To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to manage wildfire ignition risks through de-energizations while reducing load shedding. OPS problems are computationally challenging Mixed-Integer Linear Programs (MILPs) that must be solved rapidly and frequently in operational settings. For a particular power system, OPS instances share a common structure with varying parameters related to wildfire risks, loads, and renewable generation. This motivates the use of Machine Learning (ML) for solving OPS problems by exploiting shared patterns across instances. In this paper, we develop an ML-guided framework that quickly produces high-quality de-energization decisions by extending existing ML-guided MILP solution methods while integrating domain knowledge on the number of energized and de-energized lines. Results on a large-scale realistic California-based synthetic test system show that the proposed ML-guided method produces high-quality solutions faster than traditional optimization methods.
We introduce a new method for constructing local-in-time solutions of the incompressible Euler equations in Sobolev spaces on an arbitrary Sobolev bounded domain. The method is based on a construction of an analytic solution in an analytically approximated domain, after which we apply analytic persistence to extend the analytic solution using given a priori bounds in Sobolev spaces. The method does not introduce any modification or regularization of the equations themselves and appears applicable to many other PDEs.
Entrainment is a social adaptive mechanism which in human spoken interaction includes interlocutors unconsciously adjusting their vocal patterns and related behaviors to match those of their conversation partner. Entrainment offers important insights toward understanding the socio-cognitive characteristics of an individual. Quantifying entrainment patterns can also inform clinical diagnosis, long-term monitoring, and individualized interventions in neuro-developmental disorders characterized by deficits in communication and social interaction, such as Autism Spectrum Disorder (ASD). In this work, we model vocal entrainment in dyadic child-inclusive conversations to analyze behavioral traits of children with and without an autism diagnosis. Specifically, we explore contrastive-learning based unsupervised modeling to learn representations related to entrainment from speech features. We validate the proposed measures by using them to differentiate real conversations from simulated shuffled ones. Furthermore, we illustrate their utility in modeling various behaviors relevant to autism symptoms by correlation experiments and comparing the variation of the introduced measures in children under different demographic conditions.
This study explores how independent travel empowers women travel influencers and how meanings of empowerment are negotiated through interactions with their followers by examining the empowerment process at both individual and social levels. Drawing on capacity building theory and social cognitive theory, it investigates how travel experiences and digital content creation contribute to personal agency, skill development, and socially mediated expressions of empowerment. Using an interpretive paradigm and a netnographic approach, the study analyses the social media practices of 17 female travel influencers. Findings show that independent travel fosters self-efficacy, confidence, and the acquisition of skills and knowledge among influencers, while followers express inspiration, aspiration, identification, and negotiation of constraints in response to shared narratives of empowerment. The study underscores the dynamic interplay among digital influence, community engagement, and gendered empowerment, offering insights into virtual capacity building within contemporary travel cultures.
Recently, transient plasma has been shown to improve the combustion of carbon-free (i.e., "green") fuels, i.e., hydrogen and ammonia in engine applications. However, the mechanism underlying this enhancement remains poorly understood. Here, transient plasma ignition is shown to have fundamental effects on combustion that increase the ability to ignite difficult-to-ignite fuel mixtures and increase the burning rate of the flame. Using canonical constant volume combustion chamber experiments, it was observed that the pressure rise increases twice as fast, and the pressure rate (dP/dt) is as much as four times higher when the combustion of ammonia mixtures is initiated by transient plasma ignition compared to conventional spark ignition. Transient plasma ignition is induced by a series of high-voltage (similar to 15-20 kV) pulses approximately 20 ns in duration. These pulses generate fluid motion and multiscale mixing, which accelerate the burning rate during early timescales. Furthermore, the rapid discharge induces atomic hydrogen radicals (spectroscopically detected), which have been shown to decrease the ignition delay and increase the flame burn rate. The objective of this study is to examine how transient plasma discharges modify early-stage oxidation pathways in ammonia-dominated combustion. Using a 70% NH3 / 30% H-2 mixture at an equivalence ratio of 1.5 to preserve key kinetic limitations while ensuring stable experimental operation, this work focuses on mechanistic interrogation of radical-driven pathway acceleration rather than comprehensive performance mapping. By combining spectroscopy, constant-volume experiments, and kinetic modeling, we seek to clarify how plasma-generated radicals influence early heat release and reaction evolution in ammonia-rich systems.
