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Merging polymers of intrinsic microporosity and porous carbon-based zinc oxide composites in novel mixed matrix membranes for efficient gas separation

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Green Energy & Environment 2025年第1期10卷 203-213页

作者： Muning Chen Jiemei Zhou jing Ma Weigang Zheng Guanying Dong Xin Li Zhihong Tian Yatao Zhang jing Wang Yong Wang School of Chemical Engineering Zhengzhou UniversityZhengzhou450001HenanChina State Key Laboratory of Materials-Oriented Chemical Engineering and College of Chemical Engineering Nanjing Tech UniversityNanjing211816JiangsuChina Singapore Membrane Technology Centre Nanyang Environment and Water Research InstituteNanyang Technological UniversitySingapore637141Singapore Engineering Research Center for Nanomaterials Henan UniversityKaifeng475004HenanChina State Key Laboratory of Coking Coal Resources Green Exploitation Zhengzhou UniversityZhengzhou 450001China School of Energy and Environment Southeast UniversityNanjing210096JiangsuChina

Mixed matrix membranes(MMMs)have demonstrated significant promise in energy-intensive gas separations by amalgamating the unique properties of fillers with the facile processability of ***,achieving a simultaneous enhancement of permeability and selectivity remains a formidable challenge,due to the difficulty of achieving an optimal match between polymers and *** this study,we incorporate a porous carbon-based zinc oxide composite(C@ZnO)into high-permeability polymers of intrinsic microporosity(PIMs)to fabricate *** dipole–dipole interaction between C@ZnO and PIMs ensures their exceptional compatibility,mitigating the formation of non-selective voids in the resulting ***,C@ZnO with abundant interconnected pores can provide additional low-resistance pathways for gas transport in *** a result,the CO_(2) permeability of the optimized C@ZnO/PIM-1 MMMs is elevated to 13,215 barrer,while the CO_(2)/N_(2) and CO_(2)/CH4 selectivity reached 21.5 and 14.4,respectively,substantially surpassing the 2008 Robeson upper ***,molecular simulation results further corroborate that the augmented membrane gas selectivity is attributed to the superior CO_(2) affinity of C@*** summary,we believe that this work not only expands the application of MMMs for gas separation but also heralds a paradigm shift in the application of porous carbon materials.

关键词： Mixed matrix membranes Polymers of intrinsic microporosity CO_(2)separation Porous carbon materials

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基于连续谱束缚态的高Q太赫兹全介质超表面

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激光技术 2022年第5期46卷 630-635页

作者：王鹏飞贺风艳刘建军井绪峰洪治中国计量大学太赫兹技术与应用研究所杭州310018

为了研究基于连续谱束缚态(BIC)高品质因子Q谐振,提出了由双空心硅圆柱体组成太赫兹全介质超表面。采用数值模拟方法对结构的透射光谱及电磁场图进行了分析,并利用本征模分析的方法研究了超表面结构参数对BIC频率的影响,给出了该BIC超... 详细信息

为了研究基于连续谱束缚态(BIC)高品质因子Q谐振,提出了由双空心硅圆柱体组成太赫兹全介质超表面。采用数值模拟方法对结构的透射光谱及电磁场图进行了分析,并利用本征模分析的方法研究了超表面结构参数对BIC频率的影响,给出了该BIC超表面在太赫兹大频率范围工作的参数设计方法。结果表明,在3.0THz左右实现了一个可调高Q环偶极Fano谐振;本征模式的分析计算结果与入射电磁波模式的分析计算结果对称性不匹配,该超表面支持的是一个对称保护BIC。此研究为基于BIC的高Q超材料在超低阈值激光器件、非线性光学谐波产生及高灵敏度传感等领域的应用提供了理论参考。

关键词：光学器件连续谱束缚态多极子分析全介质超表面高Q谐振太赫兹

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Two paths, one goal: mechanism of calcium homeostasis to balance plant growth and immunity

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Science China Life Sciences 2025年第1期68卷 306-308页

作者： Aziz Ul Ikram Yanping jing Huan Chen Jian Chen International Genome Center Jiangsu University Joint Center for Single Cell Biology School of Agriculture and BiologyShanghai Jiao Tong University

Calcium(Ca2+) is an essential nutrient and a cellular signal in plants. The level of free Ca2+in the apoplast is ～10,000-fold higher than that in the cytoplasm at the resting state. This large Ca2+level gradient across the membrane between the interior and exterior can cause Ca2+overload and toxicity. Plants have evolved mechanisms to export Ca2+out of the cytoplasm or store it in intracellular organelles, such as endoplasmic reticulum（ER）, mitochondria, and vacuole.

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Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

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Computers, Materials & Continua 2025年第1期82卷 427-442页

作者： jing Guo Ziying Wang Yajuan Guo Haitao Jiang State Grid Jiangsu Electric Power Co. Ltd.Research InstituteState Grid Corporation of ChinaNanjing211100China

The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids *** study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation *** approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation *** employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter *** xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging *** results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging ***,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 *** research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.

关键词： Anomaly detection electric vehicle aggregation attack deep cross-network

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Domain generalization with semi-supervised learning for people-centric activity recognition

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Science China(Information Sciences) 2025年第1期68卷 171-188页

作者： jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Dielectric-ion-conductive ZnNb2O6 layer enabling rapid desolvation and diffusion for dendrite-free Zn metal batteries

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Journal of Energy Chemistry 2025年第1期100卷 693-701页

作者： Haifeng Yang Jian Wang Panpan Zhang Xiaomin Cheng Qinghua Guan jing Dong Bixian Chen Lujie Jia jing Zhang Yongzheng Zhang Yunjian Liu Hongzhen Lin School of Materials Science and Engineering Jiangsu University i-Lab & CAS Key Laboratory of Nanophotonic Materials and Devices Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Helmholtz Institute Ulm (HIU) Karlsruhe Institute of Technology (KIT) School of Materials Science and Engineering Xi'an University of Technology State Key Laboratory of Chemical Engineering East China University of Science and Technology

Rechargeable aqueous zinc-metal batteries （AZMBs） are promising candidates for large-scale energy storage systems due to their low cost and high ***,their performance and sustainability are significantly hindered by the sluggish desolvation kinetics at the electrode/electrolyte interface and the corresponding hydrogen evolution reaction where active water molecules tightly participate in the Zn（H2O）62+solvation ***,learnt from self-generated solid electrolyte interphase （SEI） in anodes,the dielectric but ion-conductive zinc niobate nanoparticles artificial layer is constructed on metallic Zn surface （ZNB@Zn）,acting as a rapid desolvation *** zincophilic and dielectric-conductive properties of ZNB layer accelerate interfacial desolvation/diffusion and suppress surface corrosion or dendrite formation,achieving uniform Zn plating/stripping behavior,as confirmed by electronic/optical microscopies and interface spectroscopical measurements together with theoretical ***,the as-prepared ZNB@Zn electrode exhibits excellent cycling stability of over 2000 h and robust reversibility （99.54%） even under high current density and depth of discharge ***,the assembled ZNB@Zn-based full cell displays high capacity-retention rate of 80.21%after 3000 cycles at 5 A g-1and outstanding rate performance up to 10 A *** large-areal pouch cell is stabilized for hundreds of cycles,highlighting the bright prospects of the dielectric but ion-conductive layer in further application of AZMBs.

关键词： Zn metal battery Dielectric artificial layer Rapid ion diffusion Zincophilic diffusion Active water inhibition

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含内部件加筋储液罐流固耦合动响应研究

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应用力学学报 2024年第5期41卷 1110-1118页

作者：景旭峰王婷英柴怡君刘宝君王春明李跃明西安交通大学航天航空学院西安710049 中国核电工程有限公司北京100840

为研究加筋储液罐动特性及其在地震载荷下动响应行为,采用缩比实验与声固耦合方法对不同液面高度下加筋储液罐动特性开展了测试与计算分析;建立了“非完全”锚固条件下含有内部件加筋储液罐的有限元模型,数值分析了不同液面高度下加筋... 详细信息

为研究加筋储液罐动特性及其在地震载荷下动响应行为,采用缩比实验与声固耦合方法对不同液面高度下加筋储液罐动特性开展了测试与计算分析;建立了“非完全”锚固条件下含有内部件加筋储液罐的有限元模型,数值分析了不同液面高度下加筋储液罐动特性、地震荷载下动响应及罐底相对基础的提离现象,揭示了内部件对储液罐流固耦合动响应的影响规律。结果表明,随着液面的升高,系统的固有频率呈现下降的趋势,且在某些液面高度时液体对于罐壁结构刚化效应大于其作为附加质量的效应;在地震载荷作用下储液罐的下半部结构承受着更大的动应力,罐内液体抑制了内部件的摆动以及罐底相对地面的提离,且随着液面升高,液体对罐中心位置内部件的摆动和罐底提离具有更加明显的抑制作用。研究方法与结果对于水平地震载荷作用下储液罐结构的安全设计具有重要参考价值。

关键词：内部件储液罐声固耦合法摆动提离

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Prospects on mangrove knowledge services in the smart era: From plant atlas to knowledge graphs

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Science China Earth Sciences 2025年第1期68卷 111-127页

作者： Zhi-Wei HOU Wenlong jing Cheng-Zhi QIN Ji YANG Qing XIA Xiaoling YIN Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences School of Traffic & Transportation Engineering Changsha University of Science and Technology

Mangroves are crucial to the ecological security of the Earth and human well-being. Their management, conservation, and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and ecology. Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves. However, this kind of service is oriented to information retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data, facing the problem of “massive data, rare knowledge”. Knowledge graphs are capable of extracting, organizing, and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers. They provide a solution for the realization of intelligent knowledge services. Focusing on the urgent need for mangrove knowledge acquisition, formal representation, and intelligent services, this paper proposes a research prospect on mangrove knowledge graphs and knowledge services. We first analyze the similarities and differences between various domain-specific concepts of Tupu. On this basis, we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the core. Then, we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services, including multiple research levels such as ubiquitous data sensing and aggregation, knowledge organization and graph construction, and intelligent mangrove knowledge services. Subsequently, the methods and workflow for constructing mangrove knowledge graphs are introduced. Finally, we discuss the challenges and possible future directions of mangrove knowledge services in the smart era, including the construction of a mangrove knowledge system that integra

关键词： Mangrove Tupu Knowledge graph Multi-modal Knowledge service

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An efficient quantum proactive incremental learning algorithm

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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Unraveling brain aging through the lens of oral microbiota

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Neural Regeneration Research 2025年第7期20卷 1930-1943页

作者： Qinchao Hu Si Wang Weiqi Zhang jing Qu Guang-Hui Liu State Key Laboratory of Membrane Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Hospital of Stomatology Guanghua School of StomatologyGuangdong Provincial Key Laboratory of StomatologySun Yat-sen UniversityGuangzhouGuangdong ProvinceChina Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Aging Translational Medicine Center International Center for Aging and CancerBeijing Municipal Geriatric Medical Research CenterXuanwu HospitalCapital Medical UniversityBeijingChina CAS Key Laboratory of Genomic and Precision Medicine Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijingChina University of Chinese Academy of Sciences BeijingChina Institute for Stem Cell and Regeneration Chinese Academy of SciencesBeijingChina Aging Biomarker Consortium BeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Beijing Institute for Stem Cell and Regenerative Medicine BeijingChina

The oral cavity is a complex physiological community encompassing a wide range of *** of oral microbiota can lead to various oral infectious diseases,such as periodontitis and tooth decay,and even affect systemic health,including brain aging and neurodegenerative *** studies have highlighted how oral microbes might be involved in brain aging and neurodegeneration,indicating potential avenues for intervention *** this review,we summarize clinical evidence demonstrating a link between oral microbes/oral infectious diseases and brain aging/neurodegenerative diseases,and dissect potential mechanisms by which oral microbes contribute to brain aging and *** also highlight advances in therapeutic development grounded in the realm of oral microbes,with the goal of advancing brain health and promoting healthy aging.

关键词： Alzheimer's disease brain aging multiple sclerosis neurodegeneration neurodegenerative diseases oral microbiota Parkinson's disease periodontitis bacteria Porphyromonas gingivalis

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