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

369 篇 会议
15 篇 期刊文献
2 册 图书

馆藏范围

386 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

321 篇 工学
- 284 篇 计算机科学与技术...
- 152 篇 软件工程
- 57 篇 信息与通信工程
- 44 篇 电气工程
- 34 篇 生物医学工程（可授...
- 22 篇 生物工程
- 20 篇 光学工程
- 17 篇 电子科学与技术（可...
- 17 篇 控制科学与工程
- 7 篇 机械工程
- 7 篇 化学工程与技术
- 6 篇 材料科学与工程（可...
- 6 篇 核科学与技术
- 5 篇 动力工程及工程热...
- 5 篇 建筑学
104 篇 理学
- 79 篇 数学
- 29 篇 统计学（可授理学、...
- 23 篇 生物学
- 23 篇 系统科学
- 18 篇 物理学
- 8 篇 大气科学
- 7 篇 化学
53 篇 管理学
- 31 篇 图书情报与档案管...
- 27 篇 管理科学与工程(可...
- 17 篇 工商管理
24 篇 医学
- 23 篇 临床医学
- 21 篇 基础医学(可授医学...
- 13 篇 药学(可授医学、理...
- 10 篇 公共卫生与预防医...
9 篇 法学
- 6 篇 社会学
5 篇 农学
4 篇 教育学
3 篇 经济学
1 篇 历史学
1 篇 军事学

主题

23 篇 big data
19 篇 cloud computing
11 篇 data mining
10 篇 soft sensors
8 篇 servers
8 篇 forecasting
7 篇 wireless sensor ...
7 篇 computational mo...
7 篇 soft computing
6 篇 data privacy
6 篇 mapreduce
6 篇 machine learning
5 篇 support vector m...
5 篇 data center
5 篇 decision trees
5 篇 genetic algorith...
5 篇 security
5 篇 classification
5 篇 encryption
5 篇 training

机构

6 篇 college of compu...
2 篇 department of co...
2 篇 zongheng sensors...
2 篇 arab academy for...
2 篇 simon fraser uni...
2 篇 centre for medic...
2 篇 school of inform...
2 篇 ici in smart man...
2 篇 faculty of compu...
2 篇 department of in...
2 篇 institute of man...
2 篇 institute of com...
2 篇 artificial intel...
2 篇 andhra univ dept...
2 篇 department of co...
2 篇 institute for ad...
2 篇 bio tech sphere ...
2 篇 college of compu...
2 篇 univ petr & ener...
2 篇 department of co...

作者

2 篇 nagaraj srinivas...
2 篇 shefali singh
2 篇 dehuri satchidan...
2 篇 sun huiyu
2 篇 raju g. s. v. pr...
2 篇 vital t. pandura...
2 篇 ritu singh
2 篇 sun xingming
2 篇 ankit singh
2 篇 shamsolmoali pou...
2 篇 chen bing
2 篇 liu yang
2 篇 haqiq abdelkrim
2 篇 jie li
2 篇 raju g. s. v. p.
2 篇 tomar pradeep
2 篇 gosain anjana
2 篇 selvamani k.
2 篇 desai jaydev p.
2 篇 zhou zhili

语言

382 篇 英文
3 篇 其他
2 篇 中文

检索条件"任意字段=1st International Conference on Soft Computing in Data Science, SCDS 2015"

共 386 条记录，以下是31-40 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：

Towards a Sustainable Future: Analyzing Water Quality Changes for Environmental Sustainability

Towards a Sustainable Future: Analyzing Water Quality Change...

引用

Smart Technology (ICE-SMARTec), IEEE international conference on

作者： Nur Suhaili Mansor Hapini Awang Nor Hazlyna Harun Mohamad Ghozali Hassan Sarkin Tudu Shehu Malami Noraishah Shamsuddin Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Malaysia Disaster Management Institute (DMI) School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Nigeria ICI in Smart Manufacturing Sirim Bhd Shah Alam Malaysia

The development of industrial areas is frequently thought to have a good impact on society because it helps to meet the population's requirements. However, it also negatively impacts the environment, such as river pollution. If a country experiences rapid economic growth without considering the effects of industrial development on the environment, then human well-being will be affected. In this context, advanced development is attained by a developed nation while ensuring the well-being of society. Uncontrolled and unsustainable development increases environmental pollution, adversely affecting human health. As water is essential for human life, water resources, such as rivers, must be properly managed and protected to ensure human well-being. Thus, the study conducted on the changes in the water quality of the Kim Kim River provides insights into how unintended consequences of urban and industrial development can be identified and addressed using three sets of satellite imagery data from 2015, 2019 and 2020. The study was led to identify research problems and achieve the study objectives to obtain research results. The results of image classification analyses indicate that the development area near Sungai Kim Kim increased to 50%, with 38% from industrial areas. The RMSE for the image classification is 1.51, significantly better than the minimum allowed value of 2.00. It provides an understanding of the change in river water quality because of urban and industrial development. By highlighting the importance of sustainable development and the need to balance economic growth with environmental protection, this study contributes to the development of effective policies and strategies for the sustainable development of industrial areas.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review on Customer Feedback Classification Employing Deep Learning Methodologies for Recommend Systems

A Systematic Review on Customer Feedback Classification Empl...

引用

international conference on Computational science and Technology (ICCst)

作者： Nidamanuri Srinu J. Paramesh K. Bala Department of CSE School of Computing Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9781665476560

An efficient recommender system's (RS) significant requirement remains to give useful data concerning users and objects for producing top-grade suggestions. Written customer review remains a copious source of data, which could provide perceptions into the RS. Because of technological development, an enormous quantity of social web data rises in volume, subjectivity, and heterogeneity turns competing to prepare physically. Deep Learning (DL) approaches are employed in dealing with this arduousness in live implementations. This study attempts to demonstrate the utility, scope, and application of DL approaches' association with customer feedback in the discipline of hospitableness and touristry. A systematized literature review is exhibited for correlating, assessing, examining, and contemplating the trials and trajectories to seek research gaps in portraying the prospective scope of this pairing. The foremost intention remains to read and assess DL approaches. This study possesses important applications for service providers concerning establishing managerial strategies for customers regarding choosing services, which fulfill their requisites. Additionally, there remains a great effect on scholars concerning future research directions.

关键词： Deep learning Systematics Scientific computing Bibliographies soft sensors Employment Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence with MRI-Guided Radiation Therapy for Cancer Treatment 1st

Artificial Intelligence with MRI-Guided Radiation Therapy fo...

引用

1st international conference on Intelligent Systems in computing and Communications, ISCComm 2023

作者： Priyadharshini, K. Mohan, Divya Bhat, Amulya S. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Sahyadri College of Engineering & Management Mangaluru India

ISBN: (纸本)9783031756047

Radiation oncology is a complex and highly technological area of medicine that relies heavily on digital data processing and computer software;New developments in offline and online MRI-guided RT are described in this paper, along with the exciting prospects they present for advancing research and clinical care, the challenges they present, and the necessity for interdisciplinary collaboration to address those challenges. One of the cornerstones of cancer care is radiation therapy (RT), which is still widely used today. Recently, a lot of work has gone into making MRI a regular part of clinical RT preparation and evaluation. MRI-guided RT is a hybrid technique that combines MRI’s capabilities with those of RT because to MRI’s enhanced contrast imaging of soft tissues, imaging of moving organs, and monitoring of physiological changes in tumors and other tissues. Clinical contexts can be evaluated quantitatively rather than qualitatively, allowing for the discovery of complicated patterns in medical data. Artificial Intelligence (AI) systems are ideally suited. As molecular imaging and personalized medicine have progressed it has interest in and importance placed on nuclear theranostic approaches, which can aid in providing individualized care for a wide range of illnesses by facilitating more accurate diagnosis, better prognosis forecasting, and more precise patient selection. In the case of prostate cancer (PC), imaging is utilized both to detect local recurrence for salvage therapy and to rule out metastases, which require systemic treatment. Hence this paper proposes AI based MRI-RT (AI-MRI-RT) to mitigate the challenges prescribed above. The purpose of this research is to investigate how prostate cancer radiation relates to diagnostic imaging and it provides a narrative and analytical report of the impact of cutting-edge approaches in the management of prostate cancer, particularly in pretreatment prognostication, radiation planning, and the incorporation of syst

关键词： Molecular imaging

来源：评论

学校读者我要写书评

暂无评论

An Efficient Numerical Method for Singularly Perturbed Parabolic Problems with Non-smooth data 1st

An Efficient Numerical Method for Singularly Perturbed Parab...

引用

1st international conference on Computational sciences - Modelling, computing and soft computing, CSMCS 2020

作者： Yadav, Narendra Singh Mukherjee, Kaushik Department of Mathematics Indian Institute of Space Science and Technology Thiruvanthapuram695547 India

ISBN: (纸本)9789811647710

This article deals with a class of singularly perturbed convection-diffusion parabolic problems with discontinuous source term exhibiting both boundary and weak interior layers. In order to solve this class of problems, we discretize the time derivative by the backward-Euler method on the uniform mesh;and for the spatial discretization, a new finite difference scheme is proposed utilizing a layer resolving piecewise-uniform Shishkin mesh. We discuss the monotonocity of the proposed method. Further, we demonstrate through the numerical experiment that the proposed method converges uniformly with respect to the perturbation parameter Ε;and is almost second-order accurate in space, regardless of the larger and smaller values of Ε. In addition to this, we compare the present numerical method with the classical implicit upwind finite difference scheme. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Boundary layers

来源：评论

学校读者我要写书评

暂无评论

MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble 1st

MRI-Based Head and Neck Tumor Segmentation Using nnU-Net wit...

引用

1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Mol, Frank N. Hoek, Luuk van der Ma, Baoqiang Nagam, Bharath Chowdhary Sijtsema, Nanna M. van Dijk, Lisanne V. Bunte, Kerstin Vlijm, Rifka Van Ooijen, Peter M. A. Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands University of Groningen University Medical Center Groningen Groningen9700 RB Netherlands

ISBN: (纸本)9783031832734

The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications chal- lenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2- weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopt- ing the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robust- ness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. The mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. These metrics determine the final ranking of methods for both tasks separately. The final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2. © The Author(s) 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Assessing Self-supervised xLstM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications 1st

Assessing Self-supervised xLSTM-UNet Architectures for Head ...

引用

作者： Qayyum, Abdul Mazher, Moona Niederer, steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031832734

Radiation therapy (RT) plays a pivotal role in treating head and neck cancer (HNC), with MRI-guided approaches offering superior soft tissue con- trast and daily adaptive capabilities that significantly enhance treatment preci- sion while minimizing side effects. To optimize MRI-guided adaptive RT for HNC, we propose a novel two-stage model for Head and Neck Tumor Segmenta- tion. In the first stage, we leverage a Self-Supervised 3D student-Teacher Learn- ing Framework, specifically utilizing the DINOv2 architecture, to learn effec- tive representations from a limited unlabeled dataset. This approach effectively addresses the challenge posed by the scarcity of annotated data, enabling the model to generalize better in tumor identification and segmentation. In the second stage, we fine-tune an xLstM-based UNet model that is specifically designed to capture both spatial and sequential features of tumor progression. This hybrid architecture improves segmentation accuracy by integrating temporal dependen- cies, making it particularly well-suited for MRI-guided adaptive RT planning in HNC. The model’s performance is rigorously evaluated on a diverse set of HNC cases, demonstrating significant improvements over state-of-the-art deep learning models in accurately segmenting tumor structures. Our proposed solu- tion achieved an impressive mean aggregated Dice Coefficient of 0.81 for pre- RT segments and 0.65 for mid-RT segments, underscoring its effectiveness in automated segmentation tasks. This work advances the field of HNC imaging by providing a robust, generalizable solution for automated Head and Neck Tumor Segmentation, ultimately enhancing the quality of care for patients undergo- ing RT. Our team name is DeepLearnAI (CEMRG). The code for this work is available at https://***/RespectKnowledge/SSL-based-DINOv2_Vision- LstM_Head-and-Neck-Tumor_Segmentation. © The Author(s) 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Plant Disease Recognition and Crop Recommendation System using Deep Learning

Plant Disease Recognition and Crop Recommendation System usi...

引用

international conference on Computational science and Technology (ICCst)

作者： Sreeraj. R Prayagdev. E Vishnu Murali Jaefer Akhilkumar K S Dept. of computer science and engineering Universal Engineering College Thrissur Kerala India

ISBN: (纸本)9781665476560

Agriculture is the foundation of our country. India, the world's second-largest producer of agricultural goods, generates over 280 million tonnes annually, which accounts for more than 15% of its GDP. A.A.R.C.S. (Advanced Agricultural Recommendation and Classification System) is a flutter-based farmer-friendly Mobile Application. One of the features of the proposed system is the crop recommendation feature that recommends correct crop by considering parameters like potassium, phosphorous and nitrogen contents, temperature, rainfall, and humidity. The second feature is seedling classification (using transfer learning), to identify the plants at an early stage. And as the third feature, the system also uses transfer learning to teach the model to detect common plant diseases. The above two models accept the image as an input and recognize and classify the image into the corresponding class. The proposed system uses edge-computing technology (Deep-Learning based model inside the mobile application) which simply means “taking action on data as near to the source as possible rather than in a central, remote data center, to reduce latency and bandwidth use”, i.e. the classification will be done on-device itself, thus overcoming the need of high latency and high bandwidth network links. The application also features a common platform to list the products of the farmers and tools and lands for rent service by providing the respective contact numbers of the owners. There will also be an encrypted community chat option which will be implemented using network sockets. The proposed system also contains an admin dashboard that provides infographics about users, common diseases, chat usage, push notification service and its analytics, etc. The mobile application will be the data source for disease-based analytics.

关键词： Productivity Plant diseases Scientific computing soft sensors Sockets Transfer learning Crops

来源：评论

学校读者我要写书评

暂无评论

Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet 1st

Head and Neck Gross Tumor Volume Automatic Segmentation Usin...

引用

作者： Twam, Awj Celaya, Adrian Lim, Evan Elsayes, Khaled Fuentes, David Netherton, Tucker Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX United States Institute of Data Science in Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Abdominal Imaging Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Radiation Physics The University of Texas MD Anderson Cancer Center HoustonTX United States

ISBN: (纸本)9783031832734

Head and neck cancer (HNC) represents a significant global health burden, often requiring complex treatment strategies, including surgery, chemotherapy, and radiation therapy. Accurate delineation of tumor volumes is critical for effective treatment, particularly in MR-guided interventions, where soft tissue contrast enhances visualization of tumor boundaries. Manual segmentation of gross tumor volumes (GTV) is labor intensive, time-consuming and prone to variability, motivating the development of automated segmentation techniques. Convolutional neural networks (CNNs) have emerged as powerful tools in this task, offering significant improvements in speed and consistency. In this study, we participated as Team Pocket in Task 1 of the HNTS-MRG 2024 Grand Challenge, which focuses on the segmentation of gross tumor volumes of the primary tumor (GTVp) and the nodal tumor (GTVn) in pre-radiotherapy MR images for HNC. We evaluated the application of PocketNet, a lightweight CNN architecture, for this task. Results for the final test phase of the challenge show that PocketNet achieved an aggregated Dice Sorensen Coefficient (DSCagg) of 0.808 for GTVn and 0.732 for GTVp, with an overall mean performance of 0.77. These findings demonstrate the potential of PocketNet as an efficient and accurate solution for automated tumor segmentation in MR-guided HNC treatment workflows, with opportunities for further optimization to enhance performance. © The Author(s) 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Tropical Cyclone Predicting System Model based on Ensemble Learning

Tropical Cyclone Predicting System Model based on Ensemble L...

引用

international conference on Computational science and Technology (ICCst)

作者： P. Kirubanantham A. Saranya D. Senthil Kumar Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Sai Ram Engineering College Chennai India

ISBN: (纸本)9781665476560

Every year, tropical cyclones cause a great deal of devastation and loss of life and property. Traditional strategies for forecasting tropical cyclone tracks pose various problems in terms of forecast reliability and precision as vast quantities of meteorological and tracking data continue to accumulate, Machine learning techniques have recently been competent enough to work with spatial and temporal features from a lot of data while being extremely efficient and accurate for forecasting data in big structures. Ensemble learning is a method of creating and combining several different models to solve a particular computational problem getting the highest precision. An ensemble can perform better and is able to make better predictions with better performance compared to a single model. The existing model uses the Numerical Weather Prediction (NWP) Model with Sanders Barotropic technology, deep learning applied with Convolutional Neural Network, and GRU using a dataset consisting of actual past hurricanes and cyclones occurring in the years 1945 to 2015, where the complex correlation of meteorological variables is not considered. Hence, we proposed, Boosting Tropical Cyclone Prediction Efficiency Through Time Series Analysis using an Ensemble Learning Approach. Hereby enabling support for Auto Correlation Analysis and reducing the dimension of input variables by removing irrelevant noise data. Ensemble Analysis is done efficiently in this use case giving us better prediction accuracy. The proposed system predicts cyclone status using real-world meteorological variables improving the overall accuracy and precision

关键词： Tropical cyclones Scientific computing Computational modeling Time series analysis Weather forecasting Predictive models Numerical models

来源：评论

学校读者我要写书评

暂无评论

data-Driven Regression-Based Compartmental Model to Identify the Dynamical Behavior of Dengue Incidences in Urban Colombo 1st

Data-Driven Regression-Based Compartmental Model to Identify...

引用

1st international conference on Computational sciences - Modelling, computing and soft computing, CSMCS 2020

作者： Erandi, K.K.W.H. Perera, S.S.N. Mahasinghe, A.C. Research and Development Centre for Mathematical Modelling Department of Mathematics University of Colombo Colombo Sri Lanka

ISBN: (纸本)9789811647710

Dengue is a mosquito–borne viral disease that has rapidly spread in tropical and subtropical regions. Understanding the transmission dynamics of dengue incidences is vital in developing appropriate strategies to control potential outbreaks of the diseases. In literature, the SIR- UV model is one of the mostly–used mathematical model to describe the dynamics of the disease, of which the solvability of the relevant system of differential equations seems to be uncertain. However, this system can be reduced down into a two–dimensional system by considering quasi–equilibrium for infected vector population. Once the reduced system is considered, its solution requires the estimation of per–capita vector density, which becomes an infeasible task due to lack of resources as well as complexity of external impacts. Since the per–capita vector density heavily depends upon the climate factor, we propose a generalized linear regression formula to estimate this by capturing the seasonal pattern of dengue dynamics according to the effect of climate factors in urban Colombo. Further, we compare the dynamical behaviour of the infected host population in the simplified model with reported dengue incidences. Our simulation shows that the dynamical behaviour of the infected host population captures the seasonality of the reported dengue incidences. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Dynamics

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39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：