Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072343 - 12 January 2026

Abstract Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes. This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) architecture, optimized using the Firefly Optimization algorithm (FO). The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data, capturing both local patterns and sequential dependencies in diagnostic features, while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance. The approach is evaluated on two benchmark biomedical datasets: one comprising diagnostic data… More >

Qian Yu^1,2, Gui Zhang^2,*, Ying Wang¹, Xin Wu², Jiangshu Xiao², Wenbing Kuang¹, Juan Zhang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072172 - 12 January 2026

Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >

Qing Long¹, Bing Yi², Haiqiao Liu^3,*, Zhiling Peng¹, Xiang Liu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072058 - 12 January 2026

Abstract Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring. However, conventional detection approaches are highly susceptible to noise, illumination variations, and complex environmental conditions, which often reduce detection accuracy and real-time performance. To address these limitations, we propose Lightweight and Precise YOLO (LP-YOLO), a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid, built upon YOLOv8. First, to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks (CNNs), we design an enhanced backbone based on Wavelet Convolutions (WTConv), which expands the… More >

Wu Feng^1,2,*, Tengku Anita Raja Hussin¹, Xu Yang³

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071664 - 08 January 2026

Abstract This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700°C by integrating experimental testing and advanced numerical modeling. A temperature-indexed Concrete Damage Plasticity (CDP) framework incorporating bond–slip effects was developed in Abaqus to capture both global stress–strain responses and localized damage evolution. Uniaxial compression tests on thermally exposed cylinders provided residual strength data and failure observations for model calibration and validation. Results demonstrated a distinct two-stage degradation regime: moderate stiffness and strength reduction up to ~400°C, followed by sharp deterioration beyond 500°C–600°C, with residual capacity at… More >

Zezhi Wen¹, Kai Zhang¹, Shanlin Liang², Liqiong Chen^1,*, Zijian Xiong¹

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.066408 - 08 January 2026

Abstract Maintaining the structural integrity of parallel natural gas pipelines during leakage-induced jet fires remains a critical engineering challenge. Existing methods often fail to account for the complex interactions among heat transfer, material behavior, and pipeline geometry, which can lead to overly simplified and potentially unsafe assessments. To address these limitations, this study develops a multiphysics approach that integrates small-orifice leakage theory with detailed thermo-fluid-structural simulations. The proposed framework contributes to a more accurate failure analysis through three main components: (1) coupled modeling that tracks transient heat flow and stress development as fire conditions evolve; (2)… More >

Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

Shengzhong Zhao¹, Daiyan Chen¹, Han Zhang^1,2,*, Junhao Yu¹, Lin Xu¹, Zhaoyi Zhuang¹, Fei Wang^1,*

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1907-1932, 2025, DOI:10.32604/fhmt.2025.071910 - 31 December 2025

Abstract A sealed portal could significantly alter the flame shape and smoke flow characteristics in inclined tunnel fires. In inclined tunnels, two typical sealing conditions could be defined, namely the upper portal sealed and the lower portal sealed. In this study, the effects of tunnel slope on flame shape, flame length, along with smoke mass flow rate and induced velocity at the tunnel portal, are numerically investigated. The results show that, in all scenarios, flames initially rise vertically but tilt toward the sealed portal during the quasi-steady stage, with the largest tilt angle observed in tunnels… More >

Hongrong Wang^1,2, Hang Geng^1,*, Jing Yuan¹, Wen Zhang², Hanmin Sheng¹, Qiuhua Wang³, Xinjian Li^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3701-3721, 2025, DOI:10.32604/cmes.2025.073513 - 23 December 2025

Abstract Machine learning has emerged as a key approach in wildfire risk prediction research. However, in practical applications, the scarcity of data for specific regions often hinders model performance, with models trained on region-specific data struggling to generalize due to differences in data distributions. While traditional methods based on expert knowledge tend to generalize better across regions, they are limited in leveraging multi-source data effectively, resulting in suboptimal predictive accuracy. This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data. An active More >

Ilze Irbe^1,*, Laura Andze¹, Inese Filipova^1,2

Journal of Renewable Materials, Vol.13, No.11, pp. 2139-2154, 2025, DOI:10.32604/jrm.2025.02025-0107 - 24 November 2025

Abstract Growing environmental concerns and the need for sustainable alternatives to synthetic materials have led to increased interest in bio-based composites. This study investigates the development and characterization of sustainable egg packaging waste (EPW) biocomposites derived from recycled wood fibers and fungal mycelium filaments as a natural binder. Three formulations were prepared using EPW as the primary substrate, with and without the addition of hemp shives and sawdust as co-substrates. The composites were evaluated for granulometry, density, mechanical strength, hygroscopic behavior, thermal conductivity, and fire performance using cone calorimetry. Biocomposites, composed exclusively of egg packaging waste,… More >

Fahd N. Al-Wesabi^1,*, Hamad Almansour², Huda G. Iskandar^3,4, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5765-5787, 2025, DOI:10.32604/cmc.2025.067571 - 23 October 2025

Abstract Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired (BVI) individuals in a limited timeframe in the event of emergencies, particularly in enclosed areas. Fire detection becomes crucial as it directly impacts human safety and the environment. While modern technology requires precise techniques for early detection to prevent damage and loss, few research has focused on artificial intelligence (AI)-based early fire alert systems for BVI individuals in indoor settings. To prevent such fire incidents, it is crucial to identify fires accurately and promptly, and alert BVI personnel… More >