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11	<b>ANIMAL GUT MICROBIOME CHARACTERIZATION FOR MICROBIAL SOURCE TRACKING AND IMPLICATIONS FOR GASTROINTESTINAL DISEASE</b> Jiangshan Wang (10725807) 30 April 2024 (has links) <p dir="ltr">The gastrointestinal tract harbors a diverse range of microorganisms, collectively constituting the gut microbiome. <a href="" target="_blank">The maintenance of a symbiotic relationship between the host and these microorganisms is essential to gastrointestinal health. Disruption of the ecological balance within the gut microbiome can result in discomfort or pathological conditions.</a> <a href="" target="_blank">This dissertation explores these alterations within the gastrointestinal tract as potential indicators for specific gastrointestinal diseases.</a> <a href="" target="_blank">In pursuit of this, I collaborated with others to develop a smart ingestible capsule that offers a non-invasive method for enhancing the effectiveness of differential diagnosis and treatment strategies for Inflammatory Bowel Disease (IBD). </a>My contributions encompassed conducting <i>in vitro</i> protein sampling and extraction experiments, as well as enteric coating dissolution tests. Following thorough characterization of the capsule, I advanced to <i>ex vivo</i> sampling experiments. As a proof of concept, the capsule's sampling capabilities have been rigorously validated both <i>in vitro</i> and <i>ex vivo</i> using calprotectin, a key biomarker for monitoring and managing IBD. Future research may explore integrating this technology with other sensors for diverse chemical and gas sensing capabilities, aiming to refine the differential diagnostics of Irritable Bowel Syndrome (IBS) and IBD.</p><p dir="ltr">Simultaneously, the potential transmission of pathogenic microorganisms from the gastrointestinal tract to the environment through fecal matter can lead to substantial public health implications if adequate surveillance is not in place. These pathogens can contaminate water and food sources from various origins, exacerbating the problem. Furthermore, conventional laboratory-based assays, while effective, have extensive turnaround times and require skilled scientists to operate them. In response to this challenge, I have undertaken the development of point-of-care assays, aiming to streamline the detection of fecal contamination. This innovation is designed to mitigate the limitations associated with traditional methods by offering a more rapid and user-friendly approach. The primary objective is to enhance the accessibility of these assays, enabling on-site personnel with varying levels of expertise to utilize them effectively. Through the widespread adoption of these point-of-care assays, the overarching goal is to ensure the consistent provision of safe and reliable water and food supplies to the public.</p> Agricultural land management Agricultural land planning Bacteriology Infectious agents Biomaterials Medical devices Biosensors -- Design and construction Ingestible Sensors disease diagnosis tool
12	GRAIN HARVESTING LOGISTICAL TRACKING – UTILIZING GPS DATA TO BETTER UNDERSTAND GRAIN HARVESTING EFFICIENCY Cheyenne Eunice/ Cox Simmons (18431367) 29 April 2024 (has links) <p dir="ltr">Precision agriculture has been around for many, many years but as technology has rapidly grown with the population, farmers are looking for more ways to improve their operation with the help of these tools. These tools help farmers manage, understand, and decide when, how and what should be done regarding the tough decisions in the field based on their machinery statues. The tools that utilize GPS location and provide farm managers with useful information and feedback on large scales of value in the Harvesting and planting operation. With previous works done focusing on identify state machine activity utilizing GPS location data with the use of loggers and algorithms this study carries on from one field to the next identifying the truth data set for each and the accuracy of the algorithm. The goal is to add a more realistic view to the states identifying turning and transporting throughout the harvesting operation in truth data and from algorithm results. Also diving into truck activity with lower standard GPS tracking to see how accurately they can be predicted to complete the harvesting cycle from vehicle to vehicle. Overall, the combine and grain cart held at 88% accuracy for labeling all state activity during the harvesting operation for multiple fields, while for the model algorithm with the grain trucks having an overall accuracy of 94%.</p> Agricultural land management Agricultural land planning grain harvest crop technology grain movement AGRICULTURE SYSTEM grain management crop management decisions harvesting decisions
13	Digital Soil Mapping of the Purdue Agronomy Center for Research and Education Shams R Rahmani (8300103) 07 May 2020 (has links) This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest. Agricultural Land Management Agricultural Land Planning Sustainable Agricultural Development Agronomy Digital soil mapping (DSM) Organic matter content Cation exchange capacity Natural soil drainage classes geostatistical methods Cubist modeling Random Forest models Universal Kriging C5.0 or see5 modeling Artificial neural network Tile drainage lines Soil spatial variation Plant Phenotyping Aerial Imagery digital elevation model (DEM) terrain attributes Soil and Landscape Very poorly drained poorly drained somewhat poorly drained moderately well drained User's accuracy producer's accuracy kappa coefficient Multinomial logit regression Tile Probe topographic wetness index Topographic Position Index slope height cross sectional curvature Relative slope position channel network distance SSURGO soils data internal validation external validation

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