Guangfei Jia^*, Jinqiu Yang, Hanwen Liang

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1057-1072, 2025, DOI:10.32604/sdhm.2025.061805 - 30 June 2025

Abstract Considering the noise problem of the acquisition signals from mechanical transmission systems, a novel denoising method is proposed that combines Variational Mode Decomposition (VMD) with wavelet thresholding. The key innovation of this method lies in the optimization of VMD parameters K and using the improved Horned Lizard Optimization Algorithm (IHLOA). An inertia weight parameter is introduced into the random walk strategy of HLOA, and the related formula is improved. The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions (IMFs), and the high-noise IMFs are identified based on a correlation coefficient-variance method. Further noise… More > Graphic Abstract

Alexey Melkikh^1,2, Sergey Rutin², Dmitrii V. Antonov³, Pavel Skripov^2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 721-737, 2025, DOI:10.32604/fhmt.2025.066528 - 30 June 2025

Abstract The article considers a relaxation of the water/polypropylene glycol-425 solution with a lower critical solution temperature (LCST) following its pulsed superheating concerning liquid-liquid and liquid-vapor equilibrium lines, as well as the liquid-liquid spinodal. Superheating was performed using the pulsed heat generation method in a micro-sized wire probe. The main heating mode was the constant (over the pulse length) power mode. Characteristic heating rates ranged from 0.05 × 10⁵ to 2 × 10⁵ K/s, while the degree of superheating concerning the spinodal was up to 200 K. The temperature of spontaneous boiling-up and the amplitude of the… More > Graphic Abstract

Stanislav Aladev^1,*, Dmitry Sokolov¹, Maxim Politko¹, Galina Kazanskaya¹, Svetlana Aidagulova^1,2, Elvira Grigorieva^1,3

BIOCELL, Vol.49, No.6, pp. 1057-1069, 2025, DOI:10.32604/biocell.2025.064100 - 24 June 2025

Abstract Background: Glucocorticoids are used as anti-inflammatory drugs for the treatment of various diseases, however, their side effects on normal brain tissue remain underinvestigated. Objectives: The study aimed to investigate dexamethasone (DXM) effects on cell composition and myelin content in the mouse brain tissue. Methods: C57Bl/6 male mice (n = 60) received single and ten multiple intraperitoneal DXM injections (2.5 mg/kg), and the studied parameters were analysed at 1, 3, 7, 10 days after a single DXM injection and 15, 30, 60, and 90 days after the multiple injections. Oligodendrocytes, microglia, and astrocytes were assayed by immunohistochemistry… More >

Mingliang Deng¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1189-1202, 2025, DOI:10.32604/cmc.2025.064509 - 09 June 2025

Abstract To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes… More >

Hanqing Sun¹, Xue Li^2,*, Qiyuan Fan³, Puming Wang³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1659-1679, 2025, DOI:10.32604/cmc.2025.061426 - 09 June 2025

Abstract The era of big data brings new challenges for information network systems (INS), simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems. In this work, we propose a data-driven intrusion detection system for Distributed Denial of Service (DDoS) attack detection. The system focuses on intrusion detection from a big data perceptive. As intelligent information processing methods, big data and artificial intelligence have been widely used in information systems. The INS system is an important information system in cyberspace. In advanced INS systems, the network architectures have become more complex. And the smart devices in… More >

Wencheng Wang^1,2,*, Baoxin Yin^1,2, Lei Li^2,*, Lun Li¹, Hongtao Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1595-1616, 2025, DOI:10.32604/cmes.2025.063595 - 30 May 2025

Abstract In low-light environments, captured images often exhibit issues such as insufficient clarity and detail loss, which significantly degrade the accuracy of subsequent target recognition tasks. To tackle these challenges, this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis. The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image, followed by image segmentation using a quadtree method. To improve the accuracy and robustness of atmospheric light estimation, the algorithm incorporates a genetic algorithm to optimize the… More >

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025

Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

Mengjiao Wei^1,*, Wenyu Liu²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2505-2523, 2025, DOI:10.32604/cmc.2025.060887 - 16 April 2025

Abstract In recent years, decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios. In these algorithms, the reference vectors of the Penalty-Based boundary intersection (PBI) are distributed parallelly while those based on the normal boundary intersection (NBI) are distributed radially in a conical shape in the objective space. To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications, this paper addresses the improvement of the Collaborative Decomposition (CoD) method, a multi-objective decomposition technique that integrates PBI and NBI, and combines it with the Elephant Clan Optimization Algorithm, introducing the… More >

Chaozhi Cai, Yuqi Ren, Yingfang Xue^*, Jianhua Ren

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >