Wanwei Huang^1,*, 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 >

Yong Wan¹, Li Li¹, Jiaxin Zou¹, Hucheng Xiao², Mengdi Zhu², Ying Su², Jin Yang^2,*

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 >

Saket Sarin¹, Sunil K. Singh¹, Sudhakar Kumar¹, Shivam Goyal¹, Brij Bhooshan Gupta^2,3,4,8,*, Wadee Alhalabi⁵, Varsha Arya^6,7

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 >

Shiming Lin^1,2,3, Ling Ye², Yijie Zhuang¹, Lingyun Lu^2,*, Shaoqiu Zheng^2,*, 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 >

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 >

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 >

Abderrezzaq Benalia^1,2,*, Kerroum Derbal², Zahra Amrouci^2,3, Ouiem Baatache², Amel Khalfaoui⁴, Antonio Pizzi^5,*

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

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 >

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 >

Xin Zou¹, Jieyu Zhang¹, Ting Cheng¹, Yangyang Guo¹, Xiao Han¹, Han Liu¹, Yuxing Qin¹, Jie Li², Dabing Xiang^1,*

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 >