LiDAR-HMR: 3D Human Mesh Recovery from LiDAR

In recent years, point cloud perception tasks have been garnering increasing attention. This paper presents the first attempt to estimate 3D human body mesh from sparse LiDAR point clouds. We found that the major challenge in estimating human pose and mesh from point clouds lies in the sparsity, noise, and incompletion of LiDAR point clouds. Facing these challenges, we propose an effective sparse-to-dense reconstruction scheme to reconstruct 3D human mesh. This involves estimating a sparse representation of a human (3D human pose) and gradually reconstructing the body mesh. To better leverage the 3D structural information of point clouds, we employ a cascaded graph transformer (graphormer) to introduce point cloud features during sparse-to-dense reconstruction. Experimental results on three publicly available databases demonstrate the effectiveness of the proposed approach. Code: https://github.com/soullessrobot/LiDAR-HMR/Comment: Code is available at: https://github.com/soullessrobot/LiDAR-HMR

Fabrication of equiatomic FeCo alloy parts with high magnetic properties by fields activated sintering

Electrical field activated sintering technology combined with micro-forming (Micro-FAST), as a new rapid powder sintering/forming method, is used to fabricate FeCo alloy parts. The successfully prepared FeCo parts have a high saturation of 214.11 emu/g and a low coercivity of 16 Oe, and these values are 20% and 10% higher than that of commercially available FeCoV alloy parts on the saturation and coercivity respectively. During the sintering process, the high current application shortened the densification time and enhanced the uniformity of the microstructure significantly. The grain sizes of FeCo alloys were in a range of 5–6 mm, and good isotropy was also shown. The low angle grain boundary (LAGB) accounted for more than 30% and the low angle misorientation accounted for more than 30% of the sample parts. Furthermore, the formation of the nano B2 phase was promoted during the Micro-FAST, and the size of the B2 phase was about 5 nm. The coherent interface between a and B2 was conducive for reducing the coercivity. As a consequence, the outstanding microstructure formed by Micro-FAST makes the FeCo alloys have high saturation and low coercivity

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance

工业生态与节能——粉煤灰基CO2 吸附沸石绿色制备

CCUS carbon capture, utilization and storage is one of the key technologies to deal with global climate change, which has been highly valued by countries all over the world and has increased research and development efforts, but there are still difficulties in industrialization. This paper introduces a green preparation process of zeolite for carbon dioxide adsorption

A Joint Reliable Transport Strategy in Internet of Vehicles

Internet of Vehicles (IoV) is promising in bringing various data services from the traditional Internet to vehicle networks. Therefore, a reliable transport service in IoV needs to cross multiple heterogeneous networks with quite different characteristics. However, no single transport protocol is able to cope with such complex scenarios comprehensively with efficient data transmission all the way. To this end, we provide a solution named the joint reliable transport strategy (JR-TS) that selects different transport protocols on demand based on various scenarios, and builds an entire end-to-end route by linking all these transport protocols head to tail. Currently, JR-TS has already included three types of transport protocols to adapt to three typical network scenarios. With the proper implementations and settings, JR-TS can improve end-to-end transport performance and cache capacity efficiency more effectively than any single transport protocol

Application-aware and Dynamic Security Function Chaining for Mobile Networks

Mobile networks have urgent demands of fine-grained, cost-effective and flexible service provision for diversified user traffic. To cope with these demands, researchers have proposed various Service Function Chaining (SFC) solutions with the rise of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. However, most of them are performed based on MAC address and/or OpenFlow protocols without Network Service Header (NSH) support, having drawbacks in complexity, scalability and flexibility. NSH-based approaches are more promising for mobile networks, since they support metadata-based packet information sharing and policy enforcement. Moreover, a hierarchical SFC (hSFC) architecture is proposed to alleviate the scalability and management problems in large-scale networks. Nevertheless, how to realize application awareness and on-demand service provision has not been investigated thoroughly in the hSFC environment. Thus, in this paper, we propose a proactive-based branching approach for application-aware and dynamic security function chaining, where application features are analyzed at first, and then carried in the metadata of NSHs for subsequent processes by the relevant security functions. In this way, the data plane is able to redirect traffic based on metadata without the participation of control plane. Besides, we verify the proposed approach through our prototype system via two typical use cases, the application-aware traffic control and lawful interception, and the related experiment results confirm its feasibility and elasticity

Analysis of core genes for colorectal cancer prognosis based on immune and stromal scores

Background Colorectal cancer (CRC) is one of the most common malignancies.An early diagnosis and an accurate prognosis are major focuses of CRC research. Tumor microenvironment cells and the extent of infiltrating immune and stromal cells contribute significantly to the tumor prognosis. Methods Immune and stromal scores were calculated based on the ESTIMATE algorithm using the sample expression profile of the The Cancer Genome Atlas (TCGA) database. GSE102479 was used as the validation database. Differentially expressed genes whose expression was significantly associated with the prognosis of CRC patients were identified based on the immune matrix score. Survival analysis was conducted on the union of the differentially expressed genes. A protein–protein interaction (PPI) network was constructed using the STRING database to identify the closely connected modules. To conduct functional enrichment analysis of the relevant genes, GO and KEGG pathway analyses were performed with Cluster Profiler. Pivot analysis of the ncRNAs and TFs was performed by using the RAID2.0 database and TRRUST v2 database. TF-mRNA regulatory relationships were analyzed in the TRRUST V2 database. Hubgene targeting relationships were screened in the TargetScan, miRTarBase and miRDB databases. The SNV data of the hub genes were analyzed by using the R maftools package. A ROC curve was drawn based on the TCGA database. The proportion of immune cells was estimated using CIBERSORT and the LM22 feature matrix. Results The results showed that the matrix score was significantly correlated with colorectal cancer stage T. A total of 789 differentially expressed genes and 121 survival-related prognostic genes were identified. The PPI network showed that 22 core genes were related to the CRC prognosis. Furthermore, four ncRNAs that regulated the core prognosis genes, 11 TFs with regulatory effects on the core prognosis genes, and two drugs, quercetin and pseudoephedrine, that have regulatory effects on colorectal cancer were also identified. Conclusions We obtained a list of tumor microenvironment-related genes for CRC patients. These genes could be useful for determining the prognosis of CRC patients. To confirm the function of these genes, additional experiments are necessary