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 >

Aljuaid Turkea Ayedh M^1,2,*, Ainuddin Wahid Abdul Wahab^1,*, Mohd Yamani Idna Idris^1,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4663-4686, 2024, DOI:10.32604/cmc.2024.055287

Abstract Organizations are adopting the Bring Your Own Device (BYOD) concept to enhance productivity and reduce expenses. However, this trend introduces security challenges, such as unauthorized access. Traditional access control systems, such as Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC), are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources. This paper proposes a method for enforcing access decisions that is adaptable and dynamic, based on multilayer hybrid deep learning techniques, particularly the Tabular Deep Neural Network TabularDNN method. This technique transforms… More >

Weiqiang Jin^1,#, Xingwu Tian^1,#, Bohang Shi¹, Biao Zhao^1,*, Haibin Duan², Hao Wu³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125

Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

Tao Zhou^1,3, Yunfeng Pan^1,3,*, Huiling Lu², Pei Dang^1,3, Yujie Guo^1,3, Yaxing Wang^1,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4813-4832, 2024, DOI:10.32604/cmc.2024.054685

Abstract Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion. Such as Positron Emission Computed Tomography (PET), Computed Tomography (CT), and PET-CT. How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions. To solve the problem, the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network (Guide-YNet) is proposed in this paper. Firstly, a double-encoder single-decoder U-Net is used as the backbone in this model, a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634

Abstract Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.… More >

Ganesh There^*, Rohit Sharma^*

Energy Engineering, Vol.121, No.10, pp. 2851-2892, 2024, DOI:10.32604/ee.2024.049506

Abstract This analysis investigates the widespread use of solar drying methods and designs in developing countries, particularly for agricultural products like fruits, vegetables, and bee pollen. Traditional techniques like hot air oven drying and open sun drying have drawbacks, including nutrient loss and exposure to harmful particles. Solar and thermal drying are viewed as sustainable solutions because they rely on renewable resources. The article highlights the advantages of solar drying, including waste reduction, increased productivity, and improved pricing. It is also cost-effective and energy-efficient. The review study provides an overview of different solar drying systems and… More > Graphic Abstract

Patricia Luna^1,*, Juan Lizarazo-Marriaga¹, Alvaro Mariño²

Journal of Renewable Materials, Vol.12, No.8, pp. 1399-1416, 2024, DOI:10.32604/jrm.2024.052669

Abstract This study focuses on treating Guadua angustifolia bamboo fibers to enhance their properties for reinforcement applications in composite materials. Chemical (alkali) and physical (dry etching plasma) treatments were used separately to augment compatibility of Guadua angustifolia fibers with various composite matrices. The influence of these treatments on the fibers’ performance, chemical composition, and surface morphology were analyzed. Statistical analysis indicated that alkali treatments reduced the tensile modulus of elasticity and strength of fibers by up to 40% and 20%, respectively, whereas plasma treatments maintain the fibers’ mechanical performance. FTIR spectroscopy revealed significant alterations in chemical composition due More > Graphic Abstract

Liqing Wang^*, Yiheng Xiao

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 619-631, 2024, DOI:10.32604/iasc.2024.052971

Abstract Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation has always been unsatisfactory. When Reinforcement Learning from Human Feedback (RLHF) is applied to low-resource machine translation, commonly encountered issues of substandard preference data quality and the higher cost associated with manual feedback data. Therefore, a more cost-effective method for obtaining feedback data is proposed. At first, optimizing the quality of preference data through the prompt engineering of the Large Language Model (LLM), More >

Zakaria Triki¹, Ahmed Selloum¹, Younes Chiba¹, Hichem Tahraoui^1,2, Dorsaf Mansour³, Abdeltif Amrane^4,*, Meriem Zamouche⁵, Mohammed Kebir⁶, Jie Zhang⁷

Frontiers in Heat and Mass Transfer, Vol.22, No.4, pp. 1107-1128, 2024, DOI:10.32604/fhmt.2024.052223

Abstract Refrigeration plays a significant role across various aspects of human life and consumes substantial amounts of electrical energy. The rapid advancement of green cooling technology presents numerous solar-powered refrigeration systems as viable alternatives to traditional refrigeration equipment. Exergy analysis is a key in identifying actual thermodynamic losses and improving the environmental and economic efficiency of refrigeration systems. In this study exergy analyze has been conducted for a solar-powered vapor compression refrigeration (SP-VCR) system in the region of Ghardaïa (Southern Algeria) utilizing R1234ze(E) fluid as an eco-friendly substitute for R134a refrigerant. A MATLAB-based numerical model was… 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 >