Rabindra Nath Mondal¹, Giulio Lorenzini^2,*, Sidhartha Bhowmick¹, Sreedham Chandra Adhikari³

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 249-278, 2025, DOI:10.32604/fhmt.2024.057990 - 26 February 2025

Abstract The present study investigates the non-isothermal flow and energy distribution through a loosely bent rectangular duct using a spectral-based numerical approach over a wide range of the Dean number . Unlike previous research, this work offers novel insights by conducting a grid-point-specific velocity analysis and identifying new bifurcation structures. The study reveals how centrifugal and buoyancy forces interact to produce steady, periodic, and chaotic flow regimes significantly influencing heat transfer performance. The Newton-Raphson method is employed to explore four asymmetric steady branches, with vortex solutions ranging from 2- to 12 vortices. Unsteady flow characteristics are… More >

Wenkai Zhang^1,2, Hao Zhang^1,2, Xianming Liu¹, Xiaoyu Guo^1,2, Xinzhe Wang¹, Shuiwang Li^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000 - 17 February 2025

Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

Jiajia Liu^1,*, Peng Xie², Wei Li², Bo Tang², Jianhua Liu²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2609-2635, 2025, DOI:10.32604/cmc.2024.058810 - 17 February 2025

Abstract As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective… More >

Murun Yang^1,*, Fuxue Li²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2061-2075, 2025, DOI:10.32604/cmc.2024.058248 - 17 February 2025

Abstract Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a… More >

Song Liu^1,2, Hongying Zhang^1,*, Xue Li¹, Xi Yang^1,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1969-1984, 2025, DOI:10.32604/cmc.2024.057662 - 17 February 2025

Abstract Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels… More >

Shaozheng Zhang, Qiuyu Zhang^*, Jiahui Tang, Ruihua Xu

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2137-2158, 2025, DOI:10.32604/cmc.2024.057269 - 17 February 2025

Abstract Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a… More >

Kai Liu¹, Meizhao Liu¹, Ming Tang¹, Chen Zhang^2,*, Junwu Zhu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2893-2908, 2025, DOI:10.32604/cmc.2024.057074 - 17 February 2025

Abstract The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to More >

Ibrahim Akinjobi Aromoye¹, Hai Hiung Lo¹, Patrick Sebastian¹, Ghulam E Mustafa Abro^2,*, Shehu Lukman Ayinla^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1155-1197, 2025, DOI:10.32604/cmes.2025.058598 - 27 January 2025

Abstract Unmanned aerial vehicles (UAVs) technology is rapidly advancing, offering innovative solutions for various industries, including the critical task of oil and gas pipeline surveillance. However, the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring, which is crucial for maintaining the integrity of pipeline infrastructure. This review paper evaluates methods for extending UAV flight endurance, focusing on their potential application in pipeline inspection. Through an extensive literature review, this study identifies the latest advancements in UAV technology, evaluates their effectiveness, and highlights the existing gaps in achieving prolonged flight… More > Graphic Abstract

Saeed Dinarvand^*, Amirmohammad Abbasi, Sogol Gharsi

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 37-60, 2025, DOI:10.32604/fdmp.2024.057545 - 24 January 2025

Abstract The present review explores the promising role of nanofluids and related hybrid variants in enhancing the efficiency of flat tube car radiators. As vehicles become more advanced and demand better thermal performance, traditional coolants are starting to fall short. Nanofluids, which involve tiny nanoparticles dispersed into standard cooling liquids, offer a new solution by significantly improving heat transfer capabilities. The article categorizes the different types of nanofluids (ranging from those based on metals and metal oxides to carbon materials and hybrid combinations) and examines their effects on the improvement of radiator performance. General consensus More >

Manisara Phiriyawirut^*, Pukrapee Rodprasert, Peerapat Kulvorakulpitak, Ratiwan Cothsila, Nattarat Kengkla

Journal of Renewable Materials, Vol.13, No.1, pp. 101-114, 2025, DOI:10.32604/jrm.2024.056830 - 20 January 2025

Abstract This work explores the development of biodegradable laminar composite foams for cushioning applications. The focus lies on overcoming the inherent brittleness of starch foams by incorporating various paper types as reinforcement. Tapioca starch and glutinous starch were blended in varying ratios (100:0–0:100) to optimize the base material’s properties. The morphology, density, flexural strength, and impact strength of these starch blends were evaluated. The results revealed a trade-off between impact strength and density, with increasing glutinous starch content favoring impact resistance but also leading to higher density. The optimal ratio of tapioca to glutinous starch for… More > Graphic Abstract