The microstructure characteristic of cohesive soil in inner shelf of the East China Sea and its engineering implication

389-396In this paper, the microstructure images and parameters of the cohesive soil were obtained via scanning electron microscope testing technology and computer image processing technology. The physical-mechanical indexes of cohesive soil was obtained through geotechnical testing. The macro physical-mechanical indexes and microstructure parameters of cohesive soil in the inner shelf of the East China Sea were analysed by the regression analysis method; and the multiple regression equations between water content, liquid limit, compressibility coefficient, vane shear strength, micro-penetration resistance, medium diameter and the microstructure parameters were established. The results show that cohesive soils in the inner shelf of the East China Sea have three types of microstructure: Granular link-bond structure, flocculent link-bond structure and clay particle matrix structure. The physical indexes of cohesive soil mainly related to the microstructure parameters characterizing the size and number of pores, while the mechanical indexes, besides the size and number, also affected by the geometry of pores. Usually, the rounder the pore is and the more complicated the profile curve of pore is, the higher is the strength of cohesive soil. The results can provide theoretical reference for in-depth understanding the engineering properties of cohesive soil in the inner shelf of the East China Sea

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

A multi-domain VNE algorithm based on load balancing in the IoT networks

The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people’s increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm’s masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.Peer ReviewedPostprint (published version

FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Community Search (CS), a crucial task in network science, has attracted considerable interest owing to its prowess in unveiling personalized communities, thereby finding applications across diverse domains. Existing research primarily focuses on traditional homogeneous networks, which cannot be directly applied to heterogeneous information networks (HINs). However, existing research also has some limitations. For instance, either they solely focus on single-type or multi-type community search, which severely lacking flexibility, or they require users to specify meta-paths or predefined community structures, which poses significant challenges for users who are unfamiliar with community search and HINs. In this paper, we propose an innovative method, FCS-HGNN, that can flexibly identify either single-type or multi-type communities in HINs based on user preferences. We propose the heterogeneous information transformer to handle node heterogeneity, and the edge-semantic attention mechanism to address edge heterogeneity. This not only considers the varying contributions of edges when identifying different communities, but also expertly circumvents the challenges presented by meta-paths, thereby elegantly unifying the single-type and multi-type community search problems. Moreover, to enhance the applicability on large-scale graphs, we propose the neighbor sampling and depth-based heuristic search strategies, resulting in LS-FCS-HGNN. This algorithm significantly improves training and query efficiency while maintaining outstanding community effectiveness. We conducted extensive experiments on five real-world large-scale HINs, and the results demonstrated that the effectiveness and efficiency of our proposed method, which significantly outperforms state-of-the-art methods.Comment: 13 page

CD24-p53 axis suppresses diethylnitrosamine-induced hepatocellular carcinogenesis by sustaining intrahepatic macrophages

It is generally assumed that inflammation following diethylnitrosamine (DEN) treatment promotes development of hepatocellular carcinoma (HCC) through the activity of intrahepatic macrophages. However, the tumor-promoting function of macrophages in the model has not been confirmed by either macrophage depletion or selective gene depletion in macrophages. Here we show that targeted mutation of Cd24 dramatically increased HCC burden while reducing intrahepatic macrophages and DEN-induced hepatocyte apoptosis. Depletion of macrophages also increased HCC burden and reduced hepatocyte apoptosis, thus establishing macrophages as an innate effector recognizing DEN-induced damaged hepatocytes. Mechanistically, Cd24 deficiency increased the levels of p53 in macrophages, resulting in their depletion in Cd24 -/- mice following DEN treatment. These data demonstrate that the Cd24-p53 axis maintains intrahepatic macrophages, which can remove hepatocytes with DNA damage. Our data establish a critical role for macrophages in suppressing HCC development and call for an appraisal of the current dogma that intrahepatic macrophages promote HCC development

The Use of Oral Histories to Identify Criteria for Future Scenarios of Sustainable Farming in the South Yangtze River, China

Agricultural practices in Jiangnan water towns have historically been identified as maintaining a balance between human activity and the local environment, but are now a significant local source of water pollution. Using a multi-methods approach, this study deduces the environmental impact of traditional practices, and the socially desired conditions for successfully reintroducing critical ones. Oral histories from 31 farmers in Tianshanzhuang village, South Yangtze River were in order to chart changes in farming practices over four historic periods, and used to estimate the nitrogen and phosphorus burdens per acre. Findings show that the use of Lan River Mud—dredged mud for fertilizer—was key in producing a positive impact, but abandoned after the 1980s. Four criteria hindering reintroduction of traditional practices were identified, and potentially useful but fragmented emerging local candidate practices are considered against these, as are recent practices in Japan. We propose that the cooperation of several stakeholders with various related government departments in China could lead to a portfolio of effective policy changes and should be studied further: to include new methods and uses of Lan River Mud; the integration of aquaculture, leisure and tourism industries with agriculture; and the production of organic produce with well-planned internet-linked sales, delivery and coordination mechanisms

Dependence of resting-state-based cerebrovascular reactivity (CVR) mapping on spatial resolution

Cerebrovascular reactivity (CVR) is typically assessed with a carbon dioxide (CO2) stimulus combined with BOLD fMRI. Recently, resting-state (RS) BOLD fMRI has been shown capable of generating CVR maps, providing a potential for broader CVR applications in neuroimaging studies. However, prior RS-CVR studies have primarily been performed at a spatial resolution of 3–4 mm voxel sizes. It remains unknown whether RS-CVR can also be obtained at high-resolution without major degradation in image quality. In this study, we investigated RS-CVR mapping based on resting-state BOLD MRI across a range of spatial resolutions in a group of healthy subjects, in an effort to examine the feasibility of RS-CVR measurement at high resolution. Comparing the results of RS-CVR with the maps obtained by the conventional CO2-inhalation method, our results suggested that good CVR map quality can be obtained at a voxel size as small as 2 mm isotropic. Our results also showed that, RS-CVR maps revealed resolution-dependent sensitivity. However, even at a high resolution of 2 mm isotropic voxel size, the voxel-wise sensitivity is still greater than that of typical task-evoked fMRI. Scan duration affected the sensitivity of RS-CVR mapping, but had no significant effect on its accuracy. These findings suggest that RS-CVR mapping can be applied at a similar resolution as state-of-the-art fMRI studies, which will broaden the use of CVR mapping in basic science and clinical applications including retrospective analysis of previously collected fMRI data