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Reducing the Risk of Groundwater Contamination by Improving Wellhead Management Conditions
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Current status of dermo disease, Perkinsus marinus, and oyster harvest in West Bay, Galveston, Texas
No full text22 slide Power Point presentation. Presented at the Coastal Conference, June 2009The coastal areas of Texas received above normal rainfall during 2006 and 2007. Thereby reducing the salinity in West Bay, the high salinity arm of the Galveston Bay system. This condition has resulted in reduced Dermo disease intensity in oysters and increased oyster harvest. West Bay water salinity usually varies from the high 20���s to the low 30���s ppt. In 2007 the salinity generally ranged 20 +/- 2ppt. Although the incidence of Dermo infections remained high, the intensity of (weighted incidence) was below usual levels. Preliminary results of studies regarding Dermo infections of oyster spat show that infection initiation is ���proximity��� dependant. Speculation is advanced regarding the observation that intertidal oyster populations appear to survive Dermo disease better than subtidal populations.Perley G. and Kathreen Parr Trust; Coastal Management Plan Project Number 09-033-000-3350. Texas General Land Offic
Data-Driven Knowledge Transfer Models and Their Transferability for Predicting Structural Performance
No full textMachine learning (ML) algorithms can provide predictions on future (new) data based on statistical models using available (past) and relevant labeled or unlabeled data. A fundamental assumption in traditional ML algorithms is that both the training and testing data not only share the same feature space and distribution but also align with the same task. When this fundamental assumption is not satisfied, the trained ML model is likely to yield subpar performance and should be reconstructed from scratch based on new data. However, training a new ML model while maintaining high performance is often impractical if the volume of new data samples is limited. Moreover, in reality, obtaining additional data samples involves additional costs and time, and frequently is not feasible. Transfer learning (TL) provides a new learning paradigm in which an algorithm extracts prior knowledge to boost the learning performance from limited training data. Thus, TL can expand the availability of ML in the fields where a large amount of labeled data is not available.
Within the realm of structural engineering, the primary purpose of this dissertation is to evaluate the impact of TL, especially when large amounts of data are not available. In pursuit of this overarching goal, this dissertation demonstrates the feasibility of TL for predicting structural performance, proposes novel TL algorithms to rapidly and accurately predict the behavior of a structure or a structural component, and estimates the capacity of knowledge transfer. Based on the research findings in this dissertation, knowledge transfer algorithms considered in this study exhibited improved prediction performance. Thus, TL can alleviate the data scarcity problem, which is often observed in structural engineering. When it comes to the accurate estimation of structural performance, the proposed TL models not only provide efficient methods to reduce the model variance associated with a small dataset, but they also outperform current design standards. Furthermore, the transferability curve introduced in this dissertation can provide insight into the nature of the transferable knowledge and how it may impact the target task, enhancing decision-making in TL model implementation
Phosphine Incorporated Metal-Organic-Framework for Pd Catalyzed Heck Reaction in Flow
No full textThe development of recyclable catalysts used in chemical applications is a vital field of research, owing to the excessive use of harmful, non-recyclable materials in industrial processes contributing to environmental damage. The MOF (UiO-66) synthesized in this research has Zirconium-oxo clusters and BDC (terephthalic acid) which have the desirable characteristics required for its application in catalysis. In this work, we describe the application of MOF-immobilized Pd recyclable catalysts for Heck Coupling Reaction. We also established the generality of this reaction by running batch reactions on substrates, that included 10 substituted aryl bromides with varying electronic properties. The catalyst synthesized, UiO-66-PPh2-Pd, was also applied for the coupling reactions under microflow conditions. UiO-66-PPh2-Pd, was analyzed by several characterization techniques including NMR, PXRD, EDS, XPS and SEM. The optimum conditions for Heck Coupling Reactions using UiO66-PPh2-Pd as the catalyst was explored for batch conditions. Using the optimized conditions, various bromo-substituted substrates (0.2 mmol) were reacted with 0.3 mmol styrene for which a highest yield of 93% was obtained for 4-bromobenzaldehyde. Other substrates such as bromobenzene, 4-bromotoleune, 4-bromobenzonitrile, 4-bromoanisole, and 4-bromoaniline similarly had a high yield ranging from 90% to 92%. We also demonstrated that, the catalyst UiO66-PPh2-Pd, can be recovered and reused for several catalytic runs. This research is vital to the understanding of the properties and applications of the synthesized MOF-catalyst UiO66-PPh2-Pd and promoting the research in MOF based heterogeneous catalysts. This work further sheds light on the potential of the recyclable, efficient and robust catalyst, for the synthesis of value-added compounds and other industrial applications
The Impacts of Furrow Diking, Terracing and Contour Cultivation on Water Conservation in Texas Agriculture
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