检索结果-内蒙古大学图书馆

Rational design of TA/rGO/CNFs aerogel films as self-supporting cathodes for high-performance zinc-ion hybrid capacitors

引用

Journal of Energy Storage 2025年 125卷

作者： Yu, Xiuwen Liu, Qifan Shi, Shuangyu Zang, Limin Qiu, Jianhui He, Minhua Yang, Chao MOE Key Laboratory of New Processing Technology for Nonferrous Metal & Materials Guangxi Key Laboratory of Optical and Electronic Materials and Devices Guangxi Colleges and Universities Key Laboratory of Natural and Biomedical Polymer Materials Guilin University of Technology Guilin541004 China Guangxi Key Laboratory of Advanced Structural Materials and Carbon Neutralization Guangxi Minzu University Nanning530006 China Department of Machine Intelligence and Systems Engineering Faculty of Systems Science and Technology Akita Prefectural University Yurihonjo015-0055 Japan Guangxi Huazheng New Energy Technology Co. Ltd. Chongzuo532299 China

Wearable flexible electronic devices have been developed rapidly driven by their distinctive characteristics, such as portability, flexibility, comfort, and diverse forms. However, the accelerated pace of technological advancement leads to a considerable increase in non-biodegradable electronic waste, which has a detrimental impact on the natural environment. In this study, flexible aerogel films consisting of tannic acid (TA), reduced graphene oxide (rGO) and cellulose nanofibers (CNFs) were prepared through a simultaneous hydrothermal reduction and self-assembly strategy. As a natural polyphenol, TA can serve as a reducing agent for GO and provide extra capacity owing to its catechol and pyrogallol units. CNFs with large length-to-diameter ratio and oxygen groups can effectively prevent rGO restacking and promote electrolyte penetration. The flexible zinc-ion hybrid capacitors based on the aerogel film cathode and zinc foil anode have excellent electrochemical properties and flexibility. In light of environmentally friendly components, green preparation processes and superior electrochemical performance, this research can pave the way for further development of environmentally friendly aerogel film electrodes for high-performance wearable electronics. © 2025 Elsevier Ltd

关键词： Cellulose films

来源：评论

学校读者我要写书评

暂无评论

GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection

arXiv

引用

arXiv 2025年

作者： Cao, Yifan Mi, Zhilong Yin, Ziqiao Guo, Binghui Dong, Jin School of Artificial Intelligence Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Hangzhou International Innovation Institute Beihang University Hangzhou311115 China Beijing Academy of Blockchain and Edge Computing Beijing100080 China

As artificial intelligence methods are increasingly applied to complex task scenarios, high dimensional multi-label learning has emerged as a prominent research focus. At present, the curse of dimensionality remains one of the major bottlenecks in high-dimensional multi-label learning, which can be effectively addressed through multi-label feature selection methods. However, existing multi-label feature selection methods mostly focus on identifying global features shared across all labels, which overlooks personalized characteristics and specific requirements of individual labels. This global-only perspective may limit the ability to capture label-specific discriminative information, thereby affecting overall performance. In this paper, we propose a novel method called GPMFS (Global Foundation and Personalized Optimization for Multi-Label Feature Selection). GPMFS firstly identifies global features by exploiting label correlations, then adaptively supplements each label with a personalized subset of discriminative features using a threshold-controlled strategy. Experiments on multiple real-world datasets demonstrate that GPMFS achieves superior performance while maintaining strong interpretability and robustness. Furthermore, GPMFS provides insights into the label-specific strength across different multi-label datasets, thereby demonstrating the necessity and potential applicability of personalized feature selection approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation

arXiv

引用

arXiv 2025年

作者： Hou, Qingshan Wang, Meng Cao, Peng Ke, Zou Liu, Xiaoli Fu, Huazhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection. Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts

arXiv

引用

arXiv 2025年

作者： Meng, Runqi Song, Sifan Jin, Pengfei Oh, Yujin Teng, Lin Wang, Yulin Sun, Yiqun Chen, Ling Li, Xiang Li, Quanzheng Guo, Ning Shen, Dinggang School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University China Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School Harvard University United States Shanghai United Imaging Intelligence Co. Ltd China Shanghai Clinical Research and Trial Center China

Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with: (1) limited incorporation of medical priors, (2) imbalance between generic and tumor-specific features, and (3) high computational costs for clinical adaptation. To address these challenges, we propose MAST-Pro (Mixture-of-experts for Adaptive Segmentation of pan-Tumors with knowledge-driven Prompts), a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation. Specifically, text and anatomical prompts provide domain-specific priors, guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning, improving segmentation accuracy across diverse tumor types. To enhance efficiency, we employ Parameter-Efficient Fine-Tuning (PEFT), optimizing MAST-Pro with significantly reduced computational overhead. Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average DSC while reducing trainable parameters by 91.04%, without compromising accuracy. © 2025, CC BY-NC-ND.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

引用

Chinese Journal of Electronics 2022年第4期31卷 752-763页

作者： LUO Junwei YU Mujian YIN Xiaolin LU Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University

Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the local residual patterns(LRP) model for steganalysis. A binary image steganalytic scheme based on symmetrical local residual patterns(SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single *** with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the support vector machine classifier. The experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.

关键词： Information hiding Steganalysis Symmetrical local residual patterns Multi-scale model

来源：评论

学校读者我要写书评

暂无评论

RGS-Sketch: An Accurate, Invertible, and Mergeable Sketch for Online Super Spreader Detection in High-Speed Data Streams

引用

Proceedings of the VLDB Endowment 2025年第4期18卷

作者： Boyu Zhang He Huang Yu-E Sun Guoju Gao Soochow University Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University

Super spreader detection in high-speed data streams is crucial for numerous applications. Although many methods have emerged, existing works can hardly concurrently achieve high memory efficiency, support online detection, enable merging data from different measurement points/periods, and offer invertibility. This makes them unable to satisfy flexible application requirements. This paper proposes RGS-Sketch, a novel sketch designed to address this problem. The core of RGS-Sketch lies in a new mergeable memory sharing design called register group sharing. This design organizes registers into groups as basic memory sharing units, accommodating the high skewness of real-world data streams and offering high memory efficiency. Besides, it enables online detection through the real-time acquisition of a group's state, which also facilitates invertibility. To enhance detection accuracy further, we propose a limited register update strategy. It blocks small flows from updating registers, thereby reducing memory overhead and estimation noises. Extensive experimental results based on four real-world datasets show that RGS-Sketch significantly outperforms the most accurate baselines in accuracy while maintaining a high throughput. Specifically, it improves the F1 scores by up to 0.643 for measurements at a single point/period and up to 0.472 across multiple points/periods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reasonable design of high-capacity MnO2/MXene/CF flexible cathodes for quasi-solid-state aqueous zinc-ion batteries

引用

Journal of Alloys and Compounds 2025年 1031卷

作者： Pang, Xinyu Liu, Qifan Li, Maokun Fan, Zhanhui Yang, Chao Qiu, Jianhui He, Minhua Zang, Limin MOE Key Laboratory of New Processing Technology for Nonferrous Metal and Materials Guangxi Key Laboratory of Optical and Electronic Materials and Devices Guangxi Colleges and Universities Key Laboratory of Natural and Biomedical Polymer Materials College of Materials Science and Engineering Guilin University of Technology Guilin541004 China Guangxi Key Laboratory of Advanced Structural Materials and Carbon Neutralization Guangxi Minzu University Nanning530006 China Department of Machine Intelligence and Systems Engineering Faculty of Systems Science and Technology Akita Prefectural University Yurihonjo015-0055 Japan Guangxi Huazheng New Energy Technology Co. Ltd. Chongzuo532299 China

Manganese-based aqueous zinc-ion batteries (AZIBs) have garnered growing interest in the area of adaptable and wearable devices because of their properties of simple manufacturing, superior safety, and ecological compatibility. Nevertheless, the actual implementation of Mn-based cathodes has faced challenges due to their inadequate conductivity and poor cycling performance. In this study, a chemical synthesis approach was employed to directly grow MnO2 on the surface and within the interlayer spaces of MXene. Subsequently, the composite was coated onto carbon felt (CF) using the impregnation method to fabricate binder-free MnO2/MXene/CF cathodes for AZIBs. The excellent conductivity and structural stability of MXene effectively enhance ion/electron transport kinetics and mitigate irreversible volume changes of MnO2 during charge and discharge processes. CF with excellent electrical conductivity and flexibility serves as an ideal current collector and substrate material for loading active materials. Moreover, its distinctive three-dimensional porous structure facilitates optimal interaction between the electrode material and the electrolyte. Consequently, the flexible quasi-solid-state AZIB, consisting of the optimized MnO2/MXene/CF cathode and zinc foil anode, exhibits high charge storage performance (a specific capacity of 429.1 mAh g−1 at 0.1 A g−1 and an excellent energy density of 343.2 Wh kg−1 at 79.9 W kg−1). Density functional theory analysis reveals that the incorporation of MXene enhances electron adsorption on MnO2 and significantly alters the density of states of MnO2, thus markedly improving its conductivity. These encouraging findings suggest that manganese-based materials hold great potential for application as cathodes in flexible quasi-solid-state AZIBs. © 2025 Elsevier B.V.

关键词： Carbon carbon composites

来源：评论

学校读者我要写书评

暂无评论

Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation

arXiv

引用

arXiv 2025年

作者： Shen, Zhiqiang Cao, Peng Yang, Jinzhu Zaiane, Osmar R. Chen, Zhaolin School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada Monash Biomedical Imaging Monash University Australia Department of Data Science and AI Monash University Australia

Due to the domain shifts between training and testing medical images, learned segmentation models often experience significant performance degradation during deployment. In this paper, we first decompose an image into its style code and content map and reveal that domain shifts in medical images involve: style shifts (i.e., differences in image appearance) and content shifts (i.e., variations in anatomical structures), the latter of which has been largely overlooked. To this end, we propose StyCona, a style content decomposition-based data augmentation method that innovatively augments both image style and content within the rank-one space, for domain generalizable medical image segmentation. StyCona is a simple yet effective plug-and-play module that substantially improves model generalization without requiring additional training parameters or modifications to the segmentation model architecture. Experiments on cross-sequence, cross-center, and cross-modality medical image segmentation settings with increasingly severe domain shifts, demonstrate the effectiveness of StyCona and its superiority over state-of-the-arts. The code is available at https://***/Senyh/StyCona. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

引用

arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization

引用

Computers in Biology and Medicine 2025年 185卷 109558-109558页

作者： Aboyeji, Sunday Timothy Ahmad, Ijaz Wang, Xin Chen, Yan Yao, Chen Li, Guanglin Tong, Michael Chi Fai Siu, Alice K.Y. Zhao, Guoru Chen, Shixiong CAS Key Laboratory of Human–Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China Surgery Division Epilepsy Center Shenzhen Children's Hospital Shenzhen518036 China Department of Neurosurgery The National Key Clinic Specialty Shenzhen Key Laboratory of Neurosurgery the First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital Shenzhen518035 China Department of Otorhinolaryngology Head and Neck Surgery The Chinese University of Hong Kong Hong Kong School of Medicine The Chinese University of Hong Kong Shenzhen Guangdong518172 China

Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurologists. However, direct analysis of EEG signals is difficult due to their complex and dynamic nature, with variation across multiple patients. Therefore, researchers have proposed the short-time Fourier transform (STFT) to capture dynamic events indicative of seizures through time-varying frequency representation of EEG signals. However, tradeoffs between time and frequency resolution limited the spectrogram's interpretability and affected clinical deployment. Hence, this study proposes extracting high-resolution channels via a novel STFT spectrogram construction algorithm encompassing taper functions for seizure diagnosis. Initially, we extracted seizure and non-seizure segments from each channel of selected patients in the CHB-MIT dataset. Next, we systematically apply taper functions like Hann and Gaussian windows to minimize the edge effect during the construction of spectrogram images. Finally, we employ Dilated Convolutional Squeeze and Excitation Networks (DCSENets) through leave-one-patient-out cross-validation (LOPOCV) to perform patient-independent seizure classification. The proposed DCSENets achieve an average accuracy of 87.20±11.48% and 87.29±10.48% with Hann and Gaussian taper functions, respectively, and 86.85±11.56% without the taper function. Most patients with high performances indicate similarity in train-test sample distribution using the Kolmogorov–Smirnov test at 0.010.05. Furthermore, the Grad CAM deep visual explainer integration enhances the interpretability of the deep learning model's decision-making process. Consequently, neurologists are provided not only with enhanced visualized spectrograms but also a transparent model for improved seizure diagnosis. © 2024 Elsevier Ltd

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：