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A Multi-Pronged Approach to Studying Human-Animal Interactions in Zoo Settings
No full textZoos of the past focused primarily on animal exhibition, yet the modern zoo has shifted to a focus on animal conservation and public education. This change has coincided with a negative shift in public opinion towards zoos after documentaries such as Blackfish, leading to comparatively more positive views of sanctuaries. These preferences seem to be influenced by the lack of animal exhibition at sanctuaries, suggesting that human influences on animals are important to public perceptions of zoos. Thus, human-animal interaction research is essential to understanding perceptions of zoos and human influences on captive animals. This dissertation addresses both factors. Study 1 assessed public opinions on zoos versus sanctuaries, and investigated how these opinions are impacted by knowledge of zoo practices. Highlighting any positive information, but particularly in relation to conservation, led to more positive public opinions. Studies 2 and 3 considered human-animal interactions through human impacts on captive animals, as further knowledge could both increase animal welfare, and then, positively influence public opinion. Study 2 examined the influence of visitors on the behaviors of zoo-housed parrots in an aviary. Birds engaged in decreased positive behaviors, increased negative behaviors, and more birds were present as visitor numbers increased. The increase in negative behaviors was minimal compared to the increase in birds present, which may indicate the birds were not negatively impacted by visitors. Study 3 evaluated the judgment biases of two ambassador animals after exposure to zoo visitors. The chicken displayed pessimism whether it was held or perched, but the tegu displayed pessimism only when no visitor touch occurred. This suggests negative effects of visitor interactions for the chicken, but touch interactions may not be aversive to the tegu. All three studies contribute to our understanding of human-animal interactions for the improvement of animal welfare and public perceptions of these facilities
Barriers to Adequate Nutrition During the Critical Fetal Period: A Postpartum Cross-Sectional Study
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The Use of Narrative Drawings to Improve the Understanding of Threshold Anatomical Concepts
No full textMinutes of the Formal Session of the Oakland University Board of Trustees, June 28, 2024
No full textA. Pledge of Allegiance | B. Call to Order | C. Roll Call | D. President's Report | E. Consent Agenda for Consideration/Action: Consent Agenda; Treasurer's Report; Minutes of the Board of Trustees Formal Session of April 12, 2024; University Personnel Actions; Acceptance of Gifts and Pledges to Oakland University for the Period of April 2, 2024 through June 17, 2024; Acceptance of Grants and Contracts to Oakland University for the Period of March 1 – April 30, 2024; Final Graduate School Report, Winter 2024 - April 24, 2024; Final Medical School Report, Winter 2024 - June 1, 2024; Approval of Honorary Degree for Austin Channing Brown, Telva McGruder; Appointment of Nelia Afonso for the Title of Dean's Distinguished Professor; Appointment of Jason Wasserman for the Title of Dean's Distinguished Professor; Intercollegiate Athletics Operating Budget for the Fiscal Year Ending June 30, 2025; Meadow Brook Estate Operating Budget for Fiscal Year Ending June 30, 2025; Oakland Center Operating Budget for the Fiscal Year Ending June 30, 2025; University Housing Operating Budget for the Fiscal Year Ending June 30, 2025; Oakland County Tactical Consortium Agreement; Name Change: Department of Art and Art History to Art, Art History and Design Department; 2025 Oakland University Board of Trustees Regular Formal Session Dates | F. New Items for Consideration/Action: Resolution Honoring Payton E. Bucki, Student Liaison to the Oakland University Board of Trustees; Resolution Honoring Red Douglas, Student Liaison to the Oakland University Board of Trustees; Bachelor of Science in Engineering Degree in Mechatronics and Robotics Engineering; Master of Science in Energy Engineering; Vehicular Wireless Communication Test System (Antenna Chamber); General Fund Budget and Tuition Rates for FY2025; Resolution Honoring Robert I. Schostak; Appointment of Board Chair and Vice Chair | G. Other Items for Consideration/Action that May Come Before the Board | H. Adjournmen
Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects
No full textThis dissertation considers three critical aspects of modern statistical analyses and machine learning, namely, (i) addressing the challenges posed by assessing the distributional assumptions of a univariate dataset, (ii) constructing graphical tests for the multivariate normality assumption, and (iii) exploring new algorithms for solving group classification problems, especially when the distance-based methods are not applicable. First, we focus on the development and evaluation of graphical statistical tests for univariate datasets, aiming to assess any specific distributional assumption. Examination of normality is given special emphasis. Recognizing the impact of outliers on the normality assumption, this study also incorporates outlier detection methodologies and provides some graphical tools for their identification. T4 plot is introduced as an additional effective tool for this purpose. Examination of multivariate normality is more challenging as, in this case, nonnormality may exhibit or mask itself in many different ways. Thus, in the second part, we emphasize graphical assessments of the multivariate normality assumption. This is done via MT3 and MT4 plots based on the derivatives of the cumulant generating function. The final segment of the dissertation shifts the discussion towards machine learning algorithms devised specifically for group classification problems. This involves the exploration of new methodologies that address the challenges inherent in classification and discrimination within complex datasets where other standard methods based on the classification of individual observation may not be very effective. For this, we rely on the data's eigenstructures. In the process, we also address the problem of dimensionality reduction. The problem of selection of copulas can be viewed as a corollary of the group classification problem. This has also been discussed in a separate chapte
