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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Chen, Shuai Wang, Juan Guo, Maozu Inner Mongolia University School of Computer Science Hohhot China Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

ISBN: (纸本)9781665468190

Protein function annotation is an important content of bioinformatics. It is unrealistic to experimentally annotate a large number of protein functions, so automated prediction of the functions of proteins is required. Studying plant proteins can help us cultivate new plant varieties. However, the existing methods mainly aim at predicting the single function of plant proteins. Most proteins only have sequence information. Therefore, the multi-function prediction method of plant protein based on sequence has become the focus of research. In this study, we propose a method based on sequence to predict the function of plant proteins, called PlantGO. We describe the functional annotation problem as a multi-label classification problem using Gene Ontology (GO) terminology, and predict multiple functions for plant proteins. PlantGO extracts features from three aspects and performs feature fusion respectively. To avoid redundant information in features, PlantGO uses the random forest to select features and obtains three groups of optimal features. PlantGO uses a multi-label learning algorithm MLKNN to predict protein function, which proves the effectiveness and accuracy of the algorithm. Finally, PlantGO adopts the ensemble learning method to integrate all models into a unified model, further improving the prediction performance. Therefore, PlantGO has achieved excellent performance. The performance metrics are Accuracy (0.936), F1 Score (0.944), Ranking loss (0.011), which is better than the previous method in the independent test. We have developed an online server to predict the functions of plant proteins. © 2022 IEEE.

关键词： Learning systems

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Deciphering theαrelaxation and the anelastic-to-plastic transition in the deep glassy state

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 131-138页

作者： Qi Hao Guanghui Xing Eloi Pineda Claudio Fusco Laurent Chazeau Jean-Marc Pelletier Yunjiang Wang Yong Yang Jichao Qiao School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an710072China Department of Physics Institute of Energy TechnologiesUniversitat Politècnica de CatalunyaBarcelona08019Spain Univ Lyon INSA LyonUniversitéClaude Bernard Lyon 1CNRSMATEISUMR5510Villeurbanne69621France State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing100190China School of Engineering Science University of Chinese Academy of SciencesBeijing100049China Department of Mechanical Engineering College of EngineeringCity University of Hong KongHong Kong999077China Department of Materials Science and Engineering College of EngineeringCity University of Hong KongHong Kong999077China State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment Northwestern Polytechnical UniversityXi’an710072China Key Laboratory of Aircraft Structural Integrity Ministry of Industry and Information TechnologyXi’an710072China

In contrast to their conventional crystalline counterparts,amorphous solids exhibit diverse dynamic relaxation mechanisms under external *** challenge to understanding their behavior lies in unifying microscopic dynamics,relaxation,and macroscopic *** study establishes a potential link by quantifying the characteristic time of the anelastic-to-plastic transition through dynamic mechanical relaxation and stress relaxation tests across a wide temperature range in both the supercooled liquid and the glassy *** is found that the stress relaxation time in the glassy solids follows an Arrhenius relationship,aligning with the mainαrelaxation time,and unveils a finding:αrelaxation continues to govern deformation even below the glass transition,challenging previous assumptions of the role of secondaryβrelaxation.A hierarchically constrained atomic dynamics model rationalizes the temperature dependence ofαrelaxation and the transition fromβtoαrelaxation,also providing evidence that the stretched exponent in the Kohlrausch-Williams-Watts equation can serve as an order *** work highlights the role ofαrelaxation in the glassy state and contributes to elucidating the potential correlation between relaxation and deformation in amorphous materials.

关键词： dynamic relaxation nonelastic deformation metallic glasses aging

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MetaCity: Data-driven sustainable development of complex cities

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The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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Top Factors Leading to Water Main Failures—An Analysis of 12 Canadian Cities

Top Factors Leading to Water Main Failures—An Analysis of 1...

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World Environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Gharaati, Sadaf Dziedzic, Rebecca Dept. of Building Civil and Environmental Engineering Gina Cody School of Engineering and Computer Science Concordia Univ. MontrealQC Canada

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

Water main deterioration is a global challenge which can jeopardize the ability of water systems to deliver clean water safely. A variety of factors can affect pipe breakage, yet previous research has focused on different subsets of data. The key objective of this study is to identify the most important factors in predicting water main failure across multiple Canadian cities. The study included data organizing, data cleaning, and dimensionality reduction analyses. Two approaches were applied to analyze the importance of factors affecting water main rate of failure, factor analysis of mixed data (FAMD) and random forest. Data from 12 water utilities across Canada were analyzed, containing information on pipe physical characteristics, historical information, protection activities, environmental factors, and operational condition. Available attributes were different for each city. Overall, the two methods rated failure month and protection as the most important factors. While random forest consistently found age to be the most important attribute, FAMD results indicated that either failure month or material was the most important factor. Random forest results were observed to be biased as they consistently rated numerical attributes as more important than categorical. Thus, FAMD results are expected to be more reliable. Future data collection should focus on pipe characteristics and protection activities. Next steps of the project will include conducting other dimension reduction approaches and running correlation analyses. Findings will support the development of a framework for pipe and break data collection. This will help water utilities develop cost-effective and accurate water main deterioration models, enabling more reliable renewal strategies for water distribution systems. © ASCE.

关键词： Factor analysis

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ReQ-tank: Fine-grained Distributed Machine Learning Flow Scheduling Approach

ReQ-tank: Fine-grained Distributed Machine Learning Flow Sch...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Quanyi Xu Kedong Yan Dan Yin Chanying Huang Shan Xiao Yuxin Yang School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China School of Computer Science and Engineering Nanjing Univ. of Science and Technology Nanjing China School of Computer Science and Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Fiberhome Communication Tech. Co. Wuhan China

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling; On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications.

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Research on automatic quantitative quality inspection method for internal geometric accuracy of remote sensing images

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Ge, Huibin Li, Ying Wang, Boyu Geng, Yu Ba, Xiaojuan Northwestern Polytechnical University School of Computer Science Shaanxi China Piesat Information Technology Co. Ltd. Beijing China Beijing University of Civil Engineering and Architecture School of Geomatics and Urban Spatial Information Beijing China

The geometric accuracy of remote sensing images is very important to the mapping accuracy of land surveying and mapping industries, and its internal geometric accuracy affects the accuracy of length and area measurement. Therefore, it is of great significance to automatically screen out remote sensing images with acceptable accuracy. We propose a quantitative quality inspection algorithm for internal geometric accuracy based on automatic image matching to construct a triangulation network, which uses the automatic matching checkpoints to construct a triangulation network, characterizes the internal error between two points of the line segment by calculating the relative error of each line segment that composes each triangle, and finally counts the middle error of the relative accuracy of all line segments in the triangulation network to characterize the internal geometric accuracy of the image. Taking the remote sensing images of plains, hills, and mountains as experimental data, the experimental results show that the quality inspection results of the proposed method are consistent with those of the manual quality inspection method, and the deviation of the quality inspection accuracy is less than 0.3 pixels. The method indicates that it has nearly achieved the objectivity of manual quality inspection. With its characteristics of quantification, automation, and batch processing, this method holds significant practical importance for the internal geometric accuracy quality inspection of massive remote sensing images. In addition, the error results are rendered by color grading using segmentation thresholds, which can quickly and intuitively identify the internal geometric accuracy quality of different areas of the image. © The Authors.

关键词： Triangulation

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Research on Attention-Based Multigranular Remote Sensing Image Extreme Object Detection Algorithm

Research on Attention-Based Multigranular Remote Sensing Ima...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Meng Li Eric C.C. Tsang Wei-Hua Xu Qiang He School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China College of Artificial Intelligence Southwest University Chongqing China School of Science Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

The detection of small objects and high aspect ratio objects is a challenge for remote sensing image object detection. In this paper, we refer to these objects as extreme objects. The Attention-Based Multigranular Extreme Object Detection Network (AMEODNet) is proposed to address these problems. It consists of two parts. The first part is a multi-granularity information fusion module. The second part is an extreme object extraction section. The proposed method is tested on the NWPU VHR-10 datasets. The results are compared with mainstream object detection algorithms to verify the effectiveness of the proposed method.

关键词： Training Attention mechanisms Object detection Detectors Hardware Remote sensing Cybernetics

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Impact of Automated Driving under Virtual Traffic Signals on Traffic Resilience of Urban Road Networks

Impact of Automated Driving under Virtual Traffic Signals on...

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International Conference on Transportation and Development 2024: Transportation Safety and Emerging Technologies, ICTD 2024

作者： Zhang, Yankai Chen, Jiajing Liang, Bo Xu, Zihuan Mo, Lei Zhang, Zhao Bian, Ruijie Key Laboratory of Intelligent Transportation Technology and System Ministry of Education School of Transportation Science and Engineering Beihang Univ. Beijing China Louisiana Transportation Research Center Dept. of Civil and Environmental Engineering Louisiana State University Baton RougeLA United States

ISBN: (纸本)9780784485514

With the rapid development of autonomous driving technology, it is imperative to study the impact of this technology on the resilience of urban road transportation networks. This study focuses on virtual traffic signal control (VTS) as a representative of autonomous driving technology and proposes a multi-dimensional resilience evaluation system to compare the impacts of three different signal-timing strategies on the resilience of urban traffic networks under various scenarios. Simulation results demonstrate that the average vehicle speed under VTS control is 38.1% and 40.1% higher than the other two control modes, respectively. In addition, the VTS approach significantly reduces delays and minimizes fuel consumption compared to the other two approaches. Moreover, this paper delves into the intrinsic mechanisms by which VTS enhances traffic resilience. It highlights the importance of integrating autonomous driving technology into traffic strategies to achieve carbon emissions reduction and promote sustainable transportation. The network resilience enhanced by VTS enables cities to effectively respond to unexpected events, thereby ensuring smooth traffic flow and safety. This research provides valuable insights into existing knowledge and promotes further research in the field of traffic resilience. © ASCE.

关键词： Autonomous vehicles

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Smart Cities Designed by Smart Communities: An Engaged Approach to Develop a Sociotechnical Network

Smart Cities Designed by Smart Communities: An Engaged Appro...

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World Environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Jenewein, Oswald Hummel, Michelle A. Bezbourah, Karabi Liu, Yonghe Masten, Kathryn School of Architecture Univ. of Texas at Arlington ArlingtonTX United States Dept. of Civil Engineering Univ. of Texas at Arlington ArlingtonTX United States Dept. of Public Affairs Univ. of Texas at Arlington ArlingtonTX United States Dept. of Computer Science and Engineering Univ. of Texas at Arlington ArlingtonTX United States Maritimatix ViennaMD United States

ISBN: (数字)9780784484852

ISBN: (纸本)9780784484852

In the process of developing a smart city framework, sensor data are crucial to enable cities and communities to make informed decisions on future plans. Involving community-based organizations and residents is an integral component of this process to ensure equity and accessibility of data. This study aims to develop a sociotechnical network to (1) identify vulnerability zones;(2) measure data on flooding, air, and water quality;and (3) inform community members and decision-makers through a data dashboard. A small coastal town in the Texas Coastal Bend region is utilized as a case study. Methodologically, this study utilizes participatory action research to frame a mixed-methods approach toward developing a data dashboard. This research project is a practical guide for engaged scholars in the social sciences, engineering, and urban design fields. The outcomes include recommendations for the engaged community and provide a data-dashbord targeting academic and non-academic audiences, residents, and decision-makers. © World Environmental and Water Resources Congress *** rights reserved

关键词： Smart city

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Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xiaoting Wang, Juan Bian, Haodong Guo, Maozu Inner Mongolia University Ecological Big Data Engineering Research Center of the Ministry of Education School of Computer Science Hohhot China Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

ISBN: (纸本)9781665468190

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins. © 2022 IEEE.

关键词： Support vector machines

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