Salma Moushi^1,*, Jaouad Ait lahcen¹, Ahmed El Hana¹, Yassine Ezaier¹, Ahmed Hader^1,2, Imane Bakassi¹, Iliass Tarras¹, Yahia Boughaleb^1,3

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2509-2521, 2024, DOI:10.32604/fdmp.2024.053501 - 28 October 2024

Abstract Multi-layer membrane filtration is a widely used technology for separating and purifying different components of a liquid mixture. This technique involves passing the liquid mixture through a series of membranes with decreasing pore sizes, which allows for the separation of different components according to their molecular size. This study investigates the filtration process of a fluid through a two-dimensional porous medium designed for seawater desalination. The focus is on understanding the impact of various parameters such as the coefficient of friction, velocity, and the number of layers on filtration efficiency. The results reveal that the More >

Jielin Jiang^1,2,3,4,*, Chao Cui¹, Xiaolong Xu^1,2,3,4, Yan Cui⁵

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024

Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

Uddagiri Sirisha^1,, Parvathaneni Naga Srinivasu^2,3,*, Panguluri Padmavathi⁴, Seongki Kim^5,, Aruna Pavate⁶, Jana Shafi⁷, Muhammad Fazal Ijaz^8,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2301-2330, 2024, DOI:10.32604/cmc.2024.053132 - 15 August 2024

Abstract Fetal health care is vital in ensuring the health of pregnant women and the fetus. Regular check-ups need to be taken by the mother to determine the status of the fetus’ growth and identify any potential problems. To know the status of the fetus, doctors monitor blood reports, Ultrasounds, cardiotocography (CTG) data, etc. Still, in this research, we have considered CTG data, which provides information on heart rate and uterine contractions during pregnancy. Several researchers have proposed various methods for classifying the status of fetus growth. Manual processing of CTG data is time-consuming and unreliable.… More >

Kexin Wang^*, Yingdong Gou, Dingrui Xue^*, Jiancheng Liu, Wanlong Qi, Gang Hou, Bo Li

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893 - 15 August 2024

Abstract The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of More >

Tiezhou Wu, Yukan Zhang^*

Energy Engineering, Vol.121, No.8, pp. 2231-2255, 2024, DOI:10.32604/ee.2024.049883 - 19 July 2024

Abstract In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs, a new multilayer equilibrium topology is designed in this paper. The structure adopts a hierarchical structure design, which includes intra-group equilibrium, primary inter-group equilibrium and secondary inter-group equilibrium. This structure greatly increases the number of equilibrium paths for lithium-ion batteries, thus shortening the time required for equilibrium, and improving the overall efficiency. In terms of control strategy, fuzzy logic control (FLC) is chosen to control the size of the equilibrium current during the equilibrium process. We… More >

Huiyan Zhao¹, Xuezhong Chen¹, Zhijian Hu^2,*, Man Chen¹, Bo Xiong³, Jianying Yang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1313-1330, 2024, DOI:10.32604/fdmp.2024.048840 - 27 June 2024

Abstract Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis, a model is developed to predict the related well production rate. This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales, as well as the flow characteristics in different types of thin layers (tight sandstone gas, shale gas, and coalbed gas). Moreover, a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir. A… More > Graphic Abstract

Zuozheng Lian^1,2, Yongchao Yin¹, Haizhen Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3939-3954, 2024, DOI:10.32604/cmc.2024.049780 - 20 June 2024

Abstract The relationship between users and items, which cannot be recovered by traditional techniques, can be extracted by the recommendation algorithm based on the graph convolution network. The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data. This paper presents a new approach to address such issues, utilizing the graph convolution network to extract association relations. The proposed approach mainly includes three modules: Embedding layer, forward propagation layer, and score prediction layer. The embedding layer models users and items according to their interaction information and… More >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917 - 25 April 2024

Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes,… More >

Dongsheng Jia, Yingjie Xu^*, Liangdi Wang, Jihong Zhu, Weihong Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 171-199, 2024, DOI:10.32604/cmes.2024.046703 - 16 April 2024

Abstract The abalone shell, a composite material whose cross-section is composed of inorganic and organic layers, has high strength and toughness. Inspired by the abalone shell, several multi-layer composite plates with different layer sequences and thicknesses are studied as bullet-proof material in this paper. To investigate the ballistic performance of this multi-layer structure, the complete characterization model and related material parameters of large deformation, failure and fracture of Al₂O₃ ceramics and Carbon Fiber Reinforced Polymer (CFRP) are studied. Then, 3D finite element models of the proposed composite plates with different layer sequences and thicknesses impacted by a More >

Amira M. Idrees^1,*, Abdul Lateef Marzouq Al-Solami²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457 - 30 January 2024

Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular… More >