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Neonicotinoids (NNs) are commonly found throughout the environment on surfaces such as seeds, soil, vegetation, and blowing dust particles. However, there is a paucity of data on the kinetics and oxidation products formed on contact with the atmosphere which limits understanding of their potentially far-reaching impacts. In this study, in situ attenuated total reflectance (ATR) FTIR spectroscopy was used to investigate the OH oxidation of thin films of three solid NNs, imidacloprid (IMD), dinotefuran (DNF) and clothianidin (CLD) at 295 ± 3 K. The experimentally measured reaction probabilities based on initial rates of NN loss are (1.6 ± 0.8) × 10 −2 for IMD, (1.5 ± 0.6) × 10 −2 for DNF and (0.9 ± 0.2) × 10 −2 for CLD (±1 σ ), suggesting initial NN lifetimes with respect to OH of 10–17 days. The kinetics were interpreted using a multiphase kinetics model, KM-SUB, which showed that the OH uptake and reaction occurred primarily in the surface layer. Products identified by mass spectrometry included carbonyl-, alcohol- and olefin-containing species formed via hydrogen abstraction from aliphatic C–H groups. Additionally, carbonyl-containing desnitro and urea derivative products were observed from secondary reactions of the initially formed photodegradation products. Reaction with OH willmore »contribute to NN loss both during the day as well as at night when there are non-photolytic sources of this radical. Thus, OH reactions with both the parent neonicotinoid and its photodegradation products should be considered in assessing their environmental impacts.« less

In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset on which minimizing the Mixup loss can provably lead to learning a classifier that does not minimize the empirical loss on the data. On the other hand, we also give sufficient conditions for Mixup training to also minimize the original empirical risk. For generalization, we characterize the margin of a Mixup classifier, and use this to understand why the decision boundary of a Mixup classifier can adapt better to the full structure of the training data when compared to standard training. In contrast, we also show that, for a large class of linear models and linearly separable datasets, Mixup training leadsmore »to learning the same classifier as standard training.« less

Revealing the formation, dynamics, and contribution to plasma heating of magnetic field fluctuations in the solar wind is an important task for heliospheric physics and for a general plasma turbulence theory. Spacecraft observations in the solar wind are limited to spatially localized measurements, so that the evolution of fluctuation properties with solar wind propagation is mostly studied via statistical analyses of data sets collected by different spacecraft at various radial distances from the Sun. In this study we investigate the evolution of turbulence in the Earth’s magnetosheath, a plasma system sharing many properties with the solar wind. The near-Earth space environment is being explored by multiple spacecraft missions, which may allow us to trace the evolution of magnetosheath fluctuations with simultaneous measurements at different distances from their origin, the Earth’s bow shock. We compare ARTEMIS and Magnetospheric Multiscale (MMS) Mission measurements in the Earth magnetosheath and Parker Solar Probe measurements of the solar wind at different radial distances. The comparison is supported by three numerical simulations of the magnetosheath magnetic and plasma fluctuations: global hybrid simulation resolving ion kinetic and including effects of Earth’s dipole field and realistic bow shock, hybrid and Hall-MHD simulations in expanding boxes that mimicmore »the magnetosheath volume expansion with the radial distance from the dayside bow shock. The comparison shows that the magnetosheath can be considered as a miniaturized version of the solar wind system with much stronger plasma thermal anisotropy and an almost equal amount of forward and backward propagating Alfvén waves. Thus, many processes, such as turbulence development and kinetic instability contributions to plasma heating, occurring on slow timescales and over large distances in the solar wind, occur more rapidly in the magnetosheath and can be investigated in detail by multiple near-Earth spacecraft.

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