Guangyu Xu^1,2, Yuxi Ban¹, Legend Zhang³, Junmin Lyu³, Feng Bao⁴, Wenfeng Zheng^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085484 - 27 July 2026

Abstract High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence (GeoAI). Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation, while attention- and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities. To address these limitations, this study proposes a Multiscale Long-Distance Feature Aggregation Network (MLFANet), a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery. MLFANet introduces three key components: a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement, an FFT-based frequency-domain branch with learnable global filtering for… More >

Bingqi Gui^1,2, Shuheng Lu^1,2, Yihui Zhu^1,2,*, Zhuangqing Fan^3,4, Haiwen Sun⁵, Xuefei Yan⁶, Ang Wang⁷, Wei Pang⁷, Wenchao Chen⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084643 - 27 July 2026

Abstract Body armor prevents projectile penetration, but thoracic visceral injury may still result from pressure-wave transmission and back-face deformation. However, thoracic responses to single and multiple impacts remain insufficiently understood, numerical human thoracic models need further validation, and load transfer between the rib cage and cardiopulmonary system remains unclear. Therefore, this study combined live-fire blunt-impact tests on a biomimetic thoracic target with a human thoracic finite element model with filled thoracic cavity gaps. Load transfer and cardiopulmonary responses under non-penetrating rifle-bullet impact were investigated using a SiC/UHMWPE composite ballistic insert. The results showed that, first, under… More >

Aoran Huang^1,2,*, Sinuo Zhang^1,2, Haoxiang Zhu^1,2, Xiaojing Fan^1,2, Huachun Zhou^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083972 - 27 July 2026

Abstract In Future Mobile Internet and convergence application scenarios, existing network attack traffic detection methods are insufficient in characterizing cross-flow correlations and structural dependencies during the attack process, and therefore still have limited generalization ability in complex scenarios and unknown attack identification tasks. To address this issue, this paper proposes a cross-flow modeling large language model framework, which extends the traditional detection paradigm based on single-flow features to joint modeling oriented toward cross-flow context and relational structure. Specifically, this paper constructs cross-flow context through flow sorting, grouping, and cross-group sampling, and combines an inter-flow relation matrix… More >

Mohammed Ansar Mohammed Manaz^1,*, Shang Ping Hong², Tzung-Lin Lee¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083270 - 27 July 2026

Abstract Silicon Carbide Metal Oxide Semiconductor Field Effect Transistors (SiC MOSFETs) have superior characteristics compared to traditional Silicon-based switching devices. SiC devices can support fast switching speeds and high blocking voltages. Due to limited historical data and rapid technological improvements, there is not enough field data to correctly evaluate the reliability of the state-of-the-art SiC MOSFETs. An accurate model of their reliability and aging characteristics is needed to expedite their rapid commercial adoption in mission-critical applications, such as offshore wind farms and electric vehicles. Classical handbook-based methods produce large errors due to their inability to correctly… More >

Md. Fahmid-Ul-Alam Juboraj¹, Fahmid Al Farid^2,3, Mahe Zabin⁴, Jia Uddin⁵, Muhammad Iqbal Hossain^1,*, Sarina Mansor^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081254 - 27 July 2026

Abstract The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves More >

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026

Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

Dominick Shoha^#, David Lei^#, Tyler Truong, Sophia Strukel, Elliot Enshaie, Vikrant Rai^*

BIOCELL, Vol.50, No.8, 2026, DOI:10.32604/biocell.2026.078846 - 27 July 2026

Abstract Diabetes, inflammation, and neurodegeneration, particularly Alzheimer’s disease (AD), are deeply interconnected (brain diabetes). Type 2 diabetes mellitus (T2DM) acts as a significant risk factor for neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s (PD) by inducing chronic inflammation, oxidative stress, and metabolic dysfunction. Hyperglycemia drives neuroinflammation and damages the blood-brain barrier (BBB), exacerbating cognitive decline and neuronal loss. Chronic inflammation acts as a central bridge, linking high blood sugar, insulin resistance, and metabolic dysfunction in the brain to the buildup of amyloid plaques, tau tangles, and neuronal damage due to shared insulin signaling issues in the More >

Yixiang Wan, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084474 - 23 July 2026

Abstract Addressing the two critical challenges of missed detection of distant small targets and difficulty in identifying occluded targets under complex road conditions, this paper proposes YOLO-PBE, an improved high-precision vehicle detection model based on YOLOv11n. First, to tackle the fine-grained feature loss caused by conventional strided convolutions during downsampling, we add a high-resolution P2 detection layer and introduce SPD-Conv, a lossless spatial-to-depth feature transformation technique, for feature extraction. By preserving complete pixel-level information, the model's perception accuracy for distant small vehicles is enhanced. For feature fusion, we design an improved BiFPN incorporating a Ghost module.… More >

Hongzhen Liu¹, Liang Xie¹, Zhiqiang Ru^2,*, Yuan Wan¹, Zhe Zhang¹, Xi Fang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026

Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >

Kexin Zhang¹, Lihua Liu^1,*, Yuting Xue¹, Tao Zhou², Fengshuai Yue¹, Ruifeng Du¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082353 - 23 July 2026

Abstract Traditional Mamba-UNet integrations employ four-stage architectures, replacing conventional five-stage UNets with VMamba blocks for global dependency modeling. Unlike Transformers, which suffer from quadratic complexity and high memory consumption in self-attention, Mamba-UNet achieves efficient global modeling through linear-complexity state space modeling. This paper proposes TriLVM-UNet, a lightweight three-stage architecture that integrates parameter-efficient VMamba blocks and enhances cross-stage feature interaction via an improved skip-attention bridge (SAB) module inspired by UltraLight VM-UNet. The model incorporates a Lightweight Vision Mamba (LVM) layer for high-resolution feature extraction, alongside multi-scale dilated convolution (MSDC) and convolutional block attention module (CBAM) for enhanced More >