检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

2,092 篇 期刊文献
1,486 篇 会议
36 册 图书

馆藏范围

3,614 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,072 篇 工学
- 1,469 篇 计算机科学与技术...
- 1,203 篇 软件工程
- 429 篇 信息与通信工程
- 350 篇 生物工程
- 302 篇 生物医学工程（可授...
- 262 篇 控制科学与工程
- 238 篇 光学工程
- 221 篇 电气工程
- 138 篇 电子科学与技术（可...
- 137 篇 化学工程与技术
- 99 篇 机械工程
- 85 篇 安全科学与工程
- 77 篇 网络空间安全
- 76 篇 动力工程及工程热...
- 74 篇 仪器科学与技术
1,580 篇 理学
- 651 篇 数学
- 645 篇 物理学
- 416 篇 生物学
- 245 篇 统计学（可授理学、...
- 182 篇 化学
- 110 篇 系统科学
603 篇 管理学
- 319 篇 管理科学与工程(可...
- 308 篇 图书情报与档案管...
- 184 篇 工商管理
316 篇 医学
- 264 篇 临床医学
- 227 篇 基础医学(可授医学...
- 142 篇 药学(可授医学、理...
- 120 篇 公共卫生与预防医...
112 篇 法学
- 91 篇 社会学
76 篇 经济学
- 76 篇 应用经济学
57 篇 农学
49 篇 教育学
8 篇 文学
5 篇 军事学
2 篇 历史学
1 篇 哲学
1 篇 艺术学

主题

130 篇 deep learning
129 篇 accuracy
115 篇 machine learning
78 篇 feature extracti...
75 篇 hadrons
73 篇 training
69 篇 data models
66 篇 artificial intel...
66 篇 convolutional ne...
61 篇 real-time system...
57 篇 semantics
55 篇 computational mo...
53 篇 predictive model...
50 篇 hadron colliders
46 篇 hadronic decays
44 篇 internet of thin...
43 篇 support vector m...
42 篇 optimization
42 篇 visualization
40 篇 data mining

机构

427 篇 department of ph...
410 篇 horia hulubei na...
404 篇 van swinderen in...
401 篇 school of physic...
400 篇 iccub universita...
398 篇 h.h. wills physi...
398 篇 cavendish labora...
381 篇 yandex school of...
381 篇 department of ph...
375 篇 imperial college...
362 篇 university of ch...
355 篇 stfc rutherford ...
353 篇 center for high ...
348 篇 institute of par...
323 篇 henryk niewodnic...
316 篇 nikhef national ...
297 篇 school of physic...
297 篇 physik-institut ...
294 篇 oliver lodge lab...
289 篇 school of physic...

作者

252 篇 barter w.
249 篇 bellee v.
241 篇 beiter a.
224 篇 casse g.
222 篇 bowcock t.j.v.
217 篇 blake t.
216 篇 borsato m.
216 篇 betancourt c.
216 篇 amato s.
215 篇 bediaga i.
215 篇 boettcher t.
210 篇 brundu d.
210 篇 baryshnikov f.
207 篇 braun s.
205 篇 borisyak m.
202 篇 bizzeti a.
195 篇 bencivenni g.
190 篇 back j.j.
189 篇 bay a.
174 篇 bursche a.

语言

3,265 篇 英文
338 篇 其他
13 篇 中文
3 篇 德文
2 篇 法文
2 篇 斯洛文尼亚文
1 篇 日文
1 篇 俄文

检索条件"机构=Faculty for Computer Science and Data Science"

共 3614 条记录，以下是481-490 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Development of Advanced Noise Filtering Techniques for Medical Image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Prediction of Heart Disease Using Machine Learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector Machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning 25

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Qattouz, Hazem Almatarneh, Sattam Mansour, Khalid Alazaidah, Raed Bulkrock, Olla Higher Colleges of Technology Computer Information Science Dept Abu Dhabi United Arab Emirates Princess Sumaya University for Technology Software Engineering Dept Amman Jordan Zarqa University Data Science & Artificial Intelligence Dept Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Arab-Open University Bahrain Faculty of Computer Studies Bahrain

ISBN: (纸本)9798331540012

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual data Mining 3

GPU-based Parallel Technique for Solving the N-Similarity Pr...

引用

3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Fazlali, Mahmood Mirhosseini, Mina Shahsavari, Mahyar Shafarenko, Alex Mashinchi, Mashallah University of Hertfordshire Cyber Security and Computing Research Group Department of Computer Science Hertfordshire United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Department of Computer and Data Sciences Tehran Iran Radboud University Donders Institute for Brain Cognition and Behaviour Ai Department Nijmegen Netherlands Shahid Bahonar University of Kerman Faculty of Mathematics and Computer Department of Statistics Kerman Iran

ISBN: (纸本)9798350381580

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method. © 2024 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

data Lakehouse for Time Series data: A Systematic Literature Review

Data Lakehouse for Time Series Data: A Systematic Literature...

引用

IEEE International Conference on Big data

作者： Matthias Pohl Nathira Dharindri Wijemanne Daniel Staegemann Christian Haertel Christian Daase Dirk Dreschel Damanpreet Singh Walia Arne Osterthun Joshua Reibert Klaus Turowski German Aerospace Center (DLR) Institute of Data Science Jena Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

As data continues to grow exponentially, the fields of data management and analytics must evolve to ensure efficient data ingestion, knowledge extraction, and scalability. The data Lakehouse architecture, which combines the best features of data Warehouses and data Lakes, has emerged as a potential solution. However, to fully leverage the capabilities of data Lakehouses for time series data, it is crucial to understand the unique challenges and opportunities they present. This literature review examines proposed data Lakehouse architectures specifically for time series data, exploring their implementation, the software technologies used, and potential real-world applications. The focus is on comparing these architectures to identify the most suitable technologies for similar implementations. Through an in-depth analysis, this study emphasizes the importance of optimizing configurations to enhance system performance and scalability, particularly for data analysis and artificial intelligence (AI) workloads.

关键词： data analysis Costs Scalability Time series analysis computer architecture data warehouses Big data applications data mining Artificial intelligence Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Toward Improved Knowledge Retention: A Template for Describing data science Projects

Toward Improved Knowledge Retention: A Template for Describi...

引用

IEEE International Conference on Big data

作者： Christian Haertel Daniel Staegemann Matthias Pohl Christian Daase Klaus Turowski Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany Institute of Data Science German Aerospace Center (DLR) Jena Germany

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

data science (DS) aims to extract knowledge from large amounts of data. Organizations can use the retrieved insights to achieve various performance improvements. However, DS projects often fail to fulfill their objectives due to the explorative nature of this discipline and technical as well as managerial challenges. Consequently, new approaches to support DS project execution are sought after. A viable contribution in this regard is improving knowledge retention in DS to predict socio-technical obstacles of an undertaking and derive best practices. Therefore, in this work, a template for describing the central characteristics of DS projects is proposed using a Design science Research approach. The artifact is structured based on the common DS project stages and features 32 fields, enabling comparability and transparency in DS. The applicability of the template is demonstrated based on three DS use cases from the literature. While further steps for evaluation are pending, the template can serve as a foundation for developing a categorization model for DS projects in the future.

关键词： Organizations data science Big data data mining Best practices

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Usability and Career Guidance Functionality of an Academic Performance Tracking Application Using the UEQ

Evaluating the Usability and Career Guidance Functionality o...

引用

Information Technology International Seminar (ITIS)

作者： Siti Mukaromah Arista Pratama Henni Endah Wahanani Mohammad Idhom Agussalim Eristya Maya Safitri Department of Information System Faculty of Computer Science University of Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia Department of Informatics Faculty of Computer Science University of Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia Department of Data Science Faculty of Computer Science University of Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This article evaluates an academic application designed to provide career guidance using the User Experience Questionnaire (UEQ). A survey involving 255 respondents assessed the application across 6 UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The results reveal the highest scores on Attractiveness 1.473 and Perspicuity 1.642, indicating that the platform is visually appealing and easy to navigate. However, lower scores on Novelty 0.774 and Dependability 1.243 highlight areas requiring improvement, particularly in building trust among users and introducing innovative features. The implications of these findings suggest that while the platform holds significant potential as a tool for academic monitoring and career guidance, enhancing user engagement and reliability will be critical to achieving broader acceptance. Addressing the gaps in Stimulation (1.135) and Novelty could lead to a more engaging and unique user experience, aligning with the evolving needs of students. This study provides actionable insights for the iterative development of user-centered educational tools, emphasizing the importance of UX in supporting informed decision-making and improving academic outcomes.

关键词： Surveys Seminars Engineering profession Navigation User experience Iterative methods Reliability Usability Information technology Monitoring

来源：评论

学校读者我要写书评

暂无评论

LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

来源：评论

学校读者我要写书评

暂无评论

The Influence of Human Factors in Ensuring Cybersecurity in Organizations: Effectiveness of PDPA and GDPR Implementation 3

The Influence of Human Factors in Ensuring Cybersecurity in ...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sharifi, Ahmad Zia Walid, Md. Abul Ala Momand, Mohammad Dawood Safi, Ihsan Ul Haq Safi, Hamidullah Khanzai Hossain, Sadia Nangarhar University Faculty of Computer Science Department of Networking and Communication Afghanistan Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science and Engineering Gazipur Bangladesh Lautsprecher Teufel GMbH Berlin Germany Bangabandhu Sheikh Mujibur Rahman Digital University Department of Internet of Things and Robotics Engineering Gazipur Bangladesh

ISBN: (纸本)9798331518592

Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critical contributors to cyber risks. These human-related risks include user misuse, user mistakes, and user malice. A large proportion of cyber risks arise when hackers use social engineering or phishing techniques to manipulate employees into disclosing confidential information. While some employees knowingly engage in these actions, some incidents occur accidentally due to a lack of awareness about cybersecurity protocols. As a result, governments around the world are enacting data protection legislation aimed at enhancing employees' cyber risk. This study explores the perception of US employees on the effectiveness of data protection legislation in countering cyber threats. It surveyed 30 randomly selected employees from various industries. The results showed that employees perceive data protection legislation as effective in mitigating employeerelated cyber risks. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共362页 << < 45 46 47 48 49 50 51 52 53 54 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：