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Dha Tab-COF filled PEBAX mixed matrix membranes for effective CO_(2)/CH_(4)separation

Chinese Journal of Chemical engineering 2025年第1期77卷 123-134页

作者： Yuankai Pan Xingmei Zhang Wenwen He Lan Zheng Xiaolong Han School of Chemical Engineering Northwest UniversityXi'an 710069China School of Chemical Engineering Xi'an UniversityXi'an 710065China School of Chemistry and Life Science Advanced Institute of Materials ScienceChangchun University of TechnologyChangchun 130012China

Covalent organic skeletons(COFs)have been widely used in gas separation due to their excellent pore structure,high crystallinity,and high specific surface *** this work,Dha Tab-COF was synthesized by solvothermal method and filled in polyether block polyamide(PEBAX)to form mixed matrix membranes(MMMs).Various characterization methods such as Fourier transform infrared spectroscopy(FT-IR),Xray photoelectron spectroscopy(XPS),scanning electron microscopy(SEM)and X-ray diffractometry(XRD)were used to characterize the structure of Dha Tab-COF as well as the *** effects of operating pressure,operating temperature and the content of Dha Tab-COF particles on the CO_(2)/CH_(4)separation performance of the membranes were *** best separation performance with a CO_(2)permeability of 295.8 barrer(1 barrer=7.52×10^(-18)m^(3)·(STP)·m^(-2)·m·s^(-1)·Pa^(-1))and a CO_(2)/CH_(4)selectivity of 21.6 was achieved when the Dha Tab-COF content is 2%(mass),which were 45.7%and 108.1%higher than that of the pure PEBAX membrane,respectively.

关键词： Covalent organic framework(COFs) Polyether block polyamide(PEBAX) Mixed matrix membranes CO_(2)separation membrane

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Post-tunable salt gel electrolytes toward flexible supercapacitor switches

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Journal of Energy Chemistry 2025年第1期100卷 114-124页

作者： Hui Wang Shuang Chen Xuming Liu Qin Zhang Xin Liu Polymeric and Soft Materials Laboratory School of Chemistry and Life Science and Advanced Institute of Materials ScienceChangchun University of TechnologyChangchun 130012JilinChina Polymeric and Soft Materials Laboratory School of Chemical Engineering and Advanced Institute of Materials ScienceChangchun University of TechnologyChangchun 130012JilinChina

Convertible hydrogel supercapacitors have emerged as promising energy storage devices in switches,diodes,and ***,inherent weaknesses in ionic conductivity,mechanical properties,and water retention of hydrogel electrolytes seriously hinder their *** by the hardness conversion of sea cucumber skin,a conductivity and mechanics dual-tunable salt gel electrolyte is successfully *** salt gel presents a reversible switching of conductors-insulators and a mechanical regulation between softness and hardness via the dissolution-crystallization transition of sodium acetate trihydrate(SAT).Meanwhile,the salt gels spontaneously grow a layer of“armor”through saturated phase-change salt crystals effectively reducing water evaporation of hydrogel ***,this phase-change soft-rigid conversion strategy will expand the capabilities of gel-based flexible supercapacitors(area capacitance:258.6 mF cm^(-2)),and the capacitance retention rate could still reach 86.9%after 3000 cycles at high ***,the salt gel supercapacitor is potentially used in over-heat alarm *** is anticipated that the strategy of conductivity and mechanics of dual-tunable salt gel would provide a new perspective on the development of energy storage devices,wearable electronics,and flexible robots.

关键词： Supercapacitors Salt gels Reversible phase transition Post-tuning Conductivity switching

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Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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Outlier-resistant state estimation for memristor-based BAM neural networks with probabilistic time-varying delays

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Neural Computing and Applications 2025年第8期37卷 6251-6265页

作者： Shao, Xiaoguang Zhang, Jie Lyu, Ming Lu, Yanjuan School of Automation Nanjing University of Science and Technology Nanjing210094 China School of Electronics and Information Engineering Changchun university of science and technology Changchun130022 China

This study considers the issue of nonfragile state estimation (SE) for memristor-based bidirectional associative memory (MBAM) neural networks with probabilistic time-varying delays. The primary objective is to develop an effective estimator for accurately assessing neuron states, which are crucial in various engineering applications. Furthermore, we consider a scenario in which the measurement outputs of the neural networks may be influenced by abnormal disturbances, which could have a negative impact on the performance of the estimator. In this case, a factitious saturation constraint is introduced to mitigate the adverse effects on the designed outlier-resistant estimator, thereby improving the reliability of the estimator. Through constructing sensible Lyapunov–Krasovskii functional (LKF), a delay-dependent criterion is derived to guarantee the exponential stability of the augmented system. Finally, the effectiveness of the desired estimation scheme is demonstrated via two simulation examples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Time delay

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GMS:A Novel Method for Detecting Reentrancy Vulnerabilities in Smart Contracts

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computers, Materials & Continua 2025年第5期83卷 2207-2220页

作者： Dawei Xu Fan Huang Jiaxin Zhang Yunfang Liang Baokun Zheng Jian Zhao School of Computer Science Changchun UniversityChangchun130012China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China School of InformationManagement for Law China University of Political Science and LawBeijing102249China

With the rapid proliferation of Internet ofThings(IoT)devices,ensuring their communication security has become increasingly *** and smart contract technologies,with their decentralized nature,provide strong security guarantees for ***,at the same time,smart contracts themselves face numerous security challenges,among which reentrancy vulnerabilities are particularly *** detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer *** address these limitations,this paper proposes a novel detection method that combines pattern matching with deep ***,we carefully identify and define three common patterns of reentrancy vulnerabilities in smart ***,we extract key vulnerability features based on these ***,we employ a Graph Attention Neural Network to extract graph embedding features from the contract graph,capturing the complex relationships between different components of the ***,we use an attention mechanism to fuse these two sets of feature information,enhancing the weights of effective information and suppressing irrelevant information,thereby significantly improving the accuracy and robustness of vulnerability *** results demonstrate that our proposed method outperforms existing state-ofthe-art techniques,achieving a 3.88%improvement in accuracy compared to the latest vulnerability detection model AME(Attentive Multi-Encoder Network).This indicates that our method effectively reduces false positives and false negatives,significantly enhancing the security and reliability of smart contracts in the evolving IoT ecosystem.

关键词： Smart contract Internet of Things reentrancy vulnerabilities graph neural network

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MILD: A Multimodal Biometric Recognition Framework Integrating Large Foundation Models 18th

MILD: A Multimodal Biometric Recognition Framework Integrati...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Lu, Huimin Zhao, Qingxin Zhang, Zexing Ma, Songzhe Lin, Chenglin School of Computer Science and Engineering Changchun University of Technology Changchun130102 China

ISBN: (纸本)9789819610709

Traditional unimodal biometric recognition technologies, wh-ile widely applied across various fields, still face limitations such as environmental interference, spoofing attacks, and individual differences, leading to insufficient accuracy and reliability. Consequently, multimodal biometric recognition technology enhances recognition performance by integrating multiple biometric features. However, effectively merging the semantic information of different modalities remains a key challenge. This paper proposes a multimodal biometric recognition framework with integrated large models (MILD). The framework incorporates foundational large models for audio, language, and images, and innovatively designs modality adapters and multimodal decoders to address the semantic alignment issue of large models. Additionally, MILD uniquely combines voiceprints, electrocardiograms (ECG), and palm prints to enhance the anti-spoofing performance of biometric recognition. Experimental results validate the effectiveness of the MILD framework in cross-modal feature fusion and accurate recognition, demonstrating the potential of foundational large models in complex scenarios, with the highest cross-dataset recognition accuracy reaching 97.65%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Target Detection-Oriented RGCN Inference Enhancement Method

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computers, Materials & Continua 2025年第4期83卷 1219-1237页

作者： Lijuan Zhang Xiaoyu Wang Songtao Zhang Yutong Jiang Dongming Li Weichen Sun School of Internet of Things Engineering Wuxi UniversityWuxi214105China College of Computer Science and Engineering Changchun University of TechnologyChangchun130012China Computer Science and Technology at Guohao Academy Tongji UniversityShanghai200092China China North Vehicle Research Institute Beijing100072China

In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield *** combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on *** to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases ***,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network *** results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target *** research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.

关键词： RGCN target detection urban battlefield YOLO visual reasoning

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A universal strategy of multi-objective active learning to accelerate the discovery of organic electrode molecules

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science China Chemistry 2024年第11期67卷 3681-3687页

作者： Jiayi Du Jun Guo Wei Liu Ziwei Li Gang Huang Xinbo Zhang State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China School of Materials Science and Engineering Changchun University of Science and TechnologyChangchun 130022China

Organic electrode molecules hold significant potential as the next generation of cathode materials for Li-ion batteries. In this study, we have introduced a multi-objective active learning framework that leverages Bayesian optimization and non-dominated sorting genetic algorithms-Ⅱ. This framework enables the selection of organic molecules characterized by high theoretical energy density and low gap(LUMO-HOMO)(LUMO, lowest unoccupied molecular orbital;HOMO, highest occupied molecular orbital). Remarkably, after only two cycles of active learning, the determination of coefficient can reach 0.962 for theoretical energy density and 0.920 for the gap with a modest dataset of 300 molecules, showcasing superior predictive capabilities. The 2,3,5,6-tetrafluorocyclohexa-2,5-diene-1,4-dione, selected by non-dominated sorting genetic algorithms-Ⅱ, has been successfully applied to Li-ion batteries as cathode materials, demonstrating a high capacity of 288 m Ah g^(-1)and a long cycle life of 1,000 cycles. This outcome underscores the high reliability of our framework. Furthermore, we have also validated the universality and transferability of our framework by applying it to two additional databases, the QM9 and OMEAD. When the training dataset of the model includes at least 500 molecules, the determination of coefficient essentially reaches approximately0.900 for four targets: gap, reduction potential, LUMO, and HOMO. Therefore, the universal framework in our work provides innovative insights applicable to other domains to expedite the screening process for target materials.

关键词： organic electrode molecules Li-ion batteries active learning multi-objective Bayesian optimization

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