Lihua Zhong¹, Feng Pan¹, Yuyao Yang¹, Lei Feng¹, Jinghe Jiang², Guo Lin², Xiaoshun Zhang^3,*

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.073240 - 27 May 2026

Abstract This paper addresses the challenge of efficiently calculating dynamic carbon emission factors (CEFs) in large-scale power systems. Traditional methods that rely on direct matrix inversion are computationally intensive and become impractical for networks with thousands of nodes. To overcome this limitation, a fast and scalable computational framework is proposed based on the incomplete LU (ILU) preconditioned biconjugate gradient stabilized (BiCGSTAB) iterative solver. The proposed approach formulates the nodal CEF model as a sparse linear system and employs Krylov subspace acceleration with ILU preconditioning to enhance convergence and numerical stability. The method is applied to synthetic… More > Graphic Abstract

Mohammed Yahya^1,*, Safaaldeen A. Sulyman², Joban Sahota¹, Gursingh Aikum Dhugga¹, Saiakash Shunmugavel¹

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.074828 - 18 May 2026

Abstract The structural integrity and longevity of wind turbine blades are critical determinants of the efficiency and reliability of wind energy systems. As the primary components responsible for converting kinetic wind energy into mechanical torque and subsequently electrical power, the aerodynamic, structural, and material characteristics of rotor blades directly influence turbine performance and operational lifespan. This research addresses the limitations of conventional blade designs, often characterized by stress concentration, fatigue damage, and dynamic instability by introducing a novel diamond-lattice internal support structure aimed at improving mechanical strength, fatigue resistance, and dynamic stability. Finite element simulations performed… More >

Kang Yang^1,2, Lizhi Cai^1,2,*, Jianhua Wu^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080632 - 08 May 2026

Abstract Accurate malware identification and family categorization remain significant challenges in large-scale Android software analysis. Although deep learning has surpassed traditional machine learning in performance, its widespread adoption is hindered by the computational overhead stemming from feature redundancy and the lack of interpretability inherent in its black-box nature. To address these issues, this paper proposes DroidNTA, a DL-based detection model that fuses network traffic and API features. The model first constructs a simplified API Call Graph by extracting the intrinsic structural attributes of applications, and subsequently generates API feature vectors from invocation sequences using a Markov More >

Feng-Cheng Lin^*, Bo-Chiao Jan, Hui-An Wu

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079393 - 08 May 2026

Abstract With the increasing refinement of ornamental fish culture, understanding fish behavioral patterns has become critical. Fish movements not only reflect daily activity ranges but also reveal responses to environmental changes such as water currents and obstacles. However, traditional manual observation is limited by manpower and time, making it difficult to record fish behaviors over long periods stably. Existing automated tracking techniques often suffer from ID switches and track interruptions caused by rapid fish movement, occlusions, or intermingling, which in turn degrade the reliability of subsequent analyses. This paper proposes a deep learning-based multi-object fish tracking… More >

Duo Li¹, Shuhao Kang¹, Yukun Liu², Yang Shen², Ruihan Li³, Yuhu Liu¹, Shujun Huang⁴, Xin Wu⁵, Huan Liu^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078734 - 08 May 2026

Abstract Tungsten plays a critical role in semiconductor electrical interconnects, and a thorough understanding of its mechanical properties is essential for optimizing its processing and performance. However, few studies have explored the effect of grain refinement on the mechanical behavior of tungsten. The work indicates a phenomenological transition around ~7.3 nm within the tested grain-size range that governs the nanoindentation response of tungsten. To establish this, we performed molecular dynamics (MD) simulations of nanoindentation for different grain sizes and analyzed surface pile-up, elastic recovery, atomic displacement, loading force, hardness, stress/strain behavior, dislocation density, and dislocation evolution. More >

Mengqi Liu, Haiqing Yao^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078181 - 08 May 2026

Abstract For the wide-coverage application scenarios, wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring, and use the mobile charger (MC) to support the sustainable charging of the network. Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability. However, the existing work tends to use fixed triggering mechanism for cluster head (CH) rotation, and may trigger the rotation either too early or too late. Besides, the existing charging triggering mechanisms cannot track the changes in… More >

Xian Wu, Liang Wan^*, Jingxia Ren, Bangfeng Zhang

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077928 - 08 May 2026

Abstract Traditional malware detection models rely on a single feature source for detection, resulting in high false positive or false negative rates due to incomplete information. In addition, conventional models depend on manual feature engineering, which is inefficient and hard to adapt to new malware variants. To address these challenges, this paper proposes a malware detection model called WAFDect based on a self-attention mechanism with multi-source feature fusion. The model consists of two key designs. First, we construct a multi-source feature extraction model that analyzes multi-source data such as API call sequences, registry operation logs, file… More >

Rabie Mezouar^1,2, Fouad Okba³, Dejan Zagorac^4,5,*, Salah Daoud², Abdelfateh Benmakhlouf ²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077724 - 08 May 2026

Abstract The compression effects on the thermoelastic and thermodynamic properties of cubic zincblende silicon–tin alloy (SiSn) were explored using a multi-methodological approach, deploying data mining methods, theoretical equation-of-state parameters, and the Quasi-Harmonic Debye Model. We analyze the relative volume, isothermal bulk modulus, thermal expansion coefficient, Debye temperature, sound velocity, and microhardness of the SiSn compound under pressures up to 8 GPa. The study commences with the data mining-based searches for a structural model and continues with an analysis of the pressure dependence of the relative volume using the Vinet equation of state, followed by an investigation… More >

Lijuan Huang^1,2, Zhixian Liu³, Xinyu Zhou⁴, Jinping Liu^4,*, Kunyi Zheng⁴, Yimei Yang^2,4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077655 - 08 May 2026

Abstract Many occluded and ambiguous ground truths exist in object detection, making detectors unable to obtain optimal training samples. In this article, we revisit the suboptimal sample issue in label assignment for object detection and propose a novel detector-agnostic strategy, termed FSS, to address it. FSS reformulates label assignment as the process of selecting high-quality sub-optimal samples and progressively transforming them into optimal ones. Specifically, for each candidate, we estimate the probability of being an optimal sample by jointly considering localization quality and classification confidence, thereby constructing an instance-wise probability matrix. Based on the spatial distribution More >

Akmalbek Abdusalomov¹, Mukhriddin Mukhiddinov^2,3, Kamola Abdurashidova², Alpamis Kutlimuratov⁴, Avazjon Marakhimov⁵, Kuanishbay Seytnazarov⁶, Young-Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077057 - 08 May 2026

Abstract Human emotions are intricate and difficult to decipher through various modalities. Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteristics of emotional expressions in both visual and textual mediums. This paper presents SYMPHONIA (Synchronizing Facial and Textual Modalities for Emotion Understanding), an innovative architecture engineered to capture and amalgamate emotional signals from facial expressions and language, attuned to contextual and modality interactions. There are two parts to SYMPHONIA: a Facial Emotion Branch that uses Vision Transformers and facial landmarks, and a Textual Emotion Branch that uses RoBERTa embeddings… More >