Reshma Siyal¹, Jun Long^1,*, Muhammad Asim^2,*, Mudasir Ahmad Wani³, Kashish Ara Shakil⁴, Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074282 - 08 May 2026

Abstract The rapid growth of the Internet of Things (IoT) has led to dense wireless sensor networks (WSNs) deployed in critical applications such as smart cities, industrial monitoring, and healthcare. However, energy constraints, unpredictable communication delays, and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan. Traditional approaches often fail to balance delay minimization with energy efficiency, especially in large-scale or dynamic networks. To address these issues, this study proposes CTSO-DRNN, a novel framework that integrates Chronological Tangent Search Optimization (CTSO) with a Deep Recurrent Neural Network (DRNN) for accurate delay prediction… More >

Yida Shen¹, Bin Dong², Quan Ma¹, Chao Dang^1,*, Congjian Li^2,*, Guojian Ren³, Shaozhan Wang^1,2, Xiaozhe Sun¹, Yong Ding⁴

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2025.077096 - 30 April 2026

Abstract This article explores the application of Physics-Informed Neural Networks (PINNs) in solving supersonic flow problems within a Laval nozzle, proposing innovative methods by integrating physical constraints and neural network optimization techniques. The main innovations of this study include the construction of a novel neural network architecture with shortcut connections to enhance the prediction of overall flow trends and local fluctuations, thereby improving convergence speed, reducing computational costs, and increasing the accuracy of flow field reconstruction. Additionally, this study designs a PINNs framework that incorporates specific physical knowledge (SPK) to improve model stability, generalization, and accuracy, More >

Faten S. Alamri¹, Noor Ayesha², Afia Zafar³, Adil Ali Saleem^4,*, Amjad R. Khan⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080335 - 27 April 2026

Abstract The use of automated skin lesion classification is still a disadvantage, since there is a great visual similarity between benign and malignant lesions. The majority of deep learning methods utilize dermoscopic images only, without taking into account clinical metadata employed by dermatologists on a regular basis. The following paper proposes a vision-graph multimodal framework that links Image encoding to graph neural networks based on metadata representation through the fusion of learnable attention. The framework focuses on three limitations, which are underutilization of clinical context, absence of interpretability, and suboptimal incorporation of modalities. Gradient-weighted Class Activation… More > Graphic Abstract

An Xu¹, Zhixiong Liu¹, Hua Rong², Liang Han¹, Wei Shi³, Jun Huang³, Jiyang Fu^1,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079375 - 27 April 2026

Abstract In structural optimization, data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost. However, conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses (e.g., top displacement), but usually fail to achieve accurate internal force predictions with conventional training data volumes. As a result, most existing studies involving surrogate models did not concern internal force constraints. To address this issue, this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network (PINN) surrogate model. By embedding static equilibrium equation… More >

Yousef Lafi A. Alshammari^1,2, Muhammad Khan^1,*, Hilal Doganay Kati^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079258 - 27 April 2026

Abstract Cracks can severely degrade the integrity and service performance of plate structures. Although most existing studies focus on identifying straight crack patterns using dynamic response data, curved crack paths have received far less attention, despite being more realistic in practice and having a stronger influence on structural behaviour. This study presents a computational and experimental framework for analyzing and identifying curved crack paths in cantilever plate structures based on dynamic response characteristics. Curved crack paths are modelled using second-order polynomial equations. Finite Element Analysis (FEA) is employed to evaluate the effects of polynomial coefficients and… More >

Chun-I Fan^1,2, Sheng-Feng Lu¹, Cheng-Han Shie¹, Ming-Feng Tsai¹, Tomohiro Morikawa^3,*, Takeshi Takahashi⁴, Tao Ban⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077799 - 27 April 2026

Abstract Due to its partly open-source architecture, which allows for application analysis and repackaging, along with its large market share, the Android operating system is a main target for malware. In recent years, researchers have widely adopted neural network-based methods for detecting Android malware, achieving impressive results but without interpretability. Interpretability is crucial for showing how models behave and identifying biases in their predictions, which helps in validating and improving them. Additionally, in urgent malware analysis situations, interpretability lets analysts quickly assess harmful behaviors and aids in future malware development and investigation. Therefore, interpretability is vital… 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

Fabiano Thulu, Zeyun Wu^*

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076328 - 27 April 2026

Abstract Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors (PWRs) during severe accidents, such as Station Blackout (SBO) and Loss-of-Coolant Accident (LOCA) is vital for structural integrity assessment. However, conventional thermal-hydraulic simulations used for such predictions are computationally intensive, limiting their applicability for real-time analysis. This study develops and compares three surrogate models: Polynomial Regression, Deep Neural Network (DNN), and a Physics-Informed Neural Network (PINN). Thermal-hydraulic simulation data generated by RELAP5-3D are integrated with physics-constrained learning techniques to model transient heat conduction in the pressurizer wall. The internal wall temperature evolution… More >

Edward Sutanto¹, Rinni Sutanto², Sara Velichkovikj³, Nikola Hadzi-Petrushev⁴, Mitko Mladenov⁴, Dimiter Avtanski^5,6,7, Radoslav Stojchevski^5,6,8,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.076157 - 22 April 2026

Abstract The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research, particularly in oncogenesis. Oncogenesis is a hallmark process in the development of cancer, involving the amplification of proto-oncogenes and the subsequent dysregulation of molecular signaling networks. These pathways—including the RAS/RAF/MEK/ERK, PI3K-AKT, JAK-STAT, TGF-β/Smad, Wnt/β-Catenin, and Notch cascades—have been studied extensively in isolation, with major strides achieved in understanding how they drive cancer. However, there are still many considerations regarding how these networks interact. Ongoing studies show that crosstalk among these pathways occurs through feedback… More >