Xinfei Li², Xiaolan Xie^1,2,*, Yigang Tang², Qiang Guo^1,2

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2707-2724, 2023, DOI:10.32604/csse.2023.037351

Abstract Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems. The permutation entropy is used… More >

Yafei Hu^1,2, Keqing Li^1,2, Bo Zhang^1,2, Bin Han^1,2,*

Journal of Renewable Materials, Vol.11, No.9, pp. 3463-3484, 2023, DOI:10.32604/jrm.2023.027532

Abstract The super-fine particle size of tailings is its drawback as a recycled resource, which is reflected in the low strength of the new construction and industrial materials formed when it is mixed with cement and other cementitious materials. Therefore, it is crucial to study the effect of tailings particle size and cementitious material on the strength of tailings wet shotcrete (TWSC) and to investigate the optimal mix proportion. In this paper, a multivariate nonlinear response model was constructed by conducting central composite experiments to investigate the effect of different factors on the strength of TWSC. The strength prediction and mix… More >

Yongdong Yan^*, Youdong Si, Chunhua Lu, Keke Wu

Structural Durability & Health Monitoring, Vol.17, No.3, pp. 225-238, 2023, DOI:10.32604/sdhm.2022.022629

Abstract This research aims to investigate the effect of frost damage on chloride transportation mechanism in ordinary and fiber concrete with both theoretical and experimental methods. The proposed theoretical model takes into account the varying damage levels caused by concrete cover depth and freeze-thaw cycles, which are the two primary parameters affecting the expression of the chloride diffusion coefficient. In the experiment, three types of concrete were prepared: ordinary Portland concrete (OPC), polypropylene fiber concrete (PFC), and steel fiber concrete (SFC). These were then immersed in NaCl solution for 120 days after undergoing 10, 25, and 50 freeze-thaw cycles. The damage… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Amel Ali Alhussan^1,*, Marwa M. Eid³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

WEI LIANG^1,2,*, ZONGWEI ZHANG^1,2, KEJU YANG^1,2,3, HONGTU HU^1,2, QIANG LUO^1,2, ANKANG YANG^1,2, LI CHANG⁴, YUANYUAN ZENG⁴

BIOCELL, Vol.47, No.6, pp. 1253-1263, 2023, DOI:10.32604/biocell.2023.027373

Abstract Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes related to DN tubular… More >

Wenhua Xu^1,3, Yuming Zhu², Yingrong Wei², Ya Su², Yan Xu^1,3, Hui Ji¹, Dehua Liu^1,3,*

Energy Engineering, Vol.120, No.7, pp. 1701-1715, 2023, DOI:10.32604/ee.2023.027703

Abstract Drilling costs of ultra-deep well is the significant part of development investment, and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost. In order to improve the prediction accuracy of ultra-deep well drilling costs, the item and the dominant factors of drilling costs in Tarim oilfield are analyzed. Then, those factors of drilling costs are separated into categorical variables and numerous variables. Finally, a BP neural network model with drilling costs as the output is established, and hyper-parameters (initial weights and bias) of the BP neural network is optimized by genetic… More >

Salah Alzghoul^1,*, Othman Smadi¹, Ali Al Bataineh², Mamon Hatmal³, Ahmad Alamm⁴

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338

Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or… More >

Shekaina Justin^1,*, Wafaa Saleh^1,2, Tasneem Al Ghamdi¹, J. Shermina³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1091-1109, 2023, DOI:10.32604/iasc.2023.032589

Abstract Renewable energy has become a solution to the world’s energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids. With unparalleled data granularity, a data-driven… 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

Rongge Xiao^a,* , Qi Zhuang^a, Shuaishuai Jin^a , Wenbo Jin^a

Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-7, 2022, DOI:10.5098/hmt.18.8

Abstract A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using Whale Optimization Algorithm (WOA) to optimize BPNN model(WOABPNN).Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area… More >