Yuanmeng Chang, Hongmei Liu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074204 - 12 March 2026

Abstract Driven by rapid advances in deep learning, object detection has been widely adopted across diverse application scenarios. However, in low-light conditions, critical visual cues of target objects are severely degraded, posing a significant challenge for accurate low-light object detection. Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images. For this purpose, this study proposes a DL-YOLO model specially tailored for low-light detection. To mitigate target feature attenuation introduced by repeated downsampling, we design a Multi-Scale Feature Convolution (MSF-Conv) module that captures rich, multi-level details via multi-scale feature learning, More >

Soo-Yeon Jeong¹, Ho-Yeon Jeong², Sun-Young Ihm^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074016 - 12 March 2026

Abstract Sign language is a primary mode of communication for individuals with hearing impairments, conveying meaning through hand shapes and hand movements. Contrary to spoken or written languages, sign language relies on the recognition and interpretation of hand gestures captured in video data. However, sign language datasets remain relatively limited compared to those of other languages, which hinders the training and performance of deep learning models. Additionally, the distinct word order of sign language, unlike that of spoken language, requires context-aware and natural sentence generation. To address these challenges, this study applies data augmentation techniques to… More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073832 - 12 March 2026

Abstract The Internet of Things (IoT) and cloud computing have significantly contributed to the development of smart cities, enabling real-time monitoring, intelligent decision-making, and efficient resource management. These systems, particularly in IoT networks, rely on numerous interconnected devices that handle time-sensitive data for critical applications. In related approaches, trusted communication and reliable device interaction have been overlooked, thereby lowering security when sharing sensitive IoT data. Moreover, it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment. In this research, an Artificial Intelligence (AI) recommended fault-tolerant framework is proposed that leverages blockchain More >

Min Liu^1,*, Zhengxiong Lu^2,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071144 - 27 February 2026

Abstract The accurate state of health (SOH) estimation in lithium-ion batteries represents a critical technological challenge with profound implications for electric vehicle performance and user experience. Precise SOH assessment not only enables reliable mileage prediction but also ensures operational safety. However, the complex and non-linear capacity fading process during battery cycling poses a challenge to obtaining accurate SOH. To address this issue, this study proposes an effective health factor derived from the local voltage range during the battery charging phase. First, the battery charging phase is divided evenly with reference to voltage intervals, and an importance… More >

Zehao Li¹, Xuting Zhang¹, Hongqi Lin¹, Wu Qin², Junyu Qi³, Zhuyun Chen^1,*, Qiang Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075436 - 26 February 2026

Abstract To ensure the safe and stable operation of rotating machinery, intelligent fault diagnosis methods hold significant research value. However, existing diagnostic approaches largely rely on manual feature extraction and expert experience, which limits their adaptability under variable operating conditions and strong noise environments, severely affecting the generalization capability of diagnostic models. To address this issue, this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning (AutoML). The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary More >

Luiz Tadeu Dias Júnior, Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa^*, Alexandre Abrahão Cury

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075433 - 26 February 2026

Abstract Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, integrity, longevity and economic efficiency of civil infrastructures. The field has undergone a profound transformation over the last few decades, evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems. This review paper analyzes this historical trajectory, beginning with the approaches that relied on modal parameters as primary damage indicators. The advent of advanced sensor technologies and increased computational power brings a significant change, making Machine Learning (ML) a viable and powerful tool for damage assessment. More recently, Deep Learning (DL) has More >

Md Shafiullah^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075269 - 26 February 2026

Abstract Various factors, including weak tie-lines into the electric power system (EPS) networks, can lead to low-frequency oscillations (LFOs), which are considered an instant, non-threatening situation, but slow-acting and poisonous. Considering the challenge mentioned, this article proposes a clustering-based machine learning (ML) framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer (PSS) parameters. To validate the proposed strategy, two distinct EPS networks are selected: the single-machine infinite-bus (SMIB) with a single-stage PSS and the unified power flow controller (UPFC) coordinated SMIB with a double-stage PSS. To… More >

Jun Zhe Tan¹, Rodney H. G. Tan^1,*, Nor Ashidi Mat Isa², Sew Sun Tiang¹, Chun Kit Ang¹, Kuo-Ping Lin^1,3,4, Wei Hong Lim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.073821 - 26 February 2026

Abstract Accurate estimation of photovoltaic (PV) parameters is essential for optimizing solar module performance and enhancing resource efficiency in renewable energy systems. This study presents a process innovation by introducing, for the first time, the Triangulation Topology Aggregation Optimizer (TTAO) integrated with parallel computing to address PV parameter estimation challenges. The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets (KC200GT and R.T.C. France solar cells) and a real-world dataset (Poly70W solar module) under single-, double-, and triple-diode configurations. Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE More >

Borja Bordel Sánchez^*, Ramón Alcarria, Tomás Robles

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.072603 - 26 February 2026

Abstract In this paper, we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks. This system enables end nodes to select the optimum time and scheme to transmit private data safely. In 6G dynamic heterogeneous infrastructures, unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy. Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service (QoS). As the transport network is built of ad hoc nodes, there is no guarantee about their trustworthiness or behavior, and transversal functionalities are delegated to the extreme nodes. However, More >

Tiffany Chen¹, Grace Kim², Yekta Rahimi³, Monisha Kamdar⁴, Eduardo Fernandez-Hernandez⁴, Karrune Woan⁴, Eric L. Tam^4,*, George Yaghmour⁴

Oncology Research, Vol.34, No.3, 2026, DOI:10.32604/or.2025.072443 - 24 February 2026

Abstract Acute myeloid leukemia (AML) remains a biologically heterogeneous disease with historically limited targeted therapies and poor outcomes. The development of menin inhibitors represents a promising shift, particularly for patients harboring KMT2A rearrangements (KMT2Ar) and NPM1 mutations (NPM1m). This manuscript reviews the molecular rationale of menin inhibition for aberrant homeobox/myeloid ectopic insertion site 1 (HOX/MEIS1)-driven gene expression and leukemogenesis, clinical trial outcomes, and safety data for menin inhibitors, with a focus on recently FDA-approved revumenib and several other agents in development, ziftomenib (KO-539), bleximenib (JNJ-75276617), and icovamenib (BMF-219). We also focused our discussion on future directions to include More >