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.
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.
Purpose Emerging evidence demonstrates cost savings when low-acuity hand procedures are performed in office-based settings compared with hospital facilities or ambulatory surgery centers (ASCs). However, it is unclear whether these advantages translated into broader adoption of office-based care. Accordingly, this study sought to evaluate temporal trends in procedural setting and reimbursement rates for three common hand procedures. Methods A retrospective analysis was conducted using Centers for Medicare & Medicaid Services Physician/Supplier Procedure Summary and Physician Fee Schedule datasets from 2011 to 2023. All fee-for-service Medicare Part B claims for carpal tunnel release (CTR), trigger finger release (TFR), and de Quervain release (DQR) were included. The primary outcome was the shift in the place of service: office, outpatient hospital, inpatient hospital, or ASC. The secondary outcome was the facility and nonfacility reimbursements for each procedure. Poisson and mixed-effects regression models were used to evaluate temporal volume and reimbursement trends. Results From 2011 to 2023, ASCs were the dominant setting, accounting for 44.9% of CTR, 50.2% of TFR, and 53.3% of DQR procedures. The proportion of office-based procedures increased modestly across all procedures (CTR: 3.94% to 5.55%; TFR: 7.54% to 12.31%; DQR: 4.31% to 6.78%). Furthermore, although nominal reimbursements increased over time, overall inflation-adjusted reimbursements declined consistently after 2013. Notably, inflation-adjusted reimbursements across the 12-year period significantly decreased for TFR from $586.70 ± $183.98 to $496.64 ± $178.48 and CTR from $585.11 ± $44.93 to $489.69 ± $40.60. Conclusions Despite evidence supporting the safety and cost savings of office-based hand procedures, minimal migration toward office-based care was observed for low-acuity hand procedures. The authors hypothesize that a realignment of reimbursement policy may incentivize office-based care. Type of study/level of evidence Economic/decision analysis III.
Background Youth nicotine/tobacco use has shifted in recent decades. Updated national estimates are essential for policy and practice. We examined changes in youth nicotine/tobacco use in the United States (US) across three annual surveys (2023–2025) and assessed sociodemographic correlates and multi-product co-use patterns in 2025. Methods Self-reported lifetime and past-30-day use of seven nicotine/tobacco products were analyzed among US 10th and 12th graders (2023–2025; n = 47,524; mean age 17.0 years [standard deviation 1.1]; 47.5% female, 48.6% male, 3.8% reported another sex or preferred not to report; 41.0% non-Hispanic White, 22.3% Hispanic, 16.5% Multiracial, 13.7% non-Hispanic Black) in Monitoring the Future’s nationally representative cross-sectional survey. Changes in prevalence across the three survey years were examined using survey-weighted log-binomial regression. Findings Lifetime nicotine pouch use increased from 3.1% in 2023 to 7.5% in 2025 (average annual prevalence ratio [PR] 1.53, 95% confidence interval [CI] 1.32–1.78, p < 0.0001), and past-30-day use rose from 1.2% to 2.9% (PR 1.45, 95% CI 1.19–1.75, p = 0.0012). Prevalences were largely unchanged for past-30-day cigar/cigarillo, combustible cigarette, e-cigarette, hookah, nicotine candy, and smokeless tobacco use and lifetime cigarette, e-cigarette, and smokeless tobacco use. In 2025, e-cigarettes were the most used product, followed by nicotine pouches. Use of several products was higher among male, rural, non-Hispanic White, and Multiracial youth, and those with lower parental education. Among users, co-use of multiple products was prevalent, with the strongest associations observed for combustible cigarette–e-cigarette co-use and nicotine pouch–smokeless tobacco co-use. Interpretation There was little evidence of change in US youth nicotine/tobacco use, except for rising nicotine pouch use. Combustible tobacco use remained at historically low levels. In 2025, use was concentrated in sociodemographic groups and marked by high multi-product use. Funding National Cancer Institute, National Institute on Drug Abuse, US Food and Drug Administration, and Tobacco-Related Disease Research Program of the University of California.
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.
