Sravani Parvathareddy^1,*, Abid Yahya¹, Lilian Amuhaya¹, Ravi Samikannu¹, Raymond S. Suglo²

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.079449 - 27 May 2026

Abstract This paper presents a novel framework for the development of a real-time energy management system for mining microgrids, which integrates the benefits of a long short-term memory (LSTM) network and a feedforward neural network (FNN) for the prediction of the load and solar power, and the optimization of the dispatch, respectively, while ensuring the safety of the microgrid through the application of a convex safety filter. In the proposed framework, the LSTM provides probabilistic multi-step forecasts of load and photovoltaic generation, capturing the high volatility characteristic of mining operations with ramp rates up to 5… More > Graphic Abstract

Xi Li¹, Yingjie Chang¹, Peng Chen^1,*, Ang Bian¹, Ning Lu^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077521 - 08 May 2026

Abstract Multivariate time series anomaly detection (MTSAD) is a critical task for real-time risk control and fault diagnosis in industrial monitoring, aerospace, and financial domains. Unsupervised MTSAD confronts three core challenges: label scarcity in practical scenarios, diverse anomaly patterns that demand adaptive modeling, and weak feature discriminability between normal and anomalous samples. To address these challenges, we propose a Prototype Memory and Contrastive Learning Based Unsupervised Anomaly Detection for Multivariate Time Series method named PC-UAD. PC-UAD comprises three core modules with hierarchical functionalities: (1) A Temporal PatchEmbedder, which adopts learnable positional encoding for dynamic temporal representation… More >

Damilola Olofintuyi, Ayesha Siddika, Abdollah Monfared, Hong Zhang, Jennifer Smith^*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.079971 - 28 April 2026

Abstract Climate change is increasing the frequency and intensity of overlapping abiotic stresses, making cross-tolerance a critical component of plant resilience. While single stress responses have been extensively characterized, plants in natural and agricultural environments frequently encounter simultaneous or sequential stresses such as drought–heat, light–drought, and drought–salinity, which trigger nonadditive and often unpredictable physiological outcomes that vary with stress intensity, timing, and species. This review synthesizes current understanding of the mechanisms underlying cross-tolerance, emphasizing how contradictory signals, stress timing, and physiological integration shape plant responses under combined stress. We highlight how stomatal regulation, leaf energy balance,… More >

Wariyo Godana Arero¹, Yaqin Zhao¹, Mudasir Ahmad Wani², Pir Noman Ahmad³, Kashish Ara Shakil^4,*, Sadique Ahmad⁵, Sidrak Habtemariam Teredda⁶, Merhawit Berhane Teklu⁷, Longwen Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080279 - 27 April 2026

Abstract Lung cancer accounts for the highest number of cancer deaths globally, underscoring the urgent need for early and precise detection to enhance patient outcomes. While deep learning has made remarkable strides in analyzing medical images, current approaches face a fundamental challenge. They cannot adequately capture detailed local patterns and broader contextual relationships within lung Computed tomography (CT) scans. To address this limitation, we introduce AMVT-NMN (adaptive multi-scale vision transformer with neuromorphic memory networks), which combines three complementary mechanisms. The dynamic adaptive kernel networks component intelligently adjusts receptive field sizes based on input characteristics, enabling flexible… More >

Najat Almutairi¹, Mohammed Messaoudi², Faisal Muteb K. Almalki³, Sayed Saber^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076371 - 30 March 2026

Abstract This paper introduces a novel fractional-order model based on the Caputo–Fabrizio (CF) derivative for analyzing computer virus propagation in networked environments. The model partitions the computer population into four compartments: susceptible, latently infected, breaking-out, and antivirus-capable systems. By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems, aspects inadequately represented by traditional integer-order models. Under Lipschitz continuity and boundedness assumptions, the existence and uniqueness of solutions are rigorously established via fixed-point theory. We develop a tailored two-step Adams–Bashforth numerical scheme for the CF framework More >

Abiodun Abiola^*, Francisca Segura, José Manuel Andújar, Antonio Javier Barragán

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.070788 - 30 March 2026

Abstract This paper presents an artificial intelligence (AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser. Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation, prevent degradation, and avoid loss of efficiency. In this sense, predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities. However, if the sensor fails, there will be an incorrect forecasting of abnormalities. Among the different types of operational faults that sensors can present are drift-related… More >

Hongbo Sun¹, Xingyu Jiang^1,*, Wenyao Sun¹, Yi Zhao¹, Jifeng Cheng², Xiaoyi Qian¹, Guo Wang³

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.073012 - 27 March 2026

Abstract The accuracy of photovoltaic (PV) power prediction is significantly influenced by meteorological and environmental factors. To enhance ultra-short-term forecasting precision, this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory (TCN-BiLSTM) architecture incorporating a spatiotemporal attention mechanism. Firstly, during data preprocessing, the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients. Subsequently, a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term… 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 >

Bo Zhu^#, Enzhi Dong^#, Zhonghua Cheng^*, Kexin Jiang, Chiming Guo, Shuai Yue

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074009 - 10 February 2026

Abstract Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with More >

Su Zhang¹, Song Deng^1,*, Qingsheng Liu²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072777 - 10 February 2026

Abstract Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term… More >