificity Toward Secondary Metabolite Production in Aspergillus niger.  Masters thesis, Concordia University.

abstract: Secondary metabolites are bioactive compounds that demonstrate a broad chemical diversity and represent a source of pharmacologically and industrially relevant compounds. Bioactivities of secondary metabolites can be improved through chemical modification, or in vivo using an organism as a cell factory. As such, secondary metabolite modifications were examined in vivo using the Aspergillus niger secondary metabolite TAN-1612/BMS-192548 (TAN/BMS). The genes involved in the biosynthesis of TAN/BMS are organized in a biosynthetic gene cluster, and consist of a polyketide synthase backbone, tailoring enzymes including an O-methyltransferase, a transporter, and a fungal-specific transcription factor. In general, methyl group addition represents a strategy used to modify and optimize drugs, and methyltransferases represent an opportunity to explore methylation in vivo. Selective methylation was examined in a TAN/BMS overexpression and knockout strain through the homologous insertion of genes. To this end, an intraspecies methyltransferase gene library was designed and overexpressed in A. niger at the glucoamylase A (glaA) locus which is often exploited for enzyme overproduction. Screening methods were used to examine the expression profiles of the gene-edited strains. The overexpression of the native TAN/BMS methyltransferase gene in glaA was shown to partially restore TAN/BMS methylation. The methyltransferase mutants did not recapitulate nor modify the methylation pattern of TAN/BMS. Results indicated that the localization of genes may play an important role in selective methylation.


identifier: Mejia, Gabriela  (2022) Chavón La Escuela de Diseño: The history and legacy of a school of design and its community of artists and designers in the Dominican Republic.  Masters thesis, Concordia University.

abstract:
Founded in 1983, Chavón La Escuela de Diseño is a post-secondary art and design school located in the Dominican Republic. It has been identified as the country’s most renowned art school, maintaining a reputation of excellence and standing apart from all other art schools in the country due to its program structure, pedagogical methods and strong community of artists.

The purpose of this research is to document the untold history and legacy of this school to understand its educational philosophy as told by its students, teachers and administrators. The analysis of the participants stories aim to provide an understanding of Chavón’s contribution to post-secondary art education in the Dominican Republic and to explore how the congruence of modernist and postmodernist orientations in Chavón La Escuela de Diseño’s curriculum has been successful in the training and professional placement of its students.

This research highlights how the school has been able to survive by going through constant changes, building a strong sense of community, promoting hard work and discipline, and teaching a curriculum based on students needs and industry demands. This has led to the creation of a strong community of artists and designers in the Dominican Republic thanks to the legacy of Chavón La Escuela de Diseño.


identifier: Laurin-Beaucage, Caroline  (2022) Choreographing collaboration: A multilayered approach to somatic and site-oriented art practices.  Masters thesis, Concordia University.

abstract: Choreographing collaboration: A multilayered approach to somatic and site-oriented art practices is a research-creation thesis project that focuses on multiple sites of collaboration between bodies and spaces (both physical and digital) and how collaboration informs and shapes a creative process in dance and choreographic practices. The creative period of this research project was informed by somatic explorations between my body, a vacant storefront located on Saint Denis Street in Montreal, and Zoom, which was used to communicate with an artist located in São Paulo, Brazil. The creative research period took place during the COVID-19 pandemic, which prompted me to critically reflect upon notions of time, my creative process, and my routines as a performer and choreographer in the field of dance for 20 years. As such, one of the goals of undertaking a creative process over a period of 30 consecutive days was to set up conditions for a different creative routine to emerge. Four main themes — intimacy, publicness, transparency, and opacity — arose in this process, and each is examined and described in relation to my analysis of the methods used to expand my approach to both collaboration and choreography.


identifier: Bergeron, Juliette  (2022) Narratives of Revival: Examining Exhibitions on Knitting and Crochet in Canada and the United States.  Masters thesis, Concordia University.

abstract: This thesis looks at exhibitions on knitting and crochet of the last twenty years in Canada and the United States in order to reflect on the different ways in which revival can be understood or interpreted through them. The two countries’ tight-knit craft alliance throughout the 20th century is mobilized as a point of departure to examine how practices of knitting and crochet, largely absent from 20th century discourse on craft, started being displayed at the turn of the millennium. The case studies consist of four exhibitions staged by museums and galleries alike: Radical Lace & Subversive Knitting (2007) at the Museum of Arts and Design in New York, She Will Always Be Younger Than Us (2009) at the Textile Museum of Canada in Toronto, À toutes mailles! (2013) at the Centre MATERIA in Quebec City and Maison de la culture Marie-Uguay in Montreal, and SUPER STRING (2006) at the Stride Gallery in Calgary. Considering the recent historical development that are 21st century exhibitions on knitting and crochet, the thesis takes the idea of revival as a common denominator and carefully unpacks how they each engage with it, consciously or not. Furthermore, revival being a common topic in craft discourse, the thesis participates by introducing into it the theory of shaped time as elaborated by art historian George Kubler, specifically the ideas of prime objects and formal sequences, to outline each case study’s role in indexing (or perhaps reviving) knitting and crochet as artistic practices. In doing so, the thesis argues that the specificities of knitting and crochet, in their materiality and as practices embedded in rich and long-standing social histories, can be conducive to a broader understanding of cyclical patterns, thus inspire a new methodology toward the critical re-examination of persistent patterns in canonical art history.


identifier: Collins, Genevieve  (2022) Speculative Space Habitats: A Future-Oriented Sensory Research-Creation Project (ETHER).  Masters thesis, Concordia University.

abstract: This research explores the possibilities of sensory perception in outer space through the lens of sensory studies and futures anthropology. It includes reflections on ETHER, an immersive sensory environment designed to engage and combine the senses in novel ways. The exhibition, inspired by the immersive multi-modal installations of David Howes and Chris Salter, blends art and anthropology to craft a discernible atmosphere capable of transporting participants and introducing them to new ‘ways of sensing.’ ETHER, staged in Montreal in 2021, included video projections, aromas, drinks, and an immersive soundscape designed to engage with speculative futures through sense perception. After progressing through the exhibition, visitors participated in small group interviews where they reacted to the experience, reflected on how their sensorium was affected, and discussed themes of outer space and futurity. Sensory ethnography, an approach involving attunement to sense perception and ‘feeling along with’ research participants, constituted a guiding research method. The resulting thesis outlines the exhibition design process, discusses the unique reactions of participants to the immersive sensory environment, and reflexively considers the research-creation methods. This study ruminates on the possibilities of immersive sensory environments to engage with futures, probe the cosmic sensorium, and ultimately inspire a sense of wonder.


identifier: Bahreini, Fardin  (2022) Ontological and Machine Learning Approaches for Inspection of Facilities Using BIM.  PhD thesis, Concordia University.

abstract:
Facilities should be kept in good condition throughout their lifecycle by rigorous inspection processes. The semantic relationships between multiple inspection information during lifecycle phases, and inspection result representation are among the most critical issues that need to be addressed. So far, many studies have been done to identify, analyze, repair, and prevent defects. However, after capturing the defect information, there is a need for an ontology to organize and integrate relevant information and future actions. Additionally, the availability of inspection robots in buildings’ construction and operation phases has led to expanding the scope of applications and increasing technological challenges. BIM models comprise useful information about the building environment’s representation, which can help the inspection robot overcome task complexity. However, the research in this area is still limited and fragmented, and there is a need to develop an integrated ontology to be used as a knowledge model for logic-based inspection of building defects.
Moreover, visual inspection using non-equipped eyes is the principal method of detecting structural surface defects, which is unsafe, time-consuming, expensive, and subjective to human errors. Using remote sensing, such as, cameras and LiDAR scanners, is one solution to overcome these shortcomings. The captured point cloud data from the real environment can assist in detecting the defects and taking further actions. Recently, machine learning methods attracted the attention of researchers for semantic segmentation and classification based on point clouds. However, no deep learning method is currently available for semantic segmentation of concrete surface defects based on raw point cloud data. Furthermore, the BIM model needs to be integrated with the results of defect semantic segmentation after the LiDAR-based inspection.
Addressing the above issues, this research has the following objectives: (1) Developing an ontology for concrete surface defects; (2) Developing BIM-based ontology to cover the different types of information and concepts related to robotic navigation and inspection tasks; (3) Developing a method for point cloud-based concrete surface defects semantic segmentation; and (4) Developing a semi-automated process for as-inspected modeling.
The first part of this research focused on the development of an ontology, called Ontology for Concrete Surface Defects (OCSD), to have a unified knowledge model where all the stakeholders can access information in a systematic manner. OCSD metrics include 333 classes, 51 relations, 27 attributes, and 31 individuals. OCSD comprises high-level knowledge of the concepts and relationships related to surface defects, inspection, diagnosis, and 3R (Repair, Rehabilitation, and Replacement) processes. The application of OCSD was investigated in a case study and a survey was designed to evaluate the semantic representation of OCSD. Based on the evaluation, OCSD was able to provide a clear understanding of the concepts and relationships in the domain, and it can help future asset management systems benefit from the provided knowledge.
The second part of this research focused on the development of an integrated ontology, called Ontology for BIM-based Robotic Navigation and Inspection Tasks (OBRNIT), to extend BIM applications for robotic navigation and inspection tasks. OBRNIT metrics include 386 classes, 45 relations, 52 attributes, and 8 individuals. OBRNIT comprises high-level knowledge of the concepts and relationships related to buildings, robots, and navigation and inspection tasks. BIM is considered as a reference that is integrated with the knowledge model. The semantic representation of OBRNIT was evaluated through a case study and a survey. The evaluation demonstrates that OBRNIT covers the domain’s concepts and relationships up to the point that satisfies the domain experts. Based on the evaluation, OBRNIT was able to give a clear understanding of the concepts and relationships in the domain, and it can help in the future in developing robotic inspection systems.
The last part of this research focused on a method for point cloud-based defect semantic segmentation based on Normal Vector Enhanced Dynamic Graph Convolutional Neural Network (NVE-DGCNN) to automate the inspection process of concrete surface defects, including cracks and spalls. This part investigates two main characteristics related to surface defects, including the normal vector and depth. The network’s performance is improved by modifying the network and augmenting the dataset. Sensitivity analysis is applied to capture the best combination of hyperparameters and investigate their effects on the network performance. NVE-DGCNN resulted in 98.56% and 96.50% recalls for semantic segmentation of cracks and spalls, respectively. Furthermore, post-processing of the results of the defects semantic segmentation is done to semi-automate the process of as-inspected modeling. This semi-automated process made it possible to manage and visualize the detected defects by extracting their dimensions and identifying the conditions on the 3D model.


identifier: Xu, Yunqi  (2022) Recognition of Graphological Wartegg Hand-drawings.  Masters thesis, Concordia University.

abstract: Wartegg Test is a drawing completion task designed to reflect the personal characteristics of the testers. A complete Wartegg Test has eight 4 cm x 4 cm boxes with a printed hint in each of them. The test subjects are required to use a pencil to draw eight pictures in the boxes after they saw these printed hints. In recent years, the trend of utilizing high-speed hardware and deep learning based model for object detection makes it possible to recognize hand-drawn objects from images. However, recognizing them is not an easy task, like other hand-drawn images, theWartegg Test images are abstract and diverse. Also,Wartegg Test images are multi-object images, the number of objects in one image, their distribution and size are all unpredictable. These factors make the recognition task on Wartegg Test images more difficult. In this thesis, we present a complete framework including PCC (Pearson’s Correlation Coefficient) to extract lines and curves, SLIC(Simple linear Iterative Clustering Algorithm) for the selection of key feature points, DBSCAN(Density-based spatial clustering of applications with noise) for object cluster, and finally utilize transfer learning to increase the converging speed during training and deploy YoloV3-SPP model(A deep learning network) for detecting shapes and objects. Our system produced an accuracy of 87.9% for one object detection and 75% for multi-object detection which surpass the previous results by a wide margin.


identifier: Nejadgholi, Mahdi  (2022) Nullification, a coercion-resistance add-on for e-voting protocols.  Masters thesis, Concordia University.

abstract:
Coercion is one of the remaining issues on internet voting. Many developing
countries are prone to this problem due to lower income rate. In this study, a novel coercion-resistant protocol has been proposed that can be integrated with previously proposed e-voting systems. We call it nullification. We present it as a part of the VoteXX e-voting protocol that has been designed and implemented through this study.

Nullification gives the voter a strategic advantage over the coercer. The voter can share her keys with a trusted proxy, called a nullifier, for later flipping that vote. Integrity and ballot secrecy are provided simultaneously through the use of zero-knowledge proofs, specifically Σ-protocols. We show how our approach is different from (and potentially composable with) re-voting or panic password techniques that have been previously proposed in the academic literature.
Through designing this protocol, we solve several issues, design new Σ-protocols and protocols for the secure evaluation of basic logic functions like exclusive-or (xor) under encryption: True XOR and Online XOR have been proposed that improves the previously proposed Mix and Match protocol for secure multi-party computation of an arbitrary function under constrained input domain.


identifier: Sun, Ying  (2022) Fairness-Aware Data-Driven Building Models (DDBMs) and Their Application in Model Predictive Controller (MPC).  PhD thesis, Concordia University.

abstract:
In recent years, the massive data collection in buildings has paved the way for the development of accurate data-driven building models (DDBMs) for various applications. However, due to the variation in data volume of different conditions, existing DDBMs may present distinct accuracy for different users/occupants or periods/conditions. Accuracy variation among users or periods may creates unfairness problems (i.e., algorithmic biases created by data-driven models). This thesis explores and tackles this research problem called fairness-aware prediction of DDBMs.
This thesis first presents a comprehensive review of the entire process involved in developing a DDBM and emphasizes the research gap on achieving fairness in DDBMs. As the first research that introduces fairness concepts into the building engineering domain, this thesis summarizes three types of commonly used fairness definitions. Among these concepts, achieving Type I and Type II fairness in DDBMs shows the beneficial for enabling users to do authority management, achieving uniform predictive performance under different periods or situations, and preserving fairness for different users. In addition, this thesis reviews the commonly used fairness improvement methods for data-driven models.
Then, with the aim of improving fairness for DDBMs to have uniform predictive performance under different conditions and letting MPCs in buildings get optimal control signals based on fair prediction, this research proposes fairness improvement methods for both classification problems and regression problems in the building engineering domain and integrates fairness-aware DDBMs into model predictive controllers (MPCs). This work is separated into three tasks: 1) Task A: For classification problems, four kinds of pre-processing methods are proposed to balance the training dataset. 2) Task B: For regression problems, four in-processing methods, which incorporate fairness-related constraints or penalties into the optimization objective function during the training process of data-driven models, are studied. 3) Task C: The fairness improvement methods proposed in Task A and Task B will be integrated into MPCs.
Case studies are conducted to implement the proposed fairness improvement methods to DDBMs for apartments, develop and integrate the fairness-aware DDBMs into MPCs to get the optimal set-point temperature for controlling the electrically heated floor system (EHF, a heating system with energy storage ability) in a bungalow building. The results show that 1) The proposed pre-processing methods could improve the predictive accuracy of minority conditions and increase fairness in terms of the accuracy rate between different conditions. 2) The proposed in-processing methods could achieve user-defined trade-off between accuracy and fairness. The Type II fairness is achieved by increasing the predictive performance similarity between different conditions. 3) Although improving predictive fairness would decrease the overall predictive accuracy, fairness-aware data-driven based MPCs would not decrease the cost saving and peak shifting ability, compared to the traditional MPC without considering fairness.


identifier: Grealish, Patrick  (2022) Private Platforms, Recommendation Algorithms and Agency: A Study of Tinkerers on YouTube.  Masters thesis, Concordia University.

abstract:
The internet and the algorithms designed by private technology companies have become an important sub-field of social science concerned with agency online. The purpose of this research is to examine contemporary online platforms and the predominance of their recommendation algorithms on users to better understand the techniques online users employ to enact agency.

This research develops as a response to apprehension for agency in an online world driven by advertisement-based algorithms, as well as the social problems associated with the sentiment of lack of agency that emerges from these models. This research aims to examine two questions: First, do platform users have agency online when considering the functions of recommendation algorithms? Second, what role, if any, do recommendation algorithms play in that possible agency?

Questions of agency necessarily raise in importance as communication and information sharing moves to online platforms that use algorithms to classify and effect human action. This research is pertinent because its goal is to create knowledge so that we may better understand and act online.

The methodology is a combination of three stages of data collection; initial documentary research of available information provided to the public by Google and independent sources; documentary and case study research of data generated from YouTube and Google; a judgement sampling method to select YouTube users and a thematic analysis of their experiences. This method considers chosen users as expert informants in cases of controversy that help explain collective existence on the platform.

The findings of this research support previous research critical of the manipulative nature of algorithms. This research also contributes to a nuanced view of agency online by finding that agency does occur within technology-literate collaborative groups of users. Conceptualizing content creators and recommendation algorithms as a network of actors within a social context better explains cases of agency experienced by users. The findings support that greater knowledge of technology allows individuals greater affordances of agency.
The research proves that platform content creators are fertile informants for a study of platforms. The typology of content creators is an expansion of previous tinkerer research that supports the continued pertinence of a tech-knowledgeable user typology in sociology concerned with a lack of agency online.


identifier: Avinash, Shourya  (2022) Additive Combinatorics.  Masters thesis, Concordia University.

title: Additive Combinatorics

abstract: Abstract - A set family F that is a subset of 2^[n], [n]={1,...,n} is said to have the Eventown property if all its component sets are even sized and the intersection of any two of these sets is even sized. The Eventown theorem states a bound for the size of F in this case, namely |F| ≤ 2^[n/2]. The aim of the thesis is to discuss a generalization of the Eventown theorem through the lens of additive combinatorics.


identifier: Khan, Sehrish  (2022) Reaction-diffusion formation of copper aspartate in gel.  Masters thesis, Concordia University.

abstract: Self-assembly of metal ions and amino acids to form coordination polymers is well known. In the past, coordination polymers have been mostly synthesized via solvothermal methods. Herein, the crystallization of coordination polymers in gels is investigated. Crystal growth in gels is dominated by diffusion and often leads to high quality single crystals. We demonstrate the crystallisation of Cu(L-Asp), Cu(D-Asp) and Cu(D/L -Asp) in  agar gel which leads to coordination polymers with spherulitic morphology. The crystallization of CuAsp in gel also displayed Liesegang pattern formation. The Liesegang ring phenomena was optimized by investigating various parameters, including amino acid and Cu2+ concentrations and chirality. The coordination polymers were characterized by scanning electron microscopy (SEM), Brunauer, Emmett, and Teller (BET) surface area analysis, circular dichroism (CD), powder X-ray diffraction (PXRD) and infrared spectroscopy (IR). The PXRD of the Cu(L-Asp), Cu(D-Asp) and the Cu(D/L-Asp) were identical which indicated conglomerate formation in the case of Cu(D/L-Asp). This result was further investigated using circular dichroism and with the use of chiral additives in gels. Based on previous studies of copper coordination polymers and the structural similarity between the Cu(L-Asp) and Cu(L-Glu), a square planar geometry is proposed for Cu(D/L-Asp) with a head-to-tail arrangement.


identifier: Mufarrij, Farah  (2022) Development and comparative lifecycle assessment of various LDPE and HDPE production processes based on CO2 capture and utilization.  Masters thesis, Concordia University.

abstract:
Low-density polyethylene (LDPE) and high-density polyethylene (HDPE) are some of the most commonly used materials worldwide. These polymers are typically produced through the polymerization of ethylene which is conventionally produced through the energy-intensive steam cracking of naphtha or ethane. However, the conventional production process for LDPE and HDPE results in significant greenhouse gas emissions (1.92 and 1.86 kg CO2/kg of polymer, respectively).
Hence, this work focuses on the design of alternative pathways based on CO2 capture and utilization (CCU) for polymer production. The proposed pathways for CCU-polymers are based on the conversion of CO2 to methanol, followed by the methanol-to-olefins (MTO) process to produce mainly ethylene, and consequently LDPE and HDPE from ethylene. The process design and simulation of the entire pathway are conducted in AspenPlus, while the life cycle assessment (LCA) is done through OpenLCA.
The LCA results showed that the CCU-MTO pathway is an environmentally attractive option, particularly in regions where renewable (low-carbon) electricity is more prominent, such as Quebec and Ontario, where negative CO2 emissions are achieved. This makes polymer production via the proposed method suitable for the permanent mitigation of CO2.


identifier: Forouhar, Vida  (2022) Weight Bias: Trends Among the Canadian Public and Relationships with Physical Activity and Sedentary Behaviour.  Masters thesis, Concordia University.

abstract:
Introduction: Weight bias is a social justice issue in Canada. It is perpetuated by negative attitudes about individuals with obesity and about the causes of obesity. Research on the association between explicit and internalized weight bias and physical activity and sedentary behaviour is limited, especially among population-based samples. Data on weight bias internalization (WBI) and beliefs about the causes of obesity among Canadians is also lacking.
Objectives: The primary objectives of this study were to describe the level of WBI among Canadians and describe how Canadians attribute obesity to different causes; and to examine the relationships between weight bias and physical activity and sedentary behaviour.
Methods: A sample of Canadian adults (N = 942; 51% female; mean body mass index [BMI]= 27.3 ± 6.7 kg/m2) completed an online survey. Questionnaires included the Anti-Fat Attitudes Questionnaire, Modified Weight Bias Internalization Scale, Causes of Obesity Questionnaire, International Physical Activity Questionnaire, and the Sedentary Behavior Questionnaire.
Results: WBI scores (3.38 ± 1.58) were higher among females and individuals with higher BMIs (p < 0.001 for all). Participants mainly endorsed behavioural causes of obesity. WBI was associated with more weekly hours of sedentary behaviour (B = 0.85, p < .001). Explicit weight bias was associated with more weekly minutes of vigorous physical activity (B = 12.87, p < .05).
Conclusions: This study highlights WBI as a problem that is associated with adverse health behaviours among all individuals across the weight spectrum. Future research should investigate the longitudinal impact of weight bias on health behaviours.


identifier: LeCocq, Mandy Rita  (2022) The Psychological and Neural Mechanisms Underlying a New Model of Reinstatement of Responding to an Alcohol-Predictive Cue.  PhD thesis, Concordia University.

abstract:
Environmental stimuli that predict alcohol availability pose a significant threat to maintaining abstinence from alcohol use. Through Pavlovian conditioning, these stimuli can become cues that predict alcohol availability which can precipitate relapse in clinical populations and preclinical models. Preclinical research has largely focused on examining the immediate impact of alcohol-cues on the relapse-like return of responding for alcohol (i.e., reinstatement); however, the delayed impact of such cues on behaviour has rarely been investigated. In the current thesis, a new reinstatement model was developed to evaluate the delayed impact of re-exposure to alcohol on the return of responding for alcohol. The psychological and neural mechanisms that underlie delayed reinstatement in the new model were then investigated.
A Pavlovian conditioning task was used in which rats learned to respond to a conditioned stimulus (CS) that was paired with alcohol, followed by extinction of this response. Re-exposure to alcohol reinstated responding to the CS 24 h later, relative to responding during extinction. Additional procedures demonstrated that re-exposure to alcohol, and not another liquid made distinct from alcohol, reinstated responding to the alcohol-CS, indicating that preferential reinstatement was produced by re-exposure to alcohol compared to a control liquid.
Behavioural studies revealed that the delayed reinstatement of responding to the alcohol-CS was driven by an association that formed between alcohol and the context in which alcohol re-exposure was conducted. Reinstatement was prevented when the context that alcohol re-exposure occurred in was extinguished. Moreover, reinstatement was reduced when alcohol re-exposure was conducted in a context that differed from the test context.
Pharmacology studies revealed that µ-opioid receptors (MORs) are necessary for the delayed reinstatement of responding to the alcohol-CS. Systemically blocking MORs attenuated reinstatement, without affecting locomotor activity. Further, it was shown for the first time that blocking MORs in the ventral hippocampus prevented reinstatement.
The novel delayed reinstatement model presented in this thesis helps establish a comprehensive understanding of how alcohol-cues can influence relapse. Moreover, a detailed understanding of the psychological and neural mechanisms underlying the delayed reinstatement model can inform the development of new behavioural and pharmacological treatment interventions against relapse.


identifier: Ahmed, Reem  (2022) Development of an Integrated Data-Driven Budget Allocation Approach for Maintenance Management in Healthcare Facilities.  PhD thesis, Concordia University.

abstract: Healthcare facilities are fundamental infrastructure assets as their number and quality are common measures of a country’s prosperity and quality of life. Despite Canada being one of the highest countries all over the globe in the health spending, the Canadian hospitals were described in multiple reports as facilities with a crumbling status with a poor condition rating. This was determined according to their high value of deferred maintenance as opposed to their current replacement values. As a result, this study was initiated with the objective of developing a comprehensive asset maintenance and renewals management framework replacing the current approaches in place. The developed framework was thus expected to enhance the performance of hospital buildings assets and efficiently utilize the funds assigned for healthcare facilities on an informed and objective basis. The objective of this study was achieved through four different phases tackling various levels of the healthcare decision-making hierarchy, namely: Asset-Level, Facility-Level and Network-Level. The first pillar introduces an automated priority setting methodology for assets in hospital facilities utilizing multi-criteria decision-making techniques as well as Python-programmed supervised learning algorithms. The following model is concerned with forecasting the possible deterioration in the hospital assets on an integrated mechanism combining between a Matlab-based fuzzy inference system, Markovian models, and metaheuristics. Moving on to a higher level in the decision-making process, a facility renewal scheduling model is advanced to incorporate healthcare-tailored objectives into the planning process. This tri-objective model aims at the reduction of associated wait times and cost while maximizing the performance enhancement gained from the renewal interventions application. Furthermore, unsupervised learning clustering algorithms were used as part of this model to generate a further reduction in the wait times related to renewal intervention applications by grouping relevant interventions together according to the resources available, their location and their priority levels. Finally, a network-level budget allocation model was established relying on the outputs of previous models as inputs for an informed and objective distribution of available budget across hospitals located within one network. The first three models were applied on case study hospitals from Canada and Egypt, and they demonstrated a significant improvement in the current status of assets and facilities. While the final model was applied on a network of hospitals in the province of Alberta and all previous models were re-applied on the current assets and facilities to enable the application of the network-level model. The model results were compared to the actually selected and implemented interventions as well as the allocated budgets, and the model proved an improvement in the prioritization of the assets of 25.97%, a reduction in the deterioration prediction error of 39.29% as compared to the currently implemented mechanism on assets. The facility-level scheduling and clustering models demonstrated a criticality weighted performance enhancement of 28.01%, while maintaining a reduction in the associated downtime of 17.40%. Lastly, applying the network-level budget allocation model resulted in an 33.64% higher network performance for the same budget allocated as per collected records. This proves the capabilities of the developed models in producing more sound and informed decisions relating to healthcare asset management and accordingly, reducing the associated wait times to renewal interventions, refining the overall healthcare performance levels while maintaining minimal budget expenditure possible.


identifier: Mohammed, Ahmed  (2022) Resilience-Based Asset Management Framework for Pavement Maintenance and Rehabilitation.  PhD thesis, Concordia University.

abstract:
Infrastructure systems play a pivotal role in developing the economy and public services, which positively affects the quality of life of the communities. Thus, it is of paramount importance to investigate the current infrastructure capacity, assess its capability to sustain the anticipated disruptions, then plan the necessary recovery strategies to reduce their detrimental significance and increase their resilience. The growing decline in roads condition has recently grasped the attention of numerous researchers and practitioners regarding road resiliency during its life-cycle. 62.6% of roads in Canada are in good condition, according to Canada Infrastructure Report (2016). Nevertheless, with current investment rates, significant road networks will suffer a decline in their condition and will be vulnerable to sudden failure (FCM 2016). On the other side, the current situation in the U.S is inferior, where roads are in poor condition, classified as grade D, and not to mention the insufficient investment required to maintain road networks (ASCE, 2017).
Accordingly, this research tackles pavement resilience from an asset management perspective where; it highlights the fact that infrastructure should maintain its resiliency during its life-cycle to maintain a minimum acceptable Level of Service (LOS). The main objective of this research is to develop a resilience-based asset management framework for pavement maintenance and rehabilitation (M&R). The proposed methodology involves a set of sequential steps as follows; 1) define infrastructure resilience, 2) investigate resilience-related indicators in the same dimension of resilience definition, 3) develop a resilience-based asset management model for M&R decisions, 4) optimize the attained M&R plan for short and long-term decisions, and 5) formulate a resilience index. First, resilience is defined based on a comprehensive review of the previous literature and targeting an integrated definition that combines both asset management and resilience concepts. Then, resilience-associated indicators are investigated based on the predefined resilience definition, and the different indicators are later classified and modeled for a pavement network.
The resilience-based asset management model is carried out through the development of five components; 1) a central database of asset inventory that includes numerous data that would serve as input for the proposed model, 2) a pavement condition and level of service (LOS) assessment models that encompass the different effects of climatic conditions on pavement condition, surface, and structural conditions, and LOS, 3) regression modeling of the effect of Freeze-Thaw on pavement and investigation of flooding effect on both pavement surface and structural conditions, 4) financial and temporal models for recovery/intervention actions are formulated through computational models that account for the intervention costs and time, then link them to the later used optimization model, and 5) an optimization model to formulate the mathematical problem for the proposed resilience assessment approach and integrate the formerly-mentioned components. The utilized optimization model employs a single objective that relies on a combination of meta-heuristic rules. Genetic algorithms are utilized as an innovative idea that formulates the mathematical denotation for the proposed resilience definition. Principle Components Analysis (PCA) is used and manipulated as a novel method to establish resilience indicators’ weights and compute the resilience index. A PCA framework is developed based on optimization model output to generate the required weights for the desired resilience index. This model offers dynamic resilience indicators’ weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems, where it differs during their lifecycle with the change in maintenance and rehabilitation plans, systems retrofit, and the occurring disruptive events throughout their life-cycle.
The proposed model serves as an initial step toward providing more resilient municipal infrastructures. The model emphasizes that recovery plans should follow proactive measures to adapt to sudden or unforeseen events rather than just adopting a reactive approach, which deals with the sudden events after their occurrence. This pavement resilience assessment framework is also beneficial for asset management experts. M&R plans would not only target enhancing or restoring pavement condition or LOS but also incorporate the implementation of proper recovery strategies for both regular and extreme events into the M&R plan while taking the natural deterioration and aging effects into account. Two case studies were undertaken to demonstrate the effectiveness of the proposed methodology.


identifier: Gouda Mohamed, Omar  (2022) Effect of GFRP-Concrete Bond Characteristics on the Flexural and Serviceability Behaviour of GFRP-RC Beams.  PhD thesis, Concordia University.

abstract:
The GFRP bars are characterized by their lower elastic modulus and higher tensile strength compared to steel; hence, the design of the GFRP reinforced concrete (RC) flexural members is controlled by the serviceability limit states, including deflection and crack width. Several research studies have experimentally investigated the flexural and serviceability behaviour of GFRP-RC flexural members. This research contributes to understanding the flexural and serviceability performance of concrete beams reinforced with GFRP bars through analytical, experimental, and numerical work. The main objective is to provide advanced knowledge about the effect of using GFRP bars as an internal reinforcement on the flexural and serviceability response of concrete beams. In addition, this research assesses the current design equations in the North American standards and guidelines; and provides design recommendations to improve the design of GFRP-RC beams.

The study started with a theoretical investigation that included a review of the different factors affecting the bond behaviour of the GFRP-RC beams. The study assessed the different parameters affecting the development length equation in the CSA S806 (2012) standard. This study was performed by analyzing 431 beam-bond tests compiled from the literature. Based on a linear regression analysis, two development length equations were proposed to represent a modified form of the current CSA S806 (2012) equation. The proposed equations were compared to other equations in different design standards and guidelines to determine the efficacy of the proposed equations.

The study then experimentally investigated the different parameters influencing the GFRP bar-concrete bond interaction by testing 24 GFRP-RC beams. Previous studies showed that the GFRP bars’ bond performance in concrete affects the crack width of the RC beams. The crack width control in the different standards and guidelines accounts for the bond interaction between the GFRP bar and the surrounding concrete through the bond-dependent coefficient, kb. The kb values of the GFRP bars were obtained from the CSA S806 (2012) crack width equation by performing a well-defined test in the CSA S806 (2012) standard. The studied parameters were the clear concrete cover to GFRP bars, concrete compressive strength, bar diameter, number of GFRP reinforcement layers, confinement effect due to closely spaced stirrups, bar surface profile, and spacing between the reinforcing rebars. The study assessed the current deflection equations in the CSA S806 (2012) standard and ACI 440.1R (2015) guideline. Moreover, the flexural design equations in the different design provisions, including the prediction of the ultimate capacity, cracking moments, and moments at different serviceability limit states, were evaluated. The experimental study evaluated the available approaches in the literature used to quantify the deformability of the GFRP-RC beams to determine the optimal method of the deformability calculation and to check the proposed limits by the CSA S6 (2014) standard. The experimental results showed that the spacing between GFRP bars (at the same reinforcement ratio) and the confinement effect due to closely spaced stirrups influence the cracking and deflection behaviour. Moreover, it was found that the proposed effective moment of inertia equation provided more conservative results than the ACI 440.1R (2015) equation.

Finally, a numerical investigation was conducted using ATENA software. The study validated the numerical model to check the effectiveness of the conducted models in simulating the real beam behaviour. After that, a parametric study was performed to investigate the sensitivity of the nominal moment capacity and deflection values at different service loading levels of the GFRP-RC beams to the different material and cross-sectional parameters. The numerical results showed that the moment capacity and deflection values at the service stage are highly sensitive to the change in the concrete compressive strengths and GFRP bars’ elastic modulus. The design of GFRP-RC can be improved through a prudent choice of the different design parameters.


identifier: Kapoor, Pranay  (2022) Silver Surfers on The Tech Wave: Privacy Analysis of Android Apps for The Elderly.  Masters thesis, Concordia University.

abstract: Like other segments of the population, elderly people are also rapidly adopting the use of various mobile apps, and numerous apps are also being developed exclusively focusing on their specific needs. Mobile apps help the elderly to improve their daily lives and connectivity, their caregivers and family members to monitor their loved ones' well-being and health-related activities. While very useful, these apps also deal with a lot of sensitive private data such as healthcare reports, live location, and Personally Identifiable Information (PII) of the elderly and caregivers. While the privacy and security issues in mobile applications for the general population have been widely analyzed, there is limited work that focuses on elderly apps. We shed light on the privacy and security issues in mobile apps intended for elderly users, using a combination of dynamic and static analysis on 146 popular Android apps from the Google Play Store. To better understand some of these apps, we also test their corresponding IoT devices. Our analysis uncovers numerous security and privacy issues, leading to the leakage of private information and allowing adversaries to access user data. We find that 95/146 apps fail to adequately preserve the security and privacy of their users in one or more ways; specifically, 15 apps allow full account takeover, and 9 apps have an improper input validation check, where some of them allow an attacker to dump the database containing elderly and caregivers' sensitive information. We hope our study will raise awareness about the security and privacy risks introduced by these apps, and direct the attention of developers to strengthen their defensive measures.


identifier: Brisbois, Alex  (2022) Assessment of the Feasibility of Pumps-as-Turbine in Water Distribution Networks with the Support of Machine Learning.  Masters thesis, Concordia University.

abstract: The water-energy-carbon nexus elucidates the potential for energy recovery and more sustainable solutions for energy-intensive water systems. Installing turbines or pumps as turbines (PaTs) in key areas of water distribution networks to recover energy can avoid wasted energy as well as reduce leakage caused by high water pressure. Previous studies have proposed models to predict PaT characteristics based on pump best efficiency point (BEP). These models apply statistical methods or artificial neural networks (ANNs) to determine the turbine characteristics. Nevertheless, other simpler machine learning methods could yield better predictive models. In addition to the PaT’s characteristics, its feasibility is also determined by the local operating hydraulic conditions. Previous PaT optimization studies generally consider one day of operation and few PaTs. The goal of the study is to develop a machine learning model to predict PaT performance, combined with a flexible optimization process to determine the most feasible selection and location of PaTs. For the predictive models, a database of 145 best efficiency points and 196 characteristic curves PaT experimental records were compiled. The machine learning models consistently outperformed all other models, including the current ANN, and previous models. A genetic algorithm (GA) was applied to maximize PaT power output and was applied to three example networks, modelled with EPANET. Combined with the large library of PaTs the GA was able to quickly converge to the best solution and find significant opportunities for energy recovery. Convergence on solutions for 3 distinct networks of different complexities was able to be determined within acceptable iterations. The solutions showed a significant power recovery of 44.16 MWh/year, 34.07MWh/year, and 124.22MWh/year with reasonable payback periods within 5 years.


identifier: Keenan, Kelly  (2022) Anatomical Imaginaries: Relational embodiments of anatomical discourse in contemporary dance training.  Masters thesis, Concordia University.

abstract: In this thesis I explore how contemporary dancers’ ‘sense-abilities’ and movement are shaped by anatomical discourse in dance training. Part 1 introduces the problem of how anatomy risks reducing conceptions of the body and asks how we may foster expansive relationships to anatomy. Part 2 exposes a troubling number of binaries that are upheld through the tendency of anatomy to universalize the body into foundational truths. Part 3 details a sensory ethnographic method that foregrounds the practice of relating to anatomy in contemporary dance. My Practice as Research project, “Anatomical Imaginaries”, invited five anatomically trained professionals to compose an audio description of the head-tail relationship. I then provided a context for contemporary dance artists to engage with these imaginaries through movement exploration while attending to their unique sensorial experiences. Part 4 understands my project thinking with: contemporary anatomy which offers an abundance of methods and models; sensory anthropology that understands culture to have influence on sense-abilities; and feminist science and technology studies that foregrounds how practices are culturally informed and informing; all of these fields trouble universalizing ways of knowing. I conclude by encouraging a proliferation of the models we dance, weaving together sociocultural and feminist theory as a means of unravelling the binaries upheld through scientific effects of truthfulness.