Rensselaer Polytechnic Institute

Pandemic-scale disruptions can trigger disaster-related buying behaviors (DRBBs) that amplify shortages and strain supply chains, making it essential to understand how households adjust essential-goods inventories during crises. Using COVID-19 as an empirical case, this study offers the first multi-country Latin American comparison of DRBBs, measured as revealed changes in household inventory-days, and integrates Prospect Theory and Liquidity Constraints Theory to explain how perceived losses and risk interact with financial capacity. Survey data collected April–June 2020 across seven Latin American countries (N = 1,821) are analyzed using a Poisson rate model with an offset for baseline inventory-days. Results indicate that households with children and those facing greater pandemic severity tend to maintain higher inventory levels of basic supplies, while the effects of income and experienced shortages vary across countries, with reduced purchasing power and constrained access often associated with lower inventory levels. These findings guide targeted interventions to reduce DRBB externalities and protect equitable access to essentials in future crises.

To guarantee the safety and reliability of Autonomous Vehicle (AV) systems, corner cases play a crucial role in exploring the system’s behavior under rare and challenging conditions within simulation environments. However, current approaches often fall short in meeting diverse testing needs and struggle to generalize to novel, high-risk scenarios that closely mirror real-world conditions. To tackle this challenge, we present AutoScenario, a multimodal Large Language Model (LLM)-based framework for realistic corner case generation. It converts safety-critical real-world data from multiple sources into textual representations, enabling the generalization of key risk factors while leveraging the extensive world knowledge and advanced reasoning capabilities of LLMs. Furthermore, it integrates tools from the Simulation of Urban Mobility (SUMO) and Car Learning to Act (CARLA) simulators to automatically interpret and execute the scenario code by LLMs. Our experiments demonstrate that AutoScenario can generate realistic and challenging test scenarios, precisely tailored to specific testing requirements or textual descriptions. Additionally, we validated its ability to produce diverse and novel scenarios derived from multimodal real-world data involving risky situations, harnessing the powerful generalization capabilities of LLMs to effectively simulate a wide range of corner cases. The implementation is available at <a ext-link-type="uri" href="https://github.com/THU-AI-Testing/AutoScenario">https://github.com/THU-AI-Testing/AutoScenario</a>.

ABSTRACT Mixed metal fluorides have been considered as a promising candidate to lower the voltage hysteresis of conversion‐type iron fluoride cathodes, but their cycling stability is limited due to transition metal dissolution and interphase growth in liquid electrolyte batteries. Here, we study the role of incorporating CuF 2 and NiF 2 in the electrochemical performance of FeF 3 cathode in halide‐based solid‐state batteries to test whether we can transfer the kinetic benefit of low voltage hysteresis to solid‐state batteries while using solid electrolyte to eliminate transition metal dissolution and stabilize the interphase. Synchrotron X‐ray absorption spectroscopy results indicated the redox reactions are attributed to Cu 0 /Cu + and Fe 0 /Fe 2+ in 25CuF 2 ‐75FeF 3 and Ni 0 /Ni 2+ and Fe 0 /Fe 3+ in 10NiF 2 ‐90FeF 3 . While no apparent improvement in electrode kinetics can be observed, the incorporation of CuF 2 and NiF 2 can largely improve the cycling stability of FeF 3 cathodes. The results demonstrate the advantages of using solid‐state concept to improve the cycling stability of conversion‐type cathodes.

Delivering judicial decisions requires interpreting complex legal texts, analyzing evidence, and reasoning over jurisprudence and legal principles. Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have shown potential to automate parts of this process; however, practical and measurable benefits in real-world judicial settings remain limited. This paper introduces SARA, an LLM-powered legal reasoning platform deployed in a regional Brazilian court, which demonstrates significant efficiency and quality gains through the integration of LLM agents with a Jurisprudential Knowledge Graph (Jur-KG). SARA automatically extracts and structures key elements from legal documents, including claims, requests, and evidence, and generates legal reasoning grounded in retrieved jurisprudential precedents. The Jur-KG is modeled through an ontology encompassing core legal concepts such as parties, facts, and legal claims, enabling semantic matching and retrieval of relevant case law. By representing cases according to the Legal Case Ontology for the Brazilian Judicial System, SARA supports traceable reasoning and addresses competence questions to assess the coverage, coherence, and justification of AI-generated outputs. Deployment results indicate measurable improvements in processing time, consistency, and explainability, while ensuring compliance with ethical and legal guidelines established by Brazil’s National Council of Justice. This work demonstrates that combining LLM-based agents with domain-specific knowledge graphs can deliver both innovative capabilities and proven impact in judicial decision-making.

Abstract A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements show that this variant has at least twice as much helical structure as the wild-type U1A and is trimeric in solution, in contrast to the monomeric wild type. In sharp contrast, structures predicted by deep-learning AI tools (AlphaFold2 and RoseTTAFold2) and transformer-based tools (OmegaFold and ESMFold) are all highly similar to the wild-type U1A (backbone RMSD &lt; 1 Å). Even more surprising, two of the substituted ionizable residues are predicted to be fully buried in the non-polar core of the protein, an outcome that contradicts well-established physico-chemical principles, as ionizable residues are normally located on the protein surface. To explore this effect further, we generated sequences containing up to all twelve residues that make up the non-polar core of U1A. Across thousands of sequences, and depending on the AI model used, the majority of predicted structures contained fully buried ionizable residues while still maintaining the overall U1A fold. We then examined two additional proteins of comparable size, acylphosphatase and the de novo-designed TOP7 fold, and observed the same phenomenon: AI models frequently predicted structures with buried ionizable residues that nevertheless retained the parent fold. When these AI-predicted structures were subjected to short (50 ns) molecular dynamics simulations using physics-based force fields such as CHARMM or AMBER, the structures rapidly relaxed into ensembles that exposed ionizable residues. We conclude that while AI-based structure prediction tools perform extremely well on naturally occurring sequences, they do not reliably encode the physico-chemical principles governing the placement of ionizable residues. A straightforward remedy is to include a brief molecular dynamics simulation as a final validation step for AI-generated structures.

Abstract Research Summary Complementing the role of AI in facilitating search and identifying combinations of high value in inventive activity, we argue that AI fundamentally alters the innovation landscape by unlocking new combinations that were previously infeasible. This effect arises because AI acts as a powerful shared layer due to its predictive capabilities and its ability to transmit solutions across domains, thereby creating a bridge between previously unconnected elements. Utilizing a matched sample of patents, we show that inventions incorporating AI exhibit a greater degree of novel recombinations compared to those without AI, and that our proposed bridging mechanism is consistent with these novel recombinations. Our study contributes by identifying a new mechanism by which recombinations emerge in inventive activity while also highlighting the role of enabling technologies such as AI in facilitating such recombinations. Managerial Summary How does AI impact inventive activity? We examine the question by studying the patenting activity of US firms over the period 2005–2023. We argue that AI fundamentally alters the innovation landscape by unlocking new combinations that were previously infeasible. This effect arises because AI acts as a powerful shared layer due to its predictive capabilities and its ability to transmit shared solutions, thereby creating a bridge between previously unconnected technological elements. Our analysis demonstrates that inventions that build on AI involve novel recombinations to a greater degree compared to inventions that don’t, and that AI bridges and connects knowledge domains that were hitherto disparate. These findings indicate that AI is more than just an invention of a new method of invention, and that it fundamentally reshapes the nature of inventive activity.

We analyze the thermal and electrical characteristics of the metasurface composed of a coplanar interdigital array of the graphene microribbons (GMRs) connected by nanobridges (NBs). These nanobridges could be implemented using graphene nanoribbons (GNRs) or black-arsenic-phosphorus (b-AsP) nanostructures. When a bias voltage applied between neighboring GMRs, it induces electron and hole two-dimensional systems within the GMRs, leading to thermionic currents that flow through the connecting NB resulting in the self-heating effect. This self-heating effect increases the thermionic currents, creating an effective positive feedback loop between the carrier effective temperature and the injected currents, and the bias voltage. This mechanism may lead to thermal breakdown enabling threshold behavior of current–voltage characteristics and yielding an S-shaped response. The devices based on the GMR/GNR and GMR/AsP metasurface structures can serve as fast voltage-controlled current switches, sensors, thermal terahertz and infrared sources, and among other applications.