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  • Open Access

    ARTICLE

    Fast and Accurate Predictor-Corrector Methods Using Feedback-Accelerated Picard Iteration for Strongly Nonlinear Problems

    Xuechuan Wang1, Wei He1,*, Haoyang Feng1, Satya N. Atluri2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1263-1294, 2024, DOI:10.32604/cmes.2023.043068

    Abstract Although predictor-corrector methods have been extensively applied, they might not meet the requirements of practical applications and engineering tasks, particularly when high accuracy and efficiency are necessary. A novel class of correctors based on feedback-accelerated Picard iteration (FAPI) is proposed to further enhance computational performance. With optimal feedback terms that do not require inversion of matrices, significantly faster convergence speed and higher numerical accuracy are achieved by these correctors compared with their counterparts; however, the computational complexities are comparably low. These advantages enable nonlinear engineering problems to be solved quickly and accurately, even with rough initial guesses from elementary predictors.… More > Graphic Abstract

    Fast and Accurate Predictor-Corrector Methods Using Feedback-Accelerated Picard Iteration for Strongly Nonlinear Problems

  • Open Access

    ARTICLE

    An Adaptive Parallel Feedback-Accelerated Picard Iteration Method for Simulating Orbit Propagation

    Changtao Wang, Honghua Dai*, Wenchuan Yang

    Digital Engineering and Digital Twin, Vol.1, pp. 3-13, 2023, DOI:10.32604/dedt.2023.044210

    Abstract A novel Adaptive Parallel Feedback-Accelerated Picard Iteration (AP-FAPI) method is proposed to meet the requirements of various aerospace missions for fast and accurate orbit propagation. The Parallel Feedback-Accelerated Picard Iteration (P-FAPI) method is an advanced iterative collocation method. With large-step computing and parallel acceleration, the P-FAPI method outperforms the traditional finite-difference-based methods, which require small-step and serial integration to ensure accuracy. Although efficient and accurate, the P-FAPI method suffers extensive trials in tuning method parameters, strongly influencing its performance. To overcome this problem, we propose the AP-FAPI method based on the relationship between the parameters and the convergence speed leveraging… More >

  • Open Access

    ARTICLE

    Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation

    Yuzheng Yang*, Zhe Tu, Ying Liu, Huachun Zhou

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3163-3184, 2023, DOI:10.32604/cmc.2023.043974

    Abstract Currently, data security and privacy protection are becoming more and more important. Access control is a method of authorization for users through predefined policies. Token-based access control (TBAC) enhances the manageability of authorization through the token. However, traditional access control policies lack the ability to dynamically adjust based on user access behavior. Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility. As a result, this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control. The TBAC system divides the access control process… 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

    The DMRTA1-SOX2 positive feedback loop promotes progression and chemotherapy resistance of esophageal squamous cell carcinoma

    RUI ZHANG1,2,#, PENG ZHOU1,3,#, XIA OU4, PEIZHU ZHAO2, XIJING GUO2, MIAN XI5,*, CHEN QING1,*

    Oncology Research, Vol.31, No.6, pp. 887-897, 2023, DOI:10.32604/or.2023.030184

    Abstract Esophageal squamous cell carcinoma (ESCC) is among the most prevalent causes of cancer-related death in patients worldwide. Resistance to immunotherapy and chemotherapy results in worse survival outcomes in ESCC. It is urgent to explore the underlying molecular mechanism of immune evasion and chemoresistance in ESCC. Here, we conducted RNA-sequencing analysis in ten ESCC tissues from cisplatin-based neoadjuvant chemotherapy patients. We found that DMRTA1 was extremely upregulated in the non-pathologic complete response (non-pCR) group. The proliferation rate of esophageal squamous carcinoma cells was markedly decreased after knockdown of DMRTA1 expression, which could increase cisplatin sensitivity in ESCC. Additionally, suppression of DMRTA1… More >

  • Open Access

    ARTICLE

    Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

    Shuiping Zhang1,2, Xi Liang3, Lin Shi2, Lei Yan4, Jun Tang1,2,*

    Sound & Vibration, Vol.57, pp. 29-44, 2023, DOI:10.32604/sv.2023.041350

    Abstract The filter-x least mean square (FxLMS) algorithm is widely used in active noise control (ANC) systems. However, because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update the filter coefficients, it has a certain delay, usually has a slow convergence speed, and the system response time is long and easily affected by the learning rate leading to the lack of system stability, which often fails to achieve the desired control effect in practice. In this paper, we propose an active control algorithm with nearest-neighbor trap structure and neural network feedback mechanism… More > Graphic Abstract

    Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

  • Open Access

    ARTICLE

    Performance Assessment and Configuration Analysis in the Study of SCADA System (Supervisory Control and Data Acquisition)

    R. Vanalakshmi1, S. Maragathasundari1,*, M. Kameswari1, B. Balamurugan2, C. Swedheetha3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1399-1419, 2023, DOI:10.32604/iasc.2023.038506

    Abstract Queuing models are used to assess the functionality and aesthetics of SCADA systems for supervisory control and data collection. Here, the main emphasis is on how the queuing theory can be used in the system’s design and analysis. The analysis’s findings indicate that by using queuing models, cost-performance ratios close to the ideal might be attained. This article discusses a novel methodology for evaluating the service-oriented survivability of SCADA systems. In order to evaluate the state of service performance and the system’s overall resilience, the framework applies queuing theory to an analytical model. As a result, the SCADA process is… More >

  • Open Access

    EDITORIAL

    Sustainable Development of Energy Systems and Climate Systems: Key Issues and Perspectives

    Bing Wang1,2,*, Lu Li1, Xinru Jiang1

    Energy Engineering, Vol.120, No.8, pp. 1763-1773, 2023, DOI:10.32604/ee.2023.027846

    Abstract Climate change and energy security issues are prominent challenges in current energy system management, which should be governed synergistically due to the feedback relationships between them. The “Energy Systems Management and Climate Change” Special Collection Issue in the journal of Energy Engineering provide insights into the field of energy systems management and climate change. From an extended perspective, this study discusses the key issues, research methods and models for energy system management and climate change research. Comprehensive and accurate prediction of energy supply and demand, the evaluation on the energy system resilience to climate change and the coupling methodology application… More > Graphic Abstract

    Sustainable Development of Energy Systems and Climate Systems: Key Issues and Perspectives

  • Open Access

    ARTICLE

    Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

    I. Mettildha Mary1,*, K. Karuppasamy2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2667-2685, 2023, DOI:10.32604/csse.2023.031114

    Abstract CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can… More >

  • Open Access

    ARTICLE

    An Endogenous Feedback and Entropy Analysis in Machine Learning Model for Stock’s Return Forecast

    Edson Vinicius Pontes Bastos1,*, Jorge Junio Moreira Antunes2, Lino Guimarães Marujo1, Peter Fernandes Wanke2, Roberto Ivo da Rocha Lima Filho1

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3175-3190, 2023, DOI:10.32604/iasc.2023.034582

    Abstract Stock markets exhibit Brownian movement with random, non-linear, uncertain, evolutionary, non-parametric, nebulous, chaotic characteristics and dynamism with a high degree of complexity. Developing an algorithm to predict returns for decision-making is a challenging goal. In addition, the choice of variables that will serve as input to the model represents a non-triviality, since it is possible to observe endogeneity problems between the predictor and the predicted variables. Thus, the goal is to analyze the endogenous origin of the stock return prediction model based on technical indicators. For this, we structure a feed-forward neural network. We evaluate the endogenous feedback between the… More >

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