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A Self-Healing Elastomer with Extremely High Toughness Achieved by Acylsemicarbazide Hydrogen Bonding and Cu^(2+)-Neocuproine Coordination Interactions

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Chinese Journal of Polymer science 2024年第10期42卷 1425-1434,I0007页

作者： Xiao-Ming An Yi-Ping Wang Tang-Song Zhu Chong Xing Xu-Dong Jia Qiu-Hong Zhang Key Laboratory of High Performance Polymer Material and Technology of MOE Department of Polymer Science and EngineeringSchool of Chemistry and Chemical EngineeringNanjing UniversityNanjing210093China State Key Laboratory of Coordination Chemistry Nanjing UniversityNanjing210093China

Elastomers with high mechanical toughness can guarantee their durability during service ***-healing ability,as well as recyclability,can also extend the life of materials and save the consuming cost of the *** efforts have been dedicated to promoting the mechanical toughness as well as the self-healing capability of elastomers at the same time,while it remains a challenge to balance the trade-off between the above properties in one *** we proposed a molecular design driven by dual interactions of acylsemicarbazide hydrogen bonding and Cu^(2+)-neocuproine coordination *** introducing the reversible multiple hydrogen bonds and strong coordination bonds,we successfully fabricated an extremely tough and self-healing *** elastomer can achieve an impressive top-notch toughness of 491 MJ/m^(3).Furthermore,it boasted rapid elastic restorability within 10 min and outstanding crack tolerance with high fracture energy(152.6kJ/m^(2)).Benefiting from the combination of dynamic interactions,the material was able to self-repair under 80℃conveniently and could be reprocessed to restore the exceptional mechanical properties.

关键词： Metal-ligand coordination Hydrogen bond Ultra-tough elastomer Self-healing

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CVTD: A Robust Car-Mounted Video Text Detector

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computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li Department of Computer Science and Engineering Chongqing University of TechnologyChongqingChina College of Information and Engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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A Solution Supporting Secure Transmission of Big Data

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Tsinghua science and technology 2023年第5期28卷 838-848页

作者： Haonan Ling Yan Gao Huibin Wang Ming Chen Institute of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China China Electronic Systems Engineering Corporation Beijing 100840China College of Computer and Information Engineering Chuzhou UniversityChuzhou 239000

Traditional Internet protocol networks cannot provide the service of selecting a secure path to transmit various types of data with specific security ***,to solve the“secure path transmission”problem,this paper first proposes a search-transmit model for secure network path transmission,i.e.,to find a multi-attribute optimized path that meets the specific security requirements in the search phase,and then transmit the packets along the optimized path in the transmission ***,we propose a solution to the search-transmit *** idea of the solution is to use the particle swarm optimization algorithm to search for a secure path that meets the multi-attribute requirements and then set the source route on the router to control the packet ***,a prototype system based on network function virtualization is developed to evaluate the feasibility and performance of the proposed *** results show that the proposed solution outperforms existing algorithms in terms of performance.

关键词： network secure path search-transmit model multi-attribute path optimization segment routing network function virtualization

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CEEMDAN-SE based Multi-predictor hybrid model for cloud resource prediction

CEEMDAN-SE based Multi-predictor hybrid model for cloud reso...

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7th International Conference on Electronics technology, ICET 2024

作者： Li, Xuxin Zhang, Yi Wu, Zebin Wei, Zhihui Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363951

Effective prediction of cloud resource demand can provide decision support for cloud service providers' resource allocation, which helps optimize resource utilization and improve service quality. In cloud data centers, the time series of cloud resource demand is affected by both explicit change trend and multi-dimensional recessive factors. The existing prediction models are difficult to meet the cloud resource prediction demand under complex change scenarios. In this paper, we propose a sample entropy-optimized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN-SE) based multiple predictor hybrid model for cloud resource prediction. Firstly, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is used to decompose time series into set of subsequences. Then the sample entropy is used to evaluate and reconstruct the subsequences set, and the original time series is divided into two parts: the low frequency trend component and the high frequency fluctuation component. Finally, we used multi-predictor hybrid model based on PatchTST and Informer to train and predict trend component and the fluctuation component respectively, and the prediction results were synthesized to obtain the final predicted value. We conducted comparative experiments on the open source dataset Google-Cluster-v2011, comparing it with popular advanced models. The results show that our model has better performance than other models in predicting the time series of cloud resources. © 2024 IEEE.

关键词： Prediction models

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Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

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computers, Materials & Continua 2024年第6期79卷 4433-4448页

作者： Tingting Su Jia Wang Wei Hu Gaoqiang Dong Jeon Gwanggil School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Cyber Science and Engineering Jinling Institute of Technology CityNanjing210000China College of Information Technology Incheon National UniversityIncheon22012Korea

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more *** has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic *** introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network *** feature extraction leads to less accurate classification *** abnormal traffic detection,the data of network traffic is high-dimensional and *** data not only increases the computational burden of model training but also makes information extraction more *** address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic *** fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is *** module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive *** proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual *** module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information *** results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

关键词： Abnormal network traffic deep learning residual network multi-scale feature extraction max-feature-map

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Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

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computer Systems science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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The state-of-the-art of atmospheric pressure plasma for transdermal drug delivery

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Plasma science and technology 2024年第4期26卷 9-26页

作者：聂兰兰刘大伟程鹤赵峰卢新培 State key laboratory of advanced electromagnetic engineering and technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People’s Republic of China School of Chemistry and Chemical Engineering Nanjing University of Science and TechnologyNanjing 210094People’s Republic of China Department of Thoracic Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022People’s Republic of China

Plasma-enhanced transdermal drug delivery(TDD) presents advantages over traditional methods,including painless application, minimal skin damage, and rapid recovery of permeability. To harness its clinical potential, factors related to plasma’s unique properties, such as reactive species and electric fields, must be carefully *** review provides a concise summary of conventional TDD methods and subsequently offers a comprehensive examination of the current state-of-the-art in plasma-enhanced TDD. This includes an analysis of the impact of plasma on HaCaT human keratinocyte cells, ex vivo/in vivo studies, and clinical research on plasma-assisted TDD. Moreover, the review explores the effects of plasma on skin physical characteristics such as microhole formation, transepidermal water loss(TEWL), molecular structure of the stratum corneum(SC), and skin resistance. Additionally, it discusses the involvement of various reactive agents in plasma-enhanced TDD, encompassing electric fields,charged particles, UV/VUV radiation, heat, and reactive species. Lastly, the review briefly addresses the temporal behavior of the skin after plasma treatment, safety considerations, and potential risks associated with plasma-enhanced TDD.

关键词： plasma transdermal drug delivery skin physical characteristics reactive agents

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A strategy for enhancing luminescence thermometry via Ta^(5+)-triggered K_(0.5)Na_(0.5)NbO_(3):Er^(3+)transparent ceramics

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Journal of Materiomics 2024年第5期10卷 984-994页

作者： Quan Zhang Zetian Yang Mingyuan Wang Yang Liu Shuangying Lei Laihui Luo Dirk Poelman Key Laboratory of MEMS of Ministry of Education School of Electrical Science and EngineeringSoutheast UniversityNanjing210096China LumiLab Department of Solid StateSciencesGhent UniversityKrijgslaan 281-S1GhentB-9000Belgium School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Department of Microelectronic Science and Engineering Ningbo UniversityNingbo315211China

Luminescence thermometry is a reliable approach for remote thermal sensing,and extensive studies have been devoted to designing a luminescence thermometer with heightened thermal ***,we report a promising luminescence thermometric material,Ta^(5+)-substituted K_(0.5)Na_(0.5)NbO_(3):0.003Er^(3+)transparent ferroelectric *** temperature sensing sensitivity is significantly improved by adjusting the concentration of Ta^(5+)in the ***,utilizing the fluorescence intensity ratio from the 2H_(11/2) and 4S_(3/2) thermally coupled states of Er^(3+)as a detecting signal within the temperature range of 273–543 K,an optimal maximum absolute sensitivity of 0.0058 K–1 and relative sensitivity of 0.0158 K–1 are achieved for K_(0.5)Na_(0.5)NbO_(3):0.65Ta^(5+)/0.003Er^(3+).Simultaneously,as the concentration of Ta5+increase,a unique evolution of structural phase transitions is observed from orthorhombic to tetragonal and then to *** is accompanied by an improvement in luminescence temperature sensing properties,and the best sensitivity is demonstrated in the cubic-phase ***,a huge change in infrared luminescence properties as a function of temperature is found around the structure transition temperature of the *** results indicate a promising potential for achieving highly sensitive thermometry or monitoring phase structure transitions through luminescence thermometry behavior in the K_(0.5)Na_(0.5)NbO_(3) host.

关键词： Transparent ceramics Temperature sensing Phase transition Upconversion emission

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