检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

来源：评论

学校读者我要写书评

暂无评论

STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

arXiv

引用

arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Deploying Cationic Cellulose Nanofiber Confinement to Enable High Iodine Loadings Towards High Energy and High-Temperature Zn-I2Battery

引用

Angewandte Chemie 2023年第5期136卷

作者： Zhenglin Li Wenwen Cao Tao Hu Yichan Hu Rong Zhang Huilin Cui Funian Mo Chaozheng Liu Chunyi Zhi Guojin Liang Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen 518055 China Co-Innovation Center of Efficient Processing and Utilization of Forest Resources College of Materials Science and Engineering Nanjing Forestry University Nanjing 210000 China Contribution: Data curation (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) School of Chemistry and Chemical Engineering University of Jinan Jinan 250022 China Contribution: Conceptualization (equal) Data curation (lead) Investigation (lead) Methodology (lead) School of Materials Science and Engineering Key Laboratory of Structure and Functional Regulation of Hybrid Materials Ministry of Education Anhui University Hefei 230601 China Contribution: Data curation (equal) Formal analysis (equal) Investigation (supporting) Methodology (supporting) Contribution: Conceptualization (equal) Data curation (supporting) Formal analysis (equal) Investigation (equal) Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong 999077 Hong Kong Contribution: Data curation (equal) Investigation (equal) Methodology (equal) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (equal) Investigation (equal) Shenzhen Key Laboratory of Flexible Printed Electronics Technology Center Harbin Institute of Technology Shenzhen Guangdong 518055 China Contribution: Conceptualization (lead) Data curation (lead) Resources (lead) Supervision (equal) Contribution: Conceptualization (equal) Data curation (lead) Investigation (lead) Supervision (equal) Contribution: Supervision (lead) Visualization (lead) Writing - review & editing (lead) Contribution: Conceptualization (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

High iodine loading and high-temperature adaptability of the iodine cathode are prerequisites to achieving high energy density at full battery level and promoting the practical application for the zinc-iodine (Zn-I 2 ) battery. However, it would aggravate the polyiodide shuttle effect when employing high iodine loading and working temperature. Here, a sustainable cationic cellulose nanofiber (cCNF) was employed to confine the active iodine species through strong physiochemical adsorption to enlarge the iodine loading and stabilize it even at high temperatures. The cCNF could accommodate dual-functionality by enlarging the iodine loading and suppressing the polyiodide shuttle effect, owing to the unique framework structure with abundant surface positive charges. As a result, the iodine cathode based on the cCNF could deliver high iodine mass loading of 14.1 mg cm −2 with a specific capacity of 182.7 mAh g −1 , high areal capacity of 2.6 mAh cm −2 , and stable cycling over 3000 cycles at 2 A g −1 , thus enabling a high energy density of 34.8 Wh kg −1 and the maximum power density of 521.2 W kg −1 at a full Zn-I 2 battery level. In addition, even at a high temperature of 60 °C, the Zn-I 2 battery could still deliver a stable cycling.

关键词： Cationic Cellulose High- Temperature Battery Iodine Shuttle-Free Zn-I2 Battery

来源：评论

学校读者我要写书评

暂无评论

GIscience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

引用

arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIscience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

An efficient matching method for dispatching autonomous vehicles*

An efficient matching method for dispatching autonomous vehi...

引用

International Conference on Intelligent Transportation

作者： Ming Li Nan Zheng Xinkai Wu Xiang Huo School of Transportation Science and Engineering Beihang University China Department of Civil Engineering Monash University Australia Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transportation Science and Engineering Beihang University China

ISBN: (纸本)9781538670255

Autonomous vehicles (AVs), as an emerging transportation tool, are widely discussed for improving traffic mobility. Particularly, when combining with prevalent shared transportation modes, i.e., car-sharing and ride-sharing, AVs show promising future. However, the efficiency of searching for an optimal match between shared autonomous vehicles and passengers has long become a critical problem. This research tackles this issue by developing a model which has better efficiency. The essential task is to dispatch vehicles to serve passengers, which is similar to vehicle routing problem. But unlike existing vehicle routing models, our method makes use of the similarity of passengers' demand instead of directly matching vehicles and passengers. In this way, a cluster-based algorithm is proposed to find near-optimal solutions. Numerical experiments on large-size are performed to present the validity of the optimization model and efficiency of the proposed algorithm. Results indicate that the cluster-based algorithm could bring benefit to minimizing the number of vehicles and total travel distance.

关键词： Intelligent transportation systems Conferences

来源：评论

学校读者我要写书评

暂无评论

Boosting the “Solid–Liquid–Solid” Conversion Reaction via Bifunctional Carbonate-Based Electrolyte for Ultra-long-life Potassium–Sulfur Batteries

引用

Angewandte Chemie 2023年第44期135卷

作者： Shufen Ye Nan Yao Dr. Xiang Chen Mingze Ma Lifeng Wang Zhihao Chen Dr. Yu Yao Prof. Qiang Zhang Prof. Yan Yu Hefei National Research Center for Physical Sciences at the Microscale Department of Materials Science and Engineering iChEM (Collaborative Innovation Center of Chemistry for Energy Materials) CAS Key Laboratory of Materials for Energy Conversion University of Science and Technology of China Hefei Anhui 230026 China Contribution: Writing - original draft (lead) Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing 100084 China Contribution: Data curation (lead) Contribution: Software (lead) Contribution: Formal analysis (equal) Contribution: Investigation (equal) Contribution: Writing - review & editing (lead) Contribution: Supervision (lead) National Synchrotron Radiation Laboratory Hefei Anhui 230026 China Contribution: Conceptualization (lead)

Potassium–sulfur (K−S) batteries have attracted wide attention owing to their high theoretical energy density and low cost. However, the intractable shuttle effect of K polysulfides results in poor cyclability of K−S batteries, which severely limits their practical application. Herein, a bifunctional concentrated electrolyte (3 mol L −1 potassium bis(trifluoromethanesulfonyl)imide in ethylene carbonate (EC)) with high ionic conductivity and low viscosity is developed to regulate the dissolution behavior of polysulfides and induce uniform K deposition. The organic groups in the cathode electrolyte interphase layer derived from EC can effectively block the polysulfide shuttle and realize a “solid–liquid–solid” reaction mechanism. The KF-riched solid-electrolyte interphase inhibits K dendrite growth during cycling. As a result, the achieved K−S batteries display a high reversible capacity of 654 mAh g −1 at 0.5 A g −1 after 800 cycles and a long lifespan over 2000 cycles at 1 A g −1 .

关键词： Carbonate Solvent Concentration Electrolytes Electrode–Electrolyte Interphase Potassium Sulfur Batteries Solid–Liquid–Solid Phase Conversion

来源：评论

学校读者我要写书评

暂无评论

AA-ICP: Iterative closest point with anderson acceleration

AA-ICP: Iterative closest point with anderson acceleration

引用

2018 IEEE International Conference on Robotics and Automation, ICRA 2018

作者： Pavlov, Artem L. Ovchinnikov, Grigory W.V. Derbyshev, Dmitry Yu. Tsetserukou, Dzmitry Oseledets, Ivan V. Skolkovo Institute of Science and Technology Skolkovo Innovation Center Space Center Moscow143026 Russia Skolkovo Institute of Science and Technology Computational and Data-Intensive Science and Engineering Russia Moscow Institute of Physics and Technology Russia Skolkovo Institute of Science and Technology Space Center Russia Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Russia

ISBN: (纸本)9781538630815

Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust, this method is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms. In this paper we propose a novel effective method for acceleration of ICP which does not require substantial modifications to the existing code. This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping. The latter is often faster than a standard Picard iteration, usually used in ICP implementations. We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness. We implement proposed approach into Point Cloud Library (PCL) and make it available online. Benchmarking on the real-world data fully supports our claims. © 2018 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

引用

Nano Research 2018年第12期11卷 6316-6324页

作者： Xiaoyang Lin Zhizhong Si Wenzhi Fu Jianlei Yang Side Guo Yuan Cao Jin Zhang Xinhe Wang Peng Liu Kaili Jiang Weisheng Zhao Fert Beijing Research Institute School ofMicroelectronics & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China Beihang-GoertekJoint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao 266000China Fert Beijing Research Institute School of Computer Science and Engineering & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics & Tsinghua-Foxconn Nanotechnology Research CenterCollaborative Innovation Center of Quantum MatterTsinghua UniversityBeijing 100084China

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.

关键词： machine-learning optical identification two-dimensional (2D) material heterostructure artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Discovery of Ganodecalone A, from Ganoderma calidophilum for the treatment of acute gastric ulcers by binding to ALOX5 via the MAPK/NF-κB/iNOS signaling

引用

Journal of ethnopharmacology 2025年 351卷 120082页

作者： Yu Liu Weikang Li Li Yang Zhao Tong Zhen Wang Youxing Zhao Duqiang Luo College of Life Sciences Institute of Life Science and Green Development and Hebei Innovation Center for Bioengineering and Biotechnology Hebei University Baoding 071002 China. Research and Development of Natural Product from Li Folk Medicine & National Key Laboratory for Tropical Crop Breeding Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agriculture Sciences Haikou 571101 China. Hebei Key Laboratory of Medical Data Science School of Medicine Hebei University of Engineering Handan Hebei Province 056038 China. Electronic address: wangzhen@***. Research and Development of Natural Product from Li Folk Medicine & National Key Laboratory for Tropical Crop Breeding Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agriculture Sciences Haikou 571101 China. Electronic address: zhaoyouxing@***. College of Life Sciences Institute of Life Science and Green Development and Hebei Innovation Center for Bioengineering and Biotechnology Hebei University Baoding 071002 China. Electronic address: duqiangluo@***.

ETHNOPHARMACOLOGICAL RELEVANCE:"Li medicine" Ganoderma calidophilum is a rare and unique medicinal fungus native to Hainan, China, traditionally used to relieve stomach pain and treat gastric diseases. However, its medicinal applications lack scientific validation despite its prominent role in ethnomedicine. AIM OF THE STUDY:This study aimed to evaluate the therapeutic potential of G. calidophilum extract against gastric ulcer (GU), isolate its main bioactive compound (GDA), and elucidate the underlying mechanisms of action. MATERIALS AND METHODS:In vivo, acute gastric mucosal-injured mice were observed for mucosal symptoms, and levels of IL-6, TNF-α, SOD, and MDA were measured. In vitro, ethanol-damaged GES-1 cells were used to test the effects of G. calidophilum extract and GDA. 28 compounds were isolated from the extract, and network pharmacology and molecular docking studied GDA influence on ALOX5 and NF-κB. RESULTS:The extract improved mouse symptoms regulated inflammatory and oxidative stress markers. GDA, the key anti-inflammatory compound, repaired cell damage, reduced inflammation and oxidative stress. Network and docking results showed GDA inhibited early-stage inflammation. CONCLUSIONS:G. calidophilum extract exhibits potent anti-GU activity, primarily attributed to GDA, which alleviates gastric mucosal inflammation by binding to ALOX5 and modulating MAPK/NF-κB/iNOS signaling and lipid peroxidation. These findings validate its traditional use and provide a mechanistic basis for its therapeutic application in acute GU.

关键词： Ganodecalone A Ganoderma calidophilum Gastric ulcer Inflammation MAPK/NF-κB/iNOS signalling Oxidative stress

来源：评论

学校读者我要写书评

暂无评论

Crystal Structure of Arpin Reveals the Important Role of Its Homodimerization in Arp2/3 Complex Inhibition

Crystal Structure of Arpin Reveals the Important Role of Its...

引用

第十七次中国暨国际生物物理大会

作者： Xuejiao Zhang Yun Zhu Rongguang Zhang Sheng Ye National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Interdisciplinary Innovation Institute of Medicine & Engineering Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Biological Science and Medical Engineering Beihang University

Cell migration plays critical roles in many physiological and pathological processes, including cancer metastasis. One of the very initial steps in cell migration involves the nucleation of a branched actin network, w... 详细信息

关键词： Crystal Structure of Arpin Reveals the Important Role of Its Homodimerization in Arp2/3 Complex Inhibition

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：