Time series forecasting is essential for planning and management across various domains. Existing models struggle to maintain long-term trends in extended predictions and overlook the interplay between time and frequency-domain dependencies. To address these challenges, we propose TFformer, a hierarchical time–frequency representation architecture with Transformer, involving two key innovations: (i) spectrum decomposition isolates long-term patterns from short-term fluctuations and (ii) sequence aggregation integrates two categories of features distinguished by different energy intensities in a hierarchical manner. Experiments on six real-world datasets show that TFformer outperforms the frequency-domain baseline (FreTS) with an average 16.54% improvement in Mean Squared Error (MSE) and surpasses the time-domain baseline (iTransformer) with an average 5.91% MSE improvement, highlighting its effectiveness in capturing both time and frequency-domain patterns.

Cyberbullying is a pervasive issue on online platforms, yet early intervention via predictive modeling remains an open challenge. This challenge is compounded by the temporal dynamics of user interactions and the sparsity of such interactions in real-world social networks, making reliable modeling difficult. Current methods predominantly focus on detecting cyberbullying after it occurs through user content and profiles, while overlooking the temporal patterns and struggling when social interaction data is limited. We propose TemSoGraph, a unified temporal social graph learning model for cyberbullying detection and prediction. The model leverages a temporal self-attention mechanism to capture time-evolving user interactions and employs joint global and local node updates to represent users with limited interactions. It further incorporates a domain adaptor that learns domain-invariant features, enhancing generalization across datasets even when labeled target data is scarce. Experiments on two real-world datasets, Instagram and Vine, show that TemSoGraph outperforms eight cyberbullying detection models in detection task and six dynamic graph neural networks in prediction task. On the prediction task, TemSoGraph achieves a recall of 97.18% on Instagram with 2.53% improvement and 93.38% on Vine with 6.25% improvement. The model supports both detection and future prediction and provides a strong benchmark for cyberbullying modeling. • We propose TemSoGraph model for cyberbullying detection and prediction. • TemSoGraph works effectively under real-world data sparsity problem. • TemSoGraph integrates domain adaptor for cross-dataset generalization.

In this article, we propose a differential evolution (DE) algorithm specifically tailored for the design of linear-quadratic-Gaussian (LQG) controllers in quantum systems. Building upon the foundational DE framework, the algorithm incorporates specialized modules, including relaxed feasibility rules, a scheduled penalty function, adaptive search range adjustment, and the "bet-and-run" initialization strategy. These enhancements improve the algorithm's exploration and exploitation capabilities while addressing the unique physical realizability requirements of quantum systems. The proposed method is applied to a quantum optical system, where three distinct controllers with varying configurations relative to the plant are designed. The resulting controllers demonstrate superior performance, achieving lower LQG performance indices compared to existing approaches. In addition, the algorithm ensures that the designs comply with physical realizability constraints, guaranteeing compatibility with practical quantum platforms. The proposed approach holds significant potential for application to other linear quantum systems in performance optimization tasks subject to physically feasible constraints.

The increasing integration of renewable energy sources and battery energy storage systems has amplified uncertainties in power system operations, necessitating advanced estimation methods that transcend traditional quality-oriented approaches. In this paper, we propose a prescription-centric estimation framework that embeds decision-making insights directly into the anticipation process. By leveraging parametric programming and implicit gradient representations, our approach establishes an analytical mapping between uncertain parameters and optimal operational decisions, thereby addressing the inherent asymmetry between uncertainty estimation and system re-balancing costs. Notably, the proposed method breaks through the limitations imposed by linearization constraints in conventional estimation networks and optimization models, paving the way for more accurate and robust decision-making. An iterative bi-level nonlinear optimization strategy is also introduced to overcome the shortcomings of purely data-driven methods. The effectiveness of the framework is demonstrated through case studies, underscoring its potential to enhance both decision accuracy and efficiency in power system operations.

Accurate estimation of the state of health (SOH) of batteries is of great importance for the safe and efficient operation of energy storage systems. However, data-driven methods are often affected by limited generalisation due to sample distribution shift between different batteries, which make them difficult to extent models to unknown domains. To this end, a Transformer-based architecture integrated with RevIN is proposed in this study, which has presented a significant enhancement on the adaptability to distribution shifts of the input data. The normalisation and denormalisation process of Reversible Instance Normalisation reduces statistical bias whilst maintaining trend information. A comparative evaluation is conducted on the NASA battery datasets across six representative models, including Random Forest, eXtreme Gradient Boosting, Multilayer Perceptron, Long Short-Term Memory, Transformer, and the proposed RevIN-Transformer, under both intra-battery and cross-battery prediction settings. Moreover, the analysis of variance is employed to assess the consistency of SOH prediction errors across different batteries. The results indicate that whilst deep learning methods generally outperform traditional models, the RevIN-Transformer method achieves superior accuracy and stability in cross-battery SOH prediction tasks against distribution shifts. In addition, they also validate the effectiveness of integrating lightweight normalisation modules in a Transformer-based architecture for battery SOH time-series forecasting.

This paper presents a new Physics-Informed Neural Network (PINN) framework to estimate the State of Health (SOH) of lithium-ion batteries. The proposed architecture, PINN-LSTM-KF, integrates long- and short-term memory (LSTM) networks with extended Kalman filtering under physics-based constraints. Conventional data-driven approaches often fail to generalize across different operating conditions due to non-linear degradation patterns. Our method addresses these challenges by enforcing electrochemical constraints within a multiscale architecture. It simultaneously captures microscopic physical processes, mesoscopic temporal dynamics, and macroscopic uncertainty quantification. Experiments on lithium-ion, lithium iron phosphate, and lithium-sulfur batteries demonstrate that the proposed framework achieves mean absolute percentage errors below 0. 01% under physics-informed configurations. Model compression techniques further reduce memory overhead, enabling real-time deployment in embedded systems. The framework also supports feature-level interpretability by quantifying contributions of physical variables to degradation, offering practical insights for battery design and management. These results highlight the potential of combining physics-based modeling with learning-based estimation to improve reliability and safety in energy storage systems, particularly in critical domains such as electric vehicles and smart grids.

In this paper, we propose a distributed and interactive Plug-in Electric Vehicle (PEV) charging scheduling approach, which is also combined with an optimal pricing strategy. This method tackles challenges such as fluctuations in charging currents, potential supply congestion, and uneven demand distribution that arise as PEV penetration increases. The objective is to improve the robust stability of the charging system while also reducing the costs for PEV users. This study designs a multi-cluster aggregative game mechanism to handle the competitive dynamics among operational clusters and the collective behavior of individual PEVs. Additionally, a strategic pricing method, based on Stackelberg game theory, is designed to refine the determination of basic electricity prices. We further introduce a distributed update method that efficiently seeks the Nash Equilibrium (NE) of the hierarchical game described. The effectiveness of the proposed architecture and solution methodology is validated through experimental studies.

Balancing supply and demand is crucial for efficient energy distribution. To achieve it, accurate short-term electrical load forecasting is essential. This study investigates the applicability of various machine learning architectures for short-term load forecasting, using an NSW load dataset from the Australian Energy Market Operator. The key finding is the superior performance of a hybrid model, which integrates LSTM and GRU layers, on the NSW load dataset. This study demonstrates hybrid neural network models can significantly improve the accuracy and reliability of energy load predictions, thereby suggesting a viable pathway for enhancing future utility management practices.

This study presents a framework for sustainable energy planning of community microgrids (MGs), integrating optimal design and decision-support tools. A rural community in New South Wales, Australia, is considered as a case study for this investigation. The proposed microgrid framework is evaluated based on economic viability, environmental sustainability, and community resilience. The economic analysis reveals an attractive net present cost of $3.26 million over the MG's 25-year lifetime, with a competitive levelized cost of energy of $0.196 per kWh. The environmental impact assessment quantifies a significant reduction of 394.429 tonnes of$CO_{2}{-}$equivalent greenhouse gas emissions annually through the integration of 200 kW of solar photovoltaic and 258 kW of wind turbines. The resilience assessment demonstrates a high energy reliability with zero unmet loads facilitated by backup systems and decision-making tools. The findings contribute to the field of sustainable energy planning by providing a comprehensive and integrated approach that addresses the complex interplay of economic, environmental, and resilience factors in the context of community MGs.

This article investigates the event-triggered observer-based output feedback load frequency control (LFC) problem for power systems. To reduce the amount of the transmitted signals, a dynamic event-triggered scheme is proposed by adding an exponential term. Moreover, an adaptive event-triggered scheme is proposed to provide a balance between the control performance and the number of the transmitted signals. Under the proposed schemes, a new model is formulated for the observer-based output feedback LFC system via a time-delay system method. By employing the Lyapunov functional method, sufficient conditions are derived for global asymptotical stability and$\mathcal L_{\infty }$performance. Then, a controller design method is developed. Finally, two examples are given to illustrate the effectiveness of the proposed schemes.

Digital networked control systems are of growing importance in safety-critical systems and perform indispensable function in most complex systems today. Networked degradations such as transmission delay and packet dropout cause such systems to fail to satisfy performance requirements, and eventually affect the overall reliability. It is necessary to get a model to verify and evaluate the system reliability in early design phase, prior to its implementation. However, existing probabilistic models only provide partial descriptions of such coupled networks and control system. In this paper, a new stochastic model represented by linear discrete-time approach is proposed, considering data packet transmissions in both channels: controller-to-actuator and sensor-to-controller. Different from pervious works, the historical behaviors of networked degradations are modeled by multistate Markov chains with uncertainties, releasing the assumption that faults of all periods are independent of each other. The concept of domain requirements for such systems is considered here, contributing to the integration of control and reliability engineering. Methodologies for quantitatively assessing the reliability of the single- and sequential-control goal are derived from the Monte Carlo method. An example of an industrial heat exchanger digital networked control system is provided to illustrate the effectiveness of the model and method.

Cyber-attacks in cyber-physical systems (CPS) are receiving much attention due to the pervasive use of communication in essential services. If cyber components in CPS are compromised by attackers, the ability to maintain stability of the physical system is lost, and performance disruptions may occur. Vulnerability analysis allows quantifying the impact of attacks based on the damage cost model. Yet, existing works partially account for uncertainties in cyber-attacks and may provide inadequate support to decision making. This paper proposes a framework for optimal defense resource allocation for minimizing unsupplied demand of CPS under uncertain cyber-attacks. The vulnerability model of cyber components is described by an attacker-defender two-stage min-max game. The unavailability of cyber components causes the loss of performance of the monitored physical components. Uncertainties in the most probable attack time and in the accuracy of its estimate by the defender are considered. Numerical studies identify the optimum strategies in terms of protection and redundancy allocation of cyber components, and demonstrate that the contest intensity largely affects the two-stage game. Furthermore, if uncertainties increase, the system damage costs also increase and the defensive resource allocation strategies converge to a constant one due to the defender's lack of information about the attack.

Control systems are among the most important subsystems for their ability to undertake indispensable functions in safety-critical systems. Since many key components of such systems follow different performance degradation paths, therefore it is important to have an approach capable of correctly estimating the performance of control systems containing a variety of degraded components. One solution is to endow an existing estimation approach to equip with a capability to cope with uncertainties and inadequate system specifications. This paper presents a hybrid model capable of improving existing approaches by applying the Laplace transform to the time-varying model of the control system while taking into account the varying behaviors of components over different time slices. Reliability is estimated through an event-based Monte Carlo simulation that does not require knowledge of the exact reliability function. System reliability is improved by using the particle swarm optimization method. The method searches for the optimal parameters of the control strategy by compensating for the loss in effectiveness caused by degraded components. The proposed approach is validated through a case study conducted on a simulated cooling system. Numerical results have shown that the proposed approach is capable of improving the reliability of control systems subject to total run time constraints.