We obtain several sharp spectral bounds, approximations, and exact values for the isoperimetric number and related edge-expansion parameters of graphs. Our results focus on graph powers and on families of graphs with rich algebraic or geometric structure, including distance-regular graphs and graphs arising from finite geometries, among others. Our proofs use techniques from spectral graph theory, linear optimization, finite geometry, and probability, yielding new machinery for analysing edge-expansion phenomena in highly structured graphs.
Grid-connected power converters encounter significant stability challenges during weak grid faults, when conventional PI-based controllers exhibit an oscillatory response and poor fault-ride-through performance. This paper addresses this problem by replacing the conventional outer PI controllers that regulate DC-link and PCC voltages with an offset-free data-driven predictive controller. The developed algorithm leverages either pre-fault or fault-time data to construct input-output predictors, yielding offset-free control without the need for physics-based modelling. Simulation results show that pre-fault offset-free DPC doubles the critical equivalent grid impedance that can be handled and reduces the root mean squared error during faults by a factor of 40, while maintaining computation times comparable to conventional PI control. These findings demonstrate that the developed offset-free data predictive controller offers a simple, robust, and computationally efficient alternative to conventional control, significantly enhancing fault-ride-through capabilities of converters in weak grids.
Accurate univariate forecasting remains a pressing need in real-world systems, such as energy markets, hydrology, retail demand, and IoT monitoring, where signals are often intermittent and horizons span both short- and long-term. While transformers and Mixture-of-Experts (MoE) architectures are increasingly favored for time-series forecasting, a key gap persists: MoE models typically require complicated training with both the main forecasting loss and auxiliary load-balancing losses, along with careful routing/temperature tuning, which hinders practical adoption. In this paper, we propose a model architecture that simplifies the training process for univariate time series forecasting and effectively addresses both long- and short-term horizons, including intermittent patterns. Our approach combines sparse MoE computation with a novel attention-inspired gating mechanism that replaces the traditional one-layer softmax router. Through extensive empirical evaluation, we demonstrate that our gating design naturally promotes balanced expert utilization and achieves superior predictive accuracy without requiring the auxiliary load-balancing losses typically used in classical MoE implementations. The model achieves better performance while utilizing only a fraction of the parameters required by state-of-the-art transformer models, such as PatchTST. Furthermore, experiments across diverse datasets confirm that our MoE architecture with the proposed gating mechanism is more computationally efficient than LSTM for both long- and short-term forecasting, enabling cost-effective inference. These results highlight the potential of our approach for practical time-series forecasting applications where both accuracy and computational efficiency are critical.
As cyber–physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns. Among these, stealthy attacks, those that evade conventional detection mechanisms, pose a critical threat to closed-loop performance. This paper introduces a controller-oriented method to enhance CPS resiliency against such attacks without compromising nominal closed-loop behavior. Specifically, we propose the concept of plant equivalent controller (PEC) realizations, representing a class of dynamic output-feedback controllers that preserve the input–output behavior of a given base controller while exhibiting distinct robustness properties in the presence of disturbances and sensor attacks. To quantify and improve robustness, we employ reachable set analysis to assess the impact of stealthy attacks on the closed-loop dynamics. Building on this analysis, we provide mathematical tools (in terms of linear matrix inequalities) to synthesize the optimal PEC realization that minimizes the reachable set under peak-bounded disturbances. The proposed framework thus provides systematic analysis and synthesis tools to enhance the attack resilience of CPSs while maintaining the desired nominal performance. The effectiveness of the approach is demonstrated on the quadruple-tank process subject to stealthy sensor attacks.
As advanced nuclear reactors demand novel irradiation-tolerant materials, this study investigates the radiation damage and mechanical degradation of the promising WTaCrV refractory high-entropy alloy (RHEA). To isolate complex nanoscale chemical effects, we propose an atomistic modeling strategy comparing Average-Atom (AA), random solid-solution (RSS), and local chemical order (LCO) configurations using newly developed interatomic potentials. Collision cascades simulations reveal that the number of Frenkel pairs follow NRSS > NLCO > NAA at the same radiation dose. While the RSS effect accelerates defect generation due to rugged energy landscapes, LCO enhances lattice cohesion to mitigate radiation damage. Despite more primary defects in the RSS and LCO configurations compared with the AA configurations, the RSS and LCO effects can suppress radiation-induced mechanical degradation. Irradiation severely degrade the homogenized AA model but exert a limited impact on the strength and flow stress of the RSS and LCO models. This exceptional resistance is driven by inherent lattice distortion resulting from interactions among different alloy elements, which outweighs point defect induced lattice disruptions. Moreover, the complex interactions between deformation twins and point defects cause confined plastic flow, elevating flow stress in the RSS and LCO models. The findings provide atomistic guidance for performance assessment of next-generation structural materials for extreme nuclear environments.
