Jing Li¹, Bo Ning^1,*, Bo Li², Xuemei Jin¹, Dezhi Qiu¹, Fenlan Ou¹

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.074335 - 06 February 2026

Abstract As a controllable power generation method requiring no energy storage, Ocean Thermal Energy Conversion (OTEC) technology demonstrates characteristics of abundant reserves, low pollution, and round-the-clock stable operation. The free-standing cold-water pipe (CWP) in the system withstands various complex loads during operation, posing potential failure risks. To reveal the deformation and stress mechanisms of OTEC CWPs, this study first analyzes wave particle velocity and acceleration to determine wave loads at different water depths. Based on the Euler-Bernoulli beam model, a quasi-static load calculation model for OTEC CWPs was established. The governing equations were discretized using the… More >

Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Burhan Baraklı^1,*, Can Yüzkollar², Tuğrul Taşçı³, İbrahim Yıldırım²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074292 - 29 January 2026

Abstract Salient object detection (SOD) models struggle to simultaneously preserve global structure, maintain sharp object boundaries, and sustain computational efficiency in complex scenes. In this study, we propose SPSALNet, a task-driven two-stage (macro–micro) architecture that restructures the SOD process around superpixel representations. In the proposed approach, a “split-and-enhance” principle, introduced to our knowledge for the first time in the SOD literature, hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions. At the macro stage, the image is partitioned into content-adaptive superpixel regions, and each superpixel is represented by a high-dimensional region-level… More >

Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073533 - 29 January 2026

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >

Zhirong Jin^1,*, Xiaorui Hou¹, Yanrong Ge¹, Tiankui Guo², Ming Chen², Shuyi Li², Tianyu Niu²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070750 - 27 January 2026

Abstract Hydraulic fracturing serves as a critical technology for reservoir stimulation in deep coalbed methane (CBM) development, where the mechanical properties of gangue layers exert a significant control on fracture propagation behavior. To address the unclear mechanisms governing fracture penetration across coal-gangue interfaces, this study employs the Continuum-Discontinuum Element Method (CDEM) to simulate and analyze the vertical propagation of hydraulic fractures initiating within coal seams, based on geomechanical parameters derived from the deep Benxi Formation coal seams in the southeastern Ordos Basin. The investigation systematically examines the influence of geological and operational parameters on cross-interfacial fracture… More >

Li Wang¹, Xuesong Xing¹, Yanan Hou¹, Heng Wen¹, Ying Zhu¹, Jingyu Zi¹, Qingwei Zeng^2,3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.068653 - 27 January 2026

Abstract The deep coal reservoir in Linxing-Shenfu block of Ordos Basin is an important part of China’s coalbed methane resources. In the process of reservoir reconstruction, the artificial fracture morphology of coal seam with gangue interaction is significantly different, which affects the efficient development of coalbed methane resources in this area. In this paper, the surface outcrop of Linxing-Shenfu block is selected, and three kinds of interaction modes between gangue and coal seam are set up, including single-component coal rock sample, coal rock sample with different thicknesses of gangue layer and coal rock sample with different… More >

Hosam O. Elansary¹, Naveed A. Noor², Syed M. Ahmad³, Humza Riaz³, Sohail Mumtaz^4,*

Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076592 - 26 January 2026

Abstract Ferrites are remarkable compounds for energy harvesting and spintronic applications. For this purpose, mechanically stable, thermodynamic, photo-catalytic, and ferromagnetic characteristics of ferrites Al₂Mn(S/Se)₄ have been investigated significantly using PBEsol-GGA and modified Becke Johnson potential (TB-mBJ). In order to determine structural stability, we calculate formation energy (E_f) and Born stability criteria that confirm the structural stability of the Al₂Mn(S/Se)₄. 2D and 3D plots of Poisson’s ratio (υ) and linear compressibility are also used to indicate the stability of these materials. Additionally, thermodynamic characteristics reveal that both ferrites are stable. Spin-polarized electronic properties indicate that both ferrites are ferromagnetic More >

Francisco Daniel García^1,2, Solange Nicole Aigner^1,2, Natalia Raffaeli³, Antonio José Barotto³, Eleana Spavento³, Mariano Martín Escobar^1,4, Marcela Angela Mansilla^1,4, Alejandro Bacigalupe^1,4,*

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0181 - 23 January 2026

Abstract This study explores the use of black soldier fly larvae protein as a bio-based adhesive to produce particleboards from sugarcane bagasse. A comprehensive evaluation was conducted, including rheological characterization of the adhesive and physical–mechanical testing of the panels according to European standards. The black soldier fly larvae-based adhesive exhibited gel-like viscoelastic behavior, rapid partial structural recovery after shear, and favorable application properties. Particleboards manufactured with this adhesive and sugarcane bagasse achieved promising mechanical performance, with modulus of rupture and modulus of elasticity values of 30.2 and 3500 MPa, respectively. Internal bond strength exceeded 0.4 MPa,… More > Graphic Abstract

Herlina Marta¹, Novita Indrianti^2,*, Allifiyah Josi Nur Aziza³, Enny Sholichah⁴, Titik Budiati³, Achmat Sarifudin⁵, Yana Cahyana¹, Nandi Sukri¹, Aldila Din Pangawikan¹

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0145 - 23 January 2026

Abstract Corn starch (CS) is a renewable, biodegradable polysaccharide valued for its film-forming ability, yet native CS films exhibit low mechanical strength, high water sensitivity, and limited thermal stability. This study improves CS-based films by blending with poly(vinyl alcohol) (PVA) or glycerol (GLY) and using citric acid (CA) as a green, non-toxic cross-linker. Composite films were prepared by casting CS–PVA or CS–GLY with CA at 0%–0.20% (w/w of starch). The influence of CA on physicochemical, mechanical, optical, thermal, and water barrier properties was evaluated. CA crosslinking markedly enhanced the tensile strength, water resistance, and thermal stability More > Graphic Abstract