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  • Open Access

    ARTICLE

    Expert Consensus on Nutritional Support for Children with Congenital Heart Disease (2023 Edition)

    Xuming Mo1,*, Wei Cai2,*, Jirong Qi1, Zhuoming Xu3, Ying Wang4, Weihui Yan4, Shoujun Li5, Nianguo Dong6, Xinxin Chen7, Jinfen Liu3, Qiang Shu8, Jimei Chen9, Haibo Zhang3, Hao Zhang10, Quansheng Xing11, Qi An12, Xiaofeng Li13, Xu Wang14, Yan He15, Junwu Su16, Taibing Fan17, Teng Ming18, Weibing Tang19, Li Hong20, Jinghao Zheng3, Ming Ye21, Guocheng Sun22, Yiqun Ding23, Liang Tao24, Yifeng Yang25, Zhongshi Wu25, Hua Cao26, Qiang Wang16, Keming Yang5, Libing Zhang27, Ping Wen28, Yanqin Cui29, Bo Zhai30, Yong Zou18, Qingya Tang31, Rui Chen11, Chun Wu32, Zhiyu Feng33, Caixia Liu34, Yaping Mi21, Rufang Zhang35, Ke Lin12, Xin Li36, Mingan Pi37, Xiangming Fan8, Shanshan Shi38, Peng Huang39, Zhengxia Pan32, Jiafeng Qi40, Renwei Chen41, Shuguang Tao42, Yaqin Shu1, Huifeng Zhang21, Lan Jiang1, Min Da1, Nishant Patel1, Liang Hu1, Cardiac Surgery Group of Pediatric Surgery Society of Chinese Medical Association, Parenteral Enteral Nutrition Society of Chinese Medical Association

    Congenital Heart Disease, Vol.18, No.6, pp. 571-593, 2023, DOI:10.32604/chd.2024.048939

    Abstract The second edition of the expert consensus on pediatric nutrition was formed based on a global update of pediatric nutrition guidelines or consensus worldwide, the management of congenital heart disease, and the results of multi-center clinical nutrition research for congenital heart disease following the first Chinese consensus edition of 2016. The consensus was also shaped by the results of three discussion sessions and two questionnaires conducted by the 13-member collaboration group. This process was informed by both clinical guidelines and expert consensus. The quality of literature, both in English and Chinese, and the level of recommendations were evaluated using the… More >

  • Open Access

    ARTICLE

    A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

    Lejun Zhang1,2,3,*, Junjie Zhang1, Kentaroh Toyoda4, Yuan Liu2, Jing Qiu2, Zhihong Tian2, Ran Guo5

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 783-800, 2024, DOI:10.32604/cmes.2023.043469

    Abstract Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant… More >

  • Open Access

    ARTICLE

    PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

    Jauzak Hussaini Windiatmaja, Delphi Hanggoro, Muhammad Salman, Riri Fitri Sari*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2309-2339, 2023, DOI:10.32604/cmc.2023.041152

    Abstract This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputation-based consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It summarizes these profile records into… More >

  • Open Access

    ARTICLE

    Consortium Chain Consensus Vulnerability and Chain Generation Mechanism

    Rui Qiao, Shi Dong*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2505-2527, 2023, DOI:10.32604/cmc.2023.043476

    Abstract Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains. Therefore, this paper proposes a new consortium chain generation model, deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes, and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security. Furthermore, the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived. When either of the… More >

  • Open Access

    ARTICLE

    Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

    Mohanad A. Mohammed*, Hala B. Abdul Wahab

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1731-1748, 2024, DOI:10.32604/cmes.2023.030528

    Abstract Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges. Concurrently, the Internet of Things (IoT) has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services, ultimately enhancing human lives. This paper presents a new approach utilizing lightweight blockchain technology, effectively reducing the computational burden typically associated with conventional blockchain systems. By integrating this lightweight blockchain with IoT systems, substantial reductions in implementation time and computational complexity can be achieved. Moreover, the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm, renowned for… More >

  • Open Access

    ARTICLE

    Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

    Yixia Chen1,2, Mingwei Lin1,2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 845-866, 2023, DOI:10.32604/cmc.2023.040970

    Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake (DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase efficiency, respectively. However, how to study the knowledge representation of the collective voting information and then select delegates is a new open problem. To ensure the fairness and effectiveness of transactions in the blockchain, in this paper, we propose a novel fine-grained knowledge representation method, which improves the DPoS scheme based on the linguistic term set (LTS) and proportional hesitant fuzzy linguistic term set (PHFLTS). To this end,… More >

  • Open Access

    ARTICLE

    CF-BFT: A Dual-Mode Byzantine Fault-Tolerant Protocol Based on Node Authentication

    Zhiruo Zhang, Feng Wang*, Yang Liu, Yang Lu, Xinlei Liu

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3113-3129, 2023, DOI:10.32604/cmc.2023.040600

    Abstract The consensus protocol is one of the core technologies in blockchain, which plays a crucial role in ensuring the block generation rate, consistency, and safety of the blockchain system. Blockchain systems mainly adopt the Byzantine Fault Tolerance (BFT) protocol, which often suffers from slow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus. In this paper, this paper proposes a new dual-mode consensus protocol based on node identity authentication. It divides the consensus process into two subprotocols: Check_BFT and Fast_BFT. In Check_BFT, the replicas authenticate the primary’s identity by monitoring its behaviors. First, assume that… More >

  • Open Access

    ARTICLE

    A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

    Rongrong Ren1,2, Luyang Su1,2, Xinyu Meng1,2, Jianfang Wang3, Meng Zhao1,2,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 429-458, 2024, DOI:10.32604/cmes.2023.027310

    Abstract With the development of big data and social computing, large-scale group decision making (LGDM) is now merging with social networks. Using social network analysis (SNA), this study proposes an LGDM consensus model that considers the trust relationship among decision makers (DMs). In the process of consensus measurement: the social network is constructed according to the social relationship among DMs, and the Louvain method is introduced to classify social networks to form subgroups. In this study, the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights. In the process of consensus improvement: A… More >

  • Open Access

    ARTICLE

    A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism

    Jian Shang1,2,*, Runmin Guan2, Wei Wang2

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2609-2626, 2023, DOI:10.32604/iasc.2023.037694

    Abstract The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years. In real-world scenarios, microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks. To meet the high hardware resource requirements, address the vulnerability to network attacks and poor reliability in the traditional centralized data storage schemes, this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph (DAG) consensus mechanism. Firstly, the microgrid data storage model is designed based on the edge computing technology. The blockchain, deployed on… More >

  • Open Access

    ARTICLE

    Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

    Yonghao Zhang1,3, Yongtang Wu2, Tao Li1, Hui Zhou1,3, Yuling Chen1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 345-361, 2023, DOI:10.32604/cmes.2023.026920

    Abstract The data in Mobile Edge Computing (MEC) contains tremendous market value, and data sharing can maximize the usefulness of the data. However, certain data is quite sensitive, and sharing it directly may violate privacy. Vertical Federated Learning (VFL) is a secure distributed machine learning framework that completes joint model training by passing encrypted model parameters rather than raw data, so there is no data privacy leakage during the training process. Therefore, the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy. Typically, the VFL requires a third party for key distribution… More >

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