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Highly sensitive flexible strain sensor based on the two-dimensional semiconductor tellurium with a negative gauge factor

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Science China(information Sciences) 2024年第7期67卷 295-304页

作者： Jiarui HE Yusong QU Shengyao CHEN Cong WANG Lena DU Xiaoshan DU Yuanyuan ZHENG Guozhong ZHAO He TIAN College of Mathematics and Physics Beijing University of Chemical Technology School of Integrated Circuits Tsinghua University Beijing China & The Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University CAS center for Excellence in Nanoscience National Center for Nanoscience and Technology &University of Chinese Academy of Sciences beijing Key Lab of Terahertz Spectroscopy and Imaging Key Lab of Terahertz OptoelectronicsMinistry of Education Department of Physics Capital Normal University

research on flexible strain sensors has advanced rapidly in recent years, with particular attention being devoted to two-dimensional(2D) semiconductor materials owing to their exceptional mechanical and electrical properties that are conducive to sophisticated sensing performance. However, resistive strain sensors based on 2D semiconductor materials typically exhibit positive gauge factors(GF), while materials for strain sensors with a negative GF remain elusive. We have identified a trend of reduction in the band gap of the emerging 2D semiconductor material tellurium(Te) under strain in simulations reported in past research,and have observed a negative GF in the Te-based strain sensor. In this study, we combined Te with a flexible polyethylene terephthalate(PET) substrate to manufacture a flexible strain sensor with a significantly negative GF. The results of tests revealed that the Te-based strain sensor achieved an impressive maximum sensitivity of-139.7 within a small range of bending-induced strain(< 1%). Furthermore, it exhibited excellent linearity and good cyclic stability, and was successfully applied to monitor limb movements. The work here verifies the significant potential for the use of Te-based strain sensors in next-generation flexible electronics.

关键词： 2D semiconductor materials 2D tellurium flexibleelectronics strain sensors negative gauge factor high sensitivity

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Wireless semantic communication based on semantic matching multiple access and intent bias multiplexing

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The Journal of China Universities of Posts and Telecommunications 2024年第1期31卷 26-36页

作者： Ren Chao He Zongrui Sun Chen Li Haojin Zhang Haijun University of Science and technology beijing Beijing 100083China research and Development center Sony(China)LimitedBeijing 100027China

This paper proposes a multi-access and multi-user semantic communication scheme based on semantic matching and intent deviation to address the increasing demand for wireless users and data.The scheme enables flexible management of long frames,allowing each unit of bandwidth to support a higher number of users.By leveraging semantic classification,different users can independently access the network through the transmission of long concatenated sequences without modifying the existing wireless communication architecture.To overcome the potential disadvantage of incomplete semantic database matching leading to semantic intent misunderstanding,the scheme proposes using intent deviation as an advantage.This allows different receivers to interpret the same semantic information differently,enabling multiplexing where one piece of information can serve multiple users with distinct purposes.Simulation results show that at a bit error rate(BER)of 0.1,it is possible to reduce the transmission by approximately 20 semantic basic units.

关键词： semantic communication multiple access multiplexing multimodal communication

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Optimal Planning of Hybrid AC/DC Low-voltage Distribution Networks Considering DC Conversion of Three-phase Four-wire Low-voltage AC Systems

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 141-153页

作者： Bo Zhang Lu Zhang Wei Tang Gen Li Chen Wang the College of information and Electrical Engineering China Agricultural UniversityBeijing 100083China the Electric Energy Group Department of Engineering TechnologyTechnical University of Denmark(DTU)2750 BallerupDenmark the Power Distribution technology center State Grid Beijing Electric Power Research InstituteBeijing 100075China

The increasing integration of distributed household photovoltaics(PVs)and electric vehicles(EVs)may further ag gravate voltage violations and unbalance of low-voltage distribu tion networks(LVDNs).DC distribution networks can increase the accommodation of PVs and EVs and mitigate mutilple pow er quality problems by the flexible power regulation capability of voltage source converters.This paper proposes schemes to es tablish hybrid AC/DC LVDNs considering the conversion of the existing three-phase four-wire low-voltage AC systems to DC op eration.The characteristics and DC conversion constraints of typical LVDNs are analyzed.In addition,converter configura tions for typical LVDNs are proposed based on the three-phase four-wire characteristics and quantitative analysis of various DC configurations.Moreover,an optimal planning method of hybrid AC/DC LVDNs is proposed,which is modeled as a bi-level programming model considering the annual investments and three-phase unbalance.Simulations are conducted to verify the effectiveness of the proposed optimal planning method.Sim ulation results show that the proposed optimal planning method can increase the integration of PVs while simultaneously reduc ing issues related to voltage violation and unbalance.

关键词： Optimal planning low-voltage distribution net work three-phase unbalance DC conversion

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Automatic velocity picking based on optimal key points tracking algorithm

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Petroleum Science 2024年第2期21卷 903-917页

作者： Yong-Hao Wang Wen-Kai Lu Song-Bai Jin Yang Li Yu-Xuan Li Xiao-Feng Gu The Institute for Artificial Intelligence(THUAI) Tsinghua UniversityBeijing100084China State Key Laboratory of Intelligent technology and Systems Tsinghua UniversityBeijing100084China beijing National research center for information Science and technology(BNRist) Beijing100084China The Department of Automation Tsinghua UniversityBeijing100084China

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

关键词： Velocity picking Multi-object tracking Density clustering Kalman filter

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SAC-Based Computation Offloading for Reconfigurable Intelligent Surface-Aided Mobile Edge Networks

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China Communications 2024年第6期21卷 261-270页

作者： Li Bin Qian Zhen Fei Zesong School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China Jiangsu Collaborative Innovation center of Atmospheric Environment and Equipment technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing 210044 School of information and Electronics Beijing Institute of TechnologyBeijing 100081China

In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

关键词： MEC RIS soft actor-critic user mobility

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Apatite Fission-Track Thermochronology in the Tamusu Area,Bayingobi Basin,NW China,and its Geological Significance

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Acta Geologica Sinica(English Edition) 2024年第3期98卷 585-601页

作者： TONG Qinlong QIN Mingkuan YE Fawang CNNC beijing research Institute of Uranium Geology National Key Laboratory of Remote Sensing Information and Image Analysis TechnologyBeijing 100029China

The Bayingobi basin is located in the middle of Central Asia Orogenic Belt,at the intersection of Paleo-Asian Ocean and Tethys Ocean,as well as the junction of multiple tectonic plates.This unique tectonic setting underpins the basin's intricate history of tectonic activity.To unravel the multifaceted tectono-thermal evolution within the southwestern region of the basin and to elucidate the implications of sandstone-hosted uranium mineralization,granitic and clastic rock samples were collected from the Zongnai Mts.uplift and Yingejing depression,and apatite fission track(AFT)dating and thermal history simulation analysis were performed.AFT dating findings reveal that the apparent ages of all samples fall within the range of 244 Ma to 112 Ma.In particular,the bedrock of the Zongnai Mts.and Jurassic detrital apatite fission tracks have undergone complete annealing,capturing the uplift-cooling age.Meanwhile,the AFT ages of Cretaceous detrital rocks are either equivalent to or notably exceed the age of sedimentary strata,signifying the cooling age of the provenance.A comprehensive examination of AFT ages and palaeocurrent direction analyses suggests that the Cretaceous source in the Tamusu area predominantly originated from the central and southern sectors of the Zongnai Mts.uplift.However,at a certain juncture during the Late Early Cretaceous,the Cretaceous provenance expanded to include the northern part of the Zongnai Mts.uplift.Based on the results of thermal history simulations and previous studies,it is considered that the Tamusu area has undergone four distinct tectonic uplift events since the Late Paleozoic.The first is the Late Permian to Early Triassic(260-240 Ma),which is associated with the closure of the Paleo-Asian Ocean and the accretionary orogeny within the Alxa region.The second uplift event took place in the Early Jurassic(190-175 Ma)and corresponded to intraplate orogeny following the closure of the Paleo-Asian Ocean.The third uplift event is the Late Jurassic to Early Cretaceous(160-120 Ma),which

关键词： apatite fission-track tectono-thermal history sandstone-hosted uranium deposit Bayingobi basin Central Asian Orogenic Belt

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of information technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland research center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese mainland

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Infectious Diseases of Poverty 2024年第1期13卷 27-41页

作者： Xiaohan Si Liping Wang Kerrie Mengersen Wenbiao Hu Ecosystem Change and Population Health research Group School of Public Health and Social WorkQueensland University of TechnologyBrisbaneQLD 4059Australia information center Chinese Center for Disease Control and PreventionBeijing 102206China School of Mathematical Sciences Queensland University of TechnologyBrisbaneQLD 4000Australia

Background Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes.However,research on seasonal influenza activities based on climate zones are still in lack.This study aims to utilize the ecological-based Koppen Geiger climate zones classif-cation system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese mainland and assess the feasibility of developing an early warning system.Methods Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report information System.Epidemic temporal indices,time series seasonality decomposition,spatial modelling theories including Moran's/and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.Results All climate zones had peaks in Winter-Spring season.Arid,desert,cold(BWk)showed up the first peak.Only Tropical,savannah(Aw)and Temperate,dry winter with hot summer(Cwa)zones had unique summer peak.Temper-ate,no dry season and hot summer(Cfa)zone had highest average incidence rate(IR)at 1.047/100,000.The Global Moran's/showed that average IR had significant clustered trend(z=53.69,Presearch finds will provide scientific evidence for developing seasonal infl

关键词： Seasonal influenza Koppen Geiger climate zones classification system Chinese mainland Seasonality decomposition Local indicators of spatial association

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Temporal and spatial evolution of global major grain trade patterns

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Journal of Integrative agriculture 2024年第3期23卷 1075-1086页

作者： Ziqi Yin Jiaxuan Hu Jing Zhang Xiangyang Zhou Lingling Li Jianzhai Wu Agricultural information Institute Chinese Academy of Agricultural SciencesBeijing 100081China Institute of Scientific and Technical information of China Beijing 100038China Zibo Agricultural Science research Institute Zibo 255000China Zibo Institute for Digital agriculture and Rural research Zibo 255000China

The complex and volatile international landscape has significantly impacted global grain supply security. This study uses a complex network analysis model to examine the evolution and trends of the global major grain trade from 1990 to 2020, focusing on network topology, centrality ranking, and community structure. There are three major findings. First, the global major grain trade network has expanded in scale, with a growing emphasis on diversification and balance. During the study period, the United States, Canada, China, and Brazil were the core nodes of the network. Grain-exporting countries were mainly situated in Asia, the Americas, and Europe, and importing countries in Asia, Africa, and Europe. Second, a significant increase in the number of high centrality countries with high export capacity occurred, benefiting from natural advantages such as fertile land and favorable climates. Third, the main global grain trade network is divided into four communities, with the Americas-Europe community being the largest and most widespread. The formation of the community pattern was influenced by geographic proximity, driven by the core exporting countries. Therefore, the world needs to enhance the existing trade model, promote the multi-polarization of the grain trade network, and establish a global vision for the future community. Countries and regions should participate actively in global grain trade security governance and institutional reform, expand trade links with other countries, and optimize import and export policies to reduce trade risks.

关键词： grain trade pattern evolution complex network

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development research center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of information Science and technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.However,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection.Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key.Additionally,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under LDP.Initially,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods.Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs.Theoretically,we demonstrate that the variance of KS-UE is lower than that of existing methods.These claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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