identifier: Law, Timothy ORCID: https://orcid.org/0000-0001-5966-6323 <https://orcid.org/0000-0001-5966-6323>  (2022) Reducing Context-Dependency in Ecology: Environmental Variation Leads to Predictable Patterns of Species Associations Across Local Communities.  Masters thesis, Concordia University.

abstract: Predicting how communities change in space and time requires an understanding of how mechanisms such as environmental filtering and biotic interactions shape distributions and abundances. However, ecological communities are often context dependent, so mechanisms that are important in certain environments may not be in others. To understand how context dependency affects the prediction of community structure, we asked: Can the environment predict the outcomes of community assembly? Using fish association networks estimated with Markov random fields for over 700 lakes in Ontario, Canada, we tested if species association patterns, representing potential community assembly mechanisms, varied as a function of the environment. We examined the effect of the environment at two scales: pairwise and community level, summarizing potential mechanisms between species and across whole communities. The environment was a strong predictor of community level species association patterns but not of pairwise patterns, suggesting that the cumulative outcome of mechanisms structuring communities can be explained by the environment. We then tested if community level patterns were associated with the uniqueness of a lake’s species composition. We found that as species association patterns became stronger, they lead to lakes with more common species compositions. Taken together, our results show that variation in the outcome of community assembly can be explained by the environment using community level patterns, offering a way for community ecologists to study context-dependency in community structure across differing environmental gradients and species compositions.


identifier: Alavipour, Mehrad  (2022) Relation Between Geodesic Polar and Isothermal Coordinates.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract: The main result of this thesis is an asymptotic formula establishing a correspondence between two at first sight unrelated systems of local coordinates on a two-dimensional real analytic Riemannian manifold: the so-called isothermal local parameter and the Riemann normal coordinates. This formula was stated without proof in 1992 Fay’s memoir on analytic torsion, a weaker statement was proved in Walsh’s 2012 PhD thesis. A survey of basic facts from differential geometry used in our proof is also given.


identifier: Mohammadshahi, Ali  (2021) A framework for developing a Risk-Adaptive Innovation-based Technology Roadmap using TRIZ, Analytic Network Process, and Bayesian Network.  Masters thesis, Concordia University.

abstract:
In recent decades, companies, industries, and even governments have been motivated by technological advances to improve alignment between their strategic objectives and technology management and innovation by applying flexible and structured methods. Technology roadmapping has been a well-accepted response to this need by organizations. Technology roadmapping is an important tool widely used within industries for collaborative technology planning. It is considered a flexible technique to support strategic and long-range planning and coordination for corporations or entire industries. The technology roadmapping (TRM) approach provides companies or industry sectors with a well-structured and often visual pathway for investigating the relationship between emerging and developing markets, products, and technologies. This technique can also benefit a turbulent business environment and protect companies from potential losses. Moreover, the flexibility and benefits of this technique have led to a rapidly increasing literature for TRM, and companies and industries have been adopting this technique increasingly.
Despite the deceptively simple format of technology roadmaps, there are significant challenges in their implementation and development as mainly the scope is generally broad and contains complex concepts and involves human interactions. Moreover, there is little practical support available for TRM, and the companies applying TRM typically need to re-invent the process and adapt it to their business situation. In addition, TRM does not cover the area of innovation when a required technology does not exist and needs to be developed. Therefore, they need to be equipped with technology development solutions and means to assess their risks.
This research proposes an innovation-based risk-adaptive TRM process for those companies that need to develop products and services for which the required technology is not yet existing. It presents an integration of the Theory of Inventive Problem Solving (TRIZ) and TRM throughout the roadmapping process as well as the Analytic Network Process (ANP) for final decision making between alternative technologies. The proposed method also uses a Bayesian Network Model to investigate risk propagation in the roadmap.


identifier: Amirifar, Tolou  (2022) An NLP-Deep Learning approach for Product Rating Prediction Based on Online Reviews and Product Features.  Masters thesis, Concordia University.

abstract:
This study focuses on predicting the popularity of a product based on its overall rating score. Unlike previous studies that focus on predicting the review rating based on sentiment analysis and polarity of the reviews, in this thesis, the effect of product features in determining its popularity is directly measured and analyzed in order to predict its overall rating score. To this end, a methodology consisting of three phases is considered. Phase 1 predicts the overall rating by feeding the general product features, extracted from the online product information available on Amazon webpages to a Deep Learning (DL) model. Phase 2 identifies other features that customers care about the most by applying the Named Entity Recognition (NER) algorithm to the customer online reviews; and lastly, Phase 3 feeds the combination of the general and custom features to the DL model to predict the overall rating score of the product.
The experimental results on a dataset of laptop products, collected from Amazon, indicate an impressive performance of the proposed approach, which is mainly attributed to including custom product features to the inputs of the DL algorithm when compared with the existing method. More precisely, the proposed model could achieve the highest accuracy score of 84.01%, 84.68% for recall, 87.63% for precision, and 84.06% for F1 score. Applying this procedure could help businesses identify the specific areas of strengths and weaknesses of their products or services from the perspective of their customers, allowing them to thrive in today's competitive markets.


identifier: Wood, Hannah, Patterson, Zachary and Bouguila, Nizar  (2022) BIDIRECTIONAL LSTM AND KALMAN FILTER FOR PASSENGER FLOW PREDICTION ON BUS TRANSPORTATION SYSTEMS.  Masters thesis, Concordia University.

creator:
Wood, Hannah
Patterson, Zachary
Bouguila, Nizar

abstract:
Forecasting travel demand is a complex problem facing public transit operators. Passenger flow prediction is useful not only for operators, used for long-term planning and scheduling, but also for transit users. The time is quickly approaching that short-term passenger flow prediction will be expected as a matter of course by transit users. To address this expectation, a
Bi-directional Long Short-Term Memory Neural Network model (BDLSTM NN) and a Bi-directional Long Short-Term Memory Neural Network Kalman Filter model (BDLSTM KF) predict short-term passenger flow and based on the dependencies between passenger count and spatial-temporal features. A comprehensive preprocessing framework is proposed leveraging historical data and extracting bidirectional features of passenger flow. The proposed model is based on [1] but adapted, applied, and analysed to produce optimal results for passenger flow forecasting on a bus route. Building on [2], a BDLSTM architecture is then combined with a Kalman filter. The Kalman filter reduces the training and testing complexity required for passenger flow forecasting. The BDLSTM-based Kalman filter produces predictions with less uncertainty than each method alone. Evaluating the BDLSTM-based Kalman filter with two months of real-world data, one year apart shows positive improvements for short-term forecasting in high complexity bus networks. It is possible to see that the BDLSTM outperforms traditional machine and deep learning techniques used in this context.


identifier: Olajide, Adeniyi  (2022) Computational Investigation of Aqueous Droplets containing Environmentally Relevant Molecules.  Masters thesis, Concordia University.

abstract: The abundance of aqueous ions plays a significant role in various processes, including environmental ones such as cloud condensation nucleation. The details of ion-water and water-water interactions are critically important in these processes and require further insights from molecular simulations. Towards elucidating the hydration of ions in clusters, molecular dynamics (MD) simulations based on first-principles, implementing the density-functional tight-binding model with the third-order expansion (DFTB3), were employed to predict the properties of water clusters of varying sizes seeded with polyatomic ions (ClOx− and NOx−). Validating the predictions of the DFTB3 model against high-level quantum chemistry data established the robustness and accuracy of DFTB3 in evaluating small cluster properties of ClOx− (H2O)n and NOx− (H2O)n. In order to enhance conformational sampling of the clusters, a parallel tempering approach of MD simulations was implemented, and the potential of mean force (PMF) along the ion to water cluster center-of-mass distance was calculated by implementing umbrella sampling in the DFTB3-MD simulations. On the basis of the probability distributions of the ions with respect to the water cluster moiety center of mass obtained from the PMF, it appears that ClOx− and NOx− ions exhibit surface behaviour in water clusters, with the larger oxides showing more preference for surface solvation. This feature may influence the chemistry of the ions as they will be readily available for reactions with other atmospheric particles.


identifier: Jorjam, Shayan  (2022) Stochastic Simulation of Construction Methods of Multi-purpose Utility Tunnels.  Masters thesis, Concordia University.

abstract: The traditional method of installing underground utilities, which is based on burying them under the roads, has been used for many decades. Repeated excavations related to this method cause many problems, which can significantly increase the actual costs as well as social costs. Multi-purpose Utility Tunnels (MUT) are a good alternative for buried utilities. Although MUTs are more expensive than the traditional method, social cost savings can make them more practical. Different factors should be investigated to determine if MUTs can be an economical and practical alternative. The construction method is one of the most important factors that should be carefully assessed to have a successful MUT project and reduce its impact on the surrounding area. Simulation can be used for investigating the different construction methods of MUTs. In this research, two stochastic discrete event simulation models depicting two MUT construction methods (i.e., microtunneling and cut-and-cover) are developed. The purpose of these models is to analyze the duration and cost of the MUT projects. Also, 4D simulation models of these methods are developed for constructability assessment of these projects. The conclusions of this research are as follows: (1) the duration of C&C method is more sensitive than microtunneling to the changes in tunnel diameter; (2) the cost of microtunneling method is more sensitive than C&C to changes in tunnel diameter; (3) in average, microtunneling is 52% more expensive and 66% faster than C&C; (4) the impact of the microtunneling on the surrounding area is less than the C&C.


identifier: Mirabi, Melika  (2022) Development of LC-HRMS Assay for the Measurement of 12 Mycotoxins in Urine.  Masters thesis, Concordia University.

abstract: Mycotoxins are the secondary metabolites of certain molds. These toxic compounds naturally contaminate food products and beverages and can cause severe health effects in humans and animals. Environmental changes and industrialization are currently promoting the spread of these mycotoxins worldwide. Health Canada regularly monitors the levels of specific mycotoxins in various food products. Complementarily to food monitoring, periodic biomonitoring is especially important to determine the exposure to mycotoxins in the Canadian population considering the variability of individual diets and metabolism. Urine biomonitoring is a non-invasive approach and sample collection is easy. The purpose of this study is to develop a sensitive and accurate LC-HRMS method for the detection in urine of 12 mycotoxins that impact human health. These mycotoxins are enniatin A (ENNA), enniatin A1 (ENNA1), enniatin B (ENNB), enniatin B1 (ENNB1), alternariol (AOH), alternariol monomethyl ether (AME), beauvericin (BEA), citrinin (CIT), fumonisin B1 (FB1), fumonisin B2 (FB2), ochratoxin A (OTA) and ochratoxin alpha (OTα). The final 24-min LC-HRMS method used CORTECS T3 reversed-phase separation and employed time-segmented polarity switching to cover the 12 analytes of interest in a single analysis. To allow high-throughput for large-scale monitoring, two sample preparation procedures were evaluated: "dilute-and-shoot" and solid-phase extraction with hydrophilic and lipophilic sorbent (HLB SPE). Evaluation of solubility and non-specific adsorption with the dilute-and-shoot method revealed that enniatins (ENNs) and BEA have low solubility in a highly aqueous solvent (H2O/ACN/FA 94/5/1 v/v) with a 70-98% decrease in signal intensity compared to a highly organic solvent (MeOH/ H2O/ FA 60/39/1 v/v). This issue also caused loss of ENNs and BEA during storage of urine samples in plastic containers. Rinsing the containers with MeOH allowed the recovery of 17, 30, 57, 44 and 67% of ENNB, ENNB1, ENNA, ENNA1, and BEA, respectively. Use of 20x dilution in the dilute-and-shoot method resulted in LOQs > 2 ng/mL for almost all mycotoxins, which are present at < 1 ng/mL in real samples. Thus, dilute-and-shoot is not sensitive enough for the intended application, so HLB SPE was used for sample clean-up and enrichment. This sample preparation method recovered 68 - 88% of all the mycotoxins tested with 10x enrichment, which led to significant ionization suppression of CIT, OTα, OTA, AOH, AME and FB1. Reducing their enrichment decreased the matrix effects for all the mycotoxins (77% - 150%) except for AOH (22%) and AME (66%), which was compensated for by the addition of an internal standard (AMEd3). An optimized HLB SPE LC-HRMS method is proposed for validation and further application to real samples in the biomonitoring of mycotoxins in urine.


identifier: Ansari, Alireza  (2022) Stresses in Soil Beneath Triangular Shell Foundations.  Masters thesis, Concordia University.

abstract:
Foundations perform a crucial role in structures to transmit the loads from the superstructure to the soil beneath. The first utilization of shells as foundations was about six decades ago in Mexico. Shells are structures that transmit loads based on their form rather than mass, allowing to preserve more material. Investigations illustrated that the shell foundations provide higher bearing capacity and better settlement characteristics relative to traditional flat footings. In order to improve the geotechnical behavior of shell foundations, researchers attempt to obtain the optimum shape of shell footings by achieving more uniform stress distributions beneath the soil.
This thesis scrutinizes the stress distributions below embedded triangular shell strip footings resting on loose, medium, and dense sands by examining the effect of the edge angle. A series of two-dimensional numerical models are developed and calibrated with conducted experimental results in the literature. Mohr-Coulomb failure yield criteria are employed to simulate the soils with elastic-perfectly plastic behavior. Also, plane strain conditions are assumed to model the soils. The results indicate an increase in average stress distributions below the shell foundations by reducing the edge angle under a particular applied load. Triangular shell foundations demonstrate higher load-carrying capacity and more uniform stress distributions beneath the soil than flat foundations. Contact pressure at the soil-foundation interface and stress distributions at different depths are attained and presented. This study reveals that the optimum edge angle for triangular strip shell foundations in loose, medium, and dense sand states is 55 degrees.


identifier: Mercuri, Marisa  (2022) Staying in Touch: Mothers’ and Infants’ Dyadic Touching Behaviours Across Time, Context, and Risk Status.  PhD thesis, Concordia University.

abstract:
Touch is the basis of infants’ social, emotional, and cognitive development. Yet, it remains largely understudied in developmental research. A series of three studies were designed to expand our limited knowledge of parents’ and infants’ touching behaviours, including the synchrony of maternal-infant touch, across various types of parent-child interactions over time.
Study 1 utilized an extensive longitudinal design whereby healthy mother-infant dyads (N=12) were observed at 1-, 3-, 5-, 7-, and 9-months postpartum and within two normative interaction contexts (face-to-face; floor play). Study 2 implemented a longitudinal research design whereby typically developing newborns, mothers, and fathers (N=22) were observed immediately after birth and following the physiological stress of labor/delivery, and again 3- months later, following the social stress of maternal emotional unavailability via the Still-Face procedure (SF; Tronick et al., 1978). Study 3 used a cross-sectional research design examining mother-infant dyads (N=41) with high vs. low risk of maternal depressive symptomatology during instances of infant crying within the context of the SF and Separation (SP; Field et al., 1986) procedures at 4-months postpartum.
Overall, results revealed that touch is a pervasive and extensive communicative modality for both infants and parents, including those at-risk, within normative and perturbed contexts, starting from birth and across the first 9-months of life. Study 1 demonstrated that during typical/playful mother-infant interactions from 1- to 9-months postpartum, dyads initially displayed behavioural matching and later transitioned toward synchronous patterns entailing the parallel use of complementary types of touch. Study 2 revealed that mothers and infants displayed an inverse pattern of tactile synchrony (coordinated, converse changes in touch) from the immediate postpartum period to the reunion period of the SF procedure 3-months later. In Study 3, mothers and infants displayed a positive pattern of tactile synchrony (coordinated, analogous changes in touch) within the context of infant crying at 4-months postpartum. However, dyads in the high depression group displayed significantly less affectionate touch. Among other findings, results from all three studies underscored the soothing and regulatory functions of touch. Findings meaningfully contribute to our knowledge of early parent-infant dynamics and support a direction toward comprehensive examinations of touch across infancy.


identifier: Panchal, Komal  (2022) An Empirical Study of Runtime Files Attached to Crash Reports.  Masters thesis, Concordia University.

abstract:
When a software system crashes, users report the crash using crash report tracking tools. A crash report (CR) is then routed to software developers for review to fix the problem. A CR contain a wealth of information that allow developers to diagnose the root causes of problems and provides fixes. This is particularly important at Ericsson, one of the world’s largest Telecom company, in which this study was conducted. The handling of CRs at Ericsson goes through multiple lines of supports until a solution is provided. To make this possible, Ericsson software engineers and network operators rely on runtime data that is collected during the crash. This data is organized into files that are attached to the CRs. However, not all CRs contain this data in the first place. Software engineers and network operators often have to request additional files after the CR is created and sent to different Ericsson support lines, a problem that often delays the resolution process.
In this thesis, we conduct an empirical study of the runtime files attached to Ericsson CRs. We focus on answering four research questions that revolved around the proportion of runtime files in a selected set of CRs, the relationship between the severity of CRs and the type of files they contain, the impact of different file types on the time to fix the CR, and the possibility to predict whether a CR should have runtime data attached to it at the CR submission time. Our ultimate goal is to understand how runtime data is used during the CR handling process at Ericsson and what recommendations we can make to improve this process.


identifier: Shalbaf, Saman  (2021) Breakup of Liquid Feedstock in Plasma Spraying.  Masters thesis, Concordia University.

abstract: Suspension plasma spray is an emerging technology to produce functional nanostructured coatings at moderate cost. In general, in this technique, the liquid is injected radially into a high-velocity high-temperature plasma flow. After liquid breakup and evaporation, solid particles remain in the field and impact the substrate. Preliminary studies have shown that liquid jet atomization is the primary phenomenon that controls the coating quality. However, due to the complex thermophysical properties of plasma and its intricate flow physics, the breakup processes of liquid jets in plasma crossflows have not been investigated comprehensively yet. In general, the gaseous Reynolds number and the liquid-to-gas density ratio in this process are around 50 and 10,000, respectively, which are far outside the limits commonly observed in engines and wind tunnels. In this regard, detailed features of the breakup phenomena of the liquid jets injected in plasma and air crossflow are provided. Moreover, a case study has been established to analyze the effect of changing the surface tension of the liquid in the plasma spray process. The finite volume scheme is used to solve the incompressible variable-density Navier-Stokes equations. In addition, the volume of fluid (VOF) approach is utilized to track the gas-liquid interfaces. Finally, qualitative results such as instantaneous snapshots and shape of the liquid jet cross-sections, in company with quantitative data like including fracture point location, length of surface waves and size of the droplets have been presented.


identifier: Farhour, Farbod ORCID: https://orcid.org/0000-0003-1637-9898 <https://orcid.org/0000-0003-1637-9898>  (2022) A Weak Supervision-based Approach to Improve Chatbots for Code Repositories.  Masters thesis, Concordia University.

abstract:
Software chatbots are growing in popularity and have been increasingly used in software projects due to their benefits in saving time, cost, and effort. Through natural language, users communicate with chatbots to perform various tasks (e.g., monitor and control services). Natural Language Understanding (NLU) component is vital for chatbots as it enables them to understand the users' queries. NLUs need to be trained on various ways a user formulates a query (typically different paraphrases of the same intent). Nevertheless, when implementing a chatbot using an NLU, chatbot practitioners face a challenge in training the NLUs as labeled training data is scarce or unavailable. Typically, such training is done manually and prohibitively expensive.

In this thesis, we propose a weak supervision-based approach to automate the query annotation and chatbot retraining process. Specifically, we leverage weak supervision to label users' queries posted to a software repository-based chatbot. To evaluate the proposed approach, we perform a case study to assess our approach on the NLU's performance. We use a software repository-based chatbot dataset that contains 749 queries, with 52 intents in our evaluation. The results show that using our approach yields to an average increase of 17.16% in the NLU's performance in terms of F1-score. Also, we find that our approach labels, on average, 99% of users' queries correctly. Finally, our results show that applying more labeling functions improves the NLU's performance in classifying the user's query. Our work helps software engineering (SE) practitioners improve their chatbot's performance while requiring minimal training by automating the labeling process of users' queries.


identifier: Sarkhouh, Ebrahim ORCID: https://orcid.org/0000-0003-0607-1654 <https://orcid.org/0000-0003-0607-1654>  (2021) Optimizing the Workload Scheduling in MEC-Assisted Intelligent Transportation Systems.  PhD thesis, Concordia University.

abstract:
Autonomous driving (AD) is rising as an efficient solution to a wide range of transportation problems. With all the capabilities utilized (sensors, 5G communication technologies, computation units), intelligent vehicles can interact with the surroundings and cooperate in instantaneously maneuvering safely and effectively. Incorporating a central agent that supports this on-the-road interaction represents a critical enabling idea that will elevate the Cooperative Autonomous Driving (CAD) performance. Multi-access Edge Computing (MEC) recently attracted a considerable focus, specifically in vehicular networks, as it provides a reliable and online response to service demands arriving from vehicles. In the context of CAD, MEC can reduce the usage of wireless communications, orchestrate the activities on the road and provide massive computation capabilities to the vehicles. In this dissertation, we investigate the potential of MEC in the context of supporting autonomous driving and managing the radio and computation resources available. We propose adequate solutions for various problems that MEC should continuously resolve as an essential component of a complete intelligent transportation system.

First, we examine the capability of MEC by formulating the problem of scheduling vehicular computational tasks over the resources as an optimization problem and solve it via integer linear programming (ILP) and Lagrangian relaxation. We prove the complexity of the problem, and thus we develop a scalable solution that reaches near-optimal solutions and around 90% speedup compared with branch-and-cut.

Second, to consolidate our work veracity, we tighten the system capacity by limiting the wireless communication resources. Also, we propose a system model that harvests the computational resources available on the vehicles' onboard units via a fog computing scheme and utilizes them along with the infrastructure edge resources.  The system aims to jointly allocate the radio and computational resources to maximize the number of admitted tasks. We provide a formal definition of the problem as multi-stage scheduling and, due to its complexity, propose a Dantzing-Wolfe decomposition method to solve the problem. We compare the performance of the proposed method with CPLEX and show that the solution is only 20% far from the optimal solution while achieving 94% speedup.

After demonstrating the merit of deploying/utilizing edge servers in a vehicular network, the third contribution particularizes more the system model to an AD environment by applying two significant modifications. First, we accurately represent an AD scenario by modeling the computational load as long-term processes that continuously receive data from multiple sources, process them together, and inform multiple destinations with decisions supporting cooperative autonomous driving applications. Such processes work as assistants to on-the-road activities such as changing lanes, taking turns, or establishing/maintaining platoons. Second, we adopt a sophisticated, more suitable metric that quantifies the freshness of the information received called Age of Information (AoI).  We aim to minimize AoI of the information continuously received in the destinations. The problem turned to be NP-hard. We propose a novel Benders decomposition technique that divides the problem into several subproblems and one integer master problem. We developed a scalable solution for each of these problems and compared the overall method with the optimal solution. The method proposed showed high scalability and efficiency in terms of the objective and computation time.

We conclude with a discussion on the outcomes of this thesis and the directions we intend to take in our future work.


identifier: Selim, Alaa ORCID: https://orcid.org/0000-0003-4056-177X <https://orcid.org/0000-0003-4056-177X>  (2022) FRSH: A Frugal and Rapid bioSensor for in-House use to assess meat spoilage.  Masters thesis, Concordia University.

abstract: Biogenic amines (BAs) represent a toxicological risk in many food products.  Putrescine is the most common BAs found in food and is frequently used as a marker for food quality.  Today there is a lack of regulation concerning safe putrescine limits in food as well as outdated food handling practices leading to unnecessary putrescine intake. Conventional methods used to evaluate BAs in food are generally time and resource heavy, limiting the options for on-site analysis.  In this work, we developed a transcription-factor based biosensor for quantification of putrescine, one of the BAs in meat products, using a naturally occurring putrescine responsive repressor-operator pair (PuuR-puuO) from Escherichia coli.  Moreover, we demonstrate the use of the putrescine biosensor with a paper-based cell-free device that enables low-cost and rapid putrescine detection.  The system was validated using a variety of consumer meat samples with comparable performance to standard well-plate analysis methods.  We propose that our system is ready for use to assess the safety of meat products will contribute to a new phase of low-cost biosensors designed for food safety.


identifier: Tavassoli, Nasim ORCID: https://orcid.org/0000-0002-5761-6653 <https://orcid.org/0000-0002-5761-6653>  (2022) To be or not to be prosocial: Judgments and experiences of prosocial action and refusal across development.  PhD thesis, Concordia University.

abstract:
As social beings, individuals regularly encounter multifaceted social situations wherein, for variety of reasons, they decide to help others or not. Although inevitable, prosocial refusal experiences are overlooked in the prosocial development literature. In two studies, the role of prosocial refusal experiences in the development of prosociality was investigated through
analyses of reasoning, emotional and moral evaluations, and prosocial decision making. Study 1 examined how contextual factors influenced prosocial judgments in costly helping, sharing, and comforting situations. A total of 165 children and adolescents evaluated six hypothetical situations wherein one child needed to be helped, shared with, or comforted and another child decided whether to provide that assistance. Prosociality was evaluated as desirable-obligatory particularly in high need situations. Children more than adolescents evaluated prosociality as desirable. In high need situations, comforting more than helping and sharing was evaluated as desirable, whereas in low need situations, sharing was the most desirable form of prosociality. To complement analyses of hypothetical vignettes, Study 2 investigated children’s, adolescents’ and young adults’ reflections on past prosocial experiences (N participants = 253; N experiences = 990); participants each recounted experiences of helping and not helping when they were glad or regretful. Developmental changes and contextual variations in prosocial (refusal) reasoning, emotional and moral evaluations, and prosocial decision making were assessed. Participants’ reasons for prosocial actions and refusals varied across age groups and event types. To explain why they were glad or regretful of their prosocial choices, participants used other-oriented reasons to evaluate prototypical experiences (e.g., helping/glad), and self-oriented reasons for non-prototypical experiences (e.g., helping/regret). Moral reasons were used to explain why prosocial engagements and refusals were both right or wrong. Non-prototypical prosocial experiences were reported to prevent future prosociality. The results of Study 2 broadened our understanding of prosocial refusal experiences, emphasized the importance of interpersonal and intrapersonal regret in evaluating prosocial choices, and revealed that not all prosocial decisions have moral roots. Overall, this dissertation contributed to the growing literature on prosocial refusal experiences by highlighting the context-specificity of prosocial judgments and shed light on how refusal experiences are relevant to prosocial development.


identifier: Moussalli, Souheila  (2022) Exploring Intelligent Personal Assistants in Second Language Acquisition.  PhD thesis, Concordia University.

abstract:
Abstract

Exploring Intelligent Personal Assistants in Second Language Acquisition

Souheila Moussalli, Ph.D.
Concordia University, 2022

The goal of this dissertation is to investigate Intelligent Personal Assistants (IPAs), a voice-controlled service that can complete various functions by orally interacting with its users, as pedagogical tools in English second language classrooms to assess their pedagogical suitability. This dissertation begins with a review of the literature focusing on the importance of using technology in the language classroom. The remainder is divided into three manuscript-based chapters in which each manuscript addresses one aspect of the general research questions: (a) What are language learners’ perceptions of the use of IPAs as learning tools? (Manuscript A); (b) Can IPAs understand different language learners, and can these learners understand IPAs? (Manuscript B); and (c) Can IPAs help English language learners improve their receptive and productive skills? (Manuscript C).
 The first manuscript investigates the use of IPAs and users’ perceptions of the technology as a language learning tool. It examines a number of variables such as the IPAs’ ease of use, options for learner self-regulation (defined as learners’ ability to understand and control their learning environment), learner motivation and, more importantly, opportunities for learner input and output practice. The second manuscript explores IPA’s ability to interact with different accented language learners of English. The focus is on exploring the IPA’s ability to understand speech from different levels of language accentedness, and vice versa: to explore learners’ ability to understand the synthesized speech. The third manuscript investigates whether the pedagogical use of IPAs can lead to improvements in learners’ phonological awareness, perception and production of the allomorphy that characterizes regular past tense -ed marking in English (example depending on the preceding phonological environment, suffix -ed can be pronounced as talk/t/, play/d/ and add/id/).
This dissertation contributes to our knowledge of learner experience and attitudes towards IPAs as it can further unfold the potentials and limitations of the technology. As far as second language phonology/pronunciation is concerned, the dissertation breaks new ground in research since little is known about IPAs and their pedagogical potential for the development of second language listening and speaking skills.


identifier: Soligon, Clara, Dover, Geoffrey C., Mihalik, Jason P., Kiefer, Adam, Sinnott, Aaron and Powell, Jacob R.  (2022) The Role of Visual Sensory Performance Outcomes in Concussions: Impact on Concussed Special Operations Forces Combat Soldiers and Possible Implications for the Future of Sports-Related Concussions.  Masters thesis, Concordia University.

creator:
Soligon, Clara
Dover, Geoffrey C.
Mihalik, Jason P.
Kiefer, Adam
Sinnott, Aaron
Powell, Jacob R.

abstract:
The Role of Visual Sensory Performance Outcomes in Concussions: Impact on Concussed Special Operations Forces Combat Soldiers and Possible Implications for the Future of Sports-Related Concussions
Clara Soligon, BS, CAT(C)
Concordia University, 2022

	This thesis details the increased concern towards concussions in athletes and Soldiers as well as the role of visual sensory performance. More studies are showing the consequences, whether short term or long term, of concussions.  The symptoms burden and multiple neurocognitive deficits faced by a concussed athlete are getting increasingly recognized by society, as well as healthcare professionals. Studies have shown how concussions can also cause visual sensory performance deficits, even when traditional assessments are normal, and the athletes are cleared to return to play. One big challenge with concussions is the lack of objective measures to diagnose a concussion, as well for medically clearing an athlete or Soldier to return to full activity. Even with the knowledge that visual deficits might be present after a concussion; most traditional assessments do not assess vision due to a lack of unified platform and test availability. Visual sensory performance is important for injury prevention and impact anticipation, as well as assuring peak occupational performance. We assessed US Special Operations Forces combat Soldiers’ visual sensory performance outcomes. Concussions are the most common traumatic injury in the US military since 2000. Visual sensory performance outcome deficits could prevent Soldiers to complete their missions and impact their safety. Finding visual sensory performance outcomes deficits could help prevent an early return to play or return to duty, an increased risk of re-injury as well as help guide rehabilitation in athletes and military alike.


identifier: Elshafei, Mohamed ORCID: https://orcid.org/0000-0002-3131-1448 <https://orcid.org/0000-0002-3131-1448>  (2022) On the Impact and Detection of Biceps Muscle Fatigue in Wearable Sensors-Based Human Activity Recognition.  PhD thesis, Concordia University.

abstract:
Nowadays, modern sport and athletic training are very interested in wearable-based Human Activity Recognition (HAR) systems due to their cost-efficiency, portability, and convenience. However, this leads the developers to compete in developing the various HAR applications with little attention to HAR's-related problems such as fatigue. In this thesis, we select the bicep curls as an example of a HAR activity to study the fatigue problem in wearable-based HAR. We approach the fatigue problem through three studies: first, we study the impact of fatigue in wearable-based HAR. Second, we detect the presence of fatigue during human activity, e.g., biceps curls exercise. Third, we improve the performance of fatigue detection models while reducing the test's data consumption. Throughout our studies, we use our dataset, which consists of 3,750 repetitions of biceps curls from twenty-five volunteers between 20–46 years and with body mass index (BMI) between 24–46.

Our first study on the impact of fatigue in wearable-based HAR shows that fatigue often occurs in later sets of biceps curls. During fatigue, the completion time of later sets extends by up to 31%, while muscular endurance decreases by 4.1%. Also, our study shows that changes in data patterns often occur during fatigue, turning some features to be statistically insignificant. This can lead to a substantial decrease in performance in both subject-specific and cross-subject models. In addition, muscle fatigue can lead to various injuries such as muscle strain and tendons rupture, which may require up to 22 weeks of treatment. Therefore, it is essential to be aware of fatigue during human activity, which we address in our second study.

The second study proposes a wearable-based approach to detect fatigue in biceps curls. We provide a set of 16 most fatigue representative features from 33 extracted features. Then, we employ these features in five models to detect fatigue in biceps curls. Our study shows that a two-layer FNN achieves the highest accuracy of 98\% and 88\% for subject-specific and cross-subject models, respectively. We observe that the cross-subject models are preferable for a large crowd since these models can utilize crowd data. However, we observe that inter-subject data variability is usually high in the large crowd due to the physical differences among the individuals, resulting in different data patterns for the same activities. As a result, researchers may suggest using subject-specific models for each user in the crowd to achieve higher performance. Still, such a performance comes with a higher data cost of the user's subject-specific model; therefore, improving fatigue detection in cross-subject models is essential, which is the goal of our third study.

In the third study, we propose a personalization approach as a solution to improve the cross-subject models' performance by utilizing data from the crowd based on similarities between the test subject and users from the crowd. We extract 11 hand-crafted features to measure the similarities between the test subject and the individuals in the crowd. Then, we employ these similarities to prioritize and select the training data from the crowd for two cross-subject models. Our study shows that the personalization approach improves the performance of the cross-subject models in terms of precision by up to 7.25%, recall by up to 5.69%, accuracy by up to 6.67%, and F1-measure by up to 6.52%. Furthermore, adding 20% of the test subject's data into the training dataset of the personalized cross-subject models can produce accurate results closer to the ones from subject-specific models.


identifier: Promwongsa, Nattakorn  (2022) Provisioning Ultra-Low Latency Services in Softwarized Network for the Tactile Internet.  PhD thesis, Concordia University.

abstract:
The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet is envisioned to deliver real-time control and physical tactile experiences remotely in addition to conventional audiovisual data to enable immersive human-to-machine interaction and allow skill-set delivery over networks. To realize the Tactile Internet, two key performance requirements, namely ultra-low latency and ultra-high reliability need to be achieved. However, currently deployed networks are far from meeting these stringent requirements and cannot efficiently cope with dynamic service arrivals/departures and the significant growth of traffic demands. To fulfill these requirements, a softwarized network enabled by network function virtualization (NFV) and software-defined network (SDN) technologies is introduced as a new promising concept of a future network due to its flexibility, agility, scalability and cost efficiency. Despite these benefits, provisioning Tactile Internet network services (NSs) in an NFV-based infrastructure remains a challenge, as network resources must be allocated for virtual network function (VNF) deployment and traffic routing in such a way that the stringent requirements are met, and network operator’s objectives are optimized. This problem is also well-known, as NFV resource allocation (NFV-RA) and can be further divided into three stages: (i) VNF composition, (ii) VNF embedding/placement and (iii) VNF scheduling.

This thesis addresses challenges on NFV-RA for Tactile Internet NSs, especially ultra-low latency NSs. We first conduct a survey on architectural and algorithmic solutions proposed so far for the Tactile Internet. Second, we propose a joint VNF composition and embedding algorithm to efficiently determine the number of VNF instances to form a VNF forward graph (VNF-FG) and their embedding locations to serve ultra-low latency NSs, as in some cases, multiple instances of each VNF type with proper embedding may be needed to guarantee the stringent latency requirements. The proposed algorithm relies on a Tabu search method to solve the problem with a reasonable time. Third, we introduce real-time VNF embedding algorithms to efficiently support ultra-low latency NSs that require fast service provisioning. By assuming that a VNF-FG is given, our proposed algorithms aim to minimize the cost while meeting the stringent latency requirement. Finally, we focus on a joint VNF embedding and scheduling problem, assuming that ultra-low latency NSs can arrive in the network any time and have specific service deadlines. Moreover, VNF instances once deployed can be shared by multiple NSs. With these assumptions, we aim to optimally determine whether to schedule NSs on already deployed VNFs or to deploy new VNFs and schedule them on newly deployed VNFs to maximize profits while guaranteeing the stringent service deadlines. Two efficient heuristics are introduced to solve this problem with a feasible time.


identifier: Mirniazy, Kian ORCID: https://orcid.org/0000-0002-1414-2726 <https://orcid.org/0000-0002-1414-2726>  (2022) Supervised Deep Learning with Finite Element Synthetic Data for Force Estimation in Robotic-assisted Surgery.  Masters thesis, Concordia University.

abstract: The prevalence of robot-assisted minimally invasive surgery on the liver has increased exponentially. Having accurate, real-time knowledge of force during robotic-assisted surgical procedures is vital for safe surgery. Many techniques have been proposed in the literature to tackle this concern, from deploying force sensors to physics-based modeling of the robot and, more recently, learning-based force prediction. For a high-fidelity force measurement, sensors should be integrated at the instrument's tip, close to the surgical site, which brings sterilization, biocompatibility, and MRI compatibility concerns. On the other hand, Dynamic robot modeling may be precise in a specific setting, but it suffers from the lack of generalization encountering unseen settings. Considering the drawbacks and deficits of mentioned methods, indirect force estimation via deflection measurement through imaging techniques is investigated as an alternative solution, generally done via machine learning methods. Almost all previous studies are either supervised learning, where data are labeled with ex-vivo ground truth, or unsupervised or semi-supervised learning, where outcomes are promising but not adequate. This study investigated indirect force prediction for the human liver through a developed deep autoencoder model as a supervised deep learning method trained via synthetic data generated by finite element (FE) simulation. This method took advantage of various patient-specific livers parameters and geometries extracted from CT images. The Hyperelastic modeling of the soft tissue is considered and assessed with various hyperelastic models. The uncertainty due to the surgical tool's occlusion is addressed in this model, and a novel state vector was proposed to improve the accuracy and generalisability of the prediction. In addition, the impact of the bounded region on the model's accuracy was evaluated. It was shown that the proposed method could predict the external force on an unseen tissue with different geometry and mechanical properties. The accuracy of force prediction considering tool occlusion noise diminishes by 4.2 percent, which is in an acceptable range. The accuracy of presented model for various scenarios ranges from 95 to 88 percent. Model's results have been evaluated by predicting the force encountering the surface deformation of an unseen liver geometry and constitutive model where the mean absolute error of prediction is 0.249 Newton.


identifier: Sigounis, Anna-Maria  (2022) Control-oriented modelling and Model Predictive Control for air-based Building-Integrated Photovoltaic/Thermal systems.  Masters thesis, Concordia University.

abstract:
This thesis presents a methodology for the development of a grey-box control-oriented model for air-based open-loop Building Integrated Photovoltaic/Thermal (BIPV/T) systems with the scope to apply Model Predictive Control (MPC) to the air flow within the BIPV/T channel to achieve optimal BIPV/T – HVAC integration. The Varennes library, the first institutional net-zero energy building (NZEB) in Canada and one of a handful of buildings worldwide hosting a full-scale fully operational BIPV/T system, is used as an archetype for this work. The methodology presented can optimize the design of air-based BIPV/T systems or be applied to commissioned systems facilitating the broader realization of net-zero energy buildings.
BIPV/T systems combine the concept of BIPV and thermal collectors producing simultaneously electricity and heat by passing a fluid underneath their surface. The waste heat can be used for various thermal applications, however a key factor for broader BIPV/T realization is optimal control of these systems to maximize the amount of useful heat. As the available heat recovered from the BIPV/T is closely related to the respective weather conditions and the applied flow rate, optimally controlling the fan speed within the BIPV/T channel can significantly improve their performance.
Both a transient and steady-state model are developed and compared. The developed BIPV/T models are calibrated and validated using experimental and real weather data. The development of the MPC strategy for the commissioned BIPV/T system covering 1/6 of the south-facing roof at the Varennes library is presented. In one studied design variation of the existing archetype, the BIPV/T system is considered to simultaneously send heat to an Energy Recovery Ventilator (ERV) and an air-to-water heat pump (HP) while the opportunity to provide excess heat to adjacent buildings is explored, facilitating in the development of net-zero energy neighborhoods. Finally, the benefits of covering additional area of the Library’s south-facing roof with BIPV/T are examined to facilitate production of useful heat for other buildings in the community.
Through this work the essential parameters an engineer should have measurements for when applying MPC to a commissioned BIPV/T system are identified. This can prove extremely useful during the design stages as to where sensors should be placed and the relative importance of each measurement.
Through MPC, the fan speed is effectively adjusted simultaneously providing heat to the HP and ERV while adequately cooling the PV panels. The COP of the solar heated air-to-water heat pump showed a maximum increase of 32% for a mild sunny day. The BIPV/T that would cover the whole roof was able to produce up to 1.17 kWh/m2 (of roof area) of heat for a mild sunny day and provide 85.5% of this amount to adjacent buildings. Specifically, for a day-care center located near the library the consumed energy related to ventilation could be decreased by up to 36.8% with the heat provided from Library BIPV/T system.


identifier: Ghosh, Sauradip, Hamou-Lhadj, Abdelwahab and Ezzati-Jivan, Naser  (2022) System and Application Performance Analysis Patterns Using Software Tracing.  Masters thesis, Concordia University.

creator:
Ghosh, Sauradip
Hamou-Lhadj, Abdelwahab
Ezzati-Jivan, Naser

abstract: Software systems have become increasingly complex, which makes it difficult to detect the root causes of performance degradation. Software tracing has been used extensively to analyze the system at run-time to detect performance issues and uncover the causes. There exist several studies that use tracing and other dynamic analysis techniques for performance analysis. These studies focus on specific system characteristics such as latency, performance bugs, etc. In this thesis, we review the literature to build a catalogue of performance analysis patterns that can be detected using trace data. The goal is to help developers debug run-time and performance issues more efficiently.  The patterns are formalized and implemented so that they can be readily referred to by developers while analyzing large execution traces. The thesis focuses on the traces of system calls generated by the Linux kernel. This is because no application is an island and that we cannot ignore the complex interactions that an application has with the operating system kernel if we are to detect potential performance issues.


identifier: Alsaig, Ammar ORCID: https://orcid.org/0000-0002-6756-9766 <https://orcid.org/0000-0002-6756-9766>  (2022) Contelog: A Formal Declarative Framework for Contextual Knowledge Representation and Reasoning.  PhD thesis, Concordia University.

abstract:
Context-awareness is at the core of providing timely adaptations in safety-critical secure applications of pervasive computing and Artificial Intelligence (AI) domains. In the current AI and application context-aware frameworks, the distinction between knowledge and context are blurred and not formally integrated. As a result, adaptation behaviors based on contextual reasoning cannot be formally derived and reasoned about. Also, in many smart systems such as automated manufacturing, decision making, and healthcare, it is essential for context-awareness units to synchronize with contextual reasoning modules to derive new knowledge in order to adapt, alert, and predict. A rigorous formalism is therefore essential to (1) represent contextual domain knowledge as well as application rules,
and (2) efficiently and effectively reason to draw contextual conclusions. This thesis is a contribution in this direction. The thesis introduces first a formal context representation and a context calculus used to build context models for applications. Then, it introduces query processing and optimization techniques to perform context-based reasoning. The formal framework that achieves these two tasks is called Contelog Framework, obtained by a conservative extension of the syntax and semantics of Datalog. It models contextual knowledge and infers new knowledge. In its design, contextual knowledge and contextual reasoning are loosely coupled, and hence contextual knowledge is reusable on its own. The significance is that by fixing the contextual knowledge, rules in the program and/or query may be changed. Contelog provides a theory of context, in a way that is independent of the application logic rules. The context calculus developed in this thesis allows exporting knowledge inferred in one context to be used in another context. Following the idea of Magic sets from Datalog, Magic Contexts together with query rewriting algorithms are introduced to optimize bottom-up query evaluation of Contelog programs. A Book of Examples has been compiled for Contelog, and these examples are implemented to showcase a proof of concept for the generality, expressiveness, and rigor of the proposed Contelog framework. A variety of experiments that compare the performance of Contelog with earlier Datalog implementations reveal a significant improvement and bring out practical merits of current stage of Contelog and its potential for future extensions in context representation and reasoning of emerging applications of context-aware computing.


identifier: Phan, Vu Hong Hai  (2022) Root Cause Analysis Frameworks for Information Systems.  Masters thesis, Concordia University.

abstract:
Telecommunications systems have evolved to include an ever-growing number of interdependent hardware and software components with complex interactions. This exponential increase in complexity affects the reliability and stability of network systems. This thesis provides two systematic approaches to improve the speed and quality of the Root Cause Analysis task in telecommunications systems.

The first approach introduces a new fault analysis framework based on association rule mining and evaluates it for telecommunication systems. The approach describes a strategy using association rules to specifically target faults while improving runtime performance relative to the standard Apache Spark implementation. It also introduces a novel filtering strategy called Cover Set filtering that prunes and merges rule sets to produce high-quality, concise and interpretable results. The proposed framework is evaluated with real-world telecommunication datasets. Compared with other strategies, we demonstrate a better rule diversity in general and a sufficiently compact fault analysis.

The second approach tackles Root Cause Analysis from the causal perspective. It is based on Counterfactuals and Nearest Neighbour Matching concepts to identify fault types and highlight the most fault contributing variables. The proposed framework is a proof of concept for finding the root cause of problems based on the causal learning technique. It is demonstrated to be highly compatible with numerical data and highly robust with noisy data.

In conclusion, the proposed frameworks improve the quality and performance of fault troubleshooting tasks in telecommunication systems. Last but not least, the proposed frameworks can be adapted to other information systems with minor modifications.


identifier: Malektaji, Sepideh  (2022) Machine Learning for Next-generation Content Delivery Networks: Deployment, Content Placement, and Performance Management.  PhD thesis, Concordia University.

abstract:
With the explosive demands for data and the growth in mobile users, content delivery networks (CDNs) are facing ever-increasing challenges to meet end-users quality-of-experience requirements, ensure scalability and remain cost-effective. These challenges encourage CDN providers to seek a solution by considering the new technologies available in today’s computer network domain. Network Function Virtualization (NFV) is a relatively new network service deployment technology used in computer networks. It can reduce capital and operational costs while yielding flexibility and scalability for network operators. Thanks to the NFV, the network functions that previously could be offered only by specific hardware appliances can now run as Virtualized Network Functions (VNF) on commodity servers or switches. Moreover, a network service can be flexibly deployed by a chain of VNFs, a structure known as the VNF Forwarding Graph or VNF-FG. Considering these advantages, the next-generation CDN will be deployed using NFV infrastructure. However, using NFV for service deployment is challenging as resource allocation in a shared infrastructure is not easy. Moreover, the integration of other paradigms (e.g., edge computing and vehicular network) into CDN will compound the complexity of content placement and performance management for the next-generation CDNs. In this regard, due to their impacts on final service and end-user perceived quality, the challenges in service deployment, content placement, and performance management should be addressed carefully.  In this thesis, advanced machine learning methods are utilized to provide algorithmic solutions for the abovementioned challenges of the next generation CDNs.
Regarding the challenges in the deployment of the next-generation CDNs, we propose two deep reinforcement learning-based methods addressing the joint problems of VNF-FG’s composition and embedding, as well as function scaling and topology adaptation. As for content placement challenges, a deep reinforcement learning-based approach for content migration in an edge-based CDN with vehicular nodes is proposed. The proposed approach takes advantage of the available caching resources in the proximity of the full local caches and efficiently migrates contents at the edge of the network.  Moreover, for managing the performance quality of an operating CDN, an unsupervised machine learning anomaly detection method is provided. The proposed method uses clustering to enable easier performance analysis for next-generation CDNs. Each proposed method in this thesis is evaluated by comparison to the state-of-the-art approaches. Moreover, when applicable, the optimality gaps of the proposed methods are investigated as well.


identifier: Huebner, Emma June ORCID: https://orcid.org/0000-0003-4921-3376 <https://orcid.org/0000-0003-4921-3376>  (2022) Museum Education Through Social Media.  Masters thesis, Concordia University.

title: Museum Education Through Social Media

abstract: The COVID-19 pandemic has forced museum educators to invent new resources, which has led to the increased use of social media as an educational tool in the museum context. This descriptive qualitative study explores museum education through social media using an adapted museum education theoretical model (Allard & Boucher, 1998; Meunier, 2011) and considers theories of connectivism (Siemens, 2004). The more specific aim was to address the approaches, experiences, and objectives of museum educators who use social media, and how youth aged 18 to 24 years respond to it. The overarching research question that guided the study was: How does using social media as an educational tool lead to new museum education practices? Twenty-eight art museum educators answered a qualitative survey, and fifteen youth participated in in-depth focus groups. The results provide an overview of museum education through social media through a thematic analysis and suggest that social media is a new horizon for museum education which can take many forms; that short-form videos are an effective tool for teaching and learning about art in museums; that the notion of a connection between educators and visitors is complicated via social media; and that the balance between high and low cultural practices is hard to strike. The study also provides practical recommendations for educators who wish to consider young people’s experience of museums’ social media content in their future utilization of networking platforms for educational purposes. This research builds a foundation for forthcoming explorations of teaching and learning mechanisms on social media platforms.


identifier: Peters, James  (2022) Preparing the Arctic: Optimally Locating Aeronautical Search and Rescue Stations along Canada’s Northwest Passage.  Masters thesis, Concordia University.

abstract: Although historically ice-covered, the Northwest Passage (NWP)—a maritime corridor located in the Canadian Arctic—has been experiencing melting trends in recent decades. Declining sea ice concentrations would lead to improved navigability along the NWP, suggesting promising opportunities for both domestic and international shippers. With vessel traffic expected to rise, and the lack of emergency response resources currently stationed in the region, Canada would be responsible for equipping its North with a search and rescue (SAR) network that is capable of providing relief to the users of its waterways. Since the Royal Canadian Air Force (RCAF) oversees the majority of SAR activities in Canada, the distribution of its response aircraft throughout the Arctic is crucial in the design of a successful response network. To address these concerns, we formulated the location problem as an integer linear program (ILP) that looked to determine optimal sites for aeronautical SAR stations and the allocation of aircraft so that the weighted primary and secondary coverage of demand points was maximized. To do so, we modelled the response capacities of the RCAF's fleet by designing a set of response functions based on each asset's performance specifications. We analyzed 29 arrangements across two cases: one in which the secondary coverage of demand points was optional (Case A), and another in which it was mandatory (Case B). Using six to seven aircraft, our approach led to three arrangements that would best address SAR concerns in the North: Arrangement 7A which was proposed for Case A, Arrangement 6B for Case B, and Arrangement 7B as a compromise of the two.


identifier: Yan, Zhen, Chen, Mingyuan and Chauhan, Satyaveer S.  (2022) Production Planning and Scheduling with Time-Related Processing Cost Considerations for a Heat Treatment Shop.  Masters thesis, Concordia University.

creator:
Yan, Zhen
Chen, Mingyuan
Chauhan, Satyaveer S.

abstract: Due to production customization and diversification, many manufacturing companies face greater challenges to cope with uncertainties related to material supply, market demands and increased cost. The purpose of this study is to optimize the production process in a multi-product heat treatment shop to reduce production cost. We consider a multi-item multi-level production planning and scheduling problem in this research with production cost related to the waiting time of the components to be processed. A non- linear integer programming model is developed to describe the considered problem. After linearization, the model is solved to optimality using IBM® ILOG® CPLEX® Optimization Studio. We also propose a heuristic solution method to solve the considered problem for fast solutions of much larger problem sizes. Computational results of numerical examples indicate the mathematical model generates optimal results in capturing detailed problem features such as larger product variety which can be missed in using the heuristic method. The results also show that the developed mathematical model can be used to solving various production planning and scheduling problems with practical considerations such as time-related manufacturing cost functions.


identifier: Roy Trudel, Elisabeth  (2022) (Re)imagining a Sensing and Feeling Human: On the Creation of Exclusions Through the Visual in International Human Rights.  PhD thesis, Concordia University.

abstract:
This thesis argues that international human rights are based on a problematic, tacit assumption of an ideal, or genuine, legal subject. To show this, it focuses on some of the ways in which dominant discourses are in tension with human rights’ fundamental claim to universality by ignoring multiple ways of being, affording limited modes of expression, and hence perpetuating social exclusions. Different theoretical and methodological approaches drawn from several disciplines are used to analyse this paradoxical tendency to produce exclusions in international human rights law and practice, notably Hannah Arendt’s figure of the refugee, posthumanist critiques and several non-Western critical theories, namely Black, Indigenous and decoloniality approaches. Often these approaches are used in isolation, but they can usefully inform each other; together, they help reveal how the dominant concept of subjectivity has always needed an ‘Other’, and that the ‘human’ at the heart of human rights is not a universal concept but is historically determined.

To illustrate these critical arguments, I pursue an analysis of visual discourses in the field of international human rights, with a focus on the ways in which exclusions are represented and entrenched through the visual in this context. I focus on emblematic images in the history of human rights, as well as on contemporary images used in the field by dominant actors like the United Nations and by people experiencing human rights violations. I then argue that international human rights are based on a vision-centred sensorium and certain processes of reasoning that exclude emotions, and therefore make problematic assumptions about subjects. This supports the broader claim that, due to its cultural and historical origins, the international human rights system is geared to recognize only certain subjects as genuine rights bearers and to provide remedies for certain forms of violations, while ignoring others. Finally, I consider whether and how it would be possible to embrace other forms of thinking and being in the world and to recognize different sensory experiences within the international human rights framework.


identifier: Wu, Ju-Feng  (2022) Arithmetic aspects of $\mathrm{GSp}_{2g}$: $p$-adic families of Siegel modular forms, eigenvarieties, and families of Galois representations.  PhD thesis, Concordia University.

abstract:
This thesis reports the three articles written by the author and his collaborators. These three papers concern various arithmetic aspects of the algebraic group $\GSp_{2g}$, which are interrelated under the theme of eigenvarieties.

We first present a construction of sheaves of overconvergnet Siegel modular forms by using the perfectoid method, originally introduced by Chojecki–Hansen–Johansson for automorphic
forms on compact Shimura curves over $\Q$. These sheaves are then proven to be isomorphic to the ones constructed by Andreatta--Iovita--Pilloni. Using perfectoid methods, we establish an overconvergent Eichler--Shimura morphism for Siegel modular forms, generalising the result of Andreatta--Iovita--Stevens for elliptic modular forms. More precisely, we establish a Hecke- and Galois-equivariant morphism from the overconvergent cohomology groups associated with $\GSp_{2g}$ to the space of overconvergent Siegel modular forms.

It was asked by Andreatta--Iovita--Pilloni whether the classical points of the eigenvariety parametrising the finite-slope cuspidal Siegel eigenforms are étale over the weight space. Inspired by Kim's pairing presented in the book of Bellaïche, which allows one to study the ramification locus of the eigencurve, we generalise Kim's pairing to study the ramification locus of the cuspidal eigenvariety for $\GSp_{2g}$, providing some partial answer to the question asked by Andreatta--Iovita--Pilloni.

Finally, it is expected that such a pairing not only allows one to study the geometry of the eigenvariety but also carries interesting arithmetic information. Inspired by the book of Bellaïche--Chenevier, we study families of Galois representations over the cuspidal eigenvariety for $\GSp_{2g}$. Under some reasonable hypotheses as well as some conditions, we deduce the vanishing of the adjoint Selmer group associated with the Galois representation attached to a cuspidal eigenclass in the cohomology of the Siegel modular variety.


identifier: Chapados Muermans, Thomas  (2022) Investigating the Use of Transformer Based Embeddings for Multilingual Discourse Connective Identification.  Masters thesis, Concordia University.

abstract: In this thesis, we report on our experiments toward multilingual discourse connective (or DC) identification and show how language-specific BERT models seem to be sufficient even with little task-specific training data and do not require any additional handcrafted features to achieve strong results. Although some languages are under-resourced and do not have large annotated discourse connective corpora. To address this, we developed a methodology to induce large synthetic discourse annotated corpora using a parallel word aligned corpus. We evaluated our models in 3 languages: English, Turkish, and Mandarin Chinese; and applied our induction methodology on English-Turkish and English-Chinese. All our models were evaluated in the context of the recent DISRPT 2021 Task 2 shared task. Results show that the F-measure achieved by our simple approach (93.12%, 94.42%, 87.47% for English, Turkish and Chinese) are near or at state-of-the-art for the 3 languages while being simple and not requiring any handcrafted features.


identifier: Matovic, Sara ORCID: https://orcid.org/0000-0001-6782-8150 <https://orcid.org/0000-0001-6782-8150>  (2022) Trajectories of Psychological Distress during the COVID-19 Pandemic among Community-Dwelling Older Adults in Quebec: A Longitudinal Study.  Masters thesis, Concordia University.

abstract:
Objective: The COVID-19 pandemic and its associated public health directives may increase the risk for psychological distress among vulnerable older adults. This longitudinal study aimed to identify predictors of psychological distress trajectories among community-dwelling older adults in Quebec.

Methods: The study spanned four time points across 16 months and three waves of the COVID-19 pandemic. The sample included 645 community-dwelling older adults ages 60 years and older in Quebec. Participants completed telephone-based interviews that included the Kessler 6-item Psychological Distress Scale (K6) to assess psychological distress at each time point as well as information on socioeconomic, medical, psychological and COVID-19 related factors. Group-based trajectory modelling (GBTM) was used to identify distinct trajectories of psychological distress across time.

Results: Three group-based trajectories of psychological distress were identified: the resilient (50.5%), reactive (34.9%), and elevated distress groups (14.6%). Individuals with mobility issues, insomnia symptoms, COVID-19 related acute stress, general health anxiety, increased loneliness symptoms, and those unable to use technology to see others were more likely to be in the reactive and elevated groups than the resilient group. Those with past mental health problems had uniquely increased odds of being in the reactive group compared to the resilient group. Individuals living in poverty and those who reported taking psychotropic medication had increased odds of being in the elevated distress group compared to the resilient group.

Conclusion: These findings characterized distinct trajectories of psychological distress in older adults and identified risk factors for elevated distress levels.


identifier: Dalhoumi, Oumayma  (2022) Bayesian Matrix Factorization and Applications.  Masters thesis, Concordia University.

abstract:
Nonnegative matrix factorization (NMF) reduces the observed nonnegative matrix into a product of two nonnegative matrices. Nonnegativity entails two major implications: non-negative components and purely additive combination. These characteristics made this method useful in a wide range of applications. In this thesis, we propose two novel Bayesian nonnegative matrix factorization techniques.

First, we propose a model dedicated to semi-bounded data where each entry of the observed matrix is supposed to follow an Inverted Beta distribution. Latent variables of the factorized parameter matrices follow a Gamma prior. Variational Bayesian inference and lower bound approximation for the objective function are used to find an analytically tractable solution for the model. An online extension of the algorithm is also proposed for more scalability. Both models are evaluated on five different applications.

Second, we propose a Bayesian NMF that can be specifically useful for non intrusive load monitoring (NILM). NILM can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. The model achieves superior performance by imposing sparsity on the activation matrix using Dirichlet priors. To estimate the parameters of the model, variational Bayesian inference is used. A novel optimization approach is proposed to find an analytically tractable solution for the model. We evaluate the model with three data sets: REDD, AMPds and IRISE, and with multiple experimental setups. The proposed model provides interpretability, flexibility and high performance.


identifier: Al-qudah, Rabiah  (2022) Peripheral Blood Smear Analyses Using Deep Learning.  PhD thesis, Concordia University.

abstract:
Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists
to assess some aspects of humans’ health status. PBS analysis is prone to human errors and
utilizing computer-based analysis can greatly enhance this process in terms of accuracy
and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry,
but due to the scarcity of labeled medical images, researchers had to find viable alternative
solutions to increase the size of available datasets. Synthetic datasets provide a promising
solution to data scarcity, however, the complexity of blood smears’ natural structure adds
an extra layer of challenge to its synthesizing process. In this thesis, we propose a method-
ology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of
synthetic blood smears. This dataset, which was automatically annotated during the gener-
ation phase, covers 17 essential categories of blood cells. The dataset also got the approval
of 5 experienced hematologists to meet the general standards of making thin blood smears.
Moreover, a platelet classifier and a WBC classifier were trained on the synthetic dataset.
For classifying platelets, a hybrid approach of deep learning and image processing tech-
niques is proposed. This approach improved the platelet classification accuracy and macro-
average precision from 82.6% to 98.6% and 76.6% to 97.6% respectively. Moreover, for
white blood cell classification, a novel scheme for training deep networks is proposed,
namely, Enhanced Incremental Training, that automatically recognises and handles classes
that confuse and negatively affect neural network predictions. To handle the confusable
classes, we also propose a procedure called "training revert". Application of the proposed
method has improved the classification accuracy and macro-average precision from 61.5%
to 95% and 76.6% to 94.27% respectively.
In addition, the feasibility of using animal reticulocyte cells as a viable solution to com-
pensate for the deficiency of human data is investigated. The integration of animal cells is implemented by employing multiple deep classifiers that utilize transfer learning in differ-
ent experimental setups in a procedure that mimics the protocol followed in experimental
medical labs. Moreover, three measures are defined, namely, the pretraining boost, the
dataset similarity boost, and the dataset size boost measures to compare the effectiveness
of the utilized experimental setups. All the experiments of this work were conducted on
a novel public human reticulocyte dataset and the best performing model achieved 98.9%,
98.9%, 98.6% average accuracy, average macro precision, and average macro F-score re-
spectively.
Finally, this work provides a comprehensive framework for analysing two main blood
smears that are still being conducted manually in labs. To automate the analysis process,
a novel method for constructing synthetic whole-slide blood smear datasets is proposed.
Moreover, to conduct the blood cell classification, which includes eighteen blood cell types
and abnormalities, two novel techniques are proposed, namely: enhanced incremental train-
ing and animal to human cells transfer learning. The outcomes of this work were published
in six reputable international conferences and journals such as the computers in biology and medicine and IEEE access journals.


identifier: Zheng, Zhou  (2022) Development and Performance Analyses of Non-Pneumatic Wheels with Honeycomb Spokes.  PhD thesis, Concordia University.

abstract:
The dynamic performance of a ground vehicle is predominantly influenced by interactions of the tires with the terrain. The designs of pneumatic tires has evolved over the past many decades to satisfy multiple performance demands such as transfer of tractive/braking forces with minimal energy losses, ease of handling and directional control, ride comfort and road holding. Despite their proven superior performance, the pneumatic tires exhibit deficiencies such as loss of vehicle performance and directional control in the event of severe air leakage or bursting, high maintenance, complex manufacturing process, and fire hazards and environmental risks of used tires. In recent years, concepts in non-pneumatic wheels (NPWs) with different spokes’ designs have emerged, which potentially offer many advantages over the pneumatic tires, particularly the elimination of the road safety risks and the routine maintenance induced by the inflation pressure loss. The reported studies on NPWs with honeycomb spokes have mostly focused on their static and dynamic responses to normal wheel loads, such as vertical deflection, modes of vibration and rolling resistance. Only minimal efforts, however, are evident in view of their out-of-plane responses such as cornering force and self-aligning moment, which are vital for adequate handling and directional control of the vehicle.

This dissertation research is focused on design and development honeycomb NPWs with an objective to achieve in-plane and out-of-plane force-deflection and force-slip characteristics comparable to those of the pneumatic tires so as to be considered as their potential substitute in general vehicular applications. Three-dimensional finite element (FE) models of the honeycomb NPW with different spokes’ configurations are initially developed using ABAQUS software in order to predict their static and dynamic responses. A mesh convergence study is conducted to determine nearly optimal element sizes for each component, which permitted convergence of responses with least computational cost. The validity of these wheel models is verified by comparisons of predicted responses with the available results.

The verified NPW models are subsequently employed to evaluate their feasibility and relative merits through comparisons of in-plane as well as out-of-plane properties with those of a reference pneumatic tire (205/55R16). These include the multi-axis stiffness properties as well as cornering force and self-aligning moment characteristics. It is shown that the honeycomb NPW designs could be easily tuned with to achieve vertical and longitudinal stiffness as those of the reference pneumatic tire, while its lateral and cornering stiffness are substantially higher, irrespective of the spokes’ configurations considered. The considerably higher cornering stiffness of the NPW designs may cause rapid saturation of the cornering force under very low side slip conditions and thus side slippage of the wheel under higher side slip angles.

Efficient parametric studies using Taguchi and response surface methodologies are further performed using the verified NPW models so as to investigate the influences of multiple design parameters and the two-factor interactions on their multi-axis and cornering stiffness characteristics as well as natural frequencies of important vibration modes. The natural frequencies are extracted for the stationary NPW with vertically movable spindle and ground contact. The results from the experiment designs are used to develop guidance for design tuning of the NPW in order to achieve desired stiffness and modal properties. The results reveal that the design parameters of the honeycomb NPW can be tuned to achieve significantly lower lateral and cornering stiffness, which are comparable to those of the reference pneumatic tire. These designs, however, in general are coupled with higher thickness of the core layer and tread, which tend to lower the longitudinal stiffness.

A novel design concept, called “symmetric helical honeycomb spokes” is subsequently proposed for the NPW. The relative merits of the proposed helical spokes design are evaluated in terms of multi-axis and cornering stiffness properties of the wheel using the verified NPW models by varying the helix angle of the spokes. The results show that introducing the “symmetric helical honeycomb spokes” offer notable reductions in both lateral and cornering stiffness and higher longitudinal stiffness with only slight increase in mass of the NPW.


identifier: Wang, Shuyue  (2022) Effect of clogged drainage pipes on the stability of railway embankments.  Masters thesis, Concordia University.

abstract:
The rising water level caused by clogged drainage pipes results in instability in railway embankments and creates dangerous riding conditions for passing trains. As such, it is crucial to identify the factors that influence the slope stability of railway embankments subjected to rising water levels and exposed to train traffic. The purpose of this thesis is to study the slope stability of railway embankments by using a coupled Biot model where pore water pressure and stresses are considered simultaneously. Slope stability analyses are performed using two-dimensional finite element shear strength reduction (FE-SSR) models in which the control variable method is employed to study the effect of key parameters (e.g., embankment geometry, water level, and train speed) on the stability of railway embankments. Maximum safe train speeds are calculated for various slope geometries and water levels to maintain a minimum factor of safety of 1.3.
The simulation results illustrate that a flatter slope with a slope ratio of 1V:3H, 3m railway embankment and the 1m natural slope presents the best result in maximum safe train speed. Consequently, a model is developed to evaluate the influence of compromised drainage on the slope stability of a railway embankment under stationary or moving train loads. The findings of this study are then used to determine desirable slope geometry parameters and maximum safe train speeds for railway embankments subjected to rising water levels.


identifier: Čech, Martin ORCID: https://orcid.org/0000-0002-1098-6198 <https://orcid.org/0000-0002-1098-6198>  (2022) Applications of multiple Dirichlet series in analytic number theory.  PhD thesis, Concordia University.

abstract:
We consider several classical problems in analytic number theory from the point of view of multiple Dirichlet series.

In Chapter 1, we review the required background and give an overview of the thesis.

In Chapter 2, we introduce multiple Dirichlet series and the relevant ideas used in later chapters.

In Chapter 3, we study the double character sum $$S(X,Y)=\sum_{\substack{m\leq X,\\m\odd}}\sum_{\substack{n\leq Y,\\n\odd}}\leg{m}{n}.$$ The asymptotic formula for this sum was obtained by Conrey, Farmer and Soundararajan. We recover this formula using our approach with an improved error term.

In Chapter 4, we use multiple Dirichlet series to give a conditional proof of the ratios conjecture for the family of real Dirichlet L-functions in some region of the shifts. As an application of our result, we compute the one-level density in our family for test functions whose Fourier transform is supported in $(-2,2)$, including all lower-order terms.

In Chapter 5, we elaborate on an ongoing work of the author with Siegfred Baluyot. We compare two methods to come up with conjectural asymptotic formulas for moments in the family of real Dirichlet L-functions -- the recipe developed by Conrey, Farmer, Keating, Rubinstein and Snaith, and the multiple Dirichlet series approach introduced by Diaconu, Goldfeld and Hoffstein. We consider shifted moments and show that the two methods are essentially equivalent, in that they give rise to the same terms which come from the functional equation of the individual L-functions.


identifier: Beaudin-Vandolder, Melissa  (2022) Seek and You Shall Find: Understanding the Divide Between Faith and Reason in American Evangelicalism.  Masters thesis, Concordia University.

abstract: The division between Christian faith and secular reason is one that can often seem rather obvious, particularly in certain American Protestant areas; however, history shows that such a separation is a relatively new, and relatively American, development in the faith-reason relationship. Christian Patriarchs used pagan Greek philosophy in their work, Reformers advocated for public schooling, and the Enlightenment itself had roots in Christianity, with several of the most prominent thinkers of the time being devout believers. It is only in America where this tense, but amicable relationship truly broke down due to the events of the 1925 Scopes Monkey Trial in Dayton, Tennessee, which resulted in the public mockery of a group self-identified as fundamentalists. This group came to view secularity in all forms as an enemy, and built itself an identity rooted in anti-intellectualism, thus reinforcing the idea that secular reason and true faith could never mix. This belief grew so central and prominent within the group that it has since expanded beyond the walls of their churches and into their public life, influencing politics and policy in America to favor them while taking down any they view as an enemy. This division is ultimately disastrous for the continuation of the Land of the Free the United States Founding Fathers had dreamed of, so the gap must be closed. It will be a long process, but with proper communication and a willingness to listen, it is fully possible.


identifier: cliche, marie-laurence  (2022) Influence of fiber direction and temperature on the tribological behavior of carbon reinforced PEEK for applications in gas turbine engines.  Masters thesis, Concordia University.

abstract:
In order to reduce the emissions of greenhouse gas (GHG), many industries are turning towards more environmentally friendly technologies. The aerospace industry is particularly targeted as gas turbine engines will be required to reduce their CO2 emissions to net zero. New challenges will arise regarding the conditions in which materials are expected to operate (e.g. high temperatures, high velocities, high pressures) in order to achieve this goal. More conventional metals and alloys currently used in engine manufacturing will most likely be limited by their mechanical and tribological properties and thus, proper selection of materials is primordial to ensure the performance and efficiency of engines. Fiber-Reinforced Polymer (FRP) composites have been widely used in the aerospace industry and is one alternative solution due to their lightweight and high-strength properties. However, when considering FRP for the purpose of gas turbine engines, it is also important to consider the orientation of the fibers. Since there are a lot of moving and contacting mechanical assemblies in the engines where a high quantity of elements such as bearings and seals are present, the orientation of the fibers in FRP play a crucial role in ensuring good tribological behaviour. Many different studies have previously performed extensive characterization of carbon reinforced polymer composites and have demonstrated the impact of the parallel and anti-parallel orientation of the fibers on the mechanical properties and tribological behavior of composites. However, FRP composite with fibers oriented in the normal direction, which have shown improved mechanical properties, have received little attention regarding the impact of orienting vertically the fibers on the tribological properties.
The purpose of this study is to fully capture the influence of the fiber direction (parallel, anti-parallel and normal direction) in fiber-reinforced composite, with an emphasis on carbon fiber/ PEEK composite, and the influence on its tribological behavior for the purpose of gas turbine engines. The research performed throughout this study is divided into two parts where the influence of fiber direction in FRP composites on the tribological behaviour have been critically examined. The main focus of the first study is to identify the influence of fiber orientation on the surface energy and tribological behavior of CF-PEEK systems for the Fan and LPC region of the gas turbine engine (i.e. room temperature). The focus of the second study is to identify the influence of temperature on the tribological behavior of carbon fiber-reinforced PEEK. The tribological tests were performed using a ball-on-disk tribometer where the samples were subjected to different testing parameters to simulate more accurately the operating environment the material would have to go through in the engine. This includes a high loading, an elevated testing temperature, and a high number of cycles. The characterization to obtain the interfacial phenomena of the different worn surfaces and counterfaces was done using Scanning Electron Microscopy (SEM), confocal laser scanning microscopy (CLSM), Energy Dispersive X-ray Spectroscopy (EDS) and Atomic Force Microscope (AFM). Overall, it was found that there is a clear benefit of using CF-PEEK reinforced with fibers in the normal direction since it showcases better tribological properties, a higher reliability and higher potential of integrations within the gas turbine engines.


identifier: Liao, Yuhui  (2022) Worthy of the name? How do bank ESG affect banks’ lending behavior during Covid-19 Pandemic?  Masters thesis, Concordia University.

abstract: This paper examined how the bank ESG affects the banks’ lending behavior during the Covid-19 Pandemic. We use a sample of the syndicated loan data, we found that the banks with ESG level are more likely to lend to the borrowing companies that are more profitable and creditworthy. The low ESG banks have higher probability to lend if the purpose of the loan facility is for acquisition and if the borrowing bank is more familiar. Our results suggest that the banks with high ESG are more welcome to the companies running in worse conditions when making lending decisions, therefore their behavior match their name of being socially responsible.


identifier: Bolourian, Neshat  (2022) Point Cloud-based Deep Learning and UAV Path Planning for Surface Defect Detection of Concrete Bridges.  PhD thesis, Concordia University.

abstract:
Over the past decades, several bridges have collapsed, causing many losses due to the lack of proper monitoring and inspection. Although several new techniques have been developed to detect bridge defects, annual visual inspection remains the main approach. Visual inspection, using naked eyes, is time-consuming and subjective because of human errors. Light Detection and Ranging (LiDAR) scanning is a new technology to collect 3D point clouds. The main strength of point clouds over 2D images is collecting the third dimension of the scanned objects. Deep Learning (DL)-based methods have attracted the researchers’ attention for concrete surface defect detection. However, no point cloud-based DL method is currently available for semantic segmentation of bridge surface defects without converting the raw point cloud dataset into other representations, which results in increasing the size of the dataset and leads to some challenges regarding storage capacity, cost, and training time. Some promising point cloud-based semantic segmentation methods (i.e., PointNet and PointNet++) have been applied in segmenting bridge components (i.e., slabs, piers), but not for segmenting surface defects (i.e., cracks, spalls).  Moreover, most of the current point cloud-based concrete surface defect detection methods focus on only one type of defects. On the other hand, in DL, a dataset plays a key role in terms of variety, diversity, accuracy, and size. The lack of publicly available point cloud datasets for bridge surface defects is one of the reasons of the lack of studies in the area of point cloud-based methods.
Furthermore, compared with terrestrial LiDAR scanning, LiDAR-equipped Unmanned Aerial Vehicle (UAV) is capable of scanning the inaccessible surfaces of the bridges at a closer distance with higher safety. Although the UAV flying path can be controlled using remote controllers, automating and optimizing UAV path planning is preferable for being able to trace a collision-free path with minimum flight time. To increase the efficiency and accuracy of this approach, it is crucial to scan all parts of the bridge with a near perpendicular view. However, in the case of obstacle existence (e.g., bridge piers), achieving full coverage with near perpendicular view may not be possible. To provide more accurate results, using overlapping views is recommended. However, this method could result in increasing the inspection cost and time. Therefore, overlapping views should be considered only for surface areas where defects are expected.
Addressing the above issues, this research aims to: (1) create a publicly available point cloud dataset for concrete bridge surface defect semantic segmentation, (2) develop a point cloud-based semantic segmentation DL method to detect different types of concrete surface defects, and (3) propose a novel near-optimal path planning method for LiDAR-equipped UAV with respect to the minimum path length and maximum coverage considering the potential locations of defects.
On this premise, a point cloud-based DL method for semantic segmentation of concrete bridge surface defects (i.e., cracks and spalls), called SNEPointNet++, is developed. To have a network with high-performance, SNEPointNet++ focuses on two main characteristics related to surface defects (i.e., normal vector and depth) and takes into account the issues related to the point cloud dataset (i.e., small size and imbalanced dataset). Sensitivity analysis is applied to capture the best combination of hyperparameters and investigate their effects on network performance. The dataset, which was collected from four concrete bridges, was annotated, augmented, and classified into three classes: cracks, spalls, and no defect. This dataset is made available for other researchers. The model was trained and evaluated using 60% and 20% of the dataset, respectively. Testing on the remaining part of the dataset resulted in 93% recall (69% IoU) and 92% recall (82.5% IoU) for cracks and spalls, respectively. Moreover, the results show that the spalls of the segments deeper than 7 cm (severe spalls) can be detected with 99% recall.
On the other hand, this research proposes a 3D path planning method for using a UAV equipped with a LiDAR for bridge inspection to have efficient data collection. The method integrates a Genetic Algorithm (GA) and A* algorithm to solve the Traveling Salesman Problem (TSP), considering the potential locations of bridge surface defects such as cracks. The objective is to minimize the time of flight while achieving maximum visibility. The method provides the potential locations of surface defects to efficiently achieve perpendicular and overlapping views for sampling the viewpoints. Calculating the visibility with respect to the level of criticality leads to giving the priority to covering the areas with higher risk levels. Applying the proposed method on a 3-span bridge in Alberta, the results reveal that considering overlapping views based on the level of criticality of the zones and perpendicular views for all viewpoints leads to accurate and time-efficient data collection.