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Family Involvement in Literacy-Infused Science Learning for Texas Rural Youth and Communities
No full textWhile much research has studied children���s informal science learning with their families at exhibitions, museums and after-school programs such as family science nights, limited research has explored family science learning using take-home science activity kits. To better understand how home science activity kits are developed and implemented to support children���s informal science learning, a systematic review was first conducted. In the second study, I described the development of Family Involvement in Science (FIS) activity kits, one of the innovative components of a larger federal funded research grant in Texas. Seven rural fifth-grade Hispanic students and their families��� home science learning experiences were compared with seven non-rural counterparts using a comprehensive and reliable multi-dimensional and multi-categorical observation instrument called Family Involvement in Science Observation Protocol (FISOP). Students��� and family members��� time allocation participating in scientific practices, including use of strategies, activity structures, communication modes, language of content, and language use were observed and analyzed using the chi-square tests of homogeneity. Statistically significant differences were found in nine out of ten sub-domains between rural and non-rural families. In the third study, family was viewed as a socio-cultural learning environment that empowered parent-child science conversations as they actively engaged in FIS activities. Parent-child science talks from nine rural Hispanic families were captured through two pairs of GoVision camera goggles, and the videos were collected, screened, transcribed, and coded using two qualitative coding schemes: literacy-infused science strategies and scientific behaviors. The findings provided initial evidence that FIS encouraged Hispanic rural children and their parents to use scientific inquiry-based approaches and strategies in scientific conversations and activities
Unfolding the Complexity of Soil Chemical Process and Remote Sensing for the Detection and Monitoring of Oil Contaminants Using AI Techniques
No full textThe rapid acceleration of global economic development has significantly increased energy demands, leading to severe environmental consequences, particularly soil contamination due to oil pollutants. This contamination not only alters soil's physical and chemical properties but also jeopardizes its ecological balance and human health. In response to these challenges, our research embarks on a comprehensive exploration of soil contamination by oil pollutants, emphasizing the need for a deep understanding of these contaminants within their ecosystems. We investigate the effectiveness of various remediation strategies, considering the intricate dynamics of soil ecosystems. Our study aims to contribute significantly to environmental science by identifying pollutants and deploying tailored remediation techniques that harmonize with soil ecosystem complexities.
First, our research utilizes an AI-assisted systematic review to understand the remediation of soils contaminated with Polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals. By employing literature databases, text mining, and interactive data mining tools, we aim to offer a holistic view of soil contamination. Results indicate a prevalence of combined treatment techniques, with biological-biological approaches being most common, highlighting the challenges and potential strategies for effective remediation.
Secondly, our efforts are directed toward transforming soil remediation techniques with the introduction of Advanced Fenton-Photo Systems, complemented by the integration of deep-learning neural networks aimed at refining petrochemical degradation processes. The empirical evidence from our research indicates remarkable oxidation rates of Total Petroleum Hydrocarbons (TPHs) and Polycyclic Aromatic Hydrocarbons (PAHs), with degradation rates reaching up to 99% in mere minutes. This highlights a substantial leap forward in the efficiency of removing contaminants from soil.
In our third objective, we harness Artificial Intelligence (AI) to enhance the capabilities of remote sensing in accurately predicting oil contamination within the Al-Burgan oil field. Our findings, derived from the application of advanced neural network models and Sentinel-2 satellite data, have significantly improved oil contamination detection, achieving flawless accuracy in certain scenarios. This approach not only refines the detection and quantification of oil contamination but also showcases the transformative potential of AI in elevating environmental surveillance and monitoring practices.
Lastly, the research is aimed at advancing the monitoring and prediction of vegetation coverage in arid ecosystems, with a specific focus on Kuwait's Burgan field, through the application of AI-integrated remote sensing. Our analysis has unveiled notable vegetation recovery following remediation efforts, highlighted by interannual and seasonal changes in vegetation cover. The utilization of Soil-Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI), processed through neural networks, has provided deep insights into vegetative dynamics. This underscores the effectiveness of remote sensing in ecological assessments, driven by the development of innovative vegetation indices and the employment of advanced remote sensing techniques. These efforts are pivotal in offering profound insights into the health and dynamics of vegetation, underlining the critical role of ecological monitoring and management.
This research will result in improved strategies for environmental management and remediation, offering novel insights and methodologies that facilitate the sustainable management of contaminated soils. Through a multifaceted approach that integrates advanced technological solutions and ecological understanding, our study contributes to the advancement of environmental restoration efforts, ensuring healthier ecosystems for future generations
John Bickham field notebook: Herp_AK1001-AK1100.pdf
No full textEach page/AK number corresponds to a karyotype slide data and/or unique specimen.Data pages for Herp_AK1001-AK1100 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
Improving Maize (Zea mays) Production: Assessing Impacts of Seed Cleaning, Treatment, and Novel White Sub-Tropical Inbred Varieties
No full textMaize is an important crop in the U.S. and globally and grain yield continues to increase from both agronomic and genetic technologies. Farmer���s successful crop establishment of maize has relied on treated and cleaned seed to reach plant population targets of healthy plants. The purpose of the first study was to determine the effects that cleaning and treating seed had on both the plant population and yield of a crop in small plots for a breeding program. Ten diverse experimental maize cultivars were chosen based on seed availability and subject to different combinations of cleaning and treating. The cultivars were tested across two years (2021 and 2022) and multiple environments. A reengineered Mater Continuous Seed Blower was used to clean the seed. Cruiser 5FS and Maxim 4FS were blended following recommendations and used as a seed treatment. Overall, the results of this experiment showed a positive correlation between cleaning and treating seeds and an increase in yield and plant population. The results help to reinforce the use of cleaning and treating techniques in seed preparation for small plot research.
The second study involved preparing a release of novel white inbred lines for white maize hybrids. The varieties proposed for release include Tx131, Tx133, Tx134, Tx148, Tx149, Tx150, Tx160 and Tx161. All inbreds listed are derived from white sub-tropical germplasm and can improve yield and genetic diversity white maize in the U.S. Current white maize varieties are limited, and this release will increase the genetic diversity providing farmers with more options for planting. These lines were crossed with a variety of commercial and within Texas A&M program testers, and resulting hybrids were grown at multiple locations over several years. Hybrids from each line produced yields that met or exceeded those of current commercial hybrids
Impacts of Hurricanes and Algae-Based Jet Fuel on Energy Production Economics and Markets
No full textThis thesis addresses energy economics aspects involving industry disruptions, low carbon technology, and market responses. The first essay explores the impacts of hurricanes on US refineries and fuel markets. Namely we examine hurricane effects on refinery input and output, crude oil imports, gasoline, diesel, and crude oil stocks, as well as pricing dynamics for gasoline and diesel. In terms of findings this study sheds light on disruptions caused by hurricanes and the resultant welfare implications. Findings reveal that hurricane landfall in the Gulf Coast is associated with decreased refinery production for up to six weeks thereafter, with varied effects beyond the Gulf Coast region across the US. Also, hurricanes lead to regional price increases. Overall, we find hurricane strikes decrease total US social welfare, but with differing effects on producers and consumers.
The second essay reports on life cycle assessment and techno-economic analysis of direct air capture supported algae cultivation systems. The analysis focuses on prospects for generating algae-based limonene that is transformed into aviation fuel and biomass for animal feed. The analysis examines the greenhouse gas and economic consequences of a potential technology that integrates CO2 capture to algae growth to limonene and animal feed productions. Challenges of CO2 source, integrating sorbent production and manufacturing, and nutrient recovery processes associated with algae cultivation are explored. The analysis reveals that achieving environmental and economic feasibility requires more than a hundredfold reuse of the CO2 sorbent and hydrogel, currently deemed unreachable. Additionally, the study underscores that utilization of renewable electricity could further diminish the carbon footprint.
In the third essay, we extend the second essay into the strategic domains of product mix and market penetration. We consider multiple limonene applications and algae biomass markets. We find that prioritizing limonene for cleaning and fragrance is superior to using it to produce jet fuel. We also find substantial penetration of the remaining algae-based biomass as animal feed and as a feedstock for electricity generation. Scenarios involving carbon credits and technology subsidies reinforce these findings. Furthermore, we investigate the influence of enhanced limonene yield which is needed for entry into the jet fuel market
