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Phytophthora megakarya and Phytophthora palmivora, Closely Related Causal Agents of Cacao Black Pod Rot, Underwent Increases in Genome Sizes and Gene Numbers by Different Mechanisms.
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- Genome Biology & Evolution, 2017, v. 9, n. 3, p. 536, doi. 10.1093/gbe/evx021
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Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning.
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- Sensors (14248220), 2021, v. 21, n. 6, p. 2240, doi. 10.3390/s21062240
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Using Machine Learning for the Calibration of Airborne Particulate Sensors.
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- Sensors (14248220), 2020, v. 20, n. 1, p. 99, doi. 10.3390/s20010099
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Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.
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- Environmental Monitoring & Assessment, 2019, v. 191, n. 7, p. N.PAG, doi. 10.1007/s10661-019-7542-9
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Combining domain filling with a self-organizing map to analyze multi-species hydrocarbon signatures on a regional scale.
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- Environmental Monitoring & Assessment, 2019, v. 191, p. N.PAG, doi. 10.1007/s10661-019-7429-9
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Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.
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- Environmental Monitoring & Assessment, 2019, v. 191, p. N.PAG, doi. 10.1007/s10661-019-7428-x
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Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS.
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- Environmental Monitoring & Assessment, 2019, v. 191, p. N.PAG, doi. 10.1007/s10661-019-7421-4
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Using machine learning to understand the temporal morphology of the PM<sub>2.5</sub> annual cycle in East Asia.
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- Environmental Monitoring & Assessment, 2019, v. 191, p. N.PAG, doi. 10.1007/s10661-019-7424-1
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Using machine learning to examine the relationship between asthma and absenteeism.
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- Environmental Monitoring & Assessment, 2019, v. 191, p. N.PAG, doi. 10.1007/s10661-019-7423-2
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Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches.
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- Remote Sensing, 2024, v. 16, n. 13, p. 2454, doi. 10.3390/rs16132454
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Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping.
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- Remote Sensing, 2024, v. 16, n. 13, p. 2430, doi. 10.3390/rs16132430
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Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction.
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- Remote Sensing, 2024, v. 16, n. 6, p. 996, doi. 10.3390/rs16060996
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High Spatial-Temporal PM 2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source.
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- Remote Sensing, 2022, v. 14, n. 3, p. 495, doi. 10.3390/rs14030495
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PM 2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD.
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- Remote Sensing, 2021, v. 13, n. 23, p. 4788, doi. 10.3390/rs13234788
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Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification.
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- Remote Sensing, 2021, v. 13, n. 16, p. 3289, doi. 10.3390/rs13163289
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Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles.
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- Remote Sensing, 2012, v. 4, n. 5, p. 1355, doi. 10.3390/rs4051355
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Low-altitude Terrestrial Spectroscopy from a Pushbroom Sensor.
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- Journal of Field Robotics, 2016, v. 33, n. 6, p. 837, doi. 10.1002/rob.21624
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Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning.
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- BioMedInformatics, 2024, v. 4, n. 2, p. 1019, doi. 10.3390/biomedinformatics4020057
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Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen.
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- International Journal of Environmental Research & Public Health, 2019, v. 16, n. 11, p. 1992, doi. 10.3390/ijerph16111992
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Estimating the global abundance of ground level presence of particulate matter (PM<sub>2.5</sub>).
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- Geospatial Health, 2014, v. 8, n. 3, p. S611, doi. 10.4081/gh.2014.292
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Cover Feature: Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry (ChemPlusChem 4/2022).
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- ChemPlusChem, 2022, v. 87, n. 4, p. 1, doi. 10.1002/cplu.202200081
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Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry.
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- ChemPlusChem, 2022, v. 87, n. 1, p. 1, doi. 10.1002/cplu.202100418
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- Article
Phytophthora megakarya and P. palmivora, Causal Agents of Black Pod Rot, Induce Similar Plant Defense Responses Late during Infection of Susceptible Cacao Pods.
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- Frontiers in Plant Science, 2017, v. 8, p. 1, doi. 10.3389/fpls.2017.00169
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Using machine learning to estimate atmospheric Ambrosia pollen concentrations in Tulsa, OK.
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- Environmental Health Insights, 2017, v. 11, p. 1, doi. 10.1177/1178630217699399
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Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods.
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- Space Weather: The International Journal of Research & Applications, 2021, v. 19, n. 6, p. 1, doi. 10.1029/2020SW002639
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Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales.
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- Sensors (14248220), 2022, v. 22, n. 11, p. 4240, doi. 10.3390/s22114240
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Data-Driven EEG Band Discovery with Decision Trees.
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- Sensors (14248220), 2022, v. 22, n. 8, p. N.PAG, doi. 10.3390/s22083048
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Machine Learning for Light Sensor Calibration.
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- Sensors (14248220), 2021, v. 21, n. 18, p. 6259, doi. 10.3390/s21186259
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Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean.
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- Applied Sciences (2076-3417), 2018, v. 8, n. 12, p. 2687, doi. 10.3390/app8122687
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