检索结果-内蒙古大学图书馆

Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction

引用

Computers in Biology and Medicine 2025年 184卷 109413-109413页

作者： Akter, Simon Bin Akter, Sumya Tuli, Moon Das Eisenberg, David Lotvola, Aaron Islam, Humayera Fernandez, Jorge Fresneda Hüttemann, Maik Pias, Tanmoy Sarkar Martin Tuchman School of Management New Jersey Institute of Technology Newark NJ07102 United States Greenlife Medical College & Hospital Dhaka Bangladesh Department of Computer Science Virginia Tech College of Engineering BlacksburgVA United States Institute for Data Science and Informatics University of Missouri Columbia United States Department of Biochemistry Microbiology and Immunology Wayne State University School of Medicine DetroitMI United States Department of Information Management and Business Analytics Montclair State University Feliciano School of Business NJ United States Department of Oncology Wayne State University School of Medicine DetroitMI United States Center for Molecular Medicine and Genetics Wayne State University School of Medicine DetroitMI United States

The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health records (EHRs) frequently suffer from class imbalances, leading to prediction biases and differences between specificity and sensitivity, which hinder reliable model development despite the valuable insights contained in these datasets. To address this, we have introduced a novel approach to enhance MI risk prediction using self-reported attributes from the Behavioral Risk Factor Surveillance system (BRFSS) and the National Health Interview Survey (NHIS) dataset. Our approach incorporates three innovative techniques: the Dual-Path Artificial Neural Network (DP-ANN) to mitigate biased decision making across imbalanced datasets, the Triple Criteria Selection (TCS) for unbiased feature selection, and Minority Weighted Sampling (MWS) to tackle challenges of uncontrolled minority class sampling. These methods collectively enhance MI prediction and feature relevance. The DP-ANN model has achieved balanced performance, with an average specificity of 80%, sensitivity of 82%, and AUC–ROC of 89.5%, improving imbalance variance by approximately 14.96% compared to prior studies. By outperforming other models across four heavily imbalanced datasets, our approach demonstrates robustness and generalizability. Additionally, SHapley Additive exPlanations (SHAP) analysis has revealed key predictors and risk factors for MI, such as coronary heart disease and bronchitis in females, and stroke among individuals aged 35–54. In conclusion, our study provides a robust model for healthcare professionals to assess MI risk through targeted factors, promoting early detection and potentially improving patient outcomes. © 2024

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Automatically Labeling Aging Scenarios with a Machine Learning Approach 1

引用

24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Design of Combined Cycle Gas Turbine and IoT in Production Electricity from KIVU Lake Methane Gas 1

引用

16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Gakiza, Joram Chang, Kuo-Chi Chu, Kai-Chun Wang, Hsiao-Chuan Amesimenu, Governor David Kwabena Ahmad, Shoaib Haque, Shamim MdObaydul Chang, Fu-Hsiang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Information Science and Engineering Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia Department of Tourism Shih-Hsin University Taipei Taiwan

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In recent years, Lake Kivu, which has high levels of methane and carbon dioxide in Rwanda, has become a danger due to the release of toxins. The research process is that after the working cycle of the first starter ends, the work of the exhaust gas continues to generate heat, and then, the second heat engine can directly extract energy from the exhaust gas thermal energy. The engine can use different working gases. By transmitting multiple workflows, the system overall efficiency can be increased to 50–60%. The overall efficiency of a simple cycle can reach 34%, while the synthetic cycle can be as high as 64%. The theoretical efficiency of Carnot cycle reaches more than 84%. Similar to single-cycle thermal energy, synthetic cycles can be developed for industry and can provide lower temperature thermal energy in specific fields and other applications. This electricity from offshore power plants is new. It will solve the power problem in Rwanda and become a new energy source worldwide. However, the purpose of this study was to design for a cycle gas turbine facility that uses a power plant of combined cycle gas turbine system and the Internet of Things to convert chemical energy in methane gas into electricity throughout the process. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermal energy

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Strategy for Reducing Childhood Obesity Using Millet

A Machine Learning Strategy for Reducing Childhood Obesity U...

引用

International Conference on Smart Structures and systems (ICSSS)

作者： M. Birundadevi G. Premalatha M. Nalini Chelladurai Iyyanar Vettrivel Arul Department of Maruthuvam Sri Sairam Siddha Medical College and Research Center Chennai Tamilnadu India Department of Data Science and Business Systems S R M Institute of Science and Technology Chennai Tamilnadu India Department of ECE Panimalar Engineering College Chennai Tamilnadu India Department cum Library Literary Research and Documentation Siddha Central Research Institute Chennai Tamilnadu India Department Of Community Medicine Vinayaka Mission's Homoeopathic Medical College & Hospital (Deemed To Be University) Salem Tamilnadu India

To gain a more comprehensive understanding of the complex correlation between millets and the management of pediatric obesity, this research employs machine learning techniques. We meticulously assess the impact of a nutritional intervention utilizing millet on the body mass index (BMI), dietary patterns, and physical activity levels among children. Employing feature engineering, data preprocessing, and model training, this study enhances its outcomes via regression and classification analyses. The findings should shed light on concealed patterns, ascertain crucial predictors, and expand our comprehension of the potential of millets in mitigating childhood obesity. This methodology presents a novel outlook on the complex issues surrounding childhood obesity and the immense potential of millets in fostering a healthy lifestyle through the integration of machine learning techniques and nutritional research.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for geoscience:Progress,challenges,and perspectives

引用

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

来源：评论

学校读者我要写书评

暂无评论

Improving Google Flu Trends for COVID-19 estimates using Weibo posts

引用

data science and Management 2021年第3期3卷 13-21页

作者： Shuhui Guo Fan Fang Tao Zhou Wei Zhang Qiang Guo Rui Zeng Xiaohong Chen Jianguo Liu Xin Lu College of Systems Engineering National University of Defense TechnologyChangsha410073China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu611713China West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu610041China Research Center of Complex Systems Science University of Shanghai for Science and TechnologyShanghai200093China MD Department of Cardiology West China HospitalSichuan UniversityChengdu610041China School of Business Central South UniversityChangsha410083China Institute of Big Data and Internet Innovations Hunan University of Technology and BusinessChangsha410205China Institute of Accounting and Finance Shanghai University of Finance and EconomicsShanghai200433China

While incomplete non-medical data has been integrated into prediction models for epidemics,the accuracy and the generalizability of the data are difficult to *** comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19,a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established,called Weibo COVID-19 Trends(WCT),based on the post dataset generated by all users in Wuhan on Sina Weibo.A genetic algorithm is designed to select the keyword set for filtering COVID-19 related *** can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction *** remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98(P<0.01)to 0.86(P<0.01)during all analysis ***,WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak *** study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction,providing useful insights for the prediction of newly emerging infectious diseases at an early stage.

关键词： COVID-19 Epidemic estimates Weibo Google Flu Trends Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Boosting Anomaly Detection in Financial Transactions: Leveraging Deep Learning with Isolation Forest for Enhanced Accuracy

Boosting Anomaly Detection in Financial Transactions: Levera...

引用

Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Prateek Kumar Bansal Divya Nimma Nripendra Narayan Das BVN Prasad Paruchuri Harishchander Anandaram M. Karthik Institute of Business Management GLA University Mathura India PhD in Computational Science Data Analyst in UMMC University of Southern Mississippi USA Department of Information Technology Manipal University Jaipur India Department of CSE KL University(Deemed to be) Vijayawada India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Anomaly detection in financial transactions is paramount for safeguarding against fraudulent activities that pose significant risks to financial institutions and customers alike. Traditional methods often struggle to accurately identify complex and evolving patterns of fraud, necessitating innovative approaches that leverage advanced techniques such as deep learning and isolation forest. In this study, we propose a novel framework for boosting anomaly detection in financial transactions by integrating deep learning with isolation forest to achieve enhanced accuracy. Firstly, it employ an autoencoder, a type of neural network, to learn complex representations of normal transaction patterns and reconstruct input data. The autoencoder's ability to capture subtle variations in transaction attributes enables it to effectively distinguish between normal and anomalous instances based on the reconstruction error. Furthermore, we augment the anomaly detection process by incorporating isolation forest, a tree-based algorithm that isolates anomalies in the feature space by recursively partitioning data subsets. By combining the representation learning capabilities of deep learning with the outlier detection prowess of isolation forest, our framework offers a comprehensive solution for detecting fraudulent activities in financial transactions. Through experimentation on real-world financial datasets, we demonstrate the superior performance of our proposed framework compared to existing methods. The proposed method is implemented in Python software and has an accuracy of about 99.12% which is 1.49% higher than other existing methods like Conv-LSTM, Convolutional Neural Network (CNN)-LSTM, and CNN-GRU (Gated Recurrent Unit). Moreover, the integration of deep learning with isolation forest enables our framework to adapt to evolving patterns of fraud, ensuring robust and reliable anomaly detection in dynamic financial environments. Overall, our study contributes to the advancement

关键词： Deep learning Accuracy Autoencoders Forestry Boosting Software Fraud Software reliability Convolutional neural networks Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial

引用

Journal of Affective Disorders 2024年 351卷 15-23页

作者： Lin, Chemin Lee, Shwu-Hua Huang, Chih-Mao Wu, Yu-Wen Chang, You-Xun Liu, Ho-Ling Ng, Shu-Hang Cheng, Ying-Chih Chiu, Chih-Chiang Wu, Shun-Chi Department of Psychiatry Keelung Chang Gung Memorial Hospital Keelung City Taiwan College of Medicine Chang Gung University Taoyuan County Taiwan Community Medicine Research Center Chang Gung Memorial Hospital Keelung Keelung Taiwan Department of Psychiatry Linkou Chang Gung Memorial Hospital Taoyuan County Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Imaging Physics University of Texas MD Anderson Cancer Center Houston TX United States Department of Head and Neck Oncology Group Linkou Chang Gung Memorial Hospital and Chang Gung University Taoyuan Taiwan Department of Diagnostic Radiology Linkou Chang Gung Memorial Hospital and Chang Gung University Taoyuan Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Department of Psychiatry China Medical University Hsinchu Hospital China Medical University Hsinchu Taiwan Research Center of Big Data and Meta-analysis Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Psychiatry Taipei City Psychiatric Center Taipei City Hospital Taipei Taiwan Department of Psychiatry Taipei Medical University Taipei Taiwan

Background: Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. Methods: A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. Results: A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. Limitations: Sample size is small with only marginal clinical effect. Conclusion: These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression. © 2024

关键词： Cognitive decline Fish oil Late-life depression Multi-scale entropy Omega-3 PUFAs Resting-state fMRI

来源：评论

学校读者我要写书评

暂无评论

Study of Reduction of Inrush Current on a DC Series Motor with a Low-Cost Soft Start system for Advanced Process Tools 6th

Study of Reduction of Inrush Current on a DC Series Motor wi...

引用

6th International Conference on Advanced Intelligent systems and Informatics, AISI 2020

作者： Amesimenu, Governor David Kwabena Chang, Kuo-Chi Sung, Tien-Wen Wang, Hsiao-Chuan Shyirambere, Gilbert Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The existence of high armature current is the main limitation while starting a DC series motor. This initial current can have dangerous effects on the DC motor such as damage of windings and reduction in the lifetime of the machine. There are two basic methods of starting of DC motor namely the resistor starting method and the soft start using solid-state devices. In this project, to lessen the inrush current, the solid-state method was adopted to start the motor smoothly. The system uses Arduino Atmega328p-PU Microcontroller to send pulses to the motor driver which regulates the running of the motor. By focusing on DC series motor, power electronic converters and microcontrollers, the system was modeled and simulated in Proteus ISIS Software. The results recorded from the soft starter system were compared with a direct online starting system considering the starting current and voltage of the motor with an open loop control system. The soft starter system was able to decrease the direct online current value from 0.996A to 0.449A which represents approximately 28.1% reduction of the starting current to protect the motor. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DC motors

来源：评论

学校读者我要写书评

暂无评论

Cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes 1

引用

作者： Chang, Kuo-Chi Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In the case of power grid cascading trip, this study puts forward a strategy to prevent cascading trip by changing the output of generator set. Firstly, according to the specific performance of the power grid under the cascade tripping, combined with the action behavior of the line current-type backup protection, and an expression equation for measuring the safety level index between the power grid and the cascading trip failure is given. Secondly, the vulnerable branch is found out through the safety level index, the output of the key power generation nodes on the branch is taken as the optimization variable, and the optimization model is established to improve the safety level index under the chain trip of the power grid. Finally, a combination of sensitivity analysis and particle swarm optimization(PSO) was used to analyze an example on the IEEE-39 node system. The results demonstrate the feasibility and effectiveness of the model and method, and provide a reference for preventing chain trip accidents. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitivity analysis

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：