identifier: Hassan, Sameh  (2019) New Approaches For Evaluation and Controlling Alkali-Silica Reaction Damage In Deteriorated Concrete.  PhD thesis, Concordia University.

abstract:
Alkali-Silica Reaction (ASR) is considered one of the most significant critical internal deterioration mechanisms for concrete. ASR produces internal stresses that causes expansion and extended cracks threatening the country's wealth of existing infrastructure. Since ASR recognition in 1940 by Stanton, many studies had been conducted to evaluate the degree of reactivity for different types of gravel. However, limited research has focused on studying the effect of specimens’ shape and size, and casting direction on the accuracy of measured ASR expansion and find a correlation between cylindrical and standard prismatic specimens. Moreover, few studies have attempted to evaluate the optimum expansion level for controlling ASR expansion by strengthening ASR-damaged concrete.
An experimental work divided into three phases was conducted to evaluate; (1) The effect of these new approaches on ASR expansion using fused silica (FS) as a fast-acting material, (2) The selection of a suitable jacketing materials based on target performance rather than focusing only on the achieved strength investigating concrete mixtures incorporating four types of fibre and fine crumb rubber aggregates (FCRA) with and without silica fume, (3) The effectiveness of six different strengthening materials as CFRP, BFRP, mortar with GG mesh, mortar with BFRP mesh, FRC, and CRC with BFRP to suppress ASR expansion, and evaluate sensitivity of strengthening time and testing time vs. the strengthening types on the concrete mechanical properties.
The results exhibited addition of FS caused a drastic increase in the expansion, and plays a crucial role to adversely affect concrete mechanical properties and durability index until age 180 day, then the effectiveness decreased until 548 days. Specimen geometry and size, and casting direction had a significant effect on the rate of expansion. Cylindrical specimens expanded at a higher rate than the prisms until 56 days in the range from 43% to 37%, and from 9% to 15% at 90 days until test termination at 548 days. Specimens cast vertically exhibited an increase in expansion over the others cast horizontally in the range from 2.63% to 8.41%. Specimens Ø100×200mm reveal lower expansion in the range from 5.89% to 9.52% than specimens Ø75×285mm.
Concrete mixtures incorporating steel, macro, and micro polypropylene, micro nylon fibres, and FCRA with and without SF were examined. Based on balancing between mechanical properties, durability indices, and electrical resistivity, FRC incorporating micro polypropylene with SF, and CRC contained FCRA with silica were selected as FRC and CRC jacketing.
Strengthening type, strengthening time, and testing time after applying strengthening materials showed a significant effect to control ASR expansion and enhanced the damaged concrete properties. For instance, CFRP exhibited a significant reduction in expansion compared to that with control specimens and followed by BFRP, CRC with BFRP, Mortar with GG, Mortar with BFRP, and FRC, respectively. Moreover, strengthening at early ages revealed decreases mechanical properties as a result of high residual expansion. However, testing at early ages showed higher results proved the exposure conditions had an adverse effect on the strengthening materials.


identifier: Shouma, Amira  (2019) Tablets in Second Language Learning: Learners’ and Teachers’ Perceptions.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract:
This study explored the perceptions of ESL learners and teachers on using tablets in their language classrooms, focusing on the device’s four inherent affordances: learnability, usability, motivation, and willingness to use tablets. Although previous research has investigated the pedagogical use of tablets in the domain of higher education, elementary and secondary education, and teacher training, there is little research exploring the perceptions of ESL students and even less involving their teachers. The themes that guided the study are commonly found in Mobile Assisted Language Learning (MALL) perception studies. What is known about users’ perceptions of MALL-based pedagogy (including tablets) is that students and their instructors view the use of these platforms positively. For instance, it has been acknowledged that the use of MALL-based pedagogy has the potential to enhance learning by: (1) improving the learning experience – learnability (Obari & Lambacher, 2015), (2) increasing learners’ level of comfort – usability (Altena, 2015), (3) boosting motivation (Cardoso, 2011), and (4) increasing students’ willingness to use the technology (Cumming & Rodriguez, 2013).

This study examined the perceptions of 45 young adult ESL learners and 11 teachers on their use of tablets (Apple iPads) in their classrooms. A mixed-method approach was adopted to collect and analyze the data. Learners’ and teachers’ surveys used a 5-point Likert scale to probe the participants’ perceptions of the four themes: (1) learnability (e.g., using iPads helps me improve my listening skills in English), (2) usability (e.g., I find it is easy for my students to use iPads in reading activities), (3) motivation (e.g., using iPads motivates me to read English texts), and (4) willingness to use tablets (e.g., I will use the iPad for teaching my classes in the future). To measure the four key themes quantitatively, the survey results were analyzed via descriptive statistics. Qualitative data were obtained through face-to-face interviews, which were subjected to a thematic analysis. Findings reveal positive perceptions from both ESL learners and their teachers on all four measures adopted, which were also corroborated by the thematic analysis of the interviews.

Keywords: tablets, MALL, ESL learners, ESL teachers


identifier: Calderon, Anna  (2019) Everyday Resistance of Domestic Workers in Lima, Peru.  Masters thesis, Concordia University.

abstract: Domestic workers in Peru represent approximately 3,4% (342 192) of the economically active population of the country (Defensoria del Pueblo 2011). These workers are overwhelmingly poor women, un- or under-educated, and from ethnic and linguistic minorities, working in an informal environment (Legua Bordon 2017; Mick 2011; Perez and Llanos 2017, and Rottenbacher de Rojas 2015). Even though in 2003 the Peruvian government introduced Law 27896, which aims to regulate domestic work and protect domestic workers, the field remains a dangerous, precarious environment for the workers employed (ILO 2011). This thesis explores the manifold everyday strategies that domestic workers in Lima, Peru, utilize to resist violence in the workplace. Instances of violence and resistance have been observed to change and evolve throughout the experiences of the participants, from the beginning of their careers, until today. Nonetheless, the presence of violence has remained a reality in the everyday lives of domestic workers interviewed. The strategies of resistance utilized to fight these forms of violence have been placed into three broader categories; flight, survival, and active resistance, all of which are observed to enjoy a different degree of success. The most effective forms of resistance observed included (1) attaining a higher education, (2) survival, in the form of staying close to the family pet or children, and (3) negotiation, in the form of threatening to leave the job.


identifier: Chan, Yin Nang Ellison  (2019) Predicting US Elections with Social Media and Neural Networks.  Masters thesis, Concordia University.

abstract:
Increasingly, politicians and political parties are engaging their electors using social media. In the US Federal Election of 2016, candidates from both parties made heavy use of Social Media, particularly Twitter. It is then reasonable to attempt to find a correlation between popularity on Twitter, and eventual popular vote in the election. In this thesis, we will focus on using the subscriber ‘location’ field in the profile of each candidate to estimate support in each state.
A major challenge is that the Twitter location field in a user profile is not constrained, requiring the application of machine learning techniques to cluster users according to state.
In this thesis, we will train a Deep Convolutional Neural Network (CNN) to classify place names by state. Then we will apply the model to the Twitter Subscriber ‘location’ field of Twitter subscribers collected from each of the two candidates, Hillary Clinton (D), and Donald Trump (R). Finally, we will compare predicted popular votes in each state, to the actual results from the 2016 Presidential Election.
The hypothesis is that a city name has a strong correlation to the people who founded it and then incorporated it. Further, it’s hypothesized that the original settlers were mostly homogeneous, relative to the country of origin and shared a common language, thus resulting in place names using the language of their origin.
In addition to learning the pattern related to the State Names, this additional information may help a machine learning model learn to classify locations by state.
The results from our experiments are very promising. Using a dataset containing 695,389 cities, correctly labelled with their state, we partitioned the cities into a training dataset containing 556,311 cities, a validation dataset containing 111,262, and a test dataset containing 27,816. After the trained model was applied to the test dataset. We achieved a Correct Prediction rate of 84.4365%, a False Negative rate of 1.6106%, and a False Positive rate of 1.0697%.
Applying the trained model on Twitter Location data of subscribers of the two candidates, the model achieved an accuracy of 90%. The trained model was able to correctly pick the winner, by popular vote, in 45 out of the 50 states. With another US and Canadian election coming up in 2019, and 2020, it would be interesting to test the model on those as well.


identifier: de Souza, Ana  (2019) Cow Protection Discourse: Categories and National Identity.  Masters thesis, Concordia University.

abstract:
ABSTRACT

Cow Protection Discourse: Categories and National Identity
Ana de Souza

This thesis focuses on contemporary cow protection discourse that Hindu nationalist (Hindutva) groups produce. In an Indian context, the phrase ‘cow protection’ broadly refers to social, political, and religious movements concerned with promoting the welfare of cows and their progeny. These movements seek to enshrine the value of both actual cows and the cow as a symbol through laws, governmental bodies, social movements educating the public about cow products and welfare, and religious movements promoting cow-related rituals and the spiritual importance of cows. Hindu nationalist groups have been invested in the cow protection movement from their origins and commonly include it in their discourse.
This thesis proposes three categories of cow protection discourse based on the the kinds of evidence each uses: environmental, social, and religious arguments. Despite the substantial difference between the categories, these arguments are often mixed together to provide broad support for cow protection. As cow protection is an essential characteristic of so many different aspects of Hindu society, concern for cow welfare becomes an integral aspect of Hindutva formulations national identity. Four specific cases will demonstrate the categories, the pattern of mixing, and its effect for national identity. A test for the concepts of Hindutva, based on Hindutva: Who is a Hindu? by V.D. Savarkar, will be applied to each case, indicating their participation in formulating national identity. This thesis will classify the cow protection discourse that has recently become more visible and impactful within the Indian public sphere and provide an analytical map for further study.


identifier: Sifat, Syed Muhammad ORCID: https://orcid.org/0000-0001-9204-4585 <https://orcid.org/0000-0001-9204-4585>  (2019) 30 GHz Broadband Bow-tie Printed Ridge Gap Waveguide Antennas.  Masters thesis, Concordia University.

abstract:
The development of wireless and satellite communication systems has led to high demand for microwave and millimeter wave application components, which play an essential role in the upcoming 5G communication. The coverage area of such systems is control by transmitted power as well as the antenna gain of the system. Hence, it is essential to design a high gain antenna that can mitigate the losses and extend the system coverage area. Higher frequencies lead to smaller sizes of RF components including antennas. However, the implementation of passive components and guiding structure becomes difficult based on traditional guiding structures such as microstrip lines, and waveguides at millimeter wave bands. Microstrip line suffers from cavity modes, which leads to surface waves and has more losses at higher frequencies. The rectangular waveguide has high power handling capability, low losses, and high Q-factors which makes it very attractive for high-frequency applications. On the other hand, at high frequencies, the wavelengths become challenging to construct with current machining techniques as ensuring good electrical contact become very challenging.
In this thesis, planar high gain antennas are designed based on Printed Ridge Gap Waveguide (PRGW). The primary objective of this work is to develop high gain, wideband antennas that can support the future demand for high data transmission. Therefore, a detailed analysis for PRGW has been introduced as well as featured designs of the high gain antenna. This antenna array can perform for future 5G communication purpose, and it fulfills all the requirements of mm-wave bands.
In this work, a groove-based wideband bow-tie slot antenna array is designed at 30 GHz based on printed ridge gap waveguide technology (PRGW). A two-section T-shaped ridge is designed to feed a bow tie slot placed on the upper ground of PRGW. The gain of the proposed slot antenna is enhanced by using a horn-like groove. Then, the proposed high gain element is deployed to build up a 1 x 4 bow-tie slot antenna array loaded with three-layer groove antenna. The proposed antenna array is fabricated and measured, where the measured results show a -10 dB impedance bandwidth from 29.5 to 37 GHz (22%). The fabricated prototype achieves a high gain of 15.5 dBi and a radiation efficiency higher than 80% over the operating frequency bandwidth. Besides, to reduce the edge diffraction in the E-plane, an artificial corrugation ring is deployed with a certain depth so that it can improve the overall antenna performance.


identifier: Vogrig Nardini, Esthel  (2019) Performing the image: improvisational and collaborative practices in non-scripted film making. Film as a catalyst for encounters.  Masters thesis, Concordia University.

format: video video video text

abstract: This thesis inquires into the performative qualities of audiovisual productions developed through improvisational and collaborative techniques. It focuses on processes in which the act of filming becomes the catalyst for the emergence of encounters. This approach to audiovisual production lies at the crossing of performative gestures and filmmaking, in order to think about what kind of situations and relations a film is putting forward during its making and in the way it circulates. The thesis includes the making of three short films, made in collaboration with children in Mexico and the Dominican Republic.


identifier: Baharloo, Mohammad Mahdi  (2019) High-Performance Control of Mean-Field Teams in Leader-Follower Networks.  Masters thesis, Concordia University.

abstract: In this thesis, a mean-field approach is used to find high-performance control strategies for multi-agent systems.  The system consists of one leader and possibly many dynamically coupled followers, and all agents are affected by noise.  The global objective of the multi-agent control system here is to achieve an agreement between the agents while minimizing coupled linear-quadratic cost functions for two cases: a disturbance-free system, and a system with disturbances. In the former case, the proposed solution under non-classical information structure is near-optimal, which converges to the optimal solution for a large number of followers. For the latter case, the problem is solved for three non-classical information structures, namely, mean-field sharing, partial mean-field sharing, and intermittent mean-field sharing. Using the minimax control technique, it is shown that the solution obtained for the first structure is a unique saddle-point strategy. On the other hand, it is proved that for the other two structures, the proposed solutions tend to the unique saddle-point strategy when the number of followers goes to infinity. The proposed strategies in both cases are linear, scalable and computationally efficient. The theoretical findings are verified by simulation results.


identifier: Forati, Tahmineh  (2019) Developing Superhydrophobic Copper-Graphene Nanoplatelet Coatings to Promote Dropwise Condensation Using Thermal Spray Processes.  Masters thesis, Concordia University.

abstract:
Water vapour condensation is frequently used as an effective means of transferring heat using dropwise condensation on non-wetting surfaces. The rate of heat transfer can be enhanced with dropwise condensation on non-wetting or hydrophobic surfaces when compared to filmwise condensation on a wetting surface. A potential method to improve dropwise condensation is the use of superhydrophobic coatings that are exceptionally water repelling. The superhydrophobicity of a surface is a result of the combination of its surface microstructure and surface chemistry. Materials with low surface energy are mostly polymeric and organic with low durability and poor thermal and chemical stability. Furthermore, these materials add thermal resistance to the coating, limiting the potential heat transfer capacity. As an alternative, developing a coating containing graphene nanoplatelets (GNP) with their hydrophobicity, high thermal conductivity and excellent mechanical properties is a promising approach to provide a hydrophobic coating for promoting dropwise condensation.
In order to develop micro-textured coatings with high water repellency and mobility, in this work, atmospheric plasma spray (APS) and high-velocity oxy-fuel (HVOF) techniques were used as scalable and efficient coating techniques to develop thin Cu-GNP coatings on a copper substrate. The main reason for combining copper with GNP is to protect the GNP against the elevated temperatures of the plasma and HVOF plumes. Additionally, copper can act as a carrier which transfers the GNP towards the substrate hence the adhesion and mechanical properties of this coating improve as the substrate of interest is also copper. A parametric study approach was used to optimize the APS and HVOF process parameters in order to achieve the best wettability in the copper/graphene nanoplatelets micro-textured coatings. Subsequently, to lower their surface energy, post-treatment by a stearic acid solution was performed.
The APS Cu-GNP coatings exhibit water contact angles as high as 152° and sliding angle less than10° while HVOF Cu-GNP coatings showed great water mobility (with a sliding angle less than 1°) as well as high water contact angle value of 164°. The image analyses of the APS coatings showed a lamellar structure. Additionally, with optimizing the plasma power, the desired microstructure that encourages the non-wetting surface was achieved. The HVOF coating showed more homogenous as well as denser morphology, and a hierarchical microstructure was observed. With optimizing the parameters, GNP embedded in the Cu matrix was more evident in HVOF coatings which can be attributed to the lower temperature of this process. Raman analysis further dem¬onstrates the presence of GNP in the coating while the defects in its structure increased after the thermal spray processes. Moreover, the above influences are more significant in APS compared to the HVOF Process. The best of the APS and HVOF coatings are then tested to evaluate their corrosion stability. It is shown that the HVOF Cu-GNP coating developed in this work can improve the corrosion resistance up to 89% when compared to the uncoated Cu surface. This coating can potentially promote dropwise condensation while offering enhanced corrosion stability.


identifier: van Lieshout, Catharina  (2019) Learning Dutch in a Self-Directed Environment Using Google Translate.  Masters thesis, Concordia University.

abstract:
The technology addressed in this study is Google Translate (GT) and its associated text-to-speech (TTS) and automatic-speech-recognition (ASR) built-in features. In the literature, the three technologies encompassed by GT have shown to have positive effects in the learning of second/foreign language (L2) vocabulary and pronunciation: Translation (Calis & Dikilitas, 2012), TTS (Soler-Urzua, 2011) and ASR (Liakin, Cardoso & Liakina, 2014). This study is an investigation of the affordances of GT and the accompanied technologies in a self-directed learning (SDL) environment.
The study examined the pedagogical use of GT as a source of L2 Dutch vocabulary and pronunciation in an SDL setting. Thirty participants used GT (its translation, TTS and ASR functions) for approximately one hour to learn a small number of “basic/beginner” words and phrases and their respective pronunciations in Dutch (e.g., how to say “hi” – “Hoi” [hoj]). The study followed a pre/post/delayed-post test design that examined the participants learning of vocabulary/phrases and their related pronunciations, combined with a qualitative analysis of video recordings of their self-directed interactions with GT. In addition, surveys about their learning experience and interviews were administered. The findings indicate that the participants were able to acquire Dutch vocabulary and pronunciation on a short-term basis, they interacted with GT’s TTS and ASR technology in different ways and to different extents, demonstrating that GT is a versatile tool. Finally, our findings suggest that participants had overall positive views of GT according to the four perception markers adopted (i.e., learnability, usability, motivation, and willingness to use the technology).


identifier: Sparkes, Carol Nancy  (2019) Flipped Classrooms versus Traditional Classrooms: A systematic review and meta-analysis of student achievement in higher education.  PhD thesis, Concordia University.

abstract:
In an attempt to understand what makes blended learning (BL) more effective than Classroom Instruction (CI), this research looked more closely at the Flipped Classroom (FC) model of BL. The FC takes a relatively consistent approach to course design by flipping what is traditionally done in the classroom (i.e., lecture) with what is traditionally done as homework (i.e., application).
Numerous studies have been conducted comparing FC with the CI on student achievement in higher education without conclusive results. To synthesize the literature, this dissertation implemented a systematic review and meta-analysis to measure the average effect size and the direction of the impact and to determine the conditions under which students learn more effectively. To ensure a transparent process the potential for bias in each step of a meta-analysis was acknowledged and addressed.
Through a systematic review of the literature from 2000 to 2017, 114 studies were included and 125 effect sizes were calculated. Using meta-analysis these effect sizes created a weighted mean effect-size of +0.30, which was statistically significant at p < 0.05 and educationally significant.
Study features were analyzed to determine if there were any attributes that made a difference but none were found to be significant. The use of quizzes, however, showed an interesting pattern and near significant difference (p = .058) when the effect sizes were grouped by STEM, non-STEM and Health-related disciplines. No publication bias was found, no outliers were found from the sensitivity analysis, and there was no significant difference between the effects from quasi-experimental and experimental designs.
While the FC significantly outperformed CI it was not to a greater extent than general BL outperformed CI. Future research is encouraged between levels of treatments, instead of between FC and CI, in order to provide more nuanced results about how to improve instructional design in future courses.


identifier: Alameddine, Hyame ORCID: https://orcid.org/0000-0002-6307-0036 <https://orcid.org/0000-0002-6307-0036>  (2019) Orchestration and Scheduling of Resources in Softwarized Networks.  PhD thesis, Concordia University.

type: Thesis PeerReviewed

abstract:
The Fifth Generation (5G) era is touted as the next generation of mobile networks that will unleash new services and network capabilities, opening up a whole new line of businesses recognized by a top-notch Quality of Service (QoS) and Quality of Experience (QoE) empowered by many recent advancements in network softwarization and providing an innovative on-demand service provisioning on a shared underlying network infrastructure. 5G networks will support the immerse explosion of the Internet of Things (IoT) incurring an expected growth of billions of connected IoT devices by 2020, providing a wide range of services spanning from low-cost sensor-based metering services to low-latency communication services touching health, education and automotive sectors among others. Mobile operators are striving to find a cost effective network solution that will enable them to continuously and automatically upgrade their networks based on their ever growing customers demands in the quest of fulfilling the new rising opportunities of offering novel services empowered by the many emerging IoT devices. Thus, departing from the shortfalls of legacy hardware (i.e., high cost, difficult management and update, etc.) and learning from the different advantages of virtualization technologies which enabled the sharing of computing resources in a cloud environment, mobile operators started to leverage the idea of network softwarization through several emerging technologies. Network Function Virtualization (NFV) promises an ultimate Capital Expenditures (CAPEX) reduction and high flexibility in resource provisioning and service delivery through replacing hardware equipment by software. Software Defined Network (SDN) offers network and mobile operators programmable traffic management and delivery. These technologies will enable the launch of Multi-Access Edge Computing (MEC) paradigm that promises to complete the 5G networks requirements in providing low-latency services by bringing the computing resources to the edge of the network, in close vicinity of the users, hence, assisting the limited capabilities of their IoT devices in delivering their needed services. By leveraging network softwarization, these technologies will initiate a tremendous re-design of current networks that will be transformed to self-managed, software-based networks exploiting multiple benefits ranging from flexibility, programmability, automation, elasticity among others.

This dissertation attempts to elaborate and address key challenges related to enabling the re-design of current networks to support a smooth integration of the NFV and MEC technologies. This thesis provides a profound understanding and novel contributions in resource and service provisioning and scheduling towards enabling efficient resource and network utilization of the underlying infrastructure by leveraging several optimization and game theoretic techniques. In particular, we first, investigate the interplay existing between network function mapping, traffic routing and Network Service (NS) scheduling in NFV-based networks and present a Column Generation (CG) decomposition method to solve the problem with considerable runtime improvement over mathematical-based formulations. Given the increasing interest in providing low-latency services and the correlation existing between this objective and the goal of network operators in maximizing their network admissibility through efficiently utilizing their network resources, we revisit the latter problem and tackle it under different assumptions and objectives. Given its complexity, we present a novel game theoretic approach that is able to provide a bounded solution of the problem. Further, we extend our work to the network edge where we promote network elasticity and alleviate virtualization technologies by addressing the problem of task offloading and scheduling along with the IoT application resource allocation problem. Given the complexity of the problem, we propose a Logic-Based Benders (LBBD) decomposition method to efficiently solve it to optimality.


identifier: Axamitnyy, Rostislav  (2019) Sequoia RAIDb-3: a new model for data distribution and replication using commodity systems.  Masters thesis, Concordia University.

abstract:
Unprecedented growth in the amount of data generated and used in the modern
world has made distributed databases an important target for contemporary research.
Today, advances in distributed databases embrace a wide range of concepts and ideas
including, but not limited to, fragmentation, replication, distributed transactions and
distributed concurrency control.
One novel idea in this area was the introduction of the Redundant Array of
Inexpensive Databases (RAIDb) initially proposed by the authors of Sequoia, a Java-
based clustering middle-ware framework. On a conceptual level, RAIDb is similar
to RAID arrays of disks; however, in contrast to traditional RAID, RAIDb utilizes
an array of individual databases. The objective of RAIDb is to provide improved
performance and fault tolerance relative to a single database while preserving the
abstraction of a standard SQL DBMS.
This thesis extends the functionality of Sequoia and proposes a distribution model
based upon full horizontal fragmentation. We refer to this new design as RAIDb-3.
We discuss details of the implementation and support its validity with an extensive
suite of test cases.


identifier: Girard, Sébastien  (2019) Relationship between back squat strength to body mass ratio and muscle activity during single-leg landing tasks in varsity athletes.  Masters thesis, Concordia University.

abstract:
The number of injuries sustained by female athletes has been on the rise. Female athletes are 4 to 8 times more at risk of suffering an anterior cruciate ligament injury (ACL). Strength is believed to be an important predictor of ACL injury in female athletes and has been a focus of improvement in injury prevention programs. However, there is a lack of consensus on the relationship between muscle strength and hip and knee kinematics during landing tasks. Only a few studies used a lower extremity relative strength measure and reported promising results, but none of them reported EMG activity. The goal of this study was to explore the relationship between back squat strength to body mass ratio and the lower limb muscle activity during single-leg landing tasks.

Twenty-eight varsity athletes were recruited (13 males and 15 females) from various sports from the universities in the Montreal, Québec region. Participants were asked to perform a standardized 1-RM back squat test followed by single-leg drop landings from 28cm and 44cm plyometric boxes. EMG data was collected 150ms pre-initial and 250ms post-initial contact for the following muscles: biceps femoris (BF), semitendinosus (ST), vatus lateralis (VL), vastus medialis (VMO), rectus femoris (RF), gluteus medius (GMED) and gluteus maximus (GMAX). Significant differences were found between male and female athletes on the back squat one-repetition maximum and squat to body mass ratio (SQ:BW). Females had significantly more neuromuscular activity of the quadriceps and GMAX, and significantly less activity of the biceps femoris in both landing phases. After controlling for strength, only significant differences in RF, BF and GMAX activity remained between sexes in the deceleration phase. Prior to landing, moderate negative correlations between VL, VMO and GMAX activity and SQ:BW were observed at both drop heights. During the post-initial contact phase, moderate negative correlations for VL and VMO activity were found at both drop heights. Our findings suggest that researchers looking at sex-based differences in muscle activity should control for relative strength differences between groups as it may influence muscle activity during landing tasks.


identifier: karandish, Seyedeh Forough  (2019) Analysis of the Performance of HOG and CNNs for Detecting Construction Equipment and Personal Protective Equipment.  Masters thesis, Concordia University.

abstract:
The construction industry remains one of the most dangerous working environments in terms of fatalities and accidents. High numbers of accidents and loss-time injuries, leads to a decrease in productivity in this industry. Therefore, new technologies are being developed to improve the safety of construction sites. Object detection on construction sites has a huge impact on the construction industry. Many researchers studied productivity, safety, and project progress. However, few efforts have been made to improve the robustness of the related datasets for detection purposes. In the meantime, it is noticed that the lack of a custom dataset leads to low accuracy and also an increase in the cost and time of training dataset preparation.
In this research, we first investigated the generation of synthetic images using 3D models of construction equipment to use them as the datasets for training purposes, namely: excavators, loaders and trucks, and then sensitivity analysis is applied. We compared the performance of CNNs and other conventional methods for classifying construction equipment. In the second part, the detection of personal protective equipment for construction workers was studied. For this purpose, several object detection architectures from the TensorFlow object detection model zoo have been evaluated to find the best and most robust detection model. The dataset used in this study contains real images from construction sites. The performance evaluation of trained object detectors are measured in terms of mean average precision.  The test results from this study showed that (1) synthetic images have a significant effect on the final detection results; and (2) comparing various object detection architectures, Faster_rcnn_resnet101 was the most suitable model in terms of accuracy of detection.


identifier: Alkhodair, Sarah  (2019) Assessing Trust and Veracity of Data in Social Media.  PhD thesis, Concordia University.

abstract:
Social media highly impacts our knowledge and perception of the world. With the tremendous amount of data that is circulating in social media and initiated by a vast number of users from all over the world, extracting useful information from such data and assessing its veracity has become much more challenging. Data veracity refers to the trustworthiness and certainty of data. The challenges of handling textual data in social media have raised the need for efficient tools to extract, understand, and assess the veracity of information circulating in social media at a given time. In this thesis, we present three research problems to address major challenges of handling textual data in social media.

First, overwhelming the user with huge volumes of short, noisy, and unstructured textual data complicates the task of understanding what topics are discussed by users in micro-blogging websites. Topic models were proposed to automatically learn a set of keywords that better describe each topic covered by a large corpus of text documents to enable fast and effective browsing and exploration of its contents. However, in order for the results of topic modeling algorithms to be useful, these results have to be interpretable. Applying topic models to social media data to get meaningful results is not a trivial task. In this thesis, we study the problem of improving interpretation of topic modeling of micro-posts in social media. We propose a new method that incorporates topic modeling, a lexical database, and the set of hashtags available in the corpus of micro-posts to produce a higher quality representation of each extracted topic. Extensive experiments on two real-life datasets collected from Twitter show that our method outperforms the state-of-the-art model in terms of perplexity, topics' coherence, and their quality.

Second, the nature and flexibility of social media facilitate the process of posting unverified information, especially during the rapid diffusion of breaking news. Efficiently detecting and acting upon unverified breaking news rumors throughout social media is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this thesis, we study the problem of assessing the veracity of information contained in micro-posts regarding emerging stories and topics of breaking news. We propose a new approach that jointly learns word embeddings and trains a neural network model with two different objectives to automatically identify unverified micro-posts spreading in social media during breaking news. Extensive experiments on real-life datasets show that our proposed model outperforms the state-of-the-art classifier as well as other baseline classifiers in terms of precision, recall, and F1.

Finally, the uncertainty and chaos associated with hot and sensitive breaking news and emergencies facilitate the explosive spread of high-engaging breaking news rumors that might be extremely damaging. In such a case, authorities have to prioritize the rumors verification process and act upon high-engaging breaking news rumors quickly to reduce their damaging consequences. However, this is an extremely challenging task. In this thesis, we study the problem of identifying rumors micro-posts that are most likely to become viral and achieve high engagement rates among recipients in social media during breaking news. We propose a multi-task neural network to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction. Extensive experiments on real-life datasets show that the performance of our joint learning model outperforms other baseline classifiers in terms of precision, recall, and F1 and is capable of identifying high-engaging breaking news rumors with high accuracy.


identifier: Babashahi Ashtiani, Kia  (2019) Automated Mechanism Design A Large Scale Optimization Approach.  Masters thesis, Concordia University.

abstract:
A set of self-interested and rational agents in a social network want to distribute their initial set of resources among themselves to obtain the set of resources they desire the most. Each agent,
being self-interested and rational has incentive to lie about its preferences towards the resources to manipulate the outcome of the system to its advantage. In the context of multi-agent systems,
Automated Mechanism Design (AMD), is a computer based design of rules that allows the reach of an equilibrium despite the selfishness of its agents.
Most of the multi-agent and AMD research has focused on one-to-one bilateral exchanges that only allow two agents to exchange resources.
Very few studies have been conducted on multilateral (many-to-many) types of trade. While several multi-agent algorithms exist for the one-to-one case, very few algorithms exists for the many-to-many case, which is more often encountered in social networks. AMD algorithms lack scalability as they rely on the enumeration of the resource allocation combinations.
We first propose three new optimization models for the AMD problem using a decomposition technique to create mechanisms that are not only scalable, i.e., one could use them to solve the
AMD problem for data sets consisting of hundreds of agents and resources in seconds, but also support different types of trades between different numbers of agents (one-to-one, many-to-one
and many-to-many). Then we illustrate a new mathematical model with a polynomial number of variables corresponding to the compact formulation of the current model of the literature that
supports the many-to-many type of trade.
Numerical experiments show that we can solve significantly larger data sets than the ones existing in the literature, i.e., up to 2,000 agents and 2,000 resources for the many-to-many type of trade in less than 24 seconds.


identifier: Bayatpour, Afshin  (2019) Moisture Effects on Properties of Polymeric Composite Laminates with Different Void Content.  Masters thesis, Concordia University.

abstract: Composite materials, especially carbon fiber reinforced polymers (CFRPs), have been broadly used due to their high specific strength and stiffness ratio compared to metallic materials in the past few decades. CFRPs are widely utilized in aerospace, civil infrastructure and marine sectors under harsh environments. As a result, there exists a strong demand to observe the mechanical behavior of composite materials in the presence of moisture in order to avoid unpredictable effects. Composite structures are often cured in an autoclave to achieve the required aerospace grade quality. However, curing large structures requires access to large autoclaves which is limited and expensive. As a result, fabrication of large structures using out-of-autoclave prepreg materials will lead to a great amount of savings in manufacturing costs. In the out-of-autoclave processing method, the presence of voids inside the laminate has been an issue due to the lack of high pressure during manufacturing. This study aims primarily to observe the moisture absorption response of composite samples containing different levels of void. By changing the vacuum level inside the bag during the manufacturing process, three different unidirectional laminates at three levels of void have been manufactured. After immersing the samples in warm water at 60˚C for about one year, the maximum moisture absorption level and rate of absorption were monitored through experiments and diffusion coefficients were calculated for all the samples using Fick’s law. Results show that the moisture absorption coefficient, its rate and the maximum level of moisture can be correlated to the void volume fraction. Moreover, the sensibility of the CFRPs under moisturized environment has been investigated. The mechanical behavior of these laminates has been studied at four different moisture levels by performing dynamic mechanical analysis (DMA) and interlaminar shear strength (ILSS) tests. Empirical results indicate that in most of the cases, interlaminar shear strength, glass transition temperature, and storage modulus decrease during the exposure of the samples with different void contents to the moisture environment. Finally, a finite element analysis has been done to study the change of moisture concentration through the thickness of the material.


identifier: Manouchehri, Narges  (2019) Finite Bivariate and Multivariate Beta Mixture Models Learning and Applications.  Masters thesis, Concordia University.

abstract:
Finite mixture models have been revealed to provide flexibility for data clustering. They have demonstrated high competence and potential to capture hidden structure in data. Modern technological progresses, growing volumes and varieties of generated data, revolutionized computers and other related factors are contributing to produce large scale data. This fact enhances the significance of finding reliable and adaptable models which can analyze bigger, more complex data to identify latent patterns, deliver faster and more accurate results and make decisions with minimal human interaction.
Adopting the finest and most accurate distribution that appropriately represents the mixture components is critical. The most widely adopted generative model has been the Gaussian mixture. In numerous real-world applications, however, when the nature and structure of data are non-Gaussian, this modelling fails. One of the other crucial issues when using mixtures is determination of
the model complexity or number of mixture components. Minimum message length (MML) is one of the main techniques in frequentist frameworks to tackle this challenging issue.
In this work, we have designed and implemented a finite mixture model, using the bivariate and multivariate Beta
distributions for cluster analysis and demonstrated its flexibility in describing the intrinsic characteristics of the observed data.
In addition, we have applied our estimation and model selection algorithms to synthetic and real datasets. Most importantly, we considered interesting applications such as in image segmentation, software modules defect prediction, spam detection  and occupancy estimation in smart buildings.


identifier: AL-Smadi, Huthaifa  (2019) Space-Based Maintenance Management for Architectural Building Systems using Multi-Objective Optimization.  Masters thesis, Concordia University.

abstract:
Public Buildings like universities and educational buildings are considered among the most challenging assets to maintain and modernize. Statistics show that the non-residential buildings are prone to a significant shortage in maintenance and rehabilitation expenditure. As per the Canadian infrastructures report card (2016), 42% of Municipal buildings rank between “fair” and “very poor”. In addition, according to the American society of civil engineering (ASCE) (2017) the school sector grade, which is the largest sector of educational buildings is D+ (poor condition). Thus, a maintenance optimization methodology is essential for these building types to appropriately plan for the maintenance of systems that are competing for limited funding. Upon reviewing the literature, a gap was revealed in the area of building maintenance, repair and rehabilitation, which is the lack of consideration of the space type inside the building and how different space types affect the maintenance planning process. Considering the fact that any building is composed of different space types having variable needs, requirements and functions that help in supporting the overall function of the building facility, therefore, not including the space types and functions as part of the maintenance plan would result in a loss in the overall functionality. Hence, the main objective of this research is to develop a maintenance optimization model which takes the space type into account and accordingly optimize the maintenance actions to be implemented inside a building.
To achieve the main objective the following sub-objectives were identified: 1) review the maintenance prioritization and optimization methods for buildings maintenance, 2)develop a deterioration models for building systems and 3) establish an optimization model for buildings maintenance. The methodology encompasses three main phases. First, a space-based condition assessment, is adopted as a part of this research to determine weights of the spaces and the different systems inside each space of the building. Second, a Weibull Distribution model, is utilized to predict the future condition of building systems inside each space by modeling their deterioration over the time. Finally, a Particle swarm optimization is employed to optimize the activity selection. A case study on educational buildings is leveraged to illustrate the applicability of the proposed model. Non-dominate solutions were established considering the defined constraints. The selected compromising solution result was 11.06 building condition with a total cost of $475,000. A Sensitivity analysis was conducted showing the impact of the systems service life on the space-based assessment and the maintenance cost. The output of this study is a framework that selects the best combinations of maintenance activities to be implemented inside a building, considering the varying space types and maintenance costs.


identifier: Perry Cox, Alexei  (2019) Finding Places to Make Places.  Masters thesis, Concordia University.

title: Finding Places to Make Places

abstract: Placed in the context of ongoing moral disaster, Finding Places to Make Places  is a discussion of language and poetic usefulness, specifically how collective discourse survives the unimaginable through personal recourse. It examines the ideology of cultural superiority and intellectual migration in public squares and private homes. With skepticism and love, this poetry and poetics attempts to explain the failure and potential at the heart of revolutions: the impulse to launch the experience of an individual into a communal existence across time. It is meant to speak, with many voices, beyond these known failures and into our many futures. It is a defense of the art of poetry as a means to evoke the necessary accommodations human beings can make to survive what is unsurvivable. The organizing principle for the poetries has been divvied into a curious binary that works to mirror both the similarities and differences in the concepts of each section. These political and/or personal poems demonstrate the culture-bound logistics and flourishings and shortcomings of certain poetic voices during the Cuban Revolution of 1953-59, the Cultural Revolution in China of 1966-79, the Civil War of Lebanon of 1975-90 and the Arab Spring in the MENA region of 2010-11, as well as ongoing revolutions in the lands currently known as the United States and Canada. The poems center around healing these wounded places in the often more ambitiously universal interiors of the psyche.


identifier: Guittard, Erika  (2019) Expériences de musicothérapeutes travaillant à court terme avec des groupes d’adolescents en santé mentale.  Masters thesis, Concordia University.

abstract: Cette étude phénoménologique a pour but de documenter la pratique de la musicothérapie en santé mentale adolescente, et plus précisément les séances réalisées avec des groupes d’adolescents à court terme. En dépit des besoins avérés de ce type de populations et des effets prometteurs de la musicothérapie en santé mentale démontrés par la recherche, la littérature canadienne ne documente encore que peu ce type spécifique de contexte. Pour cette étude, trois musicothérapeutes canadiennes travaillant principalement en santé mentale adolescente ont été rencontrées en entrevues semi-structurées afin de partager leur expérience professionnelle. Trois thèmes ont principalement émergé après analyse de leurs réponses : (a) les principaux buts visés dans ce type de contexte, (b) les structures de programmes généralement établies et (c) les compétences professionnelles requises. Si travailler avec des groupes d’adolescents aux problématiques psychiatriques et psychosociales nécessite de faire appel à des approches spécifiques, les méthodes de travail sont aussi être inhérente à chaque professionnel, et peuvent donc être variées. Elles doivent aussi être adaptables à un contexte souvent imprévisible, mais aussi à une population vulnérable, en constante exploration identitaire et en grand besoin d’ancrage. L’étude met notamment en exergue l’importance de la musicothérapie communautaire, mais aussi la nécessité de valoriser l’individualité, l’histoire personnelle de chaque individu dans le processus thérapeutique.


identifier: Daniel, Kruger  (2019) An Historical Study of the First Year of the Canadian Association of Music Therapists.  Masters thesis, Concordia University.

abstract: This study presents a historical narrative of the inaugural year (1974/75) of the Canadian Association of Music Therapists (CAMT). The purpose of the study was to examine music therapists’ lived experiences of the first year of the CAMT concomitantly with primary source documents published between the first two CAMT conferences (August 3, 1974 and May 2, 1975, respectively). The primary research question was: What are the experiences of music therapists who were involved in the CAMT during its inaugural year? The secondary research question was: How do primary source historical documents from the first year of the CAMT relate to the experiences of the participants? I combined phenomenological and historical methodologies in this qualitative interview study. I interviewed three Canadian music therapists who were active during 1974/75. The experiences they shared in their interviews were related to primary source historical documents the researcher obtained from the CAMT historical archives. Three primary themes emerged from the data analysis: “developing identity,” “defining music therapy/music therapist,” and “emerging alternative profession.” This study offers information about significant conflicts, issues, and developments in the early CAMT that was previously missing from the historical literature on Canadian music therapy.


identifier: Asaadi, Muftah Mohamed  (2019) Performance Improvement of Dense Dielectric Patch Antenna using Partially Reflective Surfaces.  PhD thesis, Concordia University.

abstract:
Recently, millimeter-wave (MMW) band is being considered as the spectrum for future wireless communication systems. Several advantages are achieved by utilizing the millimeter-wave range, including high gain with large available bandwidth, compact size, and high security. Nevertheless, attenuation loss may restrict wireless communication systems’ transmission range. Meanwhile, printed antenna technology has gained the attention of antenna designers’ due to its low profile and ease of fabrication. High-gain antennas are very desirable as a critical part of MMW systems. Designing millimeter wave antennas with high gain characteristics would be a significant advantage due to their high sensitivity to atmospheric absorption losses. Moreover, planar configurations are required in many applications, such as for wireless communication.
      The main goal of this thesis is to design and propose state of the art designs of Fabry Pérot Cavity antenna (FPCA) designs with several types of superstrates to achieve high gain, wide bandwidth, and high efficiency to satisfy the requirements of today’s advanced wireless communication systems. A dense dielectric patch (DD) antenna is used as the main radiator and designed to operate at 28 GHz. The thesis presents several contributions related to the design and analysis of FPC antennas using several types of superstrates.
    The first research theme of this thesis has two parts. The first part presents a holey dielectric superstrate applied over a 2×2 dense dielectric square patch antenna array to enhance the gain, improve the bandwidth and efficiency, as well as to reduce the side lobe levels (SLLs). A dense dielectric patch replaces the metallic patch and is used as a radiated element. The measured results show a high gain of 16 dBi, with radiation efficiency of about 93 %, wide bandwidth of 15.3 %, and a reduced SLL. The second part focusses on a partially reflective surface (PRS) unit cell composed of two thin perforated dielectric slabs. The effect of the thicknesses of the unit cell dielectric slabs is discussed in detail. An array of the proposed PRS unit cell is applied over a dense dielectric square patch antenna array to broaden the bandwidth and to enhance the gain as well.  The measured results exhibit a 3 dB gain bandwidth of 27 % with a high gain of 16.8 dBi.
     The second research theme presents an effective method to design a tapered superstrate of an FPC antenna with a DD patch element. This type of superstrate is designed to correct the phase above the superstrate to be almost uniform. The proposed single-layer perforated tapered superstrate is constructed by tapering the relative permittivity to be high in the center of the superstrate slab and then decrease gradually as it moves towards the edges. This tapered relative permittivity is then applied over a single DD patch antenna. The proposed antenna exhibits good performance in terms of the antenna gain and bandwidth.  The antenna gain becomes flat and as high as 17.6 dBi. The antenna bandwidth is about 16 %, and the side lobe level of the antenna is very promising.
     A third theme presents the implementation and design of a high gain dense dielectric patch antenna integrated with a frequency-selective surface (FSS) superstrate. A 7×7-unit cell is used to build the superstrate layer, and applied above the high DD patch antenna. A modified unit cell is proposed to generate a positive reflection phase with high reflection magnitude within the frequency design in order to broaden the antenna bandwidth. A bandwidth of 15.3 % with a high gain of 16 dBi is obtained.
     Finally, a high gain linearly polarized (LP) substrate integrated waveguide (SIW) cavity antenna based on a high-order mode is implemented, fabricated, and tested. A TE440 mode is excited at 28 GHz. In this design, 4×4 slots are cut into the top metal of the cavity, where each slot is placed above each standing wave peak. These slot cuts contributed to a high gain of 16.4 dBi and radiation efficiency of about 96 %. The LP SIW cavity antenna was then integrated with a linear-to-circular polarization converter developed as a high gain circularly polarized (CP) SIW cavity antenna with high gain and high radiation efficiency of 16 dBi and 96 %, respectively.


identifier: Moazami, Ehsan  (2019) World-to-digital microfluidics for transformation and enzymatic assays.  Masters thesis, Concordia University.

abstract:
Digital microfluidics (DMF) is a technique for the manipulation of discrete droplets on an array of electrodes, which allows the controlled movement of fluids and represents an alternative from the conventional microfluidic paradigm of transporting fluids in enclosed channels.  One of the major benefits of DMF is that fluid motion and control is achieved without external pumps and fabricated valves – it only requires the use of electric fields.  The automation component of DMF has pushed the barriers of this ‘lab-on-chip’ technology; however, integration with external components (i.e. world-to-chip) interfaces has been a challenge.  For example, the delivering of the biological fluids to the chip and integrating temperature control on a single platform are considered as two world-to-chip challenges in DMF.  To address these two challenges, my thesis describes two world-to-chip components that are integrated with the DMF device: reagent delivery and temperature control.  This new platform enables us to perform a variety of biological or chemical experiments on a chip with reduced manual intervention.  Specifically, the new platform enabled an increase in reservoir volume on the chip by 40-fold from ~10 µL to 400 µL which allowed more reproducible dispensing and eliminated the need to refill the reservoirs during the biological assay.  In addition, we integrated a closed-loop temperature control system that enabled fast and rapid changes in temperature on the chip.
To show the utility of the world-to-chip interfaces, we validated the system by automating bacterial transformation and enzymatic assay procedures, which show that both procedures require world-to-chip interfaces for accurate and precise implementation.  Overall, we propose that this system has the potential to be integrated for other types of biological assays and experiments which require fluidic control, automation, and temperature control.


identifier: Eftekharirad, Roshanak ORCID: https://orcid.org/0000-0002-4000-3877 <https://orcid.org/0000-0002-4000-3877>  (2019) Improving Fire Emergency Management Using Occupant Information and BIM-Based Simulation.  Masters thesis, Concordia University.

abstract:
The increasing complexity of buildings has brought some difficulties for emergency response. When fires occur in a building, limited perception regarding the disaster area and occupants can increase the probability of injuries and damages. Thus, the availability of comprehensive and timely information may help understand the existing conditions and plan an efficient evacuation. For this purpose, Building Information Modeling (BIM) should be integrated with three sets of information: (1) occupancy that defines the type of space usage; (2) occupants’ information; and (3) sensory data. The Industry Foundation Classes (IFC), as a standard of BIM, has the definitions for all areas, volumes, and elements of a building. IFC also has the basic definitions of sensor and occupant entities. However, these entities do not provide enough dynamic and accurate information for supporting emergency management systems.
In addition, building renovation projects have an effect on evacuation time. During the building renovation projects, space is shared between the construction crews and occupants. The construction works change the building layout and movement flow, which increase the occupants’ vulnerability, affecting their evacuation behavior under emergency conditions. Hence, the safety and wellbeing of the occupants as well as their evacuation time should be considered under emergency incidents.
This thesis aims to improve fire emergency management using occupant information and BIM-based simulation. For this purpose, a “dynamic BIM” for fire emergency real-time management is developed that captures enough dynamism regarding the building condition as well as environmental conditions and occupants’ behavior. Also, an Agent-Based Model (ABM) is used to assist in the analysis of the static and dynamic behavior of the environment and occupants in BIM.
The specific objectives of the research are: (1) extending IfcSensor entity for occupant’s sensors; (2) adding new attributes to IfcOccupant to support emergency response operations and defining a new entity for occupancy; (3) defining the relationships between sensors, occupants, occupancy, time series, and building components in the context of building evacuation; (4) creating dynamic BIM for tracking occupants and environmental states; and (5) evaluating the evacuation time for specific scenarios where additional spatio-temporal constraints exist during a fire incidence. Renovation construction operations are considered as such constraint and an ABM co-simulation framework is developed under emergency conditions. The feasibility of the proposed methods is discussed using different case studies.


identifier: Dragonas, Stephanie  (2019) Montreal’s Gay Village: A Multi-Generational Oral History and Documentary Film.  Masters thesis, Concordia University.

format: text video

abstract: Montreal's Gay Village: outlawed, out-dated, or just out late? Through a multi-generational queer oral history, this research-creation documentary project explores Montreal's Gay Village, a neighbourhood that is in an economic downturn. By unearthing the neighbourhood's and community's heavily charged history through the personal stories of local community members, this documentary project offers contextualization to a place that is changing rapidly. By including LGBTQ+ community members of all ages, this documentary attempts to put multiple generations of queer people in conversation with one another as the dynamic relationships between the place, its history, interpretations of its history, and individual's varying lived experiences within the space are examined. How has the evolution of Montreal's Gay Village both as a place (physically) and a space (its socio-political positioning) impacted identities - both personal and communal? How does intergenerational dialogue, or a lack thereof, contribute to an understanding of spaces and their significance, both symbolic and material?


identifier: Rollins, Annie  (2019) Immaterial Remains: the (im)possibilities of preserving China's shadow puppet tradition.  PhD thesis, Concordia University.

abstract:
Chinese shadow puppetry is a performance form that dates back over a millennium ago. As one of the most widespread puppet forms in China, shadow puppetry lineages became literal archives of their community's stories with just a few practitioners from each generation charged with retaining the cumulative repository of shared experiences. As modern China and increasingly globalizing forces puts pressure on the circumstances that cultivated this long tradition of shadow puppetry, questions of continuance have been on the minds of the practitioners and their audiences since the early 1900s. If the transmission of shadow puppetry ends, unable to engage apprentices to inherit or find audiences to receive, where will those thousands of years of stories and their ghostly lineage go? This thesis utilizes an apprenticeship research method to follow two main lines of inquiry: (1) what are the particular ways in which Chinese shadow puppetry, a traditional vernacular puppetry form, is resistant to current methods of preservation (2) how can new preservation theories and practices be approached to better support traditional vernacular puppet forms? Ancillary to this, can creative artists outside the lineage of Chinese shadow puppetry contribute to this endeavor?
The research shows that while current methods for preservation and safeguarding intangible cultural forms, especially puppetry, are enacting some change, the results are mixed and often detrimental to the form they are designed to protect. The goal for this dissertation is to catalyze a re-examination of safeguarding policies and current theories on the (im)possibilities of preserving traditional vernacular puppetry forms in order to posit a new approach to safeguarding that prioritizes (1) apprenticeship, (2) practitioner-driven creativity and (3) performance as the central mode of dissemination. Considering these three tenets is not intended to replace the roster of safeguarding methods currently in place, but adds to them to include necessary elements that have so far not been adequately considered. This thesis hopes to shift the current focus of preservation methods, which prioritizes objects and products, back unto practitioners as central to any method of preservation.


identifier: Aurup, Golam Mohammad Moshiuddin  (2019) Three Essays on the Effects of Bilingualism on the Response in the Two Languages.  PhD thesis, Concordia University.

abstract: This study tries to understand how the bilinguals in Canada show differential responses to questionnaires and advertisements in the two languages. Three essays are developed in this regard. In the first essay, acculturation to the two cultures, as shown by the bilingual English and French Canadians, was studied. A common factorial structure with three correlated factors- English/French Media use, English/French social interaction, and English/French cultural attachment emerged from this analysis. The effects of ethnicity and language of questionnaire on these factors were then analyzed. Overall advertising involvement with informative and emotional advertisements was then studied in the second essay. A common structure of the advertising involvement concept was proposed for the four conditions- the two language version responses by these two ethnic groups. The effects of ethnicity and language of questionnaire were then observed. This essay also developed path models for the causal relationship involving advertising involvement, brand attitudes, and behavioral intentions, and looked into the differences between first and second language responses. The third essay took a neuroscience approach to look into advertising involvement. This essay took three different strategies to analyze EEG brain signals and explained how the differences between advertisements viewed in the first and/or second languages can be explained. Advertising involvement as explained in the marketing literature and neuroscience do not always converge. This third essay worked to find a common ground in this regard.


identifier: Hoebanx, Pauline ORCID: https://orcid.org/0000-0002-1033-8289 <https://orcid.org/0000-0002-1033-8289>  (2019) Dating scripts on social media: A case study of Matthew Hussey's YouTube videos.  Masters thesis, Concordia University.

abstract: Heterosexual dating practices have been studied extensively in the social sciences. Many of these studies have employed Simon and Gagnon’s scripting theory (1986) as their theoretical framework, finding that heterosexual dating norms have not changed since the 1950s (Laner & Ventrone, 2000). Among these studies, some have used dating advice as an empirical source of data, though they have remained focused on dating advice in the print media (e.g. Eaton & Rose, 2011; Rose & Frieze, 1993). There are no existing studies about dating advice on social media, and the present study proposes to address this gap in the literature, by conducting a qualitative case study of a sample of 30 videos from dating coach Matthew Hussey’s YouTube channel (Hussey, n.d.). By changing the dynamics of information production, distribution and consumption, social media has increased the flow of accessible information, and this thesis examines whether such a transformation has affected norms surrounding dating scripts. Drawing upon Jackson’s four dimensions of the social (2006a), I propose an analysis of the intersections of heteronormativity, mononormativity (Barker, Gill, & Harvey, 2018), and neoliberalism in Matthew Hussey’s dating advice videos. This research found that the dating scripts discussed in Hussey’s videos are akin to those described in previous studies. However, an unexpected finding of this project was the limitations of scripting theory in accounting for structural power relations.


identifier: Kalsi, Jaspreet Singh  (2019) Color Image Segmentation Using Generalized Inverted Finite Mixture Models By Integrating Spatial Information.  Masters thesis, Concordia University.

abstract: In computer vision, image segmentation plays foundational role. Innumerable techniques, such as active contour, graph-cut-based, model-based, machine learning, and clustering-based methods have been proposed for tackling the image segmentation problem. But, none of them is universally applicable. Thus, the hunt for optimized and robust models for image segmentation is still under-process and also an open question. The challenges faced in image segmentation are the integration of spatial information, finding the exact number of clusters (M), and to segment the image smoothly without any inaccuracy specially in the presence of noise, a complex background, low contrast and, inhomogeneous intensity. The use of finite mixture model (FMMs) for image segmentation is very popular approach in the field of computer vision. The application of image segmentation using FMM ranges from automatic number plate recognition, content-based image retrieval, texture recognition, facial recognition, satellite imagery etc. Image segmentation using FMM undergoes some problems. FMM-based image segmentation considers neither spatial correlation among the peer pixels nor the prior knowledge that the adjacent pixels are most likely belong to the same cluster. Also, color images are sensitive to illumination and noise. To overcome these limitations,  we have used three different methods for integrating spatial information with FMM. First method uses the prior knowledge of M. In second method, we have used Markov Random Field (MRF). Lastly, in third, we have used weighted geometric and arithmetic mean template. We have implemented these methods with inverted Dirichlet mixture model (IDMM), generalized inverted Dirichlet mixture model (GIDMM) and inverted Beta Liouville mixture model (IBLMM). For experimentation, the Berkeley 500 (BSD500) and MIT's Computational Visual Cognition Laboratory (CVCL) datasets are employed. Furthermore, to compare the image segmentation results, the outputs of IDMM, GIDMM, and IBLMM are compared with each other, using segmentation performance evaluation metrics.


identifier: Abedini Sohi, Parsoua  (2019) Self-Standing Silicon Nanostructures Fabricated Using Chemical/Electrochemical Technique: Application in Gas Field Ionization Tunneling Sensor.  PhD thesis, Concordia University.

abstract:
The air surrounding us may contain different amount of hazardous gases or atmospheric pollutants. As the public health and safety is the prime priority, therefore it is very essential to have a reliable detector to distinguish the presence of these pollutions.
So far, different gas sensing technologies have been invented. However none of the traditional sensors, which are mostly based on chemical reaction between the gas and detector material, could fulfill all the requirements such as sensitivity, selectivity, reversibility, durability and fast recovery time. Therefore, it is worth investigating contemporary sensors for the detection of different target gases. An ingenious approach would be to use the ionization characteristics of distinct gases to uniquely identify the gas type. Gas ionization sensors contain two parallel electrodes separated by a narrow gap. Applied electric field between the electrodes ionizes the target gas. Ionization energy is a unique amount of energy required for the gaseous atoms to discharge electrons. In these sensors the discharge characteristics are used as a calibrating data, which provides excellent selectivity. In addition, because this technique does not involve adsorption or desorption of the gases, the sensor exhibits fast response and high reversibility and recovery. In planar parallel electrode structures the applied electric field is uniform. Applying nanostructures as one of the electrodes creates nonlinear electric field, enhanced at the vicinity of the tips of the structures. In these structures the ionization initiates at lower voltages.
There are two different types of gas ionization sensors. One is based on measuring the breakdown voltages of the gases, which is extensively discussed in the literature. The second one is an innovative concept for nanostructure-based gas ionization sensors. These devices, which are studied in this work, is based on tunneling field ionization characteristics of the gases. In this phenomenon an electron from the gas tunnels into the vacant energy levels of a semiconductor employed as the anode.
The fabrication technique of the anode involves the growth of p-type silicon nanostructures through a mask-less chemical/electrochemical etching technique. The technique and the fabrication of the silicon nanowires used in the structure of the device are described in detail. Prior to the fabrication process, the mechanism of nanowires formations was modeled for 2D and 3D structures and simulated using COMSOL multiphysics. Our studies showed that the positive carriers (holes) current greatly influences the etching rate of the silicon during the electrochemical etching process.  Our experiments showed that growth process of the nanostructures fits very well with our analytical model and helped us to adjust the fabrication parameters to control the characteristic as well as the density and concentration of the nanostructure arrays.
Finally, a miniaturized gas sensors based on the grown nanostructured was fabricated. I-V characteristics of the sensor was measured for various gases like Ar, N2, O2, and He. Charge transport mechanisms are explained based upon the gathered data.
The sensor was tested for 200 μm  and 50 μm  inter-electrode distances. Charge transport mechanism is explained based upon the gathered data for both devices. The fabricated devices are capable to characterize several gas species including inert gases. Gas ionization voltages were much lower compared to those, reported in the literature using devices fabricated by metallic nanostructures.


identifier: Niksirat, Parna  (2018) Characterizing the Effect of Reduced Gravity on Rover Wheel-Soil Interactions.  Masters thesis, Concordia University.

abstract:
The entrapment of the Mars Exploration Rover Spirit in soft regolith and the tears and punctures in the Mars Science Laboratory Curiosity rover’s wheels demonstrate some of the current mobility challenges in granular terrains on extraterrestrial planetary surfaces.

Classical wheel-terrain interaction models used in the literature are unable to sufficiently predict the effects of reduced-gravity on rover performance. Several researchers today highlight the insufficient predictive power of classical terramechanics models for planetary rovers, thus implying a need to renew the experimental underpinnings of our theories.  Only a single dataset has been reported in the literature for wheels driving in soil during reduced-g flights, and the actual data collected is limited. This thesis presents data that more than doubles the number of existing reduced-g wheel-soil interaction experiments for the study of terramechanics. One of the key contributions is that it includes the measurement of drawbar pull (i.e. net traction force) data as well as direct observation of wheel-soil interactions (through a glass sidewall), both for the first time ever in reduced gravity.

The experimentation campaign is designed to inform the upcoming ExoMars space mission, through the use of ExoMars wheel prototype and Martian soil simulant in simulated Martian gravity produced in parabolic flights. An advanced automated gantry system is developed to support this activity with improved control and repeatability over the prior published experiments. In addition to Martian gravity, wheel-soil interactions are also studied in Lunar gravity, all achieved aboard Canada’s National Research Council’s (NRC) Falcon 20 aircraft. Wheel rotation rate, horizontal advance rate, and vertical wheel loading are controlled independently. To address the constraints imposed by testing aboard an aircraft performing parabolic flights and to achieve experimental repeatability and consistency, a novel rapid automated soil preparation subsystem is developed. The consistency and repeatability of the soil preparation are studied and verified both through cone penetration tests and through examining triplicates of terramechanics (i.e. traction force, wheel sinkage) datasets.

A key observation from the terramechanics dataset is a significant reduction of traction (over 30\% less) in partial gravity experiments (PGE) compared to on-ground experiments (OGE), at the same wheel loading. The complementary visualization analysis results indicate that, with wheel normal load held equal between experiments, the amount of soil mobilized by wheel-soil interaction substantially increases as gravity decreases. The results of the visualization analysis suggest a deterioration in the soil strength at lower gravities, which thus undermines the rover mobility by reducing the net traction. The results have important implications regarding the practice of using a reduced-mass rover on Earth to assess the performance of a full-mass rover in similar soil on a reduced-gravity surface. Other details discovered in the dataset are also further elaborated in this study.

The analysis of terramechanics data and high-speed images that are collected at Lunar and Martian gravities, and contrasted against OGE, not only guide the understanding of the influence of gravity on wheel performance but also holds promise to fill the gaps of research in the literature. The congruity of analysis of computer vision/clustering techniques with terramechanics results in this campaign highlights a promising technique for studying these interactions in a planetary context. The richness of the data produced, unprecedented in the study of robot-terrain interactions, can highlight gaps and discrepancies in existing models and enables validation of new models that approach robot-terrain interactions with an appropriate and efficient level of detail.


identifier: ye, qing  (2019) classifying transport proteins using profile hidden markov models and specificity determining sites.  Masters thesis, Concordia University.

abstract:
This thesis develops methods to classifiy the substrates transported across a membrane by a given transmembrane protein. Our methods use tools that predict specificity determining sites (SDS) after computing a multiple sequence alignment (MSA), and then building a profile Hidden Markov Model (HMM) using HMMER. In bioinformatics, HMMER is a set of widely used applications for sequence analysis based on profile HMM. Specificity determining sites (SDS) are the key positions in a protein sequence that play a crucial role in functional variation within the protein family during the course of evolution.

We have established a classification pipeline which integrated the steps of data processing, model building and model evaluation. The pipeline contains similarity search, multiple sequence alignment, specificity determining site prediction and construction of a profile Hidden Markov Model.

We did comprehensive testing and analysis of different combinations of MSA and SDS tools in our pipeline. The best performing combination was MUSCLE with Xdet, and the performance analysis showed that the overall average Matthews Correlation Coefficient (MCC) across the seven substrate classes of the dataset was 0.71, which outperforms the state-of-the-art.


identifier: Fahmy, Rana  (2019) Re-Settling Woes and Rebellions: The Role of Irelands Naturall History in the Cromwellian Era.  Masters thesis, Concordia University.

abstract: Irelands Naturall History, written by the Dutch physician Gerard Boate, is a key document in the history of science during the seventeenth century. Yet, scholars have undermined its significance in the Cromwellian resettlement of Ireland and its influence on colonial projects under the umbrella of ‘science’ during the 1650s. The interconnections between the advancement of the new learning during a period of political turmoil that began with the Irish rebellion of 1641 raises new questions for colonial narratives. Thus, re-examining the significance of Irelands Naturall History also raises new perspectives on the meaning of science during the Cromwellian reconquest of Ireland. From its initial composition to its legacy in the eighteenth century, Irelands Naturall History is a multi-faceted work that served many purposes – from a guide to a successful plantation to a source on the improvement of the Irish landscape.


identifier: Fatemeh, Mohammadi  (2019) Convolutional Autoencoder for Studying Dynamic Functional Brain Connectivity in Resting-State Functional MRI.  Masters thesis, Concordia University.

abstract:
Brain is the most complex organ in human body. Understanding how different regions of the brain function and interact with one another is a challenging task. One of the most important topics in the study of the brain is the functional brain connectivity, which is defined as the correlations, each between a pair of the activation signals from the different regions of the brain. Study of the functional connectivity in the human brain provides new insights into the understanding of the healthy and diseased brains and their differences. Functional magnetic resonance imaging (fMRI) is an imaging technique that allows researchers to study the brain activity and functional connectivity. While many researchers have focused on static functional connectivity in the resting-state fMRI to study the functions of the brain, dynamic functional connectivity has received more attention recently for such a study, since it provides more detailed information about the brain functions. Within the literature for studying the dynamic brain connectivity, k-means clustering has been applied to the connectivity matrices in order to find the functional connectivity patterns. However, it is known that the k-means clustering technique is not suitable for applying it to high dimensional data such as the functional brain connectivity matrices. In this thesis, in order to overcome this problem, we propose a deep learning-based convolutional autoencoder to obtain  latent representations of the connectivity matrices prior to applying to them the k-means clustering. Use of the convolutional autoencoder, not only reduces the dimension of the connectivity matrices, but also provides a more semantic representation of these matrices. It is shown that the proposed method of clustering that consists of the use of the autoencoder followed by k-means clustering results in improving the clustering of the connectivity matrices, and consequently, to a better capturing of the functional connectivity patterns. In order to show the effectiveness of the proposed clustering method, synthetic connectivity matrices for patterns, with their classes known, are generated. The proposed method is then first applied to these syntactically generated connectivity matrices and the resulting patterns are compared with that obtained by applying k-means clustering technique to the synthetic connectivity matrices. It is shown that the proposed method classifies the various patterns more accurately.

The proposed method is then used to study the dynamic functional brain connectivity by applying it to real fMRI data captured from a group of healthy subjects and another group of subjects affected by schizophrenia. For this purpose, after preprocessing the raw fMRI data for each subject in these two groups, the group independent component analysis (ICA) is applied in order to decompose the fMRI data into statistically independent components (map of the entire brain) and their corresponding time-courses. Each independent component corresponds to a specific region of the brain. The connectivity matrix whose elements corresponds to the correlation between the time-courses within a segment of the time-courses enclosed inside a sliding window is then obtained. Next, the proposed clustering method is used to cluster all the connectivity matrices, each corresponding to one segment, into a finite number of functional connectivity patterns (states). A two-sample t-test is then performed on each state in order to determine each pair of the regions in the group of the healthy control subjects for which weather or not the correlation value is significantly different from that of the corresponding pair of the regions in the group of schizophrenia patients. It is observed through this test that there are indeed pairs of the brain regions where significant differences do exist between the two groups. It is also seen that such a difference between the two groups is even more pronounced in the visual network of the brain.

Finally, in this thesis, a study is undertaken for the evaluation of the dwell time, which is defined to be the duration for a functional connectivity pattern to remain in one state before switching to another state. It is shown through this study that the dwell time for the healthy group to stay in the state with more connectivity is longer than that for the group with schizophrenia. On the other hand, the dwell time for the group with schizophrenia to stay in the state with less connectivity is longer than that for the healthy group.


identifier: Béland, Mélanie  (2017) Association Between Exercise And Depression In Adults With A Non-Communicable Chronic Disease.  PhD thesis, Concordia University.

abstract:
People living with a non-communicable chronic disease (NCD) are about twice as likely as the general population to suffer from depression. This comorbid condition is associated with several negative consequences, such as worse prognosis and higher risks of mortality. Recent research has examined the beneficial effect of exercise on depressive symptoms and have reported positive results, similar to pharmacotherapy and psychological intervention. However, to date, no study has examined the effect of aerobic exercise on reducing depressive symptoms in NCDs. Moreover, a handful of studies have looked at the association between exercise and depressive symptoms in the asthma population. In order to address these gaps, this thesis systematically reviewed previous literature in the area of aerobic exercise and depressive symptoms in patients with NCDs and explored the association between leisure-time physical activity and depressive symptoms in adults with asthma.

The first study was a systematic review which aimed at assessing the beneficial effects of aerobic exercise on depressive symptoms in a NCD population. A total of 24 studies were included in the meta-analysis. The results revealed a moderate effect of aerobic exercise interventions on depressive symptoms in NCDs in comparison to usual care.

The second study was a cross-sectional study assessing the association between leisure-time physical activity (LTPA) and depressive symptoms in adults with asthma. A total of 953 participants from two asthma cohorts were included. Results revealed that engaging in any LTPA was associated with lower depressive symptoms. Moreover, there was a strong association between engaging in vigorous LTPA and lower depressive symptoms in participants with high levels of depressive symptoms. However, these relationships were no longer meaningful once they were adjusted for inhaled corticosteroid dose and asthma control.

In summary, the research has demonstrated that people with depressive symptoms and a NCD benefit from engaging in aerobic exercise, even at low levels. Given the results of this thesis, practitioners, such as clinical psychologists, may be instrumental in promoting aerobic exercise to clients who depressive symptoms comorbid with a NCD. Further research is needed to better understand the relationship in NCDs, most notable in respiratory diseases and type 2 diabetes.


identifier: Lee, Seungho  (2019) Three Essays on Current International Financial Markets.  PhD thesis, Concordia University.

abstract:
This dissertation consists of three essays that address recent developments in international financial markets that have been of concern for scholars, policymakers, and practitioners. The first essay examines how cultural factors can influence individual investors’ trading behavior in response to risk in nine Eurozone countries. The markets studied were particularly affected by the global financial crisis, the subsequent European banking crisis, and the European sovereign debt crisis. Using mutual fund flows as proxy of investors’ trading behavior, our evidence indicates that a country culture variable significantly affects investors’ trading responsiveness to risk. Specifically, the impact of risk on fund flows is significantly positive and is larger in scale in countries with individualist cultures.
The second essay attempts to investigate the effects of negative interest rate policies (NIRP) on foreign exchange and equity markets of eight European countries and Japan. To see the impacts of these policies, event studies and regime-switching vector autoregressive regression analyses are conducted for the nine countries that implement NIRP. The results provide valid evidence that the announcement of NIRP has a transitory effect on currency depreciation; long term effects are less evident. On the day of NIRP implementation, both currency and equity market returns reacted in response to the event efficiently and negatively, especially in Switzerland’s case. These outcomes suggest that simulative monetary policy by lowering interest rates below zero might have counter-effects from those observed when interest rates are lowered, but to rates that remain positive. Additionally, findings from the long term analyses explain that interest rate term structure and cointegration level of local and the U.S. equity index may be related to effectiveness of NIRP in currency and equity markets, respectively.
The last essay examines the determinants of the price of the leading cryptocurrency, Bitcoin. The analyses identify a number of factors that significantly affect the returns to investments in Bitcoin including: trading volume, high-low price spread, and extreme price change in the previous period. The latter result supports the assertion that recent severe price fluctuations in Bitcoin markets are primarily due to speculative investment activities. Furthermore, evidences suggested in this study explain possibility of market compromise and inefficiency of the cryptocurrency market, implying pivotal risks for Bitcoin market participants.


identifier: Kakon, Gabriel  (2018) Edmonton Obesity Staging System for Pediatrics, Quality of Life, Fitness, Adherence to Exercise in Adolescents with Obesity.  Masters thesis, Concordia University.

abstract:
Background: The Edmonton Obesity Staging System-pediatrics (EOSS-p) is based on the EOSS for adults, which has shown better predictive value for mortality than BMI. To our knowledge, no study has examined the EOSS-p in relation to health and wellbeing outcomes in a pediatric sample with obesity. The purpose of this study was to compare the associations of EOSS-p and BMI percentile with quality of life (QOL), cardiorespiratory fitness (CRF), muscular strength, and adherence to an exercise intervention in adolescents with obesity.
Methods: Participants were enrolled in the Healthy Eating Aerobics Resistance Training in Youth trial (N= 299). QOL, CRF (peak oxygen uptake, VO2peak) and muscular strength were assessed by the Pediatric QOL Inventory (PedsQL), indirect calorimetry during a maximal treadmill test, and 8-RM bench and leg press tests respectively. QOL, CRF, and muscular strength were assessed at baseline and 6-months after the intervention. Adherence was determined as a percentage of attended exercise sessions. Participants were staged from 0 to 3 (absent to severe health risk) according to EOSS-p. The association of EOSS-p and BMI percentile with outcomes were assessed using general linear models adjusting for age and sex.
Results: Baseline QOL decreased with increasing EOSS-p stages (p<0.001). QOL was 75.7 +/- 11.4 in stage 0/1, 69.1 +/- 13.1 in stage 2, and 55.4 +/- 13.0 in stage 3. Stage 3 showed smaller improvements with 6-month CRF than stage 0/1 and 2 (p=0.001, B=-3.882 mlO2/kg/min). BMI percentile was associated with baseline VO2peak (p<0.001, B=-1.044 mlO2/kg/min), bench press (p=0.029, B=0.832 kg) and leg press (p=0.003, B=3.992 kg). Similar associations were observed between BMI percentile and 6-month outcomes.
Conclusion: As EOSS-p stages increase, QOL decreases.  EOSS-p stage 3 had lower 6-month CRF, which suggests stage 3 may require a longer, more intensive or different intervention to achieve similar CRF improvements. BMI percentile showed contradicting health associations with cardiorespiratory fitness and muscular strength.


identifier: Quan, Zixi  (2019) A Framework for Parallelizing OWL Classification in Description Logic Reasoners.  PhD thesis, Concordia University.

abstract:
The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing.
This thesis reports on a black-box approach to parallelize existing description logic (DL) reasoners for theWeb Ontology Language. We focus on OWL ontology classification, which is an important inference service and supported by every major OWL/DL reasoner. To the best of our knowledge, we are the first to propose a flexible parallel framework which can be applied to existing OWL reasoners in order to speed up their classification process. There are two versions of our methods discussed: (i) the first version implements a novel thread-level parallel architecture with two parallel strategies to achieve a good speedup factor with an increasing number of threads, but does not rely on locking techniques and thus avoids possible race conditions. (ii) The improved version implements an improved data structure and various parallel computing techniques for precomputing and classification to reduce the overhead of processing ontologies and compete with other DL reasoners based on the wall clock time for classification.
In order to test the performance of both versions of our approaches, we use a real-world repository for choosing the tested ontologies. For the first version of our approach, we evaluated our prototype implementation with a set of selected real-world ontologies. Our experiments demonstrate very good scalability resulting in a speedup that is linear to the number of available cores. For the second version, its performance is evaluated by parallelizing major OWL reasoners for concept classification. Currently, we mainly focus on comparison with two popular DL reasoners: Hermit and JFact. In comparison to the selected black-box reasoners, our results demonstrate that the wall clock time of ontology classification can be improved by one order of the magnitude for most real-world ontologies in the repository.


identifier: Moallemi, Ali  (2019) Intuitionistic Databases and Cylindric Algebra.  PhD thesis, Concordia University.

abstract:
The goal of this thesis is to develop an intuitionistic relevance-logic based semantics that allows us to handle Full First Order queries similar monotone First Order queries.

Next, we fully investigate the relational model and universal nulls, showing that they can be treated on par with the usual existential nulls. To do so, we show that a suitable finite representation mechanism, called Star-Cylinders, handling universal nulls can be developed based on the Cylindric Set Algebra. Moreover, we show that any First Order Relational Calculus query over databases containing universal nulls can be translated into an equivalent expression in our star cylindric algebra, and vice versa. Furthermore, the representation mechanism is then extended to Naive Star-Cylinders, which are star-cylinders allowing existential nulls in addition to universal nulls.

    Beside the theory part, we also provide a practical approach for four-valued databases. We show that the four-valued database instances can be stored as a pair of two-valued instances. These two-valued instances store positive and negative information independently, in the format of current databases. In a similar way, we show that four-valued queries can be decomposed to two-valued queries and can be executed against decomposed instances to obtain the four-valued the result, after merging them back. Later, we show how these results can be extended to Datalog and we show that there is no need for any syntactical notion of stratification or non-monotonic reasoning when the intuitionistic logic is implemented. This is followed by presenting the complexity results.