The University of Texas System (UT System) is an American government entity of the state of Texas that includes 14 higher educational institutions throughout the state including eight universities and six health institutions. The UT System is headquartered in Downtown Austin, and has a total enrollment of nearly 240,000 students (largest university system in Texas) and employs 21,000 faculty and more than 83,000 health care professionals,researchers and support staff. The UT System's $30 billion endowment (as of the 2019 fiscal year) is the largest of any public university system in the United States. As of 2018, Reuters ranks the UT System among the top 10 most innovative academic institutions in the world.
Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we built an agentic system, PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with a customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a similar to 144% improvement in performance over baselines.
Polymer chain scission is a key mechanism for fracture of soft materials. It is well known from single-molecule force spectroscopy experiments that the critical condition for chain scission depends on the loading rate and other environmental effects (e.g., temperature and solvent). Common approaches to describing the kinetics of chain scission often assume force-controlled conditions, that is, when a polymer chain is stretched by a prescribed force. As a result of this assumption, chain scission is irreversible, excluding the possibility of healing. In many soft materials, however, self-healing has been observed after fracture, suggesting possibly reversible chain scission. Here, we show that reversible chain scission is possible under displacement-controlled conditions, that is, when a polymer chain is stretched with a prescribed end-to-end distance. We present a breakable freely-jointed chain model, assuming that a polymer chain breaks when one of its links breaks while the other links remain nearly rigid. At a prescribed end-to-end distance, the free energy of the chain has two local minima and a local maximum (the transition state), giving rise to energy barriers for chain scission and healing. As the prescribed displacement increases, the energy barrier decreases for scission but increases for healing, depending on the chain length (number of links) and the potential energy of the link. With the energy barriers, we adopt a kinetic approach to predict the statistics and kinetics of a single polymer chain under tension, first by integrating the rate equation and then by kinetic Monte Carlo simulations. Notably, the present model predicts rate-dependent chain scission, with a lower bound for the rupture force that could be several orders of magnitude lower than the upper bound (which is close to the theoretical strength of the covalent bonds).
Rising temperatures driven by climate change and development patterns affect travel behavior, including public transit use and active transport; however, most studies rely on ambient temperature measures and assume uniform behavioral responses. In this study, we investigated how individuals’ perceived heat thresholds are associated with transit use and moderate-to-vigorous physical activity (MVPA), among adults in a humid subtropical climate. We used data from a 2022 cross-sectional survey of adults in Austin, Texas (n = 1,087). Respondents self-reported their perceived heat threshold (i.e., the temperature at which they would not go on a walk outside), weekly frequency of using public transit, and MVPA levels via the International Physical Activity Questionnaire–Short Form. We used ordinal logistic regression to assess the association between perceived heat threshold and transit use (0, 1–2, ≥3 days), multinomial logistic regression to examine the association within transit use categories (0, 1–2, 3–4, ≥5 days), and multivariable linear regression to assess the association with MVPA. Individuals who reported higher perceived heat thresholds had higher odds of using public transit. This association was primarily driven by the transition from non-use to occasional transit use (1–2 days/week), with no evidence that perceived heat thresholds were associated with more frequent transit use. In contrast, perceived heat threshold was not significantly associated with MVPA. Our findings suggest that individual heat-related behavioral thresholds may help explain individual mobility decisions, but not overall physical activity levels.
Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-tuning or reinforcement learning, which poses a fundamental bottleneck to advancing AI systems toward capabilities beyond human intelligence. To overcome this limitation, we introduce R-Zero, a fully autonomous framework that generates its own training data from scratch. Starting from a single base LLM, R-Zero initializes two independent models with distinct roles, a Challenger and a Solver. These models are optimized separately and co-evolve through interaction: the Challenger is rewarded for proposing tasks near the edge of the Solver capability, and the Solver is rewarded for solving increasingly challenging tasks posed by the Challenger. This process yields a targeted, self-improving curriculum without any pre-existing tasks and labels. Empirically, R-Zero substantially improves reasoning capability across different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks.
