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

852 篇 会议
25 篇 期刊文献
2 册 图书

馆藏范围

879 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

298 篇 工学
- 210 篇 计算机科学与技术...
- 121 篇 生物工程
- 112 篇 软件工程
- 74 篇 生物医学工程（可授...
- 65 篇 电气工程
- 37 篇 信息与通信工程
- 32 篇 化学工程与技术
- 31 篇 控制科学与工程
- 13 篇 机械工程
- 11 篇 电子科学与技术（可...
- 9 篇 动力工程及工程热...
- 8 篇 材料科学与工程（可...
- 7 篇 仪器科学与技术
- 5 篇 交通运输工程
195 篇 理学
- 150 篇 生物学
- 75 篇 数学
- 32 篇 统计学（可授理学、...
- 30 篇 化学
- 25 篇 系统科学
- 17 篇 物理学
51 篇 管理学
- 30 篇 管理科学与工程(可...
- 24 篇 图书情报与档案管...
- 13 篇 工商管理
40 篇 医学
- 29 篇 基础医学(可授医学...
- 24 篇 临床医学
- 17 篇 药学(可授医学、理...
- 7 篇 公共卫生与预防医...
33 篇 农学
- 28 篇 作物学
6 篇 法学
- 6 篇 社会学
4 篇 经济学
- 4 篇 应用经济学
3 篇 文学
1 篇 哲学
1 篇 教育学

主题

588 篇 bioinformatics
289 篇 genomics
138 篇 proteins
86 篇 biological syste...
83 篇 biology computin...
82 篇 sequences
74 篇 dna
72 篇 databases
65 篇 computational mo...
62 篇 gene expression
61 篇 computer science
61 篇 computational bi...
56 篇 diseases
51 篇 humans
49 篇 genetics
46 篇 data mining
42 篇 organisms
42 篇 testing
35 篇 laboratories
34 篇 rna

机构

4 篇 university of ca...
4 篇 ieee
4 篇 tianjin institut...
4 篇 drexel univ phil...
3 篇 school of comput...
3 篇 department of el...
3 篇 california insti...
3 篇 department of co...
3 篇 pacific northwes...
2 篇 aes-tietê
2 篇 univ brasilia de...
2 篇 fudan univ sch c...
2 篇 pacific nw natl ...
2 篇 department of em...
2 篇 scylla bioinform...
2 篇 institute of com...
2 篇 department of co...
2 篇 scripps institut...
2 篇 university of de...
2 篇 mechanical engin...

作者

9 篇 v. olman
9 篇 ying xu
6 篇 tatsuya akutsu
6 篇 h. ali
5 篇 fdez-riverola fl...
5 篇 zhengchang su
4 篇 morihiro hayashi...
4 篇 y. xu
4 篇 hu xiaohua
4 篇 soda paolo
4 篇 kim sun
4 篇 atanassov krassi...
4 篇 sicilia rosa
3 篇 z. su
3 篇 zeyad mohammad
3 篇 m. baba
3 篇 p. dam
3 篇 h. peng
3 篇 x. chen
3 篇 cordelli ermanno

语言

876 篇 英文
2 篇 中文
1 篇 其他

检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 879 条记录，以下是21-30 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：

New Algorithms for the Computation of Inverse Transforms

New Algorithms for the Computation of Inverse Transforms

引用

2022 International conference on computational Science and computational Intelligence, CSCI 2022

作者： Zhang, Jun Fan, Ruzong Shen, Fangyang University of Maryland Eastern Shore Department of Computer Science and Engineering Technology Princess AnneMD21853 United States Georgetown University Department of Biostatistics Bioinformatics and Biomathematics WashingtonDC20057 United States City University of New York Dept. of Computer Systems Technology New York City College of Technology BrooklynNY11201 United States

ISBN: (纸本)9798350320282

The Laplace Transform has been widely used for about two centuries in problem solving in mathematics, engineering and sciences. The Sumudu Transform is very recent, it is as powerful as the Laplace Transform and has many nice features. Traditionally, when these transforms are used, the calculations of the inverse transforms are necessary;unfortunately, the calculations of Inverse Laplace Transform and Inverse Sumudu Transform are problematic and challenging. The authors studied Sumudu Transform in the computational approaches. We shall introduce a few novel algorithms on the computations of Inverse Sumudu Transform and Inverse Laplace Transform in this paper. The algorithms introduced here are straightforward to understand and powerful for problem solving, and some demo versions were implemented in the Maple Computer Algebra systems. Type: Full / Regular Research Paper for CSCI-RTCS © 2022 ieee.

关键词： Laplace transforms

来源：评论

学校读者我要写书评

暂无评论

Transparent AI Models for Meningococcal Meningitis Diagnosis: Evaluating Interpretability and Performance Metrics 12

Transparent AI Models for Meningococcal Meningitis Diagnosis...

引用

12th ieee International conference on Intelligent systems, IS 2024

作者： Messai, Aya Drif, Ahlem Ouyahia, Amel Guechi, Meriem Rais, Mounira Kaderali, Lars Cherifi, Hocine University of Ferhat Abbas Sétif 1 Lrsd Laboratory Sétif19000 Algeria University of Ferhat Abbas Sétif 1 Department of computer science Sétif19000 Algeria University of Ferhat Abbas Sétif 1 STDs Hiv Aids unit Department of Medicine Sétif19000 Algeria Chu Saadna Abdenour Hospital Sétif Hospital Sétif19000 Algeria University of Ferhat Abbas Sétif 1 Department of Medicine Sétif19000 Algeria Institute of Bioinformatics University Medicine Greifswald Felix-Hausdorf-Str. 8 Greifswald17475 Germany University of Burgundy Dijon Laboratoire Interdisciplinaire Carnot de Bourgogne Icb Umr 6303 Cnrs France

ISBN: (纸本)9798350350982

Meningococcal meningitis, a severe bacterial infection, demands precise diagnosis and immediate intervention. This paper explores the potential of Artificial Intelligence (AI) in improving diagnostic accuracy and clinical decision-making for this condition. We assess interpretability and reliability by analyzing the decision-making process of Machine Learning (ML) models tailored to identify Meningococcal meningitis among different etiologies of meningitis. Initially, we train and test various ML models, including logistic regression, K-nearest neighbors, support vector machine, decision tree, gradient boosting, AdaBoost, random forest, and LightGBM classifier, on a dataset comprising 460 cases of Meningococcal meningitis and 474 cases of other types of meningitis. Among these models, the gradient-boosting model exhibits superior performance metrics, including accuracy (0.88), precision (0.92), recall (0.83), AUROC (0.93), and f1-score (0.87). Subsequently, we employ Explainable AI (XAI) tools (ELI5 and LIME) to elucidate the importance of features in the ML models. Our results highlight key factors contributing to model success, such as Neisseria meningitidis identified through cerebrospinal fluid (CSF) culture and latex agglutination, gram-negative diplococci in CSF smear examination, white cell counts in CSF, and patient age. Local explanations reveal the presence of neutrophils in CSF as a characteristic feature of Meningococcal meningitis. LIME analysis indicates the significance of low lymphocyte percentages and elevated white blood cell counts in predicting this condition. These findings underscore the effectiveness of integrating global and local interpretability techniques, aligning with expert knowledge, and emphasizing the importance of transparent AI models in clinical decision-making processes. © 2024 ieee.

关键词： Cerebrospinal fluid

来源：评论

学校读者我要写书评

暂无评论

Utilizing computational Modeling to Discover the Patterns of DNA Methylation in Acute Myeloid Leukemia Among Various Racial Populations 8

Utilizing Computational Modeling to Discover the Patterns of...

引用

8th International conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2024

作者： Agustriawan, David Darmawan, Jeremie Theddy Permana, Angga Aditya Overbeck, Marlinda Vasty Yuliani, Sy. Sitorus, Budi Berlinton School of Engineering and Informatics Multimedia Nusantara University Department of Informatics Banten Tangerang Indonesia School of Life Sciences Indonesia International Institute for Life Sciences Department of Bioinformatics East Jakarta Indonesia School of Engineering and Informatics Multimedia Nusantara University Department of Information System Banten Tangerang Indonesia

ISBN: (纸本)9798350368970

Acute myeloid leukemia (AML) is a rapidly occurring disorder that is characterized by the clonal expansion of myeloid progenitors in the bone marrow and the proliferation of immature myeloid cells. Absolute monocyte count (AMC) and absolute lymphocyte count (ALC) have been used in the diagnostic and prognostic predictor of AML. Interestingly, dysregulation of DNA methylation levels is also observed in AML. Data on AML patients were downloaded from the GDC TCGA Acute Myeloid Leukemia (LAML) on *** website. Data exploration andfiltering based on selected genes and racial population groups was performed. DNMT3A, IDH1, IDH2, TET2, FLT3-ITD, NPM1, and WTl are selected as the potential methylation regulation genes in AML with low number of mutation cases. Univariate analysis was done using correlation test while the multivariate analysis was done using a forward step variable selection linear regression model. This study found that the univariate analysis showed a specific methylated gene locations have a role in AML. Moreover, it is also discovered that the highest correlated gene and CpG site for white and non-white populations are different. This study uncovered that monocyte is found to be inversely proportional while the lymphocyte is directly proportional to the methylation level found in certain genes. There may be therapeutic implication from these findings such as the use of demethylating agents. © 2024 ieee.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Using Supervised Machine Learning Algorithms For Drug Target Interaction Prediction

Using Supervised Machine Learning Algorithms For Drug Target...

引用

2022 Innovations in Intelligent systems and Applications conference, ASYU 2022

作者： Yildiz, Nilay Fatma Ozcan, Alper Akdeniz University Department of Computer Engineering Antalya Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

In conjunction with the advent of bioinformatics, the evolution occurred in the field of drug discovery worldwide. In the literature numerous machine learning (ML), deep learning, and graph theory approaches have been implemented for drug discovery tasks. However, the drug-target interaction (DTI) problem for the existing and new drugs has been anticipated for a long time in drug repurposing and drug discovery areas. The proposed study predicts the drug-target interactions and to achieve this, a computational pipeline has been developed for the heterogeneous network. Firstly, this study obtains the low dimensional vector for the graphical nodes using the node2vec method. Secondly, multiple machine learning methods have been applied to predict drug-target interactions. Lastly, for the evaluation of the proposed models, we calculated the AUROC and AUPRC values and the obtained results indicate that non-linear SVM and Logistic regression performed better than other models with the AUROC, and AUPRC values of 0.8317 and 0.8260 respectively. © 2022 ieee.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Emotion Detection Mastery: EEG Signal Analysis with Balanced vs. Unbalanced Data

Emotion Detection Mastery: EEG Signal Analysis with Balanced...

引用

2024 International conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Menaka, Jadov Sreelekshmi, G.I. Poulose, Alwin School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India University of Kerala Department of Computational Biology and Bioinformatics Kerala Thiruvananthapuram695581 India

ISBN: (纸本)9798331540067

Emotion is crucial in daily life, influencing stress levels, decision-making, relationships, and overall mental health. Recognizing emotions accurately is valuable across sectors like healthcare, education, and beyond. This paper presents a comparative study on emotion recognition, applying various deep learning models with the DEAP dataset, which includes EEG recordings from 32 participants who watched 40 short music video clips. We extracted time-domain, frequency-domain, and wavelet features from the preprocessed EEG data to capture nuanced patterns within the signals. The models assessed in this study include convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and hybrid architectures, each trained on both balanced and unbalanced datasets. To address class imbalance, data was augmented using the Synthetic Minority Oversampling Technique (SMOTE). Results indicate that the Attention GRU model achieved slightly higher performance by utilizing its attention mechanism to capture extended dependencies within the data. Additionally, model accuracy improved by 5-9% when trained on balanced datasets, underscoring the importance of data balancing in enhancing classification outcomes. © 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

Single-Cell RNA-Seq Data Clustering: Highlighting computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Mohamad Nossier Sherin M. Moussa Nagwa Badr Program of Bioinformatics Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Program of Bioinformatics Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Laboratoire Interdisciplinaire de l'Université Française d'Égypte (UFEID Lab) Université Francaise d'Égypte Cairo Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Closed-Loop Deep Brain Stimulation Biomedical Model of Parkinson’s Disease

A Closed-Loop Deep Brain Stimulation Biomedical Model of Par...

引用

ieee Symposium on computational Intelligence and bioinformatics and computational Biology (CIBCB)

作者： Timothe Petitjean Hugo Millet Mariana F. P. Araújo Renan C. Moioli Patricia A. Vargas Caetano M. Ranieri School of Mathematical And Computer Science Heriot-Watt University Edinburgh Scotland Department of Physiological Sciences Health Sciences Center Federal University of Espirito Santo (UFES) Vitoria Brazil Bioinformatics Multidisciplinary Environment Digital Metropolis Institute Federal University of Rio Grande do Norte (UFRN) Natal Brazil Institute of Geosciences and Exact Sciences Sao Paulo State University (UNESP) Rio Claro Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

In this paper, we introduce a new approach to the study of Parkinson’s Disease (PD) through the implementation of a neurorobotics model via the integration of computational neuroscience with the latest robotic technology. Using the iCub as our robot platform, we adapted a computational model of the Basal Ganglia-Thalamo-Cortical (BG-T-C) circuit to investigate the efficiency of Deep Brain Stimulation (DBS) in mitigating PD’s motor *** to our methodology is the use of closed-loop DBS, a technique that adjusts stimulation parameters in real time based on specific kinematic and neuronal biomarkers of PD severity, such as fluctuations in beta band activity and tremor movements. This dynamic approach allows for a more personalized and efficient treatment regimen compared to traditional, static open-loop systems, which cannot adapt to the patient’s changing *** findings of our study corroborate the feasibility of using a neurorobotics model to simulate the motor symptoms of PD and provide evidence that closed-loop DBS can effectively modulate these symptoms. This was achieved by reducing the power spectral density at the beta-band frequency range (8-30 Hz) of the neural activity to below threshold levels and revealing a complex relationship between the severity of the disease and the effectiveness of the treatment.

关键词： Analytical models Adaptation models computational modeling Biological system modeling Biomarkers Brain modeling Real-time systems computational efficiency Robots Diseases

来源：评论

学校读者我要写书评

暂无评论

Prediction of Autism Spectrum Disorder Using AI and Machine Learning 18

Prediction of Autism Spectrum Disorder Using AI and Machine ...

引用

18th International conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Chauhan, Ritu Mehta, Khushi Eiad, Yafi Zuhairi, Megat Farez Center for Computational Biology and Bioinformatics Amity University Artificial Intelligence and IoT lab UP India Amity Institute of Biotechnology Amity University UP India University of Technology Sydney Faculty of Engineering & Information Technology SydneyNSW Australia Malaysian Institute of Information Technology University Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798350331011

Knowing the likelihood for a generational shift in the scientific exploration of digital data which has extensively outgrown society with tons of data. Moreover, the scientific community has evolved and outgrown themselves with Artificial Intelligence based technology to compute power and detect hidden features and information from outgrown digital databases. These advanced systems have the capability to evaluate enormous amounts of data generated with unprecedent speed, which can detect valuable insights from the data. In addition, the current study of approach focuses on the application of AI with healthcare databases to detect the hidden information from autism spectrum disorder (ASD). In this research we have various algorithmic predictions for Autism Spectrum Disorder (ASD) by combining Machine Learning with the dataset provided. The proposed approach is based on the predictive method where the data preprocessing and data evaluation models are used. The study of approach focuses on ASD with several machine Learning algorithms such as, SVM, KNN, Random Forest, Decision Tree to measure the accuracy and precision among the data and determine the hidden information and patterns. The current research scenario investigated concurrent parameters which are dependable features such as Class\ASD Traits, Sex, Jaundice scores and Q chat- 10 questions by which we have come to a conclusion which corroborated the current analysis with an accuracy Score of 74% for Random Forest, KNN: 71%, SVM: 70.6% and Decision Tree: 69.1% stating whether the individual has the Autism characteristics visible or not using various parametric analysis through data visualization and analyzing results by this approach. © 2024 ieee.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

SeqCCC: A Sequence-Informed Ligand-Receptor Interaction Prediction Method for Cell-Cell Communication Inference

SeqCCC: A Sequence-Informed Ligand-Receptor Interaction Pred...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Boya Ji Hong Xia Dezun Dong Shaoliang Peng College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cell-cell communications (CCCs) mediated by ligand-receptor interactions (LRIs) play a pivotal role in coordination and function of biological systems. The two primary steps of CCC inference methods are usually filtering significant LRIs and measuring the intercellular communication strength. Biological language models have demonstrated notable progress in bioinformatics across several biological problems, inspiring by the success of language models in the field of natural language processing. Here, we proposed a computational approach for CCC inference called SeqCCC. First, SeqCCC employed two sophisticated protein language models and the protein sequences of ligands and receptors to predict potential LRIs with the help of existing LRIs. Then, SeqCCC employed permutation test to filter significantly expressed LRIs from the single-cell expression matrix based on these known and predicted LRIs. Finally, SeqCCC calculated communication strength by applying the molecular diffusion and law of mass action in chemistry, and then it improves this by eliminating non-specific CCCs using a permutation test. In the results, SeqCCC yielded an average AUC of 0.93 (mean of the five folds, with a standard deviation of 0.02). The validity and reliability of SeqCCC are further supported by a comparative analysis with common CCC inference methods, which reveals high concordance in inference results. Additionally, SeqCCC provided different visualization methods for CCC results, including Circos Plot, Heatmap Plot, Dot Plot and Heatmap for LRIs. In conclusion, SeqCCC provides a new option for CCC inference by fusing biological language models with sequence information to improve our comprehension of intercellular communications.

关键词： Proteins Heating systems Protein engineering Visualization Biological system modeling computational modeling Predictive models Reliability bioinformatics Standards

来源：评论

学校读者我要写书评

暂无评论

Predicting Cancer Driver Genes: Leveraging Graph Convolutional Networks and Ensemble Learning

Predicting Cancer Driver Genes: Leveraging Graph Convolution...

引用

ieee Symposium on computational Intelligence and bioinformatics and computational Biology (CIBCB)

作者： Renan Soares de Andrades Mariana Recamonde-Mendoza Institute of Informatics UFRGS Bioinformatics Core HCPA Porto Alegre RS Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cancer driver genes (CDGs) are crucial in cancer development and are key targets for treatment. This study proposes a novel approach utilizing Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), to predict CDGs by integrating protein-protein interaction (PPI) networks and multi-omics data. We optimized GCN hyperparameters across six different PPI networks from literature, which improved predictive accuracy. A consensus model was created using genes identified across all networks. We used ensemble-based methods, including majority voting and score aggregation, to identify high-probability CDGs. Comparisons showed that ensemble models outperformed individual models. Our approach identified several candidate genes, including some previously unclassified, as potential CDGs in various cancers. Notably, 33 genes were consistently predicted as CDGs across all networks, highlighting their potential importance in cancer development. This study highlights the effectiveness of GNNs for CDG prediction and the value of integrating multi-omics data and diverse PPI networks for advancing cancer genomics research and targeted therapy development.

关键词： Proteins Learning systems Accuracy Graph convolutional networks Medical treatment Predictive models Hyperparameter optimization Ensemble learning bioinformatics Cancer

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88页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：