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Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which comprehensively captures the impacts of climate extremes on renewable generation, and transmission and distribution networks. The model is validated with the 2022 Puerto Rico catastrophic blackout during Hurricane Fiona – a unique system-wide blackout event with complete records of weather-induced outages. The model reveals a resilience pattern that was not captured by the previous models: early failure of certain critical components enhances overall system resilience. Sensitivity analysis on various scenarios of behind-the-meter solar integration demonstrates that lower integration levels (below 45%, including the current level) exhibit minimal impact on system resilience in this event. However, surpassing this critical level without pairing it with energy storage can exacerbate the probability of catastrophic blackouts. 

The “Heliophysics Big Year” was an extended “year” when major solar events engaged the public. NASA and the National Science Foundation (NSF) funded several projects to educate the public on the science of the heliosphere and safe observing practices. In response to this initiative, we worked with other teams to create and disseminate accurate yet engaging information. We expanded our eclipse website (https://space.rice.edu/eclipse/) with activities, citizen science projects, resources, training videos, suggested equipment, and links to other compendia. We directed the Citizen CATE 2024 project, and trained state coordinators and their teams to use the specialized equipment and procedures. We trained teachers at local, regional, national, and international workshops, providing eclipse viewing cards, lenses for making “solar cup projectors,” a pattern for a safe viewing screen, and additional materials. With other teams, we gave presentations to the media at SciLine in San Antonio and hosted public events to demonstrate safe eclipse viewing techniques. The most lasting and impactful product was our planetarium show “Totality,” which was distributed free of license fees. More than 180,000 views of the show and its animations have been documented. We improved our space weather forecasting site (https://mms.rice.edu) and used our email lists (14,000+) to send out real-time warnings about the major solar storm of 10–11 May 2024. In total, we provided nearly two million people with heliophysics information. In summary, the federal/private/business partnerships meant that the events of this “year” were a fun, safe, learning experience for tens of millions of Americans. 

The current study presents the transition of large amplitude oscillations to a fixed hovering point in the context of Bio-inspired flapping robots (BIFRs). The experimental arrangement allows two degrees of freedom for the BIFRs under study: body pitching and translation. The primary objective of this investigation is to compare the flight mechanics characteristics of two almost-identical BIFR configurations: a two-winged configuration and a four-winged one that exploits wing-wing interaction for aerodynamic effects. A motion capture system is utilized to track the two degrees of freedom of each BIFR. The study reveals that the four-winged BIFR exhibits passive transition of large amplitude oscillations to a fixed point beyond a certain frequency, whereas no such transition was observed for the two-winged BIFR at any frequency within the considered range. Realizing that the main difference between the two systems lies within the wing-wing interaction, this study thus underscores the significance of the wing-wing interaction for the transitional response upon the four-winged model. This response might be due to a phenomenon called vibrational stabilization. From the study, it can be implied that wing-wing interaction promotes the transitional response beyond a critical frequency. 

This work presents a neural-network–based computational framework for approximating weak solutions of the incompressible Euler equations in the light of the variational Principle of Minimum Pressure Gradient (PMPG). Classical smooth solutions of Euler fail to capture discontinuities such as vortex sheets and separation lines. To access these weak, possibly discontinuous solutions, we leverage the universal approximation capability of neural networks together with activation functions that admit sharp gradients, such as the Gaussian Error Linear Unit (GeLU), while retaining the differentiability needed for automatic differentiation. The PMPG, derived from Gauss’ principle of least constraint, yields a pressure-free cost functional that selects the dynamically-correct flow field from the set of kinematically admissible ones by minimizing the 𝐿2 norm of the pressure-gradient. We validate the framework on canonical discontinuous configurations. First, we approximate a vortex sheet using a regularized Gaussian representation of the Dirac delta, and show that the neural network recovers the expected vorticity distribution and circulation while enabling stable evaluation of convective acceleration and the PMPG cost. Second, we reconstruct the Parkinson-Jandali family of separating flows over a circular cylinder, demonstrating accurate representation of the separated flow and the associated shear layer. 

This Perspective takes a look back at what the original 12 Principles was envisioned to be, where they are after over a quarter of a century relative to the original... 

We have developed and implemented a first-year undergraduate laboratory experiment at St. Bonaventure University that augments a well-established thermodynamics lab on the spectrophotometric determination of the equilibrium of cobalt(II) chloride hexahydrate with generative artificial intelligence (GenAI). Students utilized free ChatGPT accounts as a support tool in their analysis of laboratory data and iteratively refined their answers to chemical questions. Overall, the inclusion of GenAI in student workflow did not appear to negatively impact student performance, but there were instances where it struggled to produce correct answers for tasks. Descriptive analysis of student responses, chat logs, and instructor discussions suggests that students were resilient to incorrect GenAI output. This laboratory experiment is designed such that it can be easily adopted by faculty with an interest in exploring the use of GenAI in chemistry curricula.