Yu-Xuan Zhou¹, Kai-Qing Zhou^1,*, Wei-Lin Chen¹, Zhou-Hua Liao¹, Di-Wen Kang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075099 - 10 February 2026

Abstract The Pigeon-Inspired Optimization (PIO) algorithm constitutes a metaheuristic method derived from the homing behaviour of pigeons. Initially formulated for three-dimensional path planning in unmanned aerial vehicles (UAVs), the algorithm has attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation, coupled with advantages in real-time performance and robustness. Nevertheless, as applications have diversified, limitations in convergence precision and a tendency toward premature convergence have become increasingly evident, highlighting a need for improvement. This review systematically outlines the developmental trajectory of the PIO algorithm, with a particular focus on its core… More >

Zuyi Huang¹, Yue Wang¹, Jia Liu², Haodong Yi¹, Lejun Ai¹, Min Chen^1,3,*, Salman A. AlQahtani⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074034 - 10 February 2026

Abstract The personalized fine-tuning of large language models (LLMs) on edge devices is severely constrained by limited computation resources. Although split federated learning alleviates on-device burdens, its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning, which leads to excessive communication overhead. To address this, we propose Language-Empowered Split Fine-Tuning (LESFT), a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm. LESFT simultaneously learns from multiple logically equivalent but linguistically diverse reasoning chains, providing richer supervisory signals and improving data efficiency. This process-oriented training allows more effective reasoning More >

Tomás Bernal-Beltrán¹, Ronghao Pan¹, José Antonio García-Díaz¹, María del Pilar Salas-Zárate², Mario Andrés Paredes-Valverde², Rafael Valencia-García^1,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073629 - 10 February 2026

Abstract The malicious dissemination of hate speech via compromised accounts, automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern. Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources. In this paper, we compare two predominant AI-based approaches for the forensic detection of malicious hate speech: (1) fine-tuning encoder-only models that have been trained in Spanish and (2) In-Context Learning techniques (Zero- and Few-Shot Learning) with large-scale language models. Our approach goes beyond binary classification, proposing a comprehensive, multidimensional evaluation that labels each… More >

Maha Abdelhaq¹, Ahmad Sami Al-Shamayleh², Adnan Akhunzada^3,*, Nikola Ivković⁴, Toobah Hasan⁵

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073577 - 10 February 2026

Abstract The rapid expansion of the Internet of Things (IoT) and Edge Artificial Intelligence (AI) has redefined automation and connectivity across modern networks. However, the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistent malware attacks. These adaptive and stealthy threats can evade conventional detection, establish remote control, propagate across devices, exfiltrate sensitive data, and compromise network integrity. This study presents a Software-Defined Internet of Things (SD-IoT) control-plane-based, AI-driven framework that integrates Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks for efficient detection of evolving multi-vector, malware-driven botnet attacks.… More >

Nouman Ahmad^1,*, Changsheng Zhang¹, Uroosa Sehar^2,3,4

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073029 - 10 February 2026

Abstract Although Named Entity Recognition (NER) in cybersecurity has historically concentrated on threat intelligence, vital security data can be found in a variety of sources, such as open-source intelligence and unprocessed tool outputs. When dealing with technical language, the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques. We introduce a three-phase approach for improved NER in multi-source cybersecurity data that makes use of large language models (LLMs). To ensure thorough entity coverage, our method starts with an identification module that uses dynamic prompting techniques. To lessen hallucinations, the extraction module uses… More >

Zhongfan Sun, Kan Guo, Yongli Hu^*, Yong Zhang

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072903 - 10 February 2026

Abstract Knowledge-based Visual Question Answering (VQA) requires the integration of visual information with external knowledge reasoning. Existing approaches typically retrieve information from external corpora and rely on pretrained language models for reasoning. However, their performance is often hindered by the limited capabilities of retrievers and the constrained size of knowledge bases. Moreover, relying on image captions to bridge the modal gap between visual and language modalities can lead to the omission of critical visual details. To address these limitations, we propose the Reflective Chain-of-Thought (ReCoT) method, a simple yet effective framework inspired by metacognition theory. ReCoT effectively activates… More >

Xiaorui Zhang^1,2,*, Yingying Wang², Wei Sun³, Shiyu Zhou², Haoming Zhang⁴, Pengpai Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072626 - 10 February 2026

Abstract Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition. However, existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds, small target objects, and limited training data, leading to poor recognition. Fine-grained images exhibit “small inter-class differences,” and while second-order feature encoding enhances discrimination, it often requires dual Convolutional Neural Networks (CNN), increasing training time and complexity. This study proposes a model integrating discriminative region localization and efficient second-order feature encoding. By ranking feature map channels via a fully connected layer, it selects high-importance channels to generate an More >

Ziying Huang¹, Ruikang Ma¹, Anning Li², Yufei Cheng¹, Xiaolin Lin², Mengzhi Li³, Yu Zhang², Liping Shi¹, Linlin Dong^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.074419 - 30 January 2026

Abstract Epimedium is commonly used to treat bone injury and kidney disease, with prenylated flavonol glycosides (PFGs) as its active ingredients. It has attracted much attention due to prominent healthcare and therapeutic effects, but faces problems of adulteration with closely related species and confusion about geographical origins. In this study, multiple technical approaches were employed to identify its genetic characteristics and metabolic differences. Based on DNA barcoding, 20 batches of samples were analyzed. The genetic distances of matK, ITS and psbA-trnH within species were all smaller than those between species, and psbA-trnH along with ITS + psbA-trnH proved most effective… More >

Yingying Lan, Bo Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075706 - 29 January 2026

Abstract In this work, the Hierarchical Quadrature Element Method (HQEM) formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells. The stress resultant formulation is developed within the HQEM framework, consequently significantly simplifying the computations of residual force and stiffness matrix. The present formulation inherently avoids shear and membrane locking, benefiting from its high-order approximation property. Furthermore, HQEM’s independent nodal distribution capability conveniently supports local p-refinement and flexibly facilitates mesh generation in various structural configurations through the combination of quadrilateral and triangular elements. Remarkably, in lateral buckling… More >

Maddina Dinesh Kumar^1,#, S. U. Mamatha², Khalid Masood³, Nehad Ali Shah^4,#, Se-Jin Yook^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074680 - 29 January 2026

Abstract Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces. The trapezoidal cavity form is compared with its thermal and flow performance, and it is revealed that trapezoidal fins tend to be more efficient, particularly when material optimization is critical. Motivated by the increasing need for sustainable energy management, this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid. The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties; hence, optimising these properties… More >