检索结果-内蒙古大学图书馆

Poor-prognosis young-onset colorectal cancer is defined by the mesenchymal subtype and can be predicted by integrating molecular and histopathological characteristics

ESMO Gastrointestinal Oncology

引用

ESMO Gastrointestinal Oncology 2025年 9卷

作者： J. Ke Y. Li L. Qi X. Li W. Wang S. Ten Hoorn Y. Zhu H. Huang F. Gao L. Vermeulen X. Wang Department of Colorectal Surgery The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China Guangdong Institute of Gastroenterology Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Department of Biomedical Sciences City University of Hong Kong Kowloon Tong Hong Kong SAR Department of Surgery The Chinese University of Hong Kong Sha Tin Hong Kong SAR Li Ka Shing Institute of Health Sciences The Chinese University of Hong Kong Sha Tin Hong Kong SAR Department of Pathology The First Affiliated Hospital of Anhui Medical University Hefei China Laboratory for Experimental Oncology and Radiobiology (LEXOR) Center for Experimental Molecular Medicine (CEMM) Academic Medical Center (AMC) University of Amsterdam Amsterdam The Netherlands National Clinical Research Centre for Geriatric Disorders Department of Geriatrics Xiangya Hospital Central South University Changsha Hunan China State Key Laboratory of Digital Medical Engineering School of Biological Science and Medical Engineering Southeast University Nanjing China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Background Young-onset colorectal cancer (CRC), affecting individuals <50 years of age, presents a significant health threat worldwide. The molecular and clinical characteristics of young-onset CRC are poorly understood, complicating the development of effective biomarkers for precision oncology. This study aimed to dissect age-dependent molecular heterogeneity of CRC and establish a model for identifying high-risk young-onset patients. Methods We analyzed clinical data for 564 439 patient samples across three large cohorts. For molecular characterizations, a subset of 1874 patient samples was used. A deep learning framework was used to analyze hematoxylin–eosin-stained whole-slide images to quantify Shannon diversity indices (SDIs). Subsequently, a multivariate model, integrating SDI, microsatellite status and promoter methylation of miR-200s, was developed for predicting the consensus molecular subtype (CMS)4-mesenchymal subtype, followed by internal and external clinical validations. Results Young-onset CRC patients exhibited better overall survival but worse relapse-free survival and higher metastasis rates compared with late-onset cases. Molecular subtyping analysis found that young-onset CRC also comprises the same four subtypes (CMS1-4), but the prevalence differs from late-onset CRC. Stratified analysis suggested that the poor outcomes in young-onset CRC were due to higher prevalence of the CMS4-mesenchymal subtype. To predict CMS4, we established an effective risk-scoring model (area under the curve = 0.87) combining molecular and histological markers, with multiple independent validations. Conclusions CRC shows age-dependent molecular heterogeneity, with young-onset cases more frequently presenting the CMS4 subtype. To predict CMS4, we developed and validated a robust risk-scoring model integrating molecular and histological markers, offering a new translatable tool for more optimized management of young-onset patients.

关键词： colorectal cancer young-onset patients consensus molecular subtypes tumor heterogeneity subtype-specific biomarker

来源：评论

学校读者我要写书评

暂无评论

Gradient-based multi-area distribution system state estimation

arXiv

引用

arXiv 2019年

作者： Zhou, Xinyang Liu, Zhiyuan Guo, Yi Zhao, Changhong Chen, Lijun Power System Engineering Department National Renewable Energy Laboratory GoldeCO80401 United States Department of Computer Science University of Colorado BoulderCO80309 United States Department of Mechanical Engineering University of Texas at Dallas RichardsonTX United States Department of Information Engineering Chinese University of Hong Kong Hong Kong

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. The real-time implementation of the algorithm tracks time-varying system states with high accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： State estimation

来源：评论

学校读者我要写书评

暂无评论

Federated variance-reduced stochastic gradient descent with robustness to Byzantine attacks

arXiv

引用

arXiv 2019年

作者： Wu, Zhaoxian Ling, Qing Chen, Tianyi Giannakis, Georgios B. School of Data and Computer Science Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University GuangzhouGuangdong510006 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical and Computer Engineering Digital Technology Center University of Minnesota MinneapolisMN55455 United States

This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the 'honest' workers. This motivates us to reduce the variance of stochastic gradients as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to attain provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine attack resilient distributed SGD. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Multi-modal digital twins for industrial anomaly detection: Framework, method, and application

引用

Robotics and computer-Integrated Manufacturing 2026年 97卷

作者： Cheng Ren Ming Li Cailian Chen Xinping Guan George Q. Huang Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong China Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China Research Institute for Advanced Manufacturing The Hong Kong Polytechnic University Hung Hom Hong Kong China Collaborative Generative AI (Co-GenAI) Research Centre The Hong Kong Polytechnic University Hung Hom Hong Kong China Research Centre for Digital Transformation of Tourism The Hong Kong Polytechnic University Hung Hom Hong Kong China Centre for Transformative Garment Production Hong Kong Science Park Pak Shek Kok Hong Kong China

Anomaly detection plays a key role in maintaining the reliable and stable operation of industrial systems, especially in high-reliability fields. Conventional single-modal data cannot provide comprehensive information about the detected object, resulting in false or missed detection. To address the challenges of complex anomaly patterns and heterogeneous data in industrial scenarios, we propose MMDT-IAD, a multi-modal digital twin (DT)-based anomaly detection framework that integrates edge–cloud collaboration. By lever- aging physical, geometric, visual, and semantic modalities, MMDT-IAD constructs a comprehensive virtual representation of monitored objects and enables real-time, scalable detection across distributed industrial environments. Next, to enable efficient fusion of heterogeneous DT modalities, we propose a One-Primary- Three-Auxiliary (1P3A) cross-modal decision fusion strategy. Finally, we apply the MMDT-IAD frame-work to the anomaly detection of aviation electrical connector pins, and present a detailed application process. The experimental results prove the effectiveness of the MMDT-IAD framework in detecting abnormal pins. Moreover, we discuss the generality of MMDT-IAD framework considering several common industrial anomalies. These results highlight the potential of MMDT-IAD framework and 1P3A method to significantly improve anomaly detection in other complex industrial scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Attitude and Horizontal Speed Control system on Unmanned Aerial Vehicle Using LQR

Attitude and Horizontal Speed Control System on Unmanned Aer...

引用

International Conference on Science and Technology-computer (ICST)

作者： Andi Dharmawan Lazuardi Ichsan Hanif Baskoro Jazi Eko Istiyanto Ariesta Martiningtyas Handayani Electronics and Instrumentation Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Computer System and Networking Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical and Informatics Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

Unmanned Aerial Vehicles (UAV) or drones are expected to fly automatically, especially in performing basic maneuvers. The necessary maneuver is to fly straight and level, where no rotation occurs on the roll, pitch, and yaw axes that work on the plane. Also, to free from rotational motion, the aircraft needs to maintain its horizontal speed to prevent the vehicle from losing lift and free-fall. This research uses a Linear Quadratic Regulator (LQR) control method to obtain full-state feedback K gain from the control system. The gain of the full-state feedback is used to control the output, which is converted into Pulse Width Modulation to control the rotational speed of the brushless motor, and the angle of servo. The result of this research is that system response obtained is optimum with the desired specification.

关键词： Control systems Angular velocity Unmanned aerial vehicles Mathematical model Attitude control Force Torque

来源：评论

学校读者我要写书评

暂无评论

Challenges in cybersecurity: Lessons from biological defense systems

arXiv

引用

arXiv 2021年

作者： Schrom, Edward Kinzig, Ann Forrest, Stephanie Graham, Andrea L. Levin, Simon A. Bergstrom, Carl T. Castillo-Chavez, Carlos Collins, James P. De Boer, Rob J. Doupé, Adam Ensafi, Roya Feldman, Stuart Grenfell, Bryan T. Halderman, J. Alex Huijben, Silvie Maley, Carlo Moses, Melanie Perelson, Alan S. Perrings, Charles Plotkin, Joshua Rexford, Jennifer Tiwari, Mohit Department of Ecology and Evolutionary Biology Princeton University 106A Guyot Hall PrincetonNJ08544 United States School of Life Sciences Arizona State University TempeAZ85287 United States Julie Ann Wrigley Global Institute of Sustainability Arizona State University TempeAZ85287 United States Biodesign Center for Biocomputation Security and Society Arizona State University TempeAZ85287 United States School of Computing Informatics and Decision Sciences Engineering Arizona State University TempeAZ85287 United States Santa Fe Institute Santa FeNM87501 United States Department of Biology University of Washington SeattleWA98195 United States Arizona State University TempeAZ85287 United States Theoretical Biology and Bioinformatics Utrecht University Utrecht3584 CH Netherlands Center for Cybersecurity and Digital Forensics Global Security Initiative Arizona State University TempeAZ85287 United States Department of Electrical Engineering and Computer Science Computer Science and Engineering Division University of Michigan Ann ArborMI48109 United States Schmidt Futures New YorkNY10011 United States Princeton School of Public and International Affairs Princeton University PrincetonNJ08544 United States Center for Computer Security and Society University of Michigan Ann ArborMI48109 United States Center for Evolution and Medicine School of Life Sciences Arizona State University TempeAZ85287 United States Arizona Cancer Evolution Center Arizona State University TempeAZ85287 United States Department of Computer Science University of New Mexico AlbuquerqueNM87131 United States University of New Mexico Department of Computer Science AlbuquerqueNM87131 United States Department of Biology University of New Mexico AlbuquerqueNM87131 United States Theoretical Biology and Biophysics Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 Unite

We explore the commonalities between methods for assuring the security of computer systems (cybersecurity) and the mechanisms that have evolved through natural selection to protect vertebrates against pathogens, and how insights derived from studying the evolution of natural defenses can inform the design of more effective cybersecurity systems. More generally, security challenges are crucial for the maintenance of a wide range of complex adaptive systems, including financial systems, and again lessons learned from the study of the evolution of natural defenses can provide guidance for the protection of such *** Codes A.0. Copyright © 2021, The Authors. All rights reserved.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Auto Landing system on A Fixed Wing Unmanned Aerial Vehicle Using Linear Quadratic Approach

Auto Landing System on A Fixed Wing Unmanned Aerial Vehicle ...

引用

International Conference on Science and Technology-computer (ICST)

作者： Andi Dharmawan Ahmad Ashari Ariesta Martiningtyas Handayani Riarsari Meirani Utami I Made Tresnayana Electronics and Instrumentation Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Computer System and Networking Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical and Informatics Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

The primary control issue on Unmanned Aerial Vehicle (UAV) is to control the rotation motion in the form of pitch, roll, and yaw. These rotational movements are used for various flight phases. The flight phases are at least divided into 3, consisting of taking off, cruising, and landing phases. The landing phase is one of the most critical phases. If the flight control of a UAV in this phase is not optimal, then it can crash. The landing phase control system in this research uses the Linear Quadratic Regulator (LQR) method. This method works on a full-state feedback control system. This control system will produce style and torque, both of which will be converted to PWM (Pulse Width Modulation) signals and the servos deflection angle values. The PWM signals play a role in regulating how fast the brushless motor rotates. The control system is used to maintain the stability of the UAV upon landing so that the UAV does not experience undesirable things. Landing phase testing results show that the control system has been able to land the UAV with an optimal control response.

关键词： Unmanned aerial vehicles Control systems Sensors Nose Force Angular velocity Elevators

来源：评论

学校读者我要写书评

暂无评论

Lifespan music training experience changes duration and transition rates of EEG microstates related to working memory

引用

Brain-Apparatus Communication: A Journal of Bacomics 2025年第1期4卷

作者： Yan Li Sijia Guo Jiaxian Chen Liangfeng Feng Haoyu Bian Yuxin Chen Hua Yang Jing Lu a The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu Chinab School of Life Science and Technology Center for Information in Medicine University of Electronic Science and Technology of China Chengdu China a The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu Chinac School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China d Department of Composition Sichuan Conservatory of Music Chengdu Chinae Key Laboratory for Music Digital Intelligence of Sichuan Sichuan Conservatory of Music Chengdu China

ObjectiveAging-related compensatory scaffolding has been suggested to appear in the prefrontal regions in response to challenges posed by declines in working memory and inhibitory control associated with age. Although the potential mechanism of music intervention in cognitive aging is unclear, its effectiveness in interfering with executive function has been widely proven. A reasonable hypothesis is that music intervention alleviates cognitive aging by affecting compensatory scaffolding related to the prefrontal *** recruited three groups of participants: young control, old control, and old musicians, and calculated EEG microstate parameters. We used the classic N-back paradigm to examine subjects’ working memory performance. We also correlated the results of EEG microstates with behavioral *** found a decrease in the duration of microstate D (which has a frontal-central maximum) in all old subjects, which was related to poorer working memory performance. Meanwhile, old musicians benefit from lifespan music training experience and exhibit an opposite alleviating trend to the age effect in this outcome. We also found changes in the transition rate between *** results reflect the compensatory scaffolding of the prefrontal regions for other system functions in the process of music intervention in cognitive aging.

关键词： Music training cognitive aging working memory EEG microstate

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：