CHAO LIU^*

BIOCELL, Vol.48, No.2, pp. 339-351, 2024, DOI:10.32604/biocell.2023.047044

Abstract Introduction: Osteoarthritis (OA) is still an important health problem, and understanding its pathological mechanisms is essential for its diagnosis and treatment. There is evidence that autophagy may play a role in OA progression, but the exact mechanism remains unclear. Methods: In this study, we adopted a multi-prong approach to systematically identify the key autophagy-related genes (ARGs) associated with OA. Through weighted gene co-expression network analysis, we initially identified significant gene modules associated with OA. Subsequent differential gene analysis performed on normal and OA specimens. Further analysis later using the MCC algorithm highlighted hub ARGs. These genes were then incorporated into… More >

Qing Ai^1,2, Hao Tian^2,3,*, Hui Wang^1,*, Qing Lang¹, Xingchun Huang¹, Xinghong Jiang⁴, Qiang Jing⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1797-1827, 2024, DOI:10.32604/cmes.2023.045251

Abstract Structural Health Monitoring (SHM) systems have become a crucial tool for the operational management of long tunnels. For immersed tunnels exposed to both traffic loads and the effects of the marine environment, efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge. This study proposed a model-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel. Firstly, a dynamic predictive model-based anomaly detection method is proposed, which utilizes a rolling time window for modeling to achieve dynamic prediction. Leveraging the assumption… More >

Ming Gao^1,#, Weiwei Cai^1,#, Yizhang Jiang¹, Wenjun Hu³, Jian Yao², Pengjiang Qian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 259-277, 2024, DOI:10.32604/cmes.2023.029015

Abstract Currently, applications accessing remote computing resources through cloud data centers is the main mode of operation, but this mode of operation greatly increases communication latency and reduces overall quality of service (QoS) and quality of experience (QoE). Edge computing technology extends cloud service functionality to the edge of the mobile network, closer to the task execution end, and can effectively mitigate the communication latency problem. However, the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management, and the booming development of artificial neural networks provides us with more powerful methods… More >

Ahmed ElShafee¹, Walid El-Shafai^2,3, Abeer D. Algarni^4,*, Naglaa F. Soliman⁴, Moustafa H. Aly⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 349-374, 2023, DOI:10.32604/csse.2023.037408

Abstract COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet,… More >

Ramya Nemani¹, G. Jose Moses², Fayadh Alenezi³, K. Vijaya Kumar⁴, Seifedine Kadry^5,6,7,*, Jungeun Kim⁸, Keejun Han⁹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 919-935, 2023, DOI:10.32604/csse.2023.034213

Abstract Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning… More >

Kunpeng Zhao, Yang Wei^*, Si Chen, Kang Zhao, Mingmin Ding

Journal of Renewable Materials, Vol.11, No.6, pp. 2695-2712, 2023, DOI:10.32604/jrm.2023.025711

Abstract The destruction of recombinant bamboo depends on many factors, and the complex ambient temperature is an important factor affecting its basic mechanical properties. To investigate the failure mechanism and stress–strain relationship of recombinant bamboo at different temperatures, eighteen tensile specimens of recombinant bamboo were tested. The results showed that with increasing ambient temperature, the typical failure modes of recombinant bamboo were flush fracture, toothed failure, and serrated failure. The ultimate tensile strength, ultimate strain and elastic modulus of recombinant bamboo decreased with increasing temperature, and the ultimate tensile stress decreased from 154.07 to 96.55 MPa, a decrease of 37.33%, and the ultimate… More > Graphic Abstract

Jiachen Li^1,2, Wenlong Deng³, Shan Qing^1,2,*, Yiqin Liu⁴, Hao Zhang^1,2, Min Zheng^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2181-2200, 2023, DOI:10.32604/fdmp.2023.027299

Abstract In this study, comparing multiple models of machine learning, a multiple linear regression (MLP), multilayer feed-forward artificial neural network (BP) model, and a radial-basis feed-forward artificial neural network (RBF-BP) model are selected for the optimization of the thermal properties of TiO₂/water nanofluids. In particular, the least squares support vector machine (LS-SVM) method and radial basis support vector machine (RB-SVM) method are implemented. First, curve fitting is performed by means of multiple linear regression in order to obtain bivariate correlation functions for thermal conductivity and viscosity of the nanofluid. Then the aforementioned models are used for a predictive analysis of the… More >

R. Surendran^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3371-3386, 2023, DOI:10.32604/csse.2023.034465

Abstract High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model… More >

Ehsan Momeni¹, Biao He², Yasin Abdi^3,*, Danial Jahed Armaghani⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2527-2550, 2023, DOI:10.32604/cmes.2023.026531

Abstract When building geotechnical constructions like retaining walls and dams is of interest, one of the most important factors to consider is the soil’s shear strength parameters. This study makes an effort to propose a novel predictive model of shear strength. The study implements an extreme gradient boosting (XGBoost) technique coupled with a powerful optimization algorithm, the salp swarm algorithm (SSA), to predict the shear strength of various soils. To do this, a database consisting of 152 sets of data is prepared where the shear strength (τ) of the soil is considered as the model output and some soil index tests… More >

Yuxin Chen¹, Weixun Yong¹, Chuanqi Li², Jian Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2507-2526, 2023, DOI:10.32604/cmes.2023.025714

Abstract After the excavation of the roadway, the original stress balance is destroyed, resulting in the redistribution of stress and the formation of an excavation damaged zone (EDZ) around the roadway. The thickness of EDZ is the key basis for roadway stability discrimination and support structure design, and it is of great engineering significance to accurately predict the thickness of EDZ. Considering the advantages of machine learning (ML) in dealing with high-dimensional, nonlinear problems, a hybrid prediction model based on the random forest (RF) algorithm is developed in this paper. The model used the dragonfly algorithm (DA) to optimize two hyperparameters… More >