Learning obstacle avoidance and predation in complex reef environments with deep reinforcement learning.

The reef ecosystem plays a vital role as a habitat for fish species with limited swimming capabilities, serving not only as a sanctuary and food source but also influencing their behavioral tendencies. Understanding the intricate mechanism through which fish adeptly navigate the moving targets within reef environments within complex water flow, all while evading obstacles and maintaining stable postures, has remained a challenging and prominent subject in the realms of fish behavior, ecology, and biomimetics alike. An integrated simulation framework is used to investigate fish predation problems within intricate environments, combining deep reinforcement learning algorithms (DRL) with high-precision fluid-structure interaction numerical methods-immersed boundary lattice Boltzmann method (lB-LBM).

RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration.

Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines under large rotations, or encode local geometry that is less distinctive. To address this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant by design and Globally-Aware.

The Immunological Functions of Muramyl Dipeptide Compound Adjuvant on Humoral, Cellular-mediated and Mucosal Immune Responses to PEDV Inactivated Vaccine in Mice.

Background: The muramyl dipeptide compound adjuvant, CVC1303, was one new resigned adjuvant to PEDV inactivated vaccine. Exploring the effects of CVC1303 on the immune induction to PEDV vaccine was of vital importance to the clinical application.

Objectives: Here we explored the functions of CVC1303 on the humoral, cellular and mucosal immune response to PEDV vaccine in mice immunization.

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.].

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province.