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Swelling and erosion assisted sustained release of tea polyphenol from antibacterial ultrahigh molecular weight polyethylene for joint replacement

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Chinese Chemical Letters 2025年第2期36卷 316-320页

作者： Yue Ren Kang Li Yi-Zi Wang Shao-Peng Zhao Shu-Min Pan Haojie Fu Mengfan Jing Yaming Wang Fengyuan Yang Chuntai Liu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China Tianjian Laboratory of Advanced Biomedical Sciences Academy of Medical SciencesZhengzhou UniversityZhengzhou 450000China Department of Stomatology The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450000China

The considerable hazard posed by periprosthetic joint infections underlines the urgent need for the rapid advancement of in-situ drug delivery systems within joint ***,the pursuit of sustained antibacterial efficacy remains a formidable *** this context,we proposed a novel strategy that leverages swelling and erosion mechanisms to facilitate drug release of drug-loaded ultrahigh molecular weight polyethylene(UHMWPE),thereby ensuring its long-lasting antibacterial *** oxide(PEO),a hydrophilic polymer with fast hydrating ability and high swelling capacity,was incorporated in UHMWPE alongside the antibacterial tea polyphenol(epigallocatechin gallate,EGCG as representative).The swelling of PEO enhanced water infiltration into the matrix,while the erosion of PEO balanced the release of the encapsulated EGCG,resulting in a steady *** behavior was supported by the EGCG release profiles and the corresponding fitted release kinetic *** demonstrated by segmented antibacterial assessments,the antibacterial efficiency was enhanced 2to 3 times in the PEO/EGCG/UHMWPE composite compared to that of EGCG/***,the PEO/EGCG/UHMWPE composite exhibited favorable biocompatibility and mechanical performance,making it a potential candidate for the development of drug-releasing joint implants to combat prosthetic bacterial infections.

关键词： Tea polyphenol Antibacterial PEO UHMWPE Joint replacement

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MDIASE-Autoencoder: A Novel Anomaly Detection Method for Increasing the Performance of Credit Card Fraud Detection Models 29

MDIASE-Autoencoder: A Novel Anomaly Detection Method for Inc...

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29th International Conference on Telecommunications, ICT 2023

作者： Putrada, Aji Gautama Ramadhan, Nur Ghaniaviyanto Telkom University Advanced and Creative Networks Research Center Bandung Indonesia Institut Teknologi Telkom Purwokerto Software Engineering Faculty of Informatics Purwokerto Indonesia

ISBN: (纸本)9798350361100

Machine learning has presented diverse solutions for identifying credit card fraud by analyzing datasets with anonymous transactions. However, an unexplored avenue lies in employing autoencoders for anomaly detection in credit card fraud identification. Our research objective is to apply and assess autoencoders to enhance credit card fraud detection through machine learning-based classification methods. The study utilizes the credit card fraud dataset available on Kaggle, applying various preprocessing techniques such as standardization and over-sampling using the Synthetic Minority Oversampling Technique (SMOTE). Subsequently, a 5-layer autoencoder is applied to the modified dataset. A novel metric for evaluating reconstruction errors, Mean Decrease in the Impurity of Absolute and Squared Error (MDIASE), is employed for feature selection in the new dataset, combining mean reduction in impurity (MDI), square error, and absolute error concepts. Finally, we assess the influence of the autoencoder on four classification methods: decision tree, KNN, logistic regression, and MLP. The evaluation metrics used for comparison are AUC maximization and geometric mean (g-mean). The test results demonstrate that the autoencoder enhances the performance of four state-of-the-art models in credit card fraud classification. Mainly, MLP exhibits the most significant improvement, with its AUC increasing from 0.558 without the autoencoder to 0.999 after its implementation. © 2023 IEEE.

关键词： Anomaly detection

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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Enhanced Crystallization of Poly(butylene adipate-co-terephthalate)by a Self-assembly Nucleating Agent

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Chinese Journal of Polymer Science 2024年第5期42卷 663-674,I0010页

作者： Cheng Zhou Kun Chen Zi-Heng Zhang Meng-Fan Jing Chun-Tai Liu Chang-Yu Shen Ya-Ming Wang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou450002China

Poly(butylene adipate-co-terephthalate)(PBAT)is a promising biodegradable flexible polymer but suffers from slow crystallization rate,making it less attractive for some applications like the injection-molded products in comparison with low-density polyethylene(LDPE).This work aimed to accelerate the crystallization of PBAT by adding a self-assembly nucleating agent octamethylenedicarboxylic dibenzoylhydrazide(OMBH).PBAT/OMBH composites with various OMBH contents(0 wt%,0.5 wt%,0.7 wt%,1 wt%,2 wt%,3 wt%and 5 wt%)were prepared through *** effect of OMBH on the crystallization behavior,morphologies and mechanical properties of PBAT was *** highest nucleation efficiency value of 59.6%was achieved for PBAT with 0.7 wt%OMBH,much higher than that of 22.7%for PBAT with 0.7 wt%*** force microscopy results showed that OMBH formed fine fibers and induced the formation of transcrystalline layers of *** transform infrared spectroscopy(FTIR)combined with two-dimensional correlation spectra suggested that the intermolecular dipole-dipole N—H…O=C interactions but not hydrogen bond between OMBH and PBAT promoted the crystallization of PBAT in the initial period of *** presence of OMBH did not change the crystal form of PBAT but had positive contribution in enhancing its crystallinity and mechanical *** work is essential for preparing PBAT with high crystallization rate,enhancing its potential applications in injection-molded products.

关键词： Poly(butylene adipate-co-terephthalate) Biodegradable Self-assembly nucleating agent Crystallization

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High-performance MXene films by sequential bridging

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National Science Review 2024年第12期11卷 65-66页

作者： Xianhu Liu Jingna Zhang Chuntai Liu Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment National Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou University

MXenes—a kind of two-dimensional(2D) carbides, nitrides, oxycarbides and carbonitrides of early transition metals—have high mechanical and electrical properties and excellent photothermal conversion, biocompatibilit...

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental Sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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分子间相互作用调控丝素蛋白/聚丙烯酰胺水凝胶的网络形貌和β片结晶,赋予水凝胶优异的黏附、应变和传感性能

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Science China Materials 2024年第5期67卷 1533-1542页

作者：杨德桢孙传强韩哲袁宝刚潘栋陈金传许华杰刘春太申长雨 State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing Technologythe Key Laboratory of Advanced Materials Processing&Mold of Ministry of EducationZhengzhou UniversityZhengzhou450002China

丝素蛋白(SF)作为一种特殊的天然聚合物,具有优异的生物相容性、生物降解性和双亲性,是制备柔性传感器的良好候选者.然而,丝素蛋白的高结晶度和结晶不可控性使获得这种出色的仿生水凝胶传感器具有挑战性.纯SF水凝胶是脆性的,没有黏性.在... 详细信息

丝素蛋白(SF)作为一种特殊的天然聚合物,具有优异的生物相容性、生物降解性和双亲性,是制备柔性传感器的良好候选者.然而,丝素蛋白的高结晶度和结晶不可控性使获得这种出色的仿生水凝胶传感器具有挑战性.纯SF水凝胶是脆性的,没有黏性.在此,我们通过引入聚丙烯酰胺(PAM)到SF水凝胶中来解决这一问题.由于SF/PAM水凝胶具有较强的分子间相互作用,其网络形貌由欧几里得孔改变为非欧几里得孔.同时,其β片结晶很容易被抑制到纳米尺度.这些演变不仅使SF/PAM水凝胶具有优异的机械性能,而且具有出色的黏附性能.与纯PAM水凝胶相比,SF8/PAM水凝胶的拉伸强度、拉伸破坏应变、抗压强度(80%应变下)和黏附性能(在猪皮上)分别提高了133.1%、120.9%、610.8%和104.8%.此外,SF的双亲性可以使碳纳米管(CNTs)在水凝胶中分散良好.制备的CNT0.3/SF8/PAM水凝胶继承并进一步改善了上述性能.除此之外,它还表现出优异的自黏附传感性能,最大灵敏度因子高达10.13,工作应变范围超过1000%,大应变下稳定循环拉伸达500次以上.同时,六种人类活动的精确检测也得到了验证.本工作为实现高性能SF基水凝胶提供了新策略,并证实SF/PAM水凝胶在柔性可穿戴应变传感器中具有广阔的应用前景.

关键词： silk fibroin intermolecular interaction network morphology β-crystallization adhesion hydrogel strain sensor

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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