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 >

Huiling Yu¹, Xibei Jia², Yongfeng Niu¹, Yizhuo Zhang^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4329-4352, 2025, DOI:10.32604/cmc.2025.061882 - 19 May 2025

Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >

Hao Li¹, Cunlu Zhao², Jinhui Zhou¹, Jun Zhang³, Hui Wang¹, Yanmei Jiao^1,*, Yugang Zhao^4,5,*

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 507-528, 2025, DOI:10.32604/fhmt.2025.063359 - 25 April 2025

Abstract Based on the rapid advancements in nanomaterials and nanotechnology, the Nanofluidic Reverse Electrodialysis (NRED) has attracted significant attention as an innovative and promising energy conversion strategy for extracting sustainable and clean energy from the salinity gradient energy. However, the scarcity of research investigating the intricate multi-factor coupling effects on the energy conversion performance, especially the trade-offs between ion selectivity and mass transfer in nanochannels, of NRED poses a great challenge to achieving breakthroughs in energy conversion processes. This numerical study innovatively investigates the multi-factor coupling effect of three critical operational factors, including the nanochannel configuration,… More >

Bo Yu^1,2, Yuye Luo³, Luyao Guo⁴, Long Huang^4,*

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 553-575, 2025, DOI:10.32604/fhmt.2025.062851 - 25 April 2025

Abstract This study investigates the flow boiling heat transfer coefficient and pressure gradient of refrigerant R410A in micro-channel flat tubes. Experiments were conducted at saturation temperatures ranging from 25°C to 30°C, mass fluxes between 198 and 305 kg/m²s, and heat fluxes from 9.77 to 20.18 kW/m², yielding 99 sets of local heat transfer coefficient data. The results show that increasing heat flux and mass flux enhances the heat transfer coefficient, although the rate of enhancement decreases with increasing vapor quality. Conversely, higher saturation temperatures slightly reduce the heat transfer coefficient. Additionally, the experimental findings reveal discrepancies in More >

Chao Zhang¹, Runze Yan¹, Honghui Li¹, Qingheng Tang¹, Qinghai Zhao^1,2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 421-440, 2025, DOI:10.32604/fhmt.2025.061469 - 25 April 2025

Abstract The growing need for enhanced heat dissipation is compelling the development of more effective heat exchangers. Innovation inspired by nature bionics, four types of leaf-shaped pin fins were proposed and four combinations of them were considered. The leaf-shaped design of the cooling pin fin enhances uniformity and synergy, effectively creating an optimized flow path that boosts cooling performance. Eight three-dimensional conjugate heat transfer models in staggered arrangement were developed using ANSYS-Fluent software. Aluminum 6061 material was used as the heat sink material and single-phase liquid water flowed through the rectangular channel where the Reynolds (Re) number… More >

Shervin Fateh Khanshir¹, Saeed Dinarvand^2,*, Ramtin Fateh Khanshir³

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 663-684, 2025, DOI:10.32604/fhmt.2025.060559 - 25 April 2025

Abstract This article aims to model and analyze the heat and fluid flow characteristics of a carboxymethyl cellulose (CMC) nanofluid within a convergent-divergent shaped microchannel (Two-dimensional). The base fluid, water + CMC (0.5%), is mixed with CuO and Al₂O₃ nanoparticles at volume fractions of 0.5% and 1.5%, respectively. The research is conducted through the conjugate usage of experimental and theoretical models to represent more realistic properties of the non-Newtonian nanofluid. Three types of microchannels including straight, divergent, and convergent are considered, all having the same length and identical inlet cross-sectional area. Using ANSYS FLUENT software, Navier-Stokes equations… More > Graphic Abstract

Xiaoying Qiu^1,*, Xiaoyu Ma¹, Guangxu Zhao¹, Jinwei Yu², Wenbao Jiang¹, Zhaozhong Guo¹, Maozhi Xu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2025-2040, 2025, DOI:10.32604/cmc.2025.061869 - 16 April 2025

Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >

Veronika Dyakova^1,2,*, Olga Vlasova¹, Victor Kozlov¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.3, pp. 493-508, 2025, DOI:10.32604/fdmp.2025.060577 - 01 April 2025

Abstract An experimental investigation of the dynamics of the interface between two low-viscosity fluids with high density contrast oscillating in a fixed vertical slotted channel has been conducted. It has been found that as the amplitude of the liquid column oscillations increases, parametric oscillations of the interface are excited in the form of a standing wave located in the channel plane. In particular, depending on the interfacial tension, the standing waves have a frequency equal to that of liquid piston oscillations (harmonic response), or half of the frequency of oscillations of the liquid column in the… More >

Jia Liu¹, Hang Gu¹, Fangmei Liu¹, Hao Chen¹, Zuhe Li¹, Gang Xu², Qidong Liu², Wei Wang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 803-822, 2025, DOI:10.32604/cmc.2025.060966 - 26 March 2025

Abstract In recent years, convolutional neural networks (CNN) and Transformer architectures have made significant progress in the field of remote sensing (RS) change detection (CD). Most of the existing methods directly stack multiple layers of Transformer blocks, which achieves considerable improvement in capturing variations, but at a rather high computational cost. We propose a channel-Efficient Change Detection Network (CE-CDNet) to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection. The adaptive multi-scale feature fusion module (CAMSF) and lightweight Transformer decoder (LTD) are introduced to improve the change detection More >