检索结果-内蒙古大学图书馆

Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Hari Krishnan Andi S. Senbagam Dilip Kumar Sharma Mayura Shelke N K Senthil Kumar Ramesh Chandra Poonia Centre for Postgraduate Studies Asia Metropolitan University Malaysia Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nandu India Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science AISSMS IOIT Pune India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad Uttar Pradesh India

In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called “Fast Captioning (FC)” to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Assistive technology with device for self-inflating bag in neonatology

引用

Research on Biomedical engineering 2025年第2期41卷

作者： Marcelino, Ana Carolina Rodovanski, Giovana Pascoali Bonetti, Karoline Panes, Caio Rabinovich de Almeida Martins, Fernando Moran, Cristiane Aparecida Araranguá Brazil Graduate Program in Neurosciences and Behavior Department of Experimental Psychology of the Institute of Psychology-University of São Paulo Sao Paulo Brazil Araranguá Brazil Computer Engineering Instituto Mauá de Tecnologia São Caetano do Sul Brazil Electronic Engineering and Computing with Emphasis on Electronic Devices and Systems at Instituto Tecnológico de Aeronáutica–ITA São Caetano do Sul Brazil Department of Physiotheraphy and at the Postgraduate Program in Rehabilitation Science of the Federal University of Santa Catarina-UFCS Ararangua Brazil

Purpose: The objectives of this study were to create a device with technological development of the self-inflating bag (SIB) equipment to measure the variables tidal volume, peak inspiratory pressure, and inspiratory flow and to evaluate the knowledge of undergraduate students in handling the SIB. To reduce the risks of mortality, length of hospital stay, and infections, SIB is a resource used to ventilate the patient through the inflation of the device, but it is not capable of measuring the ventilatory parameters. Excess ventilation and incorrect handling of the bag can cause lung injury in newborns. Therefore, measuring these parameters can prevent lung injury resulting from inadequate ventilation. Method: This is a cross-sectional and experimental study, with the development of a device SIB that allows the evaluation of variables of neonatal respiratory mechanics that were tested with an artificial lung. It was approved by the Research Ethics Committee and the research was developed in three stages: the first represents the search for scientific articles for the consensus of neonatal ventilatory variables for the construction of the SIB parameters;the second stage depicts the technological development of a device capable of monitoring such variables through the SIB. In the third stage, the content validation of the device with the handling of the SIB was carried out by students of medicine and physiotherapy courses of Brazil. Results: In this work, a device that analyzes the values of the main ventilatory parameters in neonatology was developed and to generate a safety range for the use of the device, curves with a tolerance of 10% up and down were created. All electronic components were coupled to the equipment, with a display that transmits the parameter values. The web page can be used on a cell phone, tablet, computer, or SmartTv, as long as it is connected to the "RespiratorIoT_AP" network. Among the 29 participants in the study, 8 said they were familiar w

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Autism Spectrum Disorder Identification Using Multi-Model Deep Ensemble Classifier with Transfer Learning

SSRN

引用

SSRN 2022年

作者： Herath, Lakmini Meedeniya, Dulani Marasingha, Janaka Weerasinghe, Vajira Tan, Tele Postgraduate Institute of Science University of Peradeniya Peradeniya Sri Lanka Department of Computer Science and Engineering University of Moratuwa Sri Lanka Department of Radiography/Radiotherapy Faculty of Allied Health Science University of Peradeniya Sri Lanka Department of Physiology Faculty of Medicine University of Peradeniya Sri Lanka School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia

Accurate identification of Autism Spectrum Disorder (ASD) is a challenging task that remains unsolved. The traditional subjective diagnostic process of ASD relies on time-consuming behavioral and psychological observations. In this study, we introduce an Ensemble Learningbased classification model using an open-access database focusing on functional magnetic resonance imaging (fMRI). We proposed a novel Multi-Model Ensemble Classifier (MMEC) and Multisite Ensemble Classifier (MSEC) with Transfer Learning (TL) for ASD classification to improve the prediction accuracy. The MMEC is composed of four base classifiers, Inception V3, ResNet50, MobileNet, and DenseNet to boost the performance of the individual Convolutional Neural Network (CNN) models. The MSEC combined the base classifiers trained from different data sites. We evaluated the two models with ensemble averaging, weighted averaging, and stacking methods. The proposed MMEC with stacking shows the state of art performance compared to MSEC, improving the prediction accuracy by 3.41%. The obtained results have shown an accuracy of 97.82%, 97.82%, and 97.78% for ensemble averaging, weighted averaging, and stacking methods, respectively, on Multi-site datasets. The ensemble classifier MMEC performed better than a single classifier on the Multi-site dataset. The proposed MMEC opens up a new paradigm to design a universal ASD classification framework. © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Secret Key Steganography: improve security level of LSB algorithm

Secret Key Steganography: improve security level of LSB algo...

引用

IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Ahmed Imad Hammoodi Al-Jarah Jorge Luis Ortega Arjona Postgraduate in Science and Computing Engineering National Autonomous University of Mexico (UNAM) Mexico City Mexico Science Faculty National Autonomous University of Mexico (UNAM) Mexico City Mexico

ISBN: (纸本)9781665406918

Internet has revolutionized the way people share information, store data, communicate, learn, and do business. Internet has been used for information transmission; this has changed in the last decade. Today, governments, businesses and individuals are increasingly relying on-line applications; the need to secure data exchange over the Internet and store it on the cloud has gained more importance. Data security aims to keep information safe by using cryptography and/or steganography. Steganography is a suitable candidate for keeping information safe, hiding the existence of data itself inside another medium. The Least Significant Bit (LSB) algorithm is one of the most common techniques to hide secret messages in an image. The main problem with LSB is knowing whether such an image has a secret message inside, making it easy to retrieve it by collecting the least significant bit from the steg-image. This paper presents an attempt to improve the security level of the LSB algorithm by using a secret steganography key. The proposed algorithm SK LSB is encoding the secret key from the cover image, encodes the secret message using the secret key, embedding it to the cover image. Even if attackers know about the steganography, they cannot know about the secret key. The SK LSB algorithm helps to get a higher and better level of security, useful for keeping sensitive, relevant, and important information stored. The results regarding this paper are compared with LSB algorithm regarding its Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) values aiming to show this method is more secure and does not affect the image imperceptibility.

关键词： Steganography Visualization PSNR Image coding Government Receivers Mean square error methods

来源：评论

学校读者我要写书评

暂无评论

Integrating Deep Learning in Project Management: Improving Efficiency and Effectiveness in Project Planning and Execution

Integrating Deep Learning in Project Management: Improving E...

引用

Innovation in Technology (ASIANCON), Asian Conference on

作者： Sunil Shukla Jagendra Singh Hemant Sahu Shilpa Kanthalia Taufikin Muniyandy Elangovan School of Computing Graphic Era Hill University Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CS & IT Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University) Udaipur India Faculty of Management Studies Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University) Udaipur India Department of Postgraduate State Islamic Institute (IAIN) Kudus Kudus Indonesia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The purpose of this research is to explore the use of deep learning technologies, namely the K-Nearest Neighbors model within the textile manufacturing process to test the improvement of the said process. To properly implement the model, we must use real-time data from the process and evaluate the improvement depending on a number of factors: how much does the maintenance cost decrease, how accurate is the inventory management, and how well the program forecasts demand, all of which should lead to an improvement of profit. First, the analysis of the results proved that the implementation of KNN model leads to a significant decrease of maintenance costs: while without the model the reduction was 15%, with it cost maintenance cost a 25% decrease. Second, the usage of a model improves inventory management accuracy: before the use of KNN, it was 85%, while after it rose to 95%. Third, demand forecasting becomes more accurate, as the difference was 75% to the positive without the model and 90% with it. The use of a model improved all aspects, and the most improved was profit: before the implementation of the KNN model, profit was 10%, and it was 40% after, which led to the conclusion that the implementation of deep learning models, namely the KNN model, improves the effectiveness of textile manufacturing process and positively affects the profitability and the production process as a whole.

关键词： Deep learning Analytical models Costs Accuracy Manufacturing processes Production Nearest neighbor methods Predictive models Maintenance Textiles

来源：评论

学校读者我要写书评

暂无评论

BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

引用

Computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications

arXiv

引用

arXiv 2022年

作者： Rožanec, Jože M. Novalija, Inna Zajec, Patrik Kenda, Klemen Tavakoli, Hooman Suh, Sungho Veliou, Entso Papamartzivanos, Dimitrios Giannetsos, Thanassis Menesidou, Sofia Anna Alonso, Ruben Cauli, Nino Meloni, Antonello Recupero, Diego Reforgiato Kyriazis, Dimosthenis Sofianidis, Georgios Theodoropoulos, Spyros Fortuna, Blaž Mladenić, Dunja Soldatos, John Jožef Stefan International Postgraduate School Jamova 39 Ljubljana1000 Slovenia Jožef Stefan Institute Jamova 39 Ljubljana1000 Slovenia Qlector d.o.o. Rovšnikova 7 Ljubljana1000 Slovenia Ubitech Ltd Digital Security & Trusted Computing Group Athens Greece Department of Informatics and Computer Engineering University of West Attica Agiou Spyridonos Street Egaleo Athens12243 Greece R2M Solution Srl Pavia Italy Department of Computer Science University of Cagliari Cagliari Italy Department of Digital Systems University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece INTRASOFT International 19.5 KM Markopoulou Ave. Peania GR 19002 Greece Kaiserslautern67663 Germany

Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focusing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Correction: New Extension of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Method Based on Cubic Pythagorean Fuzzy Environment: A Benchmarking Case Study of Sign Language Recognition Systems

引用

International Journal of Fuzzy Systems 2022年第7期24卷 3348-3348页

作者： Alamoodi, A. H. Albahri, O. S. Zaidan, A. A. AlSattar, H. A. Ahmed, Mohamed A. Pamucar, Dragan Zaidan, B. B. Albahri, A. S. Mahmoud, Mohammed S. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Faculty of Engineering & IT The British University Dubai UAE Department of Business Administration College of Administrative Science The University of Mashreq Baghdad Iraq Department of Logistics the University of Defence in Belgrade Belgrade Serbia Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Best Employee Selection Decision Support System Using Analytical Hierarchy Process (AHP)

引用

Journal of Physics: Conference Series 2021年第1期1908卷

作者： April Lia Hananto Bayu Priyatna Ahmad Fauzi Aviv Yuniar Rahman Yulia Pangestika Tukino Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Technology and Computer Science Universitas Buana Perjuangan Karawang Indonesia Department of Informatics Engineering Universitas Widyagama Malang Indonesia Faculty of Postgraduate Management Computer Indonesia of University

The award is given by the company to the best employees always to provide the best performance for companies that carry out duties and obligations in the company. How to give rewards to employees not only for how long they have been working at the company but about the results achieved. The gift can be in the form of salary or inventory from the company. So employees have great loyalty to the company. Decision making to determine the company can do the best employee by assessing the performance that has been done by employees at a specific time. Performance evaluation of Hon Chuan Company is influenced by several criteria, namely discipline, innovation, and responsibility. The support system for the best employee selection method uses the Analytical Hierarchy Process (AHP) method. The decision-making system is carried out to assess choices based on predetermined criteria. AHP method is applied to choose the best prospective employees. The calculation of the AHP method gives the results of the order of importance of criteria, namely discipline level (0.5584), responsibility (0.3196), and innovation (0.1220) and the best employee selection results obtained by Hendra (0.189), Vienna (0.189), Yin (0.145), Paul (0.109), Kandi (0.108), Kusworo (0.101), Bayu (0.088), and Ade Muhidin (0.072).

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：