VictorJuiz/thesis huggingface dataset:
Traffic Emission Modelling for Robust Policy Design in Connected and Electric Transportation
Keywords: Connected vehicles
Electric vehicles
Greenhouse gas emissions
Low-carbon transportation
Traffic emission model
Traffic-related air pollution

Traffic emissions such as greenhouse gases (GHGs, in CO2eq), nitrogen oxides (NOx), and traffic-related air pollution lead to global problems including climate change and public health issues. In order to mitigate the impacts of growing urban traffic on emissions and air pollution, travel demand management, driving operation control, and advanced technology initiatives have been implemented in many cities. The aim of this research is to realize robust transportation policy decisions with improved emission estimation approaches at local and regional levels. In the first module of the thesis, the emission factor (EF, in grams of traffic emissions per unit distance) within one traffic condition, which is commonly defined as a single value, is expanded to a distribution. A regional emission distribution is therefore established using the EF distribution, enhancing the robustness of policy analysis. Meanwhile, the identification of the EF variation leads to the development of a machine-learning based emission estimation approach, CLustEr-based Validated Emission Re-calculation (CLEVER). The CLEVER approach can accurately estimate regional traffic emissions without heavy data collection burden through refined traffic condition categories and representative EFs using traffic data of multiple resolutions. In the second module, several traffic emission control strategies are tested from perspectives of emissions, air pollution, and energy consumption. First, a travel demand management targeting on high-emitting trips is tested. Compared to a short-distance trip management, the proposed strategy is more effective on reducing GHG emissions and improving traffic conditions. Second, a travel-time minimized routing algorithm with connected automated vehicles is applied in an urban road network and the application causes potential increases of near-road NO2 concentrations. Lastly, electric vehicle charging schedules are optimized to minimize GHG emissions from electricity generation. The optimized plan demonstrates high potentials for reducing GHG emissions. However, trade-offs between emission reductions and charging facility costs are identified by comparing the optimized plan with non-optimized plans. This research achieves a reliable regional traffic emission estimation with much less data requirement. Based on that, innovative control strategies proposed in this research and their comprehensive evaluation process can contribute to a robust transportation policy decision.
Environmental and Demographic Change and Rural Violence in Peru: A Case Study of the District of Chuschi, Ayacucho
Keywords: Chuschi
civil war
environmental scarcity
Sendero Luminoso
Shining Path
violent conflict

Considerable debate exists about whether and how human-induced pressure on the natural environment contributes to violent conflict. This dissertation examines whether human-induced environmental and demographic change helped generate rural violence in the District of Chuschi, in the South-Central Andes of Peru in the latter half of the 20th century, in the years leading up to the Sendero Luminoso insurgency in 1980. The dissertation’s findings reveal that long-term patterns of resource capture and human-induced environmental and demographic change, combined with prevailing local, regional, and national political-economic currents in Peru over the 20th century to increase inter-community competition for valuable but limited agricultural resources – over range lands, crop lands, and water resources. In particular, these changes conditioned patterns of violence between two neighbouring communities in the District – the Community of Chuschi and the Community of Quispillacta. Changes aggravated conflictual relationships between Chuschi and Quispillacta over rights to long-contested resources that were essential to increasingly pressured livelihoods in both communities. Change and contest resulted in winners and losers, with implications for social stability years into the future. Quispillacta largely emerged as the victor in these community resource conflicts. However, their victory fostered enduring resentment in Chuschi - the traditional administrative centre of the District - and among Chuschi’s associated hamlets. These grievances spilled out during the Sendero insurgency as some in rival communities falsely accused Quispillacta of being a centre of Sendero militancy, accusations likely stemming from community members who lost out in the inter-community land competition. Quispillacta suffered terribly as a result in the Peruvian military’s counter-insurgency bloodletting, disproportionately more than Chuschi and its associated hamlets – score-settling by counter-insurgency. Human pressure on the local environment and people’s adaptive choices to these pressures thus helped condition patterns of violence in the decades before the Sendero insurgency, and patterns of violence once the insurgency heated up in the area. Understanding why violence took the shape that it did in Chuschi during the insurgency and over the previous century in various small-scale confrontations requires examining the interface of human-induced environmental change and demographic change, in the relation to socio-economic changes in the region.
Investigating the use of circulating tumor DNA for cancer surveillance and early detection in (pediatric) sarcomas and Li-Fraumeni Syndrome
Keywords: ctDNA
Early Detection
Li-Fraumeni Syndrome
Liquid Biopsies
Sarcomas
Surveillance

Circulating tumor DNA (ctDNA) is a biomarker that has been shown to be valuable in a variety of contexts in clinical oncology such as determining prognosis, monitoring treatment and predicting recurrence. Early detection using ctDNA may be invaluable for patients with a genetic risk to develop cancers. Li-Fraumeni Syndrome (LFS) is a cancer predisposition syndrome caused by inherited mutations in the tumor suppressor gene, TP53. LFS is characterized by early onset of a wide spectrum of tumors and an 83-fold lifetime risk of multiple cancers. The 'Toronto Protocol' is a multi-modality clinical surveillance protocol that was developed to facilitate early cancer detection in this population. The protocol utilizes a combination of MRI scans, ultrasounds, biochemical tests, and physical examinations. Though it has been shown to be effective in reducing tumor related mortality and treatment related morbidity, reduced sensitivity and specificity make clinical surveillance challenging to implement. To address these challenges, we are using a combination of xenograft and spontaneous tumor forming animal models to study the dynamics of ctDNA in relation to tumor burden and to resolve the feasibility of capturing the development of tumors at their earliest stages. Our work using a rhabdomyosarcoma xenograft model demonstrated that there is a strong relationship between tumor burden and ctDNA concentration in the blood. Furthermore, we detected ctDNA during the development of small, early lesions using simulated metastasis models. In a follow-up study, we used a Trp53R172H/+ pre-clinical model of LFS to assess the use of ctDNA as a surveillance tool for early detection of spontaneously developing tumors. We were able to identify ctDNA in blood samples collected before and at the onset of tumor formation as confirmed by radiological imaging and subsequently used ctDNA to monitor cancer progression. Our studies provide the first evidence demonstrating the capacity of circulating tumor DNA for early cancer detection in pediatric sarcomas, specifically arising from a hereditary cancer predisposition syndrome. Given the spontaneous nature of tumor formation in LFS, the benefits of early cancer detection and subsequent diagnosis are unparalleled in this patient population by accelerating introduction of early treatment intervention and improving disease prognosis. This work illustrates the capacity for ctDNA detection to complement and enhance cancer surveillance protocols in LFS patients. Furthermore, the outcomes of this study will support the validation and implementation of ctDNA analysis for cancer surveillance in the clinic.
Hydrogen Bronzes of WO3 and MoO3 as Active (Photo)-catalyst Supports for CO2 Reduction
Keywords: Carbon dioxide
Catalysis
Hydrogen Bronze
Molybdenum Trioxide
Photocatalysis
Tungsten Trioxide

The exponentially growing global energy demands currently satisfied by the consumption of the ever-depleting sources of fossil energy has emitted hundreds of billions of tonnes of CO2 into Earth’s atmosphere since the industrial revolution. This has generated concerns over the sustainability of the fossil energy dependence and the effects the CO2 emissions will have on the future climate. A solution to both problems exist in the form of harnessing renewable energy such as sunlight to close the carbon cycle, reclaiming the CO2 emitted for feedstock as a carbon source in fine chemical and fuels. This thesis focuses on the renewable conversion of CO2 into value added products utilizing tungsten (VI) oxide (WO3) and molybdenum (VI) oxide (MoO3) based photocatalysts. The interest in WO3 and MoO3 stems from their capability to form highly defected hydrogen bronze states (HyMO3-x, M = W, Mo) under hydrogen atmospheres which have strong optical absorbance across the entire solar spectrum, high stability, and high capacity for catalytically active defect sites (OHs and oxygen vacancies). Early attempts at utilizing WO3 and MoO3 to catalyze hydrogenation and deoxygenation reactions have observed sluggish formation of defects as H2 activation and oxygen vacancy formation may have limited reactivity. Here, we demonstrated that the decoration of Pd nanocrystals as H2 spillover catalysts can significantly accelerate the formation of HyMO3-x, resulting in highly photoactive oxide supports for CO2 reduction by H2. The high cost of Pd however, motivated our search for more cost-effective catalysts for H2 spillover, leading to developing a novel method of depositing Ni onto WO3. Ni was observed to improve CO2 conversion compared to WO3, but relative to Pd, H2 activation was slower, likely due to slower H2 dissociation and hydrogen mobility. Finally, to further improve the kinetics of Pd@HyWO3-x, we functionalized the surface by hydrolyzing CuOtBu and Ba(OEt)2 over the activated HyWO3-x’s surface hydroxides. The addition of Cu and Ba atoms onto the surface was observed to greatly enhance the activity of the system, attributed to the improved surface chemistry revealed by in-situ diffuse reflectance infrared Fourier transform spectroscopy.
Modelling Riverine Dissolved Silica on Different Spatial and Temporal Scales using Statistical and Machine Learning Methods
Keywords: climate change
Holocene
machine learning
rivers
silica

Changes in riverine delivery of dissolved silica (DSi) from continents to the ocean have consequences for the global carbon cycle and marine ecosystem health because (i) rivers are the main source of DSi to the oceans, (ii) diatoms, photosynthetic algae, depend on DSi to build their siliceous frustules, (iii) diatoms are an important foundation of many marine food webs, and (iv) diatoms dominate marine primary productivity and carbon export to the deep ocean. However, despite its importance, the controls of river DSi export are not fully constrained and there is no satisfactory model of riverine DSi yield (DSiY). This thesis investigates river DSiY using statistical and machine learning (ML) methods at different spatial and temporal scales. First, relationships between DSiY and environmental variables were analysed in western Canada to explore the controls of river DSiY on a regional scale. Statistical analyses indicated that land cover and climate are the best predictors of river DSiY in western Canada. Second, a ML model was developed to predict global river DSiY using 30 environmental variables including temperature, precipitation, land cover, lithology, and terrain. Of eight ML algorithms tested, one-step boosted random forest was the best method to predict river DSiY and climate and land cover were the most important predictors in the model, indicating that vegetation affects river DSiY on a global scale. Third, the ML model was used to predict changes in global river DSiY for the future (2090-2099, 2190-2199, and 2290- 2299) and mid-Holocene (6000 years ago). Global mean DSiY is projected to decrease in the future, but predictions have high spatial variability. And, global mean mid-Holocene river DSiY was estimated to be 30% lower than present-day, indicating that changes in paleo-river DSiY may be underestimated. This work demonstrates that DSiY is not only affected by climate and land cover, but that changes in climate and land cover have the potential to influence DSiY on different time scales. This thesis contributes the first study to use ML to model river DSiY and uses a more exhaustive set of variables than other studies, particularly a wider range of land cover variables.
Stop Signal Reaction Times: New Estimations with Longitudinal, Bayesian and Time Series based Methods
Keywords: Ex-Gaussian Distribution
Mixture Models
State-space Missing Time Series
Stop Signal Reaction Times

The stop signal reaction times (SSRT), a measure of the unobserved latency of the stop signal process in Stop Signal Task (SST) has been a focal point of the estimation. It was theoretically formulated as constant and random variable indices. The first index has been formulated using the independent horse race model of go and stop trials by Gordon Logan in 1994. The second index was estimated by Eric Jan Wagenmaker and colleagues in 2012 using the Bayesian Parametric Approach (BPA) and Ex-Gaussian assumption for the underlying go reaction times (GORT), signal respond reaction times (SRRT) and SSRT. Both of the mentioned estimation methods of SSRT assume equal impact of the preceding trial type (go/stop) on the current stop trial for its measurement. In case of violation of this assumption, the appropriate estimations of SSRT are required to address the measurement error. In this dissertation, we estimate SSRT under violation of the assumption in three frequentist longitudinal, mixture Bayesian and time series based methods. The frequentist longitudinal estimation method considers two clusters of SST trials and introduces Mixture SSRT and Weighted SSRT as two new distinct indices of SSRT being asymptotically equivalent under special conditions. The Bayesian estimation method considers degenerate mixture Bayesian estimation of SSRT using the cluster type SSRT estimation with underlying Ex-Gaussian assumption for GORT, SRRT and SSRT. In our proposed method we use the Two Stage Bayesian Parametric Approach (TSBPA) with uninformative priors. We compare the new Bayesian Mixture estimation of SSRT with the current single estimation in stochastic order and discuss the results for various underlying assumptions in terms of types of distributions, priors and weights. Finally, the time series based estimation method assumes lognormal distributions for GORT and SRRT; and, applies state-space missing data EM algorithm on each subjects SST data encompassing the order of (go/stop) trials yielding the order SST data. Then, using the Logan 1994 formula for the ordered SST data, the state-space index of SSRT is calculated. In all three methods, our examples of empirical SST data and simulated data are investigated to compare the new index to the established ones.
Student Engagement: Chinese International Student Experiences in Canadian Graduate Schools
Keywords: Cultural Identity
Internationalization
Social Justice
Student Engagement
Student Motivation
Student Voice

This study investigates the learning and practical experience of Chinese international students in Canadian graduate schools to increase their engagement and enhance their experience. Specifically, this research explores the challenges and needs faced by Chinese international graduate students engaging in Canada. Qualitative data were collected in the form of student and staff member interviews and educational policies from an urban university in Canada. Generally, the participants have varied understanding of student engagement from their experiences and cultural backgrounds. The determining factors that influence Chinese international graduate students’ engagement in Canadian institutions include cultural differences, language barriers and identities, student motivation, sense of belonging and community inclusivity, the expectations of parents, instructors, and peers, personality, interests, financial burdens and employment barriers, and gender-based differences. To better engage in and out of the classroom, Chinese international graduate students need the support of their academic and social engagement and the supporting resources. They need the support from the following areas: academic and professional development, language and cultural awareness, critical awareness and engagement, the environment based on equity, diversity, and inclusivity, sense of belonging, student voice and leadership, financial resources, health life including mental health, safety and wellness, and time management. Implications from this study suggest deconstructing the Western dominant ways of the understanding of student engagement. These findings provide some practical suggestions for Chinese international graduate students to better engage in Canadian graduate institutions and for staff to better support Chinese graduate international students. They also suggest some directions for future research.
Exploring Urinary Epigenetic Biomarkers for Identification of Aggressive Prostate Cancer
Keywords: Active Surveillance
Biomarkers
DNA methylation
miRNA
Prostate Cancer
Urine-based biomarker

Prostate cancer (PCa) is the most common non-cutaneous malignancy in men. However, the majority of PCa diagnoses are localized, low-grade, clinically insignificant (CI-PCa) tumours that are unlikely to progress. The current clinical challenges for PCa patient management are to reduce the over-treatment of patients with CI-PCa and the early identification of patients with clinically significant cancer (CS-PCa). To address these challenges, most patients diagnosed with low-grade tumours are recommended enrollment into Active Surveillance (AS) protocols where they are frequently monitored with MRI if available, repeated prostate-specific antigen (PSA) tests and digital rectal exams (DREs), and periodic prostate biopsies. Around 20-30% of AS patients will show signs of progression within 5 years. They are then “reclassified” with higher-risk disease and offered definitive treatment. While AS reduces over-treatment of CI-PCa patients, monitoring via prostate biopsies is invasive and causes morbidity. Additionally, patients who will reclassify may benefit from earlier identification and treatment instead of monitoring until symptoms of progression are exhibited. In this regard, non-invasive biomarkers that can distinguish CI-PCa and CS-PCa patients upfront, are warranted. Epigenetic dysregulations, including DNA methylation and miRNAs, are hallmarks of PCa. They are beneficial to serve as potential biomarkers for PCa diagnosis and prognosis as they require small amounts of starting material, is very stable, and can be assessed in a variety of different sources including non-invasively collected liquid biopsies such as serum or urine. Previously, my lab has discovered and/or characterized several DNA methylation and miRNA biomarkers that are associated with CS-PCa. In this thesis, I assess these biomarkers in the urine samples of patients at varying stages of PCa. I developed and validated two DNA methylation biomarker panels that can differentiate between CI-PCa from CS-PCa (AUC=0.795) and predict AS patient reclassification (OR=2.695). Additionally, I developed a panel consisting of both urinary DNA methylation and miRNAs that could predict AS patient reclassification (OR=2.166). The findings presented in this thesis, with further validation, will hopefully lead to non-invasive urine-based tests that will drive clinical decision making to improve PCa patient management and reduce invasive biopsies and their associated morbidity.
Individual and Environmental Determinants of Traffic Emissions and Near-road Air Quality
Keywords: Air quality modelling
Drive cycles
Emission factors
Local traffic characterization
Machine learning approach
Traffic emisssions

On-road motor vehicles are responsible for a considerable proportion of near-road air pollution. While background levels of air pollutants are continuously tracked by regional monitoring networks, assessing near-road air quality remains a challenge in urban areas with complex built environments, traffic composition, and meteorological variation, leading to significant spatiotemporal variability in air pollution. This research addresses current gaps in the literature on local traffic emissions and near-road air quality. This thesis first investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Traffic emissions are estimated using radar data as well as simulated traffic based on various speed aggregation methods. It provides recommendations for project-level analysis and particulate matter (PM) hotspot analysis. We further compare fleet averaged emission factors (EFs) derived from a traffic emission model, the Motor Vehicle Emissions Simulator (MOVES), with EFs using plume-based measurements. This second module stresses the need to collect local traffic information for a better understanding of on-road traffic emissions. Besides, we validate default drive cycles in MOVES against representative drive cycles derived based on real-world GPS data. The validation results are helpful for transportation planners to quantify uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories. The third module develops eco-score models and evaluates the effect of various factors such as driver and trip characteristics on emission intensities. The results shed light on the impact of driving style on emissions and identify the most important factors affecting the amount of emissions generated by every individual driver. The fourth module focuses on the impact of traffic emissions on near-road air quality and presents the results of two different experiments. First, it explores the effect of various factors on near-road ultrafine particle (UFP) concentrations based on short-term fixed monitoring, which stresses the significance of using local traffic characteristics to improve near-road air quality prediction. In addition, it captures the distribution of truck movements in urban environments and investigates the impacts of land-use variables and detailed traffic information on near-road Black Carbon (BC) concentrations.