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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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MHANDTI: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Multi-Hop Attention Networks

MHANDTI: Drug-Target Interaction Prediction Model Based on H...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhang Jiaqi Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo School of Computer Science and Technology Shandong University of Technology Zibo China College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China College of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Predicting drug-target interactions (DTI) has become an important step in the drug discovery and drug repositioning process. The biological identification of DTI incurs significant financial and temporal costs, and the deployment of computational methodologies for DTI prediction can substantially curtail both the duration and economic expenditure of drug discovery or repositioning. Drawing from disparate data sources can offer a comprehensive overview and diverse perspectives for predicting drug-target interactions. However, existing methods for processing drug and target information are unable to capture long-range dependency across heterogeneous graphs. To capture more comprehensive drug and target features for DTI prediction, this article proposes a drug-target interaction prediction model based on heterogeneous graph multi-hop attention networks, called MHANDTI. Concretely, the word-level deep convolutional neural network is used to obtain the structural features of drugs and targets as attributes of drug and target entity nodes in heterogeneous graphs. For the processing of heterogeneous graphs, MHANDTI designs a multi-hop attention diffusion layer (MHADL) to establish a connection between the non-direct connection nodes, and summarize the characteristics of the adjacent nodes of the center nodes in several hops. A series of results indicate that the performance of this method is superior to the latest existing frameworks.

关键词： Drugs Measurement Costs Soft sensors Instruments Spread spectrum communication Predictive models Prediction algorithms Drug discovery Diffusion tensor imaging

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Concept maps lead to better problem statements: An empirical study measuring the effects of priming students to think in systems

Concept maps lead to better problem statements: An empirical...

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Construction Research Congress 2022: Health and Safety, Workforce, and Education, CRC 2022

作者： Ignacio, Paulo Milovanovic, Julie Shealy, Tripp Gero, John Dept. of Civil and Environmental Engineering Virginia Tech BlacksburgVA United States Dept. of Computer Science and School of Architecture Univ. of North Carolina at Charlotte CharlotteNC United States

ISBN: (数字)9780784483985

ISBN: (纸本)9780784483985

Challenges associated with the design and construction of the built environment are complex. Students need training to help them deal with this complexity and to help them explore and reframe problems early during project planning and design. Concept maps provide a visual representation of complex information and the relationships between this information. The research presented in this paper tested whether priming students to think in systems by asking them to draw concept maps changes how they construct problem statements. In total, 40 engineering students participated in the study. Half were asked to draw a concept map before constructing a problem statement about how to improve mobility systems around campus. The cognitive effort (i.e., time and words) students spent on the task and the number of unique system elements included in their problem statement were measured. Students that received the concept mapping intervention spent significantly more time thinking about the problem, developed longer problem statements, and included more unique elements of systems. These findings suggest using concept mapping can aid students' conceptualization of complex problems. © 2022 ASCE.

关键词： Students

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A Simulation Study on the Reentrant Waves in the Pacing Ventricular Tissues 5

A Simulation Study on the Reentrant Waves in the Pacing Vent...

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2020 5th International Seminar on computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Yue Shi, Xibo Sui, Dong College of Computer Science and Technology Harbin Engineering University China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China

The aim of the study is to investigate the relationship between the reentrant waves and I K1, and probe the effect of I K1 on the reentrant waves. Firstly, based on the TNNP06 model, the single pacing cell is derived by depressing I K1. And then, a 400 cells ×400 cells 2D tissue is developed by coupling the pacing cells together. Thirdly, the S1-S2 protocol is applied to inspire the reentrant waves. Next, the processes of the reentrant waves in the tissue corresponding to different I K1 are analysed. In addition, the action potentials of the cells in special locations are recorded and discussed. With the decrease of the I K1, the reentrant waves spin slowly and the period of reentrant waves increases. Meanwhile, the range of the action potential of the cells becomes larger when I K1 becomes smaller. The results suggest that with the decrease of I K1, the reentrant wave is weakened, and the systolic and diastolic functions of the tissue are enhanced at the same time. © Published under licence by IOP Publishing Ltd.

关键词： Tissue

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Effect of the Mixing Structure Parameters of a Self-reflux Burner on Combustion Characteristics and NO_(x)Emission

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Journal of Thermal science 2021年第4期30卷 1224-1236页

作者： XU Qian SHEN Meng SHI Kejian LIU Zhihui AKKURT Nevzat XIONG Yaxuan LIU Lin FENG Junxiao WANG Jiulong School of energy and environmental engineering Shunde Graduate SchoolUniversity of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Key Laboratory of HVAC Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

To solve the problem of low efficiency of NO_(x)emission reduction in self-reflux burners,this study is based on the concept of coordinated control of self-reflux burner structural and thermal *** completing the structural design and optimization of thermal parameters,we continue to adjust the two key structural parameters:the nozzle axis distance and the length of the cylindrical section,to minimize NO_(x)*** are the two parameters that chiefly affect the mixing of flue gas and fuel *** results show that increasing nozzle axis distance can delay the mixing of gas and air and create a more uniform oxygen concentration field for the combustion *** maximum combustion temperature is reduced from 1973.65 K to 1935.88 K and the volume fraction of NO_(x)in the flue gas is reduced from 188.08×10^(–6)to 143.47×10^(–6).However,compared with the nozzle axis distance,the length of the cylindrical section of the burner has little effect on the mixing of the flow *** different cylindrical section lengths,the maximum combustion temperature does not change more than 3 K,and the volume fraction of NO_(x)in the flue gas changes within 5×10~(–6).

关键词： self-reflux structure structural parameter control thermal parameter combustion characteristic NO_(x)emission

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New Classification of Drainage Pipe Defects Based on computer Vision and Database Building and Effect Analysis

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Journal of Pipeline Systems engineering and Practice 2025年第3期16卷

作者： Wang, Junling Liu, Yanhong Wang, Chenchen Wang, Honghong Zhang, Xianguo Huang, Jinyu School of Environment and Energy Engineering Beijing Univ. of Civil Engineering and Architecture Beijing100044 China Beijing Capital Eco-Environment Protection Group Co. Ltd. Building 39 No. 21 Chegongzhuang St. Xicheng District Beijing100044 China

Identifying and detecting defects in drainage pipes are essential for maintaining drainage pipe networks. This process begins by building a database for defect classification. Currently, the "Technical regulations for detection and evaluation of urban drainage pipes"is the main reference for the classification of drainage pipe defects and the building of the database. However, the regulations do not consider computer vision requirements in the classification system. This research suggests a novel computer vision-based classification scheme for drainage pipe flaws that is divided from the initial two categories of 16 defects into three categories of 12 defects. A database was constructed using engineering samples to focus on six of the most harmful defects out of the 12 categories identified categories: rupture;interface deviation;surface damage;roots;leakage;and sealing defects. Experiments were conducted on the created database using the YOLOv8 model to analyze: (1) the impact of varying library sizes on detection performance;(2) the influence of sample libraries in different environments (varying brightness and contrast);(3) augmentation and updating of sample data;and (4) analysis of the impact of sample libraries after data augmentation and updating. The experimental findings demonstrate that the model achieves a precision (P-value) of 71.6%, a recall (R-value) of 63.7%, and a mean accuracy (mAP-value) of 71.4%. This value initially ensures that the new classification system and the database are compatible with the current computer vision technology and can yield favorable outcomes. © 2025 American Society of civil Engineers.

关键词： Libraries

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Dual-Branch Point Cloud Feature Learning for 3D Object Detection

Dual-Branch Point Cloud Feature Learning for 3D Object Detec...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lun, Hengsheng Xue, Jian Lu, Ke Univ. of Chinese Academy of Sciences School of Engineering Science Beijing China China Three Gorges University Colg. of Computer and Information Tech Yichang China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Incomplete feature information is a key problem that limits 3D point cloud object detection and its applications. Many state-of-the-art detectors address this problem from different perspectives, but a comprehensive solution has not yet been obtained. In this paper, a solution is proposed that consists of two branches, one for channel-wise local feature learning and one for spatial-wise global feature learning. The combination of the local features, global contextual features, channel-wise attention features, and spatial attention features of the 3D point cloud is obtained through the two branches. Specifically, a generic spatial self-attention model is proposed that uses skeleton convolution to enhance the extraction of spatial features and combines it with a self-attention mechanism to improve the learning of global features. Further, the proposed skeleton attention mechanism focuses on object contour and rotation invariance of the point cloud. Through sufficient experimental validation, all module proposed in this paper are shown to have a substantial effect on performance and the visualization results demonstrate the importance of our approach. © 2022 IEEE.

关键词： Object detection

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Leveraging Long Short-Term Memory for Individual Water Main Failure Prediction Using Pipe Intrinsic Variables and Climate Data

Leveraging Long Short-Term Memory for Individual Water Main ...

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World Environmental and Water Resources Congress 2025: Cool Solutions to Hot Topics

作者： Khashei, Melica Dziedzic, Rebecca Roshani, Ehsan Dept. of Building Civil and Environmental Engineering Gina Cody School of Engineering and Computer Science Concordia Univ. MontrealQC Canada National Research Council Canada OttawaON Canada

ISBN: (数字)9780784486184

ISBN: (纸本)9780784486184

The optimal annual replacement of deteriorating water mains is a critical task for water utilities. Pipe failure prediction models serve as essential tools in the strategic planning of rehabilitation efforts for urban water distribution infrastructure. Numerous studies highlight that climatic factors have a significant impact on the integrity of water mains. However, existing failure prediction methods often struggle to effectively account for the complex interactions between static variables, such as pipe material and diameter, and dynamic factors, including historical failure trends and climate-related variables, thereby limiting their accuracy and reliability. This study aims to develop a novel deep learning approach for predicting the probability of failure for individual pipes within a water distribution system. The research utilizes three key datasets from Saskatoon, Canada: pipe inventory data, historical break records, and weather information. A long short-term memory (LSTM) neural network model is developed to capture the temporal dependencies and non-linear interactions in the data. The results demonstrated that incorporating invariant variables including pipe characteristics, improved the model's predictive performance, while the inclusion of climate data further enhanced the model's performance, highlighting the importance of combining static and dynamic factors in failure prediction. © 2025 ASCE.

关键词： Strategic planning

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Experimental Study on humidity sensitivity of New DC ionization 13

Experimental Study on humidity sensitivity of New DC ionizat...

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13th Asia-Pacific International Symposium on Electromagnetic Compatibility and Technical Exhibition, APEMC 2022

作者： Ronggang, Wang Liuliang, He Xiaoe, Wu Suzhou TA&A Ultra Clean Technology Co. Ltd. R&D Center Suzhou China Beijing University of Civil Engineering and Architecture College of Science Beijing China Baoji University of Arts and Sciences School of Computer Science Baoji China

ISBN: (数字)9781665416719

ISBN: (纸本)9781665416719

The offset voltage and decay time performance parameters of ionizer are greatly affected by the humidity in the working environment. In this paper, the design principle of double DC ionizer with feedback regulation system is introduced, and the humidity sensitivity is studied by experiment. By setting different humidity, the offset voltage and decay time performance parameters of this ionizer are tested. The results show that the offset voltage at each point can be controlled within ±3V in the humidity range (20%~70%). Under the conditions of ultra-low humidity (8%~15%) and ultra-high humidity (80%~90%), the performance of this DC ionizer can meet the electrostatic control requirements of electrostatic protection area. When the ambient humidity exceeds 80%, the performance is greatly affected. © 2022 IEEE.

关键词： Ionization

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Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction

arXiv

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arXiv 2023年

作者： Zhang, Hao Liu, Yang Liu, Xiaoyan Liang, Tianming Sharma, Gaurav Xue, Liang Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The *** HangZhou 310000 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. Specifically, we propose a graph view of sentence bags referring to an entity pair, which enables message-passing based aggregation of information related to the entity pair over the sentence bag. The proposed framework alleviates the common problem of noisy labeling in distantly supervised relation extraction and also effectively incorporates inter-dependencies between sentences within a bag. Extensive experiments on two large-scale biomedical relation datasets and the widely utilized NYT dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for biomedical distant supervision relation extraction while also providing excellent performance for relation extraction in the general text mining domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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