Power system operators need tools for rapid, real-time counterfactual assessments of grid security under fast-changing conditions. Traditional N-1 contingency analysis lacks dynamic evaluation, especially of frequency swings from common faults. This paper introduces a real-time dashboard framework to screen dynamic contingencies. It assumes: (a) the grid starts in a balanced state; (b) faults can occur randomly on any transmission line, temporarily de-energizing and then reconnecting it within about one second; and (c) contingencies are flagged if post-fault transients cause line flows to exceed safety thresholds. The key contributions are: (1) Overload Indicator: a system-wide metric quantifying integrated N-1 dynamic risk from a given state; (2) Scalable Fault Evaluation Algorithm: a linear-scaling method to assess dynamic fault impacts without brute-force simulations; and (3) Risk Estimation: a Cross Entropy Adaptive Importance Sampling method estimating the likelihood of low probability by high risk events, e.g. associated with potential transformer over-current. We demonstrate the framework on the Israeli transmission power grid (IG).
We study a diffuse-interface model for thermally driven phase separation in viscous incompressible mixtures. The system couples a convective Cahn-Hilliard equation for the order parameter with a Stokes subsystem for the velocity-pressure field and a heat equation for the temperature. Temperature enters the bulk free energy through a Landau-type coefficient, while the phase field feeds back on the flow through concentration-dependent density and viscosity, yielding a phenomenological temperature-coupled Cahn-Hilliard-Stokes-heat system. The model serves as a proxy for temperature-triggered condensation-like phase separation and does not include humidity, latent heat, vapor pressure, or capillary forcing; these effects are absorbed into the choice of the threshold temperature Theta(S). We motivate the chemical potential by a temperature-dependent Landau free energy and derive a priori estimates for the regularized subproblems. On the analytical side, we prove local-in-time existence of weak solutions for a regularized auxiliary model of the coupled system. This regularized formulation serves as an auxiliary analytical model for establishing local existence. On the numerical side, we use a first-order sequential finite-element discretization of a simplified quasi-static formulation. The heat equation is advanced by an implicit diffusion step, the variable-coefficient Stokes problem by a Taylor-Hood discretization, and the Cahn-Hilliard bulk derivative is evaluated at the previous time level, so every algebraic subproblem is linear. An isothermal diffusive test confirms mass conservation to roundoff and exhibits monotone discrete-energy decay for the tested parameter set. Time-step and mesh-refinement studies show first-order temporal and approximately second-order spatial behavior. The remaining computations are qualitative, parameter-specific illustrations, and no global discrete energy law is claimed for the non-isothermal sequential scheme.
Streaming EEG fatigue monitoring is challenging because fatigue signatures are both frequency-dependent and spatially distributed across electrodes, while deployment requires causal updates with bounded latency and memory. Existing Transformers weakly exploit electrode geometry and incur history-dependent cost, whereas graph models typically rely on fixed or unconstrained connectivity that is difficult to adapt as cognitive state changes. We propose HASTE, a hybrid-attention streaming topology-aware estimator that separates where interactions are allowed from how strongly they adapt: a physical k-nearest-neighbor (kNN) graph constrains spatial attention to anatomically feasible neighborhoods, and an online band-aware functional prior biases within-neighborhood attention through an additive log-bias. Causal spectral fusion tracks time-varying band relevance, and causal local temporal attention with a fixed look-back window enables constant-memory inference with respect to session length. Under leave-one-subject-out evaluation with causal normalization on SEED-VIG, SADT, and MPD-DF, HASTE achieves Pearson correlation coefficients (PCCs) of 0.776, 0.705, and 0.669, respectively, outperforming the six baselines evaluated across all three datasets. On SEED-VIG, HASTE also outperforms the added Mamba-DE baseline on all three metrics. On an NVIDIA RTX 3090, its neural forward pass on a precomputed window feature takes similar to 2.8 ms and uses similar to 47 MB of graphics processing unit (GPU) memory.
This paper presents an experimental evaluation of decentralized affine transformation (AT) in multi-agent systems using teams of mini-quadcopters. The AT framework enables an agent team to safely navigate constrained environments with narrow passages while allowing aggressive changes in inter-agent distances, which are formally characterized through the decomposition of the AT transformation matrix. We focus on two-dimensional AT, formulated as a decentralized leader-follower problem. In this formulation, three leader quadcopters are positioned at the vertices of a triangle, while all follower quadcopters remain within the triangle. The leaders know the desired trajectories prescribed by the AT, whereas the followers do not. Instead, the followers infer their trajectories through local communication governed by fixed communication weights determined by the team's initial spatial configuration. Experimental results provide evidence consistent with the predicted bounded-error tracking and convergence of decentralized AT and demonstrate its capability to safely guide multi-agent teams through constrained environments with narrow passages.
Discriminating between ice and water remains challenging in remote sensing, particularly during phase transition when surface temperature alone is insufficient for classification. In the long-wave infrared (LWIR), ice and water exhibit distinct phase-dependent refractive indices, with the strongest differences occurring between 10.7–12.0μm in the anomalous dispersion region of ice. Here, we demonstrate that LWIR spectro-polarimetry provides a repeatable signature of the ice–water phase transition under controlled conditions. Using an infrared channeled spectro-polarimeter (IRCSP), we measured the spectrally resolved polarization difference of melting ice samples under 420K blackbody illumination at viewing angles greater than 65°. Fresnel-based predictions indicate that solid ice should exhibit increasing polarization magnitude with wavelength across this region, whereas liquid water should exhibit the opposite trend. These opposing spectral trends were observed experimentally. A split-window polarization difference metric consistently reversed sign during ice melting. The total change in the split-window metric was 5–9K across three independently prepared samples, each measured at one of three viewing angles. Broadband polarization differences were smaller, approximately 1K, and varied more due to environmental factors and surface textures. Spectrally resolved LWIR polarimetry may offer an indicator of the ice–water phase transition with potential relevance to cryosphere monitoring, melt-pond detection, and cloud-phase identification.
