Texas A&M University – Corpus Christi
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Advanced Computer Architecture
No full textIntroduction: What is Computer Performance? Execution Time, Instructions Per Second (IPS), Clock Speed (GHz), Clock Cycles Per Instruction (CPI
Retention and strategic enrollment management at a community college in Texas
No full textStudent enrollment at community colleges within the U.S. has remained flat or decreased for over a decade (AACC, 2019). Before COVID-19, community colleges had been experiencing a steady decline in enrollment for approximately ten years (Irwin et al., 2021). The purpose was to determine whether demographic, environmental, and academic variables depend on a student’s decision to stay enrolled at Texas Community College (TCC) [pseudonym] for cohort years 2018-2019 and 2020-2021. The study used a quantitative, non-experimental, retrospective design (Cronk, 2020) to examine a subset of archival student data from TCC, a large public college in Texas. The subset of archival data consisted of 1,242 student records from the fall 2018 academic year (before COVID-19) and 791 student records from the fall 2020 academic year (after COVID-19). The researcher used Jamovi statistical software to conduct a chi-square test of independence to examine relationships between (a) demographic, environmental, and academic variables and (b) a student’s decision to enroll. For 2018 and 2020, descriptive statistics were generated for each variable to obtain frequency distributions of the levels within each variable. The levels with the highest number of counts for each variable included the following: RQ1: age (18-23), enrollment status (part-time), educational goal (associate degree), ethnicity (Hispanic), gender (female); RQ2: financial aid (yes); and RQ3: GPA (0.00-1.00). Additional proportion analyses indicated that the proportions of all levels within each variable were unequal (p<.01). The chi-square test of independence was conducted to determine whether a statistically significant relationship existed between each of the seven variables and students’ decision to enroll in the subsequent semester. For 2018 and 2020, the variables of age (18-23), enrollment status (part-time), financial aid (receipt of financial aid- yes/no), and academic outcome (GPA) played a critical role in student retention. Statistically significant relationships were absent between students’ decision to enroll and the following variables: educational goal, ethnicity, and gender.Educational Leadership, Curriculum & InstructionCollege of Education and Human Developmen
Advancing consistent socio-economic monitoring of coastal ecosystem restoration through collaborative metric development
No full textEcological restoration programs increasingly aim to provide socio-economic and environmental benefits. However, monitoring of socio-economic outcomes of these programs lags behind monitoring of ecological outcomes. Socio-economic methods are less established, managers have less experience, and metrics used vary, stymieing evaluation and adaptive management. Here we demonstrate that logic models and stakeholder engagement can be used to identify core socio-economic metrics across various types of restoration, focusing on coastal restoration in the Gulf of Mexico. Across four major restoration types (oyster restoration, habitat restoration, recreation enhancement, and water quality improvement), core metrics were identified as changes in jobs, restoration expenditures, recreational activity, cognitive function, and subjective well-being. These metrics can provide a starting point for increased and more consistent monitoring of socio-economic outcomes. The collaborative, science-based, and replicable process we developed to identify core metrics can be applied to other ecosystems and management actions to expand monitoring and evaluation of socio-economic impacts
Enabling resilient operations of unoccupied aerial vehicle (UAV) swarms
No full textUAVs (Unoccupied Aerial Vehicles) are used in fields such as surveying, military operations, and disaster relief due to their cost-effectiveness, temporal efficiency, operational flexibility, and uncrewed capability. Collaborative efforts among UAV swarms can further improve results. However, whereas controlling a single UAV in dynamic environments is challenging, this complexity escalates in swarm operations. Swarms need to be operationally resilient to avoid cascaded failures and maintain mission progress. Resiliency, or the ability of a system to handle disruptions, is critical yet challenging to achieve in UAV swarms. This dissertation assesses current resiliency methods in swarm systems and proposes a comprehensive framework to enhance swarm resilience from the ground up. The approach includes a unified resource tracking framework for UAVs called U-SMART, focusing on robust communication, environmental awareness, and optimized task assignments. Additionally, a novel self-healing module targets distressed agents, tracking their well-being and providing reactive measures. The methods employ a modular approach, integrating predefined schemes and response selection algorithms to improve system resilience. Finally, the impact of external disruptions on swarm behavior through a systematic experimentation methodology for simulations is explored. This study aims to bridge the gap towards a resilient UAV swarm by addressing swarm resilience from both systemic development and simulated disruption perspectives.Computing SciencesCollege of Engineerin
The relationship of teacher perceptions of technology and technology implementation in rural South Texas high schools
No full textA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Education in Educational Leadership.The purpose of this quantitative study was to examine whether perceived usefulness and perceived ease of use of technology influence the integration of technology in rural south Texas teachers’ classrooms. To investigate possible relationships, the researcher collected data through an online survey tool. The sample size for the study was 115 respondents from 44 rural high schools across 19 counties in south Texas. The theoretical foundation for the study was based on the Technology Acceptance Model. One research question was used to guide this study, RQ: Is there a difference in perceived usefulness of technology based on the frequency of technology integration when controlling for technology ease of use, professional development opportunities, and hours spent on professional development? The study supports the research which states that rural teachers face unique challenges when using technology and that more specific and continued training is needed for teacher technology usage in rural schools.Educational Leadership, Curriculum & InstructionCollege of Education and Human Developmen
Ocean Decade Vision 2030 White Papers - Challenge 9: Skills, Knowledge, Technology, and Participatory Decision-Making for All
No full textPláticas con las meras: Voices of emerging and established Latina leaders' journeys to the superintendency
No full textThe underrepresentation of Latina superintendents in Texas is a pressing concern, as Hispanic students comprise an ever-increasing majority of enrollment in the state's public school districts. This qualitative narrative study explores the perceptions and experiences of current and aspiring Latina superintendents through the lens of intersectionality theory. Employing pláticas, a culturally responsive methodology, seven Latina leaders from South Texas shared their leadership journeys, challenges, and triumphs. Four overarching themes emerged from the data analysis: (1) Overcoming Barriers and Challenges in Work-Life Integration, (2) Mentorship, Support Systems, and Advocacy, (3) Identity, Background, and Representation, and (4) Career Advancement, Qualifications, and Leadership Development. The findings illuminate the complex interplay of personal, professional, and cultural factors that shape Latina superintendents' experiences, underscoring the resilience, determination, and unique strengths they bring to their roles. The study highlights the urgent need for targeted support, mentorship, and inclusive practices to foster Latina leaders' success and address their underrepresentation in the superintendency. Implications for practice, research, and policy are discussed, emphasizing the importance of culturally responsive leadership development, equity-focused hiring practices, and systemic reforms. This research contributes to the growing body of literature on Latina leadership in education, offering a nuanced understanding of the challenges and opportunities faced by Latina superintendents in South Texas. By amplifying the voices and experiences of these leaders, the study aims to inspire and support the next generation of Latinas in their pursuit of educational leadership roles.Educational Leadership, Curriculum & InstructionCollege of Education and Human Developmen
Advancing coastal inundation forecasting: A multifaceted machine learning approach
Full text linkA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Geospatial Computer Science.The frequency and severity of coastal inundation events are increasing along most of the world’s shorelines. Global sea levels were remarkably stable for thousands of years but climate change is driving rising sea levels, presenting significant challenges for coastal management and conservation efforts. Other factors such as land subsidence and shifting weather patterns further increase the vulnerability of coastal regions to inundation. Understanding the complex interactions among these factors is necessary for developing effective strategies to mitigate the impacts of coastal inundation and improve coastal resilience. More accurate and actionable predictive models and better monitoring systems are essential in assessing and managing the risks associated with coastal inundation. This dissertation explores and assesses the potential of machine learning techniques to predict coastal inundation for short-term, i.e., hours to days, predictions on the sandy beach adjacent to the instrumented pier of Horace Caldwell Pier in Port Aransas, Texas. Traditionally, short-term water level predictions have been used to alert the public of the potential for inundation events, providing stakeholders with valuable lead time to prepare and implement mitigation measures. However, in cases where longer preparation periods are required, seasonal to multi-year water level prediction models offer extended planning for stakeholders and beach managers. This dissertation contributes to the field of coastal inundation forecasts and assessment in several ways: (1) by assessing and enhancing the performance of deep learning architectures for short-term water level predictions. Sequence-to-sequence was identified as the most appropriate architecture for the problem as it significantly improves upon existing methodologies and pushes the boundaries of reliable predictability from 48 hours to 96 hours and more for inland stations; (2) by exploring the development of machine learning models for seasonal to multi-year water level predictions in the Texas coast region, offering valuable insights for longer-term planning and adaptation strategies with lead times of three months up to three years. (3) Finally, this dissertation introduces the first predictive machine learning model for total water levels, including wave runup, while incorporating metocean variables. The development of this model was possible through the installation of a fixed camera system, resulting in a unique dataset containing 30-minute imagery of the study area for over a year, which, along with bimonthly surveys and further processing, resulted in one of the first total water level time-series data sets. This allowed for a morphological analysis that determined that for the study area, inundation events are triggered by increases in dominant wave period, average wave period, significant wave height, and average water levels. It was also found that a dominant wave period of ten seconds or more leads to temporal beach erosion, with a recovery period under two weeks. Overall, this research contributes to advancing our understanding of predictive capabilities in managing coastal inundation, thereby assisting stakeholders and policymakers in developing proactive measures to safeguard coastal communities and ecosystems.Computing SciencesCollege of Engineerin
Advanced computer architecture
No full textIntroduction: What is Memory Hierarchy? A layered system for storing and accessing data in a computer, Organized based on speed, capacity, and cos
Advanced computer architecture
No full textIntroduction: Importance of Memory, Bottleneck in performance, Large capacity gap between registers and main memory, Challenges of Memory Access, Latency (access time), Bandwidth (data transfer rate