With the development of the nuclear energy industry, small modular reactors (SMRs) have become an important option in China's energy development due to their advantages in terms of safety and economics. The helica...
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Metamorphic testing is one of the effective methods to alleviate the test oracle problem. Metamorphic relation is the core of metamorphic testing, and there is no effective automatic identification technology. This pa...
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Accurate determination of the bilayer structure of the recently discovered high transition temperature superconductor La3Ni2O7 under pressure is a prerequisite for understanding its electronic properties and revealing...
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Accurate determination of the bilayer structure of the recently discovered high transition temperature superconductor La3Ni2O7 under pressure is a prerequisite for understanding its electronic properties and revealing its dominant superconducting mechanism. Here we present the first systematic exploration of the structural evolution of La3Ni2O7 under different pressure schemes, with the long-range London dispersion force between neighboring bilayers taken into account explicitly. We find, within a broad pressure range of 10 to 35 GPa, that the structure is essentially always in-plane symmetric under hydrostatic pressure, in clear contradiction to the experimentally observed asymmetric structure. Nevertheless, when the applied pressure is anisotropic together with inclusion of a proper dispersion force, the asymmetric structure can be reproduced, accompanied by pronounced flat bands of dz2 orbitals around the Fermi level that in turn benefit superconductivity. These findings offer stringent constraints on developing mechanistic understanding of superconductivity of La3Ni2O7.
Vernier permanent magnet machine (VPMM) has been a promising candidate for low-speed large-torque applications owing to high torque density based on multiple-magnetic field-harmonics working principle However, rich fi...
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The automation of plant phenotyping using 3D imaging techniques is ***,conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction...
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The automation of plant phenotyping using 3D imaging techniques is ***,conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing *** mitigate this trade-off,we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf(the shape and distortion of that shape)separately using leaf-specific *** separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing *** evaluate the proposed method,we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species(soybean and sugar beet)and compared the results with those of conventional *** result showed that the proposed method robustly reconstructed the leaf surfaces,despite the noise and missing points for two different leaf *** evaluate the stability of the leaf surface reconstructions,we also calculated the leaf surface areas for 14 consecutive days of the target *** result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Recently, a technology called floating nuclear power plant (FNPP) has been developed around the world to maximize the use of ocean and nuclear energy. The FNPP is required to investigate the impact of a ship collision...
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Multipolar spindles are very rare in normal tissues, but they are much more prevalent in many tumors, which might be induced by the mechanical confinements from overcrowding microenvironments in tumors. However, littl...
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Multipolar spindles are very rare in normal tissues, but they are much more prevalent in many tumors, which might be induced by the mechanical confinements from overcrowding microenvironments in tumors. However, little is known about what the difference is between various forms of mechanical confinements that cells encounter in normal tissues and tumor tissues, and how they affect multipolarity and chromosome segregation fidelity. Here, we use microchannels with different heights and widths to mimic diverse forms and degrees of mechanical constraints within the tissue architecture. We find that multipolar spindles occur frequently under two-wall confinement but that they are rare under four-wall confinement, suggesting that multipolar-spindle assembly depends on the form of the three-dimensional mechanical confinement. We reveal that two-wall confinement leads to an increased fraction of multipolar spindles by pole splitting, while four-wall confinement restrains multipolarity by the enhancement of pole clustering and the inhibition of pole splitting. We further conduct numerical simulations and develop a theoretical model to investigate how mechanical confinement influences pole splitting and clustering. By exploring the energy landscape of pole-pole interactions and pole-cortex interactions and treating pole splitting and clustering as reversible reactions, we demonstrate that mechanical confinement controls cell shape and pole-cortex interactions, which, in turn, change the energy barriers of pole splitting and clustering as well as the probability of multipolar mitosis. Further experiments confirm the theoretical prediction that the pole-cortex interaction determines the probability of the multipolar spindles under various mechanical confinements. Our findings demonstrate the extent to which extracellular microenvironments and tissue architecture can affect complex cellular behaviors, indicating that normal tissue architecture may have the ability to suppress th
Because of the environmental friendliness of electric vehicles, research on electric vehicles has become a hot spot in recent years. How to charge electric vehicles faster is critical to the widespread use of electric...
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Because of the environmental friendliness of electric vehicles, research on electric vehicles has become a hot spot in recent years. How to charge electric vehicles faster is critical to the widespread use of electric vehicles. Electric vehicle integrated power unit can use bidirectional DC-DC converter for driving to achieve integrated charging. In order to improve the power density and efficiency of the integrated system, the bidirectional DC-DC for electric vehicle driving uses three-phase interleaved parallel technology. This paper proposes a method to select different interleaved phase numbers according to different output powers of the converter to improve the working efficiency of three-phase interleaved parallel DC-DC converters. This method improves the efficiency of the three-phase interleaved parallel converter under light load and widens the high-efficiency working area of the converter. Finally, simulations and experimental results are conducted to validate the efficiency of the design.
Modular multilevel converter (MMC) has many advantages, such as low loss, flexible control and so on, which has been widely used in photovoltaic grid connected system. However, the photovoltaic output has some instabi...
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ISBN:
(纸本)9781665434263
Modular multilevel converter (MMC) has many advantages, such as low loss, flexible control and so on, which has been widely used in photovoltaic grid connected system. However, the photovoltaic output has some instability, and the damping of the flexible DC system is also low, which puts forward a severe test for the stable operation of the power grid. Firstly, this paper describes the basic structure of the photovoltaic system connected by MMC. Secondly, the photovoltaic grid connected system model based on PSCAD illustrates the fault current characteristics under DC side fault, and simulates and analyzes the influence of different factors in the system on the fault current.
Graph neural networks (GNNs) have been widely applied in various graph analysis tasks. To provide more convenient and faster predictive services, many enterprises are choosing to deploy GNNs in cloud environments. How...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
Graph neural networks (GNNs) have been widely applied in various graph analysis tasks. To provide more convenient and faster predictive services, many enterprises are choosing to deploy GNNs in cloud environments. However, given the increasing privacy concerns about GNNs models and graph data, as well as the need to quickly generate embeddings for new nodes in real-world applications, a critical issue in this emerging paradigm is to ensure the security and scalability of GNN predictions. In this paper, we propose a privacy-preserving and scalable GNN prediction scheme, named PS-GNN, to address the privacy issues in cloud environments. Specifically, PS-GNN utilizes a customized array structure to store graph data and employs secret sharing to preserve the confidentiality of both the GNN model and graph data. Besides, the scalability of PS-GNN is achieved by aggregating feature information from local node neighborhoods in parallel. Through a detailed analysis, we demonstrate the security of PS-GNN. Extensive experiments on real-world datasets demonstrate that PS-GNN outperforms existing schemes in terms of computational and communication overhead, and reaches state-of-the-art performance on large graphs.
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