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  • Open Access

    ARTICLE

    Service Function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement Learning

    Wanwei Huang1,*, Qiancheng Zhang1, Tao Liu2, Yaoli Xu1, Dalei Zhang3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4875-4893, 2024, DOI:10.32604/cmc.2024.055622

    Abstract Aiming at the rapid growth of network services, which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain (SFC) under 5G networks, this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment (MADDPG-SD). Initially, an optimization model is devised to enhance the request acceptance rate, minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case. Subsequently, we model the dynamic problem as a Markov decision process (MDP), facilitating adaptation to the… More >

  • Open Access

    ARTICLE

    Effect of Shrinkage Reducing Agent and Steel Fiber on the Fluidity and Cracking Performance of Ultra-High Performance Concrete

    Yong Wan1, Li Li1, Jiaxin Zou1, Hucheng Xiao2, Mengdi Zhu2, Ying Su2, Jin Yang2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 1941-1956, 2024, DOI:10.32604/fdmp.2024.053910

    Abstract Due to the low water-cement ratio of ultra-high-performance concrete (UHPC), fluidity and shrinkage cracking are key aspects determining the performance and durability of this type of concrete. In this study, the effects of different types of cementitious materials, chemical shrinkage-reducing agents (SRA) and steel fiber (SF) were assessed. Compared with M2-UHPC and M3-UHPC, M1-UHPC was found to have better fluidity and shrinkage cracking performance. Moreover, different SRA incorporation methods, dosage and different SF types and aspect ratios were implemented. The incorporation of SRA and SF led to a decrease in the fluidity of UHPC. SRA More >

  • Open Access

    ARTICLE

    Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

    Saket Sarin1, Sunil K. Singh1, Sudhakar Kumar1, Shivam Goyal1, Brij Bhooshan Gupta2,3,4,8,*, Wadee Alhalabi5, Varsha Arya6,7

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3123-3138, 2024, DOI:10.32604/cmc.2024.051599

    Abstract In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence More >

  • Open Access

    ARTICLE

    Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning: DTRLpath

    Shiming Lin1,2,3, Ling Ye2, Yijie Zhuang1, Lingyun Lu2,*, Shaoqiu Zheng2,*, Chenxi Huang1, Ng Yin Kwee4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 299-317, 2024, DOI:10.32604/cmc.2024.051379

    Abstract In recent years, with the continuous development of deep learning and knowledge graph reasoning methods, more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning. By searching paths on the knowledge graph and making fact and link predictions based on these paths, deep learning-based Reinforcement Learning (RL) agents can demonstrate good performance and interpretability. Therefore, deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic. However, even in a small and fixed knowledge graph reasoning action… More >

  • Open Access

    ARTICLE

    MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

    Tengda Li, Gang Wang, Qiang Fu*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2559-2586, 2024, DOI:10.32604/cmes.2024.052039

    Abstract Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration… More >

  • Open Access

    ARTICLE

    CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

    Meignanamoorthi Dhandapani*, V. Vetriselvi, R. Aishwarya

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2449-2486, 2024, DOI:10.32604/cmes.2024.050986

    Abstract The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale. Network slicing is crucial in delivering services for different, demanding vertical applications in this context. Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations. However, the existing IP (Internet Protocol) over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators. Conventional inter-domain routing methods, like Border Gateway Protocol (BGP), cannot make routing decisions based on performance,… More >

  • Open Access

    REVIEW

    Application of Plant-Based Coagulants and Their Mechanisms in Water Treatment: A Review

    Abderrezzaq Benalia1,2,*, Kerroum Derbal2, Zahra Amrouci2,3, Ouiem Baatache2, Amel Khalfaoui4, Antonio Pizzi5,*

    Journal of Renewable Materials, Vol.12, No.4, pp. 667-698, 2024, DOI:10.32604/jrm.2024.048306

    Abstract This review describes the mechanisms of natural coagulants. It provides a good understanding of the two key processes of coagulation-flocculation: adsorption and charge neutralization, as well as adsorption and bridging. Various factors have influence the coagulation/flocculation process, including the effect of pH, coagulant dosage, coagulant type, temperature, initial turbidity, coagulation speed, flocculation speed, coagulation and flocculation time, settling time, colloidal particles, zeta potential, the effects of humic acids, and extraction density are explained. The bio-coagulants derived from plants are outlined. The impact of organic coagulants on water quality, focusing on their effects on the physicochemical… More > Graphic Abstract

    Application of Plant-Based Coagulants and Their Mechanisms in Water Treatment: A Review

  • Open Access

    ARTICLE

    Optimal Design of Drying Process of the Potatoes with Multi-Agent Reinforced Deep Learning

    Mohammad Yaghoub Abdollahzadeh Jamalabadi*

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 511-536, 2024, DOI:10.32604/fhmt.2024.051004

    Abstract Heat and mass transport through evaporation or drying processes occur in many applications such as food processing, pharmaceutical products, solar-driven vapor generation, textile design, and electronic cigarettes. In this paper, the transport of water from a fresh potato considered as a wet porous media with laminar convective dry air fluid flow governed by Darcy’s law in two-dimensional is highlighted. Governing equations of mass conservation, momentum conservation, multiphase fluid flow in porous media, heat transfer, and transport of participating fluids and gases through evaporation from liquid to gaseous phase are solved simultaneously. In this model, the… More >

  • Open Access

    ARTICLE

    Performance Evaluation of Multi-Agent Reinforcement Learning Algorithms

    Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 337-352, 2024, DOI:10.32604/iasc.2024.047017

    Abstract Multi-Agent Reinforcement Learning (MARL) has proven to be successful in cooperative assignments. MARL is used to investigate how autonomous agents with the same interests can connect and act in one team. MARL cooperation scenarios are explored in recreational cooperative augmented reality environments, as well as real-world scenarios in robotics. In this paper, we explore the realm of MARL and its potential applications in cooperative assignments. Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory with minimal damage. To accomplish this, we utilize the StarCraft… More >

  • Open Access

    ARTICLE

    Preparation of Tartary Buckwheat Seed Coating Agent and Its Effect on Germination

    Xin Zou1, Jieyu Zhang1, Ting Cheng1, Yangyang Guo1, Xiao Han1, Han Liu1, Yuxing Qin1, Jie Li2, Dabing Xiang1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 699-712, 2024, DOI:10.32604/phyton.2024.048469

    Abstract To mitigate the wastage of seed resources and reduce the usage of pesticides and fertilizers, seed coating agents have gained popularity. This study employs single-factor and multi-index orthogonal experimental design methods to investigate the seed coating formula and physical properties of Tartary buckwheat. The specific effects of each component on Tartary buckwheat seed germination are analyzed. The findings reveal that the seed coating agent formulated with 1.5% polyvinyl alcohol, 0.15% sodium alginate, 0.2% op-10, 0.1% polyacrylamide, 8% colorant, 3% ammonium sulfate, 1% potassium dihydrogen phosphate, and 0.15% carbendazim exhibits the most effective coating. It demonstrates… More >

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