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 >

Yuxuan Feng, Lili Liang^*, Guanglu Sun, Yanrui Wei

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081876 - 23 July 2026

Abstract In doped two-dimensional nanomaterials, magnetism is one of the important physical properties. By introducing foreign doping atoms or molecules, the electronic structure of the material can be effectively regulated, leading to changes in magnetic behavior. Currently, magnetic property prediction has achieved considerable results with the help of traditional CNNs, but there are still obvious limitations: (1) The feature extraction of dopant sites is constrained by fixed receptive fields, making it difficult to characterize local structural perturbations in the vicinity of dopant atoms and their spatial influence propagating to surrounding regions; (2) CNNs lack the capability… More >

Laura Baitenova¹, Gulnar Mukhamejanova², Gauhar Munaitbas^3,*, Saken Mambetov¹, Zhanna Mukanova¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081004 - 23 July 2026

Abstract Morphological parsing is a fundamental task in natural language processing, particularly for morphologically rich languages where words encode complex grammatical and semantic information. This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning, designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms. The proposed architecture combines convolutional layers for capturing local morphological patterns, recurrent layers for modeling sequential dependencies, and Transformer-based self-attention for learning global contextual relationships. This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding. The framework is trained using… More >

Hend Alshaya^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080758 - 23 July 2026

Abstract Video representation learning faces very challenging goals, including spurious temporal correlations, confounding visual features, and failure to learn real causal relationships between video events. Current transformer-based approaches learn statistical relationships rather than causal interactions, leading to weak generalization and high sensitivity to distribution changes. The current paper proposes a new Counterfactual Transformer Network, named CauFormer-V, that combines causal inference concepts with temporal representation learning for video. The framework was proposed and includes three main innovations, (1) a Causal Temporal Attention (CTA) mechanism, a mechanism that specifically models causal dependencies among video frames via do-calculus intervention,… More >

Chunwei Huang, Jingyi Kong, Hangxing Ren, Wanchen Zhang, Shi Jiang^*, Xianneng Sheng^*

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.085387 - 16 July 2026

Abstract HER2-positive breast cancer accounts for 15–20% of all breast cancer cases. Although the development of monoclonal antibodies (e.g., trastuzumab, pertuzumab), tyrosine kinase inhibitors (e.g., lapatinib, pyrotinib), and antibody-drug conjugates (e.g., T-DM1, trastuzumab deruxtecan) has greatly improved patient prognosis, primary or acquired resistance to anti-HER2 therapy remains a major clinical challenge, leading to treatment failure and disease progression. Recent research has elucidated diverse resistance mechanisms, including HER2 signaling pathway aberrations (such as receptor mutations, alternative splicing, and bypass activation), tumor microenvironment remodeling (involving immunosuppressive cells, metabolic reprogramming, and immune checkpoint molecules), and ADC-specific resistance (impaired internalization,… More >

Zixin Wan^1,2,#, Jingdan Quan^1,2,#, Yue Qiu^1,2, Zhiwei Zhang^1,2,*

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.082561 - 16 July 2026

Abstract Gastric cancer (GC) is one of the malignant tumors with high incidence and mortality worldwide. It has concealed early symptoms, poor prognosis for advanced patients, and limited efficacy of conventional treatments. Metabolic reprogramming is a core hallmark of cancer, among which amino acid metabolic reprogramming plays a critical regulatory role in the initiation and progression of GC. By linking intracellular energy supply, biosynthetic demands, and tumor microenvironment remodeling, it participates in immune escape, redox homeostasis maintenance, and therapeutic resistance. Dysregulation of key amino acids, including arginine, tryptophan, glutamine, branched-chain amino acids, serine/glycine, and aspartic acid,… More >

Chuan Lin^*, Weixian Chen, Guangtao Hao^*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.080605 - 12 July 2026

Abstract Accurate building electricity load forecasting (BELF) can provide a regulatory basis for building energy management systems and promote the transition of buildings toward low-carbon and intelligent operation modes. However, building electricity load is influenced by historical loads, as well as outside environmental conditions such as humidity and temperature, which reduces the prediction accuracy of models. To tackle these challenges, this study presents a BELF model, which consists of a modal component grouping approach, grouped feature attention mechanism, and multi-scale residual depthwise convolution memory module. First, the modal component grouping method analyzes building electricity load in… More >

Xiutai Cao¹, Yuxin Sun¹, Bowen Shi¹, Hao Zhang¹, Hongli Tang¹, Yongbin Bi^1,2, Huiying Zhong^1,3,*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.074439 - 12 July 2026

Abstract With the steady advancement of China’s “Dual-Carbon” goals, CO₂ huff-and-puff technology has become one of the mainstream methods for enhancing oil recovery (EOR) in oilfields. However, differences in sweep radius of CO₂, CO₂-oil interaction mechanisms, injection parameters, and huff-and-puff modes between conventional heavy-oil and light-oil reservoirs still require further investigation. The NP oilfield consists of an upper heavy-oil zone and a lower light-oil zone, with the reservoir inclined at a certain angle. Taking this oilfield as the study area, a positively rhythmic reservoir geological model was established. A compositional numerical simulation approach was employed to analyze the… More > Graphic Abstract