55 research outputs found

    Identifying the degree of luminescence signal bleaching in fluvial sediments from the Inner Mongolian reaches of the Yellow River

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    Abstract The partial bleaching of the luminescence signal prior to deposition results in age overestimation, and can be a problem in delineating fluvial evolution within an OSL chronological framework. The Inner Mongolian reaches of the Yellow River are characterised by a high sediment load and complex sources of sediments. To test the incomplete bleaching occurring in this type of environment, the residual doses and the luminescence signal characteristics of different particle size fractions from 14 modern fluvial sediment samples were investigated. Furthermore, 26 OSL ages derived from drilling cores were compared with 11 radiocarbon ages. Our results show that the residual equivalent doses principally range between 0.16 and 0.49 Gy for silt grains, and between 0.35 and 3.72 Gy for sand grains of modern samples. This suggests that medium-grained quartz has been well bleached prior to deposition, and is preferable to coarse-grained quartz when dating fluvial sediments in this region. The results also show that the De values of coarse-grained fractions display a stronger correlation with distance downstream. In addition, a comparison of OSL and radiocarbon ages from drilling cores establishes further confidence that any initial bleaching of these sediments was sufficient. As a result, we believe that the studied fluvial samples were well bleached prior to deposition.</jats:p

    Near-Space Communications: the Last Piece of 6G Space-Air-Ground-Sea Integrated Network Puzzle

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    This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space-air-ground-sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics and unique problems of NS-COM network. Afterwards, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite (StratoSat) -to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output (MIMO) and holographic MIMO, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, StratoSat-based radar detection and imaging, NS-COM assisted enhanced global navigation system, NS-COM assisted intelligent unmanned system and free space optical (FSO) communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.Comment: 28 pages, 8 figures, 2 table

    Deficient O-GlcNAc Glycosylation Impairs Regulatory T Cell Differentiation and Notch Signaling in Autoimmune Hepatitis

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    Post-translational modifications such as glycosylation play an important role in the functions of homeostatic proteins, and are critical driving factors of several diseases; however, the role of glycosylation in autoimmune hepatitis is poorly understood. Here, we established an O-GlcNAc glycosylation-deficient rat model by knocking out the Eogt gene by TALEN-mediated gene targeting. O-GlcNAc glycosylation deficiency overtly aggravated liver injury in concanavalin-A induced autoimmune hepatitis, and delayed self-recovery of the liver. Furthermore, flow cytometry analysis revealed increased CD4+ T cell infiltration in the liver of rats with O-GlcNAc glycosylation deficiency, and normal differentiation of regulatory T cells (Tregs) in the liver to inhibit T cell infiltration could not be activated. Moreover, in vitro experiments showed that O-GlcNAc glycosylation deficiency impaired Treg differentiation to inhibit the Notch signaling pathway in CD4+ T cells. These finding indicate that O-GlcNAc glycosylation plays a critical role in the activation of Notch signaling, which could promote Treg differentiation in the liver to inhibit T cell infiltration and control disease development in autoimmune hepatitis. Therefore, this study reveals a regulatory role for glycosylation in the pathogenesis of autoimmune hepatitis, and highlights glycosylation as a potential treatment target

    SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

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    Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2x in achieved throughput under varying network conditions, reduces the server cost by up to 6.8x and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile Computing and Networking (MobiCom), 202

    Hierarchical Coordinated Control for Power System Voltage Using Linear Temporal Logic

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