检索结果-内蒙古大学图书馆

npj biodiversity 2024年第1期3卷 31页

作者： Ann M Mc Cartney Giulio Formenti Alice Mouton Diego De Panis Luísa S Marins Henrique G Leitão Genevieve Diedericks Joseph Kirangwa Marco Morselli Judit Salces-Ortiz Nuria Escudero Alessio Iannucci Chiara Natali Hannes Svardal Rosa Fernández Tim De Pooter Geert Joris Mojca Strazisar Jonathan M D Wood Katie E Herron Ole Seehausen Phillip C Watts Felix Shaw Robert P Davey Alice Minotto José M Fernández Astrid Böhne Carla Alegria Tyler Alioto Paulo C Alves Isabel R Amorim Jean-Marc Aury Niclas Backstrom Petr Baldrian Laima Baltrunaite Endre Barta Bertrand BedHom Caroline Belser Johannes Bergsten Laurie Bertrand Helena Bilandija Mahesh Binzer-Panchal Iliana Bista Mark Blaxter Paulo A V Borges Guilherme Borges Dias Mirte Bosse Tom Brown Rémy Bruggmann Elena Buena-Atienza Josephine Burgin Elena Buzan Alessia Cariani Nicolas Casadei Matteo Chiara Sergio Chozas Fedor Čiampor Angelica Crottini Corinne Cruaud Fernando Cruz Love Dalen Alessio De Biase Javier Del Campo Teo Delic Alice B Dennis Martijn F L Derks Maria Angela Diroma Mihajla Djan Simone Duprat Klara Eleftheriadi Philine G D Feulner Jean-François Flot Giobbe Forni Bruno Fosso Pascal Fournier Christine Fournier-Chambrillon Toni Gabaldon Shilpa Garg Carmela Gissi Luca Giupponi Jessica Gomez-Garrido Josefa González Miguel L Grilo Björn Grüning Thomas Guerin Nadege Guiglielmoni Marta Gut Marcel P Haesler Christoph Hahn Balint Halpern Peter W Harrison Julia Heintz Maris Hindrikson Jacob Höglund Kerstin Howe Graham M Hughes Benjamin Istace Mark J Cock Franc Janžekovič Zophonias O Jonsson Sagane Joye-Dind Janne J Koskimäki Boris Krystufek Justyna Kubacka Heiner Kuhl Szilvia Kusza Karine Labadie Meri Lähteenaro Henrik Lantz Anton Lavrinienko Lucas Leclère Ricardo Jorge Lopes Ole Madsen Ghislaine Magdelenat Giulia Magoga Tereza Manousaki Tapio Mappes Joao Pedro Marques Gemma I Martinez Redondo Florian Maumus Shane A McCarthy Hendrik-Jan Megens Jose Melo-Ferreira Sofia L Mendes Matteo Montagna Joao Moreno Mai-Britt Mosbech Mónica Moura Zuzana Musilova Eugene Myers Will J N Genomics Institute University of California Santa Cruz CA USA. anmmccar@ucsc.edu. The Vertebrate Genome Laboratory The Rockefeller University New York NY USA. Department of Biology University of Florence Sesto Fiorentino Italy. InBios-Conservation Genetics Laboratory University of Liege Liege Belgium. Leibniz Institut für Zoo und Wildtierforschung Berlin Germany. Berlin Center for Genomics in Biodiversity Research Berlin Germany. Department of Biology University of Antwerp Antwerp Belgium. Institute of Zoology University of Cologne Cologne Germany. Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy. Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra) Barcelona Spain. Naturalis Biodiversity Center Leiden The Netherlands. Neuromics Support Facility VIB Center for Molecular Neurology VIB Antwerp Belgium. Neuromics Support Facility Department of Biomedical Sciences University of Antwerp Antwerp Belgium. Tree of Life Wellcome Sanger Institute Hinxton Cambridge UK. School of Biology and Environmental Science University College Dublin Belfield Ireland. Aquatic Ecology & Evolution Institute of Ecology & Evolution University of Bern Bern Switzerland. Department of Fish Ecology & Evolution Eawag Kastanienbaum Switzerland. Department of Biological and Environmental Science University of Jyvaskyla Jyvaskyla Finland. The Earlham Institute Norwich Research Park Norwich UK. Digital Science London UK. Barcelona Supercomputing Center Spanish National Bioinformatics Institute ELIXIR Spain Getafe Spain. Leibniz Institute for the Analysis of Biodiversity Change Museum Koenig Bonn Bonn Germany. CE3C-Centre for Ecology Evolution and Environmental Changes & CHANGE-Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Campo Grande Lisboa Portugal. Centro Nacional de Análisis Genómico (CNAG) Barcelona Spain. Universitat de Barcelona (UB) Barcelona Spain. CIBIO C

来源：评论

学校读者我要写书评

暂无评论

Humanoid vision system assisted fast tracking and positioning in unknown indoor environment

引用

ICIC Express Letters, Part B: Applications 2014年第2期5卷 341-347页

作者： Hsia, Kuo-Hsien Lien, Shao-Fan Su, Juhng-Perng Ciou, Wei-Yi Department of Computer Science and Information Engineering Far East University No. 49 Chung-Hwa Road Hsin-Shih Tainan 74448 Taiwan Department of Electrical Engineering National Yunlin University of Science and Technology No. 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Department of Electrical Engineering National Dong Hwa University No. 1 Section 2 Da Hsueh Road Shoufeng Hualien 97401 Taiwan

This paper presents a humanoid vision system for surveillance robot positioning and tracking. The humanoid vision system consists of two independent pan-tilt servo camera systems. These two pan-tilt servo systems are capable of operating independently or collaboratively enabling the humanoid vision system to work quickly and exibly for tracking a moving target and measuring the distance. The Extended Kalman Filter (EKF) is employed for aid to the estimation of moving target trajectory. In addition, an embedded system, PSoC, is exploited for the humanoid vision system to accomplish low-cost, small-in-size, yet reliable robot servo control. We employed LabView for realizing image recognition and tracking algorithm, in which an adaptive template matching algorithm was embodied. © 2014 ISSN 2185-2766.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Study and implementation of knowledge-based innovative social media services

引用

ICIC Express Letters 2015年第3期9卷 759-765页

作者： Fu, Meng-Hsuan Tsai, Wen-Hao Lu, Li-Zhen Kao, Chi-Yuan Horng, Mong-Fong Kuo, Yau-Hwang Hsu, Jang-Pong Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan City Taiwan National Kaohsiung University of Applied Science No. 415 Chien Kung Road Sanmin District Kaohsiung Taiwan Advanced Multimedia Internet Technology No. 28 Lane 31 Section 1 Huandong Road Sinshih Township Tainan County Taiwan

A scheme of knowledge-based innovative social media services, called as Smart Social Service Hub (S3H), is presented. S3H is a social media platform built of Network Attached Storage (NAS) to deliver innovative social services. There are several innovative methodologies developed in S3H, including personal social community, opinion mining and smart content viewer in order to build a full private social network environment and to integrate popular social media such as Facebook, Twitter, YouTube and Plurk. Moreover, the secure social connective technology, the cross-platform content management and the innovative social services are conducted through the secure mechanism that aims to raise the quality of social network environment. Finally, the objectives of S3H and the numeric results of the opinion mining techniques are shown to verify the implementation and its performance. © 2015, ICIC International.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

Intraoperative hemodynamics predict postoperative mortality in orthotopic liver transplantation

Intraoperative hemodynamics predict postoperative mortality ...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Varesh Prasad Nicola Toschi Antonio Canichella Martina Marcellucci Filadelfo Coniglione Mario Dauri Maria Guerrisi Thomas Heldt the Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA USA. the Medical Physics Section Department of Biomedicine and Prevention University of Rome "Tor Vergata" Rome Italy and the Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical the Medical Physics Section Department of Biomedicine and Prevention University of Rome "Tor Vergata" Rome Italy. the Department of Emergency and Critical Care Medicine University Hospital and University of Rome "Tor Vergata" Rome Italy. the Institute for Medical Engineering and Science and the Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA.

ISBN: (纸本)9781424492695

Liver transplantation remains the only curative treatment option for a variety of end-stage liver diseases. Prediction of major adverse events following surgery has traditionally focused on static predictors that are known prior to surgery. The effects of intraoperative management can now be explored due to the archiving of high-resolution monitoring data. We extracted intraoperative hemodynamic trend data of 55 patients undergoing orthotopic liver transplantation (OLT) and computed 12 features from the systolic arterial blood pressure (ABP), cardiac index, central venous pressure (CVP), and stroke volume variation (SVV) signals. Using a logistic regression classifier with a leave-one-out cross-validation procedure, we selected subsets of these features to predict mortality up to 180 days after surgery. Best performance was achieved with a combination of 3 features - median absolute deviation (MAD) of ABP, median CVP, and time spent with SVV < 10% - reaching an area under the receiver-operating characteristic (or c-statistic) of 0.808. Odds ratios (OR) computed from the coefficients of the multivariate logistic regression model constructed from these features showed that greater time spent with SVV < 10% (OR= 0.981 min-1, p= 0.001) and greater MAD of systolic ABP (OR= 0.696 mmHg-1, p= 0.026) were significantly associated with survival. Adding preoperative measures such as age and serum concentrations of albumin, bilirubin, and creatinine failed to improve performance of the prediction model. These results show that the course of intraoperative hemodynamics can predict 180-day mortality after OLT.

关键词： OLT OR CVP

来源：评论

学校读者我要写书评

暂无评论

Towards a data-driven approach to scenario generation for serious games

Towards a data-driven approach to scenario generation for se...

引用

作者： Luo, Linbo Yin, Haiyan Cai, Wentong Lees, Michael Othman, Nasri Bin Zhou, Suiping School of Computer Science and Technology Xidian University Xi'an China School of Computer Engineering Nanyang Technological University Singapore Section Computational Science University of Amsterdam Netherlands Institute of High Performance Computing Singapore School of Science and Technology Middlesex University United Kingdom

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario's training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance. Copyright © 2014 John Wiley & Sons, Ltd.

关键词： Serious games

来源：评论

学校读者我要写书评

暂无评论

A relationship strength-aware topic model for communities discovery in online social networks

A relationship strength-aware topic model for communities di...

引用

5th FTRA International Conference on computer Science and its Applications, CSA 2013

作者： Bi, Juan Huang, Jia Qin, Zhiguang School of Computer Science and Engineering University of Electronic Science and Technology of China Section 2 North Jianshe Road Chengdu China 16 Donghuangchenggen North Street Beijing 100717 China

ISBN: (纸本)9783642416736

Automatic discovering latent communities of users from observed textual content and their relationships is vital for understanding the cooperation and interaction patterns of users on large scale. In this paper, a novel probabilistic generative model was proposed to detect latent communities in a social network based on semantic information and the social relationships between users. In this model, it was assumed that users from the same community tend to share similar interests, and those who engage in common topics should be closely connected to each other on the topology structure of the social network. Users can have multiple interests and participate in multiple communities. Further, heterogeneous relationship strength was used in this paper to improve community discovery. The research indicated that the probabilistic generative model present in this paper has a good capability of discovering meaningful communities and topics on real-world data from Twitter. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Dense matter with eXTP

arXiv

引用

arXiv 2018年

作者： Watts, Anna L. Yu, Wenfei Poutanen, Juri Zhang, Shu Bhattacharyya, Sudip Bogdanov, Slavko Ji, Long Patruno, Alessandro Riley, Thomas E. Bakala, Pavel Baykal, Altan Bernardini, Federico Bombaci, Ignazio Brown, Edward Cavecchi, Yuri Chakrabarty, Deepto Chenevez, Jérôme Degenaar, Nathalie Santo, Melania Del Salvo, Tiziana Di Doroshenko, Victor Falanga, Maurizio Ferdman, Robert D. Feroci, Marco Gambino, Angelo F. Ge, MingYu Greif, Svenja K. Guillot, Sebastien Gungor, Can Hartmann, Dieter H. Hebeler, Kai Heger, Alexander Homan, Jeroen Iaria, Rosario in’t Zand, Jean Kargaltsev, Oleg Kurkela, Aleksi Lai, Xiaoyu Li, Ang Li, XiangDong Li, Zhaosheng Linares, Manuel Lu, FangJun Mahmoodifar, Simin Méndez, Mariano Miller, M. Coleman Morsink, Sharon Nättilä, Joonas Possenti, Andrea Prescod-Weinstein, Chanda Qu, JinLu Riggio, Alessandro Salmi, Tuomo Sanna, Andrea Santangelo, Andrea Schatz, Hendrik Schwenk, Achim Song, LiMing Šrámková, Eva Stappers, Benjamin Stiele, Holger Strohmayer, Tod Tews, Ingo Tolos, Laura Török, Gabriel Tsang, David Urbanec, Martin Vacchi, Andrea Xu, RenXin Xu, Ypeng Zane, Silvia Zhang, Guobao Zhang, ShuangNan Zhang, Wenda Zheng, ShiJie Zhou, Xia Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Shanghai Astronomical Observatory Shanghai200030 China Tuorla Observatory Department of Physics and Astronomy University of Turku Väisäläntie 20 PiikkiöFIN-21500 Finland Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-10691 Sweden Institute of High Energy Physics CAS Beijing100049 China Tata Institute of Fundamental Research Mumbai400005 India Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Institut für Astronomie und Astrophysik Tübingen Universität Tübingen Sand 1 TübingenD-72076 Germany Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands Research Center for Computational Physics and Data Processing Silesian University in Opava Bezrucovo nam. 13 OpavaCZ-74601 Czech Republic Physics Department Middle East Technical University Ankara06531 Turkey INAF Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio CatoneI-00078 Italy New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Dipartimento di Fisica Enrico Fermi University of Pisa PisaI-56127 Italy INFN Italian National Institute for Nuclear Physics Pisa Section Pisa56127 Italy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Mathematical Sciences and STAG Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom MIT Kavli Institute for Astrophysics and Space Research CambridgeMA02139 United States DTU Space Technical University of Denmark Elektrovej 327-328 Kgs Lyngby2800 Denmark INAF/IASF Palermo via Ugo La Malfa 153 PalermoI-90146 Italy Universita degli Studi di Palermo Dipartimento di Fisica e Chimica via Archira 36 Palermo90123 Italy

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry (eXTP) mission for determining the nature of dense matter;neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Science, the eXTP mission is expected to be launched in the mid 2020s. Copyright © 2018, The Authors. All rights reserved.

关键词： Pulsars

来源：评论

学校读者我要写书评

暂无评论

ICEPOLE: High-speed, hardware-oriented authenticated encryption

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8731卷 392-413页

作者： Morawiecki, Pawel Gaj, Kris Homsirikamol, Ekawat Matusiewicz, Krystian Pieprzyk, Josef Rogawski, Marcin Srebrny, Marian Wójcik, Marcin Institute of Computer Science Polish Academy of Sciences Poland Section of Informatics University of Commerce Kielce Poland Department of Computing Macquarie University Australia Electrical Engineering and Computer Science School Queensland University of Technology Brisbane Australia Cryptographic Engineering Research Group George Mason University United States Cryptography and Information Security Group University of Bristol United Kingdom Cadence Design Systems San Jose United States Intel Gdańsk Poland

This paper introduces our dedicated authenticated encryption scheme ICEPOLE. ICEPOLE is a high-speed hardware-oriented scheme, suitable for high-throughput network nodes or generally any environment where specialized hardware (such as FPGAs or ASICs) can be used to provide high data processing rates. ICEPOLE-128 (the primary ICEPOLE variant) is very fast. On the modern FPGA device Virtex 6, a basic iterative architecture of ICEPOLE reaches 41 Gbits/s, which is over 10 times faster than the equivalent implementation of AES-128-GCM. The throughput-to-area ratio is also substantially better when compared to AES-128-GCM. We have carefully examined the security of the algorithm through a range of cryptanalytic techniques and our findings indicate that ICEPOLE offers high security level. © International Association for Cryptologic Research 2014.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

引用

Nature communications 2019年第1期10卷 2956页

作者： Lohith Madireddy Kicheol Kim Xiaoming Jia Adam Santaniello Stacy J. Caillier Bruce A. C. Cree Roland Henry Stephen L. Hauser Jorge R. Oksenberg Sergio E. Baranzini Nikolaos A. Patsopoulos Philip De Jager Chris Cotsapas Matthew R. Lincoln David A. Hafler Steffan D. Bos Hanne F. Harbo Tone Berge Elisabeth G. Celius Ashley Beecham Jacob McCauley Margaret A. Pericak-Vance Till F. M. Andlauer Christiane Gasperi Bernhard Hemmer Lisa F. Barcellos Luisa Bernardinelli Filippo Martinelli-Boneschi David R. Booth Graeme J. Stewart Farren Briggs Jonathan Haines Manuel Comabella Giancarlo Comi Federica Esposito Sandra D’Alfonso Katrina Dedham Pierre Duquette Efthimios Dardiotis Bertrand Fontaine An Goris Bénédicte Dubois Pierre-Antoine Gourraud Georgios Hadjigeorgiou Clive Hawkins Seema Kalra Rogier Hintzen Dana Horakova Noriko Isobe Jun-ichi Kira Michael Khalil Ingrid Kockum Tomas Olsson Christina M. Lill Felix Luessi Frauke Zipp Roland Martin Annette Oturai Aarno Palotie Janna Saarela Adrian Ivinson Bruce V. Taylor Alastair Compston Stephen Sawcer Weill Institute for Neurosciences Department of Neurology University of California San Francisco San Francisco CA 94158 USA Bakar Institute for Computational Health Science. University of California San Francisco San Francisco CA 94158 USA Systems Biology and Computer Science Program Ann Romney Center for Neurological Diseases Department of Neurology and Division of Genetics Department of Medicine Brigham & Women’s Hospital Harvard Medical School Boston MA 02115 USA Broad Institute of Harvard University and Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Neurology Center for Translational and Computational Neuroimmunology and Multiple Sclerosis Center Columbia University Medical Center New York NY 10032 USA Departments of Neurology Yale School of Medicine 300 George St New Haven CT 06511 USA Department of Genetics Yale School of Medicine 300 George St New Haven CT 06511 USA Institute of Clinical Medicine University of Oslo Oslo 0318 Norway Department of Neurology Oslo University Hospital Oslo 0424 Norway Department of Mechanical Electronics and Chemical Engineering Oslo Metropolitan University Oslo 0167 Norway Institute of Health and Society University of Oslo Oslo 0318 Norway John P. Hussman Institute for Human Genomics University of Miami Miller School of Medicine Miami FL 33136 USA Dr. John T. Macdonald Foundation Department of Human Genetics University of Miami Miller School of Medicine Miami FL 33136 USA Department of Neurology Klinikum rechts der Isar School of Medicine Technical University of Munich 81675 Munich Germany Munich Cluster for Systems Neurology (SyNergy) 81377 Munich Germany Division of Epidemiology School of Public HealthUniversity of California 324 Stanley Hall MC#3220 Berkeley CA 94720 USA Section of Biostatistics Neurophyisiology and Psychiatry Unit of medical and genomic statistics Universita of Pavia Pavia 27100 Italy Department of Biomedical Sciences for Health Univers

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EA-based evacuation planning using agent-based crowd simulation 14

EA-based evacuation planning using agent-based crowd simulat...

引用

Simulation Winter Conference

作者： Jinghui Zhong Linbo Luo Wentong Cai Michael Lees School of Computer Engineering Nanyang Technological University SINGAPORE School of Computer Science and Technology Xidian University Xi'an CHINA Section Computational Science University of Amsterdam Amsterdam NETHERLANDS

Safety planning for crowd evacuation is an important and active research topic nowadays. One important issue is to devise the evacuation plans of individuals in emergency situations so as to reduce the total evacuation time. This paper proposes a novel evolutionary algorithm (EA)-based methodology, together with agent-based crowd simulation, to solve the evacuation planning problem. The proposed method features a novel segmentation strategy which divides the entire evacuation region into sub-regions based on a discriminant function. Each sub-region is assigned with an exit gate, and individuals in a sub-region will run toward the corresponding exit gate for evacuation. In this way, the evacuation planning problem is converted to a symbolic regression problem. Then an evolutionary algorithm, using agent-based crowd simulation as fitness function, is developed to search for the global optimal solution. The simulation results on different scenarios demonstrate that the proposed method is effective to reduce the evacuation time.

关键词： Planning Biological cells Safety Educational institutions Logic gates Evolutionary computation Simulation

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：