检索结果-内蒙古大学图书馆

作者： Lwin, Khin Nwe Minami, Mamoru Mukada, Naoki Myint, Myo Yamada, Daiki Yanou, Akira Matsuno, Takayuki Saitou, Kazuhiro Godou, Waichiro Sakamoto, Tatsuya Department of Intelligent Mechanical System Okayama University Okayama700-8530 Japan Disco Corporation Tokyo143-8580 Japan Technological University Thanlyin11292 Myanmar Graduate School of Natural Science and Technology Division of Mechanical and Systems Engineering Okayama University Okayama700-8530 Japan Department of Radiological Technology Kawasaki College of Allied Health Professions Kurashiki701-0194 Japan Faculty of Health Science and Technology Kawasaki University of Medical Welfare Kurashiki701-0193 Japan Ushimado Marine Institute Faculty of Science Okayama University Okayama701-4303 Japan

Recently, many studies have been performed worldwide to extend the persistence of underwater operations by autonomous underwater vehicles. Underwater battery recharging technology is one of the solutions even though challenges still remain. The docking function plays an important role not only in battery recharging but also in other advanced applications, such as intervention. Visual servoing in undersea environments inevitably encounters difficulties in recognizing the environment when captured images are disturbed by noise. This study describes the effective recognition performance and robustness against air bubble disturbances in images captured by a real-time position and orientation (pose) tracking and servoing system using stereo vision for a visual-servoing-type underwater vehicle. The recognition of the vehicle pose based on dynamic images captured by dual video cameras was performed by a real-time multistep genetic algorithm (RM-GA). In previous studies, the docking performance was investigated under the condition that there were no disturbances in the captured images that address image degradation. In this paper, the robustness of the RM-GA against air bubble disturbances was verified through visual servoing and docking experiments in a pool test to confirm that the system can continue to recognize the pose of the 3-D marker and can maintain the desired pose by visual servoing. Then, the effectiveness of the proposed system against real disturbances such as turbidity that may degrade the visibility of the system in the sea was confirmed by conducting the docking experiment in a real sea, having verified the practicality of the proposed method. © 1976-2012 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Impact of Population Movement on the Spread of 2019-nCoV in China

SSRN

引用

SSRN 2020年

作者： Zhang, Chi Chen, Cai Shen, Wei Tang, Feng Lei, Hao Xie, Yu Cao, Zicheng Tang, Kang Bai, Junbo Xiao, Lehan Xu, Yutian Song, Yanxin Chen, Jiwei Guo, Zhihui Guo, Yichen Wang, Xiao Xu, Modi Zou, Huachun Shu, Yuelong Du, Xiangjun Sun Yat-sen University Guangzhou China Department of Big Data in Health Science School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College Sun Yat-sen University Guangzhou China

Background: Since Dec 2019, Wuhan and other cities in China have experienced an outbreak caused by a novel coronavirus, 2019-nCoV. Even worse, this outbreak overlapped with the traditional Chinese Spring Festival, a time when billions of people travel across the country. The government has implemented a number of policies and interventions, including a travel ban from and to Wuhan since Jan 23, 2020, an extension of the Spring Festival holiday, etc. However, a considerable volume of population movement had already occurred before the travel ban, potentially causing the spread of cases to other cities in China. As people return to their place of work, this reflux of population movement is also likely to facilitate transmission of 2019-nCoV. Thus, it is urgent to evaluate the impact of existing population movement and future population reflux on the spread of 2019-nCoV, to provide evidence for precise control of the epidemic. Methods: We used domestic human mobility data from January to February (2019-20) to infer the population movement network between cities in China. We obtained data on confirmed cases from the daily official reports of the health commission of 34 provincial-level administrative units and 341 city-level units, from the time of the outbreak to Feb 14, 2020. We used a linear regression model to simulate the relationship between population flow and imported cases, and evaluate the deduction and risk of imported cases in different scenarios of human movement. Findings: There is a statistically significant positive correlation between population influx from Wuhan and confirmed cases in other cities across China (R2=0×85, P 2=0×88, P © 2020, The Authors. All rights reserved.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records 15th

Exploring the Pan-Relationship Between Disease and Symptom R...

引用

15th International Conference on intelligent Computing, ICIC 2019

作者： Yang, Ronggen Wang, Yichen Wang, Bo Gong, Lejun Faculty of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China School of Geography and Bioinformatics Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9783030267650

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient’s physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Triplet Deep Subspace Clustering via Self-Supervised data Augmentation

Triplet Deep Subspace Clustering via Self-Supervised Data Au...

引用

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Xianzhen Li Haijun Zhang Yi Yang Shuicheng Yan Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Computer Science and Technology Soochow University Suzhou China Harbin Institute of Technology (Shenzhen) Shenzhen China Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia Sea AI Lab (SAIL) & National University of Singapore Singapore

ISBN: (纸本)9781665423991

Deep subspace clustering (DSC) with the auto-encoder and self-expression layer is of great concern due to encouraging performance. However, existing methods usually adopt a “single-task” strategy based on a single dataset, without considering other related tasks or data. As such, they cannot discover other useful information to improve the clustering task. Besides, the local structure preservation of the latent codes in mapping is usually ignored. In this paper, we therefore present an effective “multi-task” strategy via the self-supervised data augmentation, and propose a new end-to-end trainable Triplet Deep Subspace Clustering Network (TDSC-net). Specifically, TDSC-net firstly generates triplet data (i.e., anchor, positive and negative data) from input data by a spectral clustering module and a self-supervised data augmentation module. This can enable it to inherit the merits of self-supervised learning and multitask learning implicitly. After that, TDSC-net builds a triplet deep autoencoder network with a self-expression layer, which takes the triplet data as input, where they share the common network layers (i.e., autoencoder and self-expression layers) over the triple tasks for complementary learning and mutual supervision. A triplet loss is also included to retain the local information of deep latent codes, which will also benefit the self-expression. Furthermore, TDSC-net separates the self-expression layer from decoding process to improve the efficiency of reconstruction. Extensive results on several public datasets demonstrate the effectiveness of our triplet-task DSC strategy.

关键词： Codes Conferences Multitasking Decoding data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

arXiv

引用

arXiv 2021年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Nodehi, Issa Mashmool, Amir Zadegan, Zeynab Kiani Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Tamadon, Tahereh Khosravi, Samiyeh Kohnehshari, Mitra Akbari Hassannatajjeloudari, Edris Sharifrazi, Danial Mosavi, Amir Loh, Hui Wen Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Engineering Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering University of Qom Qom Iran Italy Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Engineering Faculty Bu-Ali Sina University Hamedan Iran Department of Nursing School of Nursing and Allied Medical Sciences Maragheh Faculty of Medical Sciences Maragheh Iran Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Faculty of Informatics Obuda University Budapest Hungary School of Science and Technology Singapore University of Social Sciences Singapore Department of Cardiology National Heart Centre Singapore Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals. © 2021, CC BY-SA.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Decomposition of scattering phase shifts of coupled-channels systems in the complex scaling method

引用

Physical Review C 2021年第1期104卷 014325-014325页

作者： Myagmarjav Odsuren Takayuki Myo Yuma Kikuchi Manabu Teshigawara Kiyoshi Katō School of Engineering and Applied Sciences and Nuclear Research Center National University of Mongolia Ulaanbaatar 210646 Mongolia General Education Faculty of Engineering Osaka Institute of Technology Osaka 535-8585 Japan Research Center for Nuclear Physics (RCNP) Osaka University Ibaraki 567-0047 Japan Tokuyama College National Institute of Technology Yamaguchi 745-8585 Japan Canon Medical Systems Corporation Otawara 324-8550 Japan Nuclear Reaction Data Centre Faculty of Science Hokkaido University Sapporo 060-0810 Japan

The decomposition of scattering phase shifts in single-channel systems is extended to coupled-channels systems in the complex scaling method. The present method describes the contribution of the resonant states to the phase shift separately from the background in each channel phase shift of the coupled-channels system. We apply this method to the (H3+p)+(He3+n) model for He4, and show that the decomposition of phase shifts in coupled-channels systems provides useful information about the structure of scattering states.

关键词： Nuclear reactions Nuclear structure & decays Resonance reactions Optical, coupled-channel & distorted wave models

来源：评论

学校读者我要写书评

暂无评论

Highlights from the CALET observations for 7.5 years on the International Space Station 38

Highlights from the CALET observations for 7.5 years on the ...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Torii, Shoji Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Berti, E. Bigongiari, G. Binns, W.R. Bongi, M. Brogi, P. Bruno, A. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. de Nolfo, G.A. Ebisawa, K. Ficklin, A.W. Fuke, H. Gonzi, S. Guzik, T.G. Hams, T. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Kobayashi, K. Kohri, K. Krawczynski, H.S. Krizmanic, J.F. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, M. Mori, N. Motz, H.M. Munakata, K. Nakahira, S. Nishimura, J. Okuno, S. Ormes, J.F. Ozawa, S. Pacini, L. Papini, P. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Spillantini, P. Stolzi, F. Sugita, S. Sulaj, A. Takita, M. Tamura, T. Terasawa, T. Torii, S. Tsunesada, Y. Uchihori, Y. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Zober, W.V. Waseda University 17 Kikuicho Shinjuku Tokyo Japan Department of Physics University of Florence Via Sansone 1 Sesto Fiorentino50019 Italy INFN Sezione di Firenze Via Sansone 1 Sesto Fiorentino50019 Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Ibaraki Tsukuba305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa277-8582 Japan Via Madonna del Piano 10 Sesto Fiorentino50019 Italy Department of Physical Sciences Earth and Environment University of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 Pisa56127 Italy Department of Physics McDonnell Center for the Space Sciences Washington University One Brookings Drive St. LouisMO63130-4899 United States Heliospheric Physics Laboratory NASA/GSFC GreenbeltMD20771 United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA/GSFC GreenbeltMD20771 United States Center for Research and Exploration in Space Sciences and Technology NASA/GSFC GreenbeltMD20771 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Kanagawa Sagamihara252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Kanagawa Yokohama221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Aomori Hirosaki036-8561

The CALorimetric Electron Telescope (CALET) space experiment is a high-energy astroparticle physics mission installed on the International Space Station (ISS). The primary goals of the CALET mission include studying the details of galactic cosmic-ray acceleration and propagation, and searching for possible nearby sources of high-energy electrons and dark matter signatures. The CALET experiment is measuring the flux of cosmic-ray electrons (including positrons) to 20 TeV, gamma-rays to 10 TeV and nuclei with Z=1 to 40 up to 1,000 TeV. The instrument consists of two layers of segmented plastic scintillators for the identification of cosmic-rays via a measurement of their charge (CHD), a 3 radiation length thick tungsten-scintillating fiber imaging calorimeter (IMC) and a 27 radiation length thick lead-tungstate calorimeter (TASC). The instrument was launched on August 19, 2015 to the ISS and installed on the Japanese Experiment Module-Exposed Facility (JEM-EF). Since the start of operations in mid-October, 2015, CALET has been in continuous observation mode over 7.5 years and mainly triggering on high energy (>10 GeV) cosmic-ray showers without any major interruption. The number of triggered events over 10 GeV is nearly 1.86 billion events as of June 30, 2023. Here, we present the highlights of the CALET latest results, including the electron + positron energy spectrum, the spectra of protons and other nuclei, gamma-ray observations, as well as the characterization of on-orbit performance. Some results on the electromagnetic counterpart search for LIGO/Virgo gravitational wave events and the observations of solar modulation and gamma-ray bursts are also included. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

引用

Nucleic Acids Research 2024年第3期52卷 E17页

作者： Chang, Lan-Yun Lee, Meng-Zhan Wu, Yujia Lee, Wen-Kai Ma, Chia-Liang Chang, Jun-Mao Chen, Ciao-Wen Huang, Tzu-Chun Lee, Chia-Hwa Lee, Jih-Chin Tseng, Yu-Yao Lin, Chun-Yu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 300 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan Department of Food Science Nutrition and Nutraceutical Biotechnology Shih Chien University Taipei 104 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Cancer and Immunology Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge. © 2024 Oxford University Press. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos

arXiv

引用

arXiv 2023年

作者： Turishcheva, Polina Fahey, Paul G. Hansel, Laura Froebe, Rachel Ponder, Kayla Vystrčilová, Michaela Willeke, Konstantin F. Bashiri, Mohammad Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Qiur, Yongrong Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://***/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and

关键词： Mammals

来源：评论

学校读者我要写书评

暂无评论

Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning

npj Materials Degradation

引用

npj Materials Degradation 2025年第1期9卷 1-13页

作者： Weiting Chen Lingwei Ma Yiran Li Jinke Wang Zhibin Chen Xin Guo Zongbao Li Xiaogang Li Dawei Zhang Dequan Wu Kun Zhou Thee Chowwanonthapunya Beijing Advanced Innovation Center for Materials Genome Engineering School of Advanced Materials Innovation University of Science and Technology Beijing 100083 Beijing China National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing 100083 Beijing China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang 110004 China Southwest Institute of Technology and Engineering Chongqing 401329 China Faculty of International Maritime Studies Kasetsart University Sriracha Campus 199 Tungsukla Sriracha Chonburi 20230 Thailand

The corrosion failure prediction of coating materials in diverse environments is of great significance for service performance evaluation. This work proposes a two-stage machine learning method that makes use of various data, including environmental factors, physical properties, and coating barrier performance, to accurately evaluate the corrosion degradation of coatings. In the first stage, a 1-year outdoor exposure experiment of polyurethane coatings was conducted in nine representative climatic environments. A semi-supervised collaborative training regression model is established between key environmental data and physical properties of coatings (i.e., glossiness, adhesion, water contact angle, and yellowness). In the second stage, using the predicted physical property data as inputs, a machine learning model is constructed that links physical properties to the barrier performance of coatings and develops binary classification models that can distinguish between intact and damaged coatings. This two-stage modeling strategy provides enhanced prediction accuracy and scientific interpretability by incorporating intermediate physical property parameters.

关键词：

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：