bluuebunny/crossref_metadata_2025 huggingface dataset:
One-step Non-uniform Photolithography System for Three-dimensional Conically Shaped Microwell Fabrication
Authors: Manzoor Ahmad Ali
Published: March 2024

Conical microwells are utilized in a wide range of biological applications such as cell spheroid formation, drug screening, cancer diagnostics, and three-dimensional oncology models. The shape of a microwell is an important parameter that defines its applicability. Currently, there exists no facile fabrication method for the creation of such conically shaped microwells. In this study, we develop a new platform based on non-uniform photopolymerization that effectively facilitates the synthesis of three-dimensional (3D) polymer-based conical microwells on a glass substrate in a simple 2D microfluidic channel. We also numerically investigate the parabolic curvature formation of microwells using non-uniform photolithography (NUPL) via incorporation of spatiotemporal free-radical diffusion into the modeling of photopolymerization to gain insights into its key mechanisms. We also perform numerical simulations to model the 3D-shape tuning ability of NUPL for the microwell synthesis through a variation of UV light intensity induced by the presence of opaque materials. The result of this study leads to characterize the morphological microwell shape change experimentally through a variation of UV light intensity and fluid opacity. Simply by manipulating the non-uniformity of the incident UV light, we are able to fabricate 3D V-shaped microwells with various bottom shape features. Notably, our method allows one to optimize and design polymeric microwells with shape features tuned to their application.
Co-Digestion of Primary Sludge and Waste Activated Sludge for Methane Production
Authors: Mahdi Hassan
Published: March 2024

The ratio of primary sludge (PS) and thickened waste activated sludge (TWAS) is a vital factor in the production of digester gas in the wastewater plants. Many wastewater plants do not track sludge ratio, but instead, use a specific monthly or annual sludge ratio that is calculated as an average for their processed PS and TWAS. Lab scale batch biochemical methane potential (BMP) and semi-continuous experiments were used to study different PS and TWAS ratios in a municipal wastewater treatment plant. The batch BMP study showed that the mixing ratio 67% to 33% (PS:TWAS) was the optimum ratio for the highest digester gas yield of 209 mL CH 4g TCOD. The semi-continuous study showed that the ratio 72% to 28% (PS:TWAS) at 15 days hydraulic retention time was better than the ratio 67% to 33% (PS:TWAS) in producing higher yield (188 mL CH /g4TCOD compared to 163 mL CH /g TCOD4 respectively).
Decision-making Model of Distribution Watermains Renewal With Lining and Replacement Techniques
Authors: Tsang Chun Man
Published: March 2024

This project aims to formulate a Life Cycle Cost-based decision-making model for watermains renewal with lining and replacement techniques in consideration. The model enables asset managers in municipalities in Ontario, Canada, to fulfill the legislation mandates of asset management planning via setting levels of service, formulating lifecycle activities, and putting watermains management into financial planning. It also addresses concerns about the uncertainty that entails the current age-based condition estimation method adopted by most of the municipalities in Canada. The model incorporates five key components and is developed into a Microsoft Excel-based planning tool. The application of the tool is then demonstrated in a case study with a hypothetical network. The results have shown that asset managers could address the legislative requirements on levels of service, lifecycle activities, and financial planning and tackle some of the problems faced in budgeting exercise with a constrained investment.
Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Link-level
Authors: Zhang Shuwang
Published: March 2024

As a means to help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have became a key focus of many researchers. The appeal is that the predictive models will inform policymakers, and hopefully, in turn, they will bring about systematic changes. Since the transportation sector is constantly among the top GHG emission contributors, substantial effort in the field has been going into building more accurate and informative GHG prediction models. In this work, we seek to establish a predictive framework of GHG emissions at the road segment or link level of road networks. The key theme of the framework centers around model interpretability and actionability for high-level decision-makers. The main model adopted in this framework is a Discrete Choice Model (DCM). We show that, for the first time, DCM is capable of predicting link-level GHG emission levels on road networks in a parsimonious and effective manner. We also argue that since the goal of most GHG emission prediction models focuses on involving high-level decision-makers to make changes and curb emissions, the DCM-based GHG emission prediction framework is the most suitable framework to high-level decision-makers. The nature of the model framework can provide the decision-makers with ease and clarity to address the high GHG emission level with immediate and impactful strategies.
Wind Tunnel Testing on Low Rise Bunkie Structures and Its Application as Affordable Storm Shelters
Authors: Ghazal Tarek
Published: March 2024

In Canada, the number of wind-related damage to structures and losses in lives keeps increasing due to climate change. Residents of rural areas are more exposed to those fatalities due to the lack of storm shelters. Storm shelters can provide reliable protection to people during high-intensity wind events and can also be used during the re-construction phase. Currently, people in rural areas use Bunkie structures for guest accommodation and equipment storage only. However, it would be beneficial if those structures could also be used as storm shelters during extreme wind events. This dissertation focuses on wind tunnel testing of common Bunkie structures used in Ontario to assess their aerodynamic performance under boundary layer and downburst flows, to be used as storm shelters. First, a turbulence generating system was developed at Ryerson University wind tunnel in order to model boundary layer flows encountered in sparse and built environments (i.e., open, suburban, and urban). This was designed by employing Computational Fluid Dynamics Large-Eddy simulations, followed by validation tests at Ryerson University wind tunnel. This system was also used to develop a downburst generating system consisting of multiple rotating louvers to model downburst outflows. Subsequently, wind tunnel testing for the three commonly used Bunkie shapes in Ontario was conducted under both boundary layer and downburst wind fields. This led to an aerodynamic database that consists of force and moment coefficients for Bunkiestructures forboth wind fields. The databaseforboundarylayer flowswas usedto compare between experimental results and North American design codes (i.e., NBCC 2015, and ASCE 716). Moreover, the developed database was coupled with the climate analysis in Southern Ontario to quantify the actual forces and moments on Bunkie structures seen during boundary layer and downburst events. Then, Finite Element models were developed to study the behavior of the Bunkie structures and evaluate their potential usage as storm shelters. Finally, potential retrofitting solutions were suggested to strengthen Bunkie structures to resist high-intensity wind events.
Axial Load Behaviour of Non-pierced and Pierced Double Skin Composite Walls Subjected to Elevated Temperature
Authors: Rezazadeh Ghayour Khashayar
Published: March 2024

Double skin composite walls (DSCWs) consist of two skins of profiled steel sheeting and in-fill of concrete. DSCWs are proposed to act as axial and lateral load resisting elements in buildings. This research concentrates on the structural performance of pierced (having a central opening) and non-pierced DSCWs subjected to elevated temperatures of up to 600 C maintained for steady state duration of two hours. Performance of DSCWs subjected to elevated temperatures is evaluated in terms of physical changes, residual axial load capacity/stiffness, axial load-deformation response, energy absorbing capacity, steel-concrete interaction, and overall failure modes. Analytical models for the axial load capacity of DSCWs are used to compare experimental results. The recommendations of this research will be useful for the development of guidelines for the post-fire axial strength/ stiffness of DSCWs and will aid in the development of fire protection measures.
Development and Performance Evaluation of One-part Alkali-activated Binders, Mortars and Engineered Composites
Authors: Sood Dhruv
Published: March 2024

The main objective of this experimental research was to develop alkali-activated engineered composites (AAECs) with comparable performance to that of conventional engineered cementitious composites (ECCs). The use high cement content in ECCs for uniform fiber distribution encouraged the development of cement-free binders using alkali-activation technology. Alkali activated binders (AABs) were developed through activation of high calcium industrial wastes comprising of fly ash class C (FA-C), fly ash class F (FA-F) and ground granulated blast furnace slag (GGBFS) as precursors by calcium-based solid form reagent combinations (calcium hydroxide, sodium silicates, sodium metasilicate and sodium sulfate) using a one-part dry mixing technique under ambient conditions. The influences of mono/binary/ternary combinations/proportions of precursors, dosages of reagents, reagent component ratio, and the fundamental chemical ratios (SiO /2l O2,3Na O2SiO , 2aO/SiO , an2 Na O/Al2O ) p2e3ent in the precursorsand reagents on workabilityandcompressivestrength havebeeninvestigatedto suggest suitable AAB mix compositions for further development of alkali-activated mortars and strainhardening AAECs by subsequent additions of silica sand and PVA fiber, respectively. The binary (FA-C and GGBFS) composites obtained higher 56-day compressive strengths (between 48 MPa and 52 MPa) than their ternary (FA-C, FA-F and GGBFS) counterparts. The lower fracture and crack tip toughness of the binary mortars compared to their ternary counterparts facilitated the development of higher flexural strength (ranging from 5.3 MPa to 11.3 MPa) for binary composites. The tensile stress relaxation process was relatively gradual in binary composites owing to the formation of a more uniform combination of reaction products C-S-H/C-A-S-H compared to the combined formation of amorphous N-C-A-S-H/N-A-S-H and crystalline C-A-S-H binding phases with traces of C-S-H in the case of ternary composites. The self-healing performance of composites incorporating reagent-2 (calcium hydroxide: sodium sulfate = 2.5:1) was superior to their counterparts with reagent-1 (calcium hydroxide: sodium metasilicate = 1:2.5) in tensile strength and strain capacity recovery. These composites demonstrated complete recovery of or enhanced/greater tensile strength and strain capacities than their virgin counterparts at 365 days. The developed green cement-free AAECs with satisfactory performance indices (based on stress, energy, tensile and flexural ductility) demonstrated their potential for applications in construction industries.
Behaviour of Structural Elements Constructed and Rehabilitated With High Performance Concretes
Authors: Ehsani Yeganeh Ali
Published: March 2024

An extensive experimental investigation coupled with Code based/design-oriented analysis was conducted on the performance and application of high-performance concretes (HPCs) including self-consolidating concrete (SCC), engineered cementitious composite (ECC) and ultra-highperformance concrete (UHPC) in different reinforced structural elements. Ten 1/3 scale exterior shear deficient beam-column connections were tested under torsional and bending loading. Ten rd 1/3 scale building frames (three shear and seven flexure critical) and seven framed shear walls were tested under reversed cyclic lateral loading. Twenty-three 1/5 scale ECC/UHPC/SCC jacketed strengthened/repaired reinforced SCC piers/columns were tested under axial loading to failure. The incorporation of ECC and UHPC in full frame or joint region (in plastic hinge zone) as replacement of SCC in shear deficient specimens changed brittle shear failure to desired ductile flexural failure mode while significantly increasing bending/torsional/cyclic or hysteretic load and deformability/ductility capacities. ECC/UHPC made specimens exhibited higher energy absorption and damage tolerance capacities with better crack control characteristics as well as potentially lower longitudinal and hoop/shear reinforcement requirements which can significantly improve seismic behaviour. The strain hardening with multiple cracking and limiting crack width characteristics of ECC lowered the initial stiffness and stiffness degradation with the ability to maintain good damping behaviour during lateral cyclic loading of building frames and framed shear wall system. The tested ECC framed shear wall specimens under lateral cyclic loading had higher ultimate load and deformability capacities compared to their SCC counterparts. This was attributed to better transfer of shear stress through cracks due to fiber bridging and multiple cracking with small crack width in ECC compared to SCC/UHPC. Piers rehabilitated and strengthened with ECC/UHPC jackets showed higher strength. However, ECC specimens showed better performance in terms of enhanced confining effect along the height exhibiting ductile failure compared to their SCC/UHPC counterparts which showed brittle crushing failure at the top with jacket spalling. Existing Codes and other analytical equations need to be modified for the design of ECC/UHPC incorporated structural components. This study revealed great potential of ECC/UHPC repair/strengthening technology and ECC/UHPC incorporated building frames, beam-column connections and framed-shear wall system having higher torsional, flexural and shear resistance in practical construction.
Anaerobic Process Intensification With Vacuum and Combined Pretreatment Technologies
Authors: Okoye Frances Chi
Published: March 2024

Anaerobic digestion and fermentation are essential processes for transforming wastewater treatment plants into robust energy, nutrient, and water recovery facilities. However, these processes' slow microbial growth rate and low yields limit their application to larger facilities. To improve their efficiency, intensifying anaerobic digestion and fermentation (to a lesser extent) has been the subject of numerous research studies. This dissertation explored the principle of vacuum evaporation for combining sludge thickening, decoupling of hydraulic and retention times, and nutrient recovery into a single treatment unit. Gaps, including the efficiency of vacuum sludge thickening at different solids content, its impact on anaerobic bacteria and the overall physio and biochemical processes, were investigated. In batch fermentation, establishing 400 mbar of absolute pressure led to 49% solubilization of primary sludge in 72 hours compared to the conventional fermentation in which 11% solubilization was achieved. During fermentation of TWAS at 400 mbar, deterioration of solubilization and VFA yield was observed. The vacuum technology was used to decouple the hydraulic and solids retention time (HRT/SRT) in semi-continuous fermentation of mixed sludge. At SRT of 3 days and HRT of 1.5 days, the vacuum-assisted fermenter yielded 660 mg sCOD/g VSS compared to the conventional fermenter which achieved 513 mg sCOD/g VSS with equal HRT and SRT of 3 days. Additionally, 50% of the ammonia produced in the fermenter was extracted in the condensate with no chemical alterations. The results led to the development of IntensiCarb, a patent-pending technology that combines sludge thickening, digestion, and dewatering into a single unit. This dissertation also investigated the use of combined chemical-mechanical and chemical-chemical pretreatment technologies with free nitrous acid (FNA) to enhance the anaerobic digestion of TWAS. FNA is an environment-friendly chemical that can be produced in wastewater treatment facilities. In this study, FNA concentrations from 0.7 to 2.8 mg HNO2-N/L were combined with ultrasonication energy input from 600 to 3,000 kj/kg TS to explore the feasibility of lower energy input for effective sludge disintegration and enhanced anaerobic digestion. The energy input for ultrasonication could be halved from 3,000 kj/kg TS to 1,500 kj/kg TS by adding 2.8 mg HNO2-N /L while improving solubilization by 43% and methane yield by 18%. FNA was also combined with hydrogen peroxide, an oxidizing chemical that has shown effectiveness in improving biodegradability at low pH. The results showed that the pretreatment combination with the lowest chemical concentrations of 0.7 mg HNO2-N /L and 25 mg H2O2/g TS was the most effective for maximizing methane yield, increasing it by 50% compared to the untreated TWAS.
Reliability-based Design for Axially Loaded Driven Piles in Glacial Deposits
Authors: Jesswein Markus
Published: March 2024

Many factors and uncertainties, especially from the soil conditions, challenge the accuracy of a pile design for the geotechnical ultimate limit state (ULS) and serviceability limit state (SLS). The uncertainties can be contributed by the inconsistent material properties of glacial deposits or a lack of provisions by many design methods for the pile set-up, which is an increase in the pile capacity with time. Thus, for driven steel piles subjected to static and maintained axial loads, the goal of this research was to improve the predictability of these limit states by modifying reliability-based design (RBD) methods. A database was collected first in this thesis with a total of 120 piles subjected to static and/or Pile Driving Analyzer (PDA) tests. These tests were used to evaluate the accuracy of several existing design methods that predict the pile capacity with in-situ measurements, particularly by the standard penetration test (SPT) or piezocone penetration test (CPTu). Since existing methods commonly overestimated the capacity or exhibited significant variations in their predictions, correlations were conducted between results from the in-situ and pile tests to develop new design methods to better consider the soil content and set-up time. Many existing set-up prediction methods rely on time-consuming laboratory tests, but this study offers a practical CPTu-based approach. Afterwards, reliability analyses were performed to help designers account for the uncertainties in designs and estimate the factored resistance for the ULS and SLS. Although a few studies have previously incorporated set-up into RBDs for the ULS, very few, if any, have incorporated set-up for the SLS; thus, this thesis presents a series of resistance factors that were calibrated by Monte Carlos simulation for a range of set-up to end-of-driving resistance ratios and allowable settlements. The findings demonstrate the benefits of versatile methods that consider a range of conditions, such as set-up time, soil classification, and pile geometry. In addition, economic benefits may be received for designs by using a greater factored resistance at a set-up time compared to the end-of-pile-driving condition. Overall, the findings can assist practitioners to mitigate geotechnical risks and deliver more economic and reliable pile designs.
Investigation of the Effects of Leading Pedestrian Intervals and Protected Vehicular Left-turn Phasing on Pedestrian Safety
Authors: Del Rosario Ruben
Published: March 2024

Vehicle-pedestrian collisions at signalized intersections often occur when vehicles are performing a turning movement while pedestrians are at the crosswalk. There is a need to minimize these types of collisions, given their severity and prevalence. The main objective of this study is to address this need by investigating factors and treatments (such as leading pedestrian intervals and fully protected vehicular left-turns) that may impact vehicle-pedestrian conflicts (i.e., near misses) and/or collisions at signalized intersections. Collisions, simulated conflicts, and video-derived conflicts were used as measures of safety in the investigation. The results indicate, for example, that leading pedestrian intervals currently installed in Toronto have reduced pedestrian collisions by 49.3% to 82.7%, while fully protected vehicular left-turns were associated with reducing leftturning vehicle-pedestrian conflicts by 88.7%.
Sidestream Treatment of Anaerobically Digested Sludge Centrate Using Activated Sludge and Aerobic Granular Sludge Sequencing Batch Reactors
Authors: Morgan Guillian
Published: March 2024

In this research, two 1.5-L activated sludge (AS)-sequencing batch reactors (SBRs) were seeded with return activated sludge (RAS) and operated at a hydraulic retention time (HRT) of 72-hr to enable nitrification of centrate with an ammonia-nitrogen (NH -N)3concentration of 572 ± 28 mg/L. Secondly, three 1.2-L aerobic granular sludge (AGS)-SBRs were each operated at a 12-hr followed by an 8-hr HRT to cultivate aerobic granules from RAS. The AGS-SBRs were fed with a combination of diluted and synthetic centrate to perform nitrification only and simultaneous nitrification-denitrification (SND). NH3-N removal efficiencies in the AS-SBR operated with supplemental alkalinity was 100 ± 0% (26 to 93 day), while the control AS-SBR that operated th nd without supplemental alkalinity achieved 61 ± 2% (18 to 52 day) due to incomplete nitrification. The AGS-SBRs fed with diluted centrate, synthetic centrate, and diluted centrate with supplemental organic carbon displayed average NH -N 3emoval efficiencies of 93 ± 19%, 94 ± 16%, and 92 ± 9%, respectively.
Performance of Language: A Comparative Linguistic Study of News About China and Italy During the COVID-19 Pandemic in a Canadian News Program
Authors: Jian Meng
Published: March 2024

The project is a comparative linguistic study on the COVID-19 coverage about China and Italy during the first 3-months of 2020 from the Canadian news program The National. It includes a research paper and a website featuring an animation displaying information silenced by mainstream media. The theoretical framework of the paper is situated within language ideology and is guided by Corpus-Assisted Discourse Studies methodology. Through patterns discovered in the research, it aims to detect the contrareity in the language that The National used between the two countries, and the strategies employed in the performance of “respectable racism” (Antonius, 2002) on China. The animation enunciates the incongruity between the two countries by juxtaposing the prejudiced words used implicitly in China’s corpora with the words in Italy’s corpora. This project hopes to disrupt the status quo in the information dissemination dictated by mainstream media which ostracizes the Chinese community and divides Canadians.
A Combinator, N-dimensional Array Library in Smalltalk
Authors: Hoekstra Conor
Published: March 2024

My thesis is that programming in array languages with combinators (from Combinatory Logic) is a powerful and important aspect of programming and that it can naturally fit into an object-oriented language like Smalltalk. Evidence for this is provided by an experimental implementation of a combinator, n-dimensional array library in Smalltalk. A component of this thesis is a survey of combinatory logic and combinators as they currently exist in modern array programming languages and a study of what makes them so powerful and expressive.
Extracting Source Information From News Articles
Authors: Sultana Tabassum
Published: March 2024

A news article comprises information, facts, sources, reporters’ findings, and viewpoints. One of the factors for the credibility of news depends on source and source attribution. Many researchers have identified and attributed news sources for relevance and news reliability. The present work aims to build reliable software that can help journalists, researchers, or anyone curious about news contributors. First, we propose software that extracts contributor names and features describing the sources of information. Secondly, we use classification algorithms to assign sources to three categories, namely AUT (authority), EXP (expert) and OTH (others), as a first step in assessing the balance and breadth of the sourcing in a news article. Our results suggest that the software could perform 6-class categorization of sources accurately, given a more balanced data set. The preliminary software testing showed a recall of 73%, accuracy of 95% when identifying the source and overall accuracy of 78% when categorizing the source.
The Use of Simulation in Support of Managerial Decision Making in Hospitals
Authors: Zouri Nicoleta
Published: March 2024

Executive decision-making for health care organizations’ managers is a process typically based on the manager’s experience and various patient data the organization serves. These decisions that executive managers make, affect the quality of care and efficiency metrics that organizations are required to maintain or improve based on standards established by the Ministry of Health. Among the factors affecting the quality of care and efficiency are various material and human resources, as well as diagnosis and treatment complexity and occurrence of nosocomial infections. Extreme events may also occur which may require additional material and human resources. Supporting the decision-making process with simulation applications may offer managers the ability to understand better the effect of their decisions and compare different outputs. While the simulations cannot replace human experience and educated intuition, it can help augment the decision process and could lead to better outcomes for patients and more efficient use of material and human resources. Lastly, simulations could allow managers to make adequate substitutions when resources are scarce without compromising the quality of care and with greater consideration of the impact of work schedule on health care workers’ burnout and exhaustion.
Incremental Text Clustering Algorithm Using Incremental Learning in COVID-19 Research Papers
Authors: Nilufar Mahfuja
Published: March 2024

This thesis aims to build clusters of similar research papers. Text clustering for research articles is challenging because re-clustering is necessary to handle newly added papers. An incremental clustering algorithm is presented to find similar research papers for COVID-19 related literature. The proposed approach uses an incremental word embedding generation technique to extract feature vectors of the papers. The initial clustering is done by using the K-means algorithm by two NLP feature extraction models; TF-IDF and Word2vec. The clustering results show that the Word2vec outperforms the TF-IDF model. With increasing COVID-19 literature continuously, the ultimate focus is to add the newly published papers to the existing clusters without re-clustering. Title, abstract, and full body of papers are considered for testing the proposed incremental algorithm. Clustering quality is evaluated by the Microsoft language similarity package, which shows clustering of the full-text body outperforms the abstract and title of papers.
RLML: A Domain-specific Modelling Language for Reinforcement Learning
Authors: Sinani Natalie
Published: March 2024

In recent years, machine learning technologies have gained intense popularity and are being used in a wide range of domains. However, due to the complexity associated with machine learning algorithms, it is a challenge to make it user-friendly, easy to understand and implement. Machine learning applications are especially challenging for users who do not have proficiency in this area. In this work, we use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling language (DSML) to contribute towards providing a solution for this problem. We targeted reinforcement learning domain from machine learning technologies, and evaluated the proposed language with multiple applications. We built a domain-specific modelling environment to support our reinforcement learning modelling language (RLML). The tool supports syntax-directed editing, constraint checking, and automatic generation of code from RLML models. With our proposed approach, we were able to move away from the complexity of implementing machine learning algorithms in general purpose languages and offer abstraction and simplicity for non-experts, which are a few of the characteristics and benefits of modelling languages.
Methods for Improved Efficacy in Segmentation and Tracking of Echocardiographic Images
Authors: Ali Yasser
Published: March 2024

Echo cardiography is one of the major imaging modalities for quantifying heart functionality, providing advantages such as real-time features, cost-reflectiveness, imaging efficiency, and safety compared to other image modalities. Many cardiac procedures for diagnosis and treatment of cardiac disorders require real-time segmentation of the endocardium walls and robustness to potential image imperfections (such as shadows, speckle noise and possible movements). Manually segmenting the cardiac boundary is a time-consuming process, and in emergency cases, the results are required to be available instantly. Therefore, providing an autonomous segmentation method is crucial in such situations. Also, tracking the cardiac boundary could provide valuable information about the heart condition. The primary goal of this work was to address issues with accuracy in segmentation and tracking of echocardiograms. Meanwhile, the robustness to noise and motion, as well as translation, involved in cardiac operations, were taken into account.
Detection of In-car Driver Distraction Activities With Recommendations
Authors: Aljasim Mustafa
Published: March 2024

The increasing number of car accidents is a significant issue in current transpiration systems. th According to the world health organization (WHO), road accidents are the 8 highest top reason of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this project, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase the level of in-care awareness for improved safety.
Fused Attention Modules Embedded in Artificial Neural Networks for Low Dose CT Denoising With Integrated Loss Functions
Authors: Marcos Luella
Published: March 2024

X-ray Computed Tomography (CT) is a non-invasive medical diagnostic tool that has raised public concerns due to the associated health risks of radiation dose to patients. Reducing the radiation dose leads to noise artifacts, making the low-dose CT images unreliable for diagnosis. Hence, low-dose computed tomography (LDCT) image reconstruction techniques have offered a new challenge in the research area. This thesis focuses on reconstructing LDCT images using deep learning techniques to provide an efficient, effective, and accurate training regimes for LDCT image denoising. A fusion of spatial and channel attention modules integrated into a dilated residual network is proposed to improve the structural details of denoised LDCT images. Further, a combination of perceptual loss, per-pixel loss, and structural dissimilarity loss is used for the optimization of the overall network. These objective functions aim to preserve structural details, avoid edge over-smoothing and enhance the image texture, respectively. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM) are used for measuring the quantitative results. A comparative experiment was done between the proposed model and the recent denoising models, such as Block Matching and 3D Filtering (BM3D), patch Markovnian Generative Adversarial Network (patch-GAN) and dilated residual learning with edge detection (DRL-E-MP). Not only with quantitative results, but these models were also compared visually. To further strengthen the validity of the outcomes, five different CT image datasets were used. The proposed model obtained the highest PSNR/SSIM value of 34.36/0.6971 while BM3D resulted in the lowest value with 30.24/0.4461 using the chest dataset from the Mayo Clinic. Overall, the proposed network demonstrated that it could outperform state-of-the-art models.
A Decision-Making Method for Run-Time Structural Adaptation Of FPGA-Based SOCS to Variations in Workload, Power Budget, Die Temperature, and Hardware Resources
Authors: Sharma Dimple
Published: March 2024

Embedded systems for application domains like robotics, aerospace, defense, etc. nowadays are developed on System-on-Chip (SoC) platforms based on Field Programmable Gate Array (FPGA) devices to support computation-intensive multi-task multi-modal dynamic workloads common for these applications. These systems therefore face the challenge to sustain the performance of their dynamic workloads in presence of variations in power budget, die temperature, and/or occurrence of hardware faults. This work proposes Run-time Structural Adaptation (RTSA) as a mechanism for mitigating these factors. One of the major contributions of this work is creation of a run-time decision-making method, ‚ÄúExplorer‚Äù, to carry out RTSA for FPGA-based SoCs and mitigate variations in the internal and external factors simultaneously. Whenever there is a change in the system‚Äôs set of constraints, Explorer selects an appropriate variant of hardware processing circuit for each active task from a large design space to form a system conÔ¨Åguration that satisÔ¨Åes each task‚Äôs performance speciÔ¨Åcation and all other system constraints. To support the practical deployment of Explorer, this work presents a method to derive run-time power consumption and die temperature estimation models for any FPGA-based device. This novel methodology of model derivation based on the FPGA platform and application running on it is another contribution of this work. The model derivation methods are formulated after detailed experimental analysis of power consumption and thermal behaviors of recent FPGA devices. Explorer can evaluate potential system conÔ¨Ågurations using the derived models to select a suitable system conÔ¨Åguration for RTSA. Experimental implementation of Explorer on the Zynq XC7Z020 SoC shows worst case execution time of 130 us, which demonstrates its suitability for RTSA for most applications associated with multi-stream computation-intensive tasks. This work also proposes an approach for automating model derivation which helps systems self-derive their model coefficients during system production and re-derive them due to changes in system platform, application, and/or environmental conditions. This significantly reduces model derivation time while avoiding any human involvement. This work thus presents the methods and models necessary to enable real systems utilizing FPGA devices to carry out RTSA and sustain their dynamic workloads amid dynamic environmental and hardware resource constraints.
Improving Interactive Segmentation Techniques in Medical Imaging
Authors: Pirabaharan Ragavie
Published: March 2024

Recent attention has been placed on interactive segmentation for specialized tasks where specialist input is required to further amplify the segmentation performance. In this work, we propose a novel interactive segmentation architecture as well as loss function, where user clicks are dynamically altered in size based upon the current segmentation mask. A weight map is formed based upon the users selected regions and is later passed into a deep neural network as a novel weighted loss function. An interactive U-Net (IU-Net) model which applies both foreground and background user clicks as the main method of interaction is employed to evaluate our loss function. As an addition to the IU-Net, we propose the use of a two-stream fusion interactive U-Net (TSFIU-Net) which applies multimodal fusion properties to allow for the propagation of image feature information throughout the architecture. This model is also tested with the same loss function and dynamically changing click sizes to determine the increase in accuracy. We experiment on spleen and colon cancer CT data from the MSD dataset held at MICCAI 2018 and improve the overall segmentation accuracy in comparison to the standard U-Net using our weighted loss function. Dynamic user click sizes enhances accuracy by 8.88% and 2.16% respectively by utilizing only a single user interaction on the IU-Net and by 13.9% and 3.92% on the TSFIU-Net.
Multi-level Stress Assessment From Electrocardiogram Data in a Virtual Reality Environment Using Contrastive Self Supervision
Authors: Rabbani Syeda
Published: March 2024

Electrocardiogram (ECG) data is an ideal option to assess stress in Virtual Reality (VR) applications due to its non-invasive nature and high correlation to changes in stress levels. Yet, most studies on stress assessment only collect binary measurements. Developing a multi-level assessment is necessary for a biofeedback-based application. Existing studies annotate and classify a single experience (e.g. watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. Furthermore, existing works heavily rely on supervision information to train stress assessment models. However, obtaining supervised labels for training data is an expensive and labor intensive task that is not scalable on large datasets. Given the advancements in data driven technologies, supervised learning prevents us from leveraging unlabeled data excessively available nowadays. In this thesis, we report our findings on a new study on VR multi-level stress assessment. ECG data was collected from 12 participants experiencing a VR roller coaster. The VR experience was then manually labeled in 10-second segments to three stress levels. We then propose a contrastive self-supervised learning approach for stress assessment that is superior to non-contrastive self-supervised approaches. Experimental results show that when compared to non-contrastive self-supervised learning, we obtained a 9% increase in accuracy on the WESAD dataset and a 3.7% increase on our collected data at the Ryerson Multimedia Lab.
A Coordinated Optimization Model for Solar Photovoltaic Integrated DC Distribution Networks
Authors: Achiluzzi Eleonora
Published: March 2024

Solar photovoltaic (PV) systems will drive deep electrification of energy systems leading to clean energy 2050. However, connecting large amounts of solar on DC networks used in solar farms and possible future DC distribution systems would lead to over voltages, resulting in loss of solar power. Further, solar PV systems operate exclusively using maximum power point tracking algorithms, without coordinating with the remainder of the network. This thesis presents a coordinated optimization model to optimally control the settings of voltage controllers (DC-DC converters), placed at the outputs of solar PV systems and selected distribution lines, while maximizing solar power output and minimizing substation power. The proposed formulation was tested on several systems and compared to an uncoordinated situation. The results demonstrate an increase in solar power of 60.06%, in the 28-bus case. The proposed method will be an excellent tool enabling deep electrification using solar PV systems, overcoming limitations of uncoordinated systems.
Super-resolution of Audio Files Using Feed-forward Neural Networks for Music Storage and Transfer
Authors: True Sean
Published: March 2024

In this report, a system for reducing the file size of an audio signal, and then performing super-resolution on the resultant signal to estimate the original, is proposed and designed. This design takes influence from the principles of audio sampling, as well as super-resolution systems designed for visual media, and is split into an encoder and a decoder. The encoder successfully reduces the file size of the audio file by a significant amount. The super-resolution-based decoder can also successfully generate a matching high-frequency audio track that can be combined with the encoded lossy audio in order to estimate the original audio with a reasonable degree of accuracy. While a number of improvements to the system can be made in the future, it shows great promise, as it accomplishes the goals it was designed to meet.
An Interpretable Object Detection-Based Model for the Diagnosis of Neonatal Lung Diseases Using Ultrasound Images
Authors: Bassiouny Rodina
Published: March 2024

Lung Ultrasound (LUS) has been progressively used for the diagnosis of lung diseases in neonates due to its safety. Discussions with physicians revealed that a system that can detect specific lung features associated with neonatal lung diseases will be more useful than a simple image classification model. Therefore, a seven-class faster Region Proposal-based Convolutional Neural Network (fRCNN) as well as a RetinaNet were trained on lower posterior lung ultrasound videos to detect seven LUS features. Results show that fRCNN achieved a higher mean Average Precision (mAP) of 86.57% with an Intersection over Union (IoU) of 0.2 compared to RetinaNet with 61.15% mAP. A lung sliding feature detection method was proposed to differentiate between Pneumothorax and Normal scans. Using this method, we were able to classify 5 Pneumothorax (PTX) and 6 Normal video cases with 100% accuracy. We also developed a user-friendly GUI model that performs predictions on LUS video scans and outputs a video with the imposed LUS feature detections.
Implicit Entity Recognition and Linking in Tweets
Authors: Hosseini Hawre
Published: March 2024

Linking textual content to entities from the knowledge graph has received increasing attention where surface form representations of entities are linked to appropriate entities. This allows textual content, e.g., social user-generated content, to be interpreted at a higher semantic level. However, recent research has shown that at least 15% of social user-generated content do not have explicit surface form representation of entities that they discuss. In other words, the subject of the content is only implied. For such cases, existing named entity recognition and linking methods, known as explicit entity linking, cannot perform linking because entity surface form is missing. The objective of this dissertation, while introducing and publicly sharing a comprehensive gold standard dataset for the tasks of implicit named entity recognition and linking, is to propose approaches to these tasks. We formulate the problem of recognizing implicit entity mentions in tweets, where we propose to leverage categorical and linguistically inspired features based on Systemic Functional Linguistics. Our implicit named entity recognizer achieves promising results on different evaluation metrics. Additionally, we propose two approaches for linking implicit mentions in tweets. Within the first, we formulate the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge graph. We systematically compare our work with existing work showing our method is able to provide improvements on a range of retrieval measures. Within the second approach, we model implicit entity linking as a learn to rank problem where knowledge graph entities are ranked based on their relevance to the input tweet. In doing so, we introduce and systematically classify appropriate features for identifying implicit entities. In our experiments, we show that our proposed features are able to improve the state of the art. For SFL-based recognition of implicit entity mentions as well as for the ad-hoc retrieval based and learn to rank based approaches to linking of such mentions, we provide qualitative assessment of the root causes for mislabeled instances in our experiments.
Toward More Efficient and Sustainable Autotrophic Ammonia Removal From Wastewater
Authors: Ronan Evan
Published: March 2024

There is significant need to adapt biological nitrogen removal (BNR) systems to meet the evolving needs of future generations, such that the deleterious impacts of reactive nitrogen pollution can be mitigated in a cost-effective and sustainable manner. The overall goal of this research was therefore to explore autotrophic ammonia removal from a future-facing perspective, in order to generate insights that could inform potential solutions for more stable and sustainable BNR. Three chapters of this dissertation are manuscripts that focus on a particular challenge with respect to the operation of BNR systems, including: i. influent streams with very high ammonia concentrations; ii. greenhouse gas emissions during autotrophic ammonia oxidation; and iii. incomplete ammonia removal caused by alkalinity depletion. Chapter2incorporatedalargeextantofrecentresearchtoassessthelimitationsofconventionalpractices for treating high strength ammonia wastewater and provides a synthesis of emerging approaches for more cost-effective and sustainable treatment. The advantages of exploiting biofilm and aerobic granular sludge technologies were specifically investigated, as was the use of anammox-based processes as beneficial alternatives to conventional nitrification and denitrification. Chapter 3 used novel growth systems to investigate CO2 emissions during autotrophic ammonia oxidation and demonstrated a linear relationship between ammonia removal and gaseous CO2 production. The results provided evidence to suggest that the current approach of excluding bicarbonate-derived CO2 emissions from GHG accounting of BNR processes may lead to an underestimation of the climate change impact of conventional wastewater treatment systems. Chapter 4 investigated the nitrification performance of fixed-film bioreactors with different biomass retention characteristics, to determine the effect of enhanced biomass retention towards improved nitrification during periods of alkalinity induced stress. Although a higher biomass concentration offered the capacity to achieve faster ammonia removal rates in batch mode, the bioreactors exhibited similar performance in continuous-flow mode and the retention of additional nitrifying biomass did not provide any biomass concentration-dependent mechanisms for improved resilience to acidic conditions. Overall, the work described herein represents progress towards a more complete understanding of autotrophic ammonia oxidation as it applies to the stable and sustainable operation of BNR systems.