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Explainable Frontal Boundary Predictions for Applications in Operational Environments
No full textFrontal boundaries drive many high-impact weather events around the globe. Identifying fronts through various thermodynamic fields increases predictability of hazardous weather phenomena. Frontal analysis is still primarily done by human forecasters, often implementing their own subjectivity rules and criteria for determining frontal positions and types. Subjective placements of fronts can result in various solutions by different forecasters when given identical sets of data. Numerous studies have attempted to make frontal analysis more consistent through numerical frontal analysis, using sets of rules and thresholds with thermodynamic fields to locate and classify fronts. In recent years, machine learning algorithms have gained more popularity in meteorology due to their ability to learn complex relationships within large quantities of atmospheric data. We present a novel machine learning algorithm that predicts five different types of frontal boundaries - cold, warm, stationary, and occluded fronts and drylines. The algorithm was able to locate 76-86\% and 70-81\% of fronts over CONUS and NOAA's Unified Surface Analysis domain, respectively, on an independent testing dataset. We applied two Explainable Artificial Intelligence methods to the model - permutation studies and saliency maps. Permutation studies allowed us to determine variable importance for each frontal type. Saliency maps for the selected case study gave us insight as to how the model output can change as the ambient environment is modified. While more work needs to be done to improve the algorithm, we have demonstrated that machine learning can be used to develop an accurate and efficient model for detecting frontal boundaries
Multilingual Metadata: The Pan-American Authorities Initiative for Spanish Subject Headings
No full textIn 2020, a group of library information science specialists at the University of Florida Libraries (UF) formed the Pan-American Authorities (PANA) group, a bilingual (English/Spanish) metadata working group dedicated to standardizing the creation of Spanish-language metadata to improve discoverability and access to digital collection materials published in Spanish. Recognizing our growing non-English collections, we found it important to address biases in North American cataloging and metadata practices, changing them to be more inclusive and representative of materials in our collections, their creators, and their users. However, the primary challenge was finding reliable authority files for assigning Spanish metadata that captured national and regional variations of the Spanish language. Prior to the formation of PANA, UF’s bilingual metadata specialist's primary resource for assigning Spanish metadata was lcsh-es.org, a bilingual English-Spanish database that aggregates six Spanish language authority files. While the convenience of accessing Spanish subject headings through a centralized platform was invaluable, over-reliance on this resource was problematic, notably because the authority files aggregated in lcsh-es.org predominantly originate from Europe or the United States. To address this, the PANA group began establishing a workflow that would allow for Latin American authority files to be utilized. Since its origin, the group has partnered with the University of Texas Austin Libraries, who have adapted the workflow to increase their Spanish metadata translation across several digital collections sites, representing materials from across Latin America, fostering greater accessibility for its users throughout the region. Collectively, the PANA group has successfully contributed approximately 700 terms to this resource, ranging from human rights themed-subject terms to genre forms. Currently, we are constructing a publicly accessible website, and the Pan-American Authorities (code: pana) is now an officially recognized subject heading source code by the Library of Congress
FIRST PRINCIPLES MACHINE LEARNING IN RADAR: AUGMENTING SIGNAL PROCESSING TECHNIQUES WITH MACHINE LEARNING FOR DETECTION, TRACKING, AND NAVIGATION
Get PDFMachine learning (ML) provides a set of tools for learning approximate system models from data. It has the potential to improve classic radar signal processing (RSP) algorithms by allowing them to maintain performance when the environmental assumptions used to derive them are violated. This could mitigate performance degradation experienced in more challenging scenarios, like those commonly found in airborne and maritime radar. However, the integration of ML into RSP algorithms presents a unique challenge due to the strict performance requirements of radar systems and often unpredictable nature of ML.
This work examines an architectural approach to explainable ML that allows for the seamless integration of ML with more traditional algorithmic methods. This approach is then paired with causal ML concepts to develop a method for mitigating measurement drift in tracking and navigation. Next, an integrated system of low-cost ML systems are developed to enable adaptive detection algorithms to maintain CFAR-like performance across a range of interference distributions. Finally, generative ML techniques are used to reduce sample support requirements for adaptive detectors by directly constructing whitening filters from a small set of interference samples. This dissertation presents a framework for the successful integration of ML into RSP algorithms using a targeted approach based on a clear understanding of the first principles physics at play in a given application
Breaking Stereotypes in STEM: Professional Development, School Counselor STEM-Advocacy Beliefs/Practices and African American Female Student Course Enrollment
Get PDFAfrican American females are greatly underrepresented in STEM careers. Evidence suggests that one reason for this situation is that African American female students receive fewer opportunities to take advanced math and science courses in middle and high school. Theory suggests that implicit bias and/or negative stereotypes may lead counselors and school staff to track African American females into basic courses that have less academic rigor. Few analyses have sought to test whether professional development targeting negative stereotypes can produce increased enrollment in advanced math and science courses among African American students and change the behaviors/practices of school counselors regarding STEM advisement. The purpose of this study was to examine the effects of a professional development program for school counselors—STEM for All and All for STEM webinar in addition to a portion of the Engineer inclusion: Best Practices for Recruiting Students into Nontraditional STEM and CTE Programs and Pathways webinar—on the course enrollments of African American female students and STEM advocacy attitudes of school counselors. A pre-post single group survey study was performed in one district to test the effects of this professional development program. Twenty-two school counselors took the survey just before the intervention was given and 21 counselors took the survey post intervention. Wilcoxon signed-rank tests were run to determine the following: changes in overall school counselor survey pre/post scores, the intervention effect when controlling for several independent factors, and the intervention effect when controlling for single independent factors. Course enrollments for African American female students were collected and analyzed pre and post intervention. A Wilcoxon signed rank model for the proportion of Black females taking advanced math and science courses after the professional development intervention was conducted. Follow-up Wilcoxon signed rank tests were conducted by demographic characteristics including race and gender. Results revealed that there was no average improvement in school counselor STEM advocacy scores (representing advocacy beliefs and practices) or African American female STEM course enrollment as a result of the STEM intervention for school counselors. However, school counselors who identified as American Indian/Alaska Native as well as those who indicated that they work in both the middle and high school levels showed a significant improvement in STEM advocacy scores, respectively. In addition, school counselors who indicated they had 16 plus years of experience showed a marginal decline in STEM advocacy scores
DEVELOPING AN ALGORITHM INTEGRATING VOICE AND IMAGING ANALYSIS TO RECOGNIZE FACIAL FEATURES AND DEFICIENCIES AFTER ORAL SURGERY
Get PDFAccording to the National Institute of Health (NIH), oral cancer is one of several major types of head and neck cancer (HNCs) and affects approximately 54,000 individuals in the United States each year. Recognized risk factors for HNCs are primarily tobacco use, alcohol intake, and inadequate oral hygiene, the latter of which is significant for oral cavity cancer. Like treatment for other cancers, oral cancer therapies usually include surgery, radiotherapy, chemotherapy or a combination thereof. Treatments can cause loss of clear speech as a result of resecting parts of the vocal tract, which alters the vocal tract shaping and/or limits mouth movement.
For this thesis, a software application was developed to evaluate a participant’s spoken communication by simultaneously analyzing facial features and voice recordings of him or her reading a scripted passage. The effect of vocal tract changes following oral surgery was investigated using the new application, which showed measurable, quantifiable loss of speech. The goal of development and testing was providing medical doctors, speech therapists, and researchers the ability to leverage data-drive algorithms when designing strategic rehabilitation treatment plans to improve patient recovery. With the use of machine learning techniques, a model was developed for analyzing speech patterns and identifying/quantifying an emulated impact of oral surgery on generating speech. Such an approach leverages acoustic analysis and offers a non-invasive, accessible means of assessment, especially when compared to other methods (e.g., high-speed video-stroboscopy) that are known to cause side effects of swelling/pain and exclude some cancer patients. By focusing on extracting and analyzing various audio features from speech recordings and spatial dynamics of the lips—including formant frequencies—investigators are able to discern subtle changes in motor speech task characteristics. This information could indicate post-surgical complications or suggest improvements during recovery. The framework built in this thesis identifies a process for comparing speech samples and special facial dynamics both before and after surgery. Detecting impairments, like shift in speech frequencies, offers valuable feedback about a patient’s motor speech task monitoring and rehabilitation progress. Results demonstrate the effectiveness of therapeutic interventions after cancer treatment.
Experimental analyses emulated possible post-surgical scenarios for two healthy participants. The first participant was a non-native English speaker and the second participant was a native English speaker with American accent. Various speech patterns were observed under both regular conditions and those experienced as a consequence of two types of oral obstructions. Preliminary results demonstrate the potential for using the novel method detailed herein for objectively assessing speech loss and monitoring speech rehabilitation for patients who suffer from oral cancer. In short, this thesis presents a framework for non-invasive assessment of speech impairments following oral cancer treatment that bridges the gap between clinical speech therapy and computational speech analysis. The impact will enhance oral health and surgery rehabilitation.
This study was conducted under an approved IRB by the University of Oklahoma No. 17042 and title: AI For Facial Rehab Post Oral Surgery Speech Recovery
AN ORCHESTRATION ANALYSIS OF KEVIN WALCZYK’S CONCERTO GAUCHO FOR TRUMPET AND WIND ENSEMBLE (2007) IN CONTEXT WITH THE CONCERTO GENRE FOR BANDS FROM 1880 TO 2007
Get PDFKevin Walczyk’s (b. 1964) Concerto Gaucho for Trumpet and Wind Ensemble (2007) represents a significant development of original literature for soloist and full band orchestration. While the twentieth-century band movement gained momentum through many initiatives that primarily focused on developing an original body of literature for the band, new initiatives began focusing with similar intensity on developing an original repertoire featuring soloists. Since the beginning of the twenty-first century, significant progress was made in adding artistic solo literature to the band’s repertoire. This document provides context to the evolution of wind band literature with soloists in regard to addressing balance issues while utilizing the full resource of woodwind, brass, and percussion instruments. The document examines Concerto Gaucho for Trumpet and Wind Ensemble through a phrase-by-phrase analysis of orchestration techniques in regard to balance with the solo voice and clarity within the ensemble
Copper-Carbene Mediated 1,2-Cis Furanosylation Reactions
Get PDFCarbohydrates are essential biomolecules and are found in a majority of newly discovered natural products. When the fact that around 50% of novel drug molecules are either natural products or molecules based on natural products is taken into account, one would think that a large percentage of novel drug molecules contain carbohydrates. However, carbohydrates remain one of the most underrepresented moieties in drug molecules today. This underrepresentation comes from several factors. Firstly, carbohydrates are compounds that are notoriously difficult to work with, requiring numerous fine manipulations and the hands of a skilled chemist to produce the desired results. Secondly, glycosylation methods often require stoichiometric amounts of harsh reagents or the use of expensive rare-earth promoter.
These problems are often further exacerbated as there exist three main forms of glycoside: pyranosides, furanosides, and sialic acids. Pyranosides are the easiest to work with as their propensity to undergo SN2-type reactions means that stereocontrol of the anomeric position is relatively facile. This means that a majority of protocols that are developed for glycosylations are developed for pyranosides. However, due to their difference in reactivity, pyranosylation strategies are often unable to induce effective glycosylation in either furanosides or sialic acids, meaning that these methods translate poorly to furanosylation or sialylation. As a result, efficient methods of furanosylation are few and far between.
Taking these factors into account, we notice that while glycosides in general are widely underrepresented in drug molecules approved by the FDA, furanosides are even less represented. On top of that, due to a plethora of reasons, the formation of 1,2-cis furanosides is of a particular challenge and while these species are essential for many organisms and they can be potent therapeutics, are far and beyond the most underrepresented moiety in drug molecules.
We sought to tackle this challenge head on by developing novel furanosylation strategies aimed at making furanosylation reactions more approachable to industrial entities. For this, we set out with a particular set of goals in mind. Firstly, we wished to develop furanoside donors that could be readily synthesized in a facile manner at a low cost. Secondly, our strategies must be selective for the challenging 1,2-cis linkages that remain largely underrepresented for pharmaceutical applications. And thirdly, we wanted our donors to be activatable by inexpensive, mild, earth-abundant conditions, namely copper catalysis.
These goals have converged in the development of novel approaches to 1,2-cis furanosylation, each of which promoted by mild copper catalysis, featuring benchtop stable donors bearing a carbene precursor moiety. The reactions are high yielding and diastereoselective and represent an excellent potential strategy for the generation of novel therapeutics containing 1,2-cis furanosides which should increase the accessibility of these molecules in the pharmaceutical industry
Psychological Well-Being for Elderly Population with Disability: Implications of Universal Design
Get PDFLiving in a home designed with all the necessary elements for elderly people with disabilities can enhance their independence, safety, security, self-motivation, communication, and socialization. Consequently, this contributes to their psychological well-being. As populations age worldwide, the prevalence of disabilities among the elderly is increasing, this poses significant challenges to their living condition. This thesis explored the current challenges for ease of maneuvering and implications of Universal Design (UD) in their own home to address the psychological well-being of elders with disabilities. Literature has established that an individual's lifestyle and preferences are influenced by their domestic environment and there are different sets of guidelines. Despite the standards in place, people have different needs and conveniences for maneuvering and/or accessibility based on their usability, body structure, and types of disability. The study aimed to identify the most usable spaces within the home and variable differences due to demographics- age, gender, culture, location, and climate. Moreover, the discussion section suggested key elements of UD to enhance the living quality among the target community.
The data collection method included an online survey and snowball sampling, from 51 participants' responses, either older individuals with disabilities living in their own homes or their caregivers. Among them, 29 were eliminated due to incomplete responses and 22 were retained for further analysis. Through a mixed-method research design, the study collected data regarding accessibility challenges, psychological effects, and the reasons behind the challenges in different spaces within a home environment. Additionally, the thesis delved into the practical implications of UD in housing infrastructure, emphasizing the importance of collaboration efforts among policymakers, architects, urban planners, and healthcare professionals to effectively integrate UD principles into the built environment.
The result section identified the most used spaces, the level of challenges, and the effects on psychological well-being due to the challenges. Additionally, the study elaborated on the correlation between ease of maneuvering and psychological well-being, reasons for challenges during maneuvering, and variation in accessibility responses based on different demographics.
Finally, this research aims to contribute to the burgeoning field of gerontology and disability studies by elucidating the nexus between UD and psychological well-being. By implementing custom solutions at homes, it is possible to make significant strides in reducing the cost of care homes and assisted living facilities. Thesisocating for inclusive design practices, this thesis seeks to enhance the quality of life and promote independence, dignity, and empowerment for this vulnerable demographic, thus fostering a more equitable, and age-friendly society
Foster Care's Influence on Educators
Get PDFThis dissertation explores various facets of foster care, focusing on foster families' experiences and educators' attitudes toward trauma-informed care. The first study uses autoethnography to document a family's first year as foster parents, highlighting their challenges, rewards, and personal growth, along with the cultural adjustments within the family. The second study examines the disciplinary challenges faced by foster children in schools, noting the inadequacy of conventional methods like buddy classrooms and zero-tolerance policies. It investigates whether educators' personal or professional exposure to foster care influences their attitudes toward trauma-informed care, finding that those with such experience have more favorable attitudes, as measured by the ARTIC-35 assessment. Despite increased awareness of childhood trauma among educators, foster children still face higher rates of suspension and expulsion. The dissertation argues that transformative learning experiences, particularly exposure to the foster care system during teacher training and professional development, are essential for fostering empathetic and effective trauma-informed education. The findings advocate for integrating these experiences into educational programs to better support foster children
Investigations of microbial diversity and microbial interactions in a contaminated aquifer and experimental evolution of Desulfovibrio vulgaris Hildenborough
No full textInvestigating the mechanisms underlying microbial diversity is one of the challenges in
microbiology. The dimension of diversity typically includes three aspects: taxonomic diversity
(TD), phylogenetic diversity (PD), and functional diversity (FD). Anthropogenic activities,
particularly those affecting groundwater ecosystems through contamination, represent an
underexplored area of study. Microorganisms are crucial in mediating the effects of
contaminants within these ecosystems, yet our understanding of their community responses
remains partial. Thus, it is crucial to characterize the microbial community compositions,
elucidate the relationship between biodiversity and ecosystem services, and explore the dynamics
of microbial interactions with environmental pollutants for potential bioremediation applications.
The Oak Ridge Field Research Center (OR-FRC) is one of the Department of Energy’s
contamination sites with a variety of nutrients, stressors, and contaminants including uranium,
nitrates, along with various volatile organic compounds. Detailed monitoring of hydrological and
geological profiles of the OR-FRC site has made it an ideal location for investigating the
reciprocal interactions between environmental conditions and microbial ecology and function.
Microbial taxonomic diversity declined as the stress increased. However, whether the
phylogenetic and functional diversities would show the same trend as the taxonomic diversity
along the stress gradient remains unclear. We selected several groundwater wells with extremely
high levels of nitrate, uranium, and extremely low pH from the OR-FRC site to answer these
questions. Both taxonomic and phylogenetic α-diversities were declined in the most
contaminated wells. In contrast, the decrease in functional α-diversity was modest and
statistically insignificant, showing a better buffering capacity to environmental stress.
Differences in functional composition, sometimes called β-diversity, were enlarged under high contaminated wells, while convergent functional composition was observed in uncontaminated
wells. Relative abundances of most carbon degradation genes were decreased in contaminated
wells, but those of many genes associated with nitrogen cycling, sulfur cycling, and metal
homeostasis were increased. Environmental variables had a much higher explanatory power in
functional composition than taxonomic and phylogenetic compositions, suggesting that niche
selection favored microbial functionality. Together, we demonstrate that microbial functionality
is more tolerant to stress than taxonomy and extend the Anna Karenina Principle based on Leo
Tolstoy’s assertion in that microbial community adapts to a stressful environment in its own
way.
Since the functional composition is a sensitive and informative metric for evaluating the
responses of microbial communities to environmental stress, and to further test the relationships
between the functional genes’ complexity and ecosystems stability, we collected more
groundwater samples at the OR-FRC site with more fluctuations in nitrate, pH and uranium. We
used GeoChip data, a high-throughput functional gene array, to construct the functional
molecular ecological networks. Notably, stress conditions led to decreased network complexity
and stability, while network modularity increased. Functional genes associated with nitrogen
cycling and metal homeostasis were significantly reduced under high contamination levels. We
also identified deterministic assembly processes as key drivers of microbial community structure,
although this trend was not obvious with escalating stress.
Sulfate-reducing bacteria played an important role in the biogeochemistry cycles at the OR-FRC
site, and potentially involved in the bioremediation of heavy metals and radionuclides. Thus,
understanding their adaptation to the fluctuating environments are important. We used
Desulfovibrio vulgaris Hildenborough, a model sulfate-reducing bacterium, to examine the effects of prior adaptation on evolutionary responses to elevated temperatures. Two groups of
DvH populations with 5000 generations of experimental evolution under non-stress or salt stress
conditions, and one group of DvH ancestral populations without previous experimental evolution
history were evolved for 1000 generations under elevated temperature conditions. We found that
most evolved populations showed increased growth rate and all evolved populations had
increased fitness compared to their corresponding ancestor populations under heat stress
conditions. Whole-genome sequencing indicated that significant difference of mutated genes was
observed among three groups. These findings underscore the significance of evolutionary history
in shaping microbial adaptation to new environmental challenges, with phenotypic convergence
observed despite genetic divergence.
Overall, this dissertation demonstrates that the linkage between microbial taxonomic and
functional diversities is weakened in a polluted aquifer, environmental stresses decreased the
complexity and stability of the functional molecular ecological networks. It advances our
understanding of microbial ecology in contaminated environments and the adaptive mechanisms
of microorganisms under varying stress conditions