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The use of Google Community Mobility Reports to model residential waste generation behaviors during and after the COVID-19 lockdown
No full textThis is the accepted version of the original article available at https://doi.org/10.1016/j.scs.2023.104926. © 2023 Published by Elsevier Ltd.
Accepted article is CC BY-NC-ND.This paper proposed an original analytical approach to quantify waste generation and recycling behaviors in Regina, a Canadian capital city, using Google’s Community Mobility Reports (GCMR) mobility data and ANN modeling. Residential waste collection rate (RWCR) in Regina appears to have changed abruptly during the pandemic. Compared to the pre-COVID-19 lockdown period, the correlation between the percent changes in RWCR and GCMR mobility became increasingly stronger and statistically significant during and after COVID-19 lockdown. It appears that the government imposed public health measures improved the consistency of waste generation and recycling behaviors. All 16 models predicting RWCR performed satisfactorily, with R2 near or over 0.70. The univariate Model A along with multivariate Model B and Model G performed better, with MAE of 11.5 to 15.1 tonnes/day and RMSE of 14.5 to 19.7 tonnes/day. The prediction scores of Model A using a Simple Additive Weighting (SAW) based ranking system seems to be negatively influenced by the distribution of the training and testing sets. There is no distinctive trend in the SAW-based prediction scores among the univariate model and the multivariate models. Instead of absolute values, percent changes and SAW relative ranking are adopted, making the proposed approach appropriate for cross-jurisdictional studies.The research reported in this paper was supported by a grant from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-06154) to the corresponding author, using computing equipment funded by FEROF at the University of Regina
On Koszul duality between polynomial and exterior algebras
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Mathematics, University of Regina. v, 87 p.In this thesis we will study Ext-algebras over a polynomial and exterior algebra. We prove the classical fact that the Ext-algebra over a polynomial algebra is exterior and the Ext-algebra over an exterior algebra is polynomial, using the tautological Koszul complex. We also give a proof using Koszul duality for algebras.Studentye
Ambivalent attitudes inform peer perceptions of pregnant and parenting students
No full textA Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Bachelor of Science (Honours) in Psychology, University of Regina. 72 p.Student success in university is influenced by the strength of peer relationships, especially for the growing population of pregnant and parenting (P&P) students. Although the P&P student population now comprises nearly one-quarter of all students, high drop-out rates illustrate the importance of examining the interactions between P&P students and their non- pregnant and non-parenting (non-P&P) counterparts. This study examined non-P&P students’ perceptions and stereotypes about P&P students, their valance and impact on interactions between these student populations, and the values that inform ideas about accommodations for P&P students. Twenty-five undergraduate students participated in semi-structured focus groups of 1-8 participants each. Reflexive thematic analysis generated the core theme of ambivalent attitudes inform ideas about P&P students. This encompassed five secondary themes. The first secondary theme was stereotypes influence the perception of P&P students, and the second was perceptions of P&P students are influenced by an appreciation for the challenges they face. Though P&P students need a supportive environment to succeed, they are rarely noticed in the classroom, and it should remain that way was the third secondary theme. The fourth secondary theme was ambivalent attitudes about campus climate. The final secondary theme was there should be constraints around the resources available to P&P students, which had two subthemes of P&P students should have access to accommodations and other resources and accommodations should only be available for genuine, uncontrollable circumstances. Implications for this research are discussed.Studentn
Classifying ovarian cancer using machine learning methods
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xii, 151 p.Ovarian cancer is one of the most fatal cancers for women nowadays. It is ranked as fifth
most common cancer deaths among women resulting more deaths than any other cancers
in female reproductive system. According to Canadian Cancer society that about 3000
ovarian cancer patients were detected, and among them 1950 patients died in 2022 which
indicating more than 50% of mortality rate. Ovarian cancer is mainly generated from
cancerous ovarian tumour. So, it is very important to classify cancerous tumour from
noncancerous tumour to prevent false positive for ovarian cancer. Moreover, if cancerous
tumour is diagnosed in early stage, it can be prevented from spreading and thus survival
rate for ovarian cancer can be increased. Also, by separating cancer patients from benign
tumour patients, it will be easier for doctors to know the stages of the cancer and know
patient’s prognosis and life expectancy.
The principal and initial objective of this thesis is building a feasible system using
Artificial Intelligence which is easy to use and compatible to classify ovarian cancer.
Proposed study will give a new non-conventional way to classify ovarian cancer from
ovarian tumour which will be affordable for the patients. Moreover, one of the primary
benefits of this study is that doctors/physicians can detect ovarian cancer with only blood
test/ serum test. There is no need to do any expensive tests such as: ultrasound, MRI or
CT-Scan. The main concept of this research is the application of several machine
learning techniques to correctly classify ovarian cancer and finding best technique
among those in terms of Accuracy, Precision, Sensitivity, and Specificity. Original
dataset is taken from website named Kaggle (https://www.kaggle.com/). This dataset is
screened, cleaned and normalized first and then expert’s advice has been taken to extract
the most important features to do the correct classification. Later, a correlation test has
been done for better understanding of the relations and independency among the input
features. 10 input features have been selected including age, menopause, CA-125, AFP,
NEU etc. From correlation test result 7 inputs were taken again and a comparison had
been made between 10 inputs and 7 inputs. And the output is TYPE which denotes 1 for
benign ovarian tumour and 0 for ovarian cancer. Four machine learning models have
been used for classification and they are, ANN, SVM, Naïve Bayes, and k-NN. Training
of each model is performed and after training, each algorithm is tested and hence
performance is calculated and compared. After analysing results, it is found that for the
problem considered, the Artificial Neural Network (ANN) is the best classifier in terms
of accuracy giving 85.91% accurate results on test data whereas SVM, NB and k-NN
gave accuracy of 76.05%, 83.09% and 76.06% respectively. In terms of sensitivity and
precision calculation, Naïve Bayes is best, and the ANN Classifier is second best
algorithm. Taking specificity into Consideration, the ANN is best with 87.50%.
Keywords: Machine Learning Classifier, Ovarian Cancer, Benign Ovarian Tumour,
Artificial Intelligence, Artificial Neural Network (ANN), Support Vector Machine
(SVM), Naïve Bayes (NB), k-nearest Neighbour (k-NN), Confusion Matrix.Studentye
Statistical models in ecological and health economic data
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Statistics, University of Regina. xv, 227 p.Different statistical models can be used interdisciplinary areas such as health studies, epidemiology, biology, etc., Traditional models can allow for analysis in different ways. Data on methylmercury (MeHg) concentrations and other environment parameters were collected between 2006 to 2012 from wetlands in the prairie pothole region in Saskatchewan by members of the Department of Biology, University of Regina. Data for health studies were collected from previous researchers (such as Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke from Albers, G.W., et. al.). My research goal was to use statistical models to provide researchers in other fields with some ideas and statistical results, as well as an analysis of uncertainty and variation. With MeHg data, I provide the concentration trend and with health economic data, I provide possible of models for different real cases, in particular some new mixed models and methods to simplify the pure statistical models economist may use.
In this thesis, Chapter 2, I continued my MSc’s research and used the part of my MSc’s results to predict. Chapter 3 compared probabilistic analysis and deterministic analysis in three different cases, and Chapter 4 provide analytical proof and propose simplification methods and introduce a conditional probability approach to deal with more than two state transitions in a single cycle of a Markov model.
My analysis shows the MeHg concentration in water in Saskatchewan will slowly increase in next few years based on time series analysis in Chapter 2, and suggesting that probabilistic analysis may be associated with greater bias in model inputs and that model output compares to deterministic analysis and present a small extension of the regular usage with a few examples in Chapter 3 and 4.Studentye
Strategies to Promote Breastfeeding Practices of Refugee Mothers with PTSD
No full textPost-traumatic Stress Disorder (PTSD) negatively affects the breastfeeding practices of mothers. Refugee mothers are high-risk groups. This resource is developed in consultation with the patient partners and healthcare providers. It presents strategies to promote, protect and support breastfeeding practices of refugee mothers with PTSD. Our gratitude to the Saskatchewan Health Research Foundation (SHRF) and the University of Regina for supporting this work
Characterization of fracture networks using tracer tests in a hydrocarbon reservoir
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Petroleum Systems Engineering, University of Regina. xxx, 232 p.In a hydrocarbon reservoir, natural and/or hydraulic fractures can not only provide main paths for fluid flow and increase formation permeability, but also complicate flow behaviour and production performance. Tracer tests have been widely used for reservoir characterization to determine residual oil saturation, reservoir wettability, and fluid flow paths; however, few attempts have been made to characterize complex fracture networks by quantifying tracer transport behaviour due to the associated technical challenges including the composite effects of fluid flow and geomechanical dynamics, fracture geometries, and tracer transport mechanisms. It is, therefore, of fundamental and practical significance to quantitatively identify such fracture networks and accurately evaluate the well/reservoir performance.
A pragmatic technique has been developed to describe tracer flowback behaviour for a vertically fractured well by coupling fluid flow and geomechanical dynamics. TheBarton-Bandis model is employed to describe the relationship between effective stress and fracture properties, while tracer flowback profiles are quantified by taking tracer advection, tracer dispersion, and tracer adsorption into account. Effects of the fracture propagation dynamics on the tracer flowback behaviour are then examined and discussed. The tracer recovery factor with considering geomechanics is nearly 20% higher than that without considering geomechanics.
An efficient and effective numerical model based on the embedded discrete fracture model (EDFM) is then developed and applied to deal with a fractured reservoir by independently constructing matrix and fracture grids. Subsequently, non-neighbouring connections (NNCs) are employed to couple the flow of fluid and tracer between the non-neighbouring grids. The tracer flowback profiles have been investigated for a fractured horizontal well with four different fracture network patterns including bi-wing fracture network (BWFN), opening-fissure fracture network (OFFN), fractal-like fracture network (FLFN), and mutually orthogonal fracture network (MOFN). It is found that tracer flowback concentration varies greatly with different fracture network patterns.
To improve the simulation accuracy and reduce the grid orientation effects associated with fractal-like fracture networks, the EDFM based on the perpendicular bisection (PEBI) grids is then employed so that the generated grids flexibly conform to the complex fractal-like fractures. Furthermore, the tracer flowback profiles, which can reveal the complexity of fractal-like discrete fracture networks, are quantified by considering complex tracer transport mechanisms. The structured grid-based EDFM is further used to characterize the fracture distributions in a naturally fractured reservoir conditioned to interwell tracer transport behaviour. The stochastic fracture modelling approach is implemented to generate the randomly-distributed natural fractures. The response of such an interwell tracer model is found sensitive to the fracture parameters rather than tracer properties.
All of the numerical models have been validated and then extended to field cases to characterize the fracture networks. Tracer production concentration profiles are generated and beneficial to examine the effects of different parameters on the tracer transport behaviour in a hydrocarbon reservoir. Through comparison with other reservoir characterization methods (e.g., microseismic monitoring), the accuracy and efficiency of the newly developed models are further confirmed.Studentye
CO2 emissions reduction through catalytic production & use of fuels derived from biomass
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Process Systems Engineering, University of Regina. xv, 190 p.This study focused on the preparation of environmentally friendly heterogenous catalyst from biomass specifically waste egg shells, cow bones and fish scales for use in the production of biodiesel from waste cooking oil (WCO) which is also a biomass-based waste feedstock collected from households that will be an alternative fuel source and contribute to reducing CO2 emissions into the environment as well as the cost of biodiesel production To begin, the biodiesel feedstock properties needed to be checked for its suitability in making fuel because the feedstock properties were going to impart the properties of biodiesel. The first property of the oil checked was the fatty acid composition.
The need for a further purification step apart from filtration was confirmed and other properties of the feedstock such as viscosity, density, acid value and water content of the WCO were also checked. Based on these, the WCO fell in the range within which it could be used in making biodiesel, there was no need for further physical purification steps because the properties of both the crude and purified WCO were very close. In addition, the properties showed that a catalyst basic in nature was suitable for the transesterification process.
The next phase involved carrying out reactions with the conventional basic catalyst (KOH) to serve as a baseline for the work to which heterogenous catalytic reactions would be compared. The heterogenous catalysts were then synthesized from waste egg shells (ES), cow bones (CB) and fish scales (FS) separately before bi-blend (CBES, FSCB, FSES) and tri-blend mixtures (M3) of these components were made in a ratio of 1:1 and 1:1:1 respectively and characterized. The focus of the catalyst was on the performance of M3 and how CB, FS and ES contributed to that as well as its performance in comparison to biii blend mixtures that have been the kind of blends prepared in literature for biodiesel catalysts. All seven heterogenous catalysts were utilized in transesterification reaction of the WCO at the same process conditions to evaluate their performance with respect to biodiesel yield. The biodiesel yield of these catalysts followed a trend of decreasing order as follows: M3 > FSES > CBES > ES > FSCB > FS >CB mainly due to the basicity resulting from the type of active components present in these catalysts. Regression analysis was performed to further validate which characteristics affected the performance and it was confirmed that the most important characteristic of the catalysts in this work was basicity.
Furthermore, since M3 was the focus and the best performing catalyst amongst the heterogenous catalysts as well, it was compared to the yield of the homogenous reactions that was used as the baseline and a difference of averagely 27% was observed. M3 was then used in reactions to study the effect of the process variables on the biodiesel yield and the optimum conditions were found to be a temperature of 60°C, 6 hours of reaction time, stirring speed of 600 rpm, ethanol-to-oil molar ratio of 15:1 and catalyst concentration of 2wt% of the oil.Studentye
Improving applicability of the non-monotone unified estimate for missing data
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Statistics, University of Regina. viii, 115 p.In applied statistics missing data are a common problem. Performing a "complete case
analysis" by removing individuals with missing data causes a loss of statistical power
and can cause non-response bias. Inverse probability weighting is one method used to
avoid non-response bias. However, when some individuals have partially observed data
inverse probability weighting has only a limited ability to use this data. The unified
approach (Zhao and Liu, 2021) is a modification of inverse probability weighting
that uses "working models" to extract information from individuals with partially
observed data. When the probability an individual has missing data can be accurately
modeled but the distribution of the data is difficult to model the unified approach is
an attractive option. In this thesis we review the theory of the unified estimate and
its application to the Cox proportional hazards model for survival data. We present a
new R program which can be used to easily fit the unified estimate for generalized
linear models or Cox proportional hazards models. Possible hypothesis tests for the
fit of the unified estimate and directions for future research are suggested.Studentye