检索结果-内蒙古大学图书馆

Enhancing sensitivity to ambient refractive index with tunable few-layer graphene/hBN nanoribbons

Photonics Research 2019年第7期7卷 130-137页

作者： Huan Jiang Sajid Choudhury Zhaxylyk A.Kudyshev Di Wang Ludmila J.Prokopeva Peng Xiao Yongyuan Jiang Alexander V.Kildishev Institute of Modern Optics Department of PhysicsHarbin Institute of TechnologyHarbin 150001China School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue UniversityWest Lafayette Indiana 47907USA School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Key Laboratory of Micro-Optics and Photonic Technology of Heilongjiang Province Harbin 150001China Key Laboratory of Micro-Nano Optoelectronic Information System of Ministry of Industry and Information Technology Harbin 150001China

Refractive index (RI) sensing helps to identify biomolecules and chemicals in the mid-infrared range for drug discovery, bioengineering, and environmental monitoring. In this paper, we numerically demonstrate an electrically tunable RI sensor with ultrahigh sensitivity using a three-layer graphene nanoribbon array separated by hexagonal boron nitride (hBN). Unlike the weak resonance in single-layer graphene nanoribbons, a much stronger plasmon resonance featuring a higher-quality factor can be excited in the graphene/hBN few-layer ribbon array. Simultaneously, the high purity of graphene on hBN results in an outstanding charge mobility above 4 ×10^4 cm^2 · V^-1·s^-1 at 300 K, which allows a larger modulation depth. The interaction between the locally enhanced field around graphene ribbons and its surrounding analyte leads to ultrahigh sensitivity (4.207μm/RIU), with the figure of merit reaching approximately ***, this ultrasensitive detector could selectively work in different wavebands by controlling gate voltages applied to *** merits of ultrahigh sensitivity and electrical tunability are major advances compared to previous RI sensors, paving a way toward ultrasensitive detection using graphene/hBN few-layer devices.

关键词： Enhancing sensitivity refractive index nanoribbons

来源：评论

学校读者我要写书评

暂无评论

SURG-01. DIFFERENTIATION OF GLIOBLASTOMA AND MALIGNANT MENINGIOMA FROM NORMAL BRAIN BY TISSUE STIFFNESS UTILIZING MICROSURGICAL FORCEPS EQUIPPED WITH HAPTICS technology

引用

Neuro-Oncology 2023年第SUPPLEMENT_5期25卷 v260–v261页

作者： Ezaki, Taketo Kisima, Kohki Shibao, Shunsuke Matsunaga, Takuya Pareira, Eriel Sandika Kitamura, Yohei Takahara, Kento Iwama, Takashi Sampetrean, Oltea Ohnishi, Kouhei Shimono, Tomoyuki Sasaki, Hikaru Keio University School of Medicine Department of Neurosurgery Shinjuku-ku Tokyo Japan Yokohama National University School of Engineering Science Department of Educational Program for Electrical and Computer Engineering Yokohama-shi Japan Keio University School of Medicine Department of Neurosurgery Shinjuku-ku Japan Kanagawa Institute of Industrial Science and Technology Kawasaki-shi Japan Keio University School of Medicine Department of Microbiology and Immunology Shinjuku-ku Japan Keio University Haptics Research Center Kawasaki-shi Japan Tokyo Dental College Ichikawa General Hospital Department of Neurosurgery Ichikawa-shi Chiba Japan

Stiffness of human cancers strongly correlate with their pathology, and it is reported that stiffness can be used as a biomarker. Furthermore, comprehensive analysis has suggested the usefulness of cancer stiffness in diagnosis and malignancy prediction and its association with molecular expression, as well as its potential for predicting and diagnosing postoperative complications. Neurosurgeons perform tumor resection relying on the sensations that they feel with their own fingertips (tactile sensation), but it takes years of surgical experience to accurately distinguish brain tumor from the surrounding normal brain *** is a new technology regarding touching sensation. Especially, our technology, real haptics, can communicate, record, and reproduce the real sensation. It is also possible to measure and analyze the physical property such as stiffness of the touching object from the obtained haptic *** developed a surgical forceps equipped with haptics technology to distinguish brain tumor tissue from surrounding normal brain tissue. three glioblastoma cell lines (SF126, U87, U251) and a malignant meningioma cell line (IOMM-Lee) were each transplanted intracranially into 5 nude mice. Tumor size was periodically monitored and mice were euthanized at appropriate time points. The stiffness of the tumor and normal brain tissue was measured using a master-slave integrated surgical forceps prototype. The results statistically showed that all four types of brain tumor tissues were stiffer than normal brain tissues. (p = 0.001) In addition, malignant meningioma was statistically stiffer than glioblastomas. (p = 0.025) This result is consistent with actual surgical *** have shown that our surgical forceps has the ability to distinguish pathology of brain tumors as well as to distinguish brain tumor from normal brain tissue. The forceps is also helpful for the neurosurgeons to sense minute change in tissue stiffness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Fabrication of efficient and economical dye-sensitized solar cells using carbon-coated nanotextured silicon wafers counter electrodes” [Synth. Met. 301 (2024) 117537]

引用

Synthetic Metals 2024年 302卷

作者： Ahmed F. Abdelaal Muhammad Younas Ryan N. Iman Asrar Damdam Amal M. Al-Amri Turki N. Baroud Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Core Research Facilities King Fahd University of Petroleum & Minerals 31261 Dhahran Saudi Arabia Department of Physics King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Physics College of Science and Arts King Abdulaziz University Rabigh 21911 Saudi Arabia Materials Science and Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security KFUPM Dhahran 31261 Saudi Arabia

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

引用

Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

来源：评论

学校读者我要写书评

暂无评论

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

引用

arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

A Competitive Swarm Algorithm for Image Segmentation Guided ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Diego Oliva Songfeng Lu Erik Cuevas Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong university of Science and Technology Wuhan China Department of Computer Damietta University Egypt Department of Electrical Engineering Fayoum University Fayoum Egypt Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

关键词： Image segmentation Entropy Optimization Linear programming Fuzzy sets Thresholding (Imaging) Histograms

来源：评论

学校读者我要写书评

暂无评论

GPAW: An open Python package for electronic-structure calculations

arXiv

引用

arXiv 2023年

作者： Mortensen, Jens Jørgen Larsen, Ask Hjorth Kuisma, Mikael Ivanov, Aleksei V. Taghizadeh, Alireza Peterson, Andrew Haldar, Anubhab Dohn, Asmus Ougaard Schäfer, Christian Jónsson, Elvar Örn Hermes, Eric D. Nilsson, Fredrik Andreas Kastlunger, Georg Levi, Gianluca Jónsson, Hannes Häkkinen, Hannu Fojt, Jakub Kangsabanik, Jiban Sødequist, Joachim Lehtomäki, Jouko Heske, Julian Enkovaara, Jussi Winther, Kirsten Trøstrup Dulak, Marcin Melander, Marko M. Ovesen, Martin Louhivuori, Martti Walter, Michael Gjerding, Morten Lopez-Acevedo, Olga Erhart, Paul Warmbier, Robert Würdemann, Rolf Kaappa, Sami Latini, Simone Boland, Tara Maria Bligaard, Thomas Skovhus, Thorbjørn Susi, Toma Maxson, Tristan Rossi, Tuomas Chen, Xi Schmerwitz, Yorick Leonard A. Schiøtz, Jakob Olsen, Thomas Jacobsen, Karsten Wedel Thygesen, Kristian Sommer CAMd Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Riverlane Ltd St Andrews House 59 St Andrews Street CambridgeCB2 3BZ United Kingdom School of Engineering Brown University ProvidenceRI United States Department of Electrical and Computer Engineering Boston University United States Department of Physics Technical University of Denmark Lyngby2800 Denmark Science Institute Faculty of Physical Sciences VR-III University of Iceland Reykjavík107 Iceland Department of Physics Chalmers University of Technology GothenburgSE-412 96 Sweden Science Institute Faculty of Physical Sciences University of Iceland VR-III Reykjavík107 Iceland Quantum-Si 29 Business Park Drive BranfordCT06405 United States CatTheory Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Departments of Physics and Chemistry Nanoscience Center University of Jyväskylä JyväskyläFI-40014 Finland Department of Applied Physics Aalto University P.O. Box 11100 Aalto00076 Finland CSC – IT Center for Science Ltd. P.O. Box 405 EspooFI-02101 Finland SUNCAT Center for Interface Science and Catalysis SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Chemistry Nanoscience Center University of Jyväskylä JyväskyläFI-40014 Finland University of Freiburg FIT Freiburg Centre for Interactive Materials and Bioinspired Technologies Georges-Köhler-Allee 105 Freiburg79110 Germany Biophysics of Tropical Diseases Max Planck Tandem Group University of Antioquia UdeA Medellin050010 Colombia School of Physics Mandelstam Institute for Theoretical Physics University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg2001 South Africa Freiburger Materialforschungszentrum Universität Freiburg Stefan-Meier-Straße 21 FreiburgD-79104 Germany Computational Physics Laboratory Tampere University P.O. Box 692 TampereFI-33014 Finland Nanomade Department of Physics Technical University of Denmark Kgs Lyngb

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE) providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation (BSE), variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW. © 2023, CC BY-SA.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Outlier guided optimization of abdominal segmentation

arXiv

引用

arXiv 2020年

作者： Xu, Yuchen Tang, Olivia Tang, Yucheng Lee, Ho Hin Chen, Yunqiang Gao, Dashan Han, Shizhong Gao, Riqiang Savona, Michael R. Abramson, Richard G. Huo, Yuankai Landman, Bennett A. Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37212 United States 12 Sigma Technology San DiegoCA92130 United States Hematology and Oncology Vanderbilt University Medical Center NashvilleTN37235 United States Radiology Vanderbilt University Medical Center NashvilleTN37235 United States

Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image processing, as the shape and distribution of abdominal organs can vary greatly among the population and within an individual over time. While continuous integration of novel datasets into the training set provides potential for better segmentation performance, collection of data at scale is not only costly, but also impractical in some contexts. Moreover, it remains unclear what marginal value additional data have to offer. Herein, we propose a single-pass active learning method through human quality assurance (QA). We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e.g., exemplars for which the baseline algorithm failed) or inliers (e.g., exemplars for which the baseline algorithm worked). The new models were trained using the augmented datasets with 5-fold cross-validation (for outlier data) and withheld outlier samples (for inlier data). Manual labeling of outliers increased Dice scores with outliers by 0.130, compared to an increase of 0.067 with inliers (p Copyright © 2020, The Authors. All rights reserved.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Information spreading dynamics on adaptive social networks

arXiv

引用

arXiv 2020年

作者： Liu, Chuang Zhou, Nan Zhan, Xiu-Xiu Sun, Gui-Quan Zhang, Zi-Ke Alibaba Research Center for Complexity Sciences Hangzhou Normal University Hangzhou311121 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands Department of Mathematics North University of China Taiyuan Shanxi030051 China Complex Systems Research Center Shanxi University Taiyuan Shanxi030006 China

There is currently growing interest in modeling the information diffusion on social networks across multi-disciplines. The majority of the corresponding research has focused on information diffusion independently, ignoring the network evolution in the diffusion process. Therefore, it is more reasonable to describe the real diffusion systems by the co-evolution between network topologies and information states. In this work, we propose a mechanism considering the co-evolution between information states and network topology simultaneously, in which the information diffusion was executed as an SIS process and network topology evolved based on the adaptive assumption. The theoretical analyses based on the Markov approach were very consistent with simulation. Both simulation results and theoretical analyses indicated that the adaptive process, in which informed individuals would rewire the links between the informed neighbors to a random non-neighbor node, can enhance information diffusion (leading to much broader spreading). In addition, we obtained that two threshold values exist for the information diffusion on adaptive networks, i.e., if the information propagation probability is less than the first threshold, information cannot diffuse and dies out immediately;if the propagation probability is between the first and second threshold, information will spread to a finite range and die out gradually;and if the propagation probability is larger than the second threshold, information will diffuse to a certain size of population in the network. These results may shed some light on understanding the co-evolution between information diffusion and network topology. Copyright © 2020, The Authors. All rights reserved.

关键词： Information dissemination

来源：评论

学校读者我要写书评

暂无评论

Learning resolution-invariant deep representations for person re-identification

arXiv

引用

arXiv 2019年

作者： Chen, Yun-Chun Li, Yu-Jhe Du, Xiaofei Wang, Yu-Chiang Frank Department of Electrical Engineering National Taiwan University MOST Joint Research Center for AI Technology All Vista Healthcare Umbo Computer Vision

Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios might suffer from resolution loss, how to solve the resolution mismatch problem during person re-ID becomes a practical problem. Instead of applying separate image super-resolution models, we propose a novel network architecture of Resolution Adaptation and re-Identification Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy of adversarial learning, we aim at extracting resolution-invariant representations for re-ID, while the proposed model is learned in an end-to-end training fashion. Our experiments confirm that the use of our model can recognize low-resolution query images, even if the resolution is not seen during training. Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

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：