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Two new C18-norditerpenoid alkaloids from Aconitum delavayi

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Chinese Chemical Letters 2007年第4期18卷 409-411页

作者： Shan Hao Jiang Hai Qing Wang Yi Ming Li Shuang jun Lin jun Jie tan Da Yuan Zhu State Key Laboratory of Drug Research Institute of Materia Medica Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 201203 China

Further phytochemical investigation of the unique C 18-norditerpenoid alkaloids from the roots ofAconitum delavayi Franch led to the isolation of two new norditerpenoid alkaloids, delavaconitine F 1 and delavaconitine... 详细信息

关键词： Aconitum delavayi Ranunculaceae Norditerpenoid alkaloid Delavaconitine F Delavaconitine G

Targeting cholesterol trafficking to mitigate axonal degeneration in hereditary spastic paraplegia

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Neural Regeneration Research 2025年第5期20卷 1397-1398页

作者： Zhenyu Chen Xue-jun Li Department of Biomedical Sciences University of Illinois College of Medicine RockfordRockfordILUSA Department of Bioengineering University of Illinois at ChicagoChicagoILUSA

Axonal degeneration underlies many debilitating diseases including hereditary spastic paraplegia(HSP),a genetically and clinically diverse group of disorders characterized by spasticity and weakness of the lower *** is one significant cause of chronic neurodisability due to the lack of effective treatments and a wide range of onset ages from early childhood to 70 years.

关键词： degeneration diseases hereditary

High mobility group box 1 in the central nervous system:regeneration hidden beneath inflammation

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Neural Regeneration Research 2025年第1期20卷 107-115页

作者： Hanki Kim Bum jun Kim Seungyon Koh Hyo Jin Cho Xuelian Jin Byung Gon Kim jun Young Choi Department of Brain Science Ajou University School of MedicineSuwonSouth Korea Department of Biomedical Sciences Ajou University Graduate School of MedicineSuwonSouth Korea Department of Neurology Ajou University School of MedicineSuwonSouth Korea Department of Geriatrics The Affiliated Suqian First People’s Hospital of Nanjing Medical UniversitySuqianJiangsu ProvinceChina

High-mobility group box 1 was first discovered in the calf thymus as a DNA-binding nuclear protein and has been widely studied in diverse fields,including neurology and ***-mobility group box 1 in the extracellular space functions as a pro-inflammatory damage-associated molecular pattern,which has been proven to play an important role in a wide variety of central nervous system disorders such as ischemic stroke,Alzheimer’s disease,frontotemporal dementia,Parkinson’s disease,multiple sclerosis,epilepsy,and traumatic brain *** drugs that inhibit high-mobility group box 1 as a damage-associated molecular pattern,such as glycyrrhizin,ethyl pyruvate,and neutralizing anti-high-mobility group box 1 antibodies,are commonly used to target high-mobility group box 1 activity in central nervous system *** it is commonly known for its detrimental inflammatory effect,high-mobility group box 1 has also been shown to have beneficial pro-regenerative roles in central nervous system *** this narrative review,we provide a brief summary of the history of high-mobility group box 1 research and its characterization as a damage-associated molecular pattern,its downstream receptors,and intracellular signaling pathways,how high-mobility group box 1 exerts the repair-favoring roles in general and in the central nervous system,and clues on how to differentiate the pro-regenerative from the pro-inflammatory *** targeting high-mobility group box 1 in the central nervous system may benefit from differentiating between the two functions rather than overall suppression of high-mobility group box 1.

关键词： central nervous system damage-associated molecular pattern ethyl pyruvate glycyrhizzin high mobility group box 1 inflammation neural stem cells neurodevelopment oligodendrocyte progenitor cells redox status regeneration

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： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

Anomalous quality factor evolution of induced transparency in a virtualized coupled-oscillator system

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 148-155页

作者： Yunhan Yang Han Jia Tuo Liu Yuzhen Yang jun Yang Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences

The phenomenon of induced transparency, characterized by a high quality(Q) factor, holds significant importance in the fields of high-precision sensing, energy storage and wave manipulation. Conventional transparency systems are often constrained by the limited experimentally accessible parameter space and the lack of independent control over intrinsic parameters, thereby hindering advanced research on high-Q effects. Here we propose a virtualized coupled-oscillator system that enables reconfigurable induced transparency in the reflection spectrum. By virtually incorporating a pair of coupled oscillators into an individual acoustic meta-atom, we achieve the manipulation of induced transparency in a decoupled, wide-ranging, and elaborate manner. Exploiting the diverse evolutions across the intrinsic parameter space, we show experimentally a counter-intuitive increase in the Q-factor of the transparency window, which is attributed to the enhanced dissipation of the bright ***, a constant group delay at the center frequency is observed accompanying this evolution. Our work demonstrates an intriguing platform as well as a novel strategy to engineer high-Q induced transparency phenomenon, and paves the way towards applications in programmable acoustic sensors, filters and slow-wave devices.

关键词： induced transparency acoustic meta-atom high quality-factor scattering programmable intrinsic parameter

Structural and microwave absorption properties of CoFe_(2)O_(4)/residual carbon composites

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 221-232页

作者： Yuanchun Zhang Shengtao Gao Xingzhao Zhang Dacheng Ma Chuanlei Zhu jun He Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining Anhui University of Science and TechnologyHuainan 232001China School of Chemical and Blasting Engineering Anhui University of Science and TechnologyHuainan 232001China

Electromagnetic interference,which necessitates the rapid advancement of substances with exceptional capabilities for bsorbing electromagnetic waves,is of urgent concern in contemporary *** this work,CoFe_(2)O_(4)/residual carbon from coal gasification fine slag(CFO/RC)composites were created using a novel hydrothermal *** mechanisms for microwave absorption,including conductive loss,natural resonance,interfacial dipole polarization,and magnetic flux loss,are involved in these ***,compared with pure residual carbon materials,this composite offers superior capabilities in microwave *** 7.76GHz,the CFO/RC-2 composite achieves an impressive minimum reflection loss(RL_(min))of-43.99 dB with a thickness of 2.44 ***,CFO/RC-3 demonstrates an effective absorption bandwidth(EAB)of up to 4.16 GHz,accompanied by a thickness of *** study revealed the remarkable capability of the composite to diminish electromagnetic waves,providing a new generation method for microwave absorbing materials of superior quality.

关键词： coal gasification slag residual carbon hydrothermal method microwave absorption CoFe_(2)O_(4)

计算机视觉技术赋能大学英语课堂展示中的学习投入研究

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新疆师范大学学报（哲学社会科学版） 2025年第2期46卷 144-151页

作者：谭苏燕王晋军广州大学外国语学院广东广州510010

学习投入是预测学习者学习效果的重要指标,学术界对其与课堂展示间的相关性研究鲜有涉猎。文本以某综合性大学303名非英语专业本科生为研究对象,采用人工智能与计算机视觉技术模型YOLOv7对大学英语课堂中的课堂展示实录进行学习投入多... 详细信息

学习投入是预测学习者学习效果的重要指标,学术界对其与课堂展示间的相关性研究鲜有涉猎。文本以某综合性大学303名非英语专业本科生为研究对象,采用人工智能与计算机视觉技术模型YOLOv7对大学英语课堂中的课堂展示实录进行学习投入多模态数据分析,并辅以问卷调查,形成双向印证。研究发现,学生课堂展示中的行为投入、认知投入和情感投入值均等于或高于3.78,投入结果均值达4.01,说明学生在课堂展示中的学习投入情况良好。此外,不同时段课堂展示对学生行为投入的影响显著,学生专注度及参与度在课堂前10分钟的课堂展示中效果最佳。文本为课堂展示的有效性提供了智能化、多模态、多维度的实证支持,为课堂教学的数智技术应用提供了新视角。

关键词：大学英语课堂展示学习投入计算机视觉技术展示时段

Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University Institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

Mechanistic insight into the synergy between nickel single atoms and nanoparticles on N-doped carbon for electroreduction of CO2

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Journal of Energy Chemistry 2025年第1期100卷 327-336页

作者： Mingdong Sun Wenwen Guan Cailing Chen Chao Wu Xiaoling Liu Biao Meng Tao Chen Yu Han jun Wang Shibo Xi Yu Zhou State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical Engineering Nanjing Tech University Advanced Membranes and Porous Materials Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology Institute of Sustainability for Chemicals Energy and Environment A*STAR (Agency for Science Technology and Research) College of Materials Science and Engineering Sichuan University

The synergy of single atoms （SAs） and nanoparticles （NPs） has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction （CO2RR）;however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst ***,a Ni2+-loaded porous poly（ionic liquids）(PIL) precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing *** catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO2into *** Ni K-edge X-ray absorption near-edge structure （XANES） spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and *** charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active *** the electroreduction of CO2,the coexistence of Ni SAs and NPs strengthened the CO2activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO2→*COOH step,and therefore promoted the catalysis efficiency.

关键词： Single atom catalyst Synergy effect Carbon catalyst Electrocatalysis