Zhibo Song^1,*, Hao Yang¹, Wei Shen², Yixin Ren¹, Yawei Li³, Jianlong Ma¹

Energy Engineering, Vol.120, No.9, pp. 2097-2113, 2023, DOI:10.32604/ee.2023.029115

Abstract Pollution flashover is one of the major insulation accidents that can occur in modern power systems. If the local arc is not sufficiently elongated by the creepage distance, the flashover performance of the insulator will decrease. The insulator structure and equivalent salt deposition density (ESDD) are the main factors affecting the arc path and length. To investigate the forms of arc paths under different insulator structures and their influence on flashover characteristics, a platform for recording arc paths is established. This study presents a method to identify the pattern and distribution of insulator arc paths based on the effect of… More >

Zaihe Cheng¹, Yuwen Tao², Xiaoqing Gu³, Yizhang Jiang², Pengjiang Qian^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1613-1633, 2023, DOI:10.32604/cmes.2023.027708

Abstract Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-Kang (TSK) fuzzy system framework is proposed for epilepsy data classification in this study. The new method is based on the maximum mean discrepancy (MMD) method and TSK fuzzy system, as a basic model for the classification of epilepsy data. First, for medical data, the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe. Second, in view of the deviation in the data distribution between the real source domain and the target domain, MMD is used to measure the distance between dierent data distributions. The objective… More >

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional… More >

Tusongjiang Kari¹, Sun Guoliang², Lei Kesong¹, Ma Xiaojing^1,*, Wu Xian¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >

Deye Jiang¹, Yiguang Wang^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 295-309, 2023, DOI:10.32604/cmc.2023.039244

Abstract In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We… More >

Esraa E. Shalan¹, Said S. Soliman¹, Ahmed A. Mahmoud¹, Jameel M. Al-Khayri^2,*, Salha M. ALshamrani³, Fatmah A. Safhi⁴, Areej S. Jalal⁴, Diaa Abd El-Moneim⁵, Abdallah A. Hassanin^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.7, pp. 2183-2196, 2023, DOI:10.32604/phyton.2023.028537

Abstract Determination of the somaclonal variation of in vitro-propagated plants is crucial to determine the appropriate micropropagation protocol and growth regulators for commercial scale multiplication. In this research, nine multiplication media (MM) augmented with different concentrations of 6-benzyl adenine (BA), Kinetin (Kin), and Thidiazuron (TDZ), Three rooting media (RM) supplemented with three levels of α-naphthalene acetic acid (NAA) and three types of soil mixtures (v/v); Coco peat/Vermiculite/Sand (CVS), Peat moss/Perlite/Sand (PPS) and Peat moss/Perlite (PP) were used in the micropropagation protocol of daylily plants. MM2 showed the maximum shoot length and the number of leaves, while MM9 showed the maximum number… 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 >

Aravind Athimoolam^1,*, Karthik Balasubramanian²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1331-1347, 2023, DOI:10.32604/iasc.2023.033465

Abstract This paper focuses on the development of a diagnostic tool for detecting insulated gate bipolar transistor power electronic switch flaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications. High-resolution laboratory virtual instrument engineering workbench software testing tool with a sample rate data collection system, as well as specialized signal processing and soft computing technologies, are used in this proposed method. On a single-phase cascaded H-bridge multilevel inverter, simulation and experimental investigations of both open and short issues of the insulated gate bipolar transistor components are performed out. In all conceivable switch issues, the… More >

WENLI ZENG¹, FENG LING², KAINUO DANG³, QINGJIA CHI^3,*

BIOCELL, Vol.47, No.3, pp. 581-592, 2023, DOI:10.32604/biocell.2023.025957

Abstract Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast… More >

Naret Ruttanaprommarin¹, Zulqurnain Sabir^2,3, Rafaél Artidoro Sandoval Núñez⁴, Emad Az-Zo’bi⁵, Wajaree Weera⁶, Thongchai Botmart^6,*, Chantapish Zamart⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5915-5930, 2023, DOI:10.32604/cmc.2023.034362

Abstract The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3^rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons have been used based… More >