dissertation proposal announcement

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Loyola University Chicago

The graduate school, dissertation/thesis formatting.

To complete your format check submission, you must complete every registration step and fully submit your thesis or dissertation to the website listed below.

Dissertations and theses turned in as a part of the graduation requirements at Loyola University Chicago must be formatted according to the rules laid out in the  Format Manual for Theses and Dissertations created by the Graduate School. 

To ensure that theses/dissertations are formatted correctly, each thesis/dissertation must undergo a format check by the staff of the Graduate School prior to the submission of final copies. For the format check, you must submit a full electronic draft of your manuscript to the Graduate School's electronic submission website, ProQuest ETD Administrator .

Correctly formatted final copies of theses/dissertations may be submitted after the thesis approval/dissertation defense. Additional materials must also be prepared for submission with the final copies of the thesis/dissertation. Refer to the Formatting Manual and to the Submission Process link below for information about these additional materials.

Students must submit final copies with approved revisions within one semester of a successful defense (e.g., if a student’s defense falls within a Fall semester, their final copies must meet the Spring semester submission deadlines). After one full semester a student may be discontinued and be required to apply for reinstatement (Approved 4 May 2021)

Visit the  FAQs page  for answers to new questions about the electronic submission process.

• Format Check and Final Copy Submission Process
• Format Manual for Theses and Dissertations  -  Note: The Formatting Manual was updated in September 2022. Students submitting a manuscript for December 2022 conferral may use the previous Formatting Manual guidelines. If you have any questions about the formatting guidelines, or if you need a copy of the former manual, please email formatting assistant Danielle Richards at [email protected] ).
• Format Checklist Copy (Clean)

Questions regarding the format check and the final copy submission process should be directed to:  [email protected]

Information Sessions

The Graduate School hosts two info-sessions each semester about the thesis and dissertation formatting process. Make sure to follow weekly Graduate School Announcements emails for more information.

Publishing Your Work: Thesis and Dissertation Formatting Workshop (Recorded January 14, 2021)

• Instructions for Thesis and Dissertation
• Approval Ballot for Text and Oral Defense
• Sample Oral Defense Announcement

*  The Thesis/Dissertation Committee Form, Thesis/Dissertation Proposal Ballot, and the Request for Change in Degree-Seeking Status are located in the Graduate Student Progress System at  https://gsps.luc.edu/ .  Please log in to submit these forms. **  Medical Center Biomedical Science Students MUST Use LUHS Forms.

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Upcoming Dissertation Defenses

UC doctoral students participate in a public defense of their dissertations. Students announce their dissertation by entering information through the Graduation Checklist application.

Defense Announcements

''The Gendered Play Gap: How Gendered Labor Shapes Choice and Constraint in Video Games''

'' Assessing acoustic droplet vaporization efficiency and oxygen scavenging from whole blood''

'' Advances in Electrochemical, Aptamer-Based Sensors via Alternative Surface Chemistries and Aptamer Engineering''

'' Amphiphilic Co-solvent Partitioning in Biomembranes''

'' Developing Piezoelectric Materials for Tissue Engineering''

View more dissertation defense announcements .

Resources for Thesis & Dissertation Information

College of engineering and computer science – policy on dissertation/thesis defense.

The dissertation/thesis defense must be open to the public and announced  two weeks  prior to the date of the defense. You can submit your notice to  grad.cecs.ucf.edu/graddefense .

Virtual dissertation/thesis defenses (off-campus defenses) are permitted. When entering your defense information, please enter your building as “Virtual” and the room area should be your Zoom link.  Face-to-Face defenses are still an option for students and their committees.

Requests to deviate from the above policies due to extenuating circumstances will be evaluated on a case-by-case basis.  Formal requests for exceptions (especially if the two-week lead time for the announcement was missed) should be sent by the student’s advisor to Dr. Ali Gordon at  [email protected] .

Submitting a Defense Announcement

To submit a graduate thesis or dissertation defense, please

The dissertation/thesis defense must be open to the public and announced two weeks prior to the date of the defense.

College Policy on Dissertation/Thesis Defenses

Thesis/Dissertation Approval Form

Thesis/Dissertation Approval Form – This form is used by Thesis and Dissertation students who have successfully defended.  It will be started by the student once they have completed their defense and automatically sent from member to member.  Students MUST complete their final submission before Graduate Studies will approve this form.

Upcoming Defenses

2024 –  2023 – 2022 – 2021 – 2020 – 2019 – 2018 – 2017 – 2016 – 2015 – 2014 – 2013 – 2012

• One official transcript (in a sealed envelope) from each college/university attended.
• A Bachelor of Science degree in civil engineering or another closely related engineering degree.
• Résumé.
• Statement of educational, research, and professional career objectives.
• Three letters of recommendation.
• Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc. only.

For international students interested in  UCF Global Pathway Program  (immersive English-language learning), please contact  Dr. Ali P. Gordon  for guidance.

• Bachelor’s Degree in Industrial Engineering or a closely related field with GPA of 3.0 or greater
• Mathematics through Calculus II (MAC 2312 or equivalent)
• An undergraduate course in engineering probability and statistics
• Familiarity with at least one programming language (such as Python, C, C++, Visual BASIC, Java, etc.)
• Official transcripts
• Two letters of recommendations
• Goal Statement

Admission is open to those with a bachelor's degree from an accredited institution recognized by UCF. An application to the graduate certificate program and official transcripts must be submitted. Applicants must apply online. All requested materials must be submitted by the established deadline. Admission to the program is competitive on a space-available basis. Final admission is based on evaluation of the applicant's abilities, past performance and the applicant's potential for completing the certificate. • No Letters of Recommendation Required. For international students interested in  UCF Global Pathway Program  (immersive English-language learning), please contact  Dr. Ali P. Gordon  for guidance.

Applications are accepted for the fall and spring terms only. • No Letters of Recommendation Required.

• In addition, applicants to this certificate must provide: Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc only.
• Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation.
• Applications are accepted for the fall and spring terms only.
• Bachelor's degree in Electrical Engineering or closely related discipline with a minimum GPA 3.0
• No Letters of Recommendation Required.
• The GRE/GMAT is required
• A goal statement. This is your opportunity to outline in 500 words why you wish to join the program, what you think you will contribute to the program, and how you feel the program will enhance you both personally and professionally in the future.
• Updated résumé.
• Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc. only.
• A computer-based TOEFL score of 220 or 80 on the internet-based TOEFL is required if an applicant is from a country where English is not the official language, or if an applicant’s degree is not from an accredited U.S. institution, or if an applicant did not earn a degree in a country where English is the official language or a university where English is the official language of instruction. Although we prefer the TOEFL, we will accept IELTS scores of 6.5.
• The GRE is not required for admission to this program.
• Resume or Curriculum Vita
• The goal statement should discuss all relevant professional background and any previous research and/or teaching experience. The statement should explain the motivation behind the pursuit of an MSSE. Future educational and career goals after the completion of the applicant’s master study should be discussed.
• If the applicant is interested in completing a Master thesis, then the applicant must clearly describe the particular area of research interest. The applicant should identify at least one UCF faculty member who shares a similar research focus and is believed to be best suited to serve as a potential thesis advisor. 
• The goal statement should between 500 and 1,000 words.
• The letters of recommendation should be from faculty members, university administrators, and employers with a supervisory role of the applicant. The letters, which must be current to the application and must not be for another degree program, should address the educational and career goals of the applicant. The letter writers should also know the applicant well enough to discuss the applicant’s capacity to perform, excel and succeed in a graduate program. Letters for Master’s thesis students must discuss the applicant’s ability to perform graduate-level research.
• Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc. only.
• 2 Letters of recommendation

An undergraduate degree in Computer Science, Statistics, Information Technology, or Computer Engineering is desirable but not required. Applicants without a strong undergraduate background in Computer Science and Statistics must demonstrate an understanding of the material covered in upper-division undergraduate courses listed under the Articulation Section of the Curriculum Information. Applicants may choose to demonstrate their knowledge of these courses by taking these courses as non-degree seeking and scoring “B” or better in all of them.

• A bachelor’s degree in Biomedical, Mechanical or Aerospace Engineering, or a closely related discipline. 
• Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc. only.
• Bachelor’s degree in Aerospace Engineering or closely related discipline.
• TOEFL 80, IELTS 6.5
• Letters of Recommendation (Optional)
• Bachelor’s degree in Mechanical Engineering or closely related discipline.
• Bachelor’s or Master’s degree in Biomedical Engineering or Mechanical Engineering or closely related discipline.
• Official, competitive GRE score taken within the last five years.
• A written statement of experience and research, areas of current and future potential research interests, and future career goals.
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• A Bachelor’s degree or its equivalent in statistics, data analytics or a related field from an accredited U.S. institution recognized by UCF or its equivalent from a foreign institution.
• A current curriculum vitae.
• A personal statement identifying the area of research interest and a description of the applicant’s academic and professional experiences.
• The student should have a minimum cumulative GPA of 3.0 for all bachelor’s level work completed.
• Applicants to this program, except those that have earned or will earn a Masters or Doctoral degree from an accredited U.S. institution recognized by UCF, who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc. only.

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College of Liberal Arts and Sciences

Department of Statistics

Ph.d. dissertation.

Students pursuing a Ph.D. in statistics are required to take two qualifying examinations, prepare a dissertation, and defend their dissertation before they can earn their degree.

Examinations

Ph.D. students are required to pass the following two examinations:

• Ph.D. qualifying examination, which is a written test for certain basic courses in the program.
• General examination , which is an oral test that covers aspects of applied statistics, linear models, probability theory, and statistics. Contains a dissertation proposal.

Please note: In order for a student currently enrolled in our MS program to switch to the Ph.D. program or to be considered for financial support, they must first pass both parts of the Ph.D. qualifying exam at the Ph.D. level.

Dissertation and Defense

Students are also required to prepare a dissertation, which must present an original contribution to the general area of statistics and/or probability. A Ph.D. degree must include at least 15 credits of GRAD 6950. Doctoral Dissertation Research .

Once complete, Ph.D. students must present a defense of their dissertation before an audience of interested members of the Department.

For students arriving with a bachelor’s degree and receiving financial support from the Department, students should follow the following suggested timetable:

• Ph.D. qualifying examination - within three semesters from the start of the program.
• General Examination - within six semesters from the start of the program.
• Ph.D. thesis defense - no later than five years from the start of the program.

Resources and Notes

The Registrar’s web page on doctoral degrees contains all relevant information and forms that students need to prepare for their dissertations and plan their academic careers.

Please note the following:

• Doctoral students no longer need to complete and submit a Tentative Approval Page to the Registrar’s Office, the Graduate School, or Degree Audit.
• Ph.D. students are required to announce their defense on the University Events Calendar at least two weeks prior to the defense date. Visit the Registrar’s web page on doctoral degrees for instructions on how to post your defense announcement.
• Survey of Earned Doctorates Completion Certificate.
• Dissertation   Approval Page (the approval page will be routed to the Registrar's office when the final committee approval is submitted).
• Submit ONE electronic copy of your dissertation to   Submittable . Follow the instructions found in the   Submittable help file .

MS Thesis and PhD Dissertation Final Defense Announcements

Submission of announcement and list of approved defenses.

announcement at least prior to the defense date (requires 2nd review of thesis/dissertation document to have been processed by GSO) to  and at least one month in advance of your defense date.

To submit your MS Thesis or PhD Dissertation abstract for approval before the defense	To view approved MS Thesis or PhD Dissertation abstracts
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: Both links on this page require an active njit.edu Google login within the browser ( ).

Part of the submission must include your Abstract in PDF form. Submissions will be reviewed by Graduate Studies and you will receive a notification once approved. If changes are needed, you will be notified to do so via email.

IF YOUR THESIS OR DISSERTATION IS TO BE SEQUESTERED/EMBARGOED , DO NOT SUBMIT AN ABSTRACT BUT JUST INCLUDE "SEQUESTERED/EMBARGOED" INSTEAD OF AN ABSTRACT. ANYONE WHO WILL THEN ATTEND YOUR DEFENSE MUST SIGN THE NON-DISCLOSURE AGREEMENT FORM AVAILABLE AT THIS LINK . DOWNLOAD THE FORM AND GIVE IT TO YOUR MAIN ADVISOR BEFORE THE DEFENSE.

If you have any questions, please contact [email protected]  

Ph.D. in Clinical Psychology Program

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Final Dissertation Defense Announcement

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An official announcement of the dissertation defense must be distributed to all Joint Doctoral Program faculty, staff, and students following the same procedures outlined for the proposal defense two weeks prior to the scheduled final defense.  Faculty/student notification is a requirement, not simply a courtesy.  Failure to comply may delay the defense. Note:   No defense will be permitted less than two weeks prior to final copy submission deadlines. The information for the defense announcement must include the student’s name, the title, the committee and each department/university represented, date, time and location of the defense, and an abstract.

Final Dissertation Defense Announcement Template

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Cultural Anthropology Program - Doctoral Dissertation Research Improvement Grants (CA-DDRIG)

View guidelines, important information for proposers.

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.

Supports doctoral research aimed at understanding patterns, causes and consequences of human social and cultural variation, including research that has implications for confronting anthropogenic problems.

The primary objective of the Cultural Anthropology Program is to support basic scientific research on the causes, consequences and complexities of human social and cultural variability.

Contemporary cultural anthropology is an arena in which diverse research traditions and methodologies are valid in investigations of human cultural variation. Recognizing the breadth of the field's contributions to science, the Cultural Anthropology Program welcomes proposals for empirically grounded, theoretically engaged and methodologically sophisticated research in all sub-fields of cultural anthropology. Because the National Science Foundation's mission is to support basic research, the NSF Cultural Anthropology Program does not fund research that takes as its primary goal improved clinical practice, humanistic understanding or applied policy. A proposal that applies anthropological methods to a social problem but does not propose how that problem provides an opportunity to make a theory-testing and/or theory-expanding contribution to anthropology will be returned without review.

Program research priorities include, but are not limited to, research that increases our understanding of:

• Sociocultural drivers of critical anthropogenic processes such as deforestation, desertification, land cover change, urbanization and poverty.
• Resilience and robustness of sociocultural systems.
• Scientific principles underlying conflict, cooperation and altruism, as well as explanations of variation in culture, norms, behaviors and institutions.
• Economy, culture, migration and globalization.
• Variability and change in kinship and family norms and practices.
• General cultural and social principles underlining the drivers of health outcomes and disease transmission.
• Biocultural work that considers the nexus of human culture and its relationship with human biology.
• Social regulation, governmentality and violence.
• Origins of complexity in sociocultural systems.
• Language and culture: orality and literacy, sociolinguistics and cognition.
• Theoretically-informed approaches to co-production in relation to scientific understandings of human variability and environmental stewardship.
• Mathematical and computational models of sociocultural systems such as social network analysis, agent-based models, multi-level models, and modes that integrate agent-based simulations and geographic information systems (GIS).

As part of its effort to encourage and support projects that explicitly integrate education and basic research, CA provides support to enhance and improve the conduct of doctoral dissertation projects designed and carried out by doctoral students enrolled in U.S. institutions of higher education who are conducting scientific research that enhances basic scientific knowledge.

Updates and announcements

Video and slides from cultural anthropology funding opportunity webinars, may and july, 2022, program contacts.

Program Director

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Program Director		(703) 292-8740	SBE/BCS
Program Director		(703) 292-8740	SBE/BCS
Program Specialist		(703) 292-4636	SBE/BCS

Additional program resources

• AAA Statement on Protecting and Preserving Field Records and Related Links
• Sample successful CA-DDRIG proposal

Awards made through this program

Related programs.

• Cultural Anthropology Program Senior Research Awards (CA-SR)

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Dissertation Defense Announcements

Dissertation Defense Calendar

The advancement of medical imaging has significantly enhanced the ability to diagnose, monitor, and treat cancer. This dissertation focuses on the development of deep learning methodologies for the segmentation and registration of medical images, specifically Positron Emission Tomography (PET), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and pathology images, to improve the accuracy and efficiency of cancer diagnosis and treatment planning. Segmentation, the process of delineating anatomical structures and pathological regions, is a crucial step in medical image analysis. This work introduces novel high-precision deep learning models for the automatic segmentation of tumors and organs at risk (OARs). These models utilize convolutional neural networks (CNNs) and transformer-based architectures to handle the complexities and variations inherent in PET, CT, and MRI. The segmentation models are trained on multi-modal imaging datasets, incorporating advanced techniques such as data augmentation, transfer learning, and ensemble learning to enhance robustness and generalization. Evaluation on various datasets demonstrates that these models achieve superior performance compared to traditional methods, with significant improvements in accuracy and reliability.

Registration, which aligns images from different modalities or time points, is another critical component in the analysis of medical images. This dissertation presents advanced deep learning approaches for the registration of CT, MRI, and pathology images, leveraging deep neural networks (DNNs) and unsupervised learning techniques. The proposed registration methods employ spatial transformer networks (STNs) and other novel architectures to learn complex spatial transformations directly from the data, enabling accurate alignment of multi-modal images. These approaches are designed to be computationally efficient and scalable, facilitating their integration into clinical workflows.

Our final goal is to streamline these deep learning methods to real clinical applications. This dissertation explores the practical applications of the developed models, including their deployment in microservices for common radiotherapy imaging tasks. The models are made accessible via Python scripts for clinical treatment planning software such as RayStation, allowing seamless integration into existing clinical systems. Evaluation using images and treatment planning data for prostate cancer underscores the potential of these models to enhance the quality of treatment planning and streamline the overall process of planning, response assessment, and adaptation. Additionally, this dissertation investigates the potential of federated learning for collaborative model training across multiple institutions without sharing sensitive patient data. This approach could enhance model robustness and generalizability by leveraging diverse datasets from various sources.

In conclusion, this dissertation explores the critical component of medical imaging for cancer diagnosis, monitoring, and treatment with advanced deep learning methods. We hope these innovative techniques developed in this research pave the way for more precise, efficient, and individualized patient care in oncology.

Electrical fires caused by arc faults necessitate advanced detection methods for improved safety and reliability in AC and DC power systems. This research introduces innovative artificial intelligence (AI)-based methods for the efficient detection of arc faults, enhancing both safety and reliability in electrical systems. For AC systems, we developed a convolutional neural network (CNN)-based arc fault detection algorithm that autonomously extracts arc fault features without manual thresholding. Using raw current as input, the algorithm achieves an arc fault detection accuracy of 99.47%. Additionally, this research determined an optimal sampling rate of 10 kHz for the input current. The model's efficacy was verified using the Raspberry Pi 3B platform.

While traditional CNN algorithms have high accuracy, they require optimization for real-time arc fault detection on resource-limited hardware. To address this, we proposed a lightweight CNN architecture combined with a model compression technique using a knowledge distillation-based teacher-student algorithm. This model maintains a high detection accuracy of 99.31% and operates with an impressively minimal runtime of 0.20 milliseconds per sample when implemented on the Raspberry Pi 3B platform. This performance demonstrates its suitability for commercial embedded microcontrollers (MCUs) with limited computational capability.

Extending our research to DC systems, we introduced a cost-effective, AI-driven Arc Fault Circuit Interrupter (AFCI) for DC applications. Utilizing an STM32 MCU and a silicon carbide (SiC) MOSFET-based solid-state circuit breaker (SSCB), the proposed method achieves a detection accuracy of 98.15% with a remarkable arc fault interruption time of 25 milliseconds. This AFCI solution stands out for its rapid response and high reliability, promising significant improvements in safety for DC systems.

Together, these contributions signify a leap forward in electrical safety, presenting viable solutions for the timely detection and interruption of arc faults in AC and DC systems. The outcomes of this research are expected to influence future standards and practices in electrical safety management across residential, commercial, and industrial sectors.

People adopt security technologies and make security decisions based on their perceptions, or mental models, of what risks they have and what they can do to protect their devices. Thus, people rely on their mental models to decide how to use their computing devices and the consequences of these actions. Understanding why users make security decisions and addressing the misconceptions in their mental models, specifically regarding security risks, can help prevent security mistakes made by users and help determine how to help users make good security decisions. This dissertation seeks to understand how users perceive security risks, why they make security-related decisions, and where they have misconceptions. In my dissertation, I examine how users' mental models of security and privacy differ by device platform, how that impacts how people use and interact with applications on each platform, and how user’s mental models can be used to influence adoption of good device security practices. I will present the results of three user studies exploring user mental models of security and privacy and how users need an increasing awareness of security risks and measures across all types of computing platforms in order to adopt appropriate practices to protect themselves and their information.

Social media has emerged as a common platform for knowledge sharing and exchange in online communities. However, it has also become a hotbed for the diffusion of irregular content. Content moderation is crucial for maintaining a safe and healthy online environment by regulating the distribution of user-generated content (UGC). Engaging users in content moderation fosters a sense of shared responsibility and empowers them to actively shape the environment of online communities. Leveraging the expertise of moderators leads to a deeper contextual understanding of content, thereby improving the overall consistency and legitimacy of content moderation in compliance with community or platform guidelines. Nevertheless, the collaborative effort of a more inclusive and community-driven moderation process remains unexplored by previous studies. While there is increasing attention to fairness, transparency, and ethics in content moderation, prior research often assesses content moderation perceptions of users, platforms, moderators, and bystanders in isolation. This results in a lack of comprehensive understanding of user perceptions in content moderation decision-making. To address these limitations, this research proposes UMCollab, a user-moderator collaborative content moderation framework that incorporates the dynamics of user engagement and the domain knowledge of moderators into deep learning models to facilitate content moderation decision-making. Additionally, this research empirically investigates user perceptions of content moderation from the perspectives of content familiarity, content diversity, and user roles. UMCollab leverages graph learning to model user engagement, which is further enhanced by the credibility and stance of users' online discussions. It also employs attention mechanisms to learn the domain knowledge of moderators based on their decisions regarding UGC per online community rules. Moreover, this study conducts an online user study by asking participants with diverse online engagement backgrounds and roles to complete a series of content moderation decision-making tasks and evaluate their perceptions of content moderation. The findings of this dissertation research hold significant promise for promoting effectiveness, fairness, transparency, and community ownership in moderating UGC in social media, offering opportunities to improve the safety and success of online communities.

Physical activity offers a range of health benefits but can be difficult to initiate and maintain. Self-regulatory processes are one route to understanding health behavior change. While affective mechanisms like positive affect and reward processing provide a valuable neurobiological pathway to elucidate individual motivations for physical activity. Theories and models that emphasize the role of affect and intrinsic and extrinsic motivations are applied in the current study to deepen our understanding of physical activity behaviors. Psychosocial sources of individual motivations, such as improving mood or changing one’s physical appearance, can provide insight into ways to alter affective-cognitive mechanisms to encourage sustainable physical activity behaviors. The present study applied a mixed-method approach to elucidate themes of affect and motivation in social media content. A sample of 2,585 Twitter posts were collected in mid-July 2022. A motivational and health behavior change qualitative codebook was first developed to guide thematic coding analyses. Thematic coding results revealed a high frequency of extrinsic motivation and goal and change-oriented facilitators of physical activity. Intrinsic motivation included the highest percentage of positive attitudes compared to other motivation types. Health-oriented themes, satisfaction, dissatisfaction with physical appearance, and weight loss were also relevant. LIWC-22 analyses supported the role of positive affect and informed health themes. BERT topic modeling analyses provided overarching physical activity topic themes for motivation and physical activity. Interpretations of the current results were presented, and future directions were suggested.

Nanotechnology has the potential to revolutionize various fields, addressing complex issues such as cancer treatment, waste remediation, and energy storage. To achieve this, precise engineering of nanocrystals at the atomic level is essential, going beyond mere control of size and shape. The unique properties of nanomaterials, which differ from their bulk counterparts, are influenced by surface chemistry, defects, and local structure. These characteristics are determined by the synthesis methods used, making a deep mechanistic understanding of these processes crucial for engineering nanoscale structures and properties.

To contribute to the rapidly evolving field of nanoscience, this dissertation focuses on the solution-based synthesis of vanadium oxide nanocrystals. Vanadium oxides are promising candidates for applications in catalysis, sensing, cathode materials for high-density lithium batteries, smart windows, neuromorphic computing, and optical switching. However, vanadium oxides exhibit multiple oxidation states (+2 to +5) and polymorphs. Consequently, the colloidal synthesis of high-quality vanadium oxide nanocrystals in a specific oxidation state and stoichiometry remains challenging.

This dissertation advances the synthesis of vanadium oxide nanocrystals, emphasizing the effects of synthetic parameters on their oxidation state and crystal structure. Key findings include the successful synthesis of anosovite V₃O₅ nanocrystals via a hot-injection method, marking the first colloidal synthesis of this rare phase from a readily available precursor. By adjusting vanadium precursor-to-alcohol-to-amine ratio, controlled reduction of vanadium was achieved to selectively synthesize V₃O₅ and V₂O₃ nanocrystals. The dissertation also presents an alcohol-mediated valence-state controlled synthesis method for selective preparation of pure corundum-structured V₂O₃ and anosovite V₃O₅ nanocrystals. Comprehensive characterization, including spectroscopic ellipsometry and diffuse reflectance spectroscopy, reveals unique optical properties deviating from bulk behavior, attributed to the nanoscale size effects. In addition, a heat-up method was developed for synthesizing VOx nanocrystals by thermal decomposition of vanadyl acetylacetonate, demonstrating the formation of vanadium monoxide nanocrystals. The reaction pathways for the formation of these nanocrystals via hot-injection method and heat-up methods were analyzed with ATR-FTIR spectroscopy. The findings will advance the fundamental understanding of vanadium oxide nanocrystal synthesis and pave the way for their application as advanced functional nanomaterials.

Edge computing-assisted artificial intelligence (edge-AI) has enabled a new paradigm of smart applications that have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and autonomous vehicles). However, the high mobility of users and instability in wireless networks decrease the overall Quality-of-Service (QoS) of an edge-AI application running on mobile devices with non-linear battery discharge properties. The objective of this research is to provide mobile AI applications with an energy-efficient wireless infrastructure to enhance the overall QoS.

This dissertation presents a comprehensive experimental study of mobile AI applications, including a novel performance analysis modeling framework and a Gaussian process regression-based general predictive energy model, focusing on computational resource utilization, delay, and energy consumption. To enhance mobile AI performance, this dissertation presents a novel periodic predictive AoI-based service aggregation method for high-mobility AI applications, which processes information updates according to their update cycles with satisfactory latency. Furthermore, an H.264 video encoding-based edge-AI system is proposed to overcome the challenges posed by unstable wireless networks. Finally, a novel deep reinforcement learning-based smart edge-AI system is proposed in this research, where the edge server provides smart and dynamic offloading and data processing decisions.

This dissertation explores the relationship between climate change, reproductive justice, and the prosperity of families, communities, and economies through a discussion utilizing a comprehensive literature review and the results of a quantitative study to examine the relationship between the adverse impacts of a changing climate and the cost of living, represented by increases in food prices, housing costs, and health care expenditures, and the associated impact on declining birth rates.

The first chapter builds the foundation for examining how climate change, reproductive justice, and societal prosperity interact by summarizing the climate change science literature that addresses the health and social harm disparities for low-income, communities of color, particularly women of color, and advocates for a reclaiming of bodily autonomy given the communities that are most impacted by climate change. The second chapter recalls the historical legacy of settler colonialism, especially the exploitation of Indigenous women, and examines how corporate expansion, consumerism, and white feminist ideologies create and maintain corporate colonialism and the oppression of nonwhite women through the disproportionate impacts of climate change, while calling for a reconstruction of feminism. The third chapter explores how a modern society measures prosperity through both financial and nonfinancial performance measures and introduces a new prosperity model based on a four-part test to promote, protect, and advance the health and long-term viability of families, communities, economies, and ecosystems through sustainable and responsible economic principles, means, and indicators of success. Implications and considerations for private industry and public policy are discussed, along with the need for additional research to better understand the new model’s effectiveness in predicting, determining, and protecting societal prosperity.

The invention of advanced Ni-containing concentrated solid solution alloys has significantly broadened the compositional space of alloy design. Unlike conventional alloys that typically consist of a principal solvent element with minor additions of various solute atoms, concentrated solid solution alloys involve multiple elements in equal or near-equal compositions. These concentrated solid solution alloys have exhibited remarkable properties such as exceptional toughness and superior radiation resistance. The exceptional performance of these concentrated solid solution alloys is generally attributed to specific intrinsic properties of concentrated solid solution alloys such as high entropy effect, severe lattice distortion and short ordering effect. However, being relatively new emerging materials, the theoretical understanding and experimental exploration of these alloys are still ongoing and not comprehensively understood.

This dissertation work will provide a systematic investigation on surface properties including mechanical properties and radiation damage of Ni-based concentrated solid solution alloys by using ex-situ and in-situ indentation techniques. The study explores the surface properties of a batch of Ni-containing concentrated solid solution alloys with addition of different 3d transition elements including binary NiCo, NiFe, Ni80Cr20, Ni80Mn20 and quaternary NiCoFeCr. First, initial investigations use ex-situ nanoindentation to obtain mechanical properties of five alloys including hardness, elastic modulus and strain rate sensitivity. A complete methodology is developed to acquire accurate property information directly from nanoindentation in a high throughput manner, which considers the indentation size effect and pile-up effect. Furthermore, the strengthening in Ni-concentrated solid solution alloys is attributed to being driven by solid solution strengthening induced by mismatch in atomic size. Notably, the intrinsic properties of alloying elements play a more critical role in strengthening than the number of alloying elements. Second, based on previous work, nanoindentation is further employed to evaluate the early-stage irradiation induced hardening in NiCo, NiFe and NiCoFeCr. It proposes using nanoindentation to detect early-stage irradiation-induced defects and hypothesizes that interactions between these defects and dislocations carried by deformation during indentation can quantify irradiation-induced defects. This approach successfully quantifies irradiation-induced hardening in NiCo, NiFe, and NiCoFeCr. Quantitative analysis reveals that irradiation-induced defects harden NiFe and NiCoFeCr, but no significant hardening is observed in NiCo. Additionally, irradiation-induced hardening is associated with the evolution of geometrically necessary dislocations and is interpreted by changes in the plastic zone during indentation. Finally, in-situ flat-punch indentation provides real-time observations of deformation behaviors, aiming to derive accurate stress-strain curves. A new protocol addresses thermal drift effects and contact issues, often neglected in measurements.

This comprehensive study on Ni-based concentrated solid solution alloys enhances understanding of their mechanical properties and irradiation resistance by using ex-situ and in-situ nano-mechanical techniques. Methodologies are developed for nanoindentations to acquire meaningful property information directly from surface in a high throughput manner. This systematic work offers valuable insights into the strengthening mechanisms and irradiation hardening mechanisms of Ni-based concentrated solid solution alloys. The robust experimental evidence supports that the exceptional properties of concentrated solid solution alloys are not solely determined by the number of elements but also determined by the intrinsic performance of alloying elements. These results provide new insights for alloying design strategy of concentrated solid solution alloys.

Approximately 30% of individuals with autism have complex communication needs (CCN). These individuals are unable to use vocal speech as their primary form of language and typically require support across several areas of communication such as comprehension, pragmatics, phonology, semantics, and syntax (Ganz et al., 2022; Reichle, 2019). Researchers have found that communication skills can greatly impact academic, behavioral, social, and postsecondary outcomes (Carter et al., 2012; Chiang, 2008; Matson et al., 2013; Park et al., 2012; Pillay & Bronlow, 2017). Fortunately, augmentative and alternative communication (AAC) has been effectively used to increase communication for individuals with intellectual and developmental disabilities (IDD; Crowe et al., 2022). Most often, individuals with autism are only taught to request using single words or short phrases using AAC devices (Ganz et al., 2017; Muharib et al., 2018; Tincani et al., 2020). Another way to expand communication through AAC is to teach sentence structure. Researchers have used an intervention package consisting of response prompting, sentence frames, and technology like AAC to teach students with autism and CCN to construct sentences (Pennington et al., 2021; Pennington, Flick, et al. 2018; Pennington, Foreman, et al. 2018; Pennington & Rockhold, 2018). Additionally, matrix training has been used as a generative framework to increase language for individuals with autism who use vocal speech (Frampton et al., 2016, 2019; Jimenez-Gomez et al., 2019; Kohler & Malott, 2014) and AAC (Marya et al., 2021; Naoi et al., 2006; Nigam et al., 2006; Tönsing et al., 2014). This study examined the effects of matrix training, response prompting, and sentence frames on sentence writing for four students, ages 10–18 with ASD and CCN in a specialized private school located in the southeastern United States. Three teachers, ages 23–46 served as the interventionists in the study. A series of A-B designs with modifications was used to examine the effects of the intervention package on the percentage of trained and untrained correct sentences, percentage of subject-verb combinations, and the percentage of correct word selections. Teachers presented photos of subject-verb combinations for students to write about using pre-programmed arrays with words and symbol supports on speech-generating devices. Overall, results indicated that across all interventions, there were no effects on the percentage of trained and untrained correct sentences and subject-verb combinations for all participants. Two students, however, increased their percentage of correct word selections. Overall, teachers found the intervention acceptable and beneficial for students in the classroom. Furthermore, three of four students preferred this writing intervention over their typical writing instruction in the classroom. Implications of this study provide several considerations for practitioners who would like to use matrix training to teach subject-verb combinations and/or sentence writing with students who have autism and CCN.