Yong Ma¹, Han Zhao², Kunyin Guo^3,*, Yunni Xia^3,*, Xu Wang⁴, Xianhua Niu⁵, Dongge Zhu⁶, Yumin Dong⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 907-927, 2024, DOI:10.32604/cmes.2024.048759

Abstract Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network’s periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions for real-time mobile applications, especially… More >

Chengbao Hu^1,2,3, Shilin Gong^4,*, Bin Chen^1,2,3, Zhongling Zong⁴, Xingwang Bao⁵, Xiaojian Ru⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 997-1015, 2024, DOI:10.32604/cmes.2024.048640

Abstract Strain localization frequently occurs in cohesive materials with friction (e.g., composites, soils, rocks) and is widely recognized as a fundamental cause of progressive structural failure. Nonetheless, achieving high-fidelity simulation for this issue, particularly concerning strong discontinuities and tension-compression-shear behaviors within localized zones, remains significantly constrained. In response, this study introduces an integrated algorithm within the finite element framework, merging a coupled cohesive zone model (CZM) with the nonlinear augmented finite element method (N-AFEM). The coupled CZM comprehensively describes tension-compression and compression-shear failure behaviors in cohesive, frictional materials, while the N-AFEM allows nonlinear coupled intra-element discontinuities without necessitating extra nodes or… More >

Kai Wang¹, Biao He^2,*, Pijush Samui³, Jian Zhou⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569

Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset, which serves… More >

Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables, and UCS is regarded… More > Graphic Abstract

Feng Sheng^1,*, Chen Kong², Chao Li³

International Journal of Mental Health Promotion, Vol.26, No.3, pp. 221-228, 2024, DOI:10.32604/ijmhp.2024.030209

Abstract Objective: This study was conducted to explore the relationships among sedentary behavior (SB), electronic product addiction (EPA), and depression (D) in adolescents during the COVID-19 epidemic. Methods: A total of 604 adolescents (including 309 girls and 295 boys aged 12–18) were selected from Qufu City, Shandong Province, China for three rounds of investigation. The model was constructed using AMOS 23.0 software, and cross-lagged analysis was conducted. Results: SB at T1 can significantly positively predict SB and EPA at T2 (p < 0.05). EPA at T1 can significantly positively predict SB and D at T2 (p < 0.05). Physical activity level… More >

ABHIJEET SURYAWANSHI¹, NIRANJANA BEHERA^2,*

Journal of Polymer Materials, Vol.40, No.3-4, pp. 305-316, 2023, DOI:10.32381/JPM.2023.40.3-4.11

Abstract Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest… More >

FOUED KHOFFI^1,*, OMAR HARZALLAH², JEAN YVES DREAN²

Journal of Polymer Materials, Vol.40, No.1-2, pp. 59-69, 2023, DOI:10.32381/JPM.2023.40.1-2.5

Abstract The aim of this study is to analyze the interfacial and the frictional properties of copper (Cu) reinforced polyethylene terephthalate (PET) filament. This Cu-Embedded PET filament will be used as an information transmitter. This filament was prepared by a co-extrusion process. Mechanical properties of these filaments have been quantified by tensile and pull-out analyses. It is shown that the mechanical properties of composite filament were improved by adding the copper filament (from 0.82 to 1.2 GPa). The results of the pull-out test revealed some adhesion between the copper and the PET despite the existence of a slippage of the copper… More >

Muhammad Umar Nasir¹, Omar Kassem Khalil², Karamath Ateeq³, Bassam SaleemAllah Almogadwy⁴, Muhammad Adnan Khan⁵, Muhammad Hasnain Azam⁶, Khan Muhammad Adnan^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3303-3321, 2024, DOI:10.32604/cmc.2024.048035

Abstract Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions because of human error. The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well. Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being. In the current period Machine learning… More >

Haoran Huang, Weiting Chen^*, Zheming Fan

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008

Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose… More >

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships. Firstly, a dataset for… More >