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N-doped graphene quantum dot-decorated N-TiO2/P-doped porous hollow g-C_(3)N_(4) nanotube composite photocatalysts for antibiotic photodegradation and H2 production

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International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 165-178页

作者： Jingshu Yuan yao zhang Xiaoyan zhang Junjie zhang Shen’gen zhang Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China Xi’an Fanyi University Xi’an 710105China Shunde Graduate School of University of Science and Technology Beijing Foshan 528399China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao SAR 820000China

Exclusive responsiveness to ultraviolet light (~3.2 eV) and high photogenerated charge recombination rate are the two primary drawbacks of pure TiO_(2). We combined N-doped graphene quantum dots (N-GQDs), morphology regulation, and heterojunction construction strategies to synthesize N-GQD/N-doped TiO_(2)/P-doped porous hollow g-C_(3)N_(4) nanotube (PCN) composite photocatalysts (denoted as G-TPCN). The optimal sample (G-TPCN doped with 0.1wt% N-GQD, denoted as 0.1% G-TPCN) exhibits significantly enhanced photoabsorption, which is attributed to the change in bandgap caused by elemental doping (P and N), the improved light-harvesting resulting from the tube structure, and the upconversion effect of N-GQDs. In addition, the internal charge separation and transfer capability of0.1% G-TPCN are dramatically boosted, and its carrier concentration is 3.7, 2.3, and 1.9 times that of N-TiO_(2), PCN, and N-TiO_(2)/PCN(TPCN-1), respectively. This phenomenon is attributed to the formation of Z-scheme heterojunction between N-TiO_(2) and PCNs, the excellent electron conduction ability of N-GQDs, and the short transfer distance caused by the porous nanotube structure. Compared with those of N-TiO_(2), PCNs, and TPCN-1, the H2 production activity of 0.1%G-TPCN under visible light is enhanced by 12.4, 2.3, and 1.4times, respectively, and its ciprofloxacin (CIP) degradation rate is increased by 7.9, 5.7, and 2.9 times, respectively. The optimized performance benefits from excellent photoresponsiveness and improved carrier separation and migration efficiencies. Finally, the photocatalytic mechanism of 0.1% G-TPCN and five possible degradation pathways of CIP are proposed. This study clarifies the mechanism of multiple modification strategies to synergistically improve the photocatalytic performance of 0.1% G-TPCN and provides a potential strategy for rationally designing novel photocatalysts for environmental remediation and solar energy conversion.

关键词： N-doped TiO_(2) N-doped graphene quantum dots P-doped g-C_(3)N_(4) porous hollow nanotube heterojunction photocatalysis

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A state-of-the-art review of the development of self-healing concrete for resilient infrastructure

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Frontiers of Structural and Civil Engineering 2024年第2期18卷 151-169页

作者： Dong LU Xi JIANG yao zhang Shaowei zhang Guoyang LU Zhen LENG Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China Laboratory of Fire Safety School of Safety Engineering and Emergency ManagementShijiazhuang Tiedao UniversityShijiazhuang 050043China Department of Architecture and Civil Engineering City University of Hong KongHong Kong 999077China

The brittleness of cement composites makes cracks almost inevitable,producing a serious limitation on the lifespan,resilience,and safety of concrete *** address this brittleness,self-healing concrete has been developed for regaining its mechanical and durability properties after becoming cracked,thereby promising sustainable development of concrete *** paper provides a comprehensive review of the latest developments in self-healing *** begins by summarizing the methods used to evaluate the self-healing efficiency of ***,it compares strategies for achieving healing *** then discusses the typical approaches for developing self-healing ***,critical insights are proposed to guide future studies on the development of novel self-healing *** review will be useful for researchers and practitioners interested in the field of self-healing concrete and its potential to improve the durability,resilience,and safety of concrete infrastructure.

关键词： resilient infrastructure sustainable concrete self-healing cracks

Simulation and reconstruction of particle trajectories in the CEPC drift chamber

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Nuclear Science and Techniques 2024年第8期35卷 9-22页

作者： Meng‑yao Liu Wei‑Dong Li Xing‑Tao Huang yao zhang Tao Lin Ye Yuan Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China Institute of High Energy Physics Beijing 100049China University of Chinese Academy of Sciences Beijing 100049China

The circular electron-positron collider(CEPC)is designed to precisely measure the properties of the Higgs boson,study electroweak interactions at the Z-boson peak,and search for new physics beyond the Standard *** a component of the 4th conceptual CEPC detector,the drift chamber facilitates the measurement of charged *** study implemented a Geant4-based simulation and track reconstruction for the drift *** the simulation,detector construction and response were implemented and added to the CEPC simulation *** development of track reconstruction involves track finding using the combinatorial Kalman filter method and track fitting using the tool of *** the simulated data,the tracking performance was *** results showed that both the reconstruction resolution and tracking efficiency satisfied the requirements of the CEPC experiment.

关键词： CEPC Drift chamber Detector simulation Track reconstruction

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GP‐FMLNet:A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 960-972页

作者： Jing Li Dezheng zhang Yonghong Xie Aziguli Wulamu yao zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology BeijingBeijingChina University of Alberta EdmontonAlbertaCanada

Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified *** methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information,making their performance less than *** resolve the problem,the authors propose a new method,GP‐FMLNet,that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph *** method solves the problem of misspelling words influencing sentiment polarity prediction ***,the authors iteratively mine character,glyph,and pinyin features from the input comments ***,the authors use soft attention and matrix compound modules to model the phonetic features,which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ***-periments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese senti-ment analysis algorithms.

关键词： aspect‐level sentiment analysis deep learning feature extraction glyph and phonetic feature matrix compound learning

Emergence of machine language: towards symbolic intelligence with neural networks

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National Science Review 2024年第4期11卷 27-30页

作者： Yuqi Wang Xu-yao zhang Cheng-Lin Liu Tieniu Tan Zhaoxiang zhang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences

Representation learning is a core issue in artificial intelligence (AI). Currently,there exists a disparity in the choice of representation between humans and machines. Humans rely on discrete language for communicati...

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Unified Classification and Rejection:A One-versus-all Framework

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Machine Intelligence Research 2024年第5期21卷 870-887页

作者： Zhen Cheng Xu-yao zhang Cheng-Lin Liu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China

Classifying patterns of known classes and rejecting ambiguous and novel(also called as out-of-distribution(OOD))inputs are involved in open world pattern *** neural network models usually excel in closed-set classification while perform poorly in rejecting OOD *** tackle this problem,numerous methods have been designed to perform open set recognition(OSR)or OOD rejection/detection *** methods mostly take post-training score transformation or hybrid models to ensure low scores on OOD inputs while separating known *** this paper,we attempt to build a unified framework for building open set classifiers for both classification and OOD *** formulate the open set recognition of K-known-class as a(K+1)-class classification problem with model trained on known-class samples *** decomposing the K-class problem into K one-versus-all(OVA)binary classification tasks and binding some parameters,we show that combining the scores of OVA classifiers can give(K+1)-class posterior probabilities,which enables classification and OOD rejection in a unified *** maintain the closed-set classification accuracy of the OVA trained classifier,we propose a hybrid training strategy combining OVA loss and multi-class cross-entropy *** implement the OVA framework and hybrid training strategy on the recently proposed convolutional prototype network and prototype classifier on vision transformer(ViT)*** on popular OSR and OOD detection datasets demonstrate that the proposed framework,using a single multi-class classifier,yields competitive performance in closed-set classification,OOD detection,and misclassification *** code is available at https://***/zhen-cheng121/CPN_OVA_unified.

关键词： Open set recognition out-of-distribution detection misclassification detection convolutional prototype network oneversus-all Dempster-Shafer theory of evidence

Numerical Simulations of Snow Accumulation in the Bogie Region of a Train Considering Snow Particle Rotation

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Fluid Dynamics & Materials Processing 2024年第10期20卷 2337-2352页

作者： Hong Lan Jiye zhang yao zhang Lu Cai State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu610031China College of Railway Transportation Hunan University of TechnologyZhuzhou412000China GD Midea Air-Conditioning Equipment Co. Ltd.Foshan528311China

To investigate the influence of snow particle rotational motion on the accumulation of snow in the bogie region of high-speed trains,an Euler‒Lagrange numerical approach is *** study examines the effects of snow particle diameter and train speed on the ensuing *** is shown that considering snow particle rotational motion causes significant deviation in the particle trajectories with respect to non-rotating *** a deviation increases with larger snow particle diameters and higher train *** snow accumulation on the overall surface of the bogie increases,and the amount of snow on the vibration reduction device varies *** certain conditions,the amount of accumulated snow can increase by several orders of magnitudes.

关键词： High-speed train bogie snow particle rotation discrete phase model snow accumulation

Acoustic emission characteristics of damage evolution of multi-scale fiber reinforced rubberized concrete under uniaxial compression and tension after being subjected to high temperatures

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Frontiers of Structural and Civil Engineering 2024年第8期18卷 1237-1266页

作者： Shaoqi zhang yao zhang Qianru LEI Yumeng YANG Yichao WANG Fei XU Zhiguo YAN Hehua ZHU Laboratory of Fire Safety School of Safety Engineering and Emergency ManagementShijiazhuang Tiedao UniversityShijiazhuang 050043China Structural Health Monitoring and Control Key Laboratory of Hebei Province Shijiazhuang Tiedao UniversityShijiazhuang 050043China Department of Geotechnical Engineering Tongji UniversityShanghai 200092China

Recently developed multi-scale fiber(i.e.,CaCO3 whisker,polyvinyl alcohol(PVA)fiber,and steel fiber)reinforced rubberized concrete exhibits excellent mechanical properties and spalling resistance at high *** of macro properties such as strength and Young’s modulus cannot reveal and characterize damage mechanisms,particularly those relating to the multi-scale fiber strengthening *** this study,acoustic emission(AE)technology is applied to investigate the impact of multi-scale fiber on the damage evolution of rubberized concrete exposed to high temperatures,under the uniaxial compression and tension loading *** mechanical properties,AE event location,peak frequency,b-value,the ratio of rise time to amplitude(RA),average frequency(AF)values,and AE energy of specimens are *** results show that the number of events observed using AE gradually increases as the loading *** crumb rubber and fibers inhibit the generation and development of the *** is concluded that both the peak frequency and b-value reflect the extension process of *** the cracks develop from the micro scale to the macro scale,the peak frequency tends to be distributed in a lower frequency range,and the b-value decreases *** the peak stress point,the AE energy increases rapidly and the b-value *** specimens without multi-scale fibers exhibit brittle failure,while the specimens with fibers exhibit ductile *** addition,adding multi-scale fibers and crumb rubber increases the peak frequency in the medium and high frequency ranges,indicating a positive effect on inhibiting crack *** being subjected to high temperatures,the maximum and minimum b-values decrease,reflecting an increase in the number of initial cracks due to thermal ***,the RA and AF values are used to classify tensile and shear *** specimens fracture with more shear cracks under compression,and there are more tensile

关键词： multi-scale fibers acoustic emission fracture damage evolution rubberized concrete

Differentially Private Support Vector Machines with Knowledge Aggregation

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Computers, Materials & Continua 2024年第3期78卷 3891-3907页

作者： Teng Wang yao zhang Jiangguo Liang Shuai Wang Shuanggen Liu School of Cyberspace Security Xi’an University of Posts and TelecommunicationsXi’an710121China

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to *** vector machine(SVM)is one of the most elementary learning models of machine *** issues surrounding SVM classifier training have attracted increasing *** this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy *** in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of ***,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data *** at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy ***,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data *** experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

关键词： Differential privacy support vector machine knowledge aggregation data utility