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| 1 | Near UV luminescent Cs_(2)NaBi_(0.75)Sb_(0.25)Cl_(6)perovskite colloidal nanocrystals with high stability显示文摘Near UV highly luminescent colloidal Cs_(2)NaBiCl_(6)nanocrystals(NCs)were synthesized by a simple low-cost ligand-assisted reprecipitation method.In our strategy,metal chloride precursors were added to the mixture of anti-solvent and ligand at room-temperature.The obtained Cs_(2)NaBiCl_(6)NCs exhibited a bright blue emission with significantly improved photoluminescence quantum yield(PLQY)of 39.05%.The optical properties and stability were greatly enhanced by doping Sb where Cs_(2)NaBi_(0.75)Sb_(0.25)Cl_(6)showed a high PLQY of 46.57%,and both the powder and the colloidal solution exhibited superior stability. | Huixian Yang Yanmei Guo Guoning Liu Ruowei Song Jinxi Chen Yongbing Lou Yixin Zhao | 2022 | Chinese Chemical Letters2022，33，1： | 0 |
| 2 | Gauze:enabling communication-friendly block synchronization with cuckoo filter显示文摘Block synchronization is an essential component of blockchain systems.Traditionally,blockchain systems tend to send all the transactions from one node to another for synchronization.However,such a method may lead to an extremely high network bandwidth overhead and significant transmission latency.It is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of peers.However,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization delay.In this paper,we propose a novel protocol named Gauze for fast block synchronization.It utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)network.By up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or not.Based on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth resources.The evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios. | Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI | 2023 | Frontiers of Computer Science2023，17，3： | 0 |
| 3 | Two-Dimensional Transport Properties of Germanium Bicrystal at Low Temperature显示文摘Two-dimensional properties of grain boundaries in germanium bicrystals are studied at liquid helium temperature ranging from 1.6 to 4.2 K.The experimental results indicate that the magnetoresistivity for 8°tilt angle increases exponentially with the magnetic held intensity square in weak magnetic field and with the linear magnetic field intensity in strong magnetic field.The phenomena are explainable with the percolation theory. | XIA Yiben ZHENG Guozhen GUO Shaoling SUN Jinxi | 1992 | Chinese Physics Letters1992，9，7： | 0 |
| 4 | Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks显示文摘Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels. | Jinxi Guo Kai Chen Jiehui Liu Yuhao Ma Jie Wu Yaochun Wu Xiaofeng Xue Jianshen Li | 2024 | Computer Modeling in Engineering & Sciences2024，138，3： | 0 |
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