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A class of elliptic mixed boundary value problems with (p,q)-Laplacian: existence, comparison and optimal control

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arXiv 2023年

作者： Zeng, Shengda Migórski, Stanislaw Tarzia, Domingo A. Zou, Lang Nguyen, Van Thien Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Department of Mathematics Nanjing University Jiangsu Nanjing210093 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30348 Poland College of Applied Mathematics Chengdu University of Information Technology Sichuan Province Chengdu610225 China Jagiellonian University in Krakow Chair of Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland Depto. Matemática FCE Universidad Austral Paraguay 1950 RosarioS2000FZF Argentina CONICET Argentina School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City210000 China Department of Mathematics FPT University Education zone Hoa Lac High Tech Park Km29 Thang Long Highway Thach That ward Hanoi Viet Nam

The paper deals with two nonlinear elliptic equations with (p, q)-Laplacian and the Dirichlet-Neumann-Dirichlet (DND) boundary conditions, and Dirichlet-Neumann-Neumann (DNN) boundary conditions, respectively. Under mild hypotheses, we prove the unique weak solvability of the elliptic mixed boundary value problems. Then, a comparison and a monotonicity results for the solutions of elliptic mixed boundary value problems are established. We examine a convergence result which shows that the solution of (DND) can be approached by the solution of (DNN). Moreover, two optimal control problems governed by (DND) and (DNN), respectively, are considered, and an existence result for optimal control problems is obtained. Finally, we provide a result on asymptotic behavior of optimal controls and system states, when a parameter tends to *** Codes 35J25, 35J66, 35J92, 49J20, 35Bxx © 2023, CC BY.

关键词： Nonlinear equations

ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation

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computers in Biology and Medicine 2025年 194卷 110527页

作者： Xuesheng Bian Jia Liu Sen Xu Weiquan Liu Leyi Mei Chaoshen Xiao Fan Yang School Of Information Engineering Yancheng Institute Of Technology Hope Avenue Middle Road Yancheng 224051 Jiangsu China College of Computer Engineering Jimei University Yinjiang Road No. 183 Xiamen 361005 Fujian China National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility) Key Laboratory of Genetic Evolution & Animal Models and National Resource Center for Nonhuman Primates Kunming Institute of Zoology Chinese Academy of Sciences East Jiaochang Road No. 32 Kunming 650221 Yunnan China National Key Laboratory of Radar Signal Processing Xidian university No. 2 South Taibai Road Xian 710071 Shaanxi China School of Resources and Environmental Engineering Wuhan University of Science and Technology 947 Heping Avenue Qingshan District Xian 430081 Wuhan China

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies, they often perform poorly in handling issues such as similar intensity distributions, irregular lesion shapes, and blurry boundaries, leading to low segmentation accuracy. To address these issues, we propose the ThreeF-Net, a fine-grained feature fusion network. This network combines the advantages of CNNs and Transformers, aiming to simultaneously capture local features and model long-range dependencies, thereby improving the accuracy and stability of segmentation tasks. Specifically, we designed a Transformer-assisted Dual Encoder Architecture (TDE), which integrates convolutional modules and self-attention modules to achieve collaborative learning of local and global features. Additionally, we designed a Global Group Feature Extraction (GGFE) module, which effectively fuses the features learned by CNNs and Transformers, enhancing feature representation ability. To further improve model performance, we also introduced a Dynamic Fine-grained Convolution (DFC) module, which significantly improves lesion boundary segmentation accuracy by dynamically adjusting convolution kernels and capturing multi-scale features. Comparative experiments with state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that ThreeF-Net outperforms existing methods across multiple key evaluation metrics.

关键词： Attention mechanism Breast lesion segmentation Deep learning Feature fusion Ultrasound images

Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays

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Genomics, Proteomics & Bioinformatics 2018年第6期16卷 416-427页

作者： Wenfang Zhou Mojie Duan Weitao Fu Jinping Pang Qin Tang Huiyong Sun Lei Xu Shan Chang Dan Li Tingjun Hou College of Pharmaceutical Sciences Zhejiang University State Key Laboratory of Computer Aided Design and Computer Graphics (CAD&GC) Zhejiang University State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics National Center for Magnetic Resonance in WuhanWuhan Institute of Physics and MathematicsChinese Academy of Sciences Institute of Bioinformatics and Medical Engineering School of Electrical and Information EngineeringJiangsu University of Technology

Androgen receptor(AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of many severe diseases such as prostate cancer, muscle atrophy, and osteoporosis. Binding of ligands to AR triggers the conformational changes in AR that may affect the recruitment of coactivators and downstream response of AR signaling ***, AR ligands have great potential to treat these diseases. In this study, we searched for novel AR ligands by performing a docking-based virtual screening(VS) on the basis of the crystal structure of the AR ligand binding domain(LBD) in complex with its agonist. A total of 58 structurally diverse compounds were selected and subjected to LBD affinity assay, with five of them(HBP1-3, HBP1-17, HBP1-38, HBP1-51, and HBP1-58) exhibiting strong binding to AR-LBD. The IC50 values of HBP1-51 and HBP1-58 are 3.96 m M and 4.92 m M, respectively, which are even lower than that of enzalutamide(Enz, IC50= 13.87 m M), a marketed second-generation AR antagonist. Further bioactivity assays suggest that HBP1-51 is an AR agonist, whereas HBP1-58 is an AR antagonist. In addition, molecular dynamics(MD) simulations and principal components analysis(PCA) were carried out to reveal the binding principle of the newlyidentified AR ligands toward AR. Our modeling results indicate that the conformational changes of helix 12 induced by the bindings of antagonist and agonist are visibly different. In summary,the current study provides a highly efficient way to discover novel AR ligands, which could serve as the starting point for development of new therapeutics for AR-related diseases.

关键词： Androgen receptor AR ligand Virtual screening AR agonist AR antagonist

Super-resolution imaging with patchy microspheres

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arXiv 2021年

作者： Shang, Qingqing Tang, Fen Yu, Lingya Oubaha, Hamid Caina, Darwin Melinte, Sorin Zuo, Chao Wang, Zengbo Ye, Ran Key Laboratory for Opto-Electronic Technology of Jiangsu Province Nanjing Normal University Nanjing21033 China School of Artificial Intelligence Nanjing Normal University Nanjing21033 China School of Computer Science and Electronic Engineering Bangor University BangorLL57 1UT United Kingdom Institute of Information and Communication Technologies Electronics and Applied Mathematics Universiteé Catholique de Louvain Louvain-la-Neuve1348 Belgium Faculty of Engineering Physical Sciences and Mathematics Universidad Central Del Ecuador Quito170402 Ecuador School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

The diffraction limit is a fundamental barrier in optical microscopy, which restricts the smallest resolvable feature size of a microscopic system. Microsphere-based microscopy has proven to be a promosing tool for challenging the diffraction limit. Nevertheless, the microspheres have a low imaging contrast in the air, which hinders the application of this technique. In this Letter, we demonstrate that this challenge can be effectively overcome by using partially Ag-plated microspheres. The deposited Ag film acts as an aperture stop that blocks a portion of the incident beam, forming a photonic hook with oblique near-field illumination. Such a photonic hook significantly enhanced imaging contrast, as experimentally verified by imaging Blu-ray disc surface and silica particle arrays. © 2021, CC BY.

关键词： Microspheres

DeePMD-kit v2: A software package for Deep Potential models

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

Supercapacitors with alternating current line-filtering performance

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BMC Materials 2020年第1期2卷 1-20页

作者： Doudou Zhao Xiaodong Zhuang Kaiyue Jiang Sheng Han Jiantong Li Xiang Zhu Changchun Ke Emmanuel Kymakis The Meso-Entropy Matter Lab School of Chemistry and Chemical Engineering Shanghai Key Laboratory of Electrical Insulation and Thermal Aging State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong University Dongchuan Road 800 Shanghai 200240 China School of Chemical and Environmental Engineering Shanghai Institute of Technology Haiquan Road 100 Shanghai 201418 China Jiangsu Key Laboratory of Environmentally Friendly Polymeric Materials School of Materials Science and Engineering Changzhou University Changzhou 213164 China School of Information and Communication Technology KTH Royal Institute of Technology Electrum 229 16440 Kista Sweden State Key Laboratory for Oxo Synthesis and Selective Oxidation Suzhou Research Institute of Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Suzhou 215000 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos 71410 Heraklion Crete Greece

Alternating current (AC) line filters have been widely used to smooth the leftover AC ripples on direct current voltage. Currently available commercial aluminum electrolytic capacitors (AECs) are primarily used for this application. However, the bulky volume and low capacitance of AECs have become incompatible with the rapidly developed intelligent electronic devices and industry dynamics. Supercapacitors with high specific capacitance and AC line-filtering performance could become the next-generation candidates to replace AECs for smoothing leftover AC ripples. Thus, most conventional supercapacitors behave like a resistor and not a capacitor at 120 Hz mainly because complex pore structures of electrode materials prevent the diffusion of electrolyte ions. Various electrode materials have been reported to reveal supercapacitors with AC line-filtering performance; however, the balance of high specific capacitance and an excellent filtering efficiency is a prodigious challenge. This review summarizes recently reported supercapacitors based on different types of electrode materials with AC filtering performance and attempts to develop the relationship between different influencing factors and features of functional materials.

关键词： Supercapacitor Micro-supercapacitor Alternating current line filtering Phase angle Electrode

SMK-means:An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data

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computers, Materials & Continua 2018年第9期56卷 365-379页

作者： Bo Xiao Zhen Wang Qi Liu Xiaodong Liu Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computer and Software Nanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computing Edinburgh Napier University10 Colinton RoadEdinburghEH105DTUK

In recent years,the rapid development of big data technology has also been favored by more and more *** data storage and calculation problems have also been *** the same time,outlier detection problems in mass data have also come along with ***,more research work has been devoted to the problem of outlier detection in big ***,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect *** this paper,an improved algorithm is *** SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of *** this paper,several experiments are performed to compare and analyze multiple performances of the *** analysis,we know that the proposed algorithm is superior to the existing algorithms.

关键词： Big data outlier detection SMK-means Mini Batch K-means simulated annealing

Hyperspectral image classification with context-aware dynamic graph convolutional network

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arXiv 2019年

作者： Wan, Sheng Gong, Chen Zhong, Ping Pan, Shirui Li, Guangyu Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on ATR National University of Defense Technology Changsha410073 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to the same land-cover class, so they often fail to correctly discover the contextual relations among pixels in complex situations, and thus leading to imperfect classification results on some irregular or inhomogeneous regions such as class boundaries. To address this deficiency, we develop a new HSI classification method based on the recently proposed Graph Convolutional Network (GCN), as it can flexibly encode the relations among arbitrarily structured non-Euclidean data. Different from traditional GCN, there are two novel strategies adopted by our method to further exploit the contextual relations for accurate HSI classification. First, since the receptive field of traditional GCN is often limited to fairly small neighborhood, we proposed to capture long range contextual relations in HSI by performing successive graph convolutions on a learned region-induced graph which is transformed from the original 2D image grids. Second, we refine the graph edge weight and the connective relationships among image regions by learning the improved adjacency matrix and the 'edge filter', so that the graph can be gradually refined to adapt to the representations generated by each graph convolutional layer. Such updated graph will in turn result in accurate region representations, and vice versa. The experiments carried out on three real-world benchmark datasets demonstrate that the proposed method yields significant improvement in the classification performance when compared with some state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

A novel bi-hemispheric discrepancy model for eeg emotion recognition

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arXiv 2019年

作者： Li, Yang Zheng, Wenming Wang, Lei Zong, Yuan Qi, Lei Cui, Zhen Zhang, Tong Song, Tengfei Key Laboratory of Child Development and Learning Science Ministry of Education Department of Information Science and Engineering Southeast University Nanjing Jiangsu210096 China Key Laboratory of Child Development and Learning Science Ministry of Education School of Biological Sciences and Medical Engineering Southeast University Nanjing Jiangsu210096 China School of Computing and Information Technology University of Wollongong NSW2500 Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu210096 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu210096 China

The neuroscience study [1] has revealed the discrepancy of emotion expression between left and right hemispheres of human brain. Inspired by this study, in this paper, we propose a novel bi-hemispheric discrepancy model (BiHDM) to learn the asymmetric differences between two hemispheres for electroencephalograph (EEG) emotion recognition. Concretely, we first employ four directed recurrent neural networks (RNNs) based on two spatial orientations to traverse electrode signals on two separate brain regions, which enables the model to obtain the deep representations of all the EEG electrodes' signals while keeping the intrinsic spatial dependence. Then we design a pairwise subnetwork to capture the discrepancy information between two hemispheres and extract higher-level features for final classification. Besides, in order to reduce the domain shift between training and testing data, we use a domain discriminator that adversarially induces the overall feature learning module to generate emotion-related but domain-invariant feature, which can further promote EEG emotion recognition. We conduct experiments on three public EEG emotional datasets, and the experiments show that the new state-of-the-art results can be achieved. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography