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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Jiang, Hailiang Tian, Xin Tao, Ye Wang, Lulu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Beijing HAOLINJU CVS Co. Ltd Beijing100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers;fresh produce stores outperform regular stores in fruit sales of CGB;Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels. © 2024 The Authors.

关键词： community group-buying dark-colored fruits fresh produce stores subjectivity

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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A Multi-Scale Thermal Analysis Method for data Centers with Application in a Ship data Center

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Journal of Thermal Science 2021年第6期30卷 1973-1985页

作者： DAI Yanjun ZHAO Jie SHI Jiwei WANG Wei TAO Wenquan Key Laboratory of Thermo-Fluid Science and Engineering of MOE School of Energy and Power EngineeringXi'an Jiaotong UniversityXi’an 710049China Science and Technology on Thermal Energy and Power Laboratory Wuhan 430205China Key Laboratory of Data Center Energy Saving&Low Carbon Techniques of Xi’an Xi’an 710049China

Thermal analysis of data centers is in urgent need to ensure that computer chips remain below the critical temperature while the energy consumption for cooling can be *** is difficult to obtain detailed hotspot locations and temperatures of chips in large data centers containing hundreds of racks or more by direct *** this paper,a multi-scale thermal analysis method is proposed that can predict the temperature distribution of chips and solder balls in data *** multi-scale model is divided into six scales:room,rack,server,Insulated-Gate Bipolar Transistor(IGBT),chip and solder ball.A concept of sub-model is proposed and the six levels are organized into four simulation ***-model 1 contains Room,Rack and Server(RRS);Sub-model 2 contains Server and IGBT(SI);Sub-model 3 contains IGBT and Chip(IC),and Sub-model 4 contains Chip and Solder-ball(CS).These four sub-models are one-way coupled by transmitting their results as boundary conditions between *** full-field simulation method is employed to verify the efficiency and accuracy of multi-scale simulation method for a single-rack data *** two simulation results show that the highest temperature emerges in the same *** Single-rack Full-field Model(SRFFM)costs 2.5 times more computational time than that with Single-rack Multi-scale Model(SRMSM).The deviation of the highest temperature of chips and solder balls are 1.57℃and 0.2℃between the two models which indicates that the multi-scale simulation method has good prospect in the data center thermal ***,the multi-scale thermal analysis method is applied to a ship data center with 15 racks.

关键词： thermal analysis computational fluid dynamics multi-scale simulation full-field simulation data center

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具有双功能酶活性的Au/Cu多级组装颗粒的构建

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Science China Materials 2023年第4期66卷 1471-1483页

作者：李清张嘉兴王跃飞闵继伟申雨禾刘伟刘雪峰苏荣欣齐崴 State Key Laboratory of Chemical Engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Tianjin Key Laboratory of Membrane Science and Desalination Technology Tianjin UniversityTianjin 300072China Automotive Data of China Co. Ltd.Tianjin 300300China

纳米酶被定义为具有独特纳米结构和催化活性的模拟酶.受天然酶的启发,我们报道了一种具有仿多酚氧化酶和过氧化物酶活性的双功能Au/Cu多级组装颗粒(Hops).它不但能够快速氧化难降解的有毒酚类污染物,还可以用于灵敏检测生物供能物质.Au/... 详细信息

纳米酶被定义为具有独特纳米结构和催化活性的模拟酶.受天然酶的启发,我们报道了一种具有仿多酚氧化酶和过氧化物酶活性的双功能Au/Cu多级组装颗粒(Hops).它不但能够快速氧化难降解的有毒酚类污染物,还可以用于灵敏检测生物供能物质.Au/Cu Hops纳米酶表现出优异的催化活性和可回收性,并且可在极端条件下保持稳定.值得注意的是,与天然漆酶相比,在相同质量浓度下,Au/Cu Hops纳米酶催化对苯二酚氧化反应具有更高的底物亲和力和6.37倍的Vmax.此外,基于Au/Cu Hops的仿过氧化物酶活性,我们构建了级联反应系统用于检测葡萄糖浓度,检测限低至5μmol L^(-1).最后,通过分子动力学模拟,我们证明了Cu-S簇在Au/Cu Hops表面形成双催化活性位点,并通过密度泛函理论计算揭示了纳米酶的催化途径.该工作对双功能纳米酶的构建和应用具有重要意义.

关键词：密度泛函理论计算灵敏检测纳米酶可回收性极端条件分子动力学模拟模拟酶葡萄糖浓度

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Study on the influence of environmentally friendly plasticizers on the properties of polyvinyl chloride 3

Study on the influence of environmentally friendly plasticiz...

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2023 3rd International Conference on Advanced Materials and Chemical engineering, AMCE 2023

作者： Wu, Hailong Zhou, Biyun Cen, Chuanfu Cao, Yu School of Data Science and Software Engineering Wu Zhou University Guangxi Wuzhou543002 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wu Zhou University Guangxi Wuzhou543002 China School of Mechanical and Resource Engineering Wu Zhou University Guangxi Wuzhou543002 China

In this work, the torque rheology, mechanical properties, migration resistance, thermal deformation, and Vicat softening point temperature of PVC plasticized by four environmentally friendly plasticizers are investigated. The experimental results reveal that the plasticizer's heavy metal content meets production requirements while falling below the limiting standard. Plasticizer melting points differ, as do the enthalpy and torque of the entire melting process, resulting in different energy consumption when melting plasticizers. Also, the mechanical properties of PVC are not different, but the mechanical properties of PVC plasticized by epoxy soybean oil are the most prominent. Besides, the Vicat softening point temperature of PVC plasticized by four environmentally friendly plasticizers is within the normal standard range, and the migration amount in different solvents is changed in a small range, which will not affect human health. © Published under licence by IOP Publishing Ltd.

关键词： Plasticizers

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Counterfactual Faithful data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings 3

Counterfactual Faithful Data Generation Based on Disentangle...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Ding, Xu Zhu, Qiang Wang, Song Zhang, Yiqi Wang, Hui Xu, Juan Institute of Industry and Equipment Technology Hefei University of Technology Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China Bengbu Triumph Engineering Technology Co. Ltd Bengbu China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Recently, deep learning and human-out-of-the-loop methods enjoy their prosperous applications in mechanical fault diagnosis. Nonetheless, the None-IID(independent and identically distributed) issue radicated in acquired data severely limits the stability and accuracy of compound fault diagnosis of rolling bearings. This paper proposes a sample augmentation method for generating simulated signals based on the concept of counterfactuals. Firstly, disentangled representations and counterfactual faithful theory are applied to classify the original signal into two categories of properties. One is the fault semantics encoded from the original vibration signal. And the other is the sample attribute encoded by the encoder of Variational Autoencoders (VAEs). Secondly, the counterfactual faithful pseudo-samples are conjured through the Generative Adversarial Network(GAN) using the seeds of the 'factual' sample attributes and 'counterfactual' fault semantics to compensate for the drawback of distribution shift. Finally, the original samples and pseudo-samples are used as the CNN classifier dataset to realize bearing fault diagnosis. Experiments show that this method can generate counterfactual signals that are highly consistent with the original data distribution and can achieve better classification accuracy after balancing the dataset. © 2022 IEEE.

关键词： counterfactual faithfulness fault diagnosis rolling bearing structural causal model VAEGAN

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A METHOD FOR INCREMENTAL DISCOVERY OF FINANCIAL EVENT TYPES BASED ON ANOMALY DETECTION

arXiv

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arXiv 2023年

作者： Gu, Dianyue Li, Zixu Guan, Zhenhai Zhang, Rui Huang, Lan Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University Changchun China

Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints;at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios. This limitation of a small number of event types does not meet our research needs for more complex tasks such as the prediction of major financial events and the analysis of the ripple effects of financial events. In this paper, a three-stage approach is proposed to accomplish incremental discovery of event types. For an existing annotated financial event dataset, the three-stage approach consists of: for a set of financial event data with a mixture of original and unknown event types, a semi-supervised deep clustering model with anomaly detection is first applied to classify the data into normal and abnormal events, where abnormal events are events that do not belong to known types;then normal events are tagged with appropriate event types and abnormal events are reasonably clustered. Finally, a cluster keyword extraction method is used to recommend the type names of events for the new event clusters, thus incrementally discovering new event types. The proposed method is effective in the incremental discovery of new event types on real data sets. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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The default mode network is affected in the early stage of simian immunodeficiency virus infection:a longitudinal study

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Neural Regeneration Research 2023年第7期18卷 1542-1547页

作者： Zhen-Chao Tang Jiao-Jiao Liu Xue-Tong Ding Dan Liu Hong-Wei Qiao Xiao-Jie Huang Hui Zhang Jie Tian Hong-Jun Li Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijingChina Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina Beijing Youan Hospital Capital Medical UniversityBeijingChina Renji Hospital Shanghai Jiao Tong University School of MedicineShanghaiChina Institute of Laboratory Animal Sciences Chinese Academy of Medical SciencesBeijingChina

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode *** recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral ***,when brain imaging data at only one time point are analyzed,determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency ***,because infection times and other factors are often uncertain,longitudinal studies cannot be conducted directly in the ***,in this study,we performed a longitudinal study on the dynamic changes in the default mode network over time in a rhesus monkey model of simian immunodeficiency virus *** found marked changes in default mode network connectivity in 11 pairs of regions of interest at baseline and 10 days and 4 weeks after virus *** interactions between treatment and time were observed in the default mode network connectivity of regions of interest pairs area 31/V6.R and area 8/frontal eye field(FEF).L,area 8/FEF.L and caudal temporal parietal occipital area(TPOC).R,and area 31/V6.R and *** administered 4 weeks after infection not only interrupted the progress of simian immunodeficiency virus infection but also preserved brain function to a large *** findings suggest that the default mode network is affected in the early stage of simian immunodeficiency virus infection and that it may serve as a potential biomarker for early changes in brain function and an objective indicator for making early clinical intervention decisions.

关键词： acquired immune deficiency syndrome analysis of variance antiretroviral therapy default mode network functional magnetic resonance imaging human immunodeficiency virus longitudinal study rhesus monkeys simian immunodeficiency virus SIV-mac239

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Unsupervised Feature Selection for Multivariate Time Series based on Improved FINCH 6

Unsupervised Feature Selection for Multivariate Time Series ...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Wang, Ling Liu, Wei Chu, Zhongkun Shunde Innovation School University of Science and Technology of Beijing China School of Automation and Electrical Engineering University of Science and Technology of Beijing Key Laboratory of Knowledge Automation for Industrial Process Ministry of Education Beijing China

ISBN: (纸本)9798400709241

Feature selection is a crucial step in the preprocessing of data mining for multivariate time series. In order to remove irrelevant and redundant features in the multivariate time series, an unsupervised feature selection method based on improved First Integer Neighbor Clustering Hierarchy (FINCH) is proposed to select a compact subset of features containing the most important features. Firstly, the time-lagged cross-correlation (TLCC) is used to measure the redundancy between features, and Inverse Fast Fourier Transform (IFFT) is performed to reduce the computational complexity. Secondly, a new correlation-based clustering centroid and stopping criterion are defined to speed up hierarchical clustering. Finally, feature filtering is performed using the proposed improved FINCH to remove redundant features. Experiments on six benchmark datasets show that the proposed method achieve higher efficiency in dimensional reduction under the same experimental conditions. © 2023 ACM.

关键词： Feature Selection

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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