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The Effects of a Structured Literacy Computer Program Implemented at Home on the Early Literacy Skills of Preschool Children
No full textThe purpose of the current study was to examine the effects of a computer program on the early literacy skills of preschool children, the relationship between fidelity to the intervention and improvement in literacy skills, and the reported satisfaction with the program. Forty-two four- and five-year-old children were randomly assigned to an intervention group, which used the OgStar Reading Early Reader iPad computer program/app, or a control group, which used the IXL Math computer program/app. The recommendation was to engage with the program for 15- 20 minutes per day for five days a week over a period of eight weeks in the summer prior to kindergarten. Three DIBELS measures were used to assess early literacy skills: Letter Naming Fluency (LNF), Phoneme Segmentation Fluency (PSF), and Nonsense Word Fluency (NWF). Parents submitted fidelity data about the number of lessons completed and completed a survey to assess their opinions regarding the use of the program. A total of 33 children completed posttests. Using linear regression and controlling for pretest score, students in the intervention group scored statistically significantly higher on LNF posttests (g = 0.41, p = .025) and NWF- correct letter sounds posttests (g = 0.52, p = .009) over the control group. No statistically significant differences were found between the groups for PSF (g = 0.19, p = .458) or NWF- words recoded correctly (g = 0.61, p = .057). Overall, fidelity to the planned intervention varied across participants. The number of lessons completed was moderately related to participant gains in LNF (r = 0.38), NWF- correct letter sounds (r = 0.31), and NWF- words recoded correctly (r = 0.36). Parent reported level of satisfaction with the app was generally positive. Parents reported they thought their child learned new skills (4.8/6.0) and would recommend it to other parents (4.5/6.0). These findings provide some initial support that the use of the OgStar Early Reader app may improve alphabetic knowledge for preschool children. Further study of the program and its effectiveness for a variety of participants and contexts is needed
Genetic Architecture of Nodule Traits in Guar
No full textGuar (Cyamopsis tetragonoloba L.) is an annual, diploid legume crop cultivated primarily for the galactomannan gum contained in the seed endosperm, which is used widely in foods, pharmaceuticals, and other industrial applications. As a legume, guar forms root nodules to fix atmospheric nitrogen, but nodulation has been noted to be poor in the field. Phenotypic evaluation of 225 diverse guar accessions from the United States Department of Agriculture germplasm collection revealed significant variation among genotypes for plant height, nodule diameter, fresh and dry nodule weight and dry aboveground plant biomass. However, no significant differences were found for fresh aboveground plant biomass. Broad-sense heritability was highest for nodule diameter (92%) indicating potential for genetic gain through selection. Significant positive correlations occurred between nodule number and weight, and between plant biomass and nodule weight, revealing increased plant growth with greater nodule mass. Several promising genotypes such as PI 288747 were identified that can serve as parents in breeding initiatives to improve nodulation capacity. Population structure analysis on 225 accessions from India, Pakistan, and the United States genotyped with 7,000 SNPs revealed three main genetic clusters largely corresponding to geographic origin. In which Q1 included genotypes from all three countries while Q2 and Q3 included genotypes only from India. Genome-wide association studies using 19,007 filtered SNPs identified SNP markers associated with plant height, nodule number and diameter, nodule weight, and plant biomass across multiple models. Several SNPs were shown to be connected to various guar nodulation traits. SNP Chr6_56449898 showed a strong correlation with the weight and number of nodules. Chr2_51078296 and Chr2_51010513 SNPs showed a strong correlation with nodule characteristics as well as other variables including biomass and plant height. Together, these results further scientific understanding of guar genetic diversity, nodulation traits, and potential genomic regions controlling nodulation. This knowledge and germplasm can aid guar breeding programs seeking to improve nitrogen fixation and yields through enhanced nodulation, which would also increase ecological services. This research provides an important foundation and resources to enable marker-assisted breeding and selection for superior nodulation capacity and associated increased productivity in this economically important legume crop
New Directions in Indirect Detections: Gamma-Ray Observations of Globular Clusters and Dwarf Galaxies
No full textOne of the most elusive unknowns in particle physics and astrophysics today is the fundamental nature of dark matter. It is theoretically well-motivated that dark matter is a weakly interacting massive particle (WIMP) ��� a particle lying within the GeV to TeV energy ranges that interacts very weakly with Standard Model particles. Such behavior makes dark matter extremely difficult to detect with terrestrial detectors. However, there are still many ways to probe the fundamental nature of dark matter. One such way is by searching for astrophysical signatures of dark matter annihilation. Supposing that dark matter is a WIMP which self-annihilates, we can look for inexplicable excesses of Standard Model particles from astrophysical sources. In particular, we can look for high-energy gamma-rays with energies in the range of GeV to TeV. In this thesis, I will discuss recent results on both the dark matter distributions and gamma-ray emissions of a selection of Milky Way satellites and globular clusters with an emphasis on the Sagittarius dwarf galaxy system and the Omega Centauri globular cluster. In Chapter Two, I discuss an updated dynamical analysis of the Omega Centauri globular cluster. In Chapter Three, I discuss an in-depth analysis of the gamma-ray source associated with M54, the globular cluster at the center of the Sagittarius dwarf galaxy. In Chapter Four, I discuss a follow-up paper in which we use cosmological simulations to further test the physical origins of the M54/Sagittarius source. I conclude in Chapter Five by describing several unique ways in which it may be possible to differentiate between the signal of annihilating DM and that of astrophysical background sources
Non-Destructive Viability Testing of Cotton Seeds Using Raman Spectroscopy and Optical Coherence Tomography
No full textIn recent years, the field of plant breeding has been quick to adopt new technologies for the analysis of field and crop systems, with optimistic results and massive quantities of data generated. In this thesis two such optical technologies, Raman spectroscopy and Optical Coherence Tomography (OCT), are used to produce quantifiable characteristics of cotton seed that may be used for analysis of viability and determination of the features that correlate to survivability. It was found that spatial variability of the Raman spectrum, as well as the occurrence or absence of specific delineations in the OCT tomograms correlate with seed viability.
Raman spectroscopy has proven to be an invaluable tool in the nondestructive analysis of biological materials. As it was previously found that fatty acid and carbohydrate content could be linked to seed viability, Raman signatures of these compounds are of particular interest. The ability of OCT to provide three-dimensional morphological structural details non-invasively, suggests its potential use for the analysis of internal seed structures. The features of interest include damage to the seed coat as well as clear demarcations in the seed interior, or subsurface features that could impact the germination of the seed. A comparison between the features documented by both technologies on each seed paired with a germination test allows correlations about the impacts that each feature and their combination have on the survivability of seeds.
We predict seed viability using the correlation of the observations we make in the Raman and OCT measurements with seed viability. With both cotton varieties (Tamcot 73 and G11) we find that when we select only seeds predicted to be viable, the germination rate is higher than for randomly selected control seeds. While the Tamcot 73 seeds we predicted to be viable germinated slightly better than the control seeds of the same variety, the overall low germination rate of these seeds indicates that other factors not measured by our techniques can have a large remaining impact. For the most recently harvested Tamcot G11 seeds, the germination rate of the control seeds was about 85%, leaving not much room for improvement, and thus a lower level of significance. The level of significance was highest for G11 seeds harvested in 2019, indicating that combined OCT and Raman measurements of seeds can be used to predict seed viability and to improve agronomic outcomes by selecting seeds with a higher expected chance to be germinating
Anger-Induced Early Childhood Respiratory Sinus Arrhythmia and Parenting Style: Predictors of Toddler Externalizing Behavior Problems and School Readiness
No full textEarly childhood emotional regulation has been linked to favorable academic, behavioral and socioemotional outcomes. Respiratory sinus arrhythmia (RSA) in response to an emotion induction has been studied as an indicator of emotional regulation. This study investigated children���s dynamic RSA change in response to a negative-affect evoking laboratory episode, and how RSA relates to behavior problems and school readiness. The sample consisted of 58 typically developing children (males = 24, mean age = 3.1, SD = .02) and their parents (mothers = 56, mean age = 35.04, SD = 5.25) in the Bryan-College Station area. Greater RSA suppression during the anger induction was associated with lower externalizing behavioral problem scores and higher school readiness scores. Children of authoritarian parents had the highest school readiness scores across levels of RSA suppression but the highest externalizing problems for those with low emotional regulation. Girls showed more pronounced patterns of RSA suppression whereas boys��� RSA levels remained comparatively flat over the anger induction. My study contributes to the emotion regulation literature from a family study perspective while employing multi-method assessments of child temperament. The findings shed further insight to the relationship between emotion regulation and cognitive performance. This research has implications for preschool readiness, cognition and behavioral interventions
Assessing the Potential for Antibiotics to Alter Horizontal Gene Transfer Rates in Aquifers via Artificial Recharge of Treated Wastewater
No full textThe managed artificial recharge (MAR) of groundwater is a topic of growing interest regarding water resource management. MAR utilizes alternative sources of water, including treated wastewater effluent, to augment natural recharge rates and slow water table declines. MAR can be accomplished through constructed infiltration basins with high infiltration rates to the water table, or injection wells screened at the depth of the aquifer bypassing the vadose zone. One emerging contaminant commonly detected in treated wastewater is antibiotics used to treat bacterial infections in humans and agriculture. Sulfamethoxazole and trimethoprim antibiotics are commonly co-detected in treated wastewater. This thesis aims to examine the potential dissemination of antibiotic resistance in groundwater influenced by the artificial recharge of treated wastewater effluent. Recent research suggests that antibiotics can potentially modify the dissemination of antibiotic-resistant bacteria in the environment by inducing horizontal gene transfer (HGT) frequencies. HGT is the exchange of antibiotic resistance, mainly through conjugation, from resistant bacteria to susceptible bacteria. While native subsurface bacteria are often resistant to antibiotics, they pose no direct risk to humans unless pathogens acquire that resistance and an exposure pathway exists. Experimental work includes investigating the background levels of antibiotic resistance in soils and changes in antibiotic resistance after subjecting soil microbiomes to varying antibiotic concentrations. A literature review on artificial recharge, wastewater treatment, bacterial and antibiotic transport in the subsurface influenced by artificial recharge was also conducted. A series of one dimensional, variably saturated flow and solute transport simulations were conducted for an artificial recharge environment using Hydrus 1D and a range of reported concentrations and solute transport parameters in soils from the literature review. Expected results include that artificial recharge of treated wastewater may provide rapid antibiotic solute transport through the soil column and into the water table, highly dependent upon linear sorption coefficients and first-order degradation constants. Significant further research on horizontal gene transfer frequencies in native subsurface microbiomes at sulfamethoxazole and trimethoprim concentrations many times below the minimum inhibitory concentrations is needed to determine the potential risks of enhancing the environmental antibiotic resistance problem in managed artificial recharge
Analyzing the Effects of Groundwater Flow Rates on the Recovery Efficiency of Aquifer Thermal Energy Storage Systems Using Numerical Modelling
No full textThis study looks at the effects of groundwater flow velocity on the recovery efficiency of Aquifer Thermal Energy Storage (ATES) systems and how modifying the pumping rate and storage time can optimize the efficiency of an ATES system located in an aquifer with low to high regional flow rates. By examining the processes that control the efficiency of these energy storage systems, we hope to improve their effectiveness and contribute to their use as an energy conservation technology.
The ATES investigation was done by applying principles of heat and solute transport to a numerical model that represents a low-temperature doublet ATES system installed in a shallow confined aquifer. The finite difference method modeling suite MODFLOW was used as the primary modeling software, with MT3DMS serving as the solute transport engine. The model includes six regional groundwater flow rates ranging from 0-50 m/yr, two pumping regimes of 200-400 m3 /d (injection and extraction), and two storage times of 0-3 months. The ATES wells operated on annual cycles, with injection temperatures ranging from 5-30��C.
We found that groundwater velocity is a primary control in the recovery efficiency of ATES systems, with an average drop of 27% from no groundwater flow to 50 m/yr. Additionally, we found that at groundwater flow velocities higher than 30 m/yr, the injection rate is the most important secondary parameter, with lower pumping and injection rates associated with lower recovery efficiencies. Inversely, at groundwater flow velocities lower than 20 m/yr, storage time is the most important secondary parameter, with longer storage times associated with lower recovery efficiencies
A Multifaceted Investigation of Cloud Microphysics: From Improving Convective Cloud Microphysics Parameterizations to Revealing Aerosol-Cirrus Cloud Interactions
No full textCloud microphysical processes and their parameterizations are at the core of multi-scale atmospheric modeling but still remain one of the major sources of uncertainty in weather and climate simulations. The aims of this dissertation are twofold: advancing our understanding of the physical processes involved in aerosol-cloud interactions and developing new parameterizations for better representing cloud microphysics in global climate models (GCMs). This dissertation is divided into two main parts. Part I focuses on evaluating and improving cloud microphysics parameterizations for convective clouds in GCMs. Part II focuses on investigating the impacts of volcanic aerosol on cirrus cloud using satellite observations, detailed microphysical model simulations and two GCMs.
GCMs have started treating convective clouds with detailed cloud microphysics parameterizations. However, the representations of convective cloud microphysical processes are often based on those for large-scale stratiform clouds, warranting further model evaluation for the fidelity of microphysics treatments transferred among various cloud types. Here, we evaluate and improve several aspects of the convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3), including processes of hydrometeor sedimentation, graupel production, convective snow detrainment, and rain generation against ground-based and satellite observations. Our model development efforts lead to substantial improvements in the simulations of cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation over the default model settings. These improvements set a better stage for future studies of convective cloud processes and their interactions with large-scale environments and aerosols.
Explosive volcanic eruptions inject a large amount of sulfur dioxide and ash particles into the upper troposphere and lower stratosphere, where volcanic-origin aerosols (sulfate and ashes) may modify cirrus cloud microphysics through ice nucleation but to an unknown extent due to limited research on this topic. Here, we aim to narrow this knowledge gap with the advent of advanced satellite retrievals of aerosol and cloud capturing the episodes of enhanced stratospheric aerosol loadings produced by modern moderate-magnitude eruptions. An analysis of 10-yr satellite datasets shows a phenomenal decrease in number and increase in size of cirrus ice crystals in the midlatitude lower stratosphere in response to ash-rich volcanic eruptions (2008 Kasatochi, 2009 Sarychev), indicative of heterogeneous freezing on volcanic ash suppressing homogeneous freezing. Conversely, cirrus clouds for the ash-deficit scenario (2015 Calbuco) are found to have up to 2.2 times more ice crystals, implying a moderately enhanced homogeneous freezing on volcanic sulfate aerosols. These impacts of aerosol on cirrus cloud, disentangling influences from meteorological co-variability, are most likely. Cloud parcel model with detailed physical ice nucleation processes is employed to elucidate the mechanisms of aerosol effects and the modeling results corroborate the observational findings. Sensitivity modeling experiments are also performed using two GCMs, CESM2.2 and E3SM-PA. Impacts of sub-grid scale vertical velocity generated by gravity waves on cirrus ice formation and volcanic ash emissions are found absent, identifying models��� inability to accurately capture the response of cirrus clouds to volcanic emissions and areas for future model development.
The studies in this dissertation advance our understanding of volcanic aerosol-cirrus cloud interactions on the process-level, stress the necessity and importance of accurate representations of cloud microphysical processes in GCMs, and advocate iterative effort in improving parameterizations as GCMs are the only means by which we project future climate
Effect of Feedstock Chemistry and Melt Pool Dynamics on Realizing Superior Strength and Surface Quality in Additively Manufactured Ultra-High Strength Steels
No full textAdditive manufacturing (AM) methods generally utilize a layer-by-layer process, fabricating parts from the bottom up which differs from conventional subtractive manufacturing methods. The current focus of AM development involves the production of complex, functional parts. Due to an increase in demand from industries such as aerospace, biomedical and automotive, metal AM has become increasingly popular. Laser powder bed fusion (L-PBF) is widely regarded as the most versatile metal AM process, utilizing high energy density laser to selectively melt powder particles into highly intricate designs.
Laser powder bed fusion (L-PBF) of a newly developed high strength steel known as AF96 has received notable attention due to its unique microstructure and superior mechanical properties. The current study investigates the influence of initial alloy carbon content and decarburization on the mechanical properties of AF96 fabricated via L-PBF. A process parameter development study was first performed to determine optimal processing parameters that result in near defect-free as-printed parts. Test specimens were then fabricated using optimized parameter combinations for mechanical and microstructural characterization. This process was completed for three compositions of AF96 powder containing varying amounts of initial carbon content. Preliminary results show a significant increase in both yield strength and ultimate tensile strength with increasing initial carbon content.
Spatter, an inherent by-product of conventional laser welding, DED, and L-PBF (Laser Powder Bed Fusion), significantly impacts part quality. This research investigates spatter's mechanisms, effects, and in-situ monitoring techniques within the context of L-PBF. Spatter, classified into droplet (hot) and powder (cold) types, originates from vapor-driven entrainment and recoil pressure, impacting surface quality and creating internal flaws. Spatter redeposition during printing leads to recoater impediment, defect formation, and porosity, affecting mechanical properties and surface roughness. In-situ monitoring techniques encompass visible-light high-speed camera imaging, Schlieren video imaging, X-ray video imaging, and infrared video imaging, each revealing various aspects of spatter behavior. A specialized visible-light high-speed camera system for EOS M290 L-PBF, developed for this study, provides insights into spatter ejection behavior. Technical challenges in camera operation and video acquisition were addressed, achieving successful high-speed video imaging capturing spatter movement. Optical microscopy analysis of L-PBF-fabricated AF96-29 cubes reveals varied surface qualities, correlating with spatter distribution in different printability map regions. This study underscores the complexities of spatter behavior, its detrimental effects, and the significance of tailored in-situ monitoring techniques for understanding and mitigating spatter-related issues in L-PBF
Development and Verification of a Nuclear Forensics Methodology for the Attribution of Plutonium Using Data Science Methods
No full textAn advantage the global community has in preventing nuclear terrorism is the difficulty for a nonstate actor to procure special nuclear material (SNM). The regulation of SNM is essential in stymying adversaries. A nuclear forensics methodology, able to determine the provenance of SNM, like plutonium (Pu), will aid the international community in deterring nuclear smuggling. If Pu is recovered outside of regulatory control, an attribution capability would help inform conventional investigators. In both cases of theft and state hand-off of Pu, a guarantee that an offending party would be discovered and punished could force preemptive abandonment of any planned misdeeds.
The goal of this research was to develop and verify a nuclear forensics methodology for attributing unknown separated Pu samples using machine learning techniques. The methodology needed to be capable of identifying the following three attributes: the reactor-type that produced the Pu sample, the burnup of the irradiated uranium fuel that produced the Pu sample, and the time since irradiation (TSI). The methodology also needed to be robust enough to attribute samples that contain a mixture of Pu from multiple different reactor sources. A set of isotope ratios was used as the forensics signature and the training of the machine learning models utilized data from a library of Monte Carlo reactor neutronic and fuel burnup simulations. Lastly, the methodology needed to be validated by demonstrating that it could successfully attribute physical Pu samples.
Research proceeded in three main parts. First, machine learning models suitable for this application were identified, and were then trained and tested for attributing single reactor type Pu samples to assess feasibility. Second, the methodology was validated with a Pu sample separated from low enriched uranium dioxide (LEUO2) irradiated in a thermal neutron flux spectrum. Third, the machine learning methodology was adapted to attribute samples that were sourced from multiple reactor types. Additionally, a method for estimating the machine learning models��� prediction uncertainty that considered the Pu sample���s measurement uncertainty was investigated. Ultimately, all main objectives were successfully achieved. This is the first example in open literature of a methodology for attributing mixed reactor type Pu samples