Visualization and Characterization of Agricultural Sprays Using Machine Learning based Digital Inline Holography

Accurate characterization of agricultural sprays is crucial to predict in field performance of liquid applied crop protection products. Here we introduce a robust and efficient machine learning (ML) based Digital In-line Holography (DIH) to accurately characterize the droplet field for a wide range of agricultural spray nozzles. Compared to non-ML methods, our method enhances accuracy, generalizability, and processing speed. Our approach employs two neural networks: a modified U-Net to obtain the 3D droplet field from the numerically reconstructed optical field, followed by a VGG16 classifier to reduce false positives from the U-Net prediction. The modified U-Net is trained using holograms generated using a single spray nozzle at three spray locations; center, half-span, and the spray edge to create training data with various number densities and droplet size ranges. VGG16 is trained via the minimum intensity projection of the droplet 3D point spread function. Data augmentation is used to increase the efficiency of classification and make the algorithm generalizable for different measurement settings. The model is validated via NIST traceable glass beads and six agricultural spray nozzles representing various spray characteristics. The results demonstrate a high accuracy rate, with over 90% droplet extraction and less than 5% false positives. Compared to traditional spray measurement techniques, our method offers a significant leap forward in spatial resolution and generalizability. In particular, our method can extract the real cumulative volume distribution of the NIST beads, where the laser diffraction is biased towards droplets moving at slower speeds. Additionally, the ML-based DIH enables the estimation of mass and momentum flux at different locations and the calculation of relative velocities of droplet pairs, which are difficult to obtain via conventional techniques.Comment: 24 pages, 12 figure

Observations and a model of undertow over the inner continental shelf

Author Posting. © American Meteorological Society, 2008. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 38 (2008): 2341-2357, doi:10.1175/2008JPO3986.1.Onshore volume transport (Stokes drift) due to surface gravity waves propagating toward the beach can result in a compensating Eulerian offshore flow in the surf zone referred to as undertow. Observed offshore flows indicate that wave-driven undertow extends well offshore of the surf zone, over the inner shelves of Martha’s Vineyard, Massachusetts, and North Carolina. Theoretical estimates of the wave-driven offshore transport from linear wave theory and observed wave characteristics account for 50% or more of the observed offshore transport variance in water depths between 5 and 12 m, and reproduce the observed dependence on wave height and water depth. During weak winds, wave-driven cross-shelf velocity profiles over the inner shelf have maximum offshore flow (1–6 cm s−1) and vertical shear near the surface and weak flow and shear in the lower half of the water column. The observed offshore flow profiles do not resemble the parabolic profiles with maximum flow at middepth observed within the surf zone. Instead, the vertical structure is similar to the Stokes drift velocity profile but with the opposite direction. This vertical structure is consistent with a dynamical balance between the Coriolis force associated with the offshore flow and an along-shelf “Hasselmann wave stress” due to the influence of the earth’s rotation on surface gravity waves. The close agreement between the observed and modeled profiles provides compelling evidence for the importance of the Hasselmann wave stress in forcing oceanic flows. Summer profiles are more vertically sheared than either winter profiles or model profiles, for reasons that remain unclear.This research was funded by the Ocean Sciences Division of the National Science Foundation under Grants OCE-0241292 and OCE-0548961

Double-Blind Phase III Randomized Trial of the Antiprogestin Agent Mifepristone in the Treatment of Unresectable Meningioma: SWOG S9005

PURPOSE: Progesterone receptors are expressed in approximately 70% of meningiomas. Mifepristone is an oral antiprogestational agent reported to have modest activity in a phase II study. This multicenter, prospective, randomized, placebo-controlled phase III trial conducted by SWOG was planned to define the role of mifepristone in the treatment of unresectable meningioma. PATIENTS AND METHODS: Eligible patients were randomly assigned to receive either mifepristone or placebo for 2 years unless disease progressed. Patients who were stable or responding to protocol therapy after 2 years had the option to continue with the same blinded therapy. Serial follow-up allowed assessment of efficacy and toxicity. Time to treatment failure and overall survival were ascertained for all randomly assigned patients. On progression, patients receiving placebo could cross over and receive active drug. RESULTS: Among 164 eligible patients, 80 were randomly assigned to mifepristone and 84 to placebo. Twenty-four patients (30%) were able to complete 2 years of mifepristone without disease progression, adverse effects, or other reasons for discontinuation. Twenty-eight patients (33%) in the placebo arm completed the 2-year study. There was no statistical difference between the arms in terms of failure-free or overall survival. CONCLUSION: Long-term administration of mifepristone was well tolerated but had no impact on patients with unresectable meningioma