214 research outputs found

    Some Remarks on Regularity Criteria of Axially Symmetric Navier-Stokes Equations

    Full text link
    Two main results will be presented in our paper. First, we will prove the regularity of solutions to axially symmetric Navier-Stokes equations under a loglog supercritical assumption on the horizontally radial component uru^r and vertical component uzu^z, accompanied by a loglog subcritical assumption on the horizontally angular component uθu^\theta of the velocity. Second, the precise Green function for the operator −(Δ−1r2)-(\Delta-\frac{1}{r^2}) under the axially symmetric situation, where rr is the distance to the symmetric axis, and some weighted LpL^p estimates of it will be given. This will serve as a tool for the study of axially symmetric Navier-Stokes equations. As an application, we will prove the regularity of solutions to axially symmetric Navier-Stokes equations under a critical (or a subcritical) assumption on the angular component wθw^\theta of the vorticity.Comment: Final version, to appear in Comm. Pure Appl. Ana

    Constrained large solutions to Leray's problem in a distorted strip with the Navier-slip boundary condition

    Full text link
    In this paper, we will solve the Leray's problem for the stationary Navier-Stokes system in a 2D infinite distorted strip with the Navier-slip boundary condition. The existence, uniqueness, regularity and asymptotic behavior of the solution will be investigated. Moreover, we discuss how the friction coefficient affects the well-posedness of the solution. Due to the validity of the Korn's inequality, all constants in each a priori estimate are independent of the friction coefficient. The main novelty is the total flux of the velocity can be relatively large (proportional to the {\it slip length}) when the friction coefficient of the Navier-slip boundary condition is small, which is essentially different from the 3D case.Comment: 45 pages. arXiv admin note: text overlap with arXiv:2204.10578. A remark is added to state the independent accomplishment of solving the 2D Leray's problem with the Navier-slip boundary condition by our group and Professor Chunjing Xie's grou

    A Novel Driver Distraction Behavior Detection Based on Self-Supervised Learning Framework with Masked Image Modeling

    Full text link
    Driver distraction causes a significant number of traffic accidents every year, resulting in economic losses and casualties. Currently, the level of automation in commercial vehicles is far from completely unmanned, and drivers still play an important role in operating and controlling the vehicle. Therefore, driver distraction behavior detection is crucial for road safety. At present, driver distraction detection primarily relies on traditional Convolutional Neural Networks (CNN) and supervised learning methods. However, there are still challenges such as the high cost of labeled datasets, limited ability to capture high-level semantic information, and weak generalization performance. In order to solve these problems, this paper proposes a new self-supervised learning method based on masked image modeling for driver distraction behavior detection. Firstly, a self-supervised learning framework for masked image modeling (MIM) is introduced to solve the serious human and material consumption issues caused by dataset labeling. Secondly, the Swin Transformer is employed as an encoder. Performance is enhanced by reconfiguring the Swin Transformer block and adjusting the distribution of the number of window multi-head self-attention (W-MSA) and shifted window multi-head self-attention (SW-MSA) detection heads across all stages, which leads to model more lightening. Finally, various data augmentation strategies are used along with the best random masking strategy to strengthen the model's recognition and generalization ability. Test results on a large-scale driver distraction behavior dataset show that the self-supervised learning method proposed in this paper achieves an accuracy of 99.60%, approximating the excellent performance of advanced supervised learning methods

    Mimicry of a Non-ribosomally Produced Antimicrobial, Brevicidine, by Ribosomal Synthesis and Post-translational Modification

    Get PDF
    Zhao et al. describe a strategy to synthesize mimics of the recently discovered antimicrobial non-ribosomal peptide, brevicidine. The engineered mimics show antimicrobial activities against pathogens susceptible to brevicidine, which demonstrate that conversion of NRPs to RiPPs is feasible and offer great opportunities for engineering a wide range of effective antibiotics
    • …
    corecore