identifier: Nourani, Hamid ORCID: https://orcid.org/0000-0002-0563-9415 <https://orcid.org/0000-0002-0563-9415>  (2021) imaged-based tip force estimation on steerable intracardiac catheters using learning-based methods.  Masters thesis, Concordia University.

abstract: Minimally invasive surgery has turned into the most commonly used approach to treat cardiovascular diseases during the surgical procedure; it is hypothesized that the absence of haptic (tactile) feedback and force presented to surgeons is a restricting factor. The use of ablation catheters with the integrated sensor at the tip results in high cost and noise complications. In this thesis, two sensor-less methods are proposed to estimate the force at the intracardiac catheter’s tip. Force estimation at the catheter tip is of great importance because insufficient force in ablation treatment may result in incomplete treatment and excessive force leads to damaging the heart chamber. Besides, adding the sensor to intracardiac catheters adds complexity to their structures. This thesis is categorized into two sensor-less approaches: 1- Learning-Based Force Estimation for Intracardiac Ablation Catheters, 2- A Deep-Learning Force Estimator System for Intracardiac Catheters. The first proposed method estimates catheter-tissue contact force by learning the deflected shape of the catheter tip section image. A regression model is developed based on predictor variables of tip curvature coefficients and knob actuation. The learning-based approach achieved force predictions in close agreement with experimental contact force measurements. The second approach proposes a deep learning method to estimate the contact forces directly from the catheter’s image tip. A convolutional neural network extracts the catheter’s deflection through input images and translates them into the corresponding forces. The ResNet graph was implemented as the architecture of the proposed model to perform a regression. The model can estimate catheter-tissue contact force based on the input images without utilizing any feature extraction or pre-processing. Thus, it can estimate the force value regardless of the tip displacement and deflection shape. The evaluation results show that the proposed method can elicit a robust model from the specified data set and approximate the force with appropriate accuracy.


identifier: Marjaba, Bernard Joseph ORCID: https://orcid.org/0000-0002-7303-205X <https://orcid.org/0000-0002-7303-205X>  (2021) Impact of Turbulence on Detonation Dynamics.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract: This study provides a macroscopic, experimental analysis of the impact of turbulence on the dynamics of a detonation wave. The interaction of detonation waves with turbulence is widely encountered in detonation-based propulsion devices such as the rotating detonation engine (RDE), and a good understanding of this interaction is needed for suitable orifice design and wave stability and sustainability. In this study, experiments were conducted in a detonation tube with multiple inlets. One inlet is used to slowly fill the tube with a reactive mixture, while the other is used for the rapid injection of a jet into an initially quiescent medium, thereby generating turbulence. Detonation waves are initiated from one end of the tube using a high power ignition system. A schlieren visualization system is used to capture images as blasts and detonation waves pass through an optical viewing section at the other end of the tube. Five levels of turbulence were generated for each fill pressure of 10 kPa, 20 kPa, and 30 kPa. Detonation speeds were measured from the captured images and compared to those of detonations propagating in a quiescent medium at the same pressures. It was found that turbulence enhances the propagation of detonations with larger cell size (at 10 kPa) bringing its speed closer to the ideal, Chapman-Jouguet, detonation speed, and hinders the propagation of detonations with smaller cells (at 20 kPa and 30 kPa) increasing the deficit below the wave’s CJ speed at the given pressure. This result implies that there exists a critical cell size, characteristic of the reactive mixture, at which a switch in behaviour occurs under the influence of turbulence. Consequently, a minimum cell size must be attained, in order to recover lost energy of a detonation wave and use turbulence favourably.


identifier: Heshmati, Arash  (2021) The Effect of Early-Career Recognition on the Subsequent Success: Evidence from the Academy Awards.  Masters thesis, Concordia University.

abstract: The research in OB, sociology, management, and even finance has recognized an important effect of prior recognition on achieving career success. However, there is little research on whether the early attainment of such a recognition can affect the degree of subsequent success. Furthermore, the few prior studies on early recognition to date have only inspected the consequences of early recognition in the field of science. The main purpose of this study is to fill this literature gap by broadening the scope of such investigation to the field of motion pictures and more specifically, the Academy awards. We developed four hypotheses addressing the effect of early recognition (i.e., nomination for Oscar award) on different success measures of performing artists, including future productivity, quality of the roles, and the subsequent award nominations. Using a sample of 438 Academy awards nominees, we showed that, relative to those who received recognition later in their careers, actors and actresses who were nominated in the earlier stages of their careers were more successful later in terms of the number of movies they starred in, the importance or quality of their roles, and the subsequent awards and nominations they received. In addition, younger nominees were shown to have higher chances of receiving another Oscar nomination following their initial nomination. Thus, early recognition is positively related to future career success not only in science but also in other fields. These findings have important practical implications to both individuals seeking to build a successful career and to other social actors (organizations, recruiters, agents, etc.), who should consider such findings for identifying and selecting competent candidates based on their prior records and make more accurate predictions about a person’s potential for achieving future success.


identifier: Aubin, Jocelyn  (1994) La Chambre nuptiale.  Masters thesis, Concordia University.

title: La Chambre nuptiale

abstract: Ete 1976. Montreal. Sur la grande place du complexe administratif et commercial de la Place Desjardins, en plein coeur de centre-ville, s'ouvre au public ce qui de I'exterieur a failure d'un pavilion entierement blanc avec a son entree comme toute identification: "La Chambre nuptiale". Concue et realisee par Francine Larivee et le Groupe de recherche et d'action sociale par I'art et les medias de communication, La Chambre nuptiale se veut un instrument de questionnement de la cellule de base de I'organisation sociale: le couple homme-femme. Pour I'equipe de La Chambre nuptiale, I'art et le changement social se conjuguent dans une autocritique culturelle qui vise la prise en charge, par les membres de la collectivite, de leur autonomie autant individuelle que collective. Concept inusite dans I'histoire de la pratique artistique du Quebec, La Chambre nuptiale constitue et denonce tout a la fois 1'intervention de I'art dans le champ social. La Chambre nuptiale scrute la construction sociale de I'identite des femmes, celle des hommes et celle de leur relation dans le couple. La Chambre nuptiale devoile les leurres de la distinction entre le "prive" et le "public". Face a cette demarche alliant I'art au changement social, une attention particuliere sera portee quant a I'acceuil regu par La Chambre nuptiale par differents publics dont celui de la critique d'art.


identifier: Stark, Zoey  (2021) Dyslexia Simulation Font: Can We Simulate Reading Struggles of Individuals with Dyslexia?  Masters thesis, Concordia University.

abstract: Individuals with dyslexia struggle at explaining what it is like to have dyslexia and how they perceive letters and words differently. This led the designer Daniel Britton to create a font that aims to simulate the perceptual experience of how effortful reading can be for individuals with dyslexia (http://danielbritton.info/dyslexia). This font removes forty percent of each character stroke with the aim of increasing reading effort, and in turn empathy and understanding for individuals with dyslexia. However, its efficacy has not yet been empirically tested. In the present study, we compared participants without dyslexia reading texts in the dyslexia simulation font to a group of individuals with dyslexia reading the same texts in Times New Roman font. Results suggest that the simulation font amplifies the struggle of reading, surpassing that experienced by adults with dyslexia—as reflected in increased reading time and overall number of eye movements in the majority of typical readers reading in the simulation font. Future research could compare the performance of the Daniel Britton simulation font against a sample of beginning readers with dyslexia as well as seek to design and empirically test an adapted simulation font with an increased preserved percentage of letter strokes.


identifier: Martignago Zilli, Guilherme ORCID: https://orcid.org/0000-0003-1228-4920 <https://orcid.org/0000-0003-1228-4920>  (2021) Hybrid Beamforming Design for Millimeter Wave Massive MIMO Communications.  PhD thesis, Concordia University.

abstract:
Wireless connectivity is a key driver for the digital transformation that is changing how people communicate, do business, consume entertainment and search for information. As the world advances into the fifth generation (5G) and beyond-5G (B5G) of wireless mobile technology, new services and use cases are emerging every day, bringing the demand to expand the broadband capability of mobile networks and to provide ubiquitous access and specific capabilities for any device or application. To fulfill these demands, 5G and B5G systems will rely on innovative technologies, such as the ultradensification, the mmWave, and the massive MIMO. To bring together these technologies, 5G and B5G systems will employ hybrid analog-digital beamforming, which separates the signal processing into the baseband (digital) and the radio-frequency (analog) domains. Unlike conventional beamforming, where every antenna is connected to an RF chain, and the signal is entirely processed in the digital domain, hybrid beamforming uses fewer RF chains than the total number of antennas, resulting in a less expensive and less energy-consuming design. The analog beamforming is usually implemented using switching networks or phase-shifting networks, which impose severe hardware constraints making the hybrid beamforming design very challenging. This thesis addresses the hybrid analog-digital beamforming design and is organized into three parts.
In the first part, two adaptive algorithms for solving the switching-network-based hybrid beamforming design problem, also known as the joint antenna selection and beamforming (JASB) problem, are proposed. The adaptive algorithms are based on the minimum mean square error (MMSE) and minimum-variance distortionless response (MVDR) criteria and employ an alternating optimization strategy, in which the beamforming and the antenna selection are designed iteratively. The proposed algorithms can attain high levels of SINR while strictly complying with the hardware limitations. Moreover, the proposed algorithms have very low computational complexity and can track channel variations, making them suitable for non-stationary environments. Numerical simulations have validated the effectiveness of the algorithms in different operation scenarios.
The second part addresses the phase-shifting-network-based hybrid beamforming design for narrowband mmWave massive MIMO systems. A novel joint hybrid precoder and combiner design is proposed. The analog precoder and combiner design is formulated as constrained low-rank channel decomposition, which can simultaneously harvest the array gain provided by the massive MIMO system and suppress intra-user and inter-user interferences. The constrained low-rank channel decomposition is solved as a series of successive rank-1 channel decomposition, using the projected block coordinate descent method. The digital precoder and combiner are obtained from the optimal SVD-based solution for the single-user case and the regularized channel diagonalization method for the multi-user case. Simulation results have demonstrated that the proposed design can consistently attain near-optimal performance and provided important insights into the method's convergence and its performance under practical phase-shifter quantization constraints.
Finally, the phase-shifting-network-based hybrid beamforming design for frequency-selective mmWave massive MIMO-OFDM systems is considered in the third part. The hybrid beamforming design for MIMO-OFDM systems is significantly more challenging than for narrowband MIMO systems since, in these systems, the analog precoder and combiner are shared among all subcarriers and must be jointly optimized. Thus, by leveraging the OFDM systems' multidimensional structure, the analog precoder and combiner design is formulated as constrained low-rank Tucker2 tensor decomposition and solved by a successive rank-(1,1) tensor decomposition using the projected alternate least square (ALS) method. The digital precoder and combiner are obtained on a per-subcarrier basis using the techniques presented in the second part. Numerical simulations have confirmed the design effectiveness, demonstrating its ability to consistently attain near-optimal performance and outperform other existing design in nearly all scenarios. They also provided insights into the convergence of the proposed method and its performance under practical phase-shifter quantization constraints and highlighted the differences between this design and that for the narrowband massive MIMO systems.


identifier: Zhang, Tiansheng  (2021) Modeling and Solving Resource Constrained Project Scheduling Problems with Remanufacturing Activities.  Masters thesis, Concordia University.

abstract: Resource constrained project scheduling problem (RCPSP) is one of the most important problems in industrial engineering and production management. Owing to environmental concerns, companies are paying more attention to the remanufacturing of end-of-life products. In this thesis, a mathematical model is developed considering remanufacturing activities in resource constrained project scheduling problem. The mathematical model considers recycle rate in multiple operation modes and several components of cost, including bonus, penalty, and others. A set of project network instance are generated using RanGen1 for evaluation. To solve the model, a three-stage heuristic method is developed in CPLEX 12.8 environment. Result shows that proposed method can reach a close-to-optimal solution within acceptable time limit.


identifier: Hashemi Attar, Nazanin  (2021) Robust State-Based Supervisory Control of Hierarchical Discrete-Event Systems.  PhD thesis, Concordia University.

abstract:
Model uncertainty due to unknown dynamics or changes (such as faults) must be addressed in supervisory control design. Robust supervisory control, one of the approaches to handle model uncertainty, provides a solution (i.e., supervisor) that simultaneously satisfies the design objectives of all possible known plant models. Complexity has always been a challenging issue in the supervisory control of discrete-event systems, and different methods have been proposed to mitigate it. The proposed methods aim to handle complexity either through a structured solution (e.g. decentralized supervision) or by taking advantage of computationally efficient structured models for plants (e.g., hierarchical models). One of the proposed hierarchical plant model formalisms is State-Tree-Structure (STS), which has been successfully used in supervisor design for systems containing up to 10^20 states.
In this thesis, a robust supervisory control framework is developed for systems modeled by STS. First, a robust nonblocking supervisory control problem is formulated in which the plant model belongs to a finite set of automata models and design specifications are expressed in terms of state sets. A state-based approach to supervisor design is more convenient for implementation using symbolic calculation tools such as Binary Decision Diagrams (BDDs). In order to ensure that the set of solutions for robust control problem can be obtained from State Feedback Control (SFBC) laws and hence suitable for symbolic calculations, it is assumed, without loss of generality, that the plant models satisfy a mutual refinement assumption. In this thesis, a set of necessary and sufficient conditions is derived for the solvability of the robust control problem, and a procedure for finding the maximally permissive solution is obtained.
Next, the robust state-based supervisory framework is extended to systems modeled by STS. A sufficient condition is provided under which the mutual refinement property can be verified without converting the hierarchical model of STS to a flat automaton model. As an illustrative example, the developed approach was successfully used to design a robust supervisor for a Flexible Manufacturing System (FMS) with a state set of order 10^8.


identifier: Radmannia, Sepehr  (2021) Generalized Neural Mass Model Analysis and Applications Over Electroencephalogram Data.  Masters thesis, Concordia University.

abstract: Computational modeling studies and explains neuronal behaviors by modeling their underlying dynamics. Specifically, Neural Mass Model (NMM) is a part of computational modeling that refers to populations (masses) of neurons inside the brain. NMMs have a variety of applications, e.g., characterization of the neurobiological process and artificial brain. NMMs are well investigated, and the available NMMs in the literature proposed two populations of neurons in communication with each other, namely, the population of pyramidal cells and the population of intra-neurons. Recent advanced technologies that facilitate spatiotemporal resolution neuroimaging data have contributed to develop more realistic NMMs. NMM's underlying behavioral dynamics must be appropriately analyzed to understand the brain's functional and structural relationship. In this thesis, first, we develop a novel method capable of detecting the model's underlying behavioral dynamics over high dimensional parameter space. This novel idea attempts to overcome the disadvantages of the bifurcation analysis over high-dimensional space. Moreover, physiologically interesting and non-interesting dynamics are localized over high-dimensional parameter space using the proposed approach. In order to validate the presented algorithm, we use the accuracy and F1-score as our metric. Second, we develop a method that localizes epileptic changes of electroencephalogram (EEG) signal over time as an application of NMMs in the real EEG data. It is studied in the state of the art that the gradual changes over the EEG signals can be interpreted as instability over the parameters of the NMMs. Furthermore, the progressive evolution of EEG's activity in pathological cases (e.g., epileptic seizures) is supposed to be characterized by a transition of the dynamics of the NMMs. The proposed algorithm is capable of detecting transitions of dynamics over simulated multi-dynamical signals. In addition, this method is capable of detecting the dynamic results in the best fitting time series with respect to frequency.


identifier: Luo, Yang  (2021) Design and Improved Switching Transient Modeling for A GaN-based Three Phase Inverter.  Masters thesis, Concordia university.

abstract:
In recent years, wide-bandgap devices (WBG) such as silicon carbide (SiC) and gallium nitride (GaN) transistors have drawn significant research attention in power conversion applications where higher efficiency, higher power density and lower cost are required, for example, in electric vehicle (EV) applications. Owing to its better figures of merit on on-resistance, switching speed and junction temperature, enhancement-mode GaN high electron mobility transistors (HEMTs) are able to be operated with switching frequency up to the megahertz range, through which the size of passive components in the power converters can be significantly reduced. Consequently, the power converter’s integration level and power density can be increased.
In GaN-based power converters, the switching energy loss increases naturally along with the switching frequency, which is the dominant loss component. Consequently, it is always one of the top priority performances to be considered in research and development activities. For device manufacturers, with access to internal materials and dimensional parameters, physics-based device models are usually used. Although these models can reveal detailed characteristics of the devices, they are not accessible to the public and can be very time-consuming to develop. Analytical models, that can emulate the transistor’s dynamic behaviour and predict the switching energy loss without consuming too much computing resources, are always an essential tool to help provide guidelines to engineers for circuit design and performance optimization purposes. This thesis develops a computationally inexpensive and straightforward switching transient model which proves to be more accurate than conventional model through simulation and experiments.
Currently, in the commercial market, there are only a few mature designs of GaN three-phase inverter products, and most of them are costly. This unavoidable phenomenon is led by the fact that the gate driver and power loop design for GaN is demanding. Some companies such as EPC, Navitas and Power Integrations are committed to monolithic-integrated gate drivers, but they are complex and expensive. Thus, this thesis also aims to design a GaN-based three-phase inverter with low cost and low complexity in the gate drive circuit. In addition, to achieve high performance GaN-based inverter, parasitic components in the power loop have to be minimized. In this thesis study, parasitic parameters of the power loop are extracted through Ansys Q3D simulation and then validated through experimental test results. With accurate parasitic parameters, the layout of the PCBs is improved to achieve better inverter performance.


identifier: Asadi Khanouki, Mostafa  (2021) Characterization and Microstructure-based Modeling of Magnetorheological Elastomers.  PhD thesis, Concordia University.

abstract:
Multi-functional magnetorheological elastomers (MREs) with magnetic-controlled properties oﬀer great potential for enabling new technologies in a diverse range of industry sectors, such as automotive, aerospace, civil, and biomedical applications. The main objective of this research dissertation is to develop analysis models for magneto-mechanical properties of smart MREs and to propose design optimization strategies to optimally design a novel sandwich beam-type MRE-based adaptive tuned vibration absorber. The dissertation comprises three major interrelated parts. In the first part, a quasi-static microstructure-based model has been proposed to investigate the magneto-elastic properties of MREs. The elastic response of the MREs at zero magnetic field is initially studied by comparing the results of three hyperelastic material models. Then, a microscale model is developed for predicting the quasi-static response of MREs under an external magnetic field. The model considers magnetic interaction between particles distributed in the carrier elastomeric matrix according to regular lattice models for isotropic MREs and according to chain-like structure for anisotropic MREs. Several lattice models are proposed, and performance of each lattice is compared with their counterparts. Detailed explanation is provided on the characteristics of the proposed lattices and on the resulting changes in the microstructure properties of the MREs. The simulation results for different lattice models are then compared with the experimental measurements for both isotropic and anisotropic MRE samples using an advanced rheometer equipped with a magnetorheological (MR) device.
In the second part, the dynamic magneto-mechanical properties of MREs are investigated. For this purpose, a dynamic physic-based model considering the microstructure of MREs is developed to accurately predict the frequency- and field-dependent linear viscoelastic properties of the material. The proposed model considers a cubic particle network in which magnetic particles are located at the junctures and connected with elastic springs. Using Langevin dynamics, the governing equations of motion of particles are derived to evaluate the relaxation spectrum associated with particles’ motion in parallel and normal directions with respect to the applied magnetic field. A dipole magnetic saturation model is also implemented to derive the storage and loss moduli of the MREs in terms of frequency and magnetic flux density. The material parameters in the proposed dynamic microstructure-based model have been identified using experimental tests. For this purpose, oscillatory shear tests were performed using the magneto-rheometer in linear viscoelastic region under a wide range of excitation frequency varying from 2 Hz to 100 Hz in presence of various levels of applied magnetic fields ranging from 0.0 T to 1.0 T. The viscoelastic properties, namely storage and loss moduli of both isotropic and anisotropic MREs, were subsequently measured and compared with those obtained using the developed model to quantitatively evaluate its performance.
The third part of the present dissertation aims to investigate the application of MREs in developing a novel sandwich beam-shaped MRE-based adaptive tuned vibration absorber (MRE-ATVA). An MRE-ATVA comprised of a light-weight sandwich beam treated with an MRE core layer and two electromagnets installed at both free ends is proposed. The MRE-ATVA is designed to have a lightweight and compact structure and the electromagnets provide the magnetic field required to activate the MRE layer while also act as the resonator of the absorber. The finite element (FE) model of the proposed MRE-ATVA and magnetic model of the electromagnets with three different potential designs are developed and combined to evaluate the frequency range of the absorber under varying magnetic field intensity. The results of the developed models are validated in multiple stages with available analytical and simulation data. The developed models are then utilized to formulate the multidisciplinary design optimization problem to maximize the operating frequency range of the MRE-ATVA while respecting constraints of weight, size, mechanical stress, and sandwich beam deflection. The optimization problem is solved combining the gradient based sequential quadratic programming (SQP) technique and stochastic based genetic algorithm (GA) to accurately obtain the global optimum solution.  The performance of the optimal MRE-ATVAs with three potential designs are finally compared.


identifier: Saulnier, Valérie  (2021) Réflexion philosophique sur la formation vocale des musicothérapeutes.  Masters thesis, Concordia University.

abstract: Le but de cette réflexion philosophique est d’explorer les enjeux de la formation vocale pour les musicothérapeutes.  L’analyse des données s’est déroulée en deux étapes : la première étant la révision des écrits selon les mots-clés déterminés pour cette recherche, et la deuxième étant la classification des écrits et des arguments pertinents selon une organisation logique et structurée des thèmes identifiés. Les résultats de la recherche exposent les thèmes suivants : le travail vocal en musicothérapie, les compétences vocales requises en Amérique du Nord, un survol de la formation vocale en musicothérapie, les enjeux et la sensibilisation à la santé vocale des musicothérapeutes, les impacts physiques et psychologiques d’une formation vocale inadéquate et les besoins d’une formation vocale adaptée aux besoins de la profession. Finalement des pistes de réflexion nous amènent à considérer trois aspects de la voix pour réaliser une formation vocale optimale. Les implications pour la profession et des recommandations pour de future recherche sont présentées.


identifier: Katul, Mounir ORCID: https://orcid.org/0000-0001-6194-3035 <https://orcid.org/0000-0001-6194-3035>  (2021) The Influence of International – Domestic Politics on the Production of Ethnic Identities: The Case of Lebanon (2000 – 2010).  PhD thesis, Concordia University.

abstract:
Ethnic leaders’ appeals center on polarizing ethnic identities for support in general, and during elections in specific, and it has been a general consensus until recently in electoral systems-based theories that these appeals are shaped by domestic factors such as local cultural institutions and electoral systems. For example, these theories argue that electoral systems that encourage inter-communal vote-pooling also encourage ethnic parties to forge inter-ethnic coalitions to win parliamentary elections and thus to avoid appeals based on narrowly interpreted identities that alienate the supporters of their allies. However, it is the contention of this dissertation that coalition formation and, subsequently, ethnic appeals, are not always dependent on domestic causes; rather, a major influence on an ethnic leader’s choices of identities and domestic alliances is the interplay between international actors and domestic leaders. More specifically, rivalries between international state actors manifest as ethnic rivalries between ethnic leaders when the latter form coalitions on the basis of shared allegiances to the same foreign backer or bloc. Bound by their allegiances to international actors (out of either opportunism or ideological conviction), ethnic leaders re-conceptualize salient identities, revisiting specific historical narratives in order to accommodate new domestic alliances based totally or in part on international interstate blocs and to defend their domestic and international blocs.

Taking Lebanon (2000-2010) as a case study, this research focuses on four Lebanese ethnic parties (The Phalange, the Free Patriotic Movement, the Future Movement, and Hezbollah) and tracks both shifts and consistencies in their ethnic appeals before and after a major political event of international proportion in Lebanon: the assassination of ex-prime minister Rafic Hariri in February 2005 and the subsequent withdrawal of Syrian forces from Lebanon after domestic and international pressure. Quantifying over 4,500 speeches given by leaders of these four parties over the course of this decade in Lebanon, this research applies the Constructivist lens, which treats all identities as social constructions, and discourse analysis to compare the types of identities that ethnic leaders directly appeal upon for support before and after they change coalitions.


identifier: Saryazdi, Soroush  (2021) End-to-end Representation Learning for 3D Reconstruction.  Masters thesis, Concordia University.

abstract:
Physically based rendering requires the digital representation of a scene to include both 3D geometry and material appearance properties of objects in the scene. Reconstructing such 3D representations from images of real-world environments has been a long-standing goal in the fields of computer vision, computer graphics, robotics, augmented and virtual reality, etc. Recently, representation learning based approaches have transformed the landscape of several domains such as image recognition and semantic segmentation. However, despite many encouraging advances in other domains, how these learning-based approaches can be leveraged in the realm of 3D reconstruction is still an open question. In this thesis, we propose approaches for using neural networks in conjunction with the 3D reconstruction pipeline such that they can be trained end-to-end based on a single end objective (e.g., to reconstruct an accurate 3D representation). Our main contributions include the following:

- A fully differentiable dense visual SLAM framework for reconstructing the 3D geometry of a scene from a sequence of RGB-D images, called gradslam. This work, carried out in collaboration with the Robotics and Embodied AI Lab (REAL) at MILA, resulted in the release of the first open-source library for differentiable SLAM.

- We propose the disentangled rendering loss for training neural networks to estimate material appearance parameters from image(s) of a near-flat surface. The disentangled rendering loss allows the network to weigh the importance of each material appearance parameter based on its effect on the final appearance of the material, while also having desirable mathematical properties for gradient-based training.

- We describe work towards an end-to-end trainable model that can simultaneously reconstruct the 3D geometry and predict the material appearance properties of a scene. A publicly available dataset for training such a model is not currently available. Thus, we have created a dataset of material appearance properties for complex scenes which we intend to release publicly.

Our approach enjoys many of the benefits of classical 3D reconstruction approaches such as interpretability (due to the modular nature) and the ability to use well-understood components from the reconstruction pipeline. Further, this approach also enjoys representation learning benefits such as the capability of solving challenging tasks which have been difficult to solve by designing explicit algorithms (e.g., material appearance property estimation for complex scenes), and their strong performance on end-to-end training tasks.


identifier: Khan-Dobson, Delanie  (2021) “The Real Me is the Fake Me:” Applying Knowledge of the Social Sciences to Narrative Journalism to Tell Complex Stories in the Format of a Long Form Magazine Article.  Masters thesis, Concordia University.

format: text text

abstract: This research-creation project explores how a narrative-journalism long form magazine-style article can use sociological theory as a framework to turn several different intersecting topics into one relatable and interesting story. It was written in the style of narrative journalism to take both myself and my imagined audience on a journey to understand the connections between online self-performance, Instagram influencer culture, consumer-capitalism, and postfeminist digital cultures (Gill 2007). This research is made up of personal narration, academic research, and excerpts from interviews with academics as well as of “admirers” of influencers, that is, Instagram users that follow and interact with social media influencers. The personal narrative journey within this research-creation project leads me to reach conclusions about my own biases towards influencers, and come to realize the conscious and unconscious ways in which I curate and modify my own digital identity on the internet. This research demonstrates how the literary techniques and subjectivity associated with narrative journalism can take a complex topic and turn it into understandable and nuanced story. Using theories of postfeminism, this research project adds knowledge to how the self-performance of Instagram influencers on social media supports consumer-capitalism and entrenched ideas of the “ideal woman” within North American society.


identifier: Kabir, Mohammad Ekramul ORCID: https://orcid.org/0000-0003-2044-7187 <https://orcid.org/0000-0003-2044-7187>  (2021) Planning and Design for Intelligent and Secure Integration of Electric Vehicles into the Smart Grid.  PhD thesis, Concordia University.

abstract: The transition to electric vehicles (EVs) is gaining momentum around the world and government initiatives to accelerate this transition range from major tax exemptions, lower insurance payments to convenient parking incentives at shopping malls.  The major drivers for this acceleration are the rising awareness by the public for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies.  EVs acceptance however is hindered by several challenges; among them is their shorter driving range, slower charging rates, and the ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers.  Governments of developed countries as well as major car manufacturers are taking solid steps to address these challenges and set ambitious goals to make EVs the major transportation mode within few years.  Consequently, a significant number of EVs is going to connect to the existing smart grid and hence, the load pattern is expecting a paradigm shift.  This immense load will challenge the generation, transmission and distribution sector of the grid along with being a potential cyber-physical attack platform.  To attain a graceful EV penetration for curtailing GHG emission, along with the socioeconomic initiatives, an extensive research is required, especially to mitigate the range anxiety and ameliorate the load congestion on the grid.  As a consequence, to reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network for the anticipated energy demand and that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting times and shorter travel distances to charge.  This solution also maintains the voltage stability by considering the distribution grid capacity, determining transformers’ rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network.  We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands.  Though an adequate fast charging network may assist to reduce the range anxiety and propel the EV market, catering this large number of EVs using fuel based conventional grid actually shifts the carbon footprint from the transportation sector to the power generation sector. As a consequence, green energy needs to be promoted for EV charging.  However, the intermittent behavior of renewable energy (RE) generation challenges to maintain a RE based stand alone CS. In order to address this issue, we consider a photovoltaic(PV) powered station equipped with an energy storage system (ESS), which is assumed  to  be  capable  of  assigning  variable  charging  rates  to  different  EVs  to  fulfill their demands inside their declared deadlines at minimum price.  To ensure fairness, a charging rate dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate.  The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainties.  Whatever, the energy source is green or not of a CS, a static CS cannot offer the flexibility to charge an EV at any place at any time especially for an emergency case.  Fortunately, the bidirectional energy transferring capability between vehicles (i.e., vehicle to vehicle (V2V)) might be a solution to charge an EV at any place and at any time without leaning on a stationary CS. Hence, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots.  We formulate an integer linear program (ILP) to maximize the number of served EVs by determining the optimal trajectory and schedule of each truck.  Owing to its complexity, we implement Dantzig-Wolfe decomposition approach to solve this.  However, to build a prolific EV charging ecosystem,  all its entities (e.g.,  EVs,  CSs and grid) have to be connected through a communication link and that unveils a new cyber physical attack surface. As a consequence, we exploit the abundance of Electric Vehicles (EVs) to target the stability of the power grid by presenting a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid and assess the dire consequences over the power system.  Finally, a back propagation neural network (BPNN) technique is used in a proposed framework to detect such switching attacks before being executed.


identifier: Norris, Rachel  (2021) Theoretical Foundations of Music Therapy Research and Practice with Families: A Qualitative Content Analysis.  Masters thesis, Concordia University.

abstract: The purpose of this qualitative summative content analysis was to explore the theoretical foundations that inform music therapists’ work with families. An additional aim was to see if music therapists are developing or incorporating theories that are unique to music therapy to inform their work. A review of the literature indicated that music therapists are using many different theories to support their work with families. These theories also came from different fields including psychoanalysis, child development, neurology, and music therapy. Qualitative and quantitative music therapy studies were systematically reviewed for theoretical principles and assumptions. The student-researcher also explored how these assumptions related to the music therapists’ approach and evaluation or assessment tools. The analysis indicated that music therapists are actively developing and refining theoretical premises and assumptions to better understand their work with families. The student-researcher identified three ways that theory was used to inform this work: First, music therapy researchers are increasingly using the theory of communicative musicality to support their work. Second, music therapy researchers have used theory to inform the development of family-centered approaches. Third, researchers are using theories to develop music-therapy-specific evaluation and assessment tools. These findings suggest that theoretical development in this area is ongoing and the student-researcher hopes that this inquiry can support a continued development and refinement of the theories that inform music therapists’ work with families.


identifier: Lambruschini, Miranda  (2021) A Narrative-Critical Analysis Comparing Select Markan "Sandwiches" and their Synoptic Parallels.  Masters thesis, Concordia University.

abstract: This thesis provides a narrative-critical analysis of three plotlines in Mark that contain the intercalation structure; Mark 5:21-43; 6:7-20; 14:53-72, as well as its synoptic parallels; Matthew 9:18-26; 10:1-16; 14:1-12; 26:57-75 and Luke 8:40-56; 9:1-9; 22:54-71. Through the comparison performed, the relevance and theological significance that springs forth from the use of this literary device becomes apparent. Each narrative contains thematic development that is supported through repeated language and parallel actions, faith, reliance on God without a guaranteed outcome, and the consequences of affiliation with Jesus. As a literary device, intercalation leads the implied reader to an independent state of introspection. Specifically, where the device is employed the onus of the responsibility for the implied reader to reflect grows to the point of independent introspection and reflection, as models and guided examples are provided by narrator through literary changes in the presentation of the narratives.


identifier: Chen, Long  (2021) UAV Path Planning and Obstacle Avoidance Based on Fuzzy Logic and Kinodynamic RRT Methods.  Masters thesis, Concordia University.

abstract:
Path Planning is one of the important problems to be explored in unmanned aerial vehicle (UAV) to find the optimal path between starting position and destination. The aim of path planning technique is not only to find the shortest path but also to provide the collision-free path for the UAV in unknown environment. Although there have been significant advances on the methods of path planning where the map of environment is known in advance, there are still some challenges to be addressed for dynamic autonomous navigation for the UAV in unknown environment.

This thesis research proposes a new path planning method named Fuzzy Kinodynamic RRT for unmanned aerial vehicle flying in the unknown environment. This method generates a global path based on RRT [1] (Rapidly-exploring random tree) and utilizes fuzzy logic system to avoid obstacles in real time. A set of heuristics fuzzy rules are designed to lead the UAV away from unmodeled
obstacles and to guide the UAV towards the goal. The rules are also tested in different scenarios, and they are all working efficiently both in simple and complicated cases. The UAV starts to fly along the path generated by RRT, and the fuzzy logic system is then activated when it comes across the obstacle. When the sensor detects no collision within a specific distance, the fuzzy system is turned off and the UAV flies back to the previous path towards the final destination. The simulations of the developed algorithm have been carried out in various scenarios, with the sensor to detect the obstacles. The numerical simulations show the satisfactory results in various scenarios for path planning that considerably reduces the risk of colliding with other stationary and moving obstacles. A more robust and efficient fuzzy logic controller which embeds the path planning is finally proposed and the simulation shows the satisfactory results in complicated environments.


identifier: Do, Hoang-Dung  (2021) Modeling the Linux page cache for accurate simulation of data-intensive applications.  Masters thesis, Concordia University.

abstract:
The emergence of Big Data in recent years has led to a growing need in data processing and an increasing number of data intensive applications. Processing and storage of massive amounts of data require large-scale solutions and thus must data-intensive applications be executed on infrastructures such as cloud or High Performance Computing (HPC) clusters. Although there are advancements of hardware/software stack that enable larger computing platforms, some relevant challenges remain in resource management, performance, scheduling, scalability, etc. As a result, there is an increasing demand for optimizing and quantifying performance when executing data-intensive applications on those platforms. While infrastructures with sufficient computing power and storage capacity are available, the I/O performance on disks remains a bottleneck. To tackle this problem, apart from hardware improvements, the Linux page cache is an efficient architectural approach to reduce I/O overheads, but few experimental studies of its interactions with Big Data applications exist, partly due to limitations of real-world experiments. Simulation is a popular approach to address these issues, however, existing simulation frameworks do not simulate page caching fully, or even at all. As a result, simulation-based performance studies of data-intensive applications lead to inaccurate results.

This thesis proposes an I/O simulation model that captures the key features of the Linux page cache. We have implemented this model as part of the WRENCH workflow simulation framework, which itself builds on the popular SimGrid distributed systems simulation framework. Our model and its implementation enable the simulation of both single-threaded and multithreaded applications, and of both writeback and writethrough caches for local or network-based filesystems. We evaluate the accuracy of our model in different conditions, including sequential and concurrent applications, as well as local and remote I/Os. The results show that our page cache model reduces the simulation error by up to an order of magnitude when compared to state-of-the-art, cacheless simulations.


identifier: Yu, Hongjiang  (2021) DNN-Assisted Speech Enhancement Approaches Incorporating Phase Information.  PhD thesis, Concordia University.

abstract:
Speech enhancement is a widely adopted technique that removes the interferences in a corrupted speech to improve the speech quality and intelligibility. Speech enhancement methods can be implemented in either time domain or time-frequency (T-F) domain. Among various proposed methods, the time-frequency domain methods, which synthesize the enhanced speech with the estimated magnitude spectrogram and the noisy phase spectrogram, gain the most popularity in the past few decades. However, this kind of techniques tend to ignore the importance of phase processing. To overcome this problem, the thesis aims to jointly enhance the magnitude and phase spectra by means of the most recent deep neural networks (DNNs). More specifically, three major contributions are presented in this thesis.

First, we present new schemes based on the basic Kalman filter (KF) to remove the background noise in the noisy speech in time domain, where the KF acts as joint estimator for both the magnitude and phase spectra of speech. A DNN-augmented basic KF is first proposed, where DNN is applied for estimating key parameters in the KF, namely the linear prediction coefficients (LPCs). By training the DNN with a large database and making use of the powerful learning ability of DNN, the proposed algorithm is able to estimate LPCs from noisy speech more accurately and robustly, leading to an improved performance as compared to traditional KF based approaches in speech enhancement. We further present a high-frequency (HF) component restoration algorithm to extenuate the degradation in the HF regions of the Kalman-filtered speech, in which the DNN-based bandwidth extension is applied to estimate the magnitude of HF component from the low-frequency (LF) counterpart. By incorporating the restoration algorithm, the enhanced speech suffers less distortion in the HF component. Moreover, we propose a hybrid speech enhancement system that exploits DNN for speech reconstruction and Kalman filtering for further denoising. Two separate networks are adopted in the estimation of magnitude spectrogram and LPCs of the clean speech, respectively. The estimated clean magnitude spectrogram is combined with the phase of the noisy speech to reconstruct the estimated clean speech. A KF with the estimated parameters is then utilized to remove the residual noise in the reconstructed speech. The proposed hybrid system takes advantages of both the DNN-based reconstruction and traditional Kalman filtering, and can work reliably in either matched or unmatched acoustic environments.

Next, we incorporate the DNN-based parameter estimation scheme in two advanced KFs: subband KF and colored-noise KF. The DNN-augmented subband KF method decomposes the noisy speech into several subbands, and performs Kalman filtering to each subband speech, where the parameters of the KF are estimated by the trained DNN. The final enhanced speech is then obtained by synthesizing the enhanced subband speeches. In the DNN-augmented colored-noise KF system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the LPCs and the driving noise variances. The LPCs are obtained through training a multi-objective DNN, while the driving noise variances are obtained by solving an optimization problem aiming to minimize the difference between the modelled and observed AR spectra of the noisy speech. The colored-noise Kalman filter with DNN-estimated parameters is then applied
to the noisy speech for denoising. A post-subtraction technique is adopted to further remove the residual noise in the Kalman-filtered speech. Extensive computer simulations show that the two proposed advanced KF systems achieve significant performance gains when compared to conventional Kalman filter based algorithms as well as recent DNN-based methods under both seen and unseen noise conditions.

Finally, we focus on the T-F domain speech enhancement with masking technique, which aims to retain the speech dominant components and suppress the noise dominant parts of the noisy speech. We first derive a new type of mask, namely constrained ratio mask (CRM), to better control the trade-off between speech distortion and residual noise in the enhanced speech. The CRM is estimated with a trained DNN based on the input noisy feature set and is applied to the noisy magnitude spectrogram for denoising. We further extend the CRM to the complex spectrogram estimation, where the enhanced magnitude spectrogram is obtained with the CRM, while the estimated phase spectrogram is reconstructed with the noisy phase spectrogram and the phase derivatives. Performance evaluation reveals our proposed CRM outperforms several traditional masks in terms of objective metrics. Moreover, the enhanced speech resulting from the CRM based complex spectrogram estimation has a better speech quality than that obtained without using phase reconstruction.


identifier: Tellier, Russell  (2021) The Portrayal of Vegans and Veganism in the Los Angeles Times and The New York Times in 2017: A Vegan Perspective.  Masters thesis, Concordia University.

abstract:
This thesis provides the first example of a discourse analysis of North American newspaper coverage about veganism. Specifically, it examined the Los Angeles Times and The New York Times’ representation of vegans and veganism during the second half of 2017. Its objective was to uncover the dominant discourses on vegans and veganism in the newspaper coverage of 76 articles and determine if the coverage was adequate from a vegan activist perspective. The thesis found that the dominant discourse about vegans and or veganism in most of the articles in the sample was positive. Nevertheless, overall, the coverage was still inadequate from multiple vegan perspectives, due to its negative articles, lack of details about humanity’s treatment of animals, and lack of focus on
veganism’s animal-friendly and morally obligatory status.


identifier: Jing, Ming  (2021) A Study on the Interactions between Water Droplets and Solid Targets to Understand Water Droplet Erosion.  Masters thesis, Concordia University.

abstract:
Water droplet erosion (WDE) has always been a serious issue in the power generation industry. WDE is the continuous loss of material from a solid surface due to the accumulated impacts by water droplets. In gas turbines, ultra-fine droplets are purposely sprayed into the incoming air of the compressor in order to decrease the ambient temperature and increase the efficiency of the turbine. Unfortunately, this process involving water droplets, severely damages the leading edge of the compressor blades. Erosion, such as WDE, is not constant with time. It has been classified into early stages and advanced stages. Most studies on erosion have primarily focused on the early stages of erosion. Very few researchers focused on water droplets and solid interactions at the early stages of erosion, which is important in understanding WDE damage. Knowing where and how the droplets impact the target can contribute to understanding the WDE phenomenon further and propose a way to reduce WDE or even find a potential solution for WDE altogether. The purpose of our work is to explore several approaches in understanding this interaction.
One part of this work will study the effects of airflow on the trajectories of water droplets with the use of ink coated samples and computational fluid dynamics (CFD) simulation. A secondary part will investigate and compare how different solid target samples react to the impact of water droplets. The selection of samples is as follows: flat (as-received unaltered material), grooved, and metallic foam. The grooved and metallic foam samples are employed in this work in order to stabilize a water film and dissipate the energy caused by multiple droplets impacts. The erosion tests were conducted in a rotating disc rig in accordance to the ASTM-G73 standard. The size of the droplets was 460 μm and the impact speeds were set to 150 m/s, 200 m/s, and 250 m/s. Ti-6Al-4V alloy was chosen as a sample material because it is typically used in turbine compressor blades.
During the WDE tests, it was observed that the airflow had a significant influence to drift the water streak away from the rotation center. Moreover, it can affect the trajectories and the impact position of the droplets. The airflow causes the upper half of the sample to erode faster than the bottom half. This was obvious upon observing the eroded samples after WDE tests. Furthermore, the formation and distribution of secondary droplets after the initial impact could also be influenced by the airflow within the rig. This is attributable to the lower mass of the secondary smaller droplets that are easily affected by the airflow.
Metal samples having grooves of either 0.5 mm or 1 mm depth were employed in this work along with other samples having blocked grooves to prevent water flowing downward. Incubation period and maximum erosion rate are the two main erosion resistance parameters of interest in this research. The results showed that the 0.5 mm groove had a longer incubation period than the reference sample. It also fared better than the 1 mm and bottom blocked grooved samples. This was studied by CFD simulation and is concluded as the influence of water film formation in the groove by dampening the impacts of subsequent water droplets. The 0.5 mm groove had a more suitable airflow environment to stabilize the water film in the groove than the 1 mm groove. However, at the maximum erosion rate stage, the reference sample and both sizes of grooves showed similar erosion behavior. This indicates that the maximum erosion rate stage is not affected significantly by introducing grooves and the formation of water films in them.
Porous and polyurethane-blocked samples were used as both test and reference samples respectively in the current work. Their purpose is to stabilize the water film, dissipate the impact energy, and study the interactions between water droplets and porous structure further. The
results show that the porous structure can dissipate more impact energy than the solid substrate and mitigate the WDE damage to some extent.


identifier: Taher, Alhusain Mohamed  (2021) Development of Earthwork Ontology and its Application.  PhD thesis, Concordia University.

abstract:
ABSTRACT
Development of Earthwork Ontology and its Application

Alhusain Mohamed Taher
Concordia University, 2021
In a typical construction project, a significant amount of information is communicated to various stakeholders at different phases of the project lifecycle. The communication of this information tends to be informal and ad-hoc in the majority of the cases, which makes it more susceptible to loss of information or misinterpretation. Earthwork operations, which are one of the main operations of construction projects, also struggle with the challenge of effective information communication. There is an apparent shortcoming regarding the unified structure for data and information exchange in this domain. The existing models and ontologies do not address the explicit semantic representation of earthwork operations. Accordingly, there is a need for a knowledge model to formalize the efficient communication of information. An ontological model can be used to organize the domain knowledge so that it can be utilized and reused by the stakeholders.
The primary purpose of this study is to develop an ontology for the earthwork domain that can be used to create the semantics-based integration method to support the communications between the different disciplines and stakeholders in the earthwork domain. Accordingly, the objectives of this study are: (1) To extract the explicit and tacit knowledge required for the earthwork domain; (2) To formalize the extracted knowledge by developing the Earthwork Ontology (EW-Onto); (3) To develop methods for linking and coupling EW-onto with other existing relevant ontologies in the construction domain to extend its application for safety and productivity; and (4) To evaluate the integrated ontology (IEW-Onto) and apply the ontological model in supporting application development, which is a Multi-Agent System (MAS) in the earthwork domain.
In the proposed framework, the ontology integrates the different components in the domain. The extended earthwork ontology (called Integrated Earthwork Ontology or IEW-Onto) is composed of the concepts, relationships, and axioms in this domain and can represent the semantic values of the entities and the relationships. Each entity is linked with other entities with different types of relationships, such as is-a, part-of, operates, and coordinates. IEW-Onto benefits from the available ontologies in the construction domain, and links with other ontologies, such as sensor and soil ontologies. IEW-Onto is used to build the earthwork operation model as a pattern to represent the operations and processes sequences, which provide a reusable pattern for several applications such as MAS. The developed MAS can cope with the complexity of earthwork operations’ communication at the fleet level and addresses safety issues. In the MAS, every piece of equipment is represented by a dedicated computer agent. This Ontology-based MAS is expected to improve the safety of earthwork operations. Different evaluation methods were used to evaluate EW-Onto and IEW-Onto, including checking consistency, survey, data-driven and application-based validations. The evaluation results show that both ontologies have consistency and provide a high level of clarity, richness, comprehensiveness, interpretability, and effectiveness of the presented knowledge in the earthwork domain.


identifier: Mahdi, Reham  (2020) Exploring the role of experience API in supporting new trends in Educational Technology: A literature review.  Masters thesis, Concordia University.

abstract:
Despite its growth in the area of educational technology, Experience API (xAPI) continues to be under-used as a solution across the different platforms in institutions and organizations.  There is a lack of any detailed summary in the literature about the potential and the limitation of using xAPI in conjunction with learning platforms and technologies.  This thesis examines the role of xAPI in promoting, shaping and supporting learning in organizational contexts.  This discussion is developed by using cases reported in the literature and new cases from contemporary educational technologies.  The thesis illustrates the role the standard plays within current major trends in digital learning and within the context of a broader ecosystem of learning platforms and technologies. It provides a useful and thorough account of xAPI and its potential to an audience of individuals responsible for implementing xAPI within organizations.  xAPI provides to some extent a promise of improved impact to Performance Evaluation and Evaluating training Effectiveness.  However, xAPI lacks concrete cases and examples to support its utilization in the fields of Learning Analytics, Performance Management, Predictive Learning and Workforce Planning.
Keywords: Experience API (xAPI), Learning Management Systems, Learning Record Store, Learning Analytics, Microlearning, Evaluation Effectiveness, Predictive Learning, Adaptive Learning, Workforce Planning.


identifier: El-Hassan, Nada  (2021) Three Essays in Mergers and Acquisitions and Executive Compensation.  PhD thesis, Concordia University.

abstract:
Several studies in the finance literature (and other fields) focus on how compensation contracts of CEOs shape incentives and affect risk-taking behaviour. We examine how CEOs with varying risk appetites approach merger and acquisition (M&A) deals differently due to incentives driven by their compensation structure. Relying on VEGA, the sensitivity of executive’s portfolio to a one percent change in volatility of stock returns, we document that acquirers and targets behave differently vis-à-vis their compensation. We show that a longer time to completion is related to the target VEGA (and not bidder VEGA). We also find that a more risk-taking CEO (as encouraged by VEGA) selling his firm would delay completing a deal by a significant three weeks. We conjecture that target CEOs choose to delay deal completion to look for better bids that tally their need to change their portfolio of company holdings.
Next, we link executive compensation, mergers and acquisitions, and environmental, social and governance in one framework that produces new insights into how CEO’s incentives yield sub-optimal investment decisions. Our sample consists of 1,280 mergers (M&A) from the period of 1993-2018 and uses the CEO’s wealth sensitivity to stock price volatility (VEGA) as a proxy for risk-taking behaviour. We establish that there is a shift in the relation between CSR rating and cumulative abnormal announcement returns of M&As deals. After 2008, the market for corporate control no longer rewards more commitment to CSR activities. We examine the performance of our sample of mergers in comparison with that of a matched sample of non-bidding firms vis-à-vis their environmental, social and governance (ESG) profiles over the long term. Our results indicate that more CSR commitment does not translate into better long-term returns for shareholders and stakeholders if the firms participate in M&As. Moreover, we investigate how the bidder and target CSR ratings, as well as management risk-taking incentives (proxied by VEGA), affect deals total synergy estimated around the announcement day. Our results show that firms with lower CSR ratings yield more synergy gains, which are not related to both bidder and target risk-taking incentives.
Finally, we provide new evidence related to the debate whether corporate social responsibility (CSR) strategies intrinsically benefit organizations and contribute to wealth creation after controlling for CEO compensation. We utilize a sample of M&A deals spanning the period 1993 to 2018 of target firms with different CSR ratings and investigate the effect of CEOs’ executive compensation driven incentives. Our main finding is that Low CSR firms becoming targets of M&A contests in the subperiod post-2008 record the highest cumulative average abnormal returns (compared to High CSR over the same period and other subsamples). We report that Low CSR firms with High VEGA target CEOs specifically perform better in corporate control contests in later years. We justify this as Low CSR firms are characterized by lower governance and more agency costs where management seeks benefit its own interests instead of being considerate for the stakeholders at large.


identifier: Taghavi, Mona  (2021) Strategic and Blockchain-based Market Decisions for Cloud Computing.  PhD thesis, Concordia University.

abstract: The cloud computing market has been in the center of attention for years where cloud providers strive to survive by either competition or cooperation. Some cloud providers choose to compete in the market that is dominated by few large providers and try to maximize their profit without sacrificing the service quality which leads to higher user ratings. Many research proposals tried to contribute to the cloud market competition. However, the majority of these proposals focus only on pricing mechanisms, neglecting thus the cloud service quality and users satisfaction. Meanwhile, cloud providers intend to form cloud federations to enhance their services quality and revenues. Nevertheless, traditional centralized cloud federations have strict challenges that might hinder the members' motivation to participate in, such as formation of stable coalitions with long-term commitments, participants' trustworthiness, shared revenue, and security of the managed data and services. For a stable and trustworthy federation, it is vital to avoid blind-trust on the claimed SLA guarantees from the members and monitor the quality of service  considering the various characteristics of cloud services. This thesis aims to tackle the issues of cloud computing market from the two perspectives of competition and cooperation by: 1) modeling and solving the conflicting situation of revenue, user ratings and service quality, to improve the providers position in the market and increase the future users' demand; 2) proposing a user-centric game theoretical framework to allow the new and smaller cloud providers to have a share in the market and increase users satisfaction through providing high quality and added-value services; 3) motivating the cloud providers to adopt a coopetition behavior through a novel, fully distributed blockchain-based federation's structure that enables them to trade their computing resources through smart contracts; 4) introducing a new role of oracle as a verifier agent to monitor the quality of service and report to the smart contract agents deployed on the blockchain while optimizing the cost of using oracles; and 5) developing a Bayesian bandit learning oracles reliability mechanism to select the oracles smartly and optimize the cost and reliability of utilized oracles. All of the contributions are validated by simulations and implementations using real-world data.


identifier: Bathini, Srinivas ORCID: https://orcid.org/0000-0001-6600-8505 <https://orcid.org/0000-0001-6600-8505>  (2021) Lab-on-Chips for Capture, Detection, and Isolation of Cancerous Extracellular Vesicles.  PhD thesis, Concordia University.

abstract:
Cancer is a group of diseases involving abnormal growth of cells with a potential to spread to the other parts of the body. It is predicted that one in two will be diagnosed with Cancer by 2030 and one in four would die. The continuous growth and metastasis of cancer cells depend upon cell-to-cell communication. This communication largely involves the secretion of soluble factors by cancer cells within the tumor microenvironment, although these cell types have also shown to export membrane-encapsulated particles containing regulatory molecules that contribute to cell-to-cell communication. These particles are known as extracellular vesicles (EVs) and include exosomes and microvesicles. EVs are a group of extracellular communication organelles enclosed by a phospholipid bilayer secreted by all types of cells throughout the animal kingdom. These vesicles are in the range of 30 to 1000 nm containing a myriad of substances like RNA, DNA, proteins, and lipids from their origin cells, offering a good source of biomarkers. Although their characteristics provide EVs great potential as biomarkers, efficient isolation and detection techniques are still challenging. Currently, the standard method for their isolation and quantification from body fluids is by ultracentrifugation, which is not a practical method to be implemented in a clinical setting. Thus, a versatile and cutting-edge platform is required to detect and isolate exosomes selectively for further analysis at a clinical level.
This dissertation is focused on developing lab-on-chip devices for the capture, detection, and isolation of cancerous extracellular vesicles from MCF7 (breast cancer cell lines) culture media. Here, we have explored three platforms based on gold nanoparticles, namely, in-situ synthesized nanocomposites, gold nano-islands on a substrate, and colloidal platform with suspended gold nanoparticles (AuNPs).
The first step was to identify the suitable platform for the capture and isolation of EVs, based on the sensitivity of the platform. Initially, gold – poly (dimethylsiloxane) (PDMS) nanocomposites, were created by the in-situ synthesis of nanoparticles on the surface of PDMS substrate by reducing gold precursor solution with curing agent of PDMS. The Au-PDMS nanocomposites are a special category of composites, with gold nanoparticles segregated in a sub-surface layer of the polymer. The second platform is the gold nano-island platform, where AuNPs are synthesized using Turkevich’s method and then deposited on a glass substrate by thermal convection. Then, the deposited nanoparticles are heat-treated at 560°C for an hour to form gold nano-islands. The refractive index sensitivity of nanocomposite and gold nano-islands is found to be around 50 and 111nm/RIU, respectively. The lower sensitivity in the nanocomposites as the nanoparticles are under the surface covered by a thin PDMS layer, whereas the in case of gold nanoislands, they are above the surface and available to interact directly with the analyte. Considering the sensitivities, the gold nano-island platform was utilized further for the capture and detection of EVs. The gold nano-islands on a substrate platform, where only half of the islands are available for sensing and capture, exhibited a better sensitivity meaning that utilizing the whole surface of nanoparticle would result in even better sensitivity as in the case of the third platform where gold nanoparticles suspended in a colloidal solution.
This dissertation then focuses on the biosensing protocol developed based on localized surface plasmon resonance (LSPR) of gold nano-islands, for the detection of EVs. The protocol involves the adsorption or binding of multiple bio-entities to the gold nano-island and absorption spectrum measurement after each step. The molecular interactions shift the gold plasmon band in the visible spectrum toward longer wavelengths and the shift is proportional to the concentration of the entity. The sensing protocol utilizes the strong affinity between EVs, and polypeptide called Venceremin or Vn96, which is specifically designed to capture EVs by binding the heat shock proteins (HSPs) present on their surface. Physical modeling, based on the characteristics of the gold nano-islands and the bio-entities involved in the sensing, is developed to determine the detection capability of the platform, which is optimized experimentally at each stage of the sensing protocol. The results and modeling present a relationship between the plasmonic shift and the concentration of exosomes. Further, a simple microfluidic device is designed for detecting EVs consists of a glass substrate with gold nano-islands, sealed by a PDMS film that contains a microchannel with a collection chamber in between. The same biosensing protocol has been transferred to a microfluidic environment by infusing biosensing entities into the device and measurement of the spectrum after each step. The capture and detection ability of the device is validated by Atomic Force Microscopy (AFM) and the measurement of gene copy numbers using droplet digital PCR (ddPCR). The results indicate that the developed device can capture and isolate the EVs from a very low sample volume, in less than 30 minutes, without affecting their size and shape, a major advantage compared to existing methods. Later, to increase the sensitivity, a two-level microfluidic device is fabricated utilizing a double-sided gold nano-island substrate, where AuNPs are deposited on both sides. This technique can be extended to multi-level microfluidics by introducing an intermediate channel in between the nanoisland substrates.
Parallelly, the gold nanoparticles suspended in a colloidal solution were utilized for the capture, detection, and isolation of EVs using the same protocol. Initially, this technique was studied at the cuvette stage by mixing the desired volume and concentrations of bio-entities manually and measuring the absorption spectrum at each stage for LSPR shift. As the nanoparticles are freely suspended in the solution, the binding of EVs on the entire surface area contributes the nanoparticles to sediment at the bottom of the cuvette. Thus, the precipitation method was used to isolate EVs. Then, a liquid biopsy chip is designed and fabricated using soft-lithography consisting of a 3D mixer, a collection chamber, and gravity assisted sedimentation unit. The streptavidin-coated magnetic particles are utilized in the liquid biopsy chip for the isolation of EVs. The device consists of a 3D mixer to ensure capture efficiency and it also consists of a sedimentation unit, which allows EV-captured magnetic particles to settle in it. The sedimented EV-captured magnetic particles are then isolated from the chip for elution of EVs and their validation using Nanoparticle Tracking Analysis (NTA), AFM, and gene amplification. The results clearly show that the proposed liquid biopsy chip can isolate the EVs without affecting their morphology. Thus, this chip can be considered as a potential point-of-care device for diagnostics in a clinical setting and for the isolation of EVs for future therapeutics.


identifier: Golden, Charlotte  (2021) Surface Transporters and Receptors are downregulated by a two-tier system in response to diverse stimuli in Saccharomyces cerevisiae.  Masters thesis, Concordia University.

abstract: Diverse physiology relies on receptor and transporter protein down-regulation mediated by ESCRTs. Mutations in ESCRT genes are thought to block this process, underlying pathogenesis of human cancers and neurological disorders. However, when orthologous mutations are introduced into model organisms, cells thrive and surface protein down-regulation persists, suggesting other mechanisms compensate for missing ESCRT activity. To better understand this secondary process, we studied degradation of quintessential ESCRT-client proteins (transporter Mup1, receptor Ste3) when ESCRT genes (VPS27, VPS36) are deleted in Saccharomyces cerevisiae using live-cell imaging and organelle biochemistry. We find that signaling for endocytosis remains intact, but all proteins aberrantly accumulate on vacuolar lysosome membranes in mutants and some wild type cells. Here they are sorted for degradation by the intralumenal fragment (ILF) pathway, either constitutively or when triggered by substrates, misfolding or TOR activation in vivo and in vitro. Thus, the ILF pathway functions as fail–safe layer of defense when ESCRTs disregard their clients, representing a two-tiered system that ensures down-regulation of surface polytopic proteins.


identifier: Chen, Chen  (2021) Computer Vision Based Automated Monitoring and Analysis of Excavation Productivity on Construction Sites.  PhD thesis, Concordia University.

abstract:
Construction equipment is the main component of construction production, and the equipment costs are usually one of the constructor’s biggest expenditures. The productivity of construction equipment plays an important role in completing construction projects within schedule and under budget. Therefore, effectively monitoring equipment productivity is critical to improve construction productivity and control the cost. In order to monitor the productivity of the equipment, numerous research works are focusing on monitoring the operation process of the equipment. Through the continuous monitoring of equipment operations, detailed information about the productivity and performance indicators, such as activities, idling time, and work cycles can be estimated. In recent years, different information technologies, such as machine learning and Real-time Location Systems (RTLS), have been used for equipment productivity monitoring. Based on the type of the data collected, technologies can be categorized into computer vision (CV)-based methods and sensor-based methods. The CV-based methods collect equipment operation data from site surveillance cameras as videos or images. Sensor-based methods install sensors and/or tags (such as Radio Frequency Identification (RFID), Global Positioning System (GPS), Ultra-wideband (UWB), Inertial Measurement Unit (IMU), etc.) on the equipment and the construction site to collect the position and pose information of the equipment. Accordingly, the work states and activities of the equipment are identified by analyzing the data collected from cameras or sensors. The location and trajectory data collected from the sensors can be used to directly estimate the activity of the equipment. Finally, based on the activity information, the productivity of the equipment can be estimated in the form of equipment operation time or soil quantity. However, the previous studies did not consider the activity recognition and productivity analysis of multiple excavators. Moreover, the reasons that cause equipment low productivity, such as idling reasons,  have not been considered in previous CV methods. Furthermore, the sensor-based methods can be expensive and impractical to use for the large fleet of construction equipment. Compared with the sensor-based methods, CV-based methods are growing and becoming more efficient.
This research aims to monitor the productivity of the excavator using a CV-based method and has the following objectives: (1) Comparing the CV-based and sensor-based methods of earthmoving equipment productivity monitoring to identify the advantages and limitations of each approach, and proposing a roadmap for the future work directions of automated equipment productivity monitoring; (2) Developing an end-to-end CV-based method for automatically recognizing activities of multiple excavators; (3) Developing a framework to analyze the productivity of the excavator based on the activity recognition results; and (4) Improving the earthmoving productivity by identifying the idling reasons of excavators and trucks.
A systematic literature review is conducted to cover the recent studies in the area of activity recognition and to compare the CV-based methods with the sensor-based methods for earthmoving equipment productivity monitoring. Then, a roadmap is proposed to suggest future research directions for automatic equipment productivity monitoring. Accordingly, a framework that integrates three Convolutional Neural Networks (CNNs) is proposed for automatic detection, tracking, activity recognition and productivity analysis of excavators. The proposed framework has been tested with the videos recorded from real construction sites. The overall activity recognition has achieved 87.6% accuracy. The productivity calculation has achieved 83% accuracy, which indicates the feasibility of the proposed framework for automating the monitoring of excavator’s productivity. Furthermore, knowing that idling is one of the main reasons for low productivity, a CV-based method was proposed to identify the idling reasons in earthmoving operations by analyzing the workgroup and interactive work states of the excavators and trucks. In this method, the activities of the excavators and trucks were identified using CNNs. Then, the onsite camera was calibrated to estimate the proximity and identify workgroups of excavators and trucks. By calculating the relationships between each excavator and the surrounding truck(s), the potential reason for idling can be identified. The proposed method has been validated with videos from several construction sites showing promising results.
The contributions of the research are: (1) Proposing a roadmap to show the future research directions of automatic equipment productivity monitoring; (2) Proposing a CV-based method to recognize the activities of multiple excavators with the state-of-the-art three dimensional (3D) CNN; (3) Proposing a framework that integrates the detection, tracking, and activity recognition techniques to monitor the operation process and analyze the productivity of the excavator; and (4) Developing a method to automatically identify the idling reasons of excavators and trucks based on their interactive work states, which contributes to a better understanding of earthmoving productivity under the dynamic and complex construction site conditions.


identifier: Kozak, Stephanie  (2021) Investigating positive classroom literacy environments: Teacher knowledge, print exposure and planning for instruction.  PhD thesis, Concordia University.

abstract: Reading for pleasure, also referred to as print exposure, is beneficial across the lifespan. However, the journey from illiterate to literate is not straightforward. Research has shown that young readers benefit from expert instruction, and plentiful practice. Thus, elementary school teachers are tasked with satisfying these requirements. The upper elementary years are an especially interesting area of study because at the same time as students’ abilities increase, intrinsic motivation to read tends to decrease. Teachers are uniquely situated to (a) ensure that children become capable readers and (b) remain interested in reading throughout their childhood and beyond. Therefore, the goal of this dissertation was to establish how to optimally prepare teachers to help children make the transition from pre-reader to life-long reader. Study 1 investigated knowledge and ability to plan for instruction among 100 pre-service teachers before and after participating in a workshop that taught them about print exposure. In this study, two measures were created (definitions and vignettes tasks) to assess pre-service teachers’ knowledge of literacy concepts related to print exposure and classroom practices related to reading. Two further ones were adapted (print exposure checklist and an instructional planning task). The workshop was successful at significantly increasing participants’ knowledge of literacy concepts related print exposure, and this increase in knowledge was associated with more time allocated for students to read in the post-test instructional planning. Study 2 extended this work by implementing a similar workshop with in-service teachers. Here, 50 teachers completed the definitions and vignettes tasks, as well as the instructional planning task before and after participating in an online professional development workshop that taught them about the formal and informal literacy activities that are critical in positive classroom literacy environments. Teachers increased their scores on the definitions task significantly after participating in the workshop, and also designated more time to Activities that Promote Reading at post-test. The goal of this study was to establish the formal and informal components of positive classroom literacy environments The results of both studies are discussed within the context of teacher training.


identifier: Hewitt, Jessica  (2021) ya:y̓əstəl̓ (working together) with ‘letsémot (one mind/heart): Narratives of Resilience and Strategies for Resistance and Resurgence from Kwantlen First Nation, British Columbia.  Masters thesis, Concordia University.

abstract: This study explores the question ‘How can collaborative research contribute to Indigenous resistance and resurgence?’ Set in the context of the community’s broader vision of self-governance, I examine strategies and tools used by Kwantlen First Nation to consolidate and communicate their ongoing cultural connections to their lands and resources. The research process follows an Indigenous relational approach guided by Kwantlen First Nation’s seven traditional laws - šxʷʔəy̓eɬ (health), hiləkʷ (happiness), ʔəwə cen smet̕ᶿənen (humbleness), xʷlil̕əq (generosity), syəw̓enəɬ, šxʷwéləy ʔiʔ sʔəy̓éq (generations), q̓ʷal̕təl̕ (forgiveness), tə́lnəxʷ (understanding). The research followed sought for ways to (re)work with previously conducted community-driven research through open-ended and ongoing conversations with Kwantlen First Nation leadership and staff, as well as opportunities to participate in community and cultural events. The thesis is comprised of two manuscripts. The first details the steps that have been taken by Kwantlen First Nation leadership to rebuild their community through reclamation of cultural protocols, governance systems and laws. Their struggles and successes are highlighted as well as the imperatives that inform their engagement with outsiders. The second manuscript is a co-authored piece with Kwantlen First Nation collaborators and my research advisor. It demonstrates how relational research can contribute to meaningful (re)storying of previously conducted community-driven research through the creation of digital tools (ArcGIS StoryMap). The advantages and limitations of this project are discussed as well as approaches and processes of collaboration. Overall, this thesis has sought to contribute tə́lnəxʷ (understanding) through syəw̓enəɬ, šxʷwéləy ʔiʔ sʔəy̓éq (generations) of Kwantlen First Nation connection to S’ólh Téméxw (lands).


identifier: Kheirandish, Mahsa  (2021) Numerical Simulation of Benzene Transport in Shoreline Groundwater Affected by Tides.  Masters thesis, Concordia University.

abstract: The release and transport of benzene in coastal aquifers were investigated in the present study. Numerical simulations were implemented using the SEAM3D coupled with GMS, to study the behavior of benzene in the subsurface of the tidally influenced beach. The transport and fate of the benzene plume were simulated, considering advection, dispersion, sorption, biodegradation, and dissolution in the beach. Different tide amplitudes, aquifer characteristics, and pollutant release locations were studied. It was found that the tide amplitude, hydraulic conductivity, and longitudinal dispersivity were the primary factors affecting the fate and transport of benzene. Aerobic biodegradation played a significant role in plume transport and benzene fate. The tidal amplitude could influence the travel speed and the percentage of biodegradation of benzene plume in the beach. A high tidal range reduced the spreading area and enhanced the rate of benzene biodegradation. Hydraulic conductivity had an impact on plume residence time and the percentage of contaminant biodegradation. Lower hydraulic conductivity induced longer residence time in each beach portion and a higher percentage of biodegradation in the beach. The plume dispersed and the concentration decreased due to high longitudinal dispersivity.


identifier: Mustafa, Irfan  (2021) Development of Intertwined Infills to Improve Multi-Material Interfacial Bond Strength.  Masters thesis, Concordia University.

abstract:
Multi-material additive-manufacturing (MMAM) technology provides a solution to 3D print a variety of parts consisting of multiple materials without the necessity of performing complex manufacturing processes. Till now various MMAM techniques have
been developed for different applications. The wide availability of materials and recent developments in MMAM has opened doors for innovation in producing truly functional products using various materials. A lot of research is research going on in developing
efficient 3D printers for MMAM, but conventional software tools does not full-fill the modern requirements suited for 3D printing of multi-material structures. There is an existing research gap between existing CAD tools, 3D printers and slicing software 3D
printing of multi-material parts. One major concern of MMAM is the strength at the interface between materials. Based on the observation of how nature puts materials together, this research develops an initial hypothesis that if the materials are overlapped
and interlaced with each other, the interface bonding strength will be enhanced. To test this hypothesis, a computer-aided manufacturing (CAM) tool that can process overlapped material regions is needed. However, existing computational tools lack key
multi-material design processing features and have certain limitations in making full use of material information, which restricts the test of the hypothesis.
Therefore, this research also develops a new MMAM slicing framework that efficiently identifies the multi-material regions and develops interlaced infills. Based on ray-tracing technology, layered depth material images (LDMI) is developed to process the material information from computer-aided design (CAD) models for tool-path planning. Each sample point in the LDMI has an associated material and geometric properties that are used to recover the material distribution in each slice. In this research, an interlocking joint (T-joint) and an interlacing infill are developed in the regions with multiple materials. By carrying out tensile tests, it is shown that the proposed infill outperforms the interlocking joint, and a fracture occurs even outside the joint area. This validates the initial hypothesis, and the enhancement of interface strength is achieved by overlapping and interlacing materials.


identifier: Wang, Zehao  (2021) Understanding the Challenges and Providing Logging Support to Monitor Data Processing in Big Data Application.  Masters thesis, Concordia University.

abstract: To analyze large-scale data efficiently, developers have created various big data processing frameworks (e.g., Apache Spark). These big data processing frameworks provide abstractions to developers so that they can focus on implementing the logic for data analysis. In traditional software systems, developers leverage logging to monitor applications and record intermediate states to assist workload understanding and issue diagnosis. However, due to the abstraction and the peculiarity of big data frameworks, there is currently no effective monitoring approach for big data applications. In this thesis, we first manually study 1,000 randomly sampled Spark-related questions on Stack Overflow to study their root causes and the type of information, if recorded, that can assist developers with motioning and diagnosis. Then, we design an approach, DPLOG, which assists developers with monitoring Spark applications. DPLOG leverages statistical sampling to minimize performance overhead and provides intermediate information and hint/warning messages for each data processing step of a chained method pipeline. We evaluate DPLOG on six benchmarking programs and find that DPLOG has a relatively small overhead (i.e., less than 10% increase in response time when processing 5GB data) compared to without using DPLOG, and reduce the overhead by over 500% compared to the baseline. Our user study with 20 developers shows that DPLOG can reduce the needed time to debug big data applications by 63% and the participants give DPLOG 4.85/5 for its usefulness on average. Moreover, the idea of DPLOG may be applied to other big data processing frameworks, and our study sheds light on future research opportunities in assisting developers with monitoring big data applications.


identifier: Samadzadegan, Bahador ORCID: https://orcid.org/0000-0003-3048-3608 <https://orcid.org/0000-0003-3048-3608>  (2021) On the Design and Operation of Heat Pump Systems for Zero Carbon Districts.  Masters thesis, Concordia University.

abstract:
Global warming and climate change are no longer just a topic for expert panel discussions since we started to observe real-life impacts of such phenomenon for more than a decade. Reducing and gradual elimination of greenhouse gas (GHG) emissions is the best and only solution. Buildings, districts, and cities are responsible for a significant portion of GHG emissions, and concepts such as sustainability, energy efficiency, and renewable energies support the transition towards zero-carbon districts. Efficient, reliable, and accessible tools are crucial to plan, design and analyze such districts and cities.
The prepared manuscript-based thesis focuses on introducing an automated framework for designing and sizing energy systems in a zero-carbon district context. The framework has been developed using the simulation environment INSEL 8.2 combined with Python coding and contains a variety of complex components, including but not limited to heat pumps (HP), photovoltaic panels (PV), inverter, maximum power point tracker, domestic hot water tank, energy metering, and simplified battery and thermal storage systems. The integrated framework covers demand profiles, energy system sizing, components' interaction, and performance analysis.
An urban energy system model (UESM) has been developed and used for different scenarios and use cases such as sensitivity analysis, optimization using genetic algorithm (GA), economic analysis, and the comparison of different energy systems configurations (Central vs. Decentral scenario). Simulation with an hourly resolution, while considering various detailed models, is the most critical capability of this framework compared to available UESMs. Moreover, all tools developed are open-source with a high level of flexibility, which can be the foundation for other researchers by adding and modifying different domains' components.


identifier: Abubakr, Ahmed  (2021) Impact Behaviour of Alkali-Activated Slag Concrete Under Ambient and Elevated Temperature Conditions.  PhD thesis, Concordia University.

abstract:
In military applications, concrete structures are intended to withstand impact loads induced by missiles and bombs. Under such attacks, elevated temperatures induced by fire are expected to increase the concrete damage. Several research studies have explored cement-based concrete performance under such loading conditions. However, limited studies in the literature investigated alkali-activated concrete behaviour under impact loads and elevated temperatures. Therefore, this dissertation aims to cover this knowledge gap regarding alkali-activated slag (AAS) impact behaviour.
A comprehensive experimental program was conducted to investigate the effects of activator nature on mechanical and impact performance of the developed AAS binding system at ambient and elevated temperatures. The results represented a benchmark for the following phase focusing on AAS concrete mixtures properties and degradation. Finally, the study assessed the potential enhancement in the static and dynamic performance by applying different strategies (i.e. rubber and fibre incorporations).
Generally, AAS exhibited better static and dynamic performance after exposure to elevated temperatures than ordinary Portland cement (OPC) systems. Alkali activator properties significantly affected AAS impact performance. Moreover, the difference in hydration products nature between AAS and OPC systems had a dominating effect. Improvement of impact performance due to rubber addition was limited to ambient temperature and up to 200oC. At higher temperatures, the melting and decomposition of rubber eliminated the desired improvement. Fibre-reinforced alkali-activated slag concrete exhibited significantly higher mechanical properties and impact energy absorption capacity at ambient temperature. However, at elevated temperatures, the improvement was mainly controlled by the fibre stability at various temperature levels. Hence, the dissertation provides a fundamental understanding and first-time data on AAS impact behaviour at various exposure conditions, emphasizing different mixture ingredients' roles.