Daiki Nobayashi^1,*, Meiya Tanaka², Naoki Tanaka², Riku Nakamura², Kazuya Tsukamoto³, Takeshi Ikenaga¹, Shu Sekigawa⁴, Myung Lee⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077008 - 09 April 2026

Abstract To realize local production and consumption of Spatio-temporal data (STD), it is essential to address two key challenges: (1) maintaining data locality by retaining and distributing STD close to their generation area, and (2) enabling application execution on heterogeneous and resource-constrained devices through a lightweight and portable execution platform. To address these challenges, we developed a Floating Cyber-Physical System (F-CPS) that retains both STD and the functions required to process and use the STD within a specific area. In the F-CPS, the STD Retention System directly distributes STD from the generation location and maintains the… More >

Lan Xiong, Liang Wan^*, Jingxia Ren

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076986 - 09 April 2026

Abstract The semantic complexity of large-scale malicious payloads in modern network traffic severely limits the robustness and generalization of existing Intrusion Detection Systems (IDS). This limitation presents a major challenge to network security. This paper proposes a dual-branch intrusion detection method called CPS-IDS. This method fuses deep semantic features with statistical features. The first branch uses the DeBERTav2 module. It performs deep semantic modeling on the session payload. This branch also incorporates a Time Encoder. The Time Encoder models the temporal behavior of the packet arrival interval time series. A Cross-Attention mechanism achieves the joint modeling… More >

Sultan Kahla¹, Zuping Zhang^1,*, Majed Alsafyani², Ahmed Emara^3,*, Mohammod Abdullah Bin Hossain⁴, Abdulwahab Osman Sheikhdon¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076402 - 09 April 2026

Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >

Ruida Liu, Dan Wang^*, Jiaming Chen, Meng Xu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074554 - 09 April 2026

Abstract Accurate prediction of shoulder joint angles based on surface electromyography (sEMG) signals is critical in human–machine interaction and rehabilitation engineering. However, due to the shoulder joint’s complex degrees of freedom, dynamically varying muscle coordination patterns, and the susceptibility of sEMG signals to cross-talk and noise interference, achieving high-precision prediction remains challenging. In this study, LASENet (BiLSTM–Attention–SE Network) is proposed as an end-to-end deep learning framework that integrates a bidirectional long short-term memory network (BiLSTM), a multi-head self-attention (MHSA) mechanism, and a squeeze-and-excitation (SE) block to predict shoulder joint angles across three degrees of freedom directly More >

Hatim Madkhali^1,2,*, Abdullah Sheneamer², Linh Nguyen³, Gnana Bharathy¹, Ritu Chauhan⁴, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074444 - 09 April 2026

Abstract Consumer electronics, with 62 million tons of electronic waste (e-waste) generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030, pose critical challenges when it comes to national infrastructure and circular economy policies. This paper compares forecasting approaches using sparse panel data for 32 European countries (2005–2018, Eurostat/Waste Electrical and Electronic Equipment (WEEE) Directive), focusing on leakage-safe prospective validation to guarantee true predictive performance. We make one-step-ahead predictions with conservative features (primarily lagged values) to account for temporal autocorrelation but with reduced multicollinearity (Variance Inflation Factor (VIF) ≈ 1.0). Cross-paradigm comparisons… More >

Boyi Cui^1,2, Kai Long^1,*, Ayesha Saeed¹, Nianzhi Guo¹, Guangxing Wu¹, Hui Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078151 - 30 March 2026

Abstract Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication, transportation, and installation. Nonetheless, their conceptual design remains predominantly dependent on engineering experience, and a generally applicable approach is still absent. This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application. A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach, wherein modular constraints and their corresponding sensitivity expressions are explicitly included. The method is applied to a reference wind turbine model More >

Daniyal Asif^1,*, Nabil Kerdid², Muhammad Shoaib Arif³, Mairaj Bibi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077334 - 30 March 2026

Abstract Ovarian cancer (OC) is one of the leading causes of death related to gynecological cancer, with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers. Machine learning (ML) has the potential to process complex datasets and support decision-making in OC diagnosis. Nevertheless, traditional ML models tend to be biased, overfitting, noisy, and less generalized. Moreover, their black-box nature reduces interpretability and limits their practical clinical applicability. In this study, we introduce an explainable ensemble learning (EL) model, TreeX-Stack, based on a stacking architecture that employs tree-based learners such as Decision… More >

Chang He^#, An Wang^#, Youbo Wang^#, Qinyun Ma^*, Xiaofeng Chen^*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074880 - 23 March 2026

Abstract Objectives: Combined chemotherapy and photothermal therapy (PTT) represents a promising approach for enhancing cancer treatment efficacy. This study aimed to develop arsenic trioxide (ATO) and poly(cyclopentadithiophene-alt-benzothiadiazole) (PCPDTBT)-loaded nanoparticles (ATO/PCPDTBT@NPs) to evaluate their synergistic efficacy in inhibiting lung cancer growth and metastasis. Methods: Nanovesicles were synthesized via a streamlined protocol and subjected to 808 nm NIR irradiation to assess their photothermal conversion capabilities. The therapeutic efficacy was evaluated in vitro using A549 lung carcinoma cells to assess apoptosis, invasion, and migration, and in vivo to monitor tumor volume reduction. Results: The nanoparticles exhibited excellent hemocompatibility and low cytotoxicity while More >

Adam Dudáš^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076507 - 12 March 2026

Abstract It is known that correlation does not imply causality. Some relationships identified in the analysis of data are coincidental or unknown, and some are produced by real-world causality of the situation, which is problematic, since there is a need to differentiate between these two scenarios. Until recently, the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data. This has changed with the advance of large language models, which are often utilized as surrogates for such human experts, making the process automated and readily… More >

Xi Cai, Xiaoqiang Wang, Huiying Zhao, Guang Han^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075756 - 12 March 2026

Abstract Low-light image enhancement (LLIE) remains challenging due to underexposure, color distortion, and amplified noise introduced during illumination correction. Existing deep learning–based methods typically apply uniform enhancement across the entire image, which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction. To overcome these limitations, we propose a Semantic-Guided Visual Mamba Network (SGVMNet) that unifies semantic reasoning, state-space modeling, and mixture-of-experts routing for adaptive illumination correction. SGVMNet comprises three key components: (1) a semantic modulation module (SMM) that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant (LLaVA) and… More >