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Evoking immune system to potentiate nanocatalytic therapy through activation of cGAS-STING pathway

Science China Chemistry 2024年第10期67卷 3310-3319页

作者： Yi Wei Yuan Liang Pengye Du Shuyu Liu Pengpeng Lei Xiuling Liu Hongjie Zhang School of Materials Science and Engineering Changchun University of Science and TechnologyChangchun 130022China 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 Department of Chemistry Tsinghua UniversityBeijing 100084China Research Institute of Changchun University of Science and Technology in Chongqing Chongqing 401120China

Nanocatalytic therapy shows great potential for therapeutic ***,therapeutic efficiency is often limited by unsatisfactory enzyme activity and lack of the coordination of immune ***,engineering nanozymes activity enhancement while activating immune system will be an effective strategy to achieve efficient tumor ***,we synthesize a DSPE-PEG-FA modified manganese dioxide-based dual-atom nanozyme(MDF),on which iridium and platinum atoms are *** obtained MDF can simultaneously mimic four enzyme activities of catalase,oxidase,peroxidase,and glutathione oxidase,set off a reactive oxygen species(ROS)storm,cause tumor cell *** enzyme activity of MDF can be enhanced by its own photothermal ***,MDF can consume intracellular glutathione and release Mn^(2+),which can prevent generated ROS from consumption and further activate cyclic guanosine monophosphate-adenosine monophosphate synthase-stimulator of interferon genes(cGAS-STING)pathway and promote the secretion of type I interferon,which will help promote dendritic cells maturation,present antigens to T lymphocytes to help kill tumor ***,MDF shows excellent tumor suppressive *** work provides a new paradigm for the field of nanozymes and offers a new reference for involvement of cGAS-STING pathway activation in tumor catalytic therapy.

关键词： single-atom ROS immune activation cGAS-STING pathway nanocatalytic therapy

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Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： Xiaonan Wang Xilan Chen Xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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A Generative Model-Based Prediction of Aspect-Level Sentiment Quad with Stage Prompts and Semantic Enhancement 6

A Generative Model-Based Prediction of Aspect-Level Sentimen...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Li, Lingbo Zhou, Xiaotang Shi, Haidong School of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798331541750

Increasing attention has been directed toward obtaining fine-grained sentiment analysis in natural language tasks, with quadruple extraction emerging as a key focal point. In the Aspect Sentiment Quad Prediction(ASQP) task, state-of-the-art generative approaches have achieved remarkable results. However, existing research has not yet sufficiently emphasized the importance of the relationships between these elements. We propose a novel generative model for tuples that focuses on recognizing all sentiment components in a sentence. The model is based on a pre-trained model and incorporates an AMR parser to parse sentences, prompt learning to generate tuples, and the use of cross-entropy to measure loss on the model. Comprehensive experimental results confirm the effectiveness of our approach and provide additional backing for the validity of our research motivation. © 2024 IEEE.

关键词： Generative adversarial networks

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Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

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Energy Informatics 2025年第1期8卷 1-43页

作者： Rajaperumal, T.A. Columbus, C. Christopher School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure

关键词： Risk management

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Robust video question answering via contrastive cross-modality representation learning

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Science China(Information Sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer Science and Information Engineering Hefei University of Technology Institutes of Physical Science and Information Technology Anhui University School of Computer Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

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Dynamically Contrastive Clustering For Sentence Embedding 6

Dynamically Contrastive Clustering For Sentence Embedding

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Shi, Haidong Zhou, Xiaotang Li, Lingbo School of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798331541750

U nsupervised contrastive learning to obtain sentence embedding has become a widely adopted and effective method. However, existing techniques mainly focus on the construction of positive-negative sample pairs, where instance contrastive learning is performed on samples by determining the positive and negative examples, while these methods ignore the effect of temperature coefficients on the model and tend to use fixed temperature coefficients. To address these issues, we propose a new approach named DCCSE(Dynamically Contrastive Cluster for Sentence Embedding), our model smooths the sample embedding while enhancing the sample linkage. Specifically, we use sample embeddings to dynamically update the cluster embeddings, combine contrastive learning and cluster centroid while employing cluster embed dings to smooth the sample embeddings and design a dynamic temperature coefficient function to improve the linkage between sample pairs. We evaluate our approach on a standard STS dataset, and the experimental results show that our model has relatively significant advantages over other baseline models. © 2024 IEEE.

关键词： Contrastive Learning

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Introduction to the Special Issue on the Bottleneck of Blockchain Techniques Scalability,Security and Privacy Protection

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computer Modeling in engineering & Sciences 2024年第12期141卷 1933-1937页

作者： Shen Su Daojing He Neeraj Kumar Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.

关键词： services Ether limitations

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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