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Science China Mathematics 2024年第2期67卷 237-252页

作者： Hong-Lin Liao Tao Tang Tao Zhou School of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China SUSTech International Center for Mathematics Shenzhen 518055China NCMIS&LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

The positive definiteness of real quadratic forms with convolution structures plays an important rolein stability analysis for time-stepping schemes for nonlocal operators. In this work, we present a novel analysistool to handle discrete convolution kernels resulting from variable-step approximations for convolution *** precisely, for a class of discrete convolution kernels relevant to variable-step L1-type time discretizations, weshow that the associated quadratic form is positive definite under some easy-to-check algebraic conditions. Ourproof is based on an elementary constructing strategy by using the properties of discrete orthogonal convolutionkernels and discrete complementary convolution kernels. To our knowledge, this is the first general result onsimple algebraic conditions for the positive definiteness of variable-step discrete convolution kernels. Using theunified theory, we obtain the stability for some simple nonuniform time-stepping schemes straightforwardly.

关键词： discrete convolution kernels positive definiteness variable time-stepping orthogonal convolution kernels complementary convolution kernels

Factor-adjusted tests for generalized linear models with multimodal data: An application to breast cancer data

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Science China Mathematics 2024年

作者： Dongyu Li Lei Wang School of Statistics and data Science KLMDASR LEBPS and LPMCNankai University

With the advancement of modern scientific research, multimodal data is increasingly being collected from multiple sources or types. For outcomes derived from generalized linear models with high-dimensional and multimodal covariates, we develop two distinct factor-adjusted tests to assess the significance of high-dimensional modality data and specific low-dimensional linear combinations of predictors from one or more modalities,respectively. First, we propose a factor-adjusted decorrelated score test to evaluate the significance of a single modality. This approach simultaneously transforms a high-dimensional test into a fixed low-dimensional one while addressing the impact of high-dimensional nuisance parameters. Second, we construct a factor-adjusted Wald test based on partial penalized estimation to assess the significance of certain low-dimensional combinations of variables from one or more modalities. The limiting distributions of these two proposed tests are analyzed under both the null hypothesis and local alternatives to characterize the asymptotic type-I errors and *** finite sample performance of our proposed tests is evaluated through simulations and further demonstrated with a breast cancer dataset.

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Application of CFD and FEA Coupling to Predict Structural Dynamic Responses of A Trimaran in Uni-and Bi-Directional Waves

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China Ocean Engineering 2024年第1期38卷 81-92页

作者： LIAO Xi-yu XIA Jin-song CHEN Zhan-yang TANG Qin ZHAO Nan ZHAO Wei-dong GUI Hong-bin Marine Design&Research Institute of China Shanghai 200011China Ship Structure division China Ship Scientific Research CenterWuxi 214082China College of Ocean Engineering Harbin Institute of TechnologyWeihaiWeihai 264209China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China School of Foreign Studies Jiangnan UniversityWuxi 214122China

To predict the wave loads of a flexible trimaran in different wave fields,a one-way interaction numerical simulation method is proposed by integrating the fluid solver(Star-CCM+)and structural solver(Abaqus).Differing from the existing coupled CFD-FEA method for monohull ships in head waves,the presented method equates the mass and stiffness of the whole ship to the hull shell so that any transverse and longitudinal section stress of the hull in oblique waves can be ***,verification study and sensitivity analysis are carried out by comparing the trimaran motions using different mesh sizes and time step *** on the wave elevation of uni-and bi-directional waves is also carried *** a comprehensive analysis on the structural responses of the trimaran in different uni-directional regular wave and bi-directional cross sea conditions is carried out,***,the differences in structural response characteristics of trimaran in different wave fields are *** results show that the present method can reduce the computational burden of the two-way fluid-structure interaction simulations.

关键词： CFD-FEA fluid-structure coupling structural responses trimaran bi-directional cross sea

A GMM approach in coupling internal data and external summary information with heterogeneous data populations

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Science China Mathematics 2024年第5期67卷 1115-1132页

作者： Jun Shao Jinyi Wang Lei Wang Key Laboratory of Advanced Theory and Application in Statistics and data Science(East China Normal University) Ministry of EducationShanghai 200062China School of Statistics East China Normal UniversityShanghai 200062China Department of Statistics University of Wisconsin-MadisonWI 53706USA School of Statistics and data Science KLMDASRLEBPS and LPMCNankai UniversityTianjin 300071China

Because of advances in data collection and storage,statistical analysis in modern scientific research and practice now has opportunities to utilize external information such as summary statistics from similar studies.A likelihood approach based on a parametric model assumption has been developed in the literature to utilize external summary information when the populations for external and main internal data are assumed to be the *** this article,we instead consider the generalized estimation equation(GEE)approach for statistical inference,which is semiparametric or nonparametric,and show how to utilize external summary information even when internal and external data populations are not the *** approach is coupling the internal data and external summary information to form additional estimation equations and then applying the generalized method of moments(GMM).We show that the proposed GMM estimator is asymptotically normal and,under some conditions,is more efficient than the GEE estimator without using external summary *** of the asymptotic covariance matrix of the GMM estimators are also *** results are obtained to confirm our theory and quantify the improvements by utilizing external *** example is also included for illustration.

关键词： adjustment for heterogeneity constraint data integration generalized method of moments summarystatistic

Method for the classification of tea diseases via weighted sampling and hierarchical classification learning

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 211-221页

作者： Rujia Li Weibo Qin Yiting He Yadong Li Rongbiao Ji Yehui Wu Jiaojiao Chen Jianping Yang College of Big data Yunnan Agricultural UniversityKunming 650201China College of Plant Protection Jilin Agricultural UniversityChangchun 130118China

This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs,low accuracy,and time-consuming traditional tea disease recognition *** method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model,effectively alleviating the impact of high similarity between tea diseases on the model’s classification *** better solve the problem of few and unevenly distributed tea disease samples,this study introduced a weighted sampling scheme to optimize data processing,which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification *** experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea(tea algal spot disease,tea white spot disease,tea anthracnose disease,and tea leaf blight disease).After applying the“weighted sampling hierarchical classification learning method”to train 7 different efficient backbone networks,most of their accuracies have *** EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21%after adopting this learning method,which is higher than EfficientNet-b2(98.82%)and MobileNet-V3(98.43%).In addition,to better apply the results of identifying tea diseases,this study developed a mini-program that operates on *** can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple *** intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers,consumers,and related scientific researchers and has certain practical value.

关键词： tea diseases hierarchical classification learning weighted sampling classification method EfficientNet miniprogram

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

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Computers, Materials & Continua 2024年第4期79卷 1659-1677页

作者： Zhanfeng Wang Lisha Yao School of Computer Science and Artificial Intelligence Chaohu UniversityHefei238000China School of Big data and Artificial Intelligence Anhui Xinhua UniversityHefei230088China

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

关键词： Expression recognition capsule neural network convolutional neural network

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

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Computers, Materials & Continua 2024年第6期79卷 4741-4762页

作者： Zhenyu Qian Yizhang Jiang Zhou Hong Lijun Huang Fengda Li Khin Wee Lai Kaijian Xia School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Department of Medical Imaging The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Department of Neurosurgery The Changshu Affiliated Hospital of Soochow UniversityChangshu215500China Department of scientific Research The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Changshu Key Laboratory of Medical Artificial Intelligence and Big data Suzhou215500China Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

关键词： Deep subspace clustering multiscale network structure automatic hyperparameter tuning semi-supervised medical image clustering

Effect of Plastic Deformation on Microstructure and Properties of Cu-(1 wt%-6 wt%) Ag Alloy

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Journal of Wuhan University of Technology(Materials Science) 2024年第3期39卷 747-753页

作者：茹亚东 ZHANG Zhongyuan 高召顺 ZHANG Ling ZUO Tingting XUE Jiangli TANG Zhixiang DA Bo LIU Yongsheng XIAO Liye Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190China Department of Physics Shanghai University of Electric PowerShanghai 201306China University of Chinese Academy of Sciences Beijing 100190China International Joint Labora-tory for Light Alloys(MOE) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China Research and Services division of Materials data and Integrated System National Institute for Materials ScienceTsukuba 305-0047Japan

In the present study,the Cu-(1 wt%-6 wt%)Ag alloys were prepared by melting,forging and wire *** effects of plastic deformation on microstructure evolution and properties of the alloys were *** results show that non-equilibrium eutectic colonies exist in the Cu-(3 wt%-6 wt%)Ag alloy and no eutectic colonies in the 1 wt%-2 wt%Ag containing *** eutectic colonies are aligned along the drawing direction and refined with the increase of draw *** to the refinement of eutectic colonies,the Cu-Ag alloy exhibits higher strength with the increase of draw *** Cu-6Ag alloy exhibits excellent comprehensive properties with a strength of 930 MPa and a conductivity of 82%IACS when the draw ratio reaches 5.7.

关键词： Cu-Ag alloy high strength and high conductivity microstructure eutectic structure strengthening mechanism

Prostate health index can stratify patients with Prostate Imaging Reporting and data System score 3 lesions on magnetic resonance imaging to reduce prostate biopsies

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Asian Journal of Andrology 2024年第1期26卷 20-24页

作者： John Shung-Lai Leung Wai-Kit Ma Brian Sze-Ho Ho Stacia Tsun-Tsun Chun Rong Na Yongle Zhan Chi-Yuen Ng Chi-Ho Ip Ada Tsui-Lin Ng Yiu-Chung Lam division of Urology Department of SurgeryQueen Mary HospitalUniversity of Hong KongHong KongChina division of Urology Department of SurgeryPrincess Margaret HospitalHongKongChina

We aim to evaluate prostate health index as an additional risk-stratification tool in patients with Prostate Imaging Reporting and data System score 3 lesions on multiparametric magnetic resonance *** with biochemical or clinical suspicion of having prostate cancer who underwent multiparametric magnetic resonance imaging in two tertiary centers(Queen Mary Hospital and Princess Margaret Hospital,Hong Kong,China)between January 2017 and June 2022 were ***-magnetic resonance imaging fusion biopsies were performed after prostate health index *** who only had Prostate Imaging Reporting and data System score 3 lesions were further stratified into four prostate health index risk groups and the cancer detection rates were *** of the 747 patients,47.3%had Prostate Imaging Reporting and data System score 3 lesions *** detection rate of clinically significant prostate cancer in this group was 15.0%.The cancer detection rates of clinically significant prostate cancer had statistically significant differences:5.3%in prostate health indexdata System score 3 *** could be avoided in patients with low prostate health index,with a small risk of missing clinically significant prostate cancer.

关键词： biopsy fusion biopsy magnetic resonance imaging prostate cancer prostate health index

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

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Journal of Integrative Agriculture 2024年第6期23卷 1787-1802页

作者： Mansoor Sheikh Farooq Iqra Hamadani Ambreen Kumar A Pravin Manzoor Ikra Yong Suk Chung Phenomics Laboratory Department of Plant Resources and EnvironmentJeju National UniversityJeju 63234Republic of Korea Council of scientific&Industrial Research Indian Institute of Integrative MedicinePulwamaJ&K 192301India Animal and Dairy Science University of WisconsinMadisonWI 530706United States of America division of Fruit Science Faculty of HorticultureSher-e-Kashmir University of Agricultural Sciences and Technology of KashmirSrinagar 190025India

Crop improvement is crucial for addressing the global challenges of food security and sustainable *** advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large *** integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop *** algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic *** technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety ***,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop *** leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop *** aim of this review is to explore the transformative potential of integrating HTP and AI in crop *** review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

关键词： artificial intelligence crop improvement data analysis high-throughput phenotyping machine learning precision agriculture trait selection