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Punishment orientation and juror decision-making in sexual assault trials
No full textA Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Bachelor of Arts (Honours) in Psychology, University of Regina. 26 p.Objectives: The study aimed to determine whether gender differences and punishment orientation predict juror decision-making in sexual assault trials.
Hypotheses: There will be a main effect of punishment orientation, such that the more punishment-oriented someone is, the more likely they will find the defendant guilty. There will be a gender difference in verdict decision, such that women will be more likely to find the defendant guilty. This relationship will be qualified by an interaction with punishment orientation, such that there will be a gender difference in verdict only among those who are less punishment-prone.
Method: A sample of N = 211 (101 female, 110 male) Canadian jury-eligible community participants were recruited through the crowdsourcing platform Prolific. Participants read a trial in which a man is charged with sexual assault against a woman. Participants chose a verdict (guilty, not guilty) and rated their confidence in that verdict (where 0 = not at all confident, and 10 = very confident). Participants then completed measures of rape myth acceptance and punishment orientation.
Results: Consistent with previous research, we found that men showed higher rape myth endorsement than women and women were more likely to find the defendant guilty. There was no gender difference in POQ scores. POQ scores did not predict verdict decisions.
Conclusion: This study adds to the limited research on punishment orientation in sexual assault trials while helping us to better understand the role that punishment plays in guilt decisions. The study tests the current legal assumptions about the right to a fair trial and whether juries can render decisions without considering punishment
Saskatchewan fossils inform the timing and ecological evolution of extant arthropod lineages
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 Biology, University of Regina. x, 139 p.Questions remain regarding the evolutionary and ecological history of insects, in part due to their low potential for fossilization. There is a 16-million-year gap in the insect fossil record surrounding the Cretaceous-Paleogene (K-Pg) mass extinction (~66 Ma), a catastrophe that shaped the subsequent evolution of modern biotic communities. The rarity of insect fossils near the K-Pg boundary limits our understanding of how insect lineages were impacted by this event and latter half of the Angiosperm Terrestrial Revolution (100–50 Ma). Here, I report a diverse amber assemblage from the Late Cretaceous (67.04 ± 0.16 Ma) of the Big Muddy Badlands, Canada that fills this critical faunal gap. Seven arthropod orders and at least 11 insect families have been recovered, making the Big Muddy deposit the most diverse arthropod assemblage near the end-Cretaceous extinction. Amber chemistry and stable isotopes suggest the amber was produced by coniferous (Cupressaceae) trees in a subtropical swamp near the Western Interior Seaway. In addition to characterizing the palaeobiology and geochemistry of Big Muddy amber, I describe two new partial ants (Formicidae: Aneuretinae; Formicidae: Pseudomyrmecinae) from the deposit. These ants are the oldest definitive fossil records for their respective extant formicid subfamilies. Reconstructing ant paleoecology is essential for understanding their evolution and biogeography. I predicted the palaeoecology of the two Big Muddy ants using a Random Forest machine learning algorithm and compare their ecological occupations to their modern relatives and younger fossil conspecifics. The predicted palaeoecological roles of the Big Muddy Pseudomyrmecinae are different than the predicted palaeoecology of their Eocene (48–34 Ma) cousins and the known ecology of their living relatives. We recover the Big Muddy pseudomyrmecine, whose extant relatives are exclusively arboreal and restricted to the Southern Hemisphere, as leaf-litter dwelling. Our results suggest that, besides a broader Cretaceous geographic range, pseudomyrmecines may have occupied a wider range of ecological roles in the past. Their relictual biogeography may reflect a response to palaeoenvironmental changes, including the rise of flowering plants and the retraction of tropical biomes, during the Late Cretaceous and Cenozoic. The unexpected abundance of ants from extant families in Big Muddy amber and virtual absence of arthropods from common, exclusively Cretaceous families suggests that the deposit may represent a yet unsampled Late Cretaceous environment and provide evidence of a faunal transition among insect families before the end of the Cretaceous.Studentye
Canadian residential schools & diabetes
No full textA Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Bachelor of Arts (Honours) in Psychology, University of Regina. 39 p.Residential schools were once utilized as a means of assimilation for First Nation and Indigenous people across North America. With that goal in mind, it has become accepted common and documented knowledge that the environment, treatment, facilities, and nutrition were sub-par and below both historical and modern standards for the healthy development of children. Numerous physical, mental, emotional, and spiritual abuses occurred to attendees resulting in historic, intergenerational trauma. In addition to this, there is an epidemic of diabetes sweeping across Canada with rates of diabetes among Indigenous people being twice as high than those among other Canadians. A previous study showed that cultural continuity in the form of language fluency was a protective factor against diabetes and suicidal ideation in on-reserve Indigenous people. In the present study, we used data from the Aboriginal Peoples Survey 2017, which surveyed Canadian Aboriginal people living off-reserve. The data was used to explore the relationship between diabetes and attendance at former residential schools, as well as cultural continuity as a protective factor against diabetes and suicidal ideation among survivors and intergenerational survivors. Intergenerational attendance at residential schools was tracked back to the time of the parents (2nd generation) and grandparents (3rd generation) of those that responded to the survey. To explore the relationships between the binary variables involved, cross-tabulations, logistic regression models, Pearson correlations and chi square tests of independence were used. The findings of the research is discussed and disclosed.Studentn
Mindfulness and cognitive training interventions that address intersecting cognitive and aging needs of older adults
No full textSummary
Mindfulness and cognitive training interventions are promising models to address impacts (e.g., anxiety and stress) of cognitive impairment among older adults. Combining strategies may yield better outcomes than models offered in isolation. However, there are numerous uncertainties about these interventions, potential for combination, and implementation. Social workers are well placed to offer these interventions.
Findings
From an initial search of 3,538 records, 13 studies were included in the final review. Mindfulness studies focused on stress reduction or cognitive behavioral therapy. Cognitive training studies applied stimulation or activity approaches. Results indicate that the field is still emerging, as most studies were pilot or feasibility trials. A combination of mindfulness-based stress reduction and brain training activities may offer the most promising model for older adults with cognitive impairment, based on outcome assessments and other factors. A common limitation among the reports was detailed on engaging older adults with cognitive challenges in the design and implementation of these interventions.
Applications
This realist review deepens the understanding of how, why, for whom, and in what circumstances a combination of mindfulness and cognitive training could be most successful for social workers to address intersecting cognitive and aging needs of older adults. Building evidence on combining mindfulness-based stress reduction and brain training activities among older adults with cognitive impairment could yield promising results, and this review identifies implementation considerations. The review also found a need for psychometric scale development on the benefits of brain training activities.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a Planning and Dissemination Grant from the Canadian Institutes of Health Research (CIHR #478015) and a Pilot Study Grant from the CIHR Canadian HIV Trials Network (CTN #PT029)
Comparative evaluation of alpha functions and volume-translation strategies to predict saturation pressures and densities of gas(es)-heavy oil/bitumen-water systems
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 Master of Applied Science in Petroleum Systems Engineering, University of Regina. xvii, 150 p.Recently, solvent-based enhanced oil recovery (EOR) methods have gained
significant interest for heavy oil reservoirs where conventional recovery techniques find
their limits. CO2 has emerged as a popular solvent due to its ability to dilute heavy oil;
however, its solubility in heavy oil is limited. Adding alkane solvents such as C3H8 and
C4H10 into a CO2 stream helps to increase its solubility in heavy oil, leading to an improved
reduction in viscosity and density as well as an accelerated swelling effect. Therefore, it is
of a practical and fundamental importance to predict the saturation pressures and densities
of gas(es)-heavy oil/bitumen-water systems under reservoir conditions.
To accurately predict saturation pressures for gas(es)-heavy oil/bitumen-water
systems, several α functions have been selected and evaluated at a reduced temperature
(Tr) of 0.70 and 0.60 for both the Soave-Redlich-Kwong equation of state (EOS) and Peng-
Robinson EOS, respectively. More specifically, 164 data points of measured saturation
pressures of gas(es)-heavy oil/bitumen-water systems are collected from the public
domain, while all α functions developed for heavy oil-associated mixtures and water have
been reviewed and selected. At Tr=0.70, the former including three existing α functions as
well as two newly developed α function at Tr=0.70 together with three new α functions at
Tr=0.60 and the latter including two alpha functions are used to evaluate saturation
pressures for various gas(es)-heavy oil/bitumen-water systems under various conditions.
The absolute average relative deviation (AARD) between the measured saturation
pressures and their predicted ones is found to decrease with either an increase in the
pseudocomponent (PC) number or redefining at Tr=0.60 other than the conventional
one at Tr=0.70. In addition to validating the newly coded MATLAB program, the CMG
WinProp module together with its default binary interaction parameters (BIPs) is
employed to respectively quantify saturation pressures of the aforementioned systems with
an overall AARD of 27.34% and 28.39% for the PR EOS and SRK EOS. The
recommended α function at Tr=0.60 from Chen and Yang (2017) predicts saturation
pressures more accurately with an overall AARD of 3.88% and 1.64% by respectively
treating the heavy oil as one PC and six PCs.
Then, a unified, consistent, and efficient framework has been proposed to better
predict the density of a gas(es)-heavy oil/bitumen system by using the SRK EOS and PR
EOR together with α functions and volume-translation (VT) strategies. With a database
comprising of 218 experimentally measured densities for gas(es)-heavy oil/bitumen
systems, these eight α functions together with four VT strategies are selected and
evaluated. For the three original α functions defined at Tr=0.70, the VT strategies proposed
by Jhaveri and Youngren (1988), Peneloux et al. (1982), Shi et al. (2018), and Twu and
Chan (2009) lead to an overall AARD of 9.74%, 7.21%, 7.16%, and 7.02%, respectively,
for predicting the mixture densities. For the new α function defined at Tr= 0.70, these four
VT strategies predict the mixture density with an AARD of 5.01%, 3.13%, 2.92%, and
2.56%, respectively. As for the two new α functions defined at Tr=0.60, these four VT
strategies predict the mixture density with an AARD of 2.57%, 1.38%, 1.67%, and 1.34%,
respectively, among which the VT strategy proposed by Twu and Chan (2009) has a very
close prediction compared to an AARD of 1.31% obtained from the ideal mixing rule with
effective density. It is important to note that the VT strategy proposed by Peneloux et al.
(1982) together with α function #8 predicts the densities of heavy hydrocarbon mixtures
more accurately compared to the other three VT strategies together with α function #7.Studentye
The things of now
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 Arts in Creative Writing and English, University of Regina. v, 131 p.The Things of Now uses a blend of creative non-fiction and poetry to explore what it means to live in this contemporary moment. The work can be considered a memoir, one which is guided by Patricia’s Hampl’s insight that the self can be transformed into an instrument of observation. Covering aspects of my formative years living in post-apartheid South Africa, my time spent as a member of the professional managerial class in Cape Town as an adult, and my shift into economic precarity as a graduate student in Canada, the thesis touches upon my gradual awakening to the forces, both macro and micro, shaping my lived experience at this moment in time.
To reflect the complex nature of contemporaneity, The Things of Now is multi-generic in structure, placing it within the realm of autotheory. By utilizing an autotheoretical approach, the manuscript, in the words of Lauren Fournier, can be seen to “exceed existing genre categories.” This allows me not only to examine how large-scale phenomena such as capitalism, colonialism, and global warming influence our present moment, but also how my own lived experience is intertwined and shaped by these forces.
The work does not seek to provide any final conclusions regarding the questions it raises. Rather, it is meant to create a sense of disorientation in the reader, with a plethora of different perspectives converging to form an abstract whole that is nonetheless cogent in its inquest into the nature of the present.Studentye
Cellular network KPI prediction on simulated 5G-NR V2N traffic dataset using machine learning
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. x, 99 p.The arrival of 5G has brought a promise of better connectivity for users, but also a challenge for cellular networks to maintain high-quality service and energy efficiency. To optimize the network and meet user demands, a resource management system is used to allocate resources in the 5G Radio Access Network (RAN). However, manual tuning of this system is complex and time-consuming. By predicting the future behavior of Network Key Performance Indexes (KPIs) of the 5G network using Artificial Intelligence (AI) and its subfield, Machine Learning (ML), this study can automate the operations of the Resource Management system, improve resource allocation, and satisfy QoS requirements while optimizing energy consumption.
However, to develop a better performing ML model, a high-quality dataset is essential. Since there is a lack of open datasets available on 5G systems, many researchers rely on synthetically generated datasets. This thesis work utilized 5G simulation tool to simulate 5G New Radio (NR) Vehicle-to-Network (V2N) communication using OMNeT++ and SUMO simulators. The NR V2N communication was simulated in a Regina downtown scenario using the proposed simulation framework, and the simulation results were processed using the developed new_df Python module into synthetic datasets that were validated by comparing with technical specifications to ensure their quality.
The synthetic datasets were then used to develop proposed Network KPI prediction models using ML. Three ML models are trained and tested, which can predict multiple KPIs, bi-directional Signal to Interference and Noise Ratio (SINR) and classify uplink Channel Quality Indicator (CQI) respectively. The multi-output regression models have shown outstanding performance with MSE as low as 0.002, and the multi-class classification model has a high accuracy.
In summary, this study contributes to the development of efficient and automated Resource Management systems for 5G networks using AI and ML techniques. An open source V2N simulation framework was developed using OMNeT++ and SUMO simulators that can simulate 5G-NR V2N communication in a realistic urban scenario. Moreover, a new_df Python function was developed for processing simulation results into an aggregated dataset and spatiotemporal dataset, providing a high-quality dataset that can be used to train and test ML models for predicting Network KPIs of the 5G network.Studentye
Analysis and prediction of traffic accidents at urban intersections
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 Environmental Systems Engineering, University of Regina. x, 90 p.Traffic accidents generate significant harm, injuries, and fatalities on a global scale,
making their investigation an important topic of study. In the initial phase of the study, we
examine the influence of three micro traffic parameters, namely standstill distance,
headway duration, and following distance oscillation, on both traffic flow and intersection
safety. A data-driven approach is proposed for identifying the Pareto optimal sets of
parameter combinations that both maximise the flow volume and minimise the risk of
crashes. The analysis of traffic flow is conducted by considering different scenarios
involving standstill distances ranging from 0 to 5 metres, headway times of 0.5 seconds,
0.9 seconds, and 5 seconds, as well as following distances spanning from 1 to 10 metres.
The trajectories derived from the microsimulation model are further examined using the
surrogate safety assessment model to ascertain the distribution of time-to-conflict between
vehicles. This analysis facilitates the estimation of risk for crash by employing extreme
value theory. The findings from the case study of Lewvan Drive and 13th Avenue
intersection suggest that utilisation of headway times of either 0.5 or 0.9 seconds, in
conjunction with standstill distances exceeding 2 metres and following distance
fluctuations ranging from 1 to 9 metres, guarantees the mitigation of crash risks to a
minimum level, while simultaneously resulting in maximum traffic flows. The subsequent
phase of the research endeavours to construct dynamic forecasts of accident rates at
intersections by taking macro traffic variables into account. Additionally, it evaluates the
predictive efficacy of statistical models, machine learning methods, and neural network
algorithms. The initial step involves conducting Pearson's correlation and statistical
analysis to ascertain the associations between the macro variables, namely the number of
accidents, average daily traffic on weekdays for major and minor roads, number of legs at
the intersection, traffic signal conditions, intersection location, and peak hours. A threshold
value of 0.7 is employed to verify the existence of collinearity among the variables. Based
on the correlation analyses, the study further employs prediction models of three streams,
including statistical analysis (Negative Binomial Model), machine learning algorithm
(ARIMA Model), and neural network (Multi-Layer Perceptron Model). All models
leverage a common dataset, which is transformed into an hourly time series prior to the
application of the models. The results of this study underscore the progress made by
various prediction algorithms in accurately anticipating the incidence of traffic accidents
at crossings, as well as the interrelationship between traffic volume and signal
characteristics.
The study's findings offer valuable insights and a framework for policymakers to
effectively implement safety regulations and ensure satisfactory traffic flow. Additionally,
these findings can aid in reducing accidents and optimising roadway capacity.
Furthermore, the study provides valuable insights for drivers, helping them understand the
importance of maintaining safe distances while driving and ultimately reducing risky
manoeuvres while maximising traffic flow. Subsequently, the utilisation of the dynamic
accident prediction model empowers policymakers to assess and forecast the efficacy of a
safety measure with regards to factors such as traffic volume and intersection control
through a comparative analysis of the changes in the variables both before and subsequent
to the implementation of the intervention.Studentye