检索结果-内蒙古大学图书馆

1st International Conference on Renewable Energy Research and Challenge, ICoRER 2019

作者： Susanto, Herry Hendroko Setyobudi, Roy Muhammad Nur, Syukri Yandri, Erkata Saepul Uyun, Aep Yaro, Abubakar Abdullah, Kamaruddin Krido Wahono, Satriyo Burlakovs, Juris Adhi Nugroho, Yogo Department of Mechanical Engineering Darma Persada University Jl. Taman Malaka Selatan No.22 Jakarta Timur13450 Indonesia School of Renewable Energy Darma Persada University Jl. Taman Malaka Selatan No.22 Jakarta Timur13450 Indonesia Department of Agriculture Science Postgraduate Program University of Muhammadiyah Malang Jl. Raya Tlogomas No.246 Malang65145 Indonesia Institute of Health Sciences and Research Ahro Scientific Publishing 272 Bath Street GlasgowG2 4JR United Kingdom Research Division for Natural Product Technology - Indonesian Institute of Sciences Jl. Jogja Wonosari km 31.5 Gunung Kidul Yogyakarta55861 Indonesia Department of Water Management Estonian University of Life Sciences Friedrich Reinhold Kreutzwaldi la Tartu51014 Estonia Individual Researcher Alumni School of Ipb University Bogor Indonesia Data Processing Rumah Paper - Editage Services Jl. Tokala No.1 East Java65146 Indonesia

Indonesia is an archipelago that has a wealth of marine fish resources, and the country with the second-longest coastline in the world. The improvement witnessed in the economy of Indonesia in recent years, is as a result of the growth experienced in various sectors, among which is the fisheries and marine. To overcome this problem, a portable conveyor system of moving equipment for fishermen's catches was designed. It can be placed at the dock or port where the fishermen are anchored and unloading the fishermen's catches from the sea. It designed to have the ability to move up and down in the moving frame, making this conveyor system can be used both in high tides and low tides of seawater level conditions. The advantage of this portable conveyor system is could be done continuously with a little human power. Other than that, from the results of the calculations, known that with increasing motor rotation, the required traveling time will only become shorter. The resulting conveyor capacity shows an increase. Also, it can use drive motors that are available on the market with a power capacity of 0.37 kW. Therefore, the process of this portable conveyor system can be more efficient. © The Authors, published by EDP Sciences, 2020.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline

arXiv

引用

arXiv 2024年

作者： Aalbers, J. Abe, K. Adrover, M. Ahmed Maouloud, S. Althueser, L. Amaral, D.W.P. Andrieu, B. Angelino, E. Antón Martin, D. Antunovic, B. Aprile, E. Babicz, M. Bajpai, D. Balzer, M. Barberio, E. Baudis, L. Bazyk, M. Bell, N.F. Bellagamba, L. Biondi, R. Biondi, Y. Bismark, A. Boehm, C. Boese, K. Braun, R. Breskin, A. Brommer, S. Brown, A. Bruni, G. Budnik, R. Cai, C. Capelli, C. Chauvin, A. Cimental Chavez, A.P. Colijn, A.P. Conrad, J. Cuenca-García, J.J. D'Andrea, V. Daniel Garcia, L.C. Decowski, M.P. Deisting, A. Di Donato, C. Di Gangi, P. Diglio, S. Doerenkamp, M. Drexlin, G. Eitel, K. Elykov, A. Engel, R. Ferella, A.D. Ferrari, C. Fischer, H. Flehmke, T. Flierman, M. Fujikawa, K. Fulgione, W. Fuselli, C. Gaemers, P. Gaior, R. Galloway, M. Gao, F. Garroum, N. Giacomobono, R. Girard, F. Glade-Beucke, R. Glück, F. Grandi, L. Grigat, J. Größle, R. Guan, H. Guida, M. Gyorgy, P. Hammann, R. Hannen, V. Hansmann-Menzemer, S. Hargittai, N. Higuera, A. Hils, C. Hiraoka, K. Hoetzsch, L. Hoferichter, M. Hood, N.F. Iacovacci, M. Itow, Y. Jakob, J. James, R.S. Joerg, F. Kahlert, F. Kaminaga, Y. Kara, M. Kavrigin, P. Kazama, S. Keller, M. Kharbanda, P. Kilminster, B. Kleifges, M. Klute, M. Kobayashi, M. Koke, D. Kopec, A. von Krosigk, B. Kuger, F. LaCascio, L. Landsman, H. Lang, R.F. Levinson, L. Li, I. Li, A. Li, S. Liang, S. Liang, Z. Lin, Y.-T. Lindemann, S. Lindner, M. Liu, K. Loizeau, J. Lombardi, F. Long, J. Lopes, J.A.M. Lucchetti, G.M. Luce, T. Ma, Y. Macolino, C. Mahlstedt, J. Maier, B. Mancuso, A. Manenti, L. Marignetti, F. Martens, K. Masbou, J. Masson, E. Mastroianni, S. Melchiorre, A. Menéndez, J. Messina, M. Milosovic, B. Milutinovic, S. Miuchi, K. Miyata, R. Molinario, A. Monteiro, C.M.B. Morå, K. Moriyama, S. Morteau, E. Mosbacher, Y. Müller, J. Murra, M. Newstead, J.L. Ni, K. O'Hare, C. Oberlack, U. Obradovic, M. Ostrowskiy, I. Ouahada, S. Paetsch, B. Pan, Y. Pandurovic, M. Pellegrini, Q. Nikhef The University of Groningen Van Swinderen Institute Groningen9747AG Netherlands University of Tokyo Higashi-Mozumi Kamioka Gifu Hida506-1205 Japan Physik-Institut University of Zürich Zürich8057 Switzerland LPNHE Sorbonne Université CNRS IN2P3 Paris75005 France Institute for Nuclear Physics University of Münster Münster48149 Germany Department of Physics and Astronomy Rice University HoustonTX77005 United States INAF-Astrophysical Observatory of Torino Department of Physics University of Torino INFN-Torino Torino10125 Italy INFN-Laboratori Nazionali del Gran Sasso Gran Sasso Science Institute L'Aquila67100 Italy Department of Physics Enrico Fermi Institute Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Vinca Institute of Nuclear Science University of Belgrade Mihajla Petrovica Alasa 12-14 Belgrade Serbia Physics Department Columbia University New YorkNY10027 United States Department of Physics & Astronomy University of Alabama TuscaloosaAL34587-0324 United States Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe76021 Germany ARC Centre of Excellence for Dark Matter Particle Physics School of Physics The University of Melbourne VIC3010 Australia SUBATECH IMT Atlantique CNRS IN2P3 Nantes Université Nantes44307 France Department of Physics and Astronomy University of Bologna INFN-Bologna Bologna40126 Italy Max-Planck-Institut für Kernphysik Heidelberg69117 Germany Institute for Astroparticle Physics Karlsruhe Institute of Technology Karlsruhe76021 Germany School of Physics The University of Sydney Camperdown SydneyNSW2006 Australia Department of Particle Physics and Astrophysics Weizmann Institute of Science Rehovot7610001 Israel Institute of Experimental Particle Physics Karlsruhe Institute of Technology Karlsruhe76021 Germany Physikalisches Institut Universität Freiburg Freiburg79104 Germany Department of Physics and

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data and construct a one-dimensional anomaly score optimised to reject the background only hypothesis in the presence of an excess of non-background-like events. We benchmark the procedure with a sensitivity study that determines its power to reject the background-only hypothesis in the presence of an injected WIMP dark matter signal, outperforming the classical, likelihood-based background rejection test. We show that our neural networks learn relevant energy features of the events from low-level, high-dimensional detector outputs, without the need to compress this data into lower-dimensional observables, thus reducing computational effort and information loss. For the future, our approach lays the foundation for an efficient end-to-end pipeline that eliminates the need for many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

A Localization Method of High Energy Transients for All-Sky Gamma-Ray Monitor

arXiv

引用

arXiv 2022年

作者： Zhao, Yi Xue, Wangchen Xiong, Shaolin Luo, Qi Wang, Yuanhao Liu, Jiacong Yu, Heng Zhao, Xiaoyun Huang, Yue Liao, Jinyuan Sun, Jianchao Li, Xiaobo Yi, Qibin Cai, Ce Xiao, Shuo Xie, Shenglun Zheng, Chao Zhang, Yanqiu Wang, Chenwei Tan, Wenjun Guo, Zhiwei Li, Chaoyang An, Zhenghua Chen, Gang Du, Yanqi Gao, Min Gong, Ke Guo, Dongya He, Jiang He, Jianjian Li, Bing Li, Gang Li, Xinqiao Liang, Jing Liang, Xiaohua Liu, Yaqing Ma, Xiang Qiao, Rui Song, Liming Song, Xinying Sun, Xilei Wang, Jin Wang, Ping Wen, Xiangyang Wu, Hong Xu, Yanbing Yang, Sheng Zhang, Dali Zhang, Fan Zhang, Hongmei Zhang, Peng Zhang, Shu Zhang, Zhen Zheng, Shijie Zhang, Keke Han, Xingbo Wu, Haiyan Hu, Tai Geng, Hao Lu, Gaopeng Xu, Wei Lyu, Fanchao Zhang, Hongbo Lu, Fangjun Zhang, Shuangnan Department of Astronomy Beijing Normal University Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China College of Physics Hebei Normal University Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China College of physics Sciences Technology Hebei University Baoding071002 China Physics and Space Science College China West Normal University Nanchong637002 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China College of Electronic and information Engineering Tongji University Shanghai201804 China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China Electronic Information School Wuhan University Hubei Wuhan430072 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations. Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors. There are two major methods for this counts distribution localization: χ2 minimization method and the Bayesian method. Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the χ2 method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than χ2 method, especially for weak bursts. We further proposed a location-spectrum iteration approach based on the Bayesian inference, which could alleviate the problems caused by the spectral difference between the burst and location templates. Our method is very suitable for scenarios with limited computation resources or time-sensitive applications, such as in-flight localization software, and low-latency localization for rapid follow-up observations. Copyright © 2022, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Indefinite kernel spectral learning 30

Indefinite kernel spectral learning

引用

30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Dept. of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 LeuvenB-3001 Belgium

This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning. © 2018 University of Groningen. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

1325P TRIDENT: Machine learning (ML) multimodal signatures to identify patients that would benefit most from tremelimumab (T) addition to durvalumab (D) + chemotherapy (CT) with data from the POSEIDON trial

引用

Annals of Oncology 2024年 35卷 S842-S843页

作者： F. Skoulidis S. Jabbour E.B. Garon P. Iyengar G. Scagliotti L. Ferrer G. Etchepare O. Gallinato J. Faure P. Bernard T. Colin P. Menu Y. Lin L. Cai J. Faria A. Remorino L. Luciani-Silverman K.P. Miller D. Dellamonica Y. Zhang Department of Thoracic/Head and Neck Medical Oncology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Radiation Oncology Rutgers Cancer Institute of New Jersey New Brunswick NJ USA Department of Medicine David Geffen School of Medicine at UCLA Los Angeles CA USA Radiation Oncology Memorial Sloan Kettering Cancer Center New York NY USA Medical Affairs AstraZeneca Turin Italy Multimodal R&D Sophia Genetics Pessac France Multimodal R&D Sophia Genetics Boston MA USA Data Science Sophia Genetics Pessac France Image Processing Team Sophia Genetics Gradignan France Multimodal Data Engineering Sophia Genetics Le Bouscat France Product and Medical Department Sophia Genetics Rolle Switzerland Biostatistics AstraZeneca South San Francisco CA USA Oncology Biometrics AstraZeneca Gaithersburg MD USA Oncology Data Science Oncology R&D AstraZeneca Cambridge UK Oncology Digital - Precision Healthcare AstraZeneca Barcelona Spain US Medical Affairs AstraZeneca Gaithersburg MD USA Oncology Business Unit AstraZeneca Gaithersburg MD USA Digital Healthcare Delivery AstraZeneca Zug Switzerland Digital Health Transformation R&D AstraZeneca Shanghai China

来源：评论

学校读者我要写书评

暂无评论

Photodisintegration cross sections for resonant states and virtual states

引用

Physical Review C 2019年第3期99卷 034312-034312页

作者： Myagmarjav Odsuren Yuma Kikuchi Takayuki Myo Hiroshi Masui Kiyoshi Katō School of Engineering and Applied Sciences and Nuclear Research Center National University of Mongolia Ulaanbaatar 210646 Mongolia Tokuyama College National Institute of Technology Yamaguchi 745-8585 Japan Faculty of Engineering General Education Osaka Institute of Technology Osaka 535-8585 Japan Research Center for Nuclear Physics (RCNP) Osaka University Ibaraki 567-0047 Japan Information Processing Center Kitami Institute of Technology Kitami 090-8507 Japan Nuclear Reaction Data Centre Faculty of Science Hokkaido University Sapporo 060-0810 Japan

The s-wave photodisintegration cross sections observed just above the neutron breakup threshold energy are calculated for resonant and virtual states by using a complex-scaled two-body model, which simulates the Be9 nucleus with a Be8+ns-wave channel. We investigate the differences between behaviors of the resonance and virtual states in the photodisintegration cross sections near threshold energy in detail. It is shown that, in the present two-body model, resonant states very near the threshold energy produce a peak structure in the photodisintegration cross section that is similar that of the virtual state.

关键词： Energy levels & level densities Few-body systems Photonuclear reactions Resonance reactions 6 ≤ A ≤ 19

来源：评论

学校读者我要写书评

暂无评论

Support Vector Machine Improvement with Kemel-Based Nonlinear Representor

Support Vector Machine Improvement with Kemel-Based Nonlinea...

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Zhuang Xiong Benyong Liu College of Big Data and Information Engineering Guizhou University Guiyang China Institute of Intelligent Information Processing Guizhou University Guiyang China

By introducing the idea of optimal feature representation in kernel-based nonlinear representor (KNR), the traditional support vector machine (SVM) is improved in this manuscript to a new version called KNR-SVM. Firstly, the k-means unsupervised method is adopted to cluster the input patterns to form k-centers. Then, a mapping function, a point of a reproducing kernel Hilbert space (RKHS), is estimated based on the k-centers and KNR method, and the input patterns are mapped explicitly into a higher dimensional feature space, using the estimated function. Finally, the traditional SVM is taken as a classifier to classify the mapped patterns. Experimental results on handwritten digital recognition show that the proposed KNRSVM outperforms the traditional SVM.

关键词： Support vector machines Kernel Standards Training Handwriting recognition Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Discovery of Two-dimensional Hexagonal MBene HfBO and Exploration on its Potential for Lithium-Ion Storage

引用

Angewandte Chemie 2023年第36期135卷

作者： Nanxi Miao Prof. Yutong Gong Huaiyu Zhang Qing Shen Rui Yang Prof. Jianping Zhou Prof. Hideo Hosono Prof. Junjie Wang State Key Laboratory of Solidification Processing School of Materials Science and Engineering Department Northwestern Polytechnical University Xi'an Shaanxi 710072 P. R. China Contribution: Data curation (lead) Formal analysis (equal) Funding acquisition (equal) Software (lead) Writing - original draft (lead) Contribution: Data curation (equal) Formal analysis (equal) Funding acquisition (equal) Validation (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Formal analysis (equal) Software (equal) Contribution: Formal analysis (equal) Investigation (equal) Software (equal) Contribution: Investigation (equal) School of Physics & Information Technology Shaanxi Normal University Xi'an 710119 P. R. China Contribution: Formal analysis (equal) Investigation (equal) MDX Research Center for Element Strategy Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama Kanagawa 226-8503 Japan Contribution: Funding acquisition (equal) Investigation (equal) Writing - review & editing (equal) Contribution: Conceptualization (lead) Data curation (equal) Formal analysis (equal) Funding acquisition (equal) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

The practical applications of two-dimensional (2D) transition-metal borides (MBenes) have been severely hindered by the lack of accessible MBenes because of the difficulties in the selective etching of traditional ternary MAB phases with orthorhombic symmetry ( ort -MAB). Here, we discover a family of ternary hexagonal MAB ( h -MAB) phases and 2D hexagonal MBenes ( h -MBenes) by ab initio predictions and experiments. Calculations suggest that the ternary h -MAB phases are more suitable precursors for MBenes than the ort -MAB phases. Based on the prediction, we report the experimental synthesis of h -MBene HfBO by selective removal of In from h -MAB Hf 2 InB 2 . The synthesized 2D HfBO delivered a specific capacity of 420 mAh g −1 as an anode material in lithium-ion batteries, demonstrating the potential for energy-storage applications. The discovery of this h -MBene HfBO added a new member to the growing family of 2D materials and provided opportunities for a wide range of novel applications.

关键词： DFT Calculations High-Throughput Screening Lithium-Ion Batteries Two-Dimensional Materials h-MBenes

来源：评论

学校读者我要写书评

暂无评论

OpenHI2 - Open source histopathological image platform

OpenHI2 - Open source histopathological image platform

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Pargorn Puttapirat Haichuan Zhang Jingyi Deng Yuxin Dong Jiangbo Shi Hongyu He Zeyu Gao Chunbao Wang Xiangrong Zhang Chen Li National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi'an Jiaotong University Department of Power System and Automation School of Electrical Engineering Xi'an Jiaotong University Department of Pathology the First Affiliated Hospital of Xi'an Jiaotong University Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781728118680

Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive to many external factors and is known to be subjective. Only systems that can meet strict requirements in pathology would be able to run along pathological routines and eventually digitized the area, and the developed platform should comply with existing pathological routines and international standards. Currently, there are a number of available software tools which can perform histopathological tasks including virtual slide viewing, annotating, and basic image analysis, however, none of them can serve as a digital platform for pathology. Here we describe OpenHI2, an enhanced version Open Histopathological Image platform which is capable of supporting all basic pathological tasks and file formats;ready to be deployed in medical institutions on a standard server environment or cloud computing infrastructure. In this paper, we also describe the development decisions for the platform and propose solutions to overcome technical challenges including responsive region retrieval and viewing, virtual slide magnification, recording of diagnostic areas. These factors would promote OpenHI2 be used as a platform for histopathological images in real-world clinical settings. Furthermore, in research, OpenHI2 inherited the annotation functionality from the previous version, thus acquired annotations can be directly utilized by the newly added machine learning module which include popular machine learning models to perform tasks such as histology image classification and segmentation in the same environment. Addition can be made to the platform since each component is modularized and fully documented. OpenHI2 is free, open-source, and available at .

关键词： OpenHI digital pathology histopathological image analysis open source software open source software pathology Platform Software Tools open source Religious Missions External factors images

来源：评论

学校读者我要写书评

暂无评论

Weighted Feature Pyramid Networks for Object Detection

Weighted Feature Pyramid Networks for Object Detection

引用

IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Xiaohan Li Taotao Lai Shuaiyu Wang Quan Chen Changcai Yang Riqing Chen Jinxun Lin Fu Zheng Digital Fujian Institute of Big Data for Agriculture and Forestry College of Computer and information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fu Jian Shu Bo Xun Information Technology Co. Ltd

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.

关键词： Feature extraction Kernel Object detection Agriculture Forestry Semantics Big data

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：