The integration of distributed energy resources is challenging the operational stability of low-voltage distribution networks, leading to frequent voltage fluctuations that conventional devices, such as on-load tap changers (OLTCs) and capacitor banks, struggle to manage under dynamic and varied operating conditions. Voltage regulation in active distribution networks typically relies on accurate network models for optimization or on reactive-power support from inverter-based resources, which can be difficult to deploy in community-scale settings with limited observability. This paper presents a real-time, model-free voltage regulation framework that uses only residential active-power demand-side flexibility and a single-point voltage measurement at the point of common coupling, without requiring the network topology, line parameters, or reactive-power actuation for online execution. The proposed adaptive droop learning framework follows a centralized-training distributed-execution architecture: a Soft Actor-Critic (SAC) coordinator maps PCC voltage deviations to aggregate flexibility requests and learns to dynamically allocate them across load buses by learning implicit voltage–power sensitivity structure from data without explicit sensitivity calculations. Distributed SAC bus-agents disaggregate allocated requests into appliance-level setpoints using XGBoost-based flexibility bounds and a quadratic-programming feasibility layer to enforce device constraints and satisfy dynamic grid response requirements. Experimental validation on an RTDS with an IEEE 13-bus radial distribution system demonstrates 88.5% voltage compliance within a ± 10 V band, a 73% reduction in OLTC operations, and 96.9% flexibility-tracking accuracy, outperforming TD3, DDPG, and PPO baselines. The results indicate that low-observability, model-free voltage regulation is feasible for practical distribution feeders.
This paper examines the effects of macroeconomic and budget balance shocks on public debt trajectories in the euro area. Country-specific SVAR models are used to identify various shocks, which are subsequently incorporated into local projection models that use panel data to estimate the impulse responses. The analysis indicates that a positive GDP shock leads to a persistent decline in the debt-to-GDP ratio, while a positive GDP deflator shock reduces the debt ratio only temporarily. A positive interest rate shock results in a substantial and lasting increase in the debt ratio. A positive primary balance shock lowers the debt ratio considerably, albeit with a lag of around one year. There is evidence of state-dependent and non-linear effects. A positive primary balance shock is more effective in reducing debt after periods of economic expansion than after recessions, and more effective when the initial public debt is low than when it is high. Moreover, a positive GDP shock reduces the debt stock to a larger extent when the debt stock is large than when it is low.
Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena, the first comprehensive benchmark for audio deepfake detection. Speech DF Arena provides a toolkit to uniformly evaluate detection systems, currently across 14 diverse datasets and attack scenarios, standardized evaluation metrics and protocols for reproducibility and transparency. It also includes a leaderboard to compare and rank the systems to help researchers and developers enhance their reliability and robustness. We include 14 evaluation sets, 14 state-of-the-art open-source and 4 proprietary detection systems, totalling 18 systems in the leaderboard. Our study presents many systems exhibiting high EER in out-of-domain scenarios, highlighting the need for extensive cross-domain evaluation. The leaderboard is hosted on HuggingFace1 and a toolkit for reproducing results across the listed datasets is available on GitHub2.
This article presents a computational study of the Hodgkin-Huxley model and the simulation of action potential propagation in an unmyelinated axon using the finite volume method. By implementing a voltage-clamp patch and tracking detailed ionic and capacitive currents and channel gating, we achieve robust and unit-consistent simulation of action potential initiation and propagation.
The superior dynamic mechanical properties of high-entropy alloys (HEAs) have attracted great interest, while there have been limited studies on the adiabatic temperature change as well as the deformation mechanism in the additively manufactured HEAs under impact loading. In this work, the deformation mechanism of laser powder bed fusion (LPBF)-fabricated CoCrFeMnNi HEAs under high-velocity impact loading was elucidated through multiple microstructural characterization in conjunction with molecular dynamics simulation. Different from CoCrFeMnNi alloys made by thermomechanical processing, both yield strength (YS) and strain hardening behavior of LPBF-fabricated HEA samples are more sensitive to the strain rate in the range of 0.001/s to 50 0 0/s. The YS of the LPBF-fabricated HEAs shows an increasing trend from similar to 452 MPa at 0.001/s to similar to 685 MPa at 50 0 0/s. The strain hardening capacity also increases with the increase of strain rate from 0.001/s to 3000/s. When the strain rates are over 30 0 0/s, high strain hardening capacity is derived from strong dislocation multiplication induced by a high density of deformation twin boundaries. An intriguing mechanical response of the LPBF-fabricated HEAs emerges during loading: the strain hardening rate slightly decreases when the strain rate values increase from 30 0 0/s to 50 0 0/s, which is ascribed to the potential temperature rise-induced dislocation dynamic recovery. Further, in comparison to as-cast HEAs, LPBF-fabricated HEAs show significantly enhanced twinning behavior at high strain rates. The difference is related to unique as-printed microstructure of LPBF-fabricated HEAs: high-density dislocation increasing flow stress, cellular boundaries inducing dislocation dissociation and subsequent nano-twins at high strain rates. These substructures bring enhanced twinning behavior and strain rate-dependent high-velocity impact behavior in LPBF-fabricated HEAs. Our work provides a new insight into the dynamic impact behavior of additively manufactured HEA materials. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
