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Mainchain conformation ordering self-assembly tailored room temperature phosphorescence emission

Science China Chemistry 2025年第1期68卷 329-336页

作者： Yuzhu Li Yumeng Gao Feng Tian Yu Wang Yuan yao Shaoliang lin Xiang Ma Binbin Xu Shanghai Key Laboratory of Advanced Polymeric Materials Key Laboratory for Ultrafine Materials of Ministry of Education School of Materials Science and Engineering East China University of Science and Technology Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Key Laboratory for Advance Materials and Feringa Noble Prize Scientist Joint Research Centre Frontiers Science for Materiobiology and Dynamic Chemistry School of Chemistry and Molecular Engineering East China University of Science & Technology

Developing organic room-temperature phosphorescence （RTP） materials with a wide range of tunable-emission is significant in expanding the applications of RTP ***,we rationally designed amphiphilic alternating copolymers poly(1,2-bis（4-alkoxyphenyl）ethane-1,2-dione-alt-（ethylene glycol）x)(P（Bz-alt-EGx）) that combined the advantage of alternating benzil and EGxskeleton,and tunable mainchain *** a mainchain conformation ordering assembly （COA） process,P（Bz-alt-EGx） self-assembled into well-defined platelets,and such assemblies emitted blue RTP at ca.425 nm due to the ordered folded-chain conformation and confined *** contrast,traditional self-assembly （TA） of P（Bz-alt-EGx） obtained less ordered aggregates,such as asymmetric worms,vesicles,and rod-like *** resultant less ordered aggregates exhibited yellow-green RTP at ca.550 *** wide-tuning RTP emission in solution （between blue and yellow-green,over125 nm） is realized using the P（Bz-alt-EGx） alternating copolymers under the treatment of different self-assembly *** study uncovers a new strategy to tailor the RTP emission through different self-assembly pathways and holds great promise for the fabrication of advanced optical materials.

关键词： self-assembly conformation ordering alternating copolymer phosphorescence emission

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High-temperature-tolerant flexible supercapacitors: Gel polymer electrolytes and electrode materials

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Journal of Energy Chemistry 2025年第1期100卷 426-457页

作者： Chong Peng Xinyi Huang Mingwei zhao Shuling Liao Quanhong yang Nianjun yang Siyu Yu School of Chemistry & Chemical Engineering Southwest University Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Department of Chemistry & IMO-IMOMEC Hasselt University

The development of flexible supercapacitors(FSCs) capable of operating at high temperatures is crucial for expanding the application areas and operating conditions of supercapacitors. Gel polymer electrolytes and electrode materials stand as two key components that significantly impact the efficacy of hightemperature-tolerant FSCs(HT-FSCs). They should not only exhibit high electrochemical performance and excellent flexibility, but also withstand intense thermal stress. Considerable efforts have been devoted to enhancing their thermal stability while maintaining high electrochemical and mechanical performance. In this review, the fundamentals of HT-FSCs are outlined. A comprehensive overview of state-of-the-art progress and achievements in HT-FSCs, with a focus on thermally stable gel polymer electrolytes and electrode materials is provided. Finally, challenges and future perspectives regarding HT-FSCs are discussed, alongside strategies for elevating operational temperatures and *** review offers both theoretical foundations and practical guidelines for designing and manufacturing HT-FSCs, further promoting their widespread adoption across diverse fields.

关键词： Polymer membrane electrodes

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Lipomatous ependymoma with ZFTA:RELA fusion-positive:A case report

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World Journal of Clinical Cases 2025年第1期13卷 48-55页

作者： Xiao-Yu zhao Juan-Han Yu Yi-Hua Wang Yi-Xin Liu Lu Xu lin Fu Ning Yi China Medical University The First Hospital of China Medical UniversityShenyang 110001Liaoning ProvinceChina Department of Pathology The First Hospital of China Medical UniversityShenyang 110001Liaoning ProvinceChina Anshan Cancer Hospital Anshan 114000Liaoning ProvinceChina

BACKGROUND Ependymoma with lipomatous differentiation is a rare type of *** ZFTA fusion-positive supratentorial ependymoma is a novel tumor type in the 2021 World Health Organization classification of central nervous system *** fusion-positive lipomatous ependymoma has not been reported to *** SUMMARY We reported a case of a 15-year-old Chinese male who had a sudden convulsion lasting approximately six *** resonance imaging showed a round cystic shadow of approximately 1.9 cm×1.5 cm×1.9 cm under the right parieto-occipital *** examination showed characteristic perivascular pseudorosettes and adipose differentiation in the ***-chemical staining showed that the tumor cells were negative for cytokeratin,NeuN,Syn and p53,but positive for GFAP,vimentin and S-100 ***-ficant punctate intracytoplasmic EMA immunoreactivity was *** level of Ki-67 was about 5%.Genetic analysis revealed ZFTA:RELA fusion.A cranio-tomy with total excision of the tumor was *** follow-up time was 36 months,no evidence of disease recurrence was found in magnetic resonance *** Based on these findings,the patient was diagnosed as a ependymoma with ZFTA fusion and lipomatous *** case report provides information on the microscopic morphological features of ependymoma with ZFTA fusion and lipomatous differentiation,which can help pathologists to make a definitive diagnosis of this tumor.

关键词： Ependymoma Lipomatous Molecular classification Supratentorial ZFTA:RELA fusion Case report

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Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

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Nano-Micro Letters 2025年第3期17卷 168-216页

作者： Shuai Zhong Lirou Su Mingkun Xu Desmond Loke Bin Yu Yishu Zhang Rong zhao Guangdong Institute of Intelligence Science and Technology HengqinZhuhai 519031People’s Republic of China College of Integrated Circuits Zhejiang UniversityHangzhou 3112000People’s Republic of China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027People’s Republic of China Department of Science Mathematics and TechnologySingapore University of Technology and DesignSingapore 487372Singapore Department of Precision Instruments Tsinghua UniversityBeijing 100084People’s Republic of China Center for Brain‑Inspired Computing Research Tsinghua UniversityBeijing 100084People’s Republic of China IDG/McGovern Institute for Brain Research Tsinghua UniversityBeijing 100084People’s Republic of China

Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power *** fully leverage its advantages,convertingthe external analog signals to spikes is an essential *** including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area *** efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory *** can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory *** review summarizesand benchmarks the recent progress of artificial sensory *** starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial ***,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also ***,we highlight the challenges and perspectives for the future development of artificial sensory neurons.

关键词： Artificial intelligence Emerging devices Artificial sensory neurons Spiking neural networks Neuromorphic sensing

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Personalized federated few-shot node classification

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Science China(Information Sciences) 2025年第1期68卷 204-218页

作者： Yunfeng zhao Xintong HE Guoxian YU Jun WANG Yongqing ZHENG Carlotta DOMENICONI School of Software Shandong University Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University Department of Mathematics National University of Singapore Department of Computer Science George Mason University

Personalized federated learning(PFL) aims to train customized models for individual clients in a decentralized setting, with the account of non-independent and identically distributed data across clients. However, most PFL methods adopt uniform classification layers for diverse clients and give rise to error-prone predictions, due to the task heterogeneity notably prominent in decentralized graph data scenarios. Although some PFL solutions setup client-specific classification layers for each client and optimize them only locally, they are corrupted with limited local training data. We propose an innovative solution called federated parameter decoupling and node augmentation(Fed PANo) to address these problems and to achieve personalized federated few-shot node classification, which is a prevalent and challenging but unexplored topic. Specifically, Fed PANo first separates the local model into the GNN and classifier to handle unique client-specific task variations. The GNN is trained through federated learning to capture shared knowledge of graph nodes across clients, while the classifier is custom-designed and trained individually for each client. Additionally, a generic classifier shared among clients is adopted to encourage the GNN's grasp of shared information. Then Fed PANo further proposes the node generator along with its local and collaborative training strategies to deal with the node scarcity of clients. Extensive experimental results on benchmark datasets confirm that Fed PANo outperforms eight competitive baselines across different settings.

关键词： personalized federated learning few-shot learning node classification task heterogeneity parameter decoupling node augmentation

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Satellite-assisted 6G wide-area edge intelligence:dynamics-aware task offloading and resource allocation for remote IoT services

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Science China(Information Sciences) 2025年

作者： Di zhao Rui DING Bin SONG State Key Laboratory of Integrated Services Networks Xidian University Hangzhou Institute of Technology Xidian University China Satellite Network Group Co. Ltd.

Future 6G networks will integrate non-terrestrial communication with mobile edge computing to enable wide-area edge intelligence, providing ubiquitous communication and computation services for everyone and everything. In this paper, we introduce the intelligent cloud-edge-device architecture into low Earth orbit (LEO) satellite-supported remote Internet of Things(IoT) networks to facilitate latency-sensitive and compute-intensive IoT applications. However, the dynamic spatio-temporal characteristics inherent in LEO satellite networks, such as the high volatility of task traffic and the high mobility of space nodes, pose severe challenges. For efficient task execution, we investigate the joint optimization of cross-region task offloading and cross-domain resource allocation, and innovatively propose a spatio-temporal attention-based proximal policy optimization(STA-PPO) algorithm. Specifically, the temporal attention-based actor network analyzes the timing dependencies of task arrival and makes time-variant decisions. Simultaneously, the spatial attention-based critic network captures the spatial variations of satellite motion and evaluates the topology-related value. We verify the effectiveness of the cloud-edge-device collaboration through extensive simulations. Numerical results demonstrate that the STA-PPO algorithm outperforms benchmarks, showing the lowest system delay and the highest link throughput.

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A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

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Computers, Materials & Continua 2025年第1期82卷 977-996页

作者： Jianwei Zhang Zhishang zhao Zengyu Cai Yuan Feng Liang Zhu yahui Sun College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450000China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China College of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450000China College of Electronics and Information Zhengzhou University of Light IndustryZhengzhou450000China

The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term ***,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior ***,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their *** paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)*** study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention ***,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user *** results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance *** advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.

关键词： Video recommendation user interest cross-attention transformer

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and Automation Luoyang Institute of Science and Technology School of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

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Lipid droplets in the nervous system:involvement in cell metabolic homeostasis

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Neural Regeneration Research 2025年第3期20卷 740-750页

作者： Yuchen Zhang Yiqing Chen Cheng Zhuang Jingxuan Qi Robert Chunhua zhao Jiao Wang School of Life Sciences Shanghai UniversityShanghaiChina School of Medicine Shanghai UniversityShanghaiChina Institute of Basic Medical Sciences Chinese Academy of Medical Sciences School of Basic MedicinePeking Union Medical CollegeBeijingChina Center of Excellence in Tissue Engineering Chinese Academy of Medical SciencesBeijingChina Beijing Key Laboratory of New Drug Development and Clinical Trial of Stem Cell Therapy BeijingChina

Lipid droplets serve as primary storage organelles for neutral lipids in neurons,glial cells,and other cells in the nervous *** droplet formation begins with the synthesis of neutral lipids in the endoplasmic ***,lipid droplets were recognized for their role in maintaining lipid metabolism and energy homeostasis;however,recent research has shown that lipid droplets are highly adaptive organelles with diverse functions in the nervous *** addition to their role in regulating cell metabolism,lipid droplets play a protective role in various cellular stress ***,lipid droplets exhibit specific functions in neurons and glial *** of lipid droplet formation leads to cellular dysfunction,metabolic abnormalities,and nervous system *** review aims to provide an overview of the role of lipid droplets in the nervous system,covering topics such as biogenesis,cellular specificity,and ***,it will explore the association between lipid droplets and neurodegenerative *** the involvement of lipid droplets in cell metabolic homeostasis related to the nervous system is crucial to determine the underlying causes and in exploring potential therapeutic approaches for these diseases.

关键词： Alzheimer's disease lipid droplet biogenesis lipid droplets lipid metabolism nervous system neurodegenerative disorders oxidative stress Parkinson's disease

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A General Association Test for High-Dimensional Random Vectors

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Acta Mathematica Sinica,English Series 2025年

作者： Yongshuai Chen Peng lin Baoxue Zhang School of Statistics Capital University of Economics and Business

Testing the association of two high-dimensional random vectors is of fundamental importance in the statistical theory and applications. In this paper, we propose a new test statistic based on the Frobenius norm and subtracting bias technique, which is generally applicable to high-dimensional data without restricting the distributional assumptions. The limiting null distribution of the proposed test is shown to be a random variable combining a finite chi-squared-type mixture with a normal approximation. Our proposed test method can also be a normal approximation or a finite chi-squared-type mixtures under additional regularity conditions. To make the test statistic applicable, we introduce a wild bootstrap method and demonstrate its validity. The finite-sample performance of the proposed test via Monte Carlo simulations reveals that it performs better at controlling the empirical size than some existing tests, even when the normal approximation is invalid. Real data analysis is devoted to illustrating the proposed test.

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