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A Deep Learning-Based Methodology to Re-Construct Optimized Re-Structured Mesh from Architectural Presentations
No full textDuring the mid���20th century, the emergence of novel technologies presented a paradigm shift for human intelligence. Computers, in particular, became instrumental tools for designers and architects, enabling the creation of intricate systems with freeform structures. These computers were capable of generating the final shape of designs by employing predetermined algorithms. Nevertheless, the prevalence of intricate freeform constructions is a notable characteristic of modern architecture. The design and calculation of these forms pose a complex challenge, as they deviate significantly from the original target industries in terms of aesthetics, statics, scale, and manufacturing technologies. While these structures may be intricate and, in some instances, praiseworthy, their architectural application necessitates a distinct approach. The formation of novel forms, shapes, and relationships within architectural design compositions can manifest creativity, leading to the exploration and discovery of innovative notions. Architects and designers are, therefore, inclined towards circumstances in which disparities hold significance. Architects may employ or adapt preexisting shapes as a foundation for their design endeavors. Architects draw inspiration from a particular image and integrate the associated notion into their design process, resulting in a more innovative architectural building. Current methodologies enable the meticulous hand alteration of forms through the utilization of documents or photographs. The proliferation of Machine Learning technology is augmenting architectural responsibilities, enabling swift and inventive results, expediting the design process, and developing a forward-looking approach to adjust and retrace actions instantaneously. The objective of this study is to assess the viability and accuracy of incorporating machine learning capabilities into defining an algorithmic-based methodology, with the goal of enhancing the design creativity process in architecture. This will be achieved through the utilization of image-to-mesh 3D reconstruction deep learning techniques, specifically applied to complex, irregular architectural structures. Additionally, a generative mesh optimization algorithm will be employed to generate a customizable mesh surface that can be further manipulated, paneled, and subjected to morphological operations
John Bickham field notebook: AK11501-AK12000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK12001-AK12500 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
Building 0D and 2D Porous Metal-Organic Nanomaterials for Efficient Photo-Induced Energy Transfer
No full textThe concept of host-guest chemistry has been raised to study the interaction between a system and attached small molecules. These strong interactions have enabled the use of framework materials in catalytic reactions with high selectivity and turnover numbers. Recently, the introduction of metal/metal clusters has revitalized this field due to improved stability and binding capabilities. Metal-organic frameworks (MOFs), renowned for their high crystallinity, porosity, and well-determined structures, have been extensively used for host-guest studies. However, the traditional 3-dimensional bulk MOFs create a diffusion barrier that hinders the applications. To overcome this, chemists have formulated the field of 0-dimensional molecular cages, termed ���0D porous coordination cages���, which maintain homogeneity and exhibit explicit confinement in all dimensions, and the field of 2-dimensional MOF-derived nanosheets, termed ���2D MOF nanosheets���. These cages facilitate efficient catalysis by avoiding 3D stacking of pores.
In my PhD research, I aim to develop viable synthetic methodology for 0D and 2D metalorganic nanomaterials. Embedding photoactive ligands into 0D cages and 2D MOF nanosheets provides an approach to introducing catalytic ability. This can be achieved through introducing coordination functional groups such as carboxylate and azolate groups, converting various photoactive molecules into suitable ligands while maintaining their activities. Moreover, the selection of coordination/metal clusters and the host-guest interaction in these materials can lead to distinct activities. This work launched studies on the fabrication of 0D cages and 2D MOF nanosheets and their photo-catalytic reactions. The objective of this research is to study the design rationales of 0D and 2D MOF nanomaterials and inspire the discovery of novel catalysts with high selectivity and activity
Human-Centered AI for Precision Medicine: Methods and Applications
No full textAI has emerged as a powerful tool in the healthcare and biomedical domains. In the field of medicine, AI must demonstrate strong performance while adhering to human ethics. Throughout my Ph.D., I focused on designing and developing human-centered AI tools for precision medicine, with a special emphasis on addressing ethical concerns in medical AI. To bridge the gap between AI and medicine, I delved into cutting-edge AI methods, including knowledge distillation, reinforcement learning, multi-task learning, multi-modality learning and contrastive learning. To make our contribution focused, I specialized in the phenotyping, disease diagnosis, organ transplant, and health event prediction scenario which are essential medical tasks. Four-folds challenges of designing human-centered AI framework towards precision medicine are summarized as (1).Trade-off between performance and fairness, (2).AI integration in clinical workflow, (3).Multi-task prediction on related medical indicators. (4).Multi-modality EHR data. To comprehensively investigate the fairness issue in the clinical prediction algorithm, I conduct extensive experiments on the disease diagnosis to benchmark the performance and bias in the electronic phenotyping. I design a twostep debiasing strategy with unbiased knowledge distillation to predict the graft failure after liver transplant fairly and precisely. In order to support the doctor���s clinical decision, FairAlloc framework is proposed to directly generate accurate and unbiased patient prioritization decisions with reinforcement learning. To simultaneously predict highly related medical indicators, CoD-MTL is designed to take advantage of multiple highly related tasks to predict multiple cause-of-death after the liver transplant. When it comes to multimodal EHR data, I design a cross-modality knowledge distillation framework to distill the knowledge from LLM into the predictive model on structured EHR. My research efforts paved a way to design powerful and trustworthy AI frameworks to support precision medicine with human-centered AI principles
Human-Centered AI for Precision Medicine: Methods and Applications
No full textAI has emerged as a powerful tool in the healthcare and biomedical domains. In the field of medicine, AI must demonstrate strong performance while adhering to human ethics. Throughout my Ph.D., I focused on designing and developing human-centered AI tools for precision medicine, with a special emphasis on addressing ethical concerns in medical AI. To bridge the gap between AI and medicine, I delved into cutting-edge AI methods, including knowledge distillation, reinforcement learning, multi-task learning, multi-modality learning and contrastive learning. To make our contribution focused, I specialized in the phenotyping, disease diagnosis, organ transplant, and health event prediction scenario which are essential medical tasks. Four-folds challenges of designing human-centered AI framework towards precision medicine are summarized as (1).Trade-off between performance and fairness, (2).AI integration in clinical workflow, (3).Multi-task prediction on related medical indicators. (4).Multi-modality EHR data. To comprehensively investigate the fairness issue in the clinical prediction algorithm, I conduct extensive experiments on the disease diagnosis to benchmark the performance and bias in the electronic phenotyping. I design a twostep debiasing strategy with unbiased knowledge distillation to predict the graft failure after liver transplant fairly and precisely. In order to support the doctor���s clinical decision, FairAlloc framework is proposed to directly generate accurate and unbiased patient prioritization decisions with reinforcement learning. To simultaneously predict highly related medical indicators, CoD-MTL is designed to take advantage of multiple highly related tasks to predict multiple cause-of-death after the liver transplant. When it comes to multimodal EHR data, I design a cross-modality knowledge distillation framework to distill the knowledge from LLM into the predictive model on structured EHR. My research efforts paved a way to design powerful and trustworthy AI frameworks to support precision medicine with human-centered AI principles
How the Presence of In-Group Peers and Experts Influence Stem Undergraduate���s Identity, Persistence, and Academic Success
No full textEfforts to confront complex societal challenges are hindered by the lack of diversity in the science, technology, engineering, and mathematics (STEM). As education is a social enterprise, investigating how social interactions support or hinder belonging and persistence is crucial for the success and retention of historically underrepresented (HU) students. This three-article dissertation sought to understand and quantify how the presence (or lack thereof) of ingroup peers and experts in a student���s network influences domain-specific motivation and persistence intentions for HU STEM students. Study 1 uses ego-centric social network analyses to evaluate how the characteristics of mentees and mentors relate to the content of support and structure of mentor networks in a large sample of White and Hispanic/Latino(a) STEM undergraduates. Mentee perceived psychological similarity with their mentor(s) predicted both dyadic and network average levels of mentor support (i.e., psychosocial, career, role modeling) and relational satisfaction. Additionally, demographic homophily and engagement in undergraduate research were related to some mentor network structures. Study 2 extended this work by evaluating how characteristics of mentees, including if they have a mentor, and the quality and structure of their individual networks related to social integration, well-being, academic success, and persistence in STEM over two years. Having a high-quality mentor network, rather than the absence or presence of one or multiple mentors, was an important predictor of domain identity and well-being for students, which ultimately led to persistence in their STEM major. Finally, Study 3 narrowed the scope of social supports by analyzing patterns of demographic and psychological factors between students in one sophomore- and junior-level undergraduate Engineering and Computer Science (ECS) classroom. LNAM results revealed the presence of network clustering based on similar levels of domain identity. Additionally, domain values were positively related to STEM persistence intentions. This work critically addresses several significant gaps in the literature at the intersection of motivation and social networks for HU STEM undergraduates. Results inform best practices for developmental mentor network support, program developments, and interventions at the classroom level aimed to enhance social supports for students, ultimately addressing the nation���s goals to broadening participation in STEM fields
Megahertz Rate Spectroscopic Investigation of Hypervelocity Impact Flash
No full textUnderstanding hypervelocity impacts (HVI) has become crucial to several fields, such as space exploration, planetary science, aerospace engineering, and defense-related applications. HVIs are highly dynamic and extreme phenomena characterized by the immense and rapid transfer of energy between colliding bodies. One manifestation of this transformation of kinetic energy during and immediately following an HVI is the emission of an intense flash of light. Researchers investigating these optical emissions emanating from HVIs refer to this phenomenon as the ���impact flash.��� The impact flash can be noticed across a wide range of impact scenarios: both high and low impact velocities; in the vacuum of outer space, as well as the atmosphere of the earth; with both solid and liquid objects; and across metallic, ceramic, and polymer materials. Investigations into the impact flash have sought to establish connections between impact conditions, temporal evolution of different spectroscopic features, and underlying material failure mechanisms. Characterization of this phenomenon can also act as an indicator for a spatial and temporal evolution of material damage. Typical studies of the impact flash make use of a diverse array of optical diagnostics tools and methods. These include high-speed cameras, photodiodes and photomultiplier tubes, pyrometers, laser interferometers, and spectrometers. Stop-motion or relatively low-rate spectral analysis systems have been employed to measure HVI-induced light emission, providing some spectral information related to the dynamic material behavior and projectile-to-target energy transfer. The relatively short time scales (microseconds) and rapidly evolving impacted material response characteristics of HVI events necessitate the use of highspeed detectors with superior temporal resolution to capture the impact flash evolution with sufficient temporal resolution.
Hence, the objective of this thesis research is to develop two high-speed transient spectral analysis systems for investigating the evolution of the impact flash. The first system, an ultrahigh-speed spectrometer (UHSS), has been developed to capture and analyze the transient light emission induced by HVIs in a two-stage light gas gun (2SLGG) facility, with sub-microsecond (i.e., megahertz-rate) temporal resolution. The UHSS makes use of a MHz-rate complementary metal��� oxide���semiconductor (CMOS) camera coupled to an imaging spectrometer, resulting in MHz-rate spectral imaging characterization. This system was used to record time-resolved spectral images of HVIs at speeds up to 6 km/s from spherical aluminum projectiles impacting both aluminum and stainless-steel targets. The high-speed spectral images recorded allowed us to compare the evolutionary behavior of energized metallic species and combustion byproducts in the nano- and microsecond time scales following the impacts. This study found that even at similar impact conditions, the light emission behavior of aluminum-on-aluminum and aluminum-on-stainless-steel impacts can be substantially different. Aluminum target impacts showed one temporal peak in light intensity, whereas the stainless-steel targets showed an additional secondary peak of emitted light. Additionally, in stainless-steel impacts, combustion byproducts were seen to feature a stronger second peak than pure metallic species.
The second high-speed spectral analysis system, a fiber-based multi-spectral diagnostics (FMSD) system, makes use of a fiber bundle to direct light from the impact flash into a collection of highspeed silicon photodiodes. A gigahertz-rate oscilloscope receives the output voltage from these photodiodes as light reaches each photodiode. By placing unique spectral filters in front of each photodiode, these photodiodes are used to capture signals from a specific species. The design of the FMSD was validated using laser-induced plasma experiments. Engineering improvements and subsequent implementation in future HVI studies are discussed
Oebalus pugnax (Hemiptera: Pentatomidae) Resistance to ��-cyhalothrin in Texas Grain Sorghum and Efficacy of Potential Alternative Insecticides
No full textAlong the Coastal Bend of Texas, the rice stink bug, Oebalus pugnax (F.), is a pest of grain sorghum and rice that is primarily managed by application of insecticides. In 2015, a rice stink bug population from Wharton County, Texas, was found to be resistant to the pyrethroid insecticide ��-cyhalothrin, a common insecticide for rice stink bug management. In more recent years, reports of control failures have spread throughout Coastal Bend, a major hub of Texas grain sorghum production. Despite growing concern from grain sorghum producers, no studies have thoroughly evaluated the extent or geographic range of pyrethroid resistance in Texas. This thesis focuses on addressing the gaps in knowledge regarding rice stink bug pyrethroid resistance in grain sorghum and improving management tools and considerations for this pest.
Between 2021���2023, 21 rice stink bug populations across the Texas and Louisiana were evaluated for ��-cyhalothrin resistance over three grain sorghum growing seasons (2021���2023). Mortality was assessed through glass vial exposures to eight concentrations (0, 0.03, 0.1, 0.3, 1, 3, 10, and 30 ��g/vial) of the pyrethroid ��-cyhalothrin. The concentration of ��-cyhalothrin required to kill 50% (LC������) of each population was estimated by probit analysis. Furthermore, the efficacy of insecticides including pyrethroid (��-cyhalothrin), organophosphate (dimethoate), and neonicotinoid (dinotefuran) insecticides were evaluated in field experiments conducted in Nueces County, Texas in 2021. The efficacy of these insecticides was evaluated for suppression of nymph and adult rice stink bug over the course of 22 days in experimental plots.
Our results indicated numerous rice stink bug populations along the Coastal Bend were resistant to ��-cyhalothrin with LC������ values ranging from 42���1,600 times more than a susceptible population. In a 2021 field efficacy trial, ��-cyhalothrin did not provide adequate control for rice stink bugs. Dinotefuran provided excellent control of nymphs, but dimethoate provided greater control of adult rice stink bugs. This study resulted in the approval of three Section 2(ee) bulletins for dimethoate products to be used on rice stink bug in Texas grain sorghum, thereby expanding the products available for control of this pest
Essays on Hospital Performance
No full textThis dissertation consists of three essays that focus on hospital service quality and healthcare delivery performance issues. I provide insights on how hospital administrators can leverage hospital personnel, accreditation, and community outreach services to influence hospital performance.
The first essay provides empirical associations regarding impacts of the extent of use of midlevel providers (MLPs) on (i) hospital quality, as measured by the Triple Aim Performance (TAP) metrics, and (ii) hospital costs. Hospital administrators are shifting care delivery models toward an approach that uses more caregivers in the form of mid-level providers (MLPs), such as nurse practitioners, physician assistants, and clinical nurse specialists. To date, however, healthcare operations management (OM) literature remains ambiguous about longitudinal empirical associations between mid-level providers and hospital costs, quality, and other performance measures. This essay, by providing one of the first panel data analyses of midlevel provider impacts in general service hospitals, improves upon existing case studies found in the extant literature. I find that MLPs can improve hospital clinical quality, technical efficiency, and patient experience without impacting hospital costs.
The second essay analyzes associations of hospital accreditation with hospital technical efficiency. As the U.S. healthcare marketplace becomes more competitive, many hospitals face problems of communicating with potential patients about the process conformance of their services. Similarly, patients face uncertainty when choosing a hospital from which to obtain care. A possible solution to this problem involves a hospital obtaining an accreditation. Hospital accreditation is a publicly visible indicator that a hospital provides care and services that should meet a high standard, as defined by an accreditation body. Hospitals can use accreditation as a signal to potential patients to help reduce information asymmetry between the two parties. I contribute to healthcare OM literature by providing one of the first panel-based SFA analyses of associated impacts that hospital accreditation has with hospital efficiency. I find that hospital accreditation is associated with improved hospital technical efficiency, but this association is moderated by government efficiency mandate signals. Since obtaining and maintaining hospital accreditation is a resource intensive and expensive process for administrators, the findings give hospital administrators and other stakeholders useful knowledge that should help in the decision-making process of whether or not to obtain hospital accreditation.
The third essay examines the association that alignment between hospital community outreach services and community needs may have with hospital readmission and mortality rates. One of the tools hospital administrators may employ to potentially reduce readmissions and improve care quality is providing community outreach services within the surrounding community. Individuals��� health is largely determined, in part, in the geographic community and context that they live and/or work in. Community-based hospital outreach programs may serve to educate community members about the availability of care services at the hospital or inform recently discharged patients about proper post-discharge care. I investigate whether these community outreach programs are associated with reduced hospital readmissions and reduced mortality. Overall findings suggest that alignment between hospital community outreach services and community needs are not significantly associated with hospital readmission rates but may be associated with higher hospital mortality rates depending on how hospital community outreach services are operationalized
John Bickham field notebook: AK7001-AK7500.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK7501-AK8000 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection