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.
Achieving consensus among self-interested agents remains challenging in decentralized multi-agent systems, where agents often have conflicting preferences. Existing coordination methods enable agents to reach consensus without a centralized coordinator, but do not provide formal guarantees on system-level objectives such as efficiency or fairness. To address this limitation, we propose a regulated decentralized negotiation framework that augments a decentralized negotiation mechanism with limited regulatory oversight. The framework builds upon the trading auction for consensus, enabling self-interested agents with conflicting preferences to negotiate through asset trading while avoiding direct disclosure of private asset valuations. We introduce an oversight mechanism, which implements a taxation-like intervention that guides decentralized negotiation toward system-efficient and equitable outcomes while also regulating how fast the framework converges. We establish theoretical guarantees of finite-time termination and derive bounds linking system efficiency and convergence rate to the level of regulatory intervention. A case study based on the collaborative trajectory options program, a rerouting initiative in U.S. air traffic management, demonstrates that the framework can reliably achieve consensus among self-interested airspace sector managers, and reveals how the level of regulatory intervention regulates the relationship between system efficiency and convergence speed. Taken together, the theoretical and experimental results indicate that the proposed framework provides a mechanism for regulated decentralized coordination that preserves noncooperative final selection while safeguarding system-level objectives.
Motivated by a flurry of recent work on efficient tensor decomposition algorithms, we show that the celebrated moment matrix extension algorithm of Brachat, Comon, Mourrain, and Tsigaridas for symmetric tensor canonical polyadic (CP) decomposition can be made efficient under the right conditions. We first show that the crucial property determining the algorithm's complexity is the regularity of a target decomposition. This observation allows us to reduce the overall complexity of the vanilla algorithm, while also unifying results from existing literature. We introduce the notion of efficient formats: tuples (n, d, r) such that generic tensors in Sd & Copf;n+1 of rank rare efficiently decomposable via a modification of the extension algorithm-solving a linear system; moreover, this implies a new criterion for determining uniqueness of decompositions. We prove that (n, 4, r) is an efficient format for r <= 2n + 1, implying that the modified extension algorithm is efficient for ranks exceeding those allowed by simultaneous diagonalization. We then formulate a conjecture that (n, 4, r) is an efficient format for r = O(n2), implying that the modified extension algorithm is efficient for ranks asymptotically matching existing algorithms, with improved leading coefficient. Towards an affirmative answer, we give computer-assisted proofs verifying correctness for n = 2, ... , 17. Next, we demonstrate that some classes of tensors with nonunique decompositions can be decomposed efficiently via the modified extension algorithm, a first for efficient algorithms. Of particular interest is the class of monomials, for which the extension algorithm is not only efficient but also improves on existing theory by explicitly parameterizing the space of decompositions. Code for implementations of the efficient algorithm for generic tensors and monomials is provided, along with several numerical examples. (c) 2026 Published by Elsevier Ltd.
Polymer chain scission is a key mechanism for fracture of soft materials such as elastomers and hydrogels. 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 with healing 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 for the survival probability of the chain 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 in the backbone).
Purpose Sleep-related symptoms in head and neck cancer are poorly characterized and often under-recognized. We examined symptom trajectories, prevalence of moderate/severe symptoms, and associations between severe sleep-related patient-reported symptoms and overall survival (OS) in oropharyngeal cancer (OPC) patients receiving radiotherapy (RT). Methods This retrospective analysis included a longitudinal cohort of 372 patients with OPC treated with curative-intent RT (± chemotherapy) at a tertiary center. Symptoms were assessed at the earliest available pre-RT baseline, RT start, RT end, and 6 months post-RT using the MD Anderson Symptom Inventory–Head and Neck (MDASI-HN). The sleep-related symptom domains were fatigue, sleep disturbance, and drowsiness. OS was evaluated using Kaplan–Meier and multivariable Cox models. Results Severe fatigue, sleep disturbance, and drowsiness were infrequent at baseline but peaked at RT end (26%, 15%, and 18%, respectively) and returned to near-baseline levels by 6 months (6%, 4%, and 2%). Severe longitudinal fatigue (HR 5.05), sleep disturbance (HR 3.86), and drowsiness (HR 8.24) were independently associated with worse OS. Severe symptoms at RT start were also associated with worse OS (fatigue HR 3.58; sleep disturbance HR 5.08; drowsiness HR 7.76; all p < 0.05). Aside from age, severe average daily symptom scores and severe symptoms at RT start were associated with OS after multivariable adjustment. Conclusion Severe sleep-related symptoms peaked during RT. Severe fatigue, sleep disturbance, and drowsiness, both longitudinally and at RT start, were associated with worse OS. These observational associations do not establish causality; symptom assessment may help identify patients who warrant supportive evaluation.
