Search

Low‐Earth orbit (LEO) satellites often observe wisp‐like energy spectra, characterized by lower energies at higher L values of energetic electrons, resulting from the scattering effect of the narrow‐band artificial transmitter signals. Typically, the flux levels of wisp energy spectra increase as they approach the South Atlantic Anomaly (SAA). Here, we report, for the first time, the longitudinal variations of wisp positions in the energy‐L coordinate based on observations from the DEMETER satellite. The L‐values of wisps decrease at the longitudes <180° and increase at the longitudes >180°. By considering the satellite's orbital characteristics, we found that the longitudinal variations of wisp are essentially the MLT‐dependent radial transport of electrons. This finding indicates that the electron drift paths can be modified within a single drift period. We propose a duskward convection electric field as a potential mechanism to explain the longitudinal variations of wisps. By assuming a uniform duskward convection electric field with an amplitude of ∼1.5 mV/m, our simulations can reasonably reproduce the longitudinal variations of wisps observed in the southern hemisphere on 21 March 2009. Our results support the existence of a convection electric field in the inner belt and suggest that this electric field significantly contributes to the variations in energies and L values of energetic electrons within one drift period. Key Points: Longitudinal variations of wisp positions in the energy‐L coordinate are reported for the first timeWe propose a convection electric field in the inner belt to explain the observed longitudinal variations of wispsThe intensity of the proposed duskward electric field is estimated as ∼1–2 mV/m [ABSTRACT FROM AUTHOR]

Ultra‐relativistic (>3 MeV) electrons are considered as a novel, separate population in the Earth's radiation belts since their loss and acceleration features are distinct from relativistic (∼MeV) electrons. The dominant acceleration mechanism of ultra‐relativistic electrons remains a subject of ongoing debate. Some studies suggest that the acceleration mechanism of ultra‐relativistic electrons is energy‐dependent: local acceleration dominants the enhancement of ∼3–5 MeV electrons while two‐step acceleration process leading by radial diffusion effects for ∼7 MeV electrons. However, a recent study (https://doi.org/10.1126/sciadv.abc0380) theoretically demonstrated that local acceleration could accelerate electrons up to >7 MeV directly under the extreme plasma depletion. In this study, we report four enhancement events of ultra‐relativistic electrons that occurred in September and October 2017. Analysis of phase space density (PSD) radial profiles and contours demonstrate that local acceleration plays the dominant role in the enhancements of ∼7 MeV electrons in mid‐September and mid‐October, supported by persistently growing peaks in electron PSD. While enhancements of ultra‐relativistic electrons in other two events still show energy‐dependent phenomenon, our results provide the observation evidence that one‐step local acceleration process can lead to the enhancement of ∼7 MeV electrons in some events. We suggest that the acceleration process of ultra‐relativistic electrons may be subject to the efficiency of local acceleration. Key Points: Electron phase space density (PSD) radial profiles and contours are studied for ultra‐relativistic electrons in September – October 2017Growing local peaks of PSD suggest local acceleration plays the dominant role in enhancements of ∼7 MeV electrons in mid‐September/OctoberOne‐step local acceleration also exists for ∼7 MeV electrons in the outer belt while previous studies put forward the two‐step process [ABSTRACT FROM AUTHOR]

Solar wind is an intermediary in energy transfer from the Sun into the Earth's magnetosphere, and is considered as a decisive driver of energetic electron dynamics at the geosynchronous orbit (GEO). Based on machine learning technology, several models driven by solar wind parameters have been established to predict GEO electron fluxes. However, the relative contributions of different solar wind parameters on GEO electron fluxes are still unclear. Recently, a feature attribution method, Deep SHapley Additive exPlanations (Deep SHAP) is proposed to open black boxes of machine learning models. In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) BZ, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. The results suggest that solar wind speed with a lag of 1 day is the most important driver. We further investigate relative roles of different parameters in three specific electron fluxes variation events (corresponding to electron fluxes reaching a local maximum, a local minimum, and unchanged, respectively). The results suggest that high solar wind speed and southward IMF BZ (high dynamic pressures) facilitate increases (decreases) of electron fluxes. These findings help reveal the underlying physical mechanisms of GEO electron dynamics and help develop more accurate prediction models for GEO electron fluxes. Plain Language Summary: Solar wind, which transfers energy from the Sun to the Earth's magnetosphere, plays a crucial role in the dynamics of energetic electrons at geosynchronous orbit (GEO). The process is such complex that hundreds of studies have tried to clarify the correlation between solar wind and the dynamics of the GEO electron fluxes but haven't gained a clear conclusion yet. In this study, we reveal the influences of solar wind parameters on electron fluxes by opening the black box of a machine learning model based on a feature attribution method. We analyze the importance of different solar wind parameters statistically. The results show that the solar wind speed on previous day is the most important parameter for variations of GEO electron fluxes. We also investigate the influence of different solar wind parameters on electron fluxes during three specific moments. Our results indicate that high solar wind speed and southward IMF BZ (high dynamic pressures) are favorable for the increases (decreases) of electron fluxes. These findings can help us understand the key factors that affect energetic electron behaviors and contribute to the development of more accurate and reliable prediction models in the future. Key Points: The Deep SHapley Additive exPlanations method is used to quantify contributions of different solar wind parameters on dynamics of energetic electrons at geosynchronous orbit (GEO)Solar wind speed with a lag of 1 day is the most important factor for >800 keV electron flux variations at GEOHigh solar wind speed and southward interplanetary magnetic field BZ favor increases of energetic electron fluxes while high dynamic pressure inhibits the fluxes [ABSTRACT FROM AUTHOR]

Employing inorganic ion conductors as solid electrolytes (SEs) is one promising solution to develop advanced all- and quasi-solid-state batteries with high energy and safety advantages. Among numerous Li+ ion conductors, Li1.4Al0.4Ti1.6(PO4)3 (LATP) has attracted extensive attention due to its preponderances of air stability and superior Li+ conductivity. However, the practical application of the LATP electrolyte is still obsessed by serious side reactions at the Li-electrode/electrolyte interface. In this work, one kind of quasi-solid electrolyte (QSE) is designed combining anodic aluminum oxide (AAO), LATP, and liquid electrolyte [LE, LiPF6/ethylene carbonate-dimethyl carbonate (EC-DMC)], wherein well-ordered LATP arrays are constructed in the AAO framework to facilitate ionic transport, and a certain content of the LE is introduced to reduce the interfacial resistances. The characterization results suggest that the ionic conductivity of as-prepared AAO–LATP–QSE (ALQSE) is boosted up to ∼6.50 × 10−3 S cm−1 with a Li+ transference number of 0.66, especially the interval between the LATP compound and the Li-metal electrode can effectively restrain Ti4+→Ti3+ reduction at the Li-anode/electrolyte interface. Thus, the assembled LiFePO4|ALQSE|Li cell exhibits excellent electrochemical stability, delivering an initial discharge capacity of 153.3 mAh g−1 at 0.1C and remaining 152.4 mAh g−1 after 60 cycles with a fairly mild reduction of 0.028% per cycle. This study not only presents a facile strategy to prepare a robust QSE framework employing an AAO template but also promotes the rational interface design between titanium (Ti)-containing solid-state electrolytes and Li-metal anodes. [ABSTRACT FROM AUTHOR]

We, the Editors and Publisher of the journal Behaviour & Information Technology, have retracted the following article which was part of the Special Issue on Behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning: Jianhang Wang (2020) Massive information management system of digital library based on deep learning algorithm in the background of big data, Behaviour & Information Technology, DOI: After publication it came to our attention that the person named as the Guest Editor of the Special Issue was impersonated by a fraudulent entity and the articles were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract the articles. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as 'Retracted'. [ABSTRACT FROM AUTHOR]

Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent at present. In this paper, we take advantage of a prior knowledge about the homogeneity of microscopic images and radial attenuation property of vignetting to develop a self-supervised deep learning algorithm that achieves complex vignetting removal in color microscopic images. Our proposed method, vignetting correction lookup table (VCLUT), is trainable on both single and multiple images, which employs adversarial learning to effectively transfer good imaging conditions from the user visually defined central region of its own light field to the entire image. To illustrate its effectiveness, we performed individual correction experiments on data from five distinct biological specimens. The results demonstrate that VCLUT exhibits enhanced performance compared to classical methods. We further examined its performance as a multi-image-based approach on a pathological dataset, revealing its advantage over other stateof-the-art approaches in both qualitative and quantitative measurements. Moreover, it uniquely possesses the capacity for generalization across various levels of vignetting intensity and an ultra-fast model computation capability, rendering it well-suited for integration into high-throughput imaging pipelines of digital microscopy.