Study on the Tourism Industry Competitiveness of Nanyue Economic Zone

This paper analyzed the subjects of tourism development of Nanyue economic zone, such as production elements, demand status, related and supporting industries, enterprise, government and opportunities, and points out that the Nanyue economic zone tourism industry competitiveness support elements and restricting factors, and puts forward some countermeasures on how to improve the competitiveness of the industry of tourism Nanyue economic zone.Key words: Nanyue economic zone; Tourism industry; Competitiveness mode

UniWorld: Autonomous Driving Pre-training via World Models

In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots. We imbue the robot with a spatial-temporal world model, termed UniWorld, to perceive its surroundings and predict the future behavior of other participants. UniWorld involves initially predicting 4D geometric occupancy as the World Models for foundational stage and subsequently fine-tuning on downstream tasks. UniWorld can estimate missing information concerning the world state and predict plausible future states of the world. Besides, UniWorld's pre-training process is label-free, enabling the utilization of massive amounts of image-LiDAR pairs to build a Foundational Model.The proposed unified pre-training framework demonstrates promising results in key tasks such as motion prediction, multi-camera 3D object detection, and surrounding semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, UniWorld shows a significant improvement of about 1.5% in IoU for motion prediction, 2.0% in mAP and 2.0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion. By adopting our unified pre-training method, a 25% reduction in 3D training annotation costs can be achieved, offering significant practical value for the implementation of real-world autonomous driving. Codes are publicly available at https://github.com/chaytonmin/UniWorld.Comment: 8 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2305.1882

Polysaccharides from the Medicinal Mushroom Cordyceps taii Show Antioxidant and Immunoenhancing Activities in a D-Galactose-Induced Aging Mouse Model

Cordyceps taii, an edible medicinal mushroom native to south China, is recognized as an unparalleled resource of healthy foods and drug discovery. In the present study, the antioxidant pharmacological properties of C. taii were systematically investigated. In vitro assays revealed the scavenging activities of the aqueous extract and polysaccharides of C. taii against various free radicals, that is, 1,1-diphenyl-2-picrylhydrazyl radical, hydroxyl radical, and superoxide anion radical. The EC50 values for superoxide anion-free radical ranged from 2.04 mg/mL to 2.49 mg/mL, which was at least 2.6-fold stronger than that of antioxidant thiourea. The polysaccharides also significantly enhanced the antioxidant enzyme activities (superoxide dismutase, catalase, and glutathione peroxidase) and markedly decreased the malondialdehyde production of lipid peroxidation in a D-galactose-induced aging mouse model. Interestingly, the immune function of the administration group was significantly boosted compared with the D-galactose-induced aging model group. Therefore, the C. taii polysaccharides possessed potent antioxidant activity closely associated with immune function enhancement and free radical scavenging. These findings suggest that the polysaccharides are a promising source of natural antioxidants and antiaging drugs. Consequently, a preliminary chemical investigation was performed using gas chromatography-mass spectroscopy and revealed that the polysaccharides studied were mainly composed of glucose, mannose, and galactose. Fourier-transform infrared spectra also showed characteristic polysaccharide absorption bands

Phase separation and enhanced wear resistance of Cu88Fe12 immiscible coating prepared by laser cladding

In order to eliminate the microstructure segregation of Cu–Fe immiscible alloys which characterized with a liquid miscible gap, the Cu88Fe12 (wt.%) immiscible coating was prepared by laser cladding. The phase separation characteristic and wear resistance of the Cu88Fe12 (wt.%) immiscible coating were also investigated. The results show that the size of the milled Cu88Fe12 composite powder is reduced comparing to that of the un-milled powder due to fracturing during mechanical milling. Moreover, the demixing or delamination disappears in the Cu88Fe12 immiscible coating and a large amount of face-centered-cubic (fcc) γ-Fe and body-centered-cubic (bcc) α-Fe particles are dispersed in the face-centered-cubic (fcc) ɛ-Cu matrix as a result of liquid phase separation. The size of Fe-rich particles presents an increasing tendency from the bottom to the top of the immiscible coating. As a result, the microhardness of the immiscible coating is improved compared with brass (∼138 HV0.2) due to the presence of high-hardness Fe-rich particles (∼191 HV0.2) and the solid solution strengthening effect of Fe in Cu-rich matrix. Furthermore, the width of ploughing, the width and height of wear scar on the surface of the immiscible coating are much less than those on the surface of brass. Therefore, the wear resistance of the immiscible coating is remarkably enhanced compared with brass

Dynamic Complexity of an Ivlev-Type Prey-Predator System with Impulsive State Feedback Control

The dynamic complexities of an Ivlev-type prey-predator system with impulsive state feedback control are studied analytically and numerically. Using the analogue of the Poincaré criterion, sufficient conditions for the existence and the stability of semitrivial periodic solutions can be obtained. Furthermore, the bifurcation diagrams and phase diagrams are investigated by means of numerical simulations, which illustrate the feasibility of the main results presented here

Paeonol Inhibits Proliferation of Vascular Smooth Muscle Cells Stimulated by High Glucose via Ras-Raf-ERK1/2 Signaling Pathway in Coculture Model

Paeonol (Pae) has been previously reported to protect against atherosclerosis (AS) by inhibiting vascular smooth muscle cell (VSMC) proliferation or vascular endothelial cell (VEC) injury. But studies lack how VSMCs and VECs interact when Pae plays a role. The current study was based on a coculture model of VSMCs and VECs to investigate the protective mechanisms of Pae on atherosclerosis (AS) by determining the secretory function of VECs and proliferation of VSMCs focusing on the Ras-Raf-ERK1/2 signaling pathway. VECs were stimulated by high glucose. Our data showed that high concentration (35.5 mM) of glucose induced damage in VECs. Injury of VECs stimulated VSMC proliferation in the coculture model. Pae (120 μM) decreased vascular endothelial growth factor (VEGF) and platelet derivative growth factor B (PDGF-B) release from VECs and inhibited overexpression of Ras, P-Raf, and P-ERK proteins in VSMCs. The results indicate that diabetes modulates the inflammatory response in VECs to stimulate VSMC proliferation and promote the development of AS. Pae was beneficial by inhibiting the inflammatory effects of VECs on VSMC proliferation. This study suggests the inhibitory mechanism of Pae due to the inhibition of VEGF and PDGF-B secretion in VECs and Ras-Raf-ERK1/2 signaling pathway in VSMCs

Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

Current perception models in autonomous driving heavily rely on large-scale labelled 3D data, which is both costly and time-consuming to annotate. This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds using masked autoencoders (MAE). While existing masked point autoencoding methods mainly focus on small-scale indoor point clouds or pillar-based large-scale outdoor LiDAR data, our approach introduces a new self-supervised masked occupancy pre-training method called Occupancy-MAE, specifically designed for voxel-based large-scale outdoor LiDAR point clouds. Occupancy-MAE takes advantage of the gradually sparse voxel occupancy structure of outdoor LiDAR point clouds and incorporates a range-aware random masking strategy and a pretext task of occupancy prediction. By randomly masking voxels based on their distance to the LiDAR and predicting the masked occupancy structure of the entire 3D surrounding scene, Occupancy-MAE encourages the extraction of high-level semantic information to reconstruct the masked voxel using only a small number of visible voxels. Extensive experiments demonstrate the effectiveness of Occupancy-MAE across several downstream tasks. For 3D object detection, Occupancy-MAE reduces the labelled data required for car detection on the KITTI dataset by half and improves small object detection by approximately 2% in AP on the Waymo dataset. For 3D semantic segmentation, Occupancy-MAE outperforms training from scratch by around 2% in mIoU. For multi-object tracking, Occupancy-MAE enhances training from scratch by approximately 1% in terms of AMOTA and AMOTP. Codes are publicly available at https://github.com/chaytonmin/Occupancy-MAE.Comment: Accepted by TI

Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction

Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. However, existing multi-camera algorithms primarily rely on monocular image pre-training, which overlooks the spatial and temporal correlations among different camera views. To address this limitation, we propose a novel multi-camera unified pre-training framework called Occ-BEV, which involves initially reconstructing the 3D scene as the foundational stage and subsequently fine-tuning the model on downstream tasks. Specifically, a 3D decoder is designed for leveraging Bird's Eye View (BEV) features from multi-view images to predict the 3D geometry occupancy to enable the model to capture a more comprehensive understanding of the 3D environment. One significant advantage of Occ-BEV is that it can utilize a vast amount of unlabeled image-LiDAR pairs for pre-training. The proposed multi-camera unified pre-training framework demonstrates promising results in key tasks such as multi-camera 3D object detection and semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, Occ-BEV demonstrates a significant improvement of 2.0% in mAP and 2.0% in NDS for 3D object detection, as well as a 0.8% increase in mIOU for semantic scene completion. codes are publicly available at https://github.com/chaytonmin/Occ-BEV.Comment: 8 pages, 5 figure