Guglielmo D’Amico^1,*, Edoardo Lui², Filippo Petroni¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082608 - 27 July 2026

Abstract This paper develops an operational framework for short-horizon risk management in multistate stochastic systems, with application to wind farm performance. We focus on instantaneous mobility-based indicators derived from finite-state continuous-time Markov chains, which capture the local propensity of a system to transition between states. Unlike classical reliability and availability measures, these indicators provide a dynamic description of system behavior. The indicators are interpreted as policy signals to support decision-making under budget constraints. We introduce a state-conditional expected short-horizon loss, representing non-production risk, and use it to evaluate ranking-based intervention strategies. The framework is applied to More >

AL-Wesabi Ibrahim¹, Hassan M. Hussein Farh^2,*, Jiazhu Xu^1,*, Mohamad A. Alawad², Ahmed Alqurashi³, Abdullrahman A. Al-Shamma’a²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081371 - 27 July 2026

Abstract Load frequency control (LFC) in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system. Although various PI/PID and other advanced control strategies have been employed for LFC in power systems, the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems. Moreover, the optimization methods employed for the tuning of… More >

Zhongxue Li^1,*, Yu Peng¹, Jin Xu², Loc Vu-Quoc³, Bassam A. Izzuddin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081240 - 27 July 2026

Abstract This paper proposes a computational theory for multibody dynamics based on a novel co-rotational formulation of a four-node quadrilateral shell element, designed to address nonlinear dynamic problems in flexible multibody systems undergoing arbitrarily large displacements and rotations. To circumvent the numerical inefficiency caused by the asymmetric tangent stiffness matrix in conventional co-rotational approaches, an incrementally additive vectorial rotational variable is introduced, which ensures the symmetry of the element’s tangent stiffness matrix in both global and local coordinate systems. The adoption of this vectorial rotational variable substantially improves computational efficiency and numerical stability. Hamilton’s principle is… More >

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal^2,*, Mohammed K. Alzaylaee³, Heba G. Mohamed^4,5

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084409 - 23 July 2026

Abstract The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50%… More >

Amir Ijaz^*, Hashem Haghbayan, Abdul Malik, Ethiopia Nigussie, Juha Plosila

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083797 - 23 July 2026

Abstract The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent More >

Xiangyi Le¹, Deyu Lin^1,2,*, Yufei Zhao², Wang Miao³, Yong Liang Guan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083306 - 23 July 2026

Abstract The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this… More >

Mohammed Abdullah Alsuwaiket^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082979 - 23 July 2026

Abstract Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection, traffic classification, and adaptive Quality-of-Service (QoS) control. This paper proposes NeuroPulse, a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks (SNNs) with the representational power of sparse self-attention, enabling efficient deployment on neuromorphic network processors (NNPs). We propose a Rate-Coded Cross-Attention (RCCA) module, which converts population-coded spike-trains into attention queries, allowing long-range dependency modeling within sub-milliwatt (sub-mW) power budgets. NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a More >

Yasemin Onal^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082593 - 23 July 2026

Abstract Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework.… More >

Mohammad Q. Al-Jamal¹, Mahmoud Al Jamal², Bashar S. Khassawneh^3,*, Ayoub Alsarhan^4,5, Amina Salhi⁶, Tahani Alsubait⁷

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079984 - 23 July 2026

Abstract Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to… More >

Huajie Zhang^1,2, Xiaopeng Li^1,2, Hanlin Xiao^3,*, Lifeng Xing³, Wenyue Zhou^1,2

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.086505 - 12 July 2026

Abstract This article has no abstract. More >