identifier: Zheng, Zhaohan ORCID: https://orcid.org/0000-0001-5278-3295 <https://orcid.org/0000-0001-5278-3295>  (2021) A Study and Improvement for Copper Electroforming on Additively Manufactured Mandrels.  Masters thesis, Concordia University.

abstract:
An idea of combining Additive Manufacturing (AM) and electroforming is coming up for metal manufacturing, which could not only fulfill the increasing needs of low batch size production but also be able to fabricate complex structures like thin-walled components. Compared to direct metal printing, it is more cost-efficient and requests lower energy consumption. However, electroforming on AM mandrel has its limitation in several perspectives. During the process, undesired voids could be build-up, resulting in a rough surface and reduced mechanical properties. Also, the various distance between the mandrel and counter-electrode increased the non-uniformity of the deposit thickness. The outer contour of the deposition is no longer matching the original design.
On the other hand, the thin-walled structures as an essential component in several industrial areas provide several challenges to the conventional manufacturing industry. The difficulty of manufacturing increases along with the aspect ratio. Typically, these structures are fabricated with Electrical Discharge Machining (EDM). With the rise of AM technology, direct metal printing has recently become a new solution for complex metal structures. However, high porosity and high thermal stress limit direct metal printing in many situations.
In this thesis, several methods are proposed to increase the precision of the electroformed part geometrically. As well as propose a method for thin-walled structure manufacturing, which provides another potential solution for the manufacturing industry.


identifier: Al-Maadhidi, Zaineb  (2021) Analysis of Retrofitted Concrete Columns using 3D Elastic-Plastic Modeling.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract: Fiber Reinforced Polymer (FRP) jacketing is frequently used to enhance the performance of concrete columns. The strength and ductility of a concrete column increase after FRP jacketing mainly because of concrete’s improved behaviour under confinement. To achieve a reliable retrofit design, engineers often need to understand how much the performance is improved after FRP jacketing. For that purpose, numerical modelling is often necessary. Phenomenological elastic-plastic models are widely used for the numerical modelling of concrete because of their capability of representing 3D concrete behaviour considering permanent inelastic deformations. This research aims to implement an elastic-plastic model to simulate FRP-jacketed concrete columns. The 3D material model is validated against existing experimental data and comparisons with the results of models developed in ABAQUS software. It is shown that the proposed modelling approach is capable of providing an accurate behaviour of square concrete columns confined with reinforcements and FRP jackets under compression. After the validation of the model, a parametric study is conducted to illustrate the consequence of partial wrapping on the behaviour of retrofitted square concrete columns and to test the effect of FRP and concrete properties on the confined concrete strain. The obtained results from the parametric study are then used to acquire a suitable confined concrete strain equation/formula using a nonlinear regression technique. The core of this technique is based on an optimization method that finds the optimal coefficients for the proposed equation, helping in choosing the equation with the best fitting performance to the confined strain that is gained from the 3D material model. This proposed equation is recommended to improve the Canadian Standards by describing the impact of material properties on the confined concrete strain.


identifier: Tabarsaii, Soudabeh  (2021) non intrusive load monitoring using additive time series modeling via finite mixture models aggregation.  Masters thesis, Concordia University.

abstract:
Due to an exponential rise in energy consumption, it is imperative that buildings adopt sustainable energy consumption systems. A number of studies have shown that this can be achieved by providing real-time feedback on the energy consumption of each appliance to residents. It is possible to accomplish this through non-intrusive load monitoring (NILM) that disaggregates electricity consumption of individual appliances from the total energy consumption of a household. Research on NILM typically trains the inference model for a single house which cannot be generalized to other houses. In this Master thesis, a novel approach is proposed to tackle mentioned issue.This thesis proposes to use two finite mixture models namely generalized Gaussian mixture and Gamma mixture, to create a generalizable electrical signature model for each appliance type by training over labelled data and create various combinations of appliances together. By using this strategy, a model can be used on unseen houses, without extensive training on the new house.
The issue of different measurement resolutions in the NILM area is also a considerable challenge. As a rule of thumb, state-of-the-art methods are studied using high-frequency data, which is rarely applicable in real-world situations due to smart meters' limited precision. To address this issue, the model is evaluated on three different datasets with different timestamps, AMPds, REDD and IRISE datasets.  To increase the aggregation level and compare with RNN and FHMM as two well-known methods in NILM, an extension that we called DNN-Mixtures, is proposed. The results show that the proposed model can compete with state of art techniques. For evaluation, accuracy, precision, recall and F-score metrics are used.


identifier: Avorgbedor, Alan ORCID: https://orcid.org/0000-0002-4812-5553 <https://orcid.org/0000-0002-4812-5553>  (2021) African Technicity and Architectures of Being.  PhD thesis, Concordia University.

abstract:
This study examines the nature of sub-Saharan African architectural and aesthetic expression and its relationship to notions of modernity and their epistemological forms. Through an analysis of predominantly West African paradigms of embodied expression, I explore the cultural and moral grounds for individual and communal personhood that are produced in participatory, embodied, and relational capacities within natural and built environments in the African context. These culturally embedded processes help to develop indices of embodied awareness that direct epistemological, spiritual, moral, and aesthetic attunements that represent modes of an often ignored or under-appreciated suite of, what this dissertation proposes as, African Technicities. These technicities, embodied reflections, or techniques of cosmological projection orient the moral character of individuals and communities toward an ancestral home. They represent traditional African dwelling practices that help to decolonize the historically universalizing regime of Western architectural discourse.

The ancestral home and its traditional base for the maintenance of cultural values is both site-specific and materially expressed through an organic, earth-based materiality that is manifest in traditional dwellings and spatial organizations that center communal living. Importantly, I argue that the ancestral home, apart from its geographical, cartographical, and/or built expression, also represents a personal and communal projection that productively motivates responsive future imaginaries based on actual traditional values, rituals, and belief. It represents an inherently decolonial strategy, but one that authentically and actively “looks back” (much like the image of the ubiquitous SANKOFA Adinkra symbol of Akan origin from Ghana, West Africa) to progress forward and reclaim the tools of cultural dignity and determination from traditional repositories of ancestral wisdom and belonging.

As such, performance-based song and dance traditions and ritual economies of communal and spiritual cleansing and determination dynamize spaces for bodily expressivity. These performances inform the kinds of encounters and engagements that are aesthetically expressed in sacred and secular, as well as material and spiritual precincts. Traditional embodiments and cultural activity unfold avenues for individual and communal orientation that become morally projective, hodological (path-oriented), and character driven expressions. These cultural expressions, so unique to the sub-Saharan African context, exceed the conventional object-oriented, edifice-centric, or abstracted virtualizations of the natural and built environment, along with their presumptive aesthetic underpinnings, prescribed through Western architectural taste and discourse.

This research project concludes that in light of the hegemonic influence of Western architectural and aesthetics discourse—as the predominant historical, philosophical, and legitimizing agent of often problematic and racist portrayals of African architectural engagements, bodily expressivity, and their interrelations—a re-appraisal of the dynamics of African traditional living engagements from pre-colonial, colonial, and postcolonial environments can help to recenter the significance of locally produced African Technicities and their embodied wisdom. The proposal is, consequently, an anterior projection, always re-centering ancestral networks within a procession from the temporal to intemporal and from material to spirit. In other words, the cultural processes that develop African personhood and community in natural and built environments stress less the linearity of progressive form captured within conventional notions of modernity, and more the dynamic call and response or embeddedness of the ancestral spirit within diagrams of home that are embodied. Ultimately, this research aims to contribute to the growing bodies of knowledge that will counter and offer a more substantive analysis and appreciation of sub-Saharan African architectural environments and their aesthetic embodiments. Through a focus on African technicities and their contribution to epistemological and embodied modes of inhabitation, this study attempts to not only philosophically situate the contemporaneity of such traditions, but their enduring relevance for the way that we may understand how embodied dwelling practices can shape the future. This piece thus represents a major contribution to the current body of discourses that seek to develop a more plausible alternative for modes of representing the sub-Saharan African experience in and approaches to situated habitation and the built environment.


identifier: Mastronardi, Karina ORCID: https://orcid.org/0000-0001-9860-8409 <https://orcid.org/0000-0001-9860-8409>  (2021) A study of the mechanisms regulating cytokinesis and tissue morphogenesis during C. elegans embryogenesis.  PhD thesis, Concordia University.

abstract:
Morphogenesis and cytokinesis are both regulated by cytoskeletal dynamics. In Chapter 2, we explore C. elegans anterior morphogenesis, where the epidermis covers the head of the embryo and helps to form an anterior lumen with the pharynx. Mechanisms must be coordinated at both the cellular and tissue levels to give rise to complex structures, which have been challenging to study in vivo. We determined how cells from three different tissues are coordinated for anterior morphogenesis. By examining the localization of the polarity protein PAR-6, we observed how polarized projections from pharyngeal, neuroblasts (neuronal precursor cells) and epidermal cells form specific patterns. We proposed that the cells forming these patterns provide and/or respond to cues that regulate epidermal cell migration. In support of this, we found that disruption of the pharyngeal cells or neuroblasts affected the rate of epidermal cell migration and epithelialization of the pharynx, while altering epidermal cell fate caused lumen positioning defects. Our studies are the first to characterize anterior morphogenesis and lay the framework for identifying how cell patterns are controlled at the molecular level.
In Chapter 3, we show how cytokinesis is regulated in different cell types. Cytokinesis occurs at the end of mitosis due to the ingression of an actomyosin ring that cleaves the cell into two daughters. However, the pathways controlling ring assembly are not fully understood and likely vary with cell type. Our lab found that the Ran pathway spatially regulates ring position in response to chromatin in mammalian cells (Beaudet et al, 2017; Beaudet et al., 2020). We tested the hypothesis that the requirement for the Ran pathway changes with cell fate in the early C. elegans embryo. The zygote (P0) divides asymmetrically to give rise to AB and P1 cells with different fates, and we found that each cell has distinct ring assembly kinetics. The differences in AB and P1 cell kinetics are due to differences in the levels of inherited actomyosin, and the Ran pathway. Importantly, we found that the Ran pathway could regulate different downstream targets in AB vs. P1 cells that control ring assembly.


identifier: Zhao, Yunfei  (2021) Three Essays on Sustainability, Bank Solvency, and Stock Price Returns.  PhD thesis, Concordia University.

abstract:
My dissertation consists of three essays in which I explore the effect of various types of news announcements (presidential tweets, natural disasters, and corporate innovations) on the stock price performance of firms and the solvency of banks.

Using event-study methodology and cross-sectional regression analysis, my first essay explores the effects of U.S. President Donald Trump's messages (tweets) on the stock prices of media and non-media companies. For media firms, we find that positive tweets have a pronounced positive stock price impact, whereas negative and neutral tweets have little or no effect. For non-media firms, we observe the opposite: negative tweets tend to be associated with significant stock price declines whereas neutral and positive tweets incur weekly positive stock price reactions. The paper provides important insights re. the ability of political figureheads to move stock prices on one hand and investors’ ability to differentiate between presidential statements that are inconsequential for the affected firms or may have long-lasting implications on the other hand.

My second essay is based on a comprehensive dataset on natural catastrophes around the world and detailed financial statements for 9,928 banks that operate in 149 countries from 1990 to 2017. We use a variety of empirical analyses to explore (1) whether and how natural disasters affect bank solvency, (2) how accounting and regulatory measures of bank solvency reflect a bank’s true affectedness, and (3) whether the effects differ across different types of banks. This study adds to the discussion of what type of capital and capital ratio best reflects a bank’s sensitivity to risk. The main finding is that damages from disasters matter: they negatively affect capital ratios, and the severity of their impact depends on a bank’s location, capitalization, and business model. In addition, the results show that accounting measures of solvency are more sensitive to disasters than are regulatory measures.

My third essay employs data on patents and trademarks collected from the United States Patent and Trademark Office (USPTO) and on disaster data collected from the Spatial Hazard Events and Losses Database for the United States (SHELDUS) covering 12,897 publicly traded firms from 1990 to 2015. By using multiple measurements of innovation and estimating a variety of different models, we show that natural disasters have a negative impact on corporate innovations in general, with important differences among different industries. We also find several channels through which natural disasters can influence firms' innovation ability.


identifier: Bayiapodog Potkah, Gerald  (2021) Forecasting Emergency Department Arrivals via Regression with ARIMA errors and Facebook Prophet: The Case of a Montreal Hospital.  Masters thesis, Concordia University.

abstract: This thesis is motivated by a practical problem in emergency department (ED) operations management. Prolonged waiting times and overcrowding are prevalent in EDs as a result of the mismatch between demand (i.e., patient arrivals) and supply of ED services (Morley, Unwin, Peterson, Stankovich, & Kinsman, 2018). As the gateway to modern healthcare systems, EDs are faced with the arrival of patients with urgent and complex care needs, increased arrivals of the elderly, and high volume of low-acuity patient arrivals. To improve the operational efficiency and healthcare delivery, ED administrators have to make informed decisions about efficient allocation of resources; demand forecasting is a first step towards informing such decisions. Using a Montreal hospital ED as a basis for our investigation, we evaluate the effectiveness of the rarely used regression with autoregressive integrated moving average errors (regARIMA) model in forecasting future daily and hourly ED arrivals. We also experimentally evaluate the performance of Facebook Prophet (fbprophet) and demonstrate its competitiveness with established forecasting methods. This insight is particularly valuable given that in the ED arrival forecasting literature, it is viewed as a “Blackbox” or “off-the-shelf” method and has not been used for comparison with other established methods. Furthermore, we investigate the hypothesis that public sporting events, particularly hockey, lead to increased arrivals to the ED by using hockey games as a predictor within our forecasting models.


identifier: Schlenner, Anna-Lena B.  (2021) Midbrain dopamine neurons during appetitive and aversive states.  Masters thesis, Concordia University.

abstract: A key role ascribed to midbrain dopamine (DA) neurons rests with learning about rewarding events by reflecting reward prediction errors (RPE). Research has shown that during reward learning a positive prediction error (e.g. surprising reward), leads to phasic excitation, while a negative prediction error (e.g. omission of an expected reward) leads to phasic inhibition in DA neurons. It remains unclear, however, how DA regulates learning about aversive events. Using behavioral electrophysiology we recorded from DA neurons in the ventral tegmental area (VTA) during a Pavlovian task in which auditory cues were trained as predictors of either an appetitive sucrose reward or aversive footshock. Our analyses confirmed a role for VTA DA neurons in tracking reward prediction error (RPE), that is, elevation in firing rate (FR) to the reward predictor and depression in FR at time of reward omission in a correlated fashion. Further, our goal was to determine whether DA firing would represent reward and aversion in line with a valence-based prediction error signal. We found that cue related phasic DA activity to both reward and aversion predicting cues contained both information about stimulus identity, as well as valence. Additionally, outcome omission was represented as state of opposite valence. These results support the hypothesis that midbrain DA neurons support learning by signaling valence-based prediction errors.


identifier: Chen, Tiantian  (2021) Development of Durable Anti-Wetting and Anti-Fouling Membranes for Water Production via Membrane Distillation (MD).  PhD thesis, Concordia University.

abstract: Membrane distillation (MD) has gained a growing interest in seawater desalination, wastewater treatment, and separation of volatile compounds as of late due to its low operating pressure, moderate temperature requirements, and high salt rejection. However, membrane fouling and pore wetting are two major problems that hinder maintaining long-term stable flux and salt rejection of MD when treating various feed solutions. Electrospun nanofiber membranes (ENMs) are promising for MD applications due to the discernable advantages of their high hydrophobicity and porosity. Utilization of micro/nano-particles and surface fluoro-silanization are effective modification methods to enhance anti-fouling and anti-wetting properties of MD membranes. The objective of this doctoral research is to develop effective membranes with durable fouling/wetting resistance for water production by MD. To achieve this goal, a highly hydrophobic poly (vinylidene fluoridecohexafluoropropylene)/ reduced graphene oxide (PVDFHFP/ rGO) flatsheet membrane was prepared via a facile electrospinning technique. The rGO incorporated membranes exhibited excellent stability and durability with a salt rejection of over 99.97% and an average flux of 20.37 kg/m2h using 60 g/L sodium chloride as the feed solution. To expand the application of conventional MD membranes to treat challenging wastewaters that contain low surface tension contaminants, one simple modification method was applied by utilizing fluoroalkylsilane of low surface energy on the pristine PVDF-HFP/rGO substrate to achieve surface superamphiphobicity (both superhydrophobicity and near-superoleophobicity). The resulting membranes demonstrated superamphiphobicity, confirmed by their wetting resistance evaluated with water and low surface tension liquids. The antiwetting performance of the membrane was tested through desalinating 35 g/L sodium chloride solution in the presence of a surfactant (sodium dodecyl sulfate, SDS) in the direct contact membrane distillation (DCMD) unit. Finally, a highly permeable membrane for membrane distillation applications by employing porous MCM-48 silica as the nanofiller was developed. The addition of hydrophobic MCM-48 nanoparticles was found to significantly increase water vapor transport, with a flux enhancement 87.16% greater than that observed in the pristine PVDF-HFP membrane.


identifier: Delshadi, Mahmoud  (2021) Three Essays on Conditional Accounting Conservatism.  PhD thesis, Concordia University.

abstract:
This thesis consists of three essays on issues related to conditional accounting conservatism. In the first essay, we explore how options trading influences the demand for conditional accounting conservatism. Using a large sample of US firms for the period 1997-2019, we provide evidence that options trading is negatively related to the degree of conditional conservatism.  Furthermore, a difference in differences analysis provides evidence that firms reduce their level of conditional conservatism after being listed on the options market. We perform further analyses to highlight specific scenarios where an active options market leads to a lower level of conditional accounting conservatism. Overall, our findings suggest that as options trading enhances information environments and alleviates information asymmetries, it reduces the demand for conditional conservatism from users of financial statements.
The second essay explores how a peer’s bankruptcy affects financial reporting by other firms within the industry. A peer firm bankruptcy announcement raises investors’ perception of the risk of same industry firms, resulting in higher external financing costs. We argue that following a peer firm bankruptcy filing, a firm in the same industry, may exhibit a higher degree of conditional conservatism to provide more verifiable information and reassure outsiders about its operation. We find that firms use more conditional conservatism following a peer firm bankruptcy filing. Our findings survive a battery of robustness tests. To further explore how bankruptcy spillover effects of peer firms lead to more conservative reporting, we also conduct a series of cross-sectional tests.
The third essay provides a review of research on the economic consequences of conditional accounting conservatism. This survey shows that it is well documented that conditional conservatism contributes to debt contracting efficiency. The preponderance of the evidence suggests that conditional conservatism leads to positive economic consequences. However, I find some disparities in the findings of prior research. I highlight the potential sources of these disparities. Finally, I present promising future research avenues to address the disparities in the prior studies.


identifier: Hizaoui, Hedia  (2021) Yet Another Creative Adjustment of Coloniality. A Critical Discourse Analysis of Connecting Classrooms through Global Learning School Links.  Masters thesis, Concordia University.

abstract: Global Citizenship Education is increasingly being adopted by national policymakers in response to the mandates of the international agenda, which recognizes the interconnectedness and interdependence of people on a global scale and advocates for global equality and social justice. Connecting Classrooms Through Global Learning is a global program that promotes educational links between schools in the UK and schools in the Global South to achieve these aims. This thesis explores the paradoxical approaches that underpin the discursive construction of these links. In theory, they advocate reciprocity and equality through a critical and social justice-oriented pedagogy. In practice, they reproduce dominant discourses that marginalize the Global South to serve the UK national interests. Using Critical Discourse Analysis as a methodological approach, this thesis investigated the discursive indications of neoliberal, liberal humanist, and neocolonial discourses in these links and their impact on social relations, identities and subjectivities of teachers and students on both sides. Applying a decolonial framework revealed the interplay of these discourses in inculcating identities and enacting social relations that are consistent with the configurations of the global market, the humanist rhetoric of ‘common good’, and the modern/colonial imaginary, but at odds with the Global Citizenship Education pursuit of equality and social justice. While continuing to relegate the Global South ways of knowing and ways of being, binary representations and unequal power relations maintain the hegemony of the UK as a global power at a time marked by post-Brexit national anxieties and economic uncertainties.


identifier: Bertrand, Marc-Antoine  (2021) ‘Colonizing’ Childhood in Children’s Literature: A Post-Structuralist Text Analysis of Mark Twain’s The Adventures of Tom Sawyer.  Masters thesis, Concordia University.

abstract:
The present study takes an in-depth look at the position of a 19th century child fiction author in light of Perry Nodelman’s (2008) colonialist argument. Through a post-structuralist textual analysis, the thesis discusses how Mark Twain uses different stylistic manipulations in The Adventures of Tom Sawyer (1876) to colonise the landscape of childhood. The analyses of passages taken from the piece indicate that the author appeared to be aware of his position in an industry that affords young demographics with images of childhood. From this position, the author is shown to exert his potency in tailoring the specific images of childhood he wished to distribute amongst an envisioned readership. These findings contribute to strengthen Nodelman’s (2008) argument on the role of adults involved in the production of children’s popular culture. Specifically, this study emphasises how authors of child fiction can easily invade landscapes of childhood to disseminate adult-constructed images. Child readers are therefore left to absorb and/or manipulate what is external to their experience. This contributes to the ongoing conversation on the position occupied by adults in fields of children popular culture and education and how their presence can, consciously or not, bias the representation of childhood that youngsters interact with as they live in their current state.
	Keywords: Childhood, literature, popular-culture, adult-authors


identifier: Pirhadi, Amir  (2021) Registration of Volumetric Ultrasound Images Using Siamese Neural Networks.  Masters thesis, Concordia University.

abstract:
In brain tumor resection, soft tissue deformation (i.e., brain shift) causes the pre-operative images to be invalid. Intra-operative ultrasound imaging is a non-invasive, portable, real-time, and cost-efficient alternative to track the surgery. A robust registration method is required to accurately align the post-resection and pre-resection ultrasound images for maximum tumor resection. However, registering the ultrasound images before and after resection is challenging for two main reasons. First, the tumor cavity after the surgery does not have correspondence in ultrasound images before the resection. Second, the quality of the image reduces during the surgery due to the blood clotting agents, air bubbles, and the saline water around the tumor.

This thesis proposes a robust non-rigid registration method based on a landmark tracking technique for brain shift correction in intra-operative ultrasound images. Some landmarks are selected in pre-resection ultrasound image manually that allows user-interaction. A Siamese neural network is adapted to track the annotated landmarks in the post-resection ultrasound image. The 2.5D approach enables 3D tracking and outlier detection. An optimal affine transformation is calculated using Iterative re-weighted least square (IRLS), which automatically suppresses the outliers.

The proposed method is tested on two publicly available datasets of REtroSpective Evaluation of Cerebral Tumors (RESECT) and Brain Images of Tumors for Evaluation (BITE). Mean target registration error (mTRE) is exploited for registration evaluation. In the BITE dataset, the method decreases the initial miss-alignment from 3.55±2.29 mm to 1.80±0.84 mm in pre/post-resection registration. In the RESECT dataset, mTRE is decreased from 3.55±1.76 mm and 3.49±1.56 mm to 1.26±0.57 mm and 1.12±0.46 mm in pre/post-resection registration and pre/during-resection registration, respectively. The fine-tuning effect is also assessed and is shown the generality of the method. The proposed method is compared to the state-of-the-art methods with statistical tests and showed average comparable or better results. The great accuracy, flexibility, and time-efficiency of the method make it an attractive option in real clinical applications that can increase the performance in neurosurgery.


identifier: Du, Wenjie  (2021) A Deep Learning Model to Impute Missing Data in Time Series.  Masters thesis, Concordia University.

abstract: Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis. A popular solution is imputation, where the fundamental challenge is to determine what values should be filled in. In this thesis, we study imputing missing data in time series with deep learning. We first present a concrete case in telecommunication domain, where we use machine learning models to handle missing data and forecast Imminent Loss of Signal (ILOS) that is going to occur in optical networks. Subsequently, we further propose a novel model, called SAITS (Self-Attention-based Imputation for Time Series), to impute missing values in multivariate time series. SAITS uses a joint-optimization training approach to learn missing values from a weighted combination of two diagonally-masked self-attention (DMSA) blocks. DMSA explicitly captures both the temporal dependencies and feature correlations between time steps, which improves imputation accuracy and training speed. The contributions of this thesis are 1) In the motivation case, we develop a deep learning methodology based on BRITS to learn a good representation from data with massive missing values and forecast 3% ILOS with 65% precision; 2) We design a joint-optimization training approach to train self-attention models on the imputation task. Trained by this approach, Transformer achieves up to 25% smaller mean absolute error than BRITS; 3) We propose SAITS, a new imputation model based on self-attention, specifically for the time-series imputation task. Compared to the state-of-the-art (SOTA) model BRITS, SAITS obtains 12%~38% smaller mean absolute error and 2.0~2.6 times faster training speed. Experimental results demonstrate that SAITS achieves the new SOTA position on the time-series imputation task.


identifier: Alam, Syed Eqbal ORCID: https://orcid.org/0000-0003-4398-5877 <https://orcid.org/0000-0003-4398-5877>  (2021) Communication-efficient Distributed Multi-resource Allocation.  PhD thesis, Concordia University.

abstract:
Distributed resource allocation arises in many application domains such as smart cities, intelligent transportation systems, sharing economy, cloud computing, edge-computing, power systems, etcetera. In several scenarios, the agents such as Internet of Things (IoT) devices may require multiple shared resources to achieve social optimum values; furthermore, they may have heterogeneous resource demands. Such distributed resource allocation problems are challenging to solve, especially when the agents are constrained through communication infrastructure, computational capabilities or do not wish to communicate with other agents in the network due to privacy reasons. Additionally, when the cost functions of agents are non-separable and are coupled through the allocation of multiple resources, in such cases, the single resource allocation algorithms are not efficient and provide suboptimal solutions.
In the available distributed solutions for multiple resources, best to my knowledge, agents exchange their information with at least one neighboring agent that may incur communication overhead or compromise agents' privacy. We develop several solutions to solve such problems for multiple divisible and multiple indivisible resources wherein no inter-agent communication is required. Moreover, we assume that each agent has private cost functions coupled with multiple resources; these functions are strictly convex, twice continuously differentiable, and increasing in each variable. Our first contribution is the stochastic distributed algorithm that solves multi-resource allocation problems with no direct agent-to-agent communication for divisible resources; moreover, it achieves social-optimum values. In the algorithms, each agent decides its resource demands locally, and an agent is unaware of the resource allocations of other agents. We solve the divisible multi-resource allocation problem by extending the additive-increase multiplicative-decrease (AIMD) algorithm for single resource allocation by Wirth and co-authors. In the algorithm, the agents keep increasing the demands for a resource linearly until they receive a one-bit signal from a central agency. The central agency broadcasts the signal whenever one of the allocated resources reaches its capacity. Agents then respond to this signal in a probabilistic manner to decrease the resource demand. By doing so, the social optimum is achieved in long-term averages.
Our second contribution is a derandomized AIMD algorithm to solve a class of distributed optimization problems for multiple divisible shared resources. The algorithm is a derandomized version of the stochastic additive-increase and multiplicative-decrease (AIMD) algorithm. The developed solution uses a one-bit feedback signal for each resource between the system and the agents, and it does not require inter-agent communication. We show empirically that the long-term average allocations of multiple shared resources converge to optimal allocations, and the system achieves minimum social cost. Furthermore, we show that the derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm, and both approaches provide approximately the same solutions.

Our third contribution is the stochastic algorithm for multiple indivisible (unit-demand) resources, inspired by classical stochastic approximation techniques. Each agent's consumption is modeled as a Bernoulli random variable in the solution, and no inter-agent communication is required. Moreover, we provide fundamental guarantees of convergence. Additionally, we present an example illustrating the performance of the algorithm.
Finally, we study the development of Internet-of-Things (IoT) enabled sharing economy applications. In many sharing economy scenarios, agents both produce as well as consume a resource; we call them prosumers. A community of prosumers agrees to sell excess resources to another community in a prosumer market. We propose a stochastic algorithm to regulate the number of prosumers in a prosumer community; each prosumer has a cost function coupled through its time-averaged production and consumption of the resource.
Furthermore, each prosumer runs its distributed algorithm and takes (binary) decisions in a probabilistic way, whether to produce one unit of the resource or not and to consume one resource unit or not. In the developed approach, prosumers do not explicitly exchange information with each other due to privacy reasons but little with a central agency. Additionally, prosumers achieve the optimal values asymptotically.


identifier: Haddad, Mark ORCID: https://orcid.org/0000-0003-3064-2381 <https://orcid.org/0000-0003-3064-2381>  (2021) An Instance-Based Learning Statistical Framework for One-Shot and Few-Shot Human Action Recognition.  Masters thesis, Concordia University.

abstract: Along with the exponential growth of online video creation platforms such as TikTok and Instagram, state of the art research involving quick and effective human action/gesture recognition remains crucial. This thesis presents an instance-based statistical framework which addresses the challenge of classifying short human action video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action (one-shot learning). The method is based on Gunner Farneback's dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we estimate the information divergences in an attempt to find similarities between the trained actions and the ones in the test videos. Classification is then done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence value). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.


identifier: Mujahid, Suhaib ORCID: https://orcid.org/0000-0003-2738-1387 <https://orcid.org/0000-0003-2738-1387>  (2021) Effective Dependency Management for the JavaScript Software Ecosystem.  PhD thesis, Concordia University.

abstract:
Open source software ecosystems are essential to software development. Developers depend on packages from the ecosystems to utilize their functionalities and avoid having to reinvent the wheel. On the one hand, this allows developers to write less code, increasing productivity, improving quality, and delivering more features. On the other hand, the package dependencies themselves must be maintained. The overhead starts with the process of selecting a quality package to use out of a large set of packages, going through updating the dependencies and avoiding breakage-inducing versions, ending with replacing obsolete dependencies and finding better alternatives. Neglecting the maintenance of the dependencies can have an expensive negative impact on the software quality. Hence, in this thesis, we propose facilitating the dependency management activities, encouraging developers to keep healthy dependencies in their projects.

We employ information extracted from the software ecosystem to help developers better manage their software dependencies. We first present an empirical study on the factors used by developers to select dependency packages from the npm software ecosystem. Next, we propose an approach that leverages tests from the ecosystems to help identify breakage-inducing versions, which increase developers' confidence in updating the dependencies and help them to make more informed decisions when they update dependencies. Also, we propose an approach to identify packages in decline as early as possible. The underlying rationale of our approach is that the decline in community interest leads to having packages used less over time, becoming less frequently maintained, and eventually, could become abandoned. Furthermore, we propose an approach to find alternatives to replace packages in decline. Finally, we empirically evaluated our approach and characterized the alternative packages.


identifier: Bergeron, Lorraine  (2021) State-Induced Precarity of Domestic Workers? A Comparative Case Study of the Domestic Work Sector in Singapore and Argentina.  Masters thesis, Concordia University.

abstract: The extent to which paid domestic work is treated differently from regular work brings me to question how Singapore and Argentina's governance of their respective domestic work sector keeps paid domestic work at the lowest possible echelon on the labour scale so it may remain a service as flexible and affordable as possible to middle and upper-class dual-income families. Furthermore, by leaving the responsibility of early childhood care or elderly care in the hands of individuals and families, the state is able to remain relatively uninvolved financially and institutionally in the provision of such services all the while recognizing the importance of paid domestic workers to the economic prosperity of households and national economies. Despite significant differences in Singapore and Argentina's opposing domestic work sector regimes, the first being thightly regulated by the state and the second characterized by a widespread informality, the outcome for female domestic workers is the same, i.e. most experience precarious work conditions. My main hypothesis is that, despite their diverging objectives and implementation processes, existing labour policies in Argentina and Singapore effectively contribute to the precarity of domestic workers by limiting their human and labour rights as well as their occupational choices and professional trajectories. The analytical comparison of domestic work in Argentina and Singapore gives high priority to the key concept of precarity as it intersects with state labour policies that govern the domestic work sector in the dissimilar political, social, and economic contexts of Argentina and Singapore. Although all domestic workers experience some forms precarity due to the labour laws that govern their sector of activity, I find that economic precarity characterizes mainly domestic workers in Argentina as where domestic workers in Singapore suffer most importantly from affective and social precarity.


identifier: Hodges, Stefan  (2021) Recovery Landlordism: Accumulation through Confinement in the Landscape of Winnipeg’s Meth Crisis.  Masters thesis, Concordia University.

abstract: From late Victorian Model Housing tenements to the public housing projects of high modernity, western states have attempted to use housing as a tool of moral transformation. This paper investigates the case of a Winnipeg landlord who has recently transitioned their private market apartments towards a drug addiction recovery model, and asks how a new form of rehabilitative capitalism has adopted disciplinary techniques from both the welfare and the carceral state. While the landlord has rebranded into an altruistic entity, forming new relationships with community organizations and various arms of the state, they have simultaneously created polices of confinement for their tenants including curfews, a pass system, rules of partitioning, and mandatory volunteer hours. In a long line of state-led projects that have attempted to use Foucauldian techniques of totalizing surveillance and control, the cracks in the landlord’s rehabilitative project will show where disciplinary power continues to fail and to renovate itself. Interviews with tenants and other actors in the landlord’s network help to understand where this project has met resistance, where it has been accepted, and where it has had to change. Further, the case study explores how confinement has been adopted into the accumulation strategies of private capital, and how rehabilitation creates new subjectivities based on intersecting categories of ‘risk’ in order to justify intervention.


identifier: Burrill, Fred  (2021) History, Memory, and Struggle in Saint-Henri, Montreal.  PhD thesis, Concordia University.

abstract:
The working-class neighbourhood of Saint-Henri in the Southwest of Montreal was long at the heart of capitalist production in Canada. Like other industrial cities across North America, the area experienced a precipitous deindustrialization and decline in the postwar period, a process whose brutality was matched only by that of the subsequent refashioning of the sector into a post-industrial paradise of restaurants and condominiums.

Taking as its point of departure the competing historical discourses at play in present-day Saint-Henri – a post-industrial structure of feeling advanced by developers, business owners, and sanitized heritage projects; an oppositional working-class culture rooted in memories of industrial labour and the hard years of unemployment; and a common, structuring framework of settler whiteness – this thesis uses oral history and archival work to excavate the roots of gendered working-class consciousness, settler identity, and local forms of political resistance in a part of the city long seen as a quintessentially Québécois space.

Beginning with the 1967 shutdown of Dominion Textile, the thesis examines the changing patterns of capital accumulation that led to factory shutdowns and the brief 1970s overlap within community organizing around productive and reproductive labour issues, a nexus that conditioned local resistance into the 1990s. It then turns to the State and capital’s conscious redevelopment of the neighbourhood in the image of bankers and hedge funds, a process particularly difficult for the women largely responsible for the affective and reproductive labour of collective survival. Gradually, local activism became integrated into the nationalist State apparatus, as deindustrialization fed into gentrification and the “worker's neighbourhood” slowly became re-imagined as a white-coded “quartier populaire.” Finally, the dissertation turns to a more in-depth investigation of the structuring forces of settler colonialism and anti-blackness on working-class lifeways in Saint-Henri, before concluding with some thoughts on active, public history and the role it can play in imagining transformative ways of organizing local resistance.

The dissertation is an intervention into deindustrialization studies, inserting perspectives of race and gender into the discussion; gentrification studies, challenging the field to centre working-class experiences of displacement; Quebec and Canadian labour history, contesting dominant national/ist frameworks; and public history.


identifier: Pan, Jie  (2021) Rule-based Machine Learning Algorithms for Smart Automatic Quadrilateral Mesh Generation System.  PhD thesis, Concordia University.

abstract:
Mesh generation, as one of six basic research directions identified in NASA Vision 2030, is an important area in computational geometry and plays a fundamental role in numerical simulations in the area of finite element analysis (FEA) and computational fluid dynamics (CFD). With the rapid progress of high-performance computing hardware, mesh generation methods are required to handle geometric domains with more complex shapes and higher resolution in reliable and fast fashions. Yet, existing mesh generation methods suffer from high computational complexity, low mesh quality in complex geometries, and speed limitations, and have continued to be the bottleneck in those simulation tasks.

This thesis addresses the quadrilateral mesh generation problem from three aspects, element extraction, sequential decision making, and data generation, and their combinations. First, a self-learning system, FreeMesh-S, for finite element extraction system is investigated.  Element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and can be formulated into a sequential decision making process. Three kinds of primitive element extraction rules are conceptually identified. FreeMesh-S, then learns the rules by 1) sampling the element generation rules by a reinforcement learning (RL) algorithm, 2) extracting high quality samples, and 3) training the final rules by a feedforward neural network (FNN). The comprehensive experiments demonstrate the effectiveness of the self-learned meshing rules by FreeMesh-S.

Second, an RL-based computational framework for automatic mesh generation is proposed to improve algorithm automation further. A state-of-the-art RL algorithm, soft actor-critic (SAC), is used to learn the mesh generator's policy from trials. It achieves a fully automatic mesh generation without human intervention and any extra clean-up operations, which are typically needed in current commercial software. The reward function is carefully designed to balance the contradiction between the instant element quality and the remaining boundary quality, in order to achieve an overall high quality mesh. The experiments have shown the competitive performance with two representative meshing methods with respect to generalizability, robustness, and effectiveness. The potentials of mesh generation as a benchmark problem for RL are also identified.

Last, a quality function-based data generation method for the meshing algorithm is devised to increase learning efficiency and algorithm performance. For any data-driven algorithms, high quality and balanced data are essential and deterministic to the performance. This method samples the input-output of the three rules according to their feature spaces; selects high quality samples by a quality function that evaluates if the output is an appropriate solution to the input; and trains an FNN model to simulate the mapping relation via the obtained data. The experiments show that the learning time is greatly reduced while the model has competitive performance comparing with other meshing methods.

To conclude, this thesis combines artificial intelligence techniques, rule-based system, neural networks, and RL, to automate the quadrilateral mesh generation while significantly reducing the time and expertise needed during the creation of high quality mesh generation algorithm. All the techniques can be directly generalized to 3D mesh generation.


identifier: Silva, Constanza Helen ORCID: https://orcid.org/0000-0002-4843-9886 <https://orcid.org/0000-0002-4843-9886>  (2021) Translating actor-network theory: A methodology for the analysis of news stories on massive open online courses from the International New York Times 2012 – 2015.  PhD thesis, Concordia University.

abstract: Actor-network theory (ANT) principles are increasingly migrating into educational research, providing a unique entry point for the study of controversy(ies). While many educational scholars draw on ANT ideas, few have used it methodologically. ANT offers researchers a way to follow actors in unstable, quickly changing situations (Latour, 2005) when relationships that might otherwise be indiscernible become visible (Venturini, 2010a). This was the case from 2012 to 2015 when mainstream news coverage on the topic of massive open online courses (MOOCs) reached a peak. Educational scholars have recognized these years as a distinct episode in news reporting on MOOCs and, more broadly, in public debate on an educational technology. Because ANT is a set of principles and conceptual tools rather than a set methodology, the first objective was to translate ANT into a workable approach for the analysis of news stories. Drawing specifically on Latour’s classic text, Reassembling the Social (2005), the research asked: How can an ANT approach be applied to the analysis of news stories? Once translated, the second objective was to demonstrate the translated methodology by following the MOOC controversy across a corpus of 31 news stories published by the International New York Times (INYT) from July 2012 to December 2015. The research asked: What does the translated ANT approach show us about how the controversy of MOOCs in higher education is expressed in the INYT corpus? What is the social that appears? Description and mapping, navigational codes, strands and confluences emerge as a set of methods and instruments for making movements and shifts in meaning within a network of passage-actors explicit. In addition to providing insights specific to the MOOC controversy as expressed in the INYT, this ANT-inspired methodology is a contribution to the growing body of ANT texts in the field of education.


identifier: Sevdi, Ülfet  (2022) Numbers Increase As We Count... a Practice-Led Research.  Masters thesis, Concordia University.

abstract: This thesis is a practice-led artistic research with accompanying critical self-reflection on a performative protest. Numbers Increase As We Count..., is based around a simple task: counting, 1, 2, 3, and so on during the entire length of the performance... This counting refers to the estimated number of victims of sex trafficking as a direct result of the invasion of Iraq: an endless performance if we were to continue the counting until we reach the actual numbers. But this is exactly the message: every second that passes brings more women into trafficking. The piece is about imperialism, war, destruction, about the way it affects women. Numbers Increase As We Count... was presented in many different incarnations during the last four years in Montreal, in Denmark, in Brazil, as well as in talk-performances in Italy, Vancouver, Ireland and the USA. The ever-evolving performance and the ongoing self-reflection on the creative and socio-political implications offer a dialectical process and set the stage for the emergence of the notion of “Performative Acting” as a mode of authentic engagement with the materials and the theatrical experience. Beyond its essential and timely theme, Numbers Increase As We Count... also tested this method of Performative Acting, offering some insights for future artistically engaged performances.


identifier: Zhang, Shuying  (2021) Diffuseness quantification of a reverberation chamber and its uncertainty with fine-resolution measurements.  Masters thesis, Concordia University.

abstract: Insufficient diffuseness is the major cause of poor inter-laboratory reproducibility of acoustic measurements conducted in a reverberation chamber. Many previous studies have proposed new methods to quantify the diffuseness of a reverberation chamber more accurately, but there is no general agreement among researchers on the most reliable method. The number of measurement samples required for these diffuseness metrics is also unclear, even though it significantly impacts the robustness of the methods. This study, therefore, aims to quantify the diffuseness of a reverberation chamber by using the three widely used diffuseness metrics of spatial variation of sound pressure levels, the relative standard deviation of decay rates, and the degree of time-series fluctuations. The measurements were also carried out with fine resolution microphone positions and varied configurations of acoustic diffusers. With the measurement data, the minimum number of measurement samples to obtain an accurate diffuseness quantification was determined. It is shown that nine independent microphone positions are sufficient to provide the acceptable confidence interval for frequencies above 315 Hz for all three metrics. However, twenty or more microphone positions are needed for the same accuracy if lower frequencies are considered for the reverberation chamber under investigation.


identifier: Gagnon, Audrey ORCID: https://orcid.org/0000-0002-3286-1070 <https://orcid.org/0000-0002-3286-1070>  (2021) Beyond the Civic and Ethnic Divide: The Cultural Conception of National Identity and Its Influence on Opinions about Immigration and Diversity in Quebec.  PhD thesis, Concordia University.

abstract:
It is increasingly documented that individuals’ conceptions of national identity influence their opinions about immigration and ethnocultural diversity. The most well-known ideal types to capture conceptions of national identity are found in the civic-ethnic dichotomy; while the former emphasizes inclusive criteria such as citizenship and is associated with positive opinions about immigration and diversity, the latter emphasizes exclusive criteria such as ancestry and is associated with negative opinions of these same issues. Though the relationship between national identity and immigration and diversity has been extensively studied, the mechanism behind this relationship is still unclear. Moreover, the civic-ethnic dichotomy does not reflect current debates about immigration and diversity, which are increasingly framed in cultural terms. To address this gap, scholars have proposed a cultural conception of national identity. The relationship between the cultural conception of national identity and immigration and diversity, however, remains contested.

My study investigates the relationship between conceptions of national identity and immigration and diversity in the context of Quebec by looking at two cases: Quebecers from the general public and sympathizers of the identity group La Meute. The analysis of interviews conducted with these two populations shows that the civic and ethnic conceptions of national identity are respectively related to positive and negative opinions about immigration and diversity and that this relationship hinges on an evaluation mechanism whereby one evaluates if immigrants and minorities conform to different markers of identity. However, the analysis shows that the civic-ethnic dichotomy neglects a significant part of the story; that is, the salience of cultural markers of identity. It demonstrates that a cultural conception of national identity is related to both positive and negative opinions about immigration and diversity. Indeed, the evaluation mechanism inherent to this conception of national identity involves assessing whether immigrants and minorities adopt the national culture. Positive evaluations of cultural compliance are associated with positive opinions about immigration and diversity, while the opposite is true for negative evaluations. Finally, emphasizing that the evaluation mechanism does not occur in a vacuum, my study explores how evaluations are informed by traditional news media and alternative sources of information.


identifier: Moradpoor Sheikhkanloo, MohammadHossein  (2021) Distortional Buckling Formulation for the Analysis of Composite thin-walled beams.  Masters thesis, Concordia University.

abstract: Thin-walled beams are widely used in construction with various restraint arrangements and sizes. While lateral torsional buckling is a major design concern for thin-walled beams used as girders distortional effects may become important depending on restraint conditions as well as for beams with slender webs, stocky ﬂanges, and/or shorter spans. This thesis introduces a novel beam-type nonlinear Finite Element formulation that is applicable for the analysis of I-sections that are prone to lateral-torsional buckling and distortion. A linearized buckling formulation has been derived as a special case of the nonlinear beam formulation under the assumption of no pre-buckling deformations. The material is assumed elastic. The formulation was also applied for composite laminated thin-walled beams. The effect of shear deformation on the flexural and lateral-torsional buckling predictions of composite-laminated thin-walled beams were illustrated without the distortional effects. For the analysis of composite-laminated thin-walled beams the finite element formulation has the flexibility to choose one of the alternative members of the Doyle-Ericksen family of strains. As such the developed formulation is an extension of both Engesser and Haringx column buckling formulas to shear deformable thin-walled beams with rigid webs. It is shown that alternative strain definitions lead to changes in the geometric stiffness matrices in finite element buckling analysis of thin-walled beams. The effect of the selected strain definition on the elastic buckling load predictions is illustrated in numerical tests including short sections and beams with low shear modulus.


identifier: Bao, Zhu  (2021) Image Segmentation and Adaptive Contrast Enhancement for Haze Removal.  Masters thesis, Concordia University.

abstract:
Nowadays, hazing scenes are very frequent in images acquired outdoors. For such images to be used as input images of autonomous systems, it is important to restore the image details so that they can provide sufficient information to the system. As hazy images feature poor contrast due to degraded image variations, one can use a contrast enhancement method, such as CLAHE, to restore the image details. However, in case of very heavily hazy images, the image signal quality is severely degraded. Applying a strong enhancement may help to recover the details but will meanwhile generate very visible noise, affecting the image quality. In order to handle the problem of the conflict of the degree of enhancement and noise created in the process, it is thus necessary to develop a good algorithm with different enhancement based on a specific mask and signal variations in different regions.
    In this thesis, a novel dehazing algorithm is proposed, which aims at heavily hazy images. In order to restore effectively image details that are almost invisible in hazy images without over enhancement in other foreground areas, the proposed algorithm involves a new adaptive CLAHE process, in which a stronger enhancement is applied to the areas of weaker variations, different from an existing version of improved CLAHE with adaptive clip limit. This new CLAHE is applied only to the foreground areas, by means of a protective mask, so that there will not be noise enhancement in the atmospheric background and the other flat areas. Each input image is segmented into foreground and background areas to generate the mask. In case of heavily hazy images, the gradient amplitudes of the signals and the noise are in the same level and it is thus very difficult to distinguish foreground and background areas.  A new gradient matrix has been defined and a gradient feature vector proposed to detect the locally dominant gray level variations, with a view to identifying the pixels of very weak variations in foreground areas with the noise presence. This gradient vector helps to distinguish the foreground and background areas in heavily hazy images, and the segmentation can be done effectively, which makes it possible to apply the new adaptive CLAHE without noise enhancement.
    The proposed algorithm has been tested with different kinds of hazy images. In case of heavily hazy image input, it performs better, in terms of image detail restoration, than existing methods based on dark-channel-prior (DCP) or other form of CLAHE. It is effective with hazy images of high dynamic range. It is also useful in case of lightly hazy images.


identifier: Nazemi, Mohammad Hossein  (2021) Implementing a Library to Calculate Surrogate Safety Measures.  Masters thesis, Concordia University.

abstract:
Context. Road traffic is continually increasing The traveled miles of vehicles increased by almost
10% from 2010 to 2019 in the USA .With increase in traffic, the risk of collisions increases as well.
The Bureau of transportation statistics (BTS) of the USA reports that crashes data has increased by
24 percent from 2010 to 2019. BTS reports 2,740,000 injuries and 36,096 fatalities in USA in 2019.
One means to reduce the number of crashes is by analysing the safety of roads. The main parameters
to evaluate the safety of roads is statistical analysis of historical crash data. However, crashes do not
happen frequently and, thus, do not help predict future crashes. Therefore, the literature introduced
the concept of surrogate safety analysis, which uses various measurements, other than the number
of crashes, to evaluate the safety of roads.
Problem. One such measure is post encroachment times (PETs). PETs are defined as time difference
between the departure of a road user from an encroachment zone and entry of another road user
in the the same zone. PETs are computed by analysing in real time traffic. PETs can be computed
among vehicles but also between vehicles and pedestrians and cyclists. Several approaches already
exist to compute PETs but have limitations, in particular accuracy, generality, and the practicality of
their solutions. In addition to PET values, speed is another metric that can be used to determine the
severity of conflicts. Calculating the average and momentary speeds of motorized road users can be
beneficial in case studies.

Solution. This thesis presents a novel algorithm and its implementation, in the form of a library, to
calculate PETs in real time. This library can use any type of detection and perception technologies,
including lidars, optic cameras and radars as sources of inputs, and classical or new deep learning
techniques as object detection and classification algorithms. Also, it can detect and calculate PET
between any types of road users with any types of movements. The hyper parameters of the library
are customizable and can be tuned for different scenarios. Moreover our library calculates average
and momentary speed of road users to demonstrate more information for PET conflicts.
Validation. We implemented and deployed our library on the infrastructure of our industrial partner,
BlueCityTechnology. BlueCityTechnology uses Lidar technologies to monitor road intersections
in several cities world-wide. We set up an experiment to manually validate data at multiple
intersections. Using the result of this manual validation as ground truth, we validated our library
and reported 86.29% F1-score for our PET detection module .
Conclusion. Our library improves greatly the state-of-the-art and is currently used in real-world
applications. Municipalities have been using PET reports of BlueCity solution to conduct before
and after case studies to detect and solve possible problems at intersections.


identifier: Krishnan, Prakash  (2021) South Asian Instagram Community Archives: A Platform for Performance, Curation, and Identity.  Masters thesis, Concordia University.

abstract: “South Asian” is a contested term often referring to persons and cultures with ancestral ties to the Indian subcontinent. For a variety of reasons, people have been and continue to be displaced from their ancestral lands. As a result, they are either resettled within the subcontinent or in other continental regions overseas. Through the fracturing of these populations and cultures by way of colonialism and imperialism, war, genocide, and socioeconomic pursuits, historical and cultural resources become more difficult to access for the deterritorialized. This thesis explores how certain users of the popular social media site Instagram are working towards the effective reterritorialization of an online reimagining of South Asia. My research is based on a two-phase methodology, during which I: a) conducted a digital ethnography of four Instagram accounts @BrownHistory, @SouthAsia.Art, @SouthAsianNation, and @yaada_, performing visual and textual analysis of content posted to the accounts between September 2020 and August 2021; b) individually interviewed the creators of these accounts. Mobilizing performance theorists Jose Esteban Muñoz’s concept of “disidentification” and Diana Taylor’s theory of “the repertoire”, I examine how these accounts act as immaterial community archives – storing, describing, and exhibiting South Asian cultural heritage material online. I demonstrate how participation in these accounts constitute a kind of identity performance in which South Asian-ness becomes articulated through a shared process of disidentification. I conclude by exploring the emergence of more recent social movements affecting the online South Asian community, noting the capabilities and limitations of the platform to enact social change.


identifier: Metneja, Bhupinder Singh  (2021) An Algorithm to Explore Molecular Potential Energy Surfaces.  Masters thesis, Concordia University.

abstract: The work is aimed at modifying an algorithm called the Activation-Relaxation Technique (ART). The algorithm was initially envisioned by the original authors to be applied to the study of materials. This work, however, aims to take the original vision forward by applying it to the exploration of potential energy surfaces (PESs) of chemical and enzymatic reactions. To make ART more efficient in terms of the force evaluations needed and, consequently, to reduce the computational time and cost, it has been modified to include additional algorithms that significantly speed up the exploration of the PES. Each such algorithm is designed such that it allows for a strategic displacement of the atoms and subsequent sharing of atomic displacement vector information. Such a strategic displacement of atoms, for example, involves exploiting an a priori knowledge of the reactants in order to avoid atomic displacement moves that would not contribute to the tracing of their PES. A molecule like N-methylacetamide, for instance, which is a good model system for proteins, only undergoes a conformational change (a gradual dihedral rotation) which acts as the sole reaction coordinate for its PES and thus algorithms are included in ART that disallow for any other atomic displacement moves except for a gradual rotation of the dihedral. The sharing of atomic displacement vector information can prove helpful, for example, in enzymatic reactions where the enzyme mutants (which are similar in structure to the wild-type) can be made to follow the displacement vector information of the wild-type in order to avoid going astray along the PES and to have a good starting point from the beginning. The goal, ultimately, is to apply ART to the study of complex chemical reactions and enzymatic reactions involving large-scale rearrangements of the protein environment. As validation steps in the development of this algorithm, we illustrate the modified ART method on selected small molecules -- ethane, propane, methyl acetate, N-methylacetamide, etc. -- highlighting pitfalls and successes.


identifier: mouine, esma ORCID: https://orcid.org/0000-0003-2463-9733 <https://orcid.org/0000-0003-2463-9733>  (2021) An empirical assessment of the contributing factors for vulnerability detection using machine learning.  Masters thesis, Concordia University.

abstract:
There is an increasing trend to mine vulnerabilities from software repositories and use machine learning techniques to detect software vulnerabilities automatically.  A fundamental but unresolved research question is: how do different factors in the mining and learning process impact the accuracy of identifying vulnerabilities in software projects of varying characteristics? Substantial research has been dedicated in this area, including source code static analysis, software repository mining, and NLP-based machine learning. However, practitioners lack experience regarding the key factors for building a baseline model of the state-of-the-art. In addition, their lack of experience regarding how transferable the vulnerability signatures from a project to another are. This study investigates how the combination of different vulnerability features and three representative machine learning models impact vulnerability detection accuracy in 17 real-world projects.
This thesis proposes different machine learning methods to detect software vulnerabilities.
The first part of this work consists of establishing a baseline model for vulnerability prediction using NLP. For that, two types of vulnerability representations are examined: 1) code features extracted through  NLP with varying tokenization strategies and three different embedding techniques (bag-of-words, word2vec, and fastText)  and 2) a set of eight architectural metrics that capture the abstract design of the software systems. The four machine learning algorithms include a random forest model, a support vector machine model, and a residual neural network model.
The second part of the study is an effort to evaluate the baseline model sufficiently and fairly by using it to evaluate the performance of another model. More experiments are performed using a bidirectional long short-term memory (BiLSTM) combined with word2vec. The results are compared to the baseline results.
Overall, the first set of experiments, the models returned the following results. 95% of the learning metrics  (precision, recall, f1 score, etc.) are above 0.77 in the experiments out of 10 hypothesis tests and 408 experiments. Further analysis shows a recommended baseline model with signatures extracted through bag-of-words embedding, combined with the random forest, consistently increases the detection accuracy by about 4% compared to other combinations in all 17 projects. The observations also show the limitation of transferring vulnerability signatures across domains based on the experiments. Furthermore, the baseline model is shown to perform better than the BiLSTM model.


identifier: Mosallaie, Shahab  (2021) Discovering the Evolution and Co-authorship Patterns of Artificial Intelligence in Cancer Research using Machine Learning and Link Prediction.  Masters thesis, Concordia University.

abstract:
Applications of artificial intelligence play an increasingly important role in diagnosing and treating cancer. The complexity of this dynamic research field requires scientists coming from various backgrounds to continuously collaborate and create multi-disciplinary teams. However, finding the potential collaborator(s) for high-quality research effectively and efficiently is considered a difficult task for many stakeholders. In this thesis, we address these problems through developing co-authorship predictive model not only to predict potential co-authorships but also to interpret and explain which factors play essential roles in selecting potential collaborations. Finding these factors may help policymakers and research organizations investigate drivers as well as constraints in order to facilitate fruitful research collaboration and the formation of strong research teams.

The thesis has two research objectives. The first one is to characterize and map the recent research landscape in the field of artificial intelligence applications for cancer diagnosis and treatment. The second one is to explore the driving factors for different co-authorship patterns of researchers working in this field. We first used NLP techniques to characterize the evolution of artificial intelligence in the cancer research area. We observed great interest of researchers who have been gradually moving from conventional to advanced learning techniques. We then employed complex network analysis with co-authorship as a proxy for collaboration and constructed several co-authorship networks of researchers working in this field. We extracted different structure-based and attribute-based metrics related to the individual authors and their collaboration patterns. Finally, we used machine learning and interpretability techniques to predict collaboration links of co-authorship patterns and to interpret the machine learning models. We were able to successfully predict future co-authorship links for various collaboration patterns, namely new co-authorships, persistent co-authorships and discontinued co-authorships. In general, our results show that common neighbors-based and discipline similarity factors have a positive impact on the appearance of co-authorship links. We conclude that using machine learning models and interpretability techniques is a useful and effective way to predict potential co-authorships and derive driving factors for collaboration.


identifier: Naghshineh, Ali  (2021) Seismic resilience and performance design approach for concrete moment resisting frame buildings equipped with yielding restrained braces.  PhD thesis, Concordia University.

abstract:
Advanced structural dissipation devices can be classified into three major groups including passive, semi-active and active control energy dissipations. While all of these technologies play an important role in structural design, passive energy dissipation devices are the most common types of control systems which can be classified into six types including Metallic Dampers, Friction Dampers, Viscoelastic Dampers, Viscous Fluid Dampers, Tuned Mass Dampers, and Tuned Liquid Dampers. The primary purpose of this research is to decrease structural damage by minimizing the demand for main structural elements through the use of passive energy dissipation devices, particularly, in the form of Yielding Restrained Braces (YRBs) or Inline Friction Dampers (IFDs). Friction dampers dissipate energy through friction and emerge due to the sliding of two solid elements relative to one another. For instance, solid friction can control earthquake-induced vibration, another example on a smaller scale is automotive brakes which dissipate the kinetic energy of motion. The friction damper (brake) is commonly used to extract kinetic energy from a moving body, when a major earthquake occurs, conventional braces buckle which leads to unsymmetrical hysteretic behaviour and loss of stiffness, while the friction damper slips at a predetermined load before yielding occurs in members of a frame, which dissipate a major part of energy. It saves the initial cost of a new construction or retrofitting of an existing building, where the dampers provide a very high energy dissipation.
Even though damping devices can provide supplemental damping to mitigate vibration in buildings due to wind or earthquake effects, integrating them in the design is not often straightforward. For example, building design with inline friction dampers is not directly provided in the Canadian code. The NBCC 2015 contains recommendations for supplemental energy dissipation in general, but no specific provisions are available for friction dampers. In the National Building Code of Canada (2015), the minimum earthquake lateral force in a Seismic Force Resisting System(s) (SFRS) is divided by a reduction factor. This factor, known as the response modification factor, can be calculated by multiplying the overstrength factor (Ro) and the ductility-related force modification factor (Rd). As the 2015 NBCC does not provide the overstrength factor (Ro) and the ductility factor (Rd) for friction-damped systems, engineers usually work with the factor for the closest equivalent system, ductile buckling-restrained braced (BRB) frames (Rd=4, Ro=1.2). This practice is already conservative in nature mainly because the non-damage-based modification factor for a Yielding Restrained Braced (YRB) system has been found to be substantially higher, and because the system can be tested at Maximum Considered Earthquake (MCE) ground motion forces and displacement in contrast to the equivalent systems that cannot avoid uncertainty in their actual behavior.
The objectives of the present research are to (i) investigate the life safety performance of different concrete moment resisting frames (CMRFs) considering supplemental damping to estimate seismic response factors, (ii) evaluate seismic design parameters of concrete moment resisting frames (CMRFs) equipped with different energy dissipation systems to understand the relative performance of YRBs, (iii) collaborate experimental work with simulation to investigate dynamic performance and reliability of YRBs under real earthquakes, (iv) develop a set of guidelines for the use of yielding restrained braces in concrete frame buildings.
In order to achieve the above goals, a set of buildings with concrete moment resisting frames have been considered. These frames were designed for high seismic locations in Canada and the equivalent locations in the US. The design YRB systems for these frames have been adapted from ASCE/FEMA guidelines and contextualized for Canada. The results show that such an approach could be beneficial for designing buildings with inline friction dampers and could provide not only cost savings but also, enhanced seismic safety and maintainability.


identifier: Herchet, Sarah  (2021) How Female Entrepreneurs Use Family Support to Improve Well-Being Through Resilience - A Mixed-Method Study.  Masters thesis, Concordia University.

abstract: In light of growing scholarly interest in female entrepreneurship as well as entrepreneurial well-being, the purpose of this mixed-method research was to investigate a resource-based conceptualization of female entrepreneurs’ well-being stemming from family support and the mechanisms and potential constraints of this relationship. I argued that instrumental and emotional resources deriving from family support would trigger a resource-gain spiral leading female entrepreneurs to acquire greater psychological resilience and, ultimately, to experience both increased hedonic and eudaimonic well-being. To test this theoretical framework, quantitative and qualitative data was collected from two North-American samples, including 152 women entrepreneurs participating in an online survey and six female entrepreneurs partaking in individual interviews, respectively. The combined results suggest that family support and resilience – under certain boundary conditions – are valuable resources for these women and their eudaimonic well-being in particular. This research contributes to the existing literature on female entrepreneurship by responding to scholarly calls for more comprehensive investigations into the diverse facets of entrepreneurial well-being and underscores the relevance of eudaimonic well-being as an outcome variable in entrepreneurship research. This research also highlights the practical importance of further enabling access to critical resources for female entrepreneurs – within the family and beyond.


identifier: Habibi-Tanha, Mohammad Kazem  (2021) Neural Correlates of Working Memory in Chronic Primary Insomnia using The Sternberg Task: A Preliminary Analysis.  Masters thesis, Concordia University.

abstract: Chronic primary insomnia is characterized by difficulty falling sleep, maintaining sleep and waking up earlier than desired as well as daytime functioning impairment in the absence of an underlying neurological and psychological condition. Daytime functional impairment of patients with chronic primary insomnia is linked to cognitive deficits, including working memory. Functional neuroimaging studies investigating working memory in this population reported abnormal cerebral activation during cognitive abilities testing. This evidence could support altered cognitive functioning. Yet, the same results have not been replicated consistently. Hence, this study aims to provide more nuanced evidence by identifying a map of cerebral activation among patients with insomnia in different phases of working memory using the Sternberg task. This task has never been used for this population. The Sternberg task can evaluate the working memory ability for the encoding and recall phases of working memory separately. Based on our results, we observed that many brain regions that were activated in patients with insomnia were similar to those that were activated in good sleepers in response to a working memory task.  Yet, there were key regions within the frontal cortex that were not activated. The differences were more noticeable in the recall phase. This needs to be quantitatively verified by including a control cohort and investigated whether potential working memory impairment in insomnia is phase dependant in future studies. We extended the literature by identifying phase-dependant and load-dependent regions of cerebral activation and deactivation in patients with chronic primary insomnia during the Sternberg task.


identifier: Dhaliwal, James S ORCID: https://orcid.org/0000-0001-8851-7562 <https://orcid.org/0000-0001-8851-7562>  (2021) Quality Control of Oxidized RNA in Saccharomyces Cerevisiae and Chlamydomonas Reinhardtii.  PhD thesis, Concordia University.

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abstract:
Reactive oxygen species can oxidize biological molecules, rendering them inactive or toxic. Oxidized RNA can stall ribosomes during translation, leading to the production of truncated, aggregation-prone nascent polypeptides. Cells have quality control systems that recognize and target oxidatively damaged molecules for repair or degradation. Ribosome collisions that occur due to stalled translation are recognized by a quality control pathway that results in the degradation of the defective mRNA, extraction and degradation of the truncated nascent polypeptide, and recycling of the ribosome subunits for additional rounds of translation. This thesis addresses the quality control of oxidized RNA. It uses two species, Saccharomyces cerevisiae and Chlamydomonas reinhardtii to examine how oxidized RNA is compartmentalized for quality control, and how a photosynthesis protein moonlights as an oxidized RNA quality control factor.

In Chapter 2, with results of immunofluorescence microscopy, genetic analyses, biochemical fractionation, and proteomics, I identify and characterize a previously undiscovered organelle, which we name “oxidized RNA bodies” or ORBs. I show that ORBs are membraneless phase-separated organelles that compartmentalize the translation quality control pathways which handle stalled ribosomes.

In Chapter 3, using immunofluorescence microscopy, genetic analyses, and biochemical fractionation, I present preliminary results on the moonlighting functions of RbcL, the large subunit of Rubisco, in handling oxidized RNA in chloroplasts. I show that a mutant unable to assemble the Rubisco holoenzyme has altered phenotypes related to the moonlighting functions of RbcL and explore the subcellular localization of the protein in various contexts associated with translation.

A powerful approach to understand the significance and management of RNA oxidation could be to find and characterize specialized intracellular compartments dedicated to it. The research contained herein advances the field of RNA quality control by providing a repertoire of candidate components, evidence for biochemical mechanisms that can be tested in future work, and a cytological context for these processes.


identifier: Alfadel, Mahmoud  (2021) Assessing and Enhancing the Security of Software Packages.  PhD thesis, Concordia University.

abstract:
Modern software applications are developed with increasing reliance on open-source software packages (i.e., dependencies). This dependence on open-source packages is highly beneficial to software development since it speeds up development tasks and improves software quality. However, it also has implications to the security of software applications. Dependencies with security vulnerabilities have the potential to expose hundreds of applications to security breaches, potentially causing huge financial and reputation damages. Hence, it is essential to build a solid understanding of the security health of software packages and how developers react once the vulnerabilities are found in the packages they depend on.

To this end, in this thesis, we conduct empirical studies that shed light on the security state of software packages from two aspects. In the first aspect, we study the lifecycle of security vulnerabilities in packages. We analyze how long it takes to discover and fix security vulnerabilities that affect software packages, to better evaluate the response of software ecosystems to security vulnerabilities. Once the vulnerability is discovered, it is also critical to mitigate its impact on software applications. Therefore, in the second aspect, we evaluate the effectiveness of existing mechanisms in mitigating the impact of package vulnerabilities. We assess the role of two popular mechanisms for tackling security vulnerabilities in software packages. The insights from our studies in this thesis can help researchers and practitioners better understand the security implications of adopting software packages. Also, leveraging our findings in the studies, we provide a series of implications that can help improve the process of discovering, fixing and managing package vulnerabilities. Finally, the implications of our work lead us to build several prototype tools to increase developers’ awareness to vulnerable packages that affect their projects and help them better plan the maintenance of their software packages from a security perspective.


identifier: Trudeau, Austin James  (2021) The Effect Of Sexualized Advertisements On Delayed Discounting.  Masters thesis, Concordia University.

abstract:
This research builds upon the literature on the effects of sexualized advertising and seeks to examine its impact on consumers’ delayed discounting. More specifically, this research examines to what extent arousal induced by sexualized advertisements as well as consumers’ trait impulsivity influence consumers’ tendency to discount financial rewards. In a computer-based lab experiment, 263 participants were randomly assigned to sexualized advertisement exposure or exposure to neutral images (control) and completed a financial discounting task. The financial discounting task included two monetary rewards amounts ($1,000 and $25,000) across six different time delays (1 day, 1 week, 1 month, 6 months, 1 year, and 5 years) and interest rates (.000 to 1.000 in .025 and .050 increments). Trait impulsivity was a measured variable. In the context of discounting of financial rewards, this research found no significant relationship between sexualized advertising induced arousal or trait impulsivity, and delayed discounting. The results have implications for future investigations of the influence of consumers’ affective states on decision-making.

Keywords: Delayed Discounting, Affect, Arousal, Valence, Impulsivity, Financial Rewards, Financial Decision-Making


identifier: Yaftian, Pegah  (2021) Design, Development and Force estimation of a Tendon-driven Steerable Catheter-based on Image-processing and Learning Approaches.  Masters thesis, Concordia University.

abstract:
In this thesis, an image-based force estimation schema for tendon-driven steerable catheters with the application in robot-assisted tissue ablation procedures was proposed and validated.
To this end, initially, an inverse kinematics solution for modeling the catheter deformation and estimating the contact force between the catheter and cardiac tissue was proposed. Next, a tendon-driven catheter was designed and developed in-house. Afterwards, the developed catheter was integrated with a motor current control software for controlling and recording the deformation of the catheter. Furthermore, the performance of the proposed method in contact force estimation was improved by various learning approaches. Validation studies were performed on phantom tissue as well as excised porcine tissue. The results of the validation studies showed that the proposed image-based force estimation models had a maximum root-mean-square error of 0.012 N for the tests on the porcine atrial tissue. Furthermore, adopting machine learning approaches resulted in the accuracy of 97.7%, 97.6%,
98.3%, and 98.8% for the Support vector machine, Random Forest, AdaBoost, and Deep Neural Network, respectively. For yielding optimal models, hyper-parameter searches were performed for all proposed learning models. In summary, the proposed force estimation system did not necessitate the utilization of force sensors and could successfully contribute in operation rooms during cardiac catheterization.


identifier: Soltanzadeh, Milad  (2021) Mathematical modeling of lactate metabolism in the brain.  Masters thesis, Concordia University.

abstract: It is well established that the primary substrate for brain energy metabolism is glucose, but accumulating evidence suggests that lactate can also be considered as an energy substrate for the brain. Additionally, it has been shown that lactate is linked to memory, epilepsy, and traumatic brain injury, which emphasizes the importance of lactate. Considering the effects of lactate and the complexity of imaging its dynamics in neurons and glial cells, researchers are motivated to explore lactate dynamics using mathematical modeling. In this thesis, we propose a new mathematical model for brain lactate exchanges which includes four compartments. In summary, our model consists of four ordinary differential equations explaining interactions between neuronal, astrocytic, capillary, and extracellular space compartments. We simulate this model to assess its abilities to reproduce different qualitative behaviors of lactate dynamics in the brain. These scenarios include increased lactate transport to the brain due to physical exercise and increased lactate production inside neurons and astrocytes. Also, parameter estimation becomes crucial for these types of models due to its effect on the model’s ability to reproduce correct physiological and pathological behavior of the brain. Some parameters can be determined from the experiments in the literature. However, for some of them, there is not a consensus over the values. In this case, an optimization can be done to determine the parameters. In this thesis, we fixed the parameter values for lactate transport and optimized the lactate production and consumption parameters.


identifier: su, xuanbo  (2021) Fully Bayesian Inference for Finite and Infinite Discrete Exponential Mixture Models.  Masters thesis, Concordia University.

abstract:
Count data often appears in natural language processing and computer vision applications. For example, in images and textual documents clustering, each image or text can be described
by a histogram of visual words or text words. In real applications, these frequency vectors often show high-dimensional and sparsity nature. In this case, hierarchical Bayesian modeling frameworks
show the ability to model the dependence of the word repetitive occurrences ’burstiness’.
Moreover, approximating these models to exponential families is helpful to improve computing efficiency, especially when facing high-dimensional count data and large data sets. However, classical deterministic approaches such as expectation-maximization (EM) do not achieve good results in real-life complex applications. This thesis explores the use of a fully Bayesian inference for finite discrete exponential mixture models of Multinomial Generalized Dirichlet (EMGD), Multinomial
Beta-Liouville (EMBL), Multinomial Scaled Dirichlet (EMSD), and Multinomial Shifted Scaled Dirichlet (EMSSD). Finite mixtures have already shown superior performance in real data
sets clustering with EM approach. The proposed approaches in this thesis are based on Monte Carlo simulation technique of Gibbs sampling mixed with Metropolis-Hastings step, and we utilize exponential family conjugate prior information to construct the required posteriors relying on Bayesian theory. Furthermore, we also present the infinite models based on Dirichlet processes, which results in clustering algorithms that do not require the specification of the number of mixture components to be given in advance. The performance of our Bayesian approaches was tested in some challenging real-world applications concerning text sentiment analysis, fake news detection, and human face
gender recognition.


identifier: Jalilov, Emil  (2021) Development of Heuristic Model-Based Predictive Control Strategies for an Institutional Net-Zero Energy Building.  Masters thesis, Concordia University.

abstract:
This thesis presents the development of heuristic model-based predictive control strategies for an institutional NZEB building archetype with the Varennes Library selected as a case study. A heuristic model-based predictive control strategy is applied to a radiant floor heating system. Depending on anticipated weather scenarios, developed near-optimal heating temperature setpoint profiles are selected. A generalized methodology for building energy model development to be used by model predictive control (MPC), demonstrating a step-by-step approach of more details addition to the model, is presented. The resulting explicit finite difference 10th order lumped parameter resistance-capacitance (RC) thermal network model is used to describe the dynamic behaviour of the building. The selected model is validated using on-site measurements.
The thesis then develops an approach for generalizing the heuristic predictive control strategies. The proposed strategy showed the possibility of 25% energy saving on an extremely cold sunny day. Another strategy emphasizing energy flexibility displaces nearly 100 % of heating power during the morning peak and approximately 80% of the heating power during the evening peak demand event once the one-day ahead notification from the utility is received. Acceptable indoor thermal conditions recommended by ASHRAE Standard 55 are maintained under proposed strategies.
Finally, the thesis analyzes the building-integrated photovoltaic/thermal (BIPV/T) system installed in the library as a potential solution to increase energy flexibility and energy efficiency, proposes subsystem data-driven control-oriented model development and evaluates the possible enhancements of the installed system both in terms of design and control perspective.