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Correction to: Experimental Investigation and Thermodynamic Assessment of Phase Equilibria in the Cr-Mo-Ru Ternary System

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Journal of Phase Equilibria and Diffusion 2022年第6期43卷 705-705页

作者： Zhang, Yanqing Chen, Xiaodong Ren, Hui Liu, Hu Yang, Shuiyuan Lu, Yong Han, Jiajia Wang, Cuiping Liu, Xingjun College of Materials and Fujian Key Laboratory of Surface and Interface Engineering for High Performance Materials Xiamen University Xiamen People’s Republic of China Xiamen Key Laboratory of High Performance Metals and Materials Xiamen University Xiamen People’s Republic of China Institute of Materials Genome and Big Data Harbin Institute of Technology Shenzhen People’s Republic of China

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A Stacked Ensemble Learning Framework with Heterogeneous Feature Combinations for Predicting ncRNA-Protein Interaction

A Stacked Ensemble Learning Framework with Heterogeneous Fea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiguo Dai Zhaowei Wang Jinmiao Song Xiaodong Duan Maozu Guo Zhen Tian School of Computer Science and Engineering Dalian Minzu University Dalian China Key Laboratory of Big Data Applied Technology State Ethnic Affairs Commission Dalian Minzu University Dalian China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The interaction between ncRNA and protein is a kind of crucial molecular activities in a cell. Developing computational methods to predict ncRNA-protein interactions has attracted increasing attentions in recent years. In this work, a novel stacked ensemble learning framework is presented for predicting ncRNA-protein interaction based on heterogeneous feature combinations, named HFC-RPI. Firstly, the compositional features of k-mer with different orders were extracted from the primary sequence and secondary structure of RNA and protein respectively. Secondly, we trained a set of base learners using a variety of heterogeneous combinations of the extracted features respectively. Thirdly, the prediction results of these base learners were employed to train the stacked learner, which output the final prediction result at the higher layer in HFC-RPI. Moreover, in order to improve the generalization of HFC-RPI, when training the base learners, a cross-validation based method was applied. Extensive experimental results showed that the proposed learning framework HFC-RPI was effective and feasible for predicting the interaction of ncRNA and protein. By comparing with state-of-the-art methods, HFC-RPI was superior to them on most performance evaluation metrics.

关键词： Proteins Feature extraction RNA Training Predictive models Protein engineering Deep learning

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Travel Time Prediction for Multi-Airport Systems Via Multiclass Queuing Networks

Travel Time Prediction for Multi-Airport Systems Via Multicl...

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Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Kailin Chen Shaoyu Wang Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Guangdong China Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

In this paper, we consider predicting travel time for aircraft operated in multi-airport systems by modeling and simulating a multiclass queuing network, which can systematically capture the complicated coupling relationship among multiple airports and terminal airspace and the complex nature of flight trajectories following different traffic flow patterns. In this multiclass queuing network model, each class of queuing network, named a class of customers, is modeled with the data of a traffic flow pattern, which is identified for a cluster of flight trajectories. Airports and airspace sectors are correspondingly modeled as networked servers with nonhomogeneous and time-varying arrival rate, service rate and server capacity to serve those classes of customers following their specific routing probabilities. Then, all of the parameters for setting up the multiclass queuing network model can be properly estimated using historical 4D flight trajectory data. To illustrate the superiority of this model, both average travel time for each class of customers, i.e., aircraft following a particular flow pattern, and the arrival time for an individual flight are predicted via simulations of a multiclass queuing network, and furthermore, compared with the real travel time. A typical example of a multi-airport system, the Guangdong-Hong Kong-Macau Greater Bay Area in China, is utilized to showcase the prediction performance of the proposed multiclass queuing network simulation model. The simulation experiments of the case study demonstrate that the proposed model well fits this multi-airports system. For most of the time periods, the percentage error (PE) of simulated average travel time and real average travel time is less than 5%. The travel time prediction for a random individual flight can achieve around 1% of the percentage error in terms of point estimation.

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Cycle extendability of hamiltonian strongly chordal graphs

arXiv

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arXiv 2020年

作者： Rong, Guozhen Li, Wenjun Wang, Jianxin Yang, Yongjie School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Faculty of Human and Business Sciences Saarland University Saarbrücken Germany

In 1990, Hendry conjectured that all Hamiltonian chordal graphs are cycle extendable. After a series of papers confirming the conjecture for a number of graph classes, the conjecture is yet refuted by Lafond and Seamone in 2015. Given that their counterexamples are not strongly chordal graphs and they are all only 2-connected, Lafond and Seamone asked the following two questions: (1) Are Hamiltonian strongly chordal graphs cycle extendable? (2) Is there an integer k such that all k-connected Hamiltonian chordal graphs are cycle extendable? Later, a conjecture stronger than Hendry’s is proposed. In this paper, we resolve all these questions in the negative. On the positive side, we add to the list of cycle extendable graphs two more graph classes, namely, Hamiltonian 4-FAN-free chordal graphs where every induced K5 − e has true twins, and Hamiltonian {4-FAN, A}-free chordal graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamiltonians

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Prevention of external force damage to overhead power lines based on network and intelligent positioning

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 551-583页

作者： Xie, Bo-Lin Pan, Jeng-Shyang Lin, Yuh-Chung Xie, Yu-Hong Pan, Tien-Szu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Science and Technology Sanda University Shanghai201209 China Guangdong Power Grid Heyuan Power Supply Bureau Transmission Management Office China Southern Power Grid Heyuan517000 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan

In the research on the prevention of external damage to power transmission lines, it is a difficult problem to prevent large machinery vehicles from damaging the overhead transmission lines. After the emergence of video surveillance technology, the accident rate has dropped significantly. But still unable to completely eliminate such accidents. The anti-external damage solution based on network and intelligent positioning is very effective for early warning of accidents. This gives us the opportunity to completely eliminate such accidents. In this paper we utilize the satellite positioning data of the power towers to establish a warning zone along the power line. When the system detects that some large machinery vehicles enter the warning zone, early warning messages will be sent to these vehicles. Afterward, the connection between vehicle drivers and management center can be established such that further instructions can be ordered to avoid accidents. According to our simulations, it presents our proposed mechanism can effectively prevent the damage occurrences which may be caused by large machinery vehicles. © 2021 Journal of Network Intelligence.

关键词： Accidents

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Using LandTrendr to analyze forest disturbance, recovery, and attribution in Hunan province from 2001 to 2024

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Ecological Indicators 2025年 176卷

作者： Panlin Cao Zhuo Zang Meng Zhang Xu Wang Xian Tang Jiahong Xiang Shu Tang Jing Wang Yanan Zhang Research Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry and Technology Changsha 410004 China Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area Changsha 410004 China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Changsha 410004 China Research Institute of Tropical Forestry Chinese Academy of Forestry Guangzhou 510520 China Sanya Academy of Forestry Sanya 572023 China Hunan Zhonglinyao Forestry Remote Sensing Technology Co. Ltd. Changsha 410004 China

Forest dynamics monitoring is an important basis for assessing the stability of regional ecosystems and formulating sustainable management strategies, and forest evolution patterns under the interaction of frequent human activities and natural disturbances in subtropical regions have not been fully revealed. This study uses Google Earth Engine and Landsat data (2001–2024) with the LandTrendr algorithm to detect spatiotemporal forest disturbances and recovery in Hunan Province, applying a random forest classifier to identify disturbance types. The results showed that from 2001 to 2024, the forest disturbance area in the study region was 4,723.46 km 2 , accounting for 3.48 % of the total forest area; the recovery area was 4,380.33 km 2 , accounting for 3.23 %; and the net loss was 343.13 km 2 , representing only 0.25 %. Among the disturbance types, weak disturbances accounted for 53.35 % of the total disturbed area, while low-level recovery accounted for 70.4 % of the total recovery area. Fire was the primary disturbance factor, accounting for 40.7 %, followed by land use conversion, which accounted for 27.3 %. This study provides a scientific basis for adaptive management, supporting targeted restoration and policy-making to enhance forest resilience and promote sustainable forest use in subtropical regions.

关键词： Change detection Attribution LandTrendr Random forest Hunan

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PC-RGNN: Point cloud completion and graph neural network for 3D object detection

arXiv

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arXiv 2020年

作者： Zhang, Yanan Huang, Di Wang, Yunhong 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 School of Computer Science and Engineering Beihang University Beijing100191 China

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012]

Franklin Open

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Franklin Open 2024年 9卷

作者： Xingyue Liu Kaibo Shi Kun Zhou Shiping Wen Yiqian Tang Lin Tang School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 PR China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 PR China Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 PR China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 PR China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

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A Method for Authenticity Identification of Fritillaria Cirrhosa D. Don Based on Deep Learning 4

A Method for Authenticity Identification of Fritillaria Cirr...

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4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019

作者： Hu, Ke Hu, Pan Cao, Dong Yan, Xin Yu, Xi Liu, Chang Chengdu University College of Information Science and Engineering Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China Research Institute of Big Data Chengdu University China Chengdu Institute of Chinese Herbal Medicine China

ISBN: (纸本)9781728123257

Since the authentic Fritillaria Cirrhosa D. Don resources are scarce due to its high price and valuable medical uses, it is difficult to meet the clinical needs. Therefore, the problem of adulteration in the market is more and more serious. At present, the identification of Fritillaria mainly relies on traditional trait identification, microscopic identification, physical and chemical identification and other methods, which is subjective and requires high practical experience for operators, and the pretreatment work is cumbersome. In this paper, the concept of deep learning is introduced into the identification of Fritillaria for the first time. First, we have created the first multi-angle Fritillaria standard datasets, including Fritillaria Cirrhosa D. Don and Fritillary bulb. We then have proposed a novel deep learning framework SE-DPU to classify Fritillaria datasets, from which we can get the highest classification accuracy. The framework is a hybrid of Squeeze-and-Excitation block (SE unit), U-Net and Dual Path Network (DPN). It reuses the features and explores the new features adaptively recalibrates channel-wise feature responses obtains abundance features while training. Experimental results with Fritillaria Cirrhosa D. Don data have indicated that the proposed method have provided competitive performance. © 2019 IEEE.

关键词： Deep learning

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Effect of the Indigenous Microbial Community on Coal Flotation

SSRN

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SSRN 2022年

作者： Wang, Xizhuo Meng, Delong Li, Jianbo Lu, Zijing Zhang, Zijia Zhang, Cui Song, Shaoxian Peng, Yonghe Xia, Ling Hubei Key Laboratory of Mineral Resources Processing and Environment Wuhan University of Technology Luoshi Road 122 Hubei Wuhan430070 China School of Resources and Environmental Engineering Wuhan University of Technology Luoshi Road 122 Hubei Wuhan430070 China School of Minerals Processing and Bioengineering Central South University South Lushan Road 932 Changsha410083 China Instituto de Física de la Universidad Autónoma de San Luis Potosí Álvaro Obregón 64 San Luis Potosí78000 Mexico Rizhao Big Data Research Institute of Geology and Geographic Information Shandong Rizhao276826 China

The indigenous microorganisms might have great potential to influence coal flotation, however, have never been emphasized. This study focused on the relationship between indigenous microorganisms, flotation efficiency, and pulp physicochemical properties in a coal beneficiation plant. The results showed that the microbial community in the coal flotation system had high diversity (Shannon index ranged from 6.38 to 7.38). The microorganisms were endogenous from raw coal and persisted in the flotation system with dominant taxa including Proteobacteria and Acidobacteria . The community structure of indigenous microorganisms changed with the addition of flotation reagents, especially nitrogen-containing reagents, which might be the key factors shaping community structures. Most bacteria were demonstrated by Spearman correlation analysis that had negative impacts on the flotation performance (ash content) of coal, except for Nitrososphaera . It is the first study to concentrate on the performance of indigenous microorganisms in coal flotation systems, providing guidance for the optimization of coal flotation from the perspective of microorganisms and contributing to the efficient usage of endangered energy sources. © 2022, The Authors. All rights reserved.

关键词： Microorganisms

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