Bioactive polysaccharides from lotus as potent food supplements: a review of their preparation, structures, biological features and application prospects

Lotus is a famous plant of the food and medicine continuum for millennia, which possesses unique nutritional and medicinal values. Polysaccharides are the main bioactive component of lotus and have been widely used as health nutritional supplements and therapeutic agents. However, the industrial production and application of lotus polysaccharides (LPs) are hindered by the lack of a deeper understanding of the structure–activity relationship (SAR), structural modification, applications, and safety of LPs. This review comprehensively comments on the extraction and purification methods and structural characteristics of LPs. The SARs, bioactivities, and mechanisms involved are further evaluated. The potential application and safety issues of LPs are discussed. This review provides valuable updated information and inspires deeper insights for the large scale development and application of LPs

Self-Distillation for Robust LiDAR Semantic Segmentation in Autonomous Driving

We propose a new and effective self-distillation framework with our new Test-Time Augmentation (TTA) and Transformer based Voxel Feature Encoder (TransVFE) for robust LiDAR semantic segmentation in autonomous driving, where the robustness is mission-critical but usually neglected. The proposed framework enables the knowledge to be distilled from a teacher model instance to a student model instance, while the two model instances are with the same network architecture for jointly learning and evolving. This requires a strong teacher model to evolve in training. Our TTA strategy effectively reduces the uncertainty in the inference stage of the teacher model. Thus, we propose to equip the teacher model with TTA for providing privileged guidance while the student continuously updates the teacher with better network parameters learned by itself. To further enhance the teacher model, we propose a TransVFE to improve the point cloud encoding by modeling and preserving the local relationship among the points inside each voxel via multi-head attention. The proposed modules are generally designed to be instantiated with different backbones. Evaluations on SemanticKITTI and nuScenes datasets show that our method achieves state-of-the-art performance. Our code is publicly available at https://github.com/jialeli1/lidarseg3d

OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images

Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP

Anchor-Free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud

Most of the existing single-stage and two-stage 3D object detectors are anchor-based methods, while the efficient but challenging anchor-free single-stage 3D object detection is not well investigated. Recent studies on 2D object detection show that the anchor-free methods also are of great potential. However, the unordered and sparse properties of point clouds prevent us from directly leveraging the advanced 2D methods on 3D point clouds. We overcome this by converting the voxel-based sparse 3D feature volumes into the sparse 2D feature maps. We propose an attentive module to fit the sparse feature maps to dense mostly on the object regions through the deformable convolution tower and the supervised mask-guided attention. By directly regressing the 3D bounding box from the enhanced and dense feature maps, we construct a novel single-stage 3D detector for point clouds in an anchor-free manner. We propose an IoU-based detection confidence re-calibration scheme to improve the correlation between the detection confidence score and the accuracy of the bounding box regression. Our code is publicly available at https://github.com/jialeli1/MGAF-3DSSD

From Voxel to Point: IoU-Guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder

In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder. To preserve the necessary information from all raw points and maintain the high box recall in voxel based Region Proposal Network (RPN), we propose a residual voxel-to-point decoder to extract the point features in addition to the map-view features from the voxel based RPN. We use a 3D Region of Interest (RoI) alignment to crop and align the features with the proposal boxes for accurately perceiving the object position. The RoI-Aligned features are finally aggregated with the corner geometry embeddings that can provide the potentially missing corner information in the box refinement stage. We propose a simple and efficient method to align the estimated IoUs to the refined proposal boxes as a more relevant localization confidence. The comprehensive experiments on KITTI and Waymo Open Dataset demonstrate that our method achieves significant improvements with novel architectures against the existing methods. The code is available on Github URLhttps://github.com/jialeli1/From-Voxel-to-Point

Aeroelastic-aerodynamic analysis and bio-inspired flow sensor design for boundary layer velocity profiles of wind turbine blades with active external flaps

© Copyright 2017 Techno-Press, Ltd. The characteristics of boundary layers have significant effects on the aerodynamic forces and vibration of the wind turbine blade. The incorporation of active trailing edge flaps (ATEF) into wind turbine blades has been proven as an effective control approach for alleviation of load and vibration. This paper is aimed at investigating the effects of external trailing edge flaps on the flow pattern and velocity distribution within a boundary layer of a NREL 5MW reference wind turbine, as well as designing a new type of velocity sensors for future validation measurements. An aeroelastic-aerodynamic simulation with FAST-AeroDyn code was conducted on the entire wind turbine structure and the modifications were made on turbine blade sections with ATEF. The results of aeroelastic-aerodynamic simulations were combined with the results of two-dimensional computational fluid dynamic simulations. From these, the velocity profile of the boundary layer as well as the thickness variation with time under the influence of a simplified load case was calculated for four different blade-flap combinations (without flap, with -5°, 0°, and +5° flap). In conjunction with the computational modeling of the characteristics of boundary layers, a bio-inspired hair flow sensor was designed for sensing the boundary flow field surrounding the turbine blades, which ultimately aims to provide real time data to design the control scheme of the flap structure. The sensor element design and performance were analyzed using both theoretical model and finite element method. A prototype sensor element with desired bio-mimicry responses was fabricated and validated, which will be further refined for integration with the turbine blade structures