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3rd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Zhang, Xingxing Li, Shaobo Yu, Liya Dong, Rongzhi Song, Qisong Wang, Zhongyu State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China Department of Computer Science and Engineering University of South Carolina SC United States Guizhou University Guiyang China

ISBN: (纸本)9783800759330

In this paper a Fully Connected Neural Network (FCNN) model is developed for the prediction of the material thermal conductivity. The traditional theoretical experimental research and computational simulation could not meet the needs of scientists' exploration of new materials. In recent years, data-driven machine learning algorithm has been used in material screening and performance prediction. Thermoelectric materials have become a hot topic in the field of materials research because of their ability to realize the interactive conversion of electric energy and thermal energy, small size, light weight, long service life and environmental friendliness. The hypothesis verification method based on a large number of experimental observations and the method of system calculation using the first principle are difficult to meet the requirements for rapid discovery of new materials in the current era due to the long research period. Therefore, the use of machine learning algorithms to assist in the development of new thermoelectric materials is of great significance. In this paper, a material thermal conductivity prediction model based on Fully Connected Neural Network (FCNN) is established, which is applied to the thermal conductivity dataset in the Materials Project database. The model is evaluated by ten-fold cross-verification, and good prediction results are obtained. The mapping model between descriptive and thermal conductivity of materials is established by using machine learning algorithm, which can be used for large-scale material screening and guiding experimental research. © VDE VERLAG GMBH.

关键词： Forecasting

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Superhydrophobic Photothermal Coating Comprising Sio2-Grafted Fe3o4 and Ag-Decorated Reduced Graphene for Anti-Icing and De-Icing

SSRN

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SSRN 2024年

作者： Zhang, Pan Wu, Changmei Xie, Lan Xue, Bai Jiang, Yi Department of Polymer Materials and Engineering College of Materials and Metallurgy Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Considering the current challenges associated with fossil fuel scarcity and ice accumulation on solid surfaces, we propose a sustainable superhydrophobic photothermal coating approach to harnesses solar energy for ice mitigation and de-icing. We have successfully integrated the Fe3O4@SiO2@FAS/Ag-rGO composites into an epoxy matrix to create a photothermal superhydrophobic coating. The innovative coating boasts outstanding superhydrophobicity with a water contact angle of 153.8°, resulting in a delayed freezing time of 1200 s, which is four times longer than that of a pure EP coating. Additionally, it exhibits an impressive photothermal effect, raising the surface temperature to 58.8 °C under 1 sun. The coating also demonstrates rapid de-icing capabilities, clearing ice in just 120 s under 40 mW/cm2. By leveraging the light-trapping effect, utilizing the plasma resonance of silver, and incorporating a high surface roughness micro-nano hierarchical structure, the coating achieves exceptional photothermal conversion efficiency and anti-icing performance. Moreover, the coating's chemical and mechanical durability enhance its practicality and potential utility across various applications. In conclusion, this innovative superhydrophobic coating with photothermal properties offers a promising solution for various practical applications in addressing challenges related to ice accumulation and removal. © 2024, The Authors. All rights reserved.

关键词： Coatings

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lncRNA7 and lncRNA2 modulate cell wall defense genes to regulate cotton resistance to Verticillium wilt (kiac041, 2022)

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PLANT PHYSIOLOGY 2022年第3期189卷 1881-1881页

作者： Zhang, Lin Liu, Jinlei Cheng, Jieru Sun, Quan Zhang, Yu Liu, Jinggao Li, Huimin Zhang, Zhen Wang, Ping Cai, Chaowei Chu, Zongyan Zhang, Xiao Yuan, Youlu Shi, Yuzhen Cai, Yingfan State Key Laboratory of Cotton Biology Henan Key Laboratory of Plant Stress Biology School of Life Sciences School of Computer and Information Engineering Henan University Kaifeng 475001 China College of life science and agricultural engineering Nanyang Normal University Nanyang 473000 China Chongqing Key Laboratory of Big Data for Bio Intelligence College of Bioinformation Chongqing University of Posts and Telecommunications Chongqing 400065 China Southern Plains Agricultural Research Center Agricultural Research Service USDA College Station Texas 77845 USA Kaifeng Academy of Agriculture and Forestry Kaifeng 475000 China State Key Laboratory of Cotton Biology Key Laboratory of Biological and Genetic Breeding of Cotton The Ministry of Agriculture Institute of Cotton Research Chinese Academy of Agricultural Sciences Anyang 455000 China

In plants, long noncoding RNAs (lncRNAs) regulate disease resistance against fungi and other pathogens. However, the specific mechanism behind this regulation remains unclear. In this study, we identified disease resistance-related lncRNAs as well as their regulating genes and assessed their functions by infection of cotton (Gossypium) chromosome segment substitution lines withVerticillium dahliae. Our results demonstrated thatlncRNA7and its regulating genePectin methylesterase inhibitor 13(GbPMEI13) positively regulated disease resistance via the silencing approach, while ectopic overexpression ofGbPMEI13in Arabidopsis (Arabidopsis thaliana) promoted growth and enhanced resistance toV. dahliae. In contrast,lncRNA2and its regulating genePolygalacturonase 12(GbPG12) negatively regulated resistance toV. dahliae. We further found that fungal disease-related agents, including the pectin-derived oligogalacturonide (OG), could downregulate the expression oflncRNA2andGbPG12, leading to pectin accumulation. Conversely, OG upregulated the expression oflncRNA7, which encodes a plant peptide phytosulfokine (PSK-α), which was confirmed bylncRNA7overexpression and Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS) experiments. We showed that PSK-α promoted 3-Indoleacetic acid (IAA) accumulation and activatedGbPMEI13expression through Auxin Response Factor 5. Since it is an inhibitor of pectin methylesterase (PME), GbPMEI13 promotes pectin methylation and therefore increases the resistance toV. dahliae. Consistently, we also demonstrated that GbPMEI13 inhibits the mycelial growth and spore germination ofV. dahliaein vitro. In this study, we demonstrated that lncRNA7, lncRNA2, and their regulating genes modulate cell wall defense againstV. dahliaevia auxin-mediated signaling, providing a strategy for cotton breeding.

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Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China 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 Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks 24

MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yue, Xianghu Zhang, Xueyi Chen, Yiming Zhang, Chengwei Lao, Mingrui Zhuang, Huiping Qian, Xinyuan Li, Haizhou Department of Electrical and Computer and Engineering National University of Singapore Singapore Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China School of Electronic Electrical and Communication Engineering University of the Chinese Academy of Sciences Beijing China National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio-Visual Class-Incremental Learning (AVCIL), the effective integration and utilization of heterogeneous modalities, with their complementary and enhancing characteristics, remains largely unexplored. To bridge this gap, we propose the Multi-Modal Analytic Learning (MMAL) framework, an exemplar-free solution for AVCIL that employs a closed-form, linear approach. To be specific, MMAL introduces a modality fusion module that re-formulates the AVCIL problem through a Recursive Least-Square (RLS) perspective. Complementing this, a Modality-Specific knowledge Compensation (MSKC) module is designed to further alleviate the under-fitting limitation intrinsic to analytic learning by harnessing individual knowledge from audio and visual modality in tandem. Comprehensive experimental comparisons with existing methods show that our proposed MMAL demonstrates superior performance with the accuracy of 76.71%, 78.98%, and 76.19% on AVE, Kinetics-Sounds, and VGGSounds100 datasets, respectively, setting new state-of-the-art AVCIL performance. Notably, compared to those memory-based methods, our MMAL, being an exemplar-free approach, provides good data privacy and can better leverage multi-modal information for improved incremental accuracy. © 2024 ACM.

关键词： Contrastive Learning

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CT-based radiomics:predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 402-416页

作者： Jia Fu Mengjie Fang Zhiyong Lin Jianxing Qiu Min Yang Jie Tian Di Dong Yinghua Zou Department of Interventional Radiology and Vascular Surgery Peking University First HospitalBeijing 100043China Department of Radiology Peking University First HospitalBeijing 100043China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

This study aimed to comprehensively evaluate non-contrast computed tomography(CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis(ARAS)after percutaneous transluminal renal angioplasty(PTRA).A total of 52 patients were retrospectively recruited,and their clinical characteristics and pretreatment CT images were *** a median follow-up period of 3.7 mo,18 patients were confirmed to have benefited from the treatment,defined as a 20%improvement from baseline in the estimated glomerular filtration rate.A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype *** features,comprising 116 handcrafted features and 78 deep learning features,were extracted from the affected renal and perirenal adipose *** features from the latter were correlated with early outcomes,as determined by univariate analysis,and were visually represented in radiomics heatmaps and volcano *** using consensus clustering and the least absolute shrinkage and selection operator method for feature selection,five machine learning models were *** regression yielded the highest leave-one-out cross-validation accuracy of 0.780(95%CI:0.660-0.880)for the renal signature,while the support vector machine achieved 0.865(95%CI:0.769-0.942)for the perirenal adipose *** Additive exPlanations was used to visually interpret the prediction mechanism,and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature,*** analysis revealed that both signatures served as independent predictive *** combined,they achieved an area under the receiver operating characteristic curve of 0.888(95%CI:0.784-0.992),indicating that the imaging phenotypes from both regions complemented each *** conclusion,non-contrast CT-based radio

关键词： Atherosclerotic renal artery stenosis Percutaneous transluminal renal angioplasty Computed tomography Radiomics Deep learning Explainability

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Monotonicity-Induced Health Indicator for Axle-Box Bearings of Urban Rail Vehicles 6th

Monotonicity-Induced Health Indicator for Axle-Box Bearings ...

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6th International Conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Wang, Yiran Xin, Ge An, Guoping Li, Yilei School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Center of Safety Technology National Railway Administration Beijing100160 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819993147

Health indicators of axle-box bearings play an important role in failure prediction for urban rail vehicles. Vibration-based signal processing technology is a promising tool to reveal the degradation process with fruitful time-frequency information. However, the processes usually have non-stationary features caused by complex working conditions, which may be confused with the unknown temporal variations under different frequency bands. To address this issue, this paper proposes a new index, named monotonicity-induced health indicator, which is adaptive to weight the optimal frequency bands. First, frequency domain analysis is used to extract features from vibration signals. The monotonicity of these features is then estimated and their weights are optimized by means of maximizing the monotonicity. Eventually, all the features and weights are fused together to obtain a health indicator. The experimental result proves that the proposed health indicator shows its superiority in terms of monotonicity while accurately characterizing the degradation process of the bearings. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Frequency domain analysis

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AMOD-Net: Atention-based Multi-Scale Object Detection Network for X-Ray Baggage Security Inspection 5

AMOD-Net: Atention-based Multi-Scale Object Detection Networ...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhu, Xiaoke Zhang, Jitong Chen, Xiaopan Li, Danyang Wang, Yufei Zheng, Minghao School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

X-ray baggage security checking is an extremely important task, which can detect various dangerous objects in airports, stations and other public places to prevent crimes and protect personal safety. However, at present, most of the recognition is done manually, which is inefficient and error-prone. As a complementary, object detection algorithm is beneficial to avoiding errors caused by manual detection. Although the universal object detection is well developed and the performance of the universal detectors is very advanced, the performance of these detectors in X-ray image detection is mediocre. In this paper, we propose an Attention-based Multi-Scale Object Detection Network (called AMOD-Net) for X-ray baggage security inspection. To solve the problems of stacking and occlusion existed in the X-ray baggage image, we design a channel selection attention module for AMOD-Net. To make better use of the feature information, we construct a deep feature fusion structure for AMOD-Net. Experiments on the X-ray baggage dataset demonstrate that our approach achieves very competitive results. © 2021 ACM.

关键词： Object detection

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CLDG: Contrastive Learning on Dynamic Graphs

CLDG: Contrastive Learning on Dynamic Graphs

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International Conference on data engineering

作者： Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG.

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Prediction of Hepatocellular Carcinoma Diseases Based on Methylation data and Screening of Hub Genes 5

Prediction of Hepatocellular Carcinoma Diseases Based on Met...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhang, Yawei Ren, Fangtao Liu, Xi Zhang, Fan School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC. © 2021 ACM.

关键词： Machine learning

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