Rafael de Oliveira Silva^1,2,*, Roberto Outa³, Fábio Roberto Chavarette⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080607 - 27 April 2026

Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080287 - 27 April 2026

Abstract The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to More >

Mustaqeem Khan¹, Ufaq Khan², Mamoun Awad¹, Nazar Zaki¹, Guiyoung Son³, Soonil Kwon^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079697 - 27 April 2026

Abstract Knowledge distillation has shown impressive results in different fields, including detection, recognition, and generation. These models are excellent at tasks such as speech recognition, but they need to be shrunk down using adaptive knowledge distillation (AKD). The use of AKD can improve human-computer interactions and streamline data collection in the field of Speech Emotion Recognition (SER). This study presents a high-level approach that employs a novel adaptive knowledge distillation (AKD) with spatio-temporal transformers to acquire advanced semantic features from the input signal. This method uses an instance-by-instance correlation between the teacher and a student to determine the More >

Muhammad Waqar¹, Zeshan Aslam Khan^1,*, Arthur Chang^2,*, Zhishan Guo³, Chun-Liang Lai⁴, Chuan-Yu Chang⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078892 - 27 April 2026

Abstract Lung cancer (LC) is among the dangerous cancers spreading progressively, and a timely LC diagnosis becomes a dire need of the time. Various imaging-based studies have been conducted for accurate LC examination through computed tomography (CT), X-ray, and histopathology. Worldwide, the proportion of LC-affected patients in hospitals is growing, thereby increasing imaging data for fast processing and early examination. To facilitate histopathological imaging-based automated and timely decision making for accurate LC prediction, a Context Aware Fusion Network (CAFNet) for holistic feature learning and spatially localized feature learning is proposed in this study for the efficient… More >

Liam Revell¹, Hyunjae Kang^1,*, Jung Taek Seo², Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078467 - 27 April 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices has increased the importance of network intrusion detection systems (NIDS) for protecting modern networks. However, many machine learning and deep learning based NIDS rely on large volumes of labeled attack data, which is often impractical to obtain for newly emerging or rare attacks. This paper presents a benchmark-style systematic evaluation of meta-learning-based Few-Shot Learning (FSL) classifiers for detecting previously unseen intrusions with limited labeled data. We investigate three representative FSL models, namely Prototypical Networks, Relation Networks, and MetaOptNet, and further examine two decision-level ensemble strategies based… More >

Sergio Isai Palomino-Resendiz^1,2, César Ulises Solís-Cervantes^1,*, Luis Alberto Cantera-Cantera^1,3, Jorge de Jesús Morales-Mercado¹, Diego Alonso Flores-Hernández⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077726 - 27 April 2026

Abstract Many linear-in-parameters models arising in identification and control can be expressed as single-layer artificial neural networks (ANNs) with linear activation, enabling online learning via first-order optimization. In practice, however, standard gradient descent often exhibits slow convergence, large intermediate weights, and stagnation when the regressor data are ill-conditioned or computations are performed under finite precision. This paper proposes Gradient Descent with Time-Decaying Regularization (GD-TDR), a training algorithm that augments the quadratic loss with a regularization term whose weight decays exponentially in time. The proposed schedule enforces uniform strong convexity during early iterations, effectively mitigating neural-paralysis-like behavior associated More >

Zhukui Tan¹, Xiaoyong Cao^2,*, Qihui Feng¹, Dong Liu², Xiayu Chen³, Fei Chen²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.078440 - 27 April 2026

Abstract The rapid integration of photovoltaic (PV) generation and energy storage systems has significantly increased the operational complexity of low-voltage direct current (LVDC) distribution networks in zero-carbon parks. Under highly variable operating conditions, conventional DC protection schemes relying on fixed overcurrent thresholds often suffer from maloperation or failure to trip, particularly during fluctuations in PV power, load switching, and changes in network topology. To address these challenges, this paper proposes an adaptive DC protection strategy based on an artificial neural network (ANN)-driven dynamic threshold optimization mechanism. The proposed method replaces static protection settings with an adaptive… More > Graphic Abstract

Aimable Ngendahayo^1,*, Adrià Junyent-Ferré², Joan Marc Rodriguez Bernuz³

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.077594 - 27 April 2026

Abstract Bugesera, a historically drought-prone region in Rwanda, is undergoing transformation through investment in modern irrigation and sustainable agricultural practices. However, extending the national electrical grid to numerous dispersed smallholder farms poses a major challenge. The persistent water scarcity and rising conventional energy costs necessitate the development of innovative and sustainable solutions. This study investigates the use of photovoltaic (PV) pumping systems as a green energy alternative for off-grid rural areas, supporting both agricultural irrigation and domestic water supply. A model system serving five one-hectare market-gardening plots and 25 inhabitants was analyzed, with a total daily… More >

Rajanand Patnaik Narasipuram^1,*, Md M. Pasha², Suresh Badugu³, Saleha Tabassum⁴, Attuluri R.Vijay Babu⁵, Bharath Kumar N⁵, Amit Singh Tandon⁶

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076542 - 27 April 2026

Abstract Supercapacitors are increasingly deployed as high power buffers in modern energy systems, yet their broader impact is constrained by limited energy density, fragmented testing practices, and incomplete understanding of lifecycle implications. This article presents a critical, method driven review based on a structured literature survey and explicit inclusion criteria, aggregating quantitative performance data for major electrode families (carbon materials, transition metal oxides, conducting polymers, biomass derived carbons, MXenes, and hybrid composites), electrolytes (aqueous, organic, ionic liquid, and gel/solid state), and device architectures (flexible, micro, solid state, lithium ion capacitors, and structural supercapacitors) under harmonized metrics… More > Graphic Abstract

Hong Zhang¹, Bin Xu¹, Jinzhong Li¹, Xiaoxiao Meng^2,*, Cheng Qian², Wei Ma¹, Yuguang Xie¹

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.073542 - 27 April 2026

Abstract The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network. The traditional load balancing methods are mainly based on network reconfiguration, which have problems such as a long time scale and poor adaptability. In response to these issues, this paper proposes a distributed iterative learning control (ILC) strategy for load balancing in flexible AC/DC hybrid distribution systems. This method combines the consensus algorithm with the ILC mechanism to construct a multi-terminal AC/DC flexible interconnection system model. It is only necessary to measure the load… More >