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Release: University of Regina marks National Day of Remembrance and Action on Violence Against Women
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Release: University of Regina expands career-ready learning opportunities with the opening of its new Centre for Experiential and Service Learning
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Understanding how Saskatchewan parents promote children’s mental health: A grounded theory study
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 Education, University of Regina. xi, 256 p.This dissertation contributes to MHP scholarship by focusing on how parents from a prairie province in Canada understand and promote their children’s mental health. Middle childhood was chosen because it is a ‘sensitive period’ of human development (DelGuidce, 2018) that is often overlooked and understudied (Montreuil et al., 2018). The salutogenic perspective underpins this research; therefore, the origins of health and how it is promoted were of interest in contrast to the prevention of illness and dis-ease. To guide this inquiry, the qualitative research tradition was chosen, and the constructivist grounded theory methodology served as its roadmap. In total, 23 parents of children between the ages of 7 and 12 agreed to participate. Each parent took part in a 1- hour intensive interview that was audio recorded and subsequently transcribed. Interview data were analyzed using initial and focused coding techniques, as is common in constructivist grounded theory (CGT). Memo writing was also an integral component of the anlytic process in order to aid in theory development. From the analyses, five categories were co-constructed from participants’ thoughts and experiences, as well as my own; they are: defining mental health, reflecting on mental health, attending to mental health needs, living with digital media, and prioritizing connection. These categories underpin raising ‘good’ people, a substantive grounded theory to conceptualize the basic social process Saskatchewan parents undergo to promote their children’s mental health, which is one that establishes social well-being as a fundamental component of subjective well-being and children’s mental health.Studentye
Hot Tensile Behavior of Ti-6Al-4V Alloy Using Artificial Neural Network and Constitutive Modeling
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. xix, 138 p.Nowadays a considerable quantity of metallic parts has been manufactured via metal forming processes, such as rolling, stamping, drawing, etc. at the ambient temperature. However, limited workability of some alloys may lead to shaping them at higher temperatures, where the material can flow more easily and the part would contain less defects. Since the stress at low temperature is a function of strain (the relative amount of deformation), the flow behavior of material at these temperature is rather straightforward. At high temperature other parameters, namely temperature and strain rates, should be also taken into account in predicting the flow stress. Our goal in this research is to predict the high temperature behavior by introducing different constitutive and numerical approaches.
The Ti-6Al-4V alloy’s promising mechanical properties such as excellent strength-to-weight ratio and great chemical and thermal resistance make it a great choice to be applied in different industries such as medical, marine, aerospace. This Thesis focuses on predicting the behavior of this alloy under tension at high temperatures and aims to find a model which can describe the stress-strain relevance accurately under desired external circumstances required for any deformation process.
There are various models trying to exploit the non-linear stress dependencies on temperature, strain, and strain rate. Herein, the constitutive and the Artificial Neural Network (ANN) models will be explained with all their benefits and drawbacks in details. For the constitutive model the material constants will be derived, the model
will be developed, and the obtained results are considered for prediction accuracy comparisons.
Afterwards, the ANN multi-layer feedforward with backpropagation and Radial Basis Function (RBFN) network will be discussed. These networks can operate as a Blackbox to predict the unknown and highly nonlinear relationship between input and output parameters. Two ANNs feed forward networks with different layers and neurons in each layer, as well as an RBFN are trained and simulated using the MATLAB Toolbox. The RFBN is very efficient for fitting the data especially when there is not a sharp change or interruption in the corresponding true results and there is a function to approximate.
The results of all models are compared to each other, first of all in case of a well fitted model by analyzing the statistical measurements such as Correlation Coefficient (R), Average Absolute Relative Error (AARE) and Root Mean Square Error (RMSE). The results demonstrate a clear improvement from the constitutive model to ANN feed forward networks, and later RBFN with 0.999, 2.17 %, and 1.59 for the corresponding modules. The other important criterion is how cumbersome it would be to obtain some models, especially constitutive models constants finding or search of a global minimum for feed forward network. So the RBFN is found to be the best method of training a favorable network.
Recently, there are many studies aiming to reduce the number of neurons in the RBFN. Those methods could be really helpful to figure out the important data points giving the most useful information about the stress-strain curves and later finding the optimal experiments giving the perfect results. This could be a great opportunity as reducing the cost of investigation could help to predict behavior of a wide range of materials properly, and pave the way for industries to exploit new desired mechanical properties.Studentye
Generalized fiducial inference on the means of zero-inflated Binomial and Binomial hurdle models
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 Statistics, University of Regina. xii, 73 p.The zero-inflated binomial and binomial hurdle models are two models to address the problem of excess zeros for proportional data. Both model structures are similar, which are composed of two distributions. A degenerate process is responsible for producing zeros, which are called structural zeros. The other distribution could be a binomial or zero truncated binomial distribution which depends on the type of model in this study. Zero-inflated binomial (ZIB) model involves the sampling zeros. On the other hand, binomial hurdle(BH) model address the structure zeros only in the zero-inflated part, which is a difference between the two models. Generalized fiducial inference is a popular statistics analysis which is independent of frequentist school and Bayesian school. This method was first proposed by R.A. Fisher to challenge Bayesian school. The core idea is to switch the role of parameters and data, and to construct a distribution for parameters that contain all of the information of data. One significant advantage of generalized fiducial inference is that it does not rely on a prior distribution. In this research, the generalized fiducial inference is introduced to construct the confidence intervals for the means of zero-inflated binomial and binomial hurdle models and the formulation of confidence intervals are derived. A simulation will be conducted to test the performance of generalized fiducial inference on single parameters of ZIB model and BH model. Then the performance of generalized fiducial inference on the means of ZIB model and BH model will be tested through observing the coverage probability and mean width for the generalized confidence interval (GCI). We make a comparison between generalized confidence intervals and bootstrap confidence intervals. Two special cases of ZIB model and BH model are also considered. Generalized fiducial inference is also illustrated using the whitefly data. Through this data, we can test the performance of generalized fiducial inference under different numbers of trials and sample sizes. We construct generalized confidence intervals and bootstrap confidence intervals to show an excellent property of the generalized fiducial inference.Studentye
Release: University of Regina and University of Saskatchewan kickoff the province's largest varsity sports competition
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Why do people self-censor on social media? A metacognitive approach
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 Experimental and Applied Psychology, University of Regina. viii, 47 p.A lot of focus has been put on the fact that people are sharing too much misinformation online; however, the failure to share accurate and high-quality content may be just as problematic. Indeed, although there is a growing body of work examining the psychological mechanisms that contribute to the spread of misinformation, little focus has been directed on why people fail to share accurate content online. One potential explanation is self-censorship—when an individual does not share their opinion with a group that they perceive to hold differing opinions. One possibility is that self-censorship may emerge from metacognitive factors, such as self-reflection about what is appropriate to share. If this is true, then it may be that the people who are most likely to self-censor are also those who should be sharing the most. Consistent with this, I found that people who were better able to distinguish between true and false news were actually more willing to self-censor. Self-censorship was also modestly associated with lower closemindedness and less overconfidence. However, counter to expectations, it was found that preference for effortful thought was negatively associated with self-censorship. These findings indicate that metacognitive factors do, in fact, impact willingness to self-censor but additional work is still needed as the effects were small.Studentye
Feature Story: University of Regina Engineering Capstone Project Day 2022 to be held in person
No full textThe innovation and creativity of University of Regina students graduating from the Faculty of Engineering and Applied Science will be on display this Saturday at the annual Engineering Capstone Project Day event. After two years of online Project Days, the Faculty is excited to return to an in person event.
The event runs from 8:30 a.m. to 3:30 p.m., Saturday, April 9 on the Main Floor of the Education Building. Admission and parking is free, and the public is welcome to attend the presentations, trade show, and poster session.Staffn
Seasonal variability of CO 2, CH 4 , and N2 O content and fluxes in small agricultural reservoirs of the northern Great Plains.
No full textInland waters are important global sources, and occasional sinks, of CO 2 , CH 4, and N 2 O to the atmosphere, but relatively little is known about the contribution of GHGs of constructed waterbodies, particularly small sites in agricultural regions that receive large amounts of nutrients (carbon, nitrogen, phosphorus). Here, we quantify the magnitude and controls of diffusive CO 2 , CH4 , and N 2 O fluxes from 20 agricultural reservoirs on seasonal and diel timescales. All gases exhibited consistent seasonal trends, with CO 2 concentrations highest in spring and fall and lowest in mid-summer, CH 4 highest in mid-summer, and N 2 O elevated in spring following ice-off. No discernible diel trends were observed for GHG content. Analyses of GHG covariance with potential regulatory factors were conducted using generalized additive models (GAMs) that revealed CO 2 concentrations were affected primarily by factors related to benthic respiration, including dissolved oxygen (DO), dissolved inorganic nitrogen (DIN), dissolved organic carbon (DOC), stratification strength, and water source (as δ18 O water ). In contrast, variation in CH 4 content was correlated positively with factors that favoured methanogenesis, and so varied inversely with DO, soluble reactive phosphorus (SRP), and conductivity (a proxy for sulfate content), and positively with DIN, DOC, and temperature. Finally, N 2 O concentrations were driven mainly by variation in reservoir mixing (as buoyancy frequency), and were correlated positively with DO, SRP, and DIN levels and negatively with pH and stratification strength. Estimates of mean CO 2 -eq flux during the open-water period ranged from 5,520 mmol m−2 year 1 (using GAM- predictions) to 10,445 mmol m−2 year−1 (using interpolations of seasonal data) reflecting how extreme values were extrapolated, with true annual flux rates likely falling between these two estimates.Financial support for data collection and analyses were
provided in part by Government of Saskatchewan (Award
200160015), Natural Sciences and Engineering Research
Council of Canada Discovery grants (to KF, GS, HB, and PL),
the Canada Foundation for Innovation (Award RGPIN–2018-
0490), University of Regina.Facultyye
