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science Bulletin 2024年第18期69卷 2833-2841页

作者： LHAASO Collaboration Zhen Cao F.Aharonian Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi W.Bian A.V.Bukevich Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen H.X.Chen Liang Chen Lin Chen long Chen M.J.Chen M.L.Chen Q.H.Chen S.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang J.H.Fang K.Fang C.F.Feng H.Feng L.Feng S.H.Feng X.T.Feng Y.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han M.Hasan H.H.he H.N.he J.Y.he Y.he Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang X.L.Ji H.Y.Jia K.Jia K.jiang X.W.jiang Z.J.jiang M.Jin M.M.Kang I.Karpikov D.Kuleshov K.Kurinov B.B.Li C.M.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li Jian Li Jie Li K.Li S.D.Li W.L.Li W.L.Li X.r.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu D.B.Liu H.Liu H.D.Liu J.Liu J.L.Liu M.Y.Liu r.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu Q.Luo Y.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.r.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov L.J.Ou p.pattarakijwanich Z.Y.pei J.C.Qi M.Y.Qi B.Q.Qiao J.J.Qin A.raza D.ruffolo A.Sáiz M.Saeed D.Semikoz L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su D.X.Sun Q.N.Sun X.N.Sun Z.B.Sun J.Takata p.H.T.Tam Q.W.Tang r.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang Kai Wang Kai Wang L.p.Wang L.Y.Wang p.H.Wang r.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.r.Wu Q.W.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia G.M.Xiang D.X.Xiao G.Xiao Y.L.Xin Y.Xing D.r.Xiong Z.Xiong D.L.Xu r.F.Xu r.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang C.Y.Yang F.Yang F.F.Yang L.L.Yang M.J.Yang r.Z.Yang W.X.Yang Y.H.Yao Z.G.Yao L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang H.Zhang 不详 Key Laboratory of particle Astrophysics&Experimental physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of sciences Beijing 100049China TIANFU Cosmic ray research Center ChengduChina Dublin institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-planck-Institut for Nuclear physics P.O.Box 103980Heidelberg 69029Germany school of physical science and technology&school of information science and technology Southwest Jiaotong UniversityChengdu 610031China school of Astronomy and Space science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Tsung-Dao Lee institute&school of physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China institute for Nuclear research of russian Academy of sciences Moscow 117312Russia hebei Normal University Shijiazhuang 050024China University of science and technology of china Hefei 230026China State Key Laboratory of particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China Key Laboratory for research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China school of physics and Astronomy Yunnan UniversityKunming 650091China Key Laboratory of Cosmic rays(Tibet University) Ministry of EducationLhasa 850000China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China school of physics and Astronomy(Zhuhai)&school of physics(Guangzhou)&Sino-French institute of Nuclear Engineering and technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275GuangdongChina institute of Frontier and Interdisciplinary science Shandong UniversityQingdao 266237China ApC UniversitéParis Cité

Theγ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays(Crs)accelerated by the shock of supernova remnant(SNr)W51C and the dense molecular clouds in the adjacent star-forming region,***,the maximum acceleration capability of W51C for Crs remains *** on observations conducted with the Large High Altitude Air Shower Observatory(LHAASO),we report a significant detection ofγrays emanating from the W51 complex,with energies from 2 to 200 *** LHAASO measurements,for the first time,extend theγ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of *** combining the"π^(0)-decay bump"featured data from Fermi-LAT,the broadbandγ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision *** observed spectral bending feature suggests an exponential cutoff at~400 TeV or a power-law break at~200 TeV in the Cr proton spectrum,most likely providing the first evidence of SNrs serving as Cr accelerators approaching the peV ***,two young star clusters within W51B could also be theoretically viable to produce the most energeticγrays observed by *** findings strongly support the presence of extreme Cr accelerators within the W51 complex and provide new insights into the origin of Galactic Crs.

关键词： Uhe c-ray Cosmic rays SNr W51C Star clusters

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the school of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the shandong Key Laboratory of Smart Transportation(preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

Frontier research of ultra-high-speed ultra-large-capacity and ultra-long-haul optical transmission

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Frontiers of Optoelectronics 2016年第2期9卷 123-137页

作者： Daojun XUE Shaohua YU Qi YANG Nan CHI Lan rAO xiangjun XIN Wei LI Songnian FU Sheng CUI Demin LIU Zhuo LI Aijun WEN Chongxiu YU Xinmei WANG State Key Laboratory of Optical Communication Technologies and Networks Wuhan Research Institute of Posts and Telecommunications Wuhan 430074 China school of information science and technology Fudan University Shanghai 200433 China State Key Laboratory of information photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China school of Optics and Electronic information Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710126 China

Ultra-high-speed, ultra-large-capacity and ultra-long-haul （3U） are the forever pursuit of optical communication. As a new mode of optical communication, 3U transmission can greatly promote next generation optical internet and broadband mobile communication network development and technological progress, therefore it has become the focus of international high-tech intellectual property competition ground. This paper introduces the scientific problems, key technologies and important achievements in 3U transmission research.

关键词： ultra-high-speed ultra-large-capacity ultra-long-haul optical transmission high spectral efficiency,parametric amplification dispersion management

Examining health of Wetlands with Multiple Ecosystem Services as Targets in china’s Coastal regions

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Chinese Geographical science 2020年第4期30卷 600-613页

作者： ZHOU Yangming DOU Yuehan YU Xiubo ZHANG Li HUANG Chong WANG Yuyu LI Xiaowei LI he JIA Yifei BAKKEr Martha CArSJENS Gerrit Jan ZHOU Yan DUAN Houlang school of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Land Use planning Group Wageningen University and ResearchWageningen 6700HBthe Netherlands Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of resources and Environment University of Chinese Academy of SciencesBeijing 100190China Key Laboratory for Biodiversity science and Ecological Engineering Ministry of EducationCollege of Life SciencesBeijing Normal UniversityBeijing 100875China State Key Laboratory of resources and Environment information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China school of Nature Conservation Beijing Forestry UniversityBeijing 100083China Key Laboratory of Coastal Environmental processes and Ecological remediation Yantai Institute of Coastal Zone ResearchChinese Academy of SciencesYantai Shandong 264003China College of Biology and Environment Nanjing Forestry UniversityNanjing 210037China

Coastal zones are key interconnectors of terrestrial and marine *** to the degradation and fragmentation of coastal wetlands,there is an urgent need to develop assessment methodology to compare the health of wetland ecosystems at different spatial *** will help efficiently develop and implement protections using easy-to-access ecosystem health *** study aims to understand the spatial distribution of coastal and inland wetland health for china’s coastal regions.A Wetland health Index(WHI)was developed to provide a basis for policy and decision *** utilization models—long Term Model,Open Model,Nature reserve Model,and protected and Economic Model—were defined in the context of china’s coastal regions to specifically examine wetland *** show that the average WHI score was 63.6 with the range of 44.8–84.3 for 35 National Nature reserves(NNrs),and the southern NNrs generally performed better than the northern *** wetlands in the southern provinces/municipalities are relatively healthier than their northern *** competent authority has slight influence on WHI scores but duration of conservation establishment does not show a clear *** increasing economic activity,the differences in health conditions(WHI scores)of china’s coastal regions also *** Domestic product(GDp)or economic intensification does not relate to WHI *** trade-offs between wetland management and economic development could contribute to improve health conditions,conservation and utilization of coastal and inland wetlands.

关键词： Wetland health Index(WHI) ecosystem services wetland utilization coastal zones

退役工业固废填埋场地再利用的全寿命环境风险特征

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中国环境科学 2020年第12期40卷 5511-5519页

作者：能昌信唐美琴徐亚董路刘玉强刘景财杨健刘凡贺亚楠山东工商学院信息与电子工程学院山东烟台264005 中国环境科学研究院环境基准与风险评估国家重点实验室中国环境科学研究院固体废物污染控制技术研究所北京100012

通过现场采样和过程模型模拟等方法研究了典型退役工业固废填埋场地(DISWL)原位开发条件下的健康风险及长期演化规律.结果表明,经过近20a的浸出和降解,86%的废物浸出浓度依然处于有害水平,70%的废物不宜直接作为建设用地土壤.直接作为... 详细信息

通过现场采样和过程模型模拟等方法研究了典型退役工业固废填埋场地(DISWL)原位开发条件下的健康风险及长期演化规律.结果表明,经过近20a的浸出和降解,86%的废物浸出浓度依然处于有害水平,70%的废物不宜直接作为建设用地土壤.直接作为建筑用地开发利用条件下,由于DISWL的性能退化会导致有害组分的浸出和渗漏增加,由此导致地下水水质超标概率经历从无(短期)到有(中期,个别物质如总氰化物T-CN和易释放氰化物F-CN),再到后期的较大概率超标(T-CN和F-CN)的渐变过程;同时,场地利用过程的健康风险也逐步增加,来自于As的致癌风险和自于T-CN的非致癌风险,分别超过风险可接受水平的81~179倍和55.32~224.3倍.上述结果提示DISWL场地开发再利用的风险评估和管控策略应重点考虑长期风险.对于长期风险不可接受的场地,通过降低废物中毒性物质的浸出浓度可实现长期风险可接受,并提出了相应浸出浓度限值的计算框架和方法.

关键词：退役工业固体废物填埋场填埋场地原位开发健康风险风险管控策略

煤矿智能化(初级阶段)技术体系研究与工程进展

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煤炭科学技术 2020年第7期48卷 1-27页

作者：王国法任怀伟庞义辉曹现刚赵国瑞陈洪月杜毅博毛善君徐亚军任世华程建远刘思平范京道吴群英孟祥军杨俊哲余北建宣宏斌孙希奎张殿振王海波中煤科工开采研究院有限公司北京100013 天地科技股份有限公司北京100013 西安科技大学陕西西安710054 辽宁工程技术大学辽宁阜新123000 北京大学地球与空间科学学院北京100871 煤炭工业规划设计研究院有限公司北京100120 中煤科工集团西安研究院有限公司陕西西安710077 北京交通大学北京100044 陕西煤业化工集团有限责任公司陕西西安710065 陕西陕煤陕北矿业有限公司陕西神木719301 兖矿集团有限公司山东邹城273500 神华神东煤炭集团有限责任公司陕西神木719315 阳泉煤业(集团)有限责任公司山西阳泉045000 大同煤矿集团有限责任公司山西大同037003 淄博矿业集团有限责任公司山东淄博272000 山东能源新汶矿业集团有限责任公司山东新泰271200 中煤科工集团常州研究院有限公司江苏常州213015

煤炭是实现清洁高效利用的最经济、最可靠的能源,煤炭资源的智能、安全、高效开发与低碳清洁利用是实现我国煤炭工业高质量发展的核心技术支撑。基于我国煤矿智能化初级阶段的发展要求,开展了煤矿智能化技术体系研究和工程建设,进行了... 详细信息

煤炭是实现清洁高效利用的最经济、最可靠的能源,煤炭资源的智能、安全、高效开发与低碳清洁利用是实现我国煤炭工业高质量发展的核心技术支撑。基于我国煤矿智能化初级阶段的发展要求,开展了煤矿智能化技术体系研究和工程建设,进行了智能化煤矿顶层设计研究,以"矿山即平台"的理念将智能化煤矿整体架构分为设备层、基础设施层、服务层与应用层,实现煤矿生产、安全、生态、保障的智能化闭环管理。针对智能化煤矿存在的信息孤岛问题,开展了多源异构数据建模、特征提取与数据挖掘等技术研究,研发了基于数据驱动的信息实体建模与更新技术;研究了智能化煤矿高精度三维地质模型构建方法,通过在刮板输送机上布设巡检机器人与三维激光扫描仪,将三维激光扫描数据与地质模型数据、采煤机位姿数据、采煤机摇臂截割数据进行有效融合,获取采煤机的实时截割曲线,通过比对采煤机实际截割曲线与地质模型的煤岩层分界面曲线,实现基于地质模型动态更新的煤层厚度自适应截割控制方法;研发了工作面采掘接续智能设计技术,实现了接续工作面图纸、规程、规范的智能设计,大幅降低了采掘接续过程中的重复劳动;研究了掘锚一体机的位姿检测与导航技术、自动打锚杆技术、自动铺网技术、巷道三维建模与质量监测技术,探索了基于远程视频监控的巷道智能高效掘进技术与装备;以"有人巡视,无人操作"为特征的智能化开采工作面在全国逐渐推广应用,开展了基于三维地质模型动态更新的采煤机自适应截割技术研发与实践,在部分矿区取得较好的试验效果。分析了智能分选技术、智能辅助运输技术、5G通信技术在煤矿井上下应用存在的技术难点及解决的技术路径,从技术研发角度系统分析了制约智能化煤矿建设的关键技术难题。详细阐述了神东煤炭集团、兖矿集团、同煤集团、阳煤集团、淄矿集团、新汶矿业集团等国内大型煤炭生产企业现阶段在智能化煤矿建设中取得的阶段性成果,从技术研发与现场实践相结合的角度分析了智能化煤矿建设过程中存在的主要技术难题与发展方向。同时对煤矿智能化标准体系进行研究,提出了煤矿智能化标准体系框架,起草制定了"智能化煤矿分类、分级技术条件与评价指标体系"、"智能化综采工作面分类、分级评价技术条件与指标体系"等相关标准,为智能化煤矿建设提供标准支撑。

关键词：煤矿智能化顶层设计智能化开采智能掘进三维地质模型标准体系

下一代互联网互联设备关键技术专利导航研究综述

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中国科学：信息科学 2022年第5期52卷 765-783页

作者：贾宏君周静乔开文张丹谢祥刘杰娄颖张铭马跃张柏秋何笑冬张凌宇郑晔晴邹昊刘科同济大学上海国际知识产权学院上海200092 国家专利导航项目(高校)研究和推广中心北京100044 北京交通大学经济管理学院院北京100044 华北电力大学科学技术研究院北京102206 扬州大学社会合作与服务处扬州225009 山东大学机械工程学院济南250100 大连理工大学科学技术研究院大连116023 北京大学信息科学技术学院北京100871 吉林大学图书馆长春130021 北京交通大学川藏铁路研究院北京100044

下一代互联网是全球各国推动科技产业革命和重塑国家长期竞争力的基础设施和先导领域,同样也是高科技密布、专利密集的战略性新兴产业领域,技术研发与创新竞争异常激烈.本文运用专利导航方法对全球下一代互联网互联设备相关技术进行了分... 详细信息

下一代互联网是全球各国推动科技产业革命和重塑国家长期竞争力的基础设施和先导领域,同样也是高科技密布、专利密集的战略性新兴产业领域,技术研发与创新竞争异常激烈.本文运用专利导航方法对全球下一代互联网互联设备相关技术进行了分析,通过对本导航领域多位技术专家的访谈,澄清和界定了下一代互联网互联设备关键技术的基本定义与范畴,从总体趋势、地域、核心申请人、核心发明人、专利强度、我国向国外申请专利,以及技术分支等方面分析了全球及我国下一代互联网互联设备关键技术的专利布局现状、竞争前沿和未来趋势,结合本导航领域国内外典型案例的总结,对研发创新和专利运用提出了相应的导航建议.

关键词：下一代互联网未来网络互联技术专利导航

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ICpSr科学数据中心的建设经验与启示

中国科技资源导刊 2017年第6期49卷 100-107页

作者：王明明王卷乐赵强高孟绪李舸韩雪华王玉洁中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室北京100101 山东理工大学建筑工程学院山东淄博255049 江苏省地理信息资源开发与利用协同创新中心江苏南京210023 中国矿业大学(北京)地球科学与测绘工程学院北京100083 国家科技基础条件平台中心北京100862 中国科学院大学北京100049

科学数据中心是科学数据管理的重要载体之一。在当前大数据和数据驱动科学研究范式的信息化时代,我国在科学数据中心建设上发展迅速但缺乏经验,迫切需要了解和借鉴发达国家的科学数据中心建设方面的先进理念和做法,促进我国的科学数据... 详细信息

科学数据中心是科学数据管理的重要载体之一。在当前大数据和数据驱动科学研究范式的信息化时代,我国在科学数据中心建设上发展迅速但缺乏经验,迫切需要了解和借鉴发达国家的科学数据中心建设方面的先进理念和做法,促进我国的科学数据管理。从网络调研、文献跟踪、实地访谈等方法,对在人文社会科学领域科学数据中心建设方面极具代表性的美国密西根大学的大学间政治社会研究联盟开展调研,从组织机制、政策标准、数据获取、数据归档、数据认证、数据引用和数据服务等方面对其进行了详细阐述并总结其建设经验。进一步结合我国科学数据中心数据管理现状,给出我国数据中心发展的启示,预期为我国学科领域的科学数据中心建设、数据管理和国际化发展等方面提供参考和借鉴。

关键词：科学数据管理科学数据中心国际经验发展启示