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Origin of enhanced charge density wave in the kagome superconductor CsV3−xMoxSb5

Physical Review B 2024年第5期110卷 054518页

作者： Jiangpeng Song Tao Han Zekun Zhou Qingge Mu Xingyuan Hou Zongyuan Zhang Yubing Tu Lei Shan School of Materials Science and Engineering Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education

The topological kagome superconductor CsV3Sb5 exhibits rich quantum phenomenology of correlated electronic phases including unconventional charge order and superconductivity. Understanding how the singularities inherent to the kagome lattice are linked to the observed many-body phases is a topic of great interest. Here, by using Shubnikov–de Haas oscillation measurements, we report the detailed evolution of electronic band structures in CsV3−xMoxSb5 single crystals, where Mo substitution causes a suppression of superconductivity and an enhanced charge density wave. The obvious decrease of oscillation frequency (Fβ) corresponding to the nontrivial band from 72 to 59 T and smaller cyclotron effective mass (∼0.069me) reveal that the van Hove singularities from the vanadium orbitals near M are abnormally lifted and promote the nesting condition for the charge density wave. Meanwhile, the electric conduction changes from a hole-dominated multiband feature to a single electron band feature and the anomalous Hall effect becomes stronger. In conjunction with the simple schematics of band structures, the promoted Fermi-surface nesting is dominant in the unusual enhanced charge density wave, offering insight to comprehend the delicate interaction between the intertwined orders in this kagome system.

关键词： Charge density waves Doping effects Fermi surface Quantum transport Superconductors

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Synergistically enhancing cycleability and rate performance of sodium titanate nanowire anode via hydrogenation and carbon coating for advanced sodium ion batteries

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Rare Metals 2022年第12期41卷 4075-4085页

作者： De-Liang Ba Wei-Hua Zhu Yuan-Yuan Li Jin-Ping Liu School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China School of Chemistry Chemical Engineering and Life ScienceState Key Laboratory of Advanced Technology for Materials Synthesis and ProcessingWuhan University of TechnologyWuhan 430070China State Center for International Cooperation on Designer Low-Carbon and Environmental Materials and School of Materials Science and Engineering Zhengzhou UniversityZhengzhou 450001China

Layered alkali-metal titanate materials are considered as attractive anodes for sodium ion batteries due to their favorable safety and low ***,their practical implementation faces major challenges of low electronic conductivity and inevitable volume variation during Na^(+)intercalation and de-intercalation,which are generally difficult to conquer by a single modification ***,a synergistic ally enhancing strategy to promote the electrochemical performance of Na_(2)Ti_(2)O_(5)nanowire array anode via simultaneous hydrogenation and carbon coating is *** leads to partially reduced titanium;together with conductive carbon layer,it endows Na_(2)Ti_(2)O_(5)with fast electron transport and structural *** resulting H-Na_(2)Ti_(2)O_(5)@C anode exhibits enhanced rate capability(8.0C,165 mAh·g^(-1))and stable cycle performance up to 1000 times in sodium-ion half-cells(the capacity of H-Na_(2)Ti_(2)O_(5)without carbon fades drastically after only 100 cycles).In addition,a newcoupling full cell is further designed with graphene hybridized high-voltage Na_(3)(VO_(0.5))_(2)(PO4)_(2)F_(2)as cathode,capable of delivering a high specific energy density of 212.1 Wh·kg^(-1)(based on the mass of both anode and cathode)and good rate and cycling *** work may offer inspiration for synergistic optimization of electrode materials for advanced electrochemical energy storage devices.

关键词： Sodium ion batteries(SIBs) Na2Ti2O5 nanowire Hydrogenation Carbon coating Enhanced performance

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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer

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Complex & Intelligent systems 2025年第8期11卷 1-14页

作者： Wang, Chengdong Wang, Jianming Gao, Wenbo Guo, Lei School of Computer and Software Engineering Anhui Institute of Information Technology Wuhu China Yunnan Key Laboratory of Computer Technology Applications Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.

关键词： Pedestrian trajectory prediction Social Interaction Transformer GCN

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The motor-clutch model in mechanobiology and mechanomedicine

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Mechanobiology in Medicine 2024年第3期2卷 22-30页

作者： Zhao Xu Feng Xu Bo Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and TechnologyXi'an Jiaotong UniversityShaanxi710049PR China Bioinspired Engineering and Biomechanics Center(BEBC) Xi'an Jiaotong UniversityShaanxi710049PR China

Cellular behaviors such as migration,spreading,and differentiation arise from the interplay of cell–matrix *** comprehension of this interplay has been advanced by the motor-clutch model,a theoretical framework that captures the binding-unbinding kinetics of mechanosensitive membrane-bound proteins involved in mechanochemical signaling,such as *** its introduction and subsequent development as a computational tool,the motor clutch model has been instrumental in elucidating the impact of biophysical factors on cellular *** review aims to provide a comprehensive overview of recent advances in the motor-clutch modeling framework,its role in elucidating the relationships between mechanical forces and cellular processes,and its potential applications in mechanomedicine.

关键词： Mechanobiology Mechanomedicine Molecular-clutch model Integrin Cell migration Cell behavior

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Enhanced Shack–Hartmann wavefront sensor with propagation correction for freeform mirror measurement

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Optics Letters 2025年第11期50卷 3708-3711页

作者： Yin, Xuao Chen, Peng Ma, Xiaorui Wu, Yunjie Cheng, Qiang Wang, Shixiang Kong, Lingbao Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing School of Information Science and Technology Fudan University Shanghai200438 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Advanced Manufacturing for Optical Systems Changchun130033 China

The Shack–Hartmann wavefront sensor (SHWS) is widely used due to its efficiency and reliability. Its accuracy in freeform mirror measurement is limited by wavefront propagation distortions and inaccuracies in conventional reflection mapping models. This Letter presents a propagation-correction SHWS (PC-SHWS) to address these challenges. The proposed method establishes a wavefront propagation-correction model (PC model) that combines wavefront backward tracing and phase-conjugate reflection wavefront mapping. Both simulations and experiments confirm the enhanced accuracy and stability of the proposed approach. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： 3D printing Beam splitters Microlens arrays Optical components Optical systems Wavefront distortions

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Visual analysis and frontier predictions of industrial big data

Visual analysis and frontier predictions of industrial big d...

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2022 Global Conference on Robotics, Artificial Intelligence and information technology, GCRAIT 2022

作者： Dou, Xin Wu, Jiajing Xu, Jiali Jia, Dongning School of Information Science and Engineering Ocean University of China Qingdao China Pilot National Laboratory for Marine Science and Technology Qingdao China School of Information Science and Engineering Ocean University of China Pilot National Laboratory for Marine Science and Technology Qingdao China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

With the numerous development and continuous optimization of industrial technology, industrial big data is faced with the challenge of massive volume, multi-source, and heterogeneity. Industrial big data achievements present an exponential trend of growth because of the development strategy of national industry. Currently, massive scholars devote themselves to industrial big data researches, but the consistency of analysis and management on industrial big data results is inadequate, especially with the assistance of bibliometrics methods and visualization tools to intuitively and systematically demonstrate the regularity and value of data. It uses CiteSpace to conduct bibliometric analysis on the published literature (1901-2020) from the Web of science Core Collection with a subject search related to industrial big data. In total, 1352 papers are synthetically reviewed by collaboration networks and co-citation networks involving countries, institutions, authors, journals, keywords, references. The objection of this study is to reveal the present status and potential frontiers for researchers in the field of industrial big data, providing them with valuable information and efficient guidance. © 2022 IEEE.

关键词： Big data

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Blockchain and Quantum-based Collaborative Communication Framework for Telehealth

Blockchain and Quantum-based Collaborative Communication Fra...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Gajjar, Harshal Jivani, Dirgha Trivedi, Chinmay Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

This paper introduces a novel telehealth communication system, designed to enhance the security and integrity of medical data exchange. In the rapidly evolving digital healthcare landscape, the protection of sensitive patient information is paramount. To address this, our system uniquely combines quantum cryptography, specifically the BB84 protocol, with blockchain technology, offering a dual-layered security framework. The Quantum Layer, underpinned by the BB84 protocol, establishes quantum-secure communication channels, effectively encrypting data exchanges between patients, doctors, and hospitals. This layer guarantees that medical information remains confidential and safe from potential quantum-level eavesdropping threats. The subsequent Blockchain Layer further strengthens the system by storing these encrypted communications in an immutable blockchain ledger. This approach not only secures the data against unauthorized alterations but also provides a transparent and permanent record of all transactions, thereby enhancing the auditability of medical communications. © 2024 IEEE.

关键词： Electronic health record

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The Development and Industry Risks of New Energy Vehicles in China

The Development and Industry Risks of New Energy Vehicles in...

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2022 International Conference on Statistics, Data science, and Computational Intelligence, CSDSCI 2022

作者： Tang, Xi-Ru Wang, Bin Institute of Urban System Engineering Beijing Academy of Science and Technology Beijing100035 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510661325

The development of the new energy automobile industry is an important direction to accelerate the construction of a powerful automobile country, and it is also an effective means to break the constraints of energy and environment, which is of great strategic significance to China. In this article, based on the perspective of the development of the overall new energy vehicle industry, we analyze the market size and development stage of China's new energy vehicle industry, and explore the characteristics of the industry's development. Further, this paper analyzes the industry risks of the future development of new energy vehicles under the background of the imminent withdrawal of government financial subsidies and the transition of the new energy vehicle industry from policy-driven to market-driven, and provides theoretical support for the healthy and sustainable development of Chinese new energy vehicle industry. © 2023 SPIE.

关键词： Automotive industry

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Fault Diagnosis of Chiller Based on ALSTM-FCN

Fault Diagnosis of Chiller Based on ALSTM-FCN

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2024 International Conference on Artificial Intelligence and Power systems, AIPS 2024

作者： Gao, Xuejin Jiang, Yiwei Han, Huayun Faculty of Information Technology Beijing University of Technology Beijing100124 China Engineering Research Center of Ministry of Education Beijing100124 China Beijing Laboratory of Association of Metros Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350392210

Maintaining the effective operation of chillers depends heavily on prompt and precise fault diagnosis. A chiller fault diagnosis method based on attention and long and short-term memory fully convolutional neural network (ALSTM-FCN) was proposed in light of the nonlinearity of chillers' operation data and the challenge of fault diagnosis models in identifying effective features, which results in low model accuracy. First, an FCN fault diagnosis model was established according to the feature selection results, and the proposed Lastly, the attention mechanism was included to the model with the intention of emphasizing the significance of features and enhancing the accuracy of chiller problem diagnosis. Furthermore, the ASHRAE 1043-RP dataset was used to confirm the efficacy of the suggested approach. © 2024 IEEE.

关键词： Convolutional neural networks

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