identifier: Amini, Amir ORCID: https://orcid.org/0000-0002-2134-4406 <https://orcid.org/0000-0002-2134-4406>  (2020) Event-triggered Consensus Frameworks for Multi-agent Systems.  PhD thesis, Concordia University.

abstract:
Recently, distributed multi-agent systems (MAS) have been widely studied for a variety of engineering applications, including cooperative vehicular systems, sensor networks, and electrical power grids. To solve the allocated tasks in MASs, each agent autonomously determines the appropriate actions using information available locally and received from its neighbours. Many cooperative behaviours in MAS are based
on a consensus algorithm. Consensus, by definition, is to distributively agree on a parameter of interest between the agents. Depending on the application, consensus has different configurations such as leader-following, formation, synchronization in robotic arms, and state estimation in sensor networks. Consensus in MASs requires local measurements and information exchanges between the neighbouring agents. Due to the energy restriction, hardware limitation, and bandwidth constraint, strategies that reduce the amount of measurements and information exchanges between the
agents are of paramount interest. Event-triggering transmission schemes are among the most recent strategies that efficiently reduce the number of transmissions. This dissertation proposes a number of event-triggered consensus (ETC) implementations
which are applicable to MASs. Different performance objectives and physical constraints, such as a desired convergence rate, robustness to uncertainty in control realization, information quantization, sampled-data processing, and resilience to denial
of service (DoS) attacks are included in realization of the proposed algorithms. A novel convex optimization is proposed which simultaneously designs the control and event-triggering parameters in a unified framework. The optimization governs the trade-off between the consensus convergence rate and intensity of transmissions. This co-design optimization is extended to an advanced class of event-triggered schemes,
known as the dynamic event-triggering (DET), which is able to substantially reduce the amount of transmissions. In the presence of DoS attacks, the co-design optimization simultaneously computes the control and DET parameters so that the number of transmissions is reduced and a desired level of resilience to DoS is guaranteed. In addition to consensus, a formation-containment implementation is proposed, where the
amount of transmissions are reduced using the DET schemes. The performance of the proposed implementations are evaluated through simulation over several MASs. The
experimental results demonstrate the effectiveness of the proposed implementations and verify their design flexibility.


identifier: Abdelkader, Eslam Mohammed  (2020) An Integrated Method for Optimizing Bridge Maintenance Plans.  PhD thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract:
Bridges are one of the vital civil infrastructure assets, essential for economic developments and public welfare. Their large numbers, deteriorating condition, public demands for safe and efficient transportation networks and limited maintenance and intervention budgets pose a challenge, particularly when coupled with the need to respect environmental constraints. This state of affairs creates a wide gap between critical needs for intervention actions, and tight maintenance and rehabilitation funds. In an effort to meet this challenge, a newly developed integrated method for optimized maintenance and intervention plans for reinforced concrete bridge decks is introduced. The method encompasses development of five models: surface defects evaluation, corrosion severities evaluation, deterioration modeling, integrated condition assessment, and optimized maintenance plans. These models were automated in a set of standalone computer applications, coded using C#.net in Matlab environment. These computer applications were subsequently combined to form an integrated method for optimized maintenance and intervention plans. Four bridges and a dataset of bridge images were used in testing and validating the developed optimization method and its five models.
The developed models have unique features and demonstrated noticeable performance and accuracy over methods used in practice and those reported in the literature. For example, the accuracy of the surface defects detection and evaluation model outperforms those of widely-recognized machine leaning and deep learning models; reducing detection, recognition and evaluation of surface defects error by 56.08%, 20.2% and 64.23%, respectively. The corrosion evaluation model comprises design of a standardized amplitude rating system that circumvents limitations of numerical amplitude-based corrosion maps. In the integrated condition, it was inferred that the developed model accomplished consistent improvement over the visual inspection procedures in-use by the Ministry of Transportation in Quebec. Similarly, the deterioration model displayed average enhancement in the prediction accuracies by 60% when compared against the most commonly-utilized weibull distribution. The performance of the developed multi-objective optimization model yielded 49% and 25% improvement over that of genetic algorithm in a five-year study period and a twenty five-year study period, respectively. At the level of thirty five-year study period, unlike the developed model, classical meta-heuristics failed to find feasible solutions within the assigned constraints. The developed integrated platform is expected to provide an efficient tool that enables decision makers to formulate sustainable maintenance plans that optimize budget allocations and ensure efficient utilization of resources.


identifier: Assad, Ahmed ORCID: https://orcid.org/0000-0002-7363-6646 <https://orcid.org/0000-0002-7363-6646>  (2020) Resilience-Driven Management of Water Distribution Networks.  PhD thesis, Concordia University.

abstract:
Water distribution networks (WDNs) are critical infrastructure systems that secure needed supply of potable water to the public. Efficient management of WDNs has always been a primary concern for decision-makers, particularly in events of natural disasters, deliberate attacks, human-made accidents and/or sudden failures. Aging and deterioration of WDNs further exacerbate their vulnerability and likelihood of service disruption. Previous hazards reveal that classical risk-based approaches are not sufficient to prevent disruptions of WDNs. As a result, the concept of resilient WDNs has emerged to cope up with inevitable disruptions that are becoming more frequent.
The objective of this research is to develop a holistic resilience-based management model for WDNs. In this context, WDNs is sought to be strong enough to withstand unforeseen disruptions with a minimum performance impact and to recover rapidly after a service interruption. Firstly, a multi-attribute metric is developed for assessing resilience of WDNs based on robustness and redundancy. Attributes from graph theory are employed to quantify the network redundancy. Robustness is measured by integrating the reliability and criticality of pipe segments of the network. Multi-attribute utility theory and Fuzzy analytical network process are exploited to estimate the criticality of water segments based on a set of economic, social, and environmental factors. Survival analysis and maximum likelihood estimate are employed to dynamically determine reliability of pipe segments. Censored inter-failure time data are leveraged to model the deterioration behavior of homogenous cohorts of pipe. The developed metric was used to measure the resilience of a real-life WDN in the City of London, Ontario. The results obtained showed an average of 5% variation when compared to previously developed flow-based and topology-based metrics.
In the second step, rapidity and resourcefulness qualities are considered to develop a resilience-based restoration model. A failure scenario causing multiple simultaneous breaks across the network is simulated to investigate the recovery process. A stochastic multi-objective optimization model that maximizes resilience while minimizing the total time and cost of the recovery process is then formulated. This model accounts for different restoration methods, relocation time and cost of restoration crews, and uncertainties in recovery estimates. The optimum restoration plan encompasses a sequence of failed segments restorations along with the restoration method. This plan achieved 4% cost saving, 48% duration reduction, and 4% resilience improvement when compared to current planning practices.
The last step involves developing a multi-objective resilience enhancement model so that WDNs can be better prepared for future disruptions. The aim is to maximize resilience of WDNs while minimizing the life cycle cost and carbon emissions of enhancement actions. Optimum enhancement interventions are firstly determined and clustered into work packages before an optimized schedule is generated considering various operational and managerial factors. Applied to a section of an actual WDN of 34 km in a length and average age of 40 years, resilience was increased by 20% with CAD 1.65 million of current investment. The study of that network indicates that a cost-saving of 32% could be attained when adopting the developed model over ongoing portfolio management practices.
The novel resilience-driven management model introduced in this research is expected to assist decision-makers better assess and enhance resilience of WDNs and improve restoration planning. The developed model can assist city mangers in allocation and utilization of resources more effectively in development of optimized plans for resilient and sustainable WDNs.


identifier: Ahluwalia, Jasmeen Kaur ORCID: https://orcid.org/0000-0003-0978-7148 <https://orcid.org/0000-0003-0978-7148>  (2020) A Cloud Infrastructure as a Service for an Efficient Usage of IoT Capabilities.  Masters thesis, Concordia University.

abstract:
The Internet of Things comprises of a system of devices (or objects) connected to the Internet and interacting with each other to satisfy various tasks or goals. These objects could be sensors, actuators, smart phones, smart appliances, etc. With the ever-increasing demand of IoT in daily life as well as in the industry, and billions of devices being connected over the internet, most IoT applications aim for cost and energy efficiency, scalability, and minimal latency in terms of resource provisioning.
To fulfill these requirements, Cloud Computing might prove beneficial. Cloud Computing provides on demand access to configurable computing resources (servers, memory, network, etc.) in the cloud, which require minimal management by the end user. It comprises of three service models, which are: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The cloud IaaS aims at an efficient usage of resources. In the specific case of IoT, these resources are the sensing and actuation capabilities. However, there are still many challenges that the design and implementation of an IoT IaaS faces. Some examples are the heterogeneity of the sensors and actuators, orchestration, provision of bare metal access, and also publication and discovery of the capabilities of IoT devices. This thesis aims at the design and implementation of an architecture for IoT IaaS. First, it lays down a set of requirements essential to the architecture. This is followed by a thorough review of the state of the art. Next, it proposes an architecture for IoT IaaS that utilizes node level virtualization for an efficient usage of IoT capabilities. Functional entities are proposed as well as interfaces relying on RESTful Web services. The interfaces include a low-level interface for homogeneously accessing all the heterogenous capabilities of IoT devices, as well as high level interfaces which allow the IoT cloud users (e.g. PaaS or individual applications) to access these capabilities in an efficient manner. We have implemented a prototype using real-life as well as simulated Temperature sensors & Humidity sensors, and EV3 LEGO Mindstorms robots. The architecture is validated by concrete measurements on the prototype and by extensive simulations.


identifier: Raznahan, Mohammadmehdi  (2020) Numerical Simulation of the Nearshore Oil Behaviors Based on Computational Fluid Dynamics.  Masters thesis, Concordia University.

abstract:
Oil spills are a serious environmental problem. To better support risk assessment and pollution control for oil spills, a good understanding of oil transport in the environment is required. This information is essential for managing response priorities and preparing contingency and mitigating measures. This study focused on the numerical simulation of the nearshore oil behaviors based on computational fluid dynamics. Based on the Reynolds-averaged Navier-Stokes momentum equations for an incompressible viscous fluid and volume of fluid (VOF) method, a 3D numerical model of three-phase transient flow was developed.
It was found that the wave number, averaged flow velocity, and oil properties would affect the oil spread extent and the oil volume fraction for open water. The higher the averaged flow velocity and wave number, the lower the oil concentration, and the faster the oil's horizontal movement. The spilled oil may move to contact the seafloor by increasing the averaged flow velocity at the inlet boundary. By increasing the wave number, the oil would stay near the water surface. In nearshore, where the wave is the main seawater motion, the oil containment boom should be set preferentially to the direction of wave transmission for oil cleaning. It was also shown that by doubling the wave number and increasing the averaged flow velocity (ten times) simultaneously, the maximum oil volume fraction would be reduced by around 32%. Finally, it was found that water temperature had no significant impact on oil migration, and the impact of evaporation can be further considered in the future simulation.
In addition, this study showed that the presence of ice would make the spreading of spilled oil slower in horizontal direction because the ice can build natural barriers to oil movement. The higher the ice concentration, the slower spilled oil migrates in all directions, and the maximum oil volume fraction will vary by increasing the ice coverage on the water surface area. The wave frequency, the averaged flow velocity, and oil properties would affect the oil spread extent and the oil volume fraction. The dumping effect of the wave due to the presence of ice also makes the impact of this factor less critical than those in the open water.


identifier: Lezzar, Mohamed Yacine  (2020) Optimized Scheduling of Ultra-Reliable Low-Latency Communications Traffic for 5G Networks.  Masters thesis, Concordia University.

abstract:
The increasingly ubiquitous applications of Ultra-Reliable Low-Latency Communications (URLLC) require innovative solutions that can only be achieved through a flexible communication system such as the The Fifth Generation (5G) New Radio (NR). Recent studies on the resource allocation for URLLC have proposed the Grant-Free (GF) scheduling instead of the traditional high latency Grant-Based (GB) scheduling, adopted in 4G Long Term Evolution (LTE). Although the GF scheduling over shared resources offers reduced latency, the possibility of achieving the reliability requirement of URLLC may be compromised due to the increased likelihood of collisions. Therefore, we propose a solution for the uplink transmissions that is capable of realizing the reliability requirement in compliance with URLLC’s stringent latency budget.
The main strategy of the proposed solution is to transmit multiple uplink copies of the same packet, utilizing both dedicated and shared resources. In order to avoid additional delays, retransmissions are carried out independent of the conventional feedback from the Base Station (BS). Therefore, each packet is transmitted a pre-determined number of times, resulting in a fixed latency value for packets in the network. The network considered in this study consists of users with both periodic and sporadic traffic. Users in the network are grouped into classes according to their packet generation probabilities. Classes with high packet generation rates are characterized as periodic-traffic classes, while sporadic-traffic classes have low generation rates.
Users gain access to the available resources in the network via three different scheduling schemes. While all users access shared resources through GF scheduling, access to dedicated resources is done in two different ways, namely, Periodic Scheduling (PS) and GB scheduling. To avoid under-utilization of resources, the PS scheme is only assigned for users with high packet generation rates, while sporadic-type users access dedicated resources through the GB scheme. Although recent studies were disinclined towards the GB scheme due to its high latency, we show that the exploitation of 5G NR’s new scalable numerology results in significant reductions to GB’s latency, making it suitable for the URLLC use case. Following this latency examination, we present probabilistic expressions representing the reliability of our proposed solution.
The main contribution of this thesis to the available literature of URLLC is the presented system optimization. We optimize the system’s performance in terms of minimizing the required bandwidth or maximizing the supported traffic capacity, while satisfying the reliability requirements. Optimal performance of the system is achieved through determining the optimal allocation of resources between the considered scheduling schemes, as well as the optimal classification of user classes in the network as periodic-type or sporadic-type classes. In addition, we find the optimal packet length (for a fixed number of information bits) that results in the minimum amount of bandwidth required.


identifier: Savard, Christopher  (2020) The Politics of Resistance: Gentrification and Opposition in the Triangle.  Masters thesis, Concordia University.

abstract: Urban development and the gentrification that accompanies it have quickly become issues that cities face worldwide. Conventional theories surrounding the topic often state that development is a response to a market force. Academic work has focused mostly on how and why the process of gentrification takes place, but not the politics surrounding that process. It has also largely overlooked what happens when a community resists development, and if resistance is effective. This study serves to explore these issues in the case of the Triangle neighborhood in Montreal, what political forces allow gentrification to take place, and how communities resist. How residents and community groups have reacted to development in the Triangle was explored through interviews and reviewing documents. Resisting development may ultimately determine how the final product is shaped.


identifier: Mohammady, Meisam  (2020) Novel Approaches to Preserving Utility in Privacy Enhancing Technologies.  PhD thesis, Concordia University.

abstract:
Significant amount of individual information are being collected and analyzed today through a wide variety of applications across different industries. While pursuing better utility by discovering
knowledge from the data, an individual’s privacy may be compromised during an analysis: corporate networks monitor their online behavior, advertising companies collect and share their private
information, and cybercriminals cause financial damages through security breaches. To this end,
the data typically goes under certain anonymization techniques, e.g., CryptoPAn [Computer Networks’04], which replaces real IP addresses with prefix-preserving pseudonyms, or Differentially
Private (DP) [ICALP’06] techniques which modify the answer to a query by adding a zero-mean
noise distributed according to, e.g., a Laplace distribution. Unfortunately, most such techniques
either are vulnerable to adversaries with prior knowledge, e.g., some network flows in the data, or
require heavy data sanitization or perturbation, both of which may result in a significant loss of data
utility. Therefore, the fundamental trade-off between privacy and utility (i.e., analysis accuracy) has
attracted significant attention in various settings [ICALP’06, ACM CCS’14]. In line with this track
of research, in this dissertation we aim to build utility-maximized and privacy-preserving tools for
Internet communications. Such tools can be employed not only by dissidents and whistleblowers,
but also by ordinary Internet users on a daily basis. To this end, we combine the development of
practical systems with rigorous theoretical analysis, and incorporate techniques from various disciplines such as computer networking, cryptography, and statistical analysis. During the research,
we proposed three different frameworks in some well-known settings outlined in the following.

First, we propose The Multi-view Approach to preserve both privacy and utility in network trace
anonymization, Second, The R2DP Approach which is a novel technique on differentially private
mechanism design with maximized utility, and Third, The DPOD Approach that is a novel framework on privacy preserving Anomaly detection in the outsourcing setting.


identifier: Chen, Jinfu  (2020) Performance Regression Detection in DevOps.  PhD thesis, Concordia University.

abstract:
Performance is an important aspect of software quality. The goals of performance are typically defined by setting upper and lower bounds for response time and throughput of a system and physical level measurements such as CPU, memory, and I/O. To meet such performance goals, several performance-related activities are needed in development (Dev) and operations (Ops). Large software system failures are often due to performance issues rather than functional bugs. One of the most important performance issues is performance regression. Although performance regressions are not all bugs, they often have a direct impact on users’ experience of the system. The process of detection of performance regressions in development and operations is faced with challenges. First, the detection of performance regression is conducted after the fact, i.e., after the system is built and deployed in the field or dedicated performance testing environments. Large amounts of resources are required to detect, locate, understand, and fix performance regressions at such a late stage in the development cycle. Second, even we can detect a performance regression, it is extremely hard to fix it because other changes are applied to the system after the introduction of the regression.
These challenges call for further in-depth analyses of the performance regression. In this thesis, to avoid performance regression slipping into operation, we first perform an exploratory study on the source code changes that introduce performance regressions in order to understand root-causes of performance regression in the source code level. Second, we propose an approach that automatically predicts whether a test would manifest performance regressions in a code commit. Most of the performance issues are related to configurations. Therefore, third, we propose an approach that predicts whether a configuration option manifests a performance variation issue. To assist practitioners to analyze system performance with operational data, we propose an approach to recovering field-representative workload that can be used to detect performance regression.


identifier: Mohseni Ardehali, Nader  (2020) Hard Magnetorheological Elastomers: Experimental Characterization, Modeling and Application.  Masters thesis, Concordia University.

abstract: Magnetoactive elastomers also known as Magnetorheological elastomers (MREs) are solid analogue of the well-known magnetorheological (MR) fluids. MREs are smart composite materials made of elastomeric medium embedded with micron-sized ferromagnetic particles.    Unlike MR fluids which can mainly provide variable damping, MREs can provide simultaneous variations in damping as well as stiffness properties. Moreover, MREs do not exhibit limitation of MR fluids such as sedimentation of iron particles and leakage. Hard magnetorheological elastomers (H-MREs) are MREs in which the soft magnetic particles in conventional MREs are replaced by hard micron-sized permanent magnetic particles which provide magnetic poles inside the elastomeric medium. By applying the magnetic field in the same or opposite direction of the magnetic poles, respectively, the stiffness of the H-MREs can be increased or decreased. While conventional MREs have been widely studied during the past decade, very limited studies exist to demonstrate the response behavior of H-MREs. The objectives of the present research thesis are to 1- experimentally characterize the viscoelastic properties of H-MREs under varying operating conditions and applied magnetic field, 2- develop simple phenomenological models to predict the viscoelastic response behavior of MREs, 3- explore the potential application of H-MREs in an adaptive vibration isolator. For this purpose, H-MREs with 15% volume fraction of neodymium–iron–boron (NdFeB) magnetic particles have been fabricated and then tested under oscillatory shear motion using a rotational magneto-rheometer to investigate their viscoelastic behavior under varying excitation frequency and magnetic flux density. The influence of the shear strain amplitude and driving frequency are also examined under various levels of applied magnetic field ranging from -0.2 T to 1.0 T. Moreover, field-dependent phenomenological models have been proposed to predict the variation of storage and loss moduli of H-MREs under varying excitation frequency and applied magnetic flux density. The results show that the proposed model can accurately predict the viscoelastic behavior of H-MREs under various fields and operating conditions. Finally, a tunable vibration isolator based on H-MRE is developed and the influence of applying positive and negative magnetic fields on the transmissibility of the HMRE-based isolator is investigated.


identifier: Kuruvinashetti, Kirankumar  (2020) Arrayed Micro-Photosynthetic Power Cell: Modeling, Testing and Performance Enhancement.  PhD thesis, Concordia University.

abstract:
Recent developments in a microbial fuel cell (MFC) research have demonstrated that this electrochemical technique is an effective method of producing electrical power from organic compounds through microbial activities during the respiration process. This led to the development of micro-photosynthetic power cell (µPSC) that generates electrical power by harnessing solar energy through photosynthesis and the respiration process of photosynthetic organisms. Micro-photosynthetic power cells convert light energy to electricity using photosynthetic microorganisms such as microalgae and cyanobacteria. Herein we describe the readily producible inexpensive, and economically feasible novel fabrication of µPSC through a thin arrayed metal grid. Several key factors limiting the performance efficiency, including the optimal photosynthetic cell culture, light intensity, performance of the µPSC in the dark condition, are investigated in detail. These experiments suggested that high illumination may damage the photosynthetic pigments of photosynthetic organisms and lead to reduced performance. An illumination intensity of 2-5 µEm-2s-1 was found to be the optimal illumination necessary for the enhanced performance.
Nano-bio interactions enhanced the current limitation of the low power density of the µPSC through the incorporation of inorganic efficient light absorbers in the form of gold nanoparticles into photosynthetic cells. The photosynthetic parameters such as maximum quantum yield, operational quantum yield, photochemical quenching, excitation pressure were studied through a pulse width modulated fluorometry. The experimental results demonstrated that by incorporating gold nanoparticles of size 24 ± 2 nm of 50 µg/ml into 1 ml of algal cells, photosynthetic efficiency was improved by 30.2 %. The spectral characteristics with gold nanoparticle internalized algal cells studied through dark-field hyperspectral microscopy demonstrated a redshift of 120 – 130 nm depending upon size and aggregations compared to control algal cells. Indicating the biocompatible light-absorbing inorganic particles internalized into photosynthetic cells could improve the light absorption in the whole visible wavelength range. Results showed that the synergy between nanotechnology and living photosynthetic cells could increase natural photosynthesis functionality.  Through the amalgamation of gold nanoparticle concentration of 50 µg/ml into 1 ml of photosynthetic cells, the power density of the µPSC was increased by 46%.  The study provides an understanding of the interaction of inorganic nanoparticles and living cells, which will improve the performance of nano-bio hybrid photosynthetic systems for energy applications.
A novel method for diagnosing intracellular conditions and organelles of cells with Localized Surface Plasmonic Resonance (LSPR) by directly internalizing the gold nanoparticles into the cells and measuring their plasmonic properties through hyperspectral imaging is studied. For the proof of the concept, a well-established cell line, the HeLa cancer cell line, was utilized. This technique will be useful for the direct diagnosis of cellular organelles, which have the potential for cellular biology, proteomics, pharmaceuticals, and drug discovery.
In order to overcome the thermodynamic limits of performance of single µPSC, arrayed configurations such as series, parallel, and combinations of series and parallel configurations were studied in detail. Experimental results suggested that combinations of series and parallel configurations are ideal for real-time electrical loading conditions. In order to demonstrate the capability of the arrayed µPSC for low power application, the light-emitting diode of rating 2 V, 2 mA, and 1.7 V, 2 mA was powered both in light and dark conditions. The electrical equivalent model of arrayed µPSCs has been developed and validated with the experimental results. The model has successfully predicted the electrical loading conditions, polarization characteristics, and power- current characteristics for all the array configurations. The results indicated that the model could be utilized to predict the performance of all kinds of array configurations. Further, the economics of the µPSCs has been studied and compared with the well-established photovoltaic cell technology. The cost comparison of both technologies has been carried out for low (mobile battery) and ultra-low power (humidity sensors) applications. Currently, the photovoltaic technology outperforms the µPSC. Nevertheless, opportunities for improving the µPSC’s performance and niche applications have also been reported.
With this thesis, it has been demonstrated that the potential of harnessing energy from photosynthesis is enormous. In this thesis, simple novel fabrication of µPSCs, optimal operating parameter testing, the necessity of array configurations, and enhancement of power output by nano-bio interactions using gold nanoparticles, the feasibility of the µPSC in comparison with photovoltaic technology, arrayed electrical equivalent model has been demonstrated.  With this approach, we envision the µPSC to power low power applications such as mobile phones, e-readers, headphones, earphones, and ultra-low power applications such as humidity, temperature, and IoT sensors, in the future. One such prospective application to the automotive sector has also been provided. As the µPSCs could be installed indoors or outdoors and in any manner like vertical or horizontal, this could open a new avenue for low and ultra-low power applications.


identifier: Hassane, Omar  (2020) A Model Traceability Framework for Network Service Management.  Masters thesis, Concordia University.

abstract:
Automating the enactment of processes using model-driven methods and tools paves  the way  for streamlining or optimizing these processes. Establishing traceability in automated processes is instrumental in carrying out analysis of the process and the involved artifacts.

In this thesis, we propose a traceability information generation, visualization and analysis approach integrated with process modelling and enactment. A process model (PM) defined as an Activity Diagram has associated model transformations implementing the various activities and actions in the process. Enactment of the PM is carried out with the use of model transformation chaining in cooperation with model management means, in particular, megamodelling. We have incorporated both traceability in the small (at the model transformation level) and traceability in the large (at the PM level) in our approach. The traceability information is retained in the megamodel and forms the basis for traceability analysis of the enacted process. We have built a change impact analysis which allows the impact of a change in a model involved in the process to be assessed with the help of the derived megamodel.

We further extended our approach with the notion of intents. We propose the usage of intents at both the PM and model-transformation levels as part of our traceability information. We define intents as information representing the objective of the PM actions/activities and their implementations. Furthermore, we have incorporated traceability visualization support to visualize trace links relating models at different levels through the captured intents. The intent-enriched traceability information and the enhanced visualization enable semantically richer traceability analysis.
We applied our work to Network Service (NS) management in the context of the Network Functions Virtualization (NFV) paradigm.We believe automation of the orchestration and management of network services can progress rapidly with the help of model-driven engineering methods and tools. We applied our approach on a NS design process to analyze the impact of changing input models on output models as well as to show the benefits of intents not only in the context of this process, but also for the whole NS lifecycle management operations.

Our work is concretized in a tool, MAPLE-T, built as an Eclipse plugin. It extends MAPLE, an integrated process modelling and enactment environment.


identifier: Ansari, Mohd Safwan Ahmad Mohd Ibrahim  (2020) Hybrid Statistical and Deep Learning Models for Diagnosis and Prognosis in Manufacturing Systems.  Masters thesis, Concordia University.

abstract:
In today’s highly competitive business environment, every company seeks to work at their full potential to keep up with competitors and stay in the market. Manager and engineers, therefore, constantly try to develop technologies to improve their product quality. Advancements in online sensing technologies and communication networks have reshaped the competitive landscape of manufacturing
systems, leading to exponential growth of Condition Monitoring (CM) data. High-dimensional data sources can be particularly important in process monitoring and their efficient utilization can help systems reach high accuracy in fault diagnosis and prognosis. While researches in Statistical Process Control (SPC) tools and Condition-Based Maintenance (CBM) are tremendous, their applications
considering high-dimensional data sets has received less attention due to the complexity and challenging nature of such data and its analysis. This thesis adds to this field by designing a
Deep Learning (DL) based survival analysis model towards CBM in the prognostic context and a DL and SPC based hybrid model for process diagnosis, both using high dimensional data. In the
first part, we a design support system for maintenance decision making by considering degradation signals obtained from CM data. The decision support system in place can predict system’s failure
probability in a smart way. In the second part, a Fast Region-based Convolutional Network (Fast R-CNN) model is applied to monitor the video input data. Then, specific statistical features are derived from the resulting bounding boxes and plotted on the multivariate Exponentially Weighted Moving Average (EWMA) control chart to monitor the process.


identifier: Hou, Luyang ORCID: https://orcid.org/0000-0001-6958-7389 <https://orcid.org/0000-0001-6958-7389>  (2020) Advanced Mechanism Design for Electric Vehicle Charging Scheduling in the Smart Infrastructure.  PhD thesis, Concordia University.

abstract:
Electric vehicle (EV) continues to grow rapidly due to low emission and high intelligence. This thesis considers a smart infrastructure (SI) as an EV-centered ecosystem, which is an integrated and connected multi-modal network involving interacting intelligent agents, such as EVs, charging facilities, electric power grids, distributed energy resources, etc. The system modeling paradigm is derived from distributed artificial intelligence and modelled as multi-agent systems (MAS), where the agents are self-interested and reacting strategically to maximize their own benefits.

The integration, interaction, and coordination of EVs with SI components will raise various features and challenges on the transportation efficiency, power system stability, and user satisfaction, as well as opportunities provided by optimization, economics, and control theories, and other advanced technologies to engage more proactively and efficiently in allocating the limited charging resources and collaborative decision-making in a market environment. A core challenge in such an EV ecosystem is to trade-off the two objectives of the smart infrastructure, of system-wide efficiency and at the same time the social welfare and individual well-being against agents’ selfishness and collective behaviors. In light of this, scheduling EVs' charging activities is of great importance to ensure an efficient operation of the smart infrastructure and provide economical and satisfactory charging experiences to EV users under the support of two-way flow of information and energy of charging facilities.

In this thesis, we develop an advanced mechanism design framework to optimize the charging resource allocation and automate the interaction process across the overall system. The key innovation is to design specific market-based mechanisms and interaction rules, integrated with concepts and principles of mechanism design, scheduling theory, optimization theory, and reinforcement learning, for charging scheduling and dynamic pricing problem in various market structures.

Specifically, this research incorporates three synergistic areas: (1) Mathematical modelling for EV charging scheduling. We have developed various mixed-integer linear programs for single-charge with single station, single-charge with multiple stations, and multi-charge with multiple stations in urban or highway environments. (2) Market-based mechanism design. Based on the proposed mathematical models, we have developed particular market-based mechanisms from the resource provider’s prospective, including iterative bidding auction, incentive-compatible auction, and simultaneous multi-round auction. These proposed auctions contain bids, winner determination models, and bidding procedure, with which the designer can compute high quality schedules and preserve users’ privacy by progressively eliciting their preference information as necessary. (3) Reinforcement learning-based mechanism design. We also proposed a reinforcement mechanism design framework for dynamic pricing-based demand response, which determines the optimal charging prices over a sequence of time considering EV users’ private utility functions. The learning-based mechanism design has effectively improved the long-term revenue despite highly-uncertain requests and partially-known individual preferences of users.

This Ph.D. dissertation presents a market prospective and unlocks economic opportunities for MAS optimization with applications to EV charging related problems; furthermore, applies AI techniques to facilitate the evolution from manual mechanism design to automated and data-driven mechanism design when gathering, distributing, storing, and mining data and state information in SI. The proposed advanced mechanism design framework will provide various collaboration opportunities with the research expertise of reinforcement learning with innovative collective intelligence and interaction rules in game theory and optimization tools, as well as offers research thrust to more complex interfaces in intelligent transportation system, smart grid, and smart city environments.


identifier: Thike, Rajendra ORCID: https://orcid.org/0000-0002-4371-1895 <https://orcid.org/0000-0002-4371-1895>  (2020) Modeling and Parameter Measurement of Special Electric Machines.  PhD thesis, Concordia University.

abstract:
Recent engineering applications such as electric and hybrid electric vehicles require higher performance electric machines. Accordingly, there have been a significant increase on research on performance improvement of electric machines for transportation applications. Opposed to the conventional machines, new and innovative traction machines have higher power density, higher efficiency, faster dynamic response, wider speed range and higher reliability. The modern simulation and manufacturing tools made it possible to obtain the desired performance requirements in the new and special designs at reasonable cost. Due to the special designs to improve a certain output variable subjected to several constraints, it is required to have advanced machine models for control and operation of these machines. Moreover, parameter measurement of these special electric machines are gaining a significant interest due to new and advanced motor drive testing systems such as power hardware in the loop emulation which use a look-up table based machine model for fast and accurate solution of machine dynamics.
This PhD work develops a novel automated current control method to measure the parameters of special electric machines. In contrast to the existing parameter measurement method that applies a voltage pulse excitation to the test motor for flux linkage measurement, the current control method developed in this thesis uses a current pulse with closed loop control. While the voltage pulse method requires a higher sampling frequency for machines with lower time constants, the number of samples available during the transient for a fixed sampling rate can be modified by using a current pulse to measure the flux linkages. In addition, the use of a current pulse in closed loop makes it possible to measure the machine flux linkages at operating points unattainable with the voltage pulse method due to inverter dead time and device drops. This PhD work also develops a current controller design methodology for the developed parameter measurement method, which aids in the automation of the measurement process in a real time system.
This PhD research also develops an experimental method to obtain the static torque angle curves and torque-ripple of  synchronous reluctance machines (SynRM). The developed technique is used to study the performance of a SynRM using cold rolled grain oriented (CRGO) laminations against a regular SynRM using cold rolled non-grain oriented laminations. The SynRM using CRGO laminations are designed to have a higher saliency, and thus a higher torque per ampere and higher power factor. This thesis presents a comparative study of the torque performance of the CRGO and CRNGO SynRMs.
This PhD work also develops the mathematical model of an interior permanent magnet synchronous machine (IPMSM) with aligned magnet and reluctance torques. It is a new class of IPMSM also called shifted IPMSM, which is designed to have higher torque for lower magnet volume. The torque characteristics of the shifted IPMSM is different from conventional IPMSMs. For the analysis and operation of this new class of machine, a suitable mathematical model is lacking in the literature. The PhD work develops the mathematical model of the novel shifted IPMSM, and validates it using the experimentally obtained inductance, torque angle and torque-speed curves.
This thesis also develops a mathematical model of a novel hybrid variable flux machine (VFM) having rare-earth magnets in series with AlNiCo magnets for power hardware in the loop emulation applications. In order to emulate the VFM, emulation of the change in magnetization state is crucial. This thesis models the magnetization and demagnetization characteristics of the VFM as look-up tables. The current control method to measure the machine parameter proposed in this thesis is used to measure the flux linkage characteristics of the VFM, and a complete VFM model suitable for power hardware in the loop emulation is developed. The developed model is validated experimentally through the comparison of transient and steady state machine behavior.
The works presented in chapters 2 and 3 are primarily useful in generating the flux-linkages, inductances and torque look-up tables for behavioral model special electric machine models. Such a model is used in advanced model based controls that would not be possible with basic two axis machine models. Moreover, the developed techniques are useful in validation of finite element analysis simulation of special electric machines. Within the thesis, Chapters 4 and 5 used the techniques developed in chapters 2 and 3 to develop and validate the machine models of shifted interior permanent magnet synchronous machine and hybrid variable flux machine. The developed models are useful in drive operations of these motors using advanced control algorithms.


identifier: Luo, Tongdan  (2020) How Loyal are you to Media Content Subscription? A Cross-cultural Comparison of North America and China.  Masters thesis, Concordia University.

abstract:
This thesis is a cross-cultural study of eWOM, comparing the contexts of North America and China. Media content subscription, featured by invisibleness, is hard to predict before the subscription, thus eWOM becomes an externalization of the perceived quality of media content websites. Consumers from diverse cultural backgrounds may be affected to a different extent by positive and negative eWOM. This study also analyzes consumer behavior under the framework of the six Hofstede (2001) cultural variables: collectivism, power distance, uncertainty avoidance, long-term orientation, masculinity, and indulgence.
Electronic Word-of-Mouth(eWOM) has an impact on consumers’ perceived quality and brand loyalty of the website they subscribe to. This study will focus on both positive and negative eWOM to explore different patterns of the valence of eWOM by the following structure: first, analyze the mediation effect of perceived quality in the relationship between eWOM and brand loyalty; second, analyze the moderation effect of Hofstede’s six cultural variables.


identifier: Elkabalawy, Moaaz ORCID: https://orcid.org/0000-0001-6654-2281 <https://orcid.org/0000-0001-6654-2281>  (2020) Optimized Resource-Constrained Method for Project Schedule Compression.  Masters thesis, Concordia University.

abstract:
Construction projects are unique and can be executed in an accelerated manner to meet market conditions. Accordingly, contractors need to compress project durations to meet client changing needs and related contractual obligations and recover from delays experienced during project execution. This acceleration requires resource planning techniques such as resource leveling and allocation. Various optimization methods have been proposed for the resource-constrained schedule compression and resource allocation and leveling individually. However, in real-world construction projects, contractors need to consider these aspects concurrently.
For this purpose, this study proposes an integrated method that allows for joint consideration of the above two aspects. The method aims to optimize project duration and costs through the resources and cost of the execution modes assigned to project activities. It accounts for project cost and resource-leveling based on costs and resources imbedded in these modes of execution. The method's objective is to minimize the project duration and cost, including direct cost, indirect cost, and delay penalty, and strike a balance between the cost of acquiring and releasing resources on the one hand and the cost of activity splitting on the other hand.
The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting. The proposed method utilizes the fuzzy set theory (FSs) for modeling uncertainty associated with the duration and cost of project activities and genetic algorithm (GA) for scheduling optimization. The method has five main modules that support two different optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; sensitivity
IV
analysis module; and decision-support module. The two optimization methods use the genetic algorithm as an optimization engine to find a set of non-dominated solutions. One optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the other uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization method is coded in python as a stand-alone automated computerized tool to facilitate the needed iterative rescheduling of project activities and project schedule optimization.
The developed method is applied to a numerical example to demonstrate its use and to illustrate its capabilities. Since the adopted numerical example is not a resource-constrained optimization example, the proposed optimization methods are validated through a multi-layered comparative analysis that involves performance evaluation, statistical comparisons, and performance stability evaluation. The performance evaluation results demonstrated the superiority of the NSGA-II against the dynamic weighted optimization genetic algorithm in finding better solutions. Moreover, statistical comparisons, which considered solutions’ mean, and best values, revealed that both optimization methods could solve the multi-objective time-cost optimization problem. However, the solutions’ range values indicated that the NSGA-II was better in exploring the search space before converging to a global optimum; NSGA-II had a trade-off between exploration (exploring the new search space) and exploitation (using already detected points to search the optimum). Finally, the coefficient of variation test revealed that the NSGA-II performance was more stable than that of the dynamic weighted optimization genetic algorithm.
It is expected that the developed method can assist contractors in preparation for efficient schedule compression, which optimizes schedule and ensures efficient utilization of their resources.


identifier: Zhang, Chucen  (2020) Exploring the Dark Side of Hunger Marketing and Brand Hate: Differences across Hofstede Cultural Dimensions.  Masters thesis, Concordia University.

abstract: The hunger marketing strategy has been implemented by many manufacturers to stimulate customers purchase intentions. Previous literature has shown that scarcity appeals would influence customer decision making. However, the dark side of the hunger marketing strategy has not been fully explored. As a result, this study focuses on whether the excessive hunger marketing strategy is the cause of customer brand hate. Two comprehensive research models are proposed to explore how the need for uniqueness and Hofstede's (1980, 2001) six cultural dimensions influence the relationship between the hunger marketing strategy and brand hate. Results suggest the need for uniqueness positively influences the effect of hunger marketing strategy on brand hate. The results of the experimentation suggest that cultural values such as power distance, individualism, and masculinity have a moderating effect on the relationship between hunger marketing strategy and brand hate.


identifier: Al-Sakkaf, Abobakr ORCID: https://orcid.org/0000-0002-1072-5895 <https://orcid.org/0000-0002-1072-5895>  (2020) Sustainability Assessment of Heritage Buildings.  PhD thesis, Concordia University.

abstract:
Many existing rating systems such as LEED, CASBEE and ITACA have been developed to evaluate the performance of buildings from a sustainability perspective. These systems however vary in their assessment of sustainability since each system has been designed to evaluate several criteria based on its respective local context. None of the existing rating systems considers energy in its assessment nor proposes a definitive guideline for sustainability assessment of Heritage Buildings (HBs). This limits the processes of decision-making when considering the best renovation alternatives for HBs. Currently, 1092 locations are considered as heritage in the world, of which 77% and 23% are categorized as cultural and natural heritage locations, respectively  (Giovine, 2019).
The objectives of this research work were to: i) identify and study the criteria, factors and indicators that impact the sustainability of HBs; ii) design a hybrid, multi-criteria sustainability-rating model and scale for HBs; iii) determine sustainability-based energy savings for the life cycle phases of HBs; and iv) design an automated assessment tool and perform sensitivity analysis for the developed model. To accomplish the first objective, a literature review was done to analyze 12 major existing rating systems and identify the principal criteria, factors and indicators that affect sustainability of buildings. A questionnaire, targeted to experts in Canada and Saudi Arabia, was also prepared and used to determine the importance of the principal criteria, factors and indicators with respect to sustainability of HBs. To accomplish the second objective, sustainability model development for HBs involved the application of ‘fuzzy logic’ using Fuzzy TOPSIS to calculate the weights and indices of the model parameters. Information from the questionnaire was also useful to examine if the calculated weights reflect reality. To accomplish the third objective, expert responses, regarding the percent energy consumption in each of the six life cycle phases of HBs, were applied on real energy consumption and cost data from two case studies. The exact energy consumption and cost in each life cycle phase was then estimated and compared with simulated energy consumption and cost results from the newly developed model. To accomplish the fourth objective, a web-based graphical user interface was created to automate the assessment process. A webpage, built on Ruby on Rails (for the backend) and HTML, CSS and Javascript (for the front end), was built based on weight calculations. The proposed scale was also applied to obtain the sustainability rating for HBs. Sensitivity analysis for the sustainability model was performed using two case studies and multi-criteria decision-making methods such as Fuzzy TOPSIS, Simple Additive Weight SAW, Weighted Sum Model WSM, Weighted Product Model WPM, and Operational Competitiveness Rating Analysis OCRA. Two HBs, Murabba Palace (MP) and Grey Nuns (GN), were studied to determine weight values for each of the tested factors.
Results showed that energy was the most important factor for both HBs in the case studies, with a sum indicator index of 1.623 and 1.891 for MP and GN, respectively. Water use was of the least importance, with a sum indicator index of 0.121 and 0.055 for MP and GN, respectively. Moreover, the total Sustainability Assessment for Heritage Buildings (SAHB) was 48% and 63% for MP and GN, respectively. This corresponds to Unsatisfied and Satisfied for MP and GN, respectively, based on the established scale for the newly developed model (Unsatisfied – < 49%, Pass – 50-59%, Satisfied – 60-69%, Bronze – 70-79%, Silver – 80-89%, and Gold - >90%). The newly developed sustainability model will be beneficial to decision-makers, HB specialists, engineers, architects, and project managers in their efforts to improve the sustainability of their HBs. It will also beneficial in the planning of rehabilitation projects for HBs.


identifier: make_name_string expected hash reference  (2020) Stock return autocorrelation, beta, and data frequency.  Masters thesis, Concordia University.

abstract: In this thesis, I empirically test the autocorrelation function of stock returns and how the frequency of data affects the measurement of the beta of stock returns. Different data frequencies, from daily to monthly, of stock and market returns from the Center for Research in Security Prices over a 30-year period from 1983 to 2012 are used in the empirical analysis. I find that infrequent rebalancing generates a certain pattern in the autocorrelation function of stock returns and that return autocorrelations can switch sign and become positive. Furthermore, idiosyncratic risk strongly affects the detection of autocorrelation. In addition, the stock beta increases with the measurement time interval. The findings suggest that beta depends heavily on the shape of stock returns' autocorrelation function due to short-term stock reversal.


identifier: Khokhar, Rashid Hussain ORCID: https://orcid.org/0000-0002-2941-1239 <https://orcid.org/0000-0002-2941-1239>  (2020) Anonymizing and Trading Person-specific Data with Trust.  PhD thesis, Concordia University.

abstract:
In the past decade, data privacy, security, and trustworthiness have gained tremendous attention from research communities, and these are still active areas of research with the proliferation of cloud services and social media applications. The data is growing at a rapid pace. It has become an integral part of almost every industry and business, including commercial and non-profit organizations. It often contains person-specific information and a data custodian who holds it must be responsible for managing its use, disclosure, accuracy and privacy protection. In this thesis, we present three research problems. The first two problems address the concerns of stakeholders on privacy protection, data trustworthiness, and profit distribution in the online market for trading person-specific data. The third problem addresses the health information custodians (HICs) concern on privacy-preserving healthcare network data publishing.
Our first research problem is identified in cloud-based data integration service where data providers collaborate with their trading partners in order to deliver quality data mining services. Data-as-a-Service (DaaS) enables data integration to serve the demands of data consumers. Data providers face challenges not only to protect private data over the cloud but also to legally adhere to privacy compliance rules when trading person-specific data. We propose a model that allows the collaboration of multiple data providers for integrating their data and derives the contribution of each data provider by valuating the incorporated cost factors. This model serves as a guide for business decision-making, such as estimating the potential privacy risk and finding the sub-optimal value for publishing mashup data. Experiments on real-life data demonstrate that our approach can identify the sub-optimal value in data mashup for different privacy models, including K-anonymity, LKC-privacy, and ϵ-differential privacy, with various anonymization algorithms and privacy parameters.
Second, consumers demand a good quality of data for accurate analysis and effective decision- making while the data providers intend to maximize their profits by competing with peer providers. In addition, the data providers or custodians must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a two-fold solution: (1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semi-trusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup, and (2) we incorporate the Vickrey-Clarke-Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.
Finally, we address the concerns of HICs of exchanging healthcare data to provide better and more timely services while mitigating the risk of exposing patients’ sensitive information to privacy threats. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.


identifier: Ehsan, Sharifnia  (2020) Overcoming Healthcare Transportation Barriers: A Case Study.  Masters thesis, Concordia University.

abstract:
Transportation remains a major barrier in receiving cancer treatment in Canada. The situation is especially alarming for those living in rural areas and in the light of COVID pandemic, poses another risk in the long list of health challenges to patients with pre-existing conditions. In this dissertation we set out find a solution to this problem by providing a framework for a personalized healthcare transportation system tailored to the needs of this population.
A three-step approach is proposed. First, a review of literature and initiatives employed by global transportation providers is conducted to identify major methods used for healthcare industry. Second, a transportation strategy is proposed, and key performance indicators identified through analysis of data and interviews with industry best practices in order to determine key aspects of such operations having the most impact on the overall service level. Finally, a discrete event simulation is provided and tested through various scenarios to understand how such operations would behave in real life and how they react as the environment evolves through time. A case study of a major nonprofit organization for whom this strategy was originally outlined is provided for further context. In the end, the key findings from this research are formulated as a decision-making tool for future guidelines in managing similar operations.

Keywords: Transportation; Strategy; Simulation; Healthcare; Ride Sharing; Breast Cancer


identifier: Pourali Rahdari, Mehrdokht  (2020) Measuring and monitoring urban sprawl in Canada from 1991 to 2011.  Masters thesis, Concordia University.

abstract:
Urban population growth and the expansion of urban areas has resulted in numerous negative environmental impacts. In Canada, built-up areas increased by more than 150% between 1971 and 2011, significantly faster than the number of inhabitants. Much of this increase took the form of dispersed, low-density urban development commonly referred to as urban sprawl. However, serious attempts to rigorously quantify and compare urban sprawl across Canada are lacking. This thesis measures the degree of sprawl for all 34 Canadian Census Metropolitan area (CMA) and the 469 Census Subdivisions (CSD) located within the boundaries of the CMAs and assesses temporal changes in urban sprawl between 1991 and 2011. This thesis uses the metrics of Weighted Urban Proliferation (WUP) and Weighted Sprawl per Capita (WSPC) to quantitatively assess the degree of urban sprawl. The value of WUP answers the question of how strongly the landscape within each reporting unit is sprawled per km2. WSPC quantifies the amount that, on average, each inhabitant or workplace contributes to urban sprawl in a reporting unit. The results demonstrate that urban sprawl increased considerably in all CMAs between 1991 and 2011. Montreal scored highest in 2011 among the CMAs (18.24 UPU/m2), followed by Victoria (17.93 UPU/m2), Kitchener-Cambridge-Waterloo (17.70 UPU/m2), Vancouver (17.24 UPU/m2), and Toronto (16.75 UPU/m2). Between 1991 and 2011, the Victoria CMA experienced the highest increase in WUP among all the CMAs. In the first decade between 1991 and 2001, urban sprawl increased in all CMAs. CMAs also showed a clear, continuous increase in urban sprawl in the second decade (2001-2011), except for Guelph and Ottawa-Gatineau-ON, where it decreased. WSPC also increased in more than half (59%) of the CMAs between 1991 and 2011. Saint John CMA (NB) obtained the highest value in 2011, followed by Thunder Bay and Greater Sudbury. The lowest WSPC values were observed in Toronto, due to the lowest land uptake per inhabitant or job, followed by Montreal, Vancouver, and Calgary. The period 1991-2001 witnessed an increase in WSPC in most of the CMAs (76%). In contrast, the value of WSPC decreased from 2001 to 2011 in most (76%) of the CMAs. The results presented here are especially useful for environmental monitoring and sustainability monitoring and to guide future planning seeking to reduce urban sprawl and its negative impacts. Although some sustainable development policies and Transit-Oriented Development (TOD) plans have been established to control urban sprawl in several cities (e.g., Vancouver, Toronto, and Montreal), progress towards controlling urban sprawl in the Canadian CMAs overall has been weak and sprawl continues to be a major threat to sustainable land use in Canada. Increased efforts are needed to more consistently and more effectively monitor and control urban sprawl and to transition to more sustainable forms of development e.g., smart growth. The insights from this study are particularly relevant to urban, regional, and land-use planning, and to the planning of future transport infrastructure.

Keywords: Built-up area, Dispersion, Land up-take, Monitoring, Urban development, Urban growth, Urban permeation (UP), Urban Sprawl, Utilization density (UD), Weighted urban proliferation (WUP), Weighted Sprawl per Capita (WSPC)


identifier: Kochukrishnan, Aneesh  (2020) Assessment of the impact of climate change on Fraser River low flows.  Masters thesis, Concordia University.

abstract: During the winter season, rivers in cold regions typically carry low discharges. Long-term forecast of river low flows is of practical importance. For example, authorities need the forecast to decide on water resources allocations and permits of the maximum allowable waste-effluent discharges from the land-based industry into a receiving river. Low river flows have important implications for river water quality and the health of aquatic life. Previously, there have been many investigations of the influence of climate change on river discharge, both low and high flows. Some studies of the Fraser River in British Columbia, Canada, dealt with the influence of climate change on flow and water temperatures. However, there is a lack of studies that consider the impact on low river flows under climate change scenarios. This study aims at improving the understanding of the impact of rising temperatures due to climate change on the low flows of the Fraser River. Specially, this study will reveal how the magnitudes of historic Fraser River low flows are related to freezing temperatures and will answer the question of to what extent low flows will change in response to projected changes in atmospheric temperature. The scope of work includes statistical analyses of the correlation between historic observations of Fraser River flows and watershed air temperatures over a time period of more than 100 years. The temperature data input is derived based on averaging values from 52 stations in the Fraser River basin. The variations in flow discharge with varying temperatures show uncertainties over the years. This study considers the cumulative freezing and thawing effects of fluctuating temperatures and divides the observed winter low flows into a lower limb and an upper limb. The correlation between discharge, Q, and temperature is established on the basis of the lower limb, yielding Q as a function of cumulative temperature, , in terms of z scores of the two variables. Global land and ocean surface temperature anomalies in the historical data are noted in several of the historic years, but their influence is removed while establishing this Q- relationship. A confidence bound relationship between flow and temperature is established for the thawing days, where the flow increases from its lowest value. The Q- relationship is further applied for forecast of future low flows, with input of temperatures from six Global Climate models (GCMs) with Representative Concentration Pathways (RCPs) 4.5 & 8.5 of the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset. Long term forecast of river low flows is obtained. The results show an increase in the minimum flow discharge up to 48% under high emission scenarios by the end of the 21st century. Under low emission scenarios, the minimum flow discharge can increase by 11%. The methods developed in this study can be applied to other cold region rivers. This is useful for addressing the issue of climate change impacts on river low flows, making necessary adjustments to the hydraulic design of water resources infrastructures, and planning the protection of aquatic life.


identifier: Kheshtgar Darvazeh, Salar ORCID: https://orcid.org/0000-0002-4903-4821 <https://orcid.org/0000-0002-4903-4821>  (2020) 3-D simulation of suppressed vortex shedding from bridge pier at high Reynolds number using attached splitter plate.  Masters thesis, Concordia University.

abstract:
Turbulent flow around in-stream bridge piers in open channels is complicated, with vortices and eddy motions of various length and time scales. These vortices cause significant channel-bed erosion and sediment transport, known as the scour problem. Consequences of scour include bridge failures, which can cause losses of property and, in some cases, of human life. Sediment scour from around the bridge piers has been a primary reason for all bridge failures and has incurred costs of millions of dollars to repair and rebuild. Researchers have tried to find ways to suppress vortices and reduce scour. They have suggested active and passive methods of suppressing vortices. One of the practical passive methods is to attach a splitter plate to the pier on either the downstream or the upstream side of the pier. \par
  This research uses the computational fluid dynamic (CFD) method to simulate three-dimensional turbulent flow around a cylinder with or without an attached splitter plate. The objectives are to understand how effective the attached splitter plate is in reducing vortices, Turbulence Kinetic Energy (TKE), channel-bed shear stress around the cylinder, and to explore the optimal splitter plate length and direction. So far, detailed knowledge of the above-mentioned aspects is incomplete. This study provides a comparison of performance between splitter plates attached on the downstream and upstream side and having different lengths. The CFD simulations used the mesh-based numerical method, with sufficiently fine resolutions for flow regions around the cylinder and near the channel-bed in order to capture detailed turbulence structures. The simulations solved the three-dimensional unsteady Navier-Stokes equations. The scope of work covers an assessment of the suitability of the shear-stress transport (SST) K-ω, the K-ω model, and the K-ɛ model for turbulence closure, as well as sensitivity tests to ensure the independence of numerical results on mesh configuration and time step.

  The results show that a splitter plate fitted on the downstream or the upstream side of the cylinder can reduce drag and lift force coefficients. In particular, a downstream splitter plate with a longitudinal length of the pier diameter is the most desirable for minimizing the force coefficients. The downstream splitter plate can reduce TKE and channel-bed shear stress values by 38.7% and 32.81%, respectively. The downstream splitter plate shifts the peak TKE region further downstream, which has positive effect for in-stream pier stability. The force coefficients and TKE value are shown to compare well with the results from other studies reported in the literature. Splitter plates are shown to reduce cross-channel velocities in the channel-centre plane, compared to the case of no plate. The findings from this research are of practical values for the safe protection of bridge piers.


identifier: Benwell, Emma  (2020) Canadian Regulatory Standards and Governance of Children’s Advertising Practices.  Masters thesis, Concordia University.

abstract: Entrenched in signifying practices, advertising is more than a brand selling its products or services to an audience; it is an indication of wider social and cultural structures, relationships, and processes. For children, advertising has invariably deep consequences, in that its ubiquity can form the social contours of childhood and act as a site for broad cultural negotiations. Through a historical survey of children’s cultural sentimentalization and the simultaneous formation of their hypercommercialized media environment, this thesis is attentive to the role that Canadian public policy and legislation play in regulating child-directed commercial messaging, and how this governance fundamentally shapes, and is shaped by, discursive tropes of the child consumer. Emphasis is placed on investigating how boundaries of contemporary product placement regimes are set, as they operate across both broadcast television and streaming services in Canada. This research also contemplates a more general inquiry into policies pertaining to the food and beverage industry. As the Canadian media landscape is the focus of this research, Advertising Standards Canada (“ASC”) and the Canadian Radio-television and Telecommunications Commission (“CRTC”) are examined to highlight the complicated and political nature of policy-making. Doing so acknowledges the ways in which institutions draw on social categories, resources, and meanings, which simultaneously mould the individual and their relationship with the state. Competing jurisdictions, namely the British and American industries, are used as points of comparison.


identifier: Fang, Xiaoyi  (2020) A Discrete and Hybrid Approach to Predicting Diametrical Errors in Slender Shaft Turning.  Masters thesis, Concordia University.

abstract: Slender shafts have a high length-to-radius ratio, low rigidity, and are often machined on a lathe to comply with the tight tolerance requirement. The machined accuracy for slender shafts, which is reflected in its diametrical deviations, is very sensitive to forces exerted by the cutting tool. To compensate for such errors effectively, this research aims to predict the diametrical deviations in slender shafts turning process efficiently and accurately. First, based on the geometric principles in turning processes, a mathematical model is built, in order to relate the given depth of cut and the shaft deflection due to the force of the cutting tool to the diametrical deviations. Then a novel finite element model considering the practical machining situation is developed to solve the aforementioned mathematical model. Compared with traditional finite element methods, the novel method addresses the interaction between the depth of cut and the cutting force throughout the machining process. A discretization method is employed to handle this coupled interaction. The approach to diametrical deviations prediction is verified with the experimental data and situations for various machining parameters and stock materials are discussed. The approach is also extended to the generic case involving workpieces with different diameters features along the shaft.


identifier: Ling, Chen  (2020) Low-Power and Low-Cost Modular Smart Sensor Platform with Automatic Node Networking and Information Management.  Masters thesis, Concordia University.

abstract: The development of industry and computer networks and the requirements of automation in daily life have led to the need for greater networking capability between smarter computer programs, services, and environments. In the past few decades, because of the rapid development of integrated circuits, the computing ability of chips increased rapidly, and the size of peripheral components and devices decreased rapidly. Nowadays, computer systems must handle far more than single calculations, and people expect them to be increasingly smarter to make their work easier. Systems are based on a modular design to respond rapidly to changing requirements while consuming less energy. Companies have developed many kinds of devices and software to satisfy various requirements, including environmental sensing, data processing, and automatic controlling. These products provide hardware and software to help researchers collect various kinds of environmental data. However, while not all products facilitate remote data collection, those that do often entail increased communication costs and power consumption, especially when deploying many devices. Many public places have been closed in 2020 due to COVID-19, which poses new challenges to data collection, including gathering environmental data when access to the site is prohibited and how to transfer data via cellular networks while minimizing costs and power consumption. This thesis proposes a design that encompasses hardware and software that will solve the problems related to remote data collection, edge computing, remote system control, low power data collection, the modular system, data security, and backup. The ultimate goals include reducing the costs of system maintenance, minimizing the difficulties of data collection and processing, simplifying system configuration, and controlling and ensuring data integrity.


identifier: Dastpeyman, Samaneh  (2020) Spatiotemporal tracking of heme-bound and heme-free yeast cytochrome c peroxidase in live cells and probing its regulation of the H2O2 stimulon at the proteome level.  PhD thesis, Concordia University.

abstract: Independently of its peroxidase activity, yeast cytochrome c peroxidase (Ccp1) functions in H2O2 sensing and signaling, including regulation of heme catalase A, Cta1. Since the maturation of Cta1, a highly efficient H2O2 detoxifying enzyme, involves the recruitment of Ccp1’s heme in respiring yeast mitochondria, I aimed to track the heme status of Ccp1 in live cells spatiotemporally. To do this, I had to develop a tool to investigate when and where an endogenous heme-binding protein such as Ccp1 is heme-loaded or heme-free in live cells. As described in Chapter 2, analysis of GFP fluorescence lifetimes by time-correlated single photon counting (TCSPC) indicates that heme efficiently quenches green fluorescent protein (GFP) in a recombinant Ccp1-GFP fusion protein in vitro and the amplitude of the quenched lifetime increases linearly with heme binding to Ccp1. Thus, in Chapter 3, I used fluorescence lifetime imaging microscopy (FLIM) of cells chromosomally expressing the same Ccp1-GFP fusion under the control of Ccp1’s native promotor to show that Ccp1-GFP is fully heme-loaded and resides in the mitochondria of two-day respiring cells but more than half of Ccp1-GFP is heme-free and extra-mitochondrial in seven-day respiring cells. Since Ccp1 is the first heme-based H2O2-sensing protein to be identified and its W191F variant behaves as an amplified H2O2 sensor, the second goal of my research was to compare the H2O2-responsive proteins regulated by Ccp1 and Ccp1W191F in cells. Using mass spectrometry-based label-free proteomics, a switch of cellular activity from biosynthesis to defense and a redirection of carbohydrate metabolism to NADPH and trehalose production were found in both yeast strains on H2O2 challenge as reported in Chapter 4. However, cells expressing Ccp1W191F mounted a significantly more sustained response to H2O2 and the in vitro experiments described in Chapter 5 suggest that Ccp1W191F is a dedicated H2O2 sensing/signaling protein in cells. Overall, my research findings confirm that Ccp1 functions as a heme-donor protein in cells and that it acts as a regulator of the H2O2 stimulon.


identifier: Tahaoglu, Cagdas ORCID: https://orcid.org/0000-0003-0566-1679 <https://orcid.org/0000-0003-0566-1679>  (2020) Three Essays on Financial Markets.  PhD thesis, Concordia University.

title: Three Essays on Financial Markets

abstract:
This dissertation consists of three essays that address recent topics in financial markets that concern for scholars, policymakers, and investors. The first essay examines the benefits of international diversification for US investors, while accounting for market development, corporate governance, market cap effects, and structural change across countries over period August 1996 –July 2013. Improved risk adjusted returns are obtained from a diversified portfolio consisting of a mix of developed and emerging countries. Additionally, we find that diversification benefits are not significant for most of the small-cap foreign assets when an investor already holds position in corresponding countries large-cap assets. Diversification benefits based on the governance effectiveness of a country’s companies are not ubiquitous. We find that economically significant improvements in risk-return performance can be attained by adding large caps of developed countries with high and low overall Governance Metrics International (GMI) ratings and large and small caps of emerging countries with low overall GMI ratings to the investment universe containing the assets of common law developed countries. However, diversification benefits are economically significant only for large and small caps of low GMI emerging countries when short selling is not allowed.

The second essay looks at the market impact of recent regulatory changes in Canada that provide for trading halts on individual stocks that experience large upside or downside movements. The focus is on all stocks traded on the Toronto Stock Exchange since the inception of the single stock circuit breaker rule (SSCB) in February 2012, to replace the short-sale uptick rule. The results support pricing efficiency: material information that caused the circuit breaker is incorporated in stock prices on the day of the halt (neither overreaction nor underreaction), with no decline in market liquidity. Using trade-by-trade data constructed on 5-minute trading intervals, we refine the daily results, and show that shocks in realized volatility are focused in the ten-minute trading interval surrounding the halts. While circuit breakers provide a limited “safety net” for investors when their stocks are subject to severe volatility, they do not provide for a quick turnaround for stocks experiencing severe price decline events.

The last essay re-examines the historical vs implied volatility spread anomaly, reported by Goyal and Saretto (2009) using a second-order stochastic dominance (SSD) criterion. The approach incorporates transaction frictions, and is robust to model specification problems, return distributions, as well as preferences. It is found that option trading frictions such as cash collateral requirements and option trading costs significantly reduce but do not eliminate returns to a long-short straddle trading strategy pre-2006 period. However, the anomaly disappears after 2006, consistent with market efficiency. The SSD test results confirm the findings.


identifier: Lichocki, Mitchell  (2020) Optimal Sampling-Based Trajectory Planning For Autonomous Systems in Urban Environments.  Masters thesis, Concordia University.

abstract: Motivated by autonomous aerial vehicles, this thesis provides a methodology for optimal trajectory planning of affine systems in non-convex environments. The resulting approximation of the optimal trajectory can then be provided to a flight controller as a reference trajectory, which compares the actual state of the system with the reference trajectory and performs the necessary control input corrections. More specifically, a modified trajectory planner inspired by Kinodynamic RRT* is presented to solve optimal control problems for input constrained affine systems with non-convex state spaces. As a result, if a solution is obtained then the solution is guaranteed to verify the state and control input constraints of the problem. Additionally, a randomized sampler function is proposed for Kinodynamic RRT* using a Gaussian distribution across the system’s state space. When the distribution is adequately sized lower cost approximate solutions of the optimal trajectory problem is obtained in less computation time when compared with other methods in the literature. The results are successfully applied to optimal control problems for an affine double integrator with drift that is subject to a maximum control input magnitude in non-convex environments.


identifier: Yang, Sidi  (2020) Uncertainty Quantification and Estimation on Medical Imaging Classification Tasks.  Masters thesis, Concordia University.

abstract:
This thesis presents an uncertainty quantification (UQ) system on medical classification imaging tasks and its practical use. Deep Neural Networks have shown tremendous success in numerous AI-related fields, for example, object detection, recognition, and health care. However, despite Deep Neural Networks exhibiting remarkable performance, we usually can not guarantee the modelling predictions to be absolutely correct. Therefore, estimation and quantification of uncertainty have become an essential parameter in Deep Learning practical applications, especially in medical imaging. Measuring uncertainty can help with better decision making, early diagnosis, and a variety of tasks.

In this thesis, we explore uncertainty quantification (UQ) approaches and propose an uncertainty estimation system for general medical imaging classification tasks. In experiments, we apply the UQ system for three medical imaging databases, including All-IDB2 (an acute lymphoblastic leukemia database), SARS-CoV2 (a coronavirus disease 2019 database) and BreaKHis (a breast cancer histopathological imaging database). Besides, we discuss how to apply UQ methods to obtain more information on the database and its modelling. We can capture the samples with the  uncertainty values and predict the most uncertain category. We also discover that we can receive more accurate results than initial modelling results by removing a percentage of data with higher uncertainty results. In summary, we find great potential for UQ research on complex medical classification tasks and consider it to become probably one of the future's essential research directions.


identifier: Su, Hongyu  (2020) Effect of Soft Ground Tunneling-Induced Displacements on Railway Embankments.  Masters thesis, Concordia University.

abstract: With the rapid development of urban infrastructure, certain transportation lines, utilities and pipelines are needed to be excavated under operating railway lines. When a tunnel under a railway is excavated, it will inevitably cause disturbance to the track structures, and the disturbance could influence the safety of railway operations. Consequently, the alleviation of ground surface displacement is of great significance to ensure the safety of both railway operations and tunnel construction. This thesis is a fundamental study of the surface displacements due to the construction of both shallow (near-surface) and deep (away from the surface) tunnels. The analysis of displacement along the surface of railway embankments is performed via two-dimensional finite element modeling. The freight train operating speed, tunnel diameter and tunnel depth are the three key factors that affect the surface displacement. The results illustrate that a 3 m diameter tunnel at depths greater than 3 m or a 4 m diameter tunnel at any depth greater than 16 m can be constructed beneath an existing railway without significantly affecting the safety of railway operations by considering subsidence control standards. Thus, this thesis contributes to determination of the maximum displacement of railway embankments induced by tunnel excavation as a function of various factors considered. Also, the findings of this thesis can help to guide future tunnel design and displacement control measures for excavations under operating freight railway lines.


identifier: Shrivastava, Mithun  (2020) The Impact of Rating Favorability on Purchase Intentions for Private Label Brands.  Masters thesis, Concordia University.

abstract: This research examines whether rating favorability for private label products impacts product attitude and purchase intentions. Three experimental studies provide converging evidence of an effect of rating favorability on purchase intentions for private label products that is fully mediated by product attitude. In the first experiment, our findings revealed a significant indirect effect of rating favorability on purchase intentions, completely mediated by product attitude; however, no significant pattern in direct effects or interactions involving rating frequency emerged. The second experiment established the robustness of first experiment’s results, and provided support for generalizability of findings to a different brand and a new set of products associated with higher prices. The third experiment examined the moderating role of brand familiarity and product novelty; findings are consistent with experiment 1 and 2 outcomes, and revealed no significant direct or interaction effects. Thus, consumers’ lack of motivation for information processing for low-involvement products leads them to the peripheral route of persuasion with reference to Petty and Cacioppo's Elaboration Likelihood Model. However, the finding that for consumer-packaged goods offered by private labels, rating frequency, brand familiarity, and product novelty do not influence consumers’ purchase intentions is contrary to previous findings, especially involving experience products and established brands. The current research suggests that private labels are unique with reference to the direct and indirect effects of rating favorability on purchase intentions, and discusses theoretical and managerial implications. Future research into the generalizability of these findings to other product categories (e.g., apparel, electronic goods, etc.), and a comparison to national brands may be fruitful.


identifier: Vass, Brian  (2020) Sunday Morning.  Masters thesis, Concordia University.

title: Sunday Morning

abstract: Sunday Morning is a memoir of eight chapters, each of which unfolds the story of its author’s life, a meditation on the nature of character and identity, and reflection on, or demonstration of, aesthetic and religious strategies for preserving and modeling the self.


identifier: Shayegan, Zahra  (2020) Modification of Titanium Dioxide for Photocatalytic Degradation of Indoor Volatile Organic Compounds: Under UV and Visible Light.  PhD thesis, Concordia University.

abstract:
The indoor air quality (IAQ) concern has risen since people spend most of their time (>90%) in indoor environments. Volatile organic compounds (VOCs) are categorized as a major group of gas pollutants. Indoor VOCs, known as hazardous compounds with several proven adverse health effects. Among various purification techniques, a heterogeneous photocatalytic oxidation process (PCO) is a promising technology for removing indoor VOC contaminants. Titanium dioxide (TiO2) is the leading candidate for PCO given its unique properties. However, no TiO2-based photocatalysts completely satisfy all practical requirements, considering the photoexcited charge carriers’ short lifetime and a wide band gap requiring ultraviolet (UV) radiation. Moreover, the application of PCO for VOCs degradation is greatly hindered at high humidity levels.
Herein, TiO2 modification techniques that include approaches for overcoming the inherent TiO2 limitations and improving the photocatalytic degradation of VOCs are studied. In this research, strategies for improving TiO2 photocatalyst activities by doping with different metal and/or non-metal ions as well as surface modification have been examined.
Accordingly, the adsorption capacity and photocatalytic activity of P25 and surface fluorinated P25 coated on nickel foam were evaluated for VOCs removal. In addition, the photoactivity of visible-light-driven photocatalysts including; anatase/rutile carbon-doped P25, anatase/brookite cerium-doped TiO2, and anatase/brookite iron-doped TiO2 coated on nickel foam were evaluated for degradation of VOCs under both UV and visible light irradiation. Surface fluorination was then applied to reduce the surface hydrophilicity of Ce-TiO2 and Fe-TiO2 photocatalysts with the optimum Ce and Fe contents. Notably, their photocatalytic performance was investigated in continuous flow mode-of-operation reactors with small residence time, different relative humidity levels, and low-level inlet contaminant concentration. These techniques can improve PCO performance through the following mechanisms: i) by introducing an electron capturing level in the band gap that would generate some defects in the TiO2 lattice and help capture charge carriers and can also be excited under visible irradiation; ii) by slowing down the charge carrier recombination rate and increasing VOCs degradation; and iii) by reducing the surface hydrophilicity, which increases VOCs’ adsorption capacity at high humid conditions.


identifier: Ahmed, Shakil  (2020) Real-Time Interactive 3D Reconstruction of Indoor Environments With High Accuracy.  Masters thesis, Concordia University.

abstract: 3D registration of depth images has been studied extensively in the past. With emergence of low cost RGB-D cameras, many applications have emerged. Yet, the quality of alignment has much to improve. It remains a challenge to create a 3D model of the environment with high accuracy which could be used for engineering applications. Moreover, challenging scenarios where features are scarce, handling large areas as scene, enabling the user to freely roam around the scene for image acquisition in a practical manner, guiding the user in taking better images and accomplishing all of this with low cost RGB-D camera in real time is a subject which is yet to be improved. In this thesis, we address all this challenges by designing and implementing a mobile system that rely on markers. Our system detects and matches markers in real-time with very low CPU load. Moreover, this mobile 3D reconstruction system is interactive which enables a user not only to move freely while doing 3D reconstruction, but also makes the user aware of current status of 3D reconstruction in real-time.


identifier: Oyewunmi, Oyejide Damilola  (2020) Development of a Dual-Modal Microfluidic Paper-Based Analytical Device for the Quantitative and Qualitative Detection of The Total Hardness of Water.  Masters thesis, Concordia University.

abstract:
A dip-and-read microfluidic paper-based analytical device (μPAD) was developed for the qualitative and quantitative detection of the total hardness of water. To create well-defined hydrophobic barriers on filter paper, a regular office printer and a commercially available permanent marker pen were utilized as a quick and simple technique with easily accessible equipment/materials to fabricate μPAD in new or resource-limited laboratories without sophisticated equipment. After a wettability and barrier efficiency analysis on the permanent marker colors, the blue and green ink markers exhibited favorable hydrophobic properties and were utilized in the fabrication of the developed test devices. The device had five reaction and detection zones modeled after the classification given by the World Health Organization (WHO), so qualitatively it determined whether the water was ‘soft’, ‘moderately hard’, ‘hard’, or ‘very hard’ by changing color from blue to pink in about 3 min. The device was also used to introduce an alternative colorimetric reaction for quantitative analysis of the water hardness without the need for ethylenediaminetetraacetic acid (EDTA) and without compromising the simplicity and low cost of the device. The developed μPAD showed a calculated limit of detection (LOD) of 0.02 mM, which is at least 80% less than those of commercially available test strips and other reported μPADs, and the results of the real-world samples were consistent with those of the standard titration (with EDTA). In addition, the device exhibited stability for 2 months at room and frigid condition (4 °C) and at varying harsh temperatures from 25 to 100 °C. The results demonstrate that the developed paper-based device can be used for rapid, on-site analysis of water with no interferences and no need for a pipette for sample introduction during testing.
A mathematical estimation of the flow of liquid water and blood serum on the fabricated paper device was computed using a geometrically modified version of the Lucas-Washburn equation to predict the signal time of the paper sensor during each test. The estimation correlated excellently with experimental data and observation, hence making the modification of the Lucas-Washburn equation valid specifically for the fabricated μPAD.
Finally, all-inclusive pullulan tablets were fabricated as an alternative analytical platform to detect the total hardness of water. The assay was used to compute a calibration curve which can be used to quantify the total hardness of water in about five minutes by just dropping the required number of tablets in the water sample, and a limit of detection of 0.0140 mM was achieved.