Throwing arm injuries are common because of the demand on the shoulder. Shoulder exams and pitching mechanics are regularly monitored by team physicians. Excessive instability and joint loading in baseball pitching are risk factors for throwing arm injuries. Altering baseball pitching mechanics affects both performance and the risk of injury. The purpose of this study is to investigate the relationship among injuries, shoulder exam variables, and pitching biomechanics in collegiate baseball pitchers. Pitching biomechanics, shoulder exam tests, and self-reported injury questionnaires were used to study 177 collegiate baseball pitchers. Pitching biomechanics where high-speed cameras record the athlete pitching. This allows us to capture both the athletes body position and calculate joint loadings. Shoulder exam tests where the athletes lay on their backs and their shoulder range of motion, flexibility, and stiffness is measured. Injury questionnaires is where the athletes report if they have had any injuries or surgeries. Our findings show that the shoulder exam, pitching biomechanics, and injury questionnaire variables are related. The ability to understand the relationship between shoulder exam variables, baseball pitching mechanics, and injuries helps further our knowledge and pushes forward the underlying goal of this study which is to improve performance and reduce injuries.
Random anti-reflective nanostructured surfaces (rARSS) enhance optical transmission through suppression of Fresnel reflection at layered-media boundaries. Windows with rARSS treatment are characterized (transmittance, reflectance, and scatter) using spectrophotometry and scatterometry to assess transmissive scatter performance over various spectral bands. Using measured spectral data, partial-integrated scatter values were obtained, allowing the comparison of random anti-reflective surface performance to optically flat surfaces.
Using a transfer function approach, an approximation of far-field light scatter can be modeled based on surface statistics. rARSS feature topology was determined using optical profilometry to obtain statistical surface roughness parameters, to assess the structured-surface feature scales. Random rough surfaces are well-modeled by Gaussian statistics, making them ideal candidates for a surface transfer function approach of surface scatter analysis.
The Generalized Harvey-Shack surface scatter theory was used to calculate surface feature diffractive effects. Scatter distributions predicted using a Gaussian two-parameter model of a random surface and structured surface metrology data were compared to measured scatter data for assessment of the transfer function model validity within the bandlimit of interest. Results show that prediction of wide angle rARSS optical scatter is viable using the transfer function approach, but the theory fails to predict transmission enhancement due to the inclusion of roughness.
The AI-digital era is characterized by an unprecedented surge in data usage, spanning from data centers to IoT devices. This growth has driven the evolution of AI-optimized networks, designed to fuse AI capabilities with advanced network solutions seamlessly. However, these networks grapple with challenges such as the complexity of network layer protocols, discrepancies between simulated AI models and their real-world implementations, and the need for decentralized AI training due to network distribution.
To address these challenges, we propose the AI-oriented Network Operating System (AINOS). At the core of AINOS are two foundational sub-platforms: the "Network Gym," tailored for AI-driven network training, and "Federated Computing," designed for decentralized training methodologies. AINOS provides a comprehensive toolkit for rapid prototyping, deployment, and validation of AI-optimized networks, bridging the gap from simulation to real-world deployment.
Harnessing the powerful features of AINOS, we prototyped AI-optimized networking solutions using a safe Reinforcement Learning (RL) strategy for Traffic Engineering (TE) at both the link and network layers. At the link layer, we implemented a scalable RL-based traffic splitting mechanism that learns optimal traffic split ratios across Wi-Fi and LTE through guided exploration. For the network layer, we devised an online Multi-agent Reinforcement Learning (MA-RL) approach with domain-specific refinements to determine optimal paths in real-time for wireless multi-hop networks. In our exploration of Network Assisted AI optimization, we reduced Federated Learning training time with our MA-RL multi-routing approach and proposed a robust Decentralized Federated Learning solution that leverages single-hop connections for enhanced network performance. Our results demonstrate the strengths of AI-enhanced networks in proficiently managing heterogeneity and latency.
Limited studies address high school gifted students' social and emotional needs (Knudsen, 2018; Kregel, 2015). Additionally, there is a lack of research regarding high school gifted students' and AIG directors' perspectives on the social and emotional strategies implemented locally within their school districts (Clinkenbeard, 2012; Kitsantas et al., 2017). Therefore, the purpose of this dissertation was (a) to discover the services school districts proposed to implement to meet the social and emotional needs of high school gifted students and (b) to explore high school gifted students’ and AIG directors’ perspectives about these services. Using purposeful sampling, this qualitative research included five participants from two school districts. The data collection methods implemented during this study were compiling school documents (i.e., 2022–2025 Local AIG Plans ) and conducting five separate interviews. I used document analysis to analyze data from the Local AIG Plans and thematic analysis to analyze data from the interviews. Results from the document analysis yielded three themes: program-level and curricula strategies, resources and support, and collaboration and counseling strategies. Results from the thematic analysis of interviews yielded three themes on how schools implement social and emotional services from the participants' perspective: social and emotional services, interaction, and gathering and sharing information. Further, the thematic analysis of participants' in-depth perspectives about these services yielded three themes: satisfaction and awareness, counseling, and limitations and improvements.
This dissertation explores the fascinating realm of metamaterials and electromagnetic engineering, with a focus on metasurfaces, dual-polarization metascreens, novel dual-band antennas, and time-varying components. Metasurfaces, two-dimensional arrays of subwavelength structures, offer compact solutions to manipulate electromagnetic waves, finding applications in optical devices, imaging systems, communication, and sensing.
The pivotal contribution of this research is the development of dual-polarization metascreens, which enable simultaneous control of horizontal and vertical polarizations. This innovation enhances radar systems, wireless communication, and remote sensing by rapidly switching between polarizations, improving data rates and accuracy.
The dissertation further explores dual-layered antennas operating in Ka and W bands, addressing unique challenges and expanding the boundaries of electromagnetic engineering. This breakthrough has applications in connectivity technology, radar systems, and millimeter-wave technologies.
Additionally, the study emphasizes the importance of rapid dispersion curve calculations and 3D printing for antenna design, accelerating research and development in communication, sensing, and connectivity technologies.
The dissertation concludes by delving into the emerging field of time-varying parameters, particularly time-varying networks composed of lumped elements. This research introduces a novel approach involving aperiodic time modulation of a single capacitor to capture energy from arbitrary pulses. These innovations promise new functionalities in electromagnetic systems, highlighting the interdisciplinary and innovative nature of this research.
In summary, this dissertation covers a wide range of topics in electromagnetic engineering and photonics, showcasing innovative applications and pushing the boundaries of the field.
Faithful repair of DNA lesions is central to maintaining genomic integrity. Illegitimate repair of chromosomal DNA damage, especially double-strand breaks (DSBs), can lead to mutations and genome rearrangements. Homologous recombination (HR) is a highly conserved molecular process that plays an important role in the repair of DSBs and the maintenance of genome stability. However, it is not fully understood which cell populations at which developmental stages in vivo have the potential to use this “error-free” repair mechanism. Further, although HR is considered to be “error-free”, illegitimate inter-chromosomal HR has been linked to the formation of chromosomal translocations that are a hallmark of leukemias, lymphomas, and sarcomas. For my studies, I engineered a transgenic mouse “Rainbow Mouse” model to induce specific chromosomal DSBs in vivo and score for inter-chromosomal HR repair in multiple tissues and cell types. I used the Rainbow Mouse to address critical biological questions- What is the relative frequency of inter-chromosomal HR repair among different tissue subpopulations? Which cell types are more likely to utilize this mechanism? Can DSBs induced in utero be repaired by inter-chromosomal HR repair? I hypothesized a significant difference in inter-chromosomal HR observed in different cell types based on their cellular differentiation state.
Overall, my research demonstrates a function reporter model to evaluate inter-chromosomal HR in vivo. My research identified specific cell types, such as pancreatic duct cells and hematopoietic stem cell enriched LIN-/CD34+ populations that undergo DSB-induced inter-chromosomal HR leading to mutation. The findings from my research highlight developmental and cell type-specific differences in the potential for inter-chromosomal HR to be used in the repair of DSBs. The Rainbow mouse model utilized in this study has the potential for long-term application in assessing the mutagenic effects of various environmental and dietary compounds, as well as understanding the role of specific proteins involved in repairing DNA damage induced by these compounds.
A staggering number of Internet-of-Things (IoT) devices harbor intrinsic security vulnerabilities in firmware. Memory errors especially predominate as a potent category among these vulnerabilities. Memory errors not only permit remote attackers to achieve Turing-complete access to compromised IoT devices but also provide a means to orchestrate massive Distributed Denial-of-Service (DDoS) attacks, capable of destabilizing even the most resilient Internet infrastructures. Standard protection techniques against memory errors, such as ASLR, can be easily bypassed, undermining their effectiveness. While certain advanced defense measures, such as software diversity and control-flow integrity, have been adapted for IoT devices, their constraints and associated overheads often render them impractical for deployment in real-world IoT devices.
This dissertation presents a holistic approach to securing memory error vulnerabilities in IoT firmware as four research thrusts: (1) we investigate remote attack strategies that exploit memory error vulnerabilities in ARM and x86 IoT firmware in the presence of standard software defenses such as DEP and ASLR; we also demonstrate man-in-the-middle attack strategies on actual IoT devices using tools such as Wi-Fi Pineapple; (2) we build and validate a testbed capable of hosting real-world IoT binaries in a simulated network, for deploying authentic DDoS scenarios; (3) we develop an IoT software diversity defense technique to resist memory error exploits; our technique generates multiple, semantically equivalent, syntactically distinct variations of IoT firmware that thwart mass duplication of identical firmware, thereby making it more challenging for attackers to deduce implementation details (crucial for memory error exploits) of any of these firmware binaries; and (4) we create cybersecurity educational modules for undergraduate and graduate students for teaching memory error exploit and defense techniques; we deploy our modules in multiple sections of an undergraduate introductory cybersecurity course at UNC Charlotte and analyze data collected through surveys on learning outcomes, engagement and experience.
In the continuous pursuit of advanced therapeutics, the field of bioinformatics has innovated tools that allow unprecedented control over the proteome, profoundly shaping our understanding and manipulation of biological domains. Computational approaches to protein design grapple with the intricacies of protein behavior, encompassing everything from interaction dynamics to stability challenges. Methods in structural bioinformatics for peptide design typically hinge on the datasets of structures that have statistics applied to ascertain the effectiveness of protein design and modulation. When dealing with proteins that are poorly resolved, disordered, or niche, this task usually falls to experts in structural biology and often requires significant laboratory resources.
This thesis discusses an automated pipeline, devised to integrate remote sequence homology, structural modeling, and binding simulations of peptides to disordered proteins. Significant design and testing underpin this pipeline, aiming to generate binding peptides to any sequence, sidestepping the absolute requirement for an expert or a tedious process to produce leads. The utility of this pipeline is assessed across a diverse set of protein systems to refine its methodology. With the recent rise of machine learning-driven predictive or generative models, we explore their potential when integrated with our pipeline in attempt to address challenges in the computation of peptide binder design.
The significance of this study was to give an active voice to the experiences of women superintendents. By giving voice to the lived experiences of women superintendents, the study sought to further understand the phenomenon of women dominating the teaching profession and other entry-level positions in education yet having a noticeably limited presence in the superintendency. More specifically, studying the barriers and supports women superintendents encounter could lead to significant opportunities to narrow the gender gap of women in the superintendency. Bringing awareness to the barriers and supports women superintendents experience could also foster more equitable workplaces. This qualitative, exploratory study aimed to identify barriers and supports faced by women school district superintendents as they ascended into the role and while they serve in the role. In this basic qualitative study, the researcher’s data sources involved semi-structured, one-on-one interviews with women superintendents. Results of the study indicate that participants felt that advancement factors were multifaceted and systematic, and employment pathways impacted options. In addition, personal obstacles acted as a barrier to reaching the superintendency. Also, gender discrimination was present while ascending to the superintendency and while serving in the role. Results also concluded that women superintendents credited their ongoing success to mentors and professional development. Implications included the need for awareness of leadership development opportunities for women in education, elimination of the glass ceiling, and additional research from women who aspire to be superintendents.
With adolescent mental health problems on the rise, secondary school teachers are in a prime position to support students. This study sought to fill a scholarly research gap and inform future mental health literacy (MHL) training needs for teachers and to identify implications for their professional practice. Specifically, this study sought to address the limited research available on the newly deemed “at-risk” population of students in high achieving schools (HASs) enrolled in accelerated courses taught by Advanced Placement (AP) teachers. The purpose of this basic interpretive qualitative study was to investigate the perceptions of high school AP teachers in HASs regarding MHL by understanding how they perceive and develop their MHL knowledge base, the effectiveness of training they have received, and the relationship between their MHL knowledge and professional practice. Results of the study from semi-structured one-on-one interviews with five high school Advanced Placement (AP) teachers within a HAS indicated that the MHL knowledge base of these teachers was inadequate for supporting students with mental health problems. Further, results indicated that the MHL training that they have received was insufficient, leading them to rely on experience beyond in-school training to develop knowledge. Implications of the study suggest a need for targeted, comprehensive pre-service and school-level MHL training and curriculum for high school AP teachers to be developed, integrated across courses, and monitored by leadership.
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