This article presents a novel model-free distributionally robust framework for a challenging equilibrium-seeking problem (ESP) under fully unknown coupled dynamics. We consider a scenario in the ESP where the state transitions of players are governed by an unknown coupled dynamic system, and each player aims to minimize its own cost function. By predicting the stochastic distribution of player states through Gaussian process regression, we propose a novel distributionally robust approximation (DRA) that transforms the complex ESP with unknown coupled dynamic system into a solvable distributionally robust optimization problem. The gradient of the DRA's objective function is quantified, ensuring solvability. The effectiveness of the proposed DRA framework is evaluated through a nonlinear system, demonstrating comparable performance to model-based methods without requiring any dynamic model.

Crises faced by regional or national governments are usually caused by either natural or human disasters or by the actions of terrorists or other nations. Decision making in the face of a crisis is difficult because the context typically is complex, and decision makers often have insufficient time or information to thoughtfully make decisions that will manage the crisis well. As a result, they often rely on brief discussions and past experiences that omit key dimensions of the crisis, which results in selection of an inferior response alternative. This article describes concepts and procedures to guide the initial phases of planning for crises so that if and when a crisis occurs, a proactively developed framework provides a sound foundation for quickly structuring decisions and implementing more detailed analyses in advance of taking specific crisis-response actions. Case-study examples are used to illustrate three main elements of our suggested approach: identifying the main dimensions of the decisions to be faced, articulating the objectives that are to be achieved, and generating a set of potentially desirable alternatives to best achieve these objectives. Although the decision analysis and behavioral science methods that we rely on are not novel, proactive structuring of crisis decisions that likely will need to be made quickly has been given only limited attention by analysts and decision makers. Improved recognition of the importance of decision-focused proactive planning should result in better decisions when a crisis occurs, leading to a reduction in the adverse consequences for countries and their citizens. Funding: This work was supported by Ben Delo and Longview Philanthropy.

Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle-to-everything (V2X) communication to share perceptual data, enhances detection but is vulnerable to channel impairments, such as noise, fading, and interference. To strengthen the reliability of intelligent transportation systems, this work improves the robustness of V2X cooperative perception under communication conditions that reflect common channel impairments. This paper proposes an Adaptive Feature Fusion Transformer (AFFormer), a Transformer-based framework that mitigates the adverse effects of corrupted features by modeling temporal, inter-agent, and spatial correlations. AFFormer introduces three key modules: Multi-Agent and Temporal Aggregation for context-aware fusion across agents and over time, Dual Spatial Attention for efficient modeling of spatial dependencies, and Uncertainty-Guided Fusion for entropy-driven refinement of fused features. A teacher-student knowledge distillation strategy further enhances robustness by aligning fused features with reliable early-collaboration supervision. AFFormer is validated on the V2XSet and DAIR-V2X datasets, where it consistently outperforms existing methods under both ideal and impaired communication conditions, demonstrating improved robustness to communication-induced feature degradation while maintaining a competitive efficiency-accuracy trade-off.

Abstract Public attitudes toward nuclear weapons remain a critical issue in international security, yet the thinking behind individuals’ support or opposition to their use is not well understood. This study examines how the American public reasons about whether to deploy nuclear weapons in a hypothetical war between the United States and Iran. Participants were asked to state their preference between continuing a ground war, deploying a nuclear strike resulting in 100,000 civilian casualties, or deploying a strike killing 2 million civilians. They then provided an open-ended answer where they described the reasons for their decision. Using Structural Topic Modeling, we identified 10 distinct patterns in participants’ thinking. Some responses emphasized concerns about deterrence or saving lives, while others focused on national security, or retaliation, among other reasons. The type of thinking participants employed was found to be related to their preceding choice, as well as to individual characteristics, such as gender, political affiliation, punitive–authoritarian–nationalist attitudes, and the influence of the relative emotional impact of the 2 bombs (i.e., the better bomb effect). These findings highlight the complexity of the thinking underlying nuclear decision making and help shed light on potential avenues for reducing the risk of a nuclear weapon being deployed again.

Rooftop photovoltaic (PV) potential assessments have advanced significantly through high-resolution geospatial methods. However, most studies remain focused on well-planned urban environments and primarily consider geometric or radiative factors, often neglecting material constraints and deployment realism in heterogeneous cities of the Global South. This study addresses these gaps by developing an automated LiDAR- and GIS-based methodology to estimate rooftop PV potential in Cartagena, Colombia, explicitly integrating cadastral constraints, geometric feasibility, and roof material exclusion. The workflow combines LiDAR-derived elevation data, parcel-based segmentation, slope and aspect filtering, and post-processing techniques to identify PV-suitable rooftops, validated against 482 manually delineated polygons. The optimal configuration (45° slope threshold; 0.25 m buffer) achieved RMSE values of 6.79° (slope) and 20.95° (aspect). A geometry-constrained panel fitting algorithm estimated 3,599,631 panels across 146,091 rooftops, representing 7.06 km2 of suitable area. Compared to simple area-based methods, this approach reduced capacity estimates by approximately 15.3%, demonstrating the importance of geometric realism. A key contribution is the integration of asbestos-cement (AC) roof exclusion, which reduced suitable rooftop area by ~65%, resulting in a final capacity of 1,281,202 panels. Estimated annual generation decreased from 1891.9 GWh/year to 673.4 GWh/year, equivalent to supplying 53.4–126.8% of Cartagena’s households. The proposed methodology provides a scalable framework for realistic urban PV assessment and introduces a dual-purpose planning tool that enables authorities to both prioritize solar deployment and identify areas requiring roof remediation, supporting safer and more controlled energy transitions in developing-country cities.

This paper investigates a data-driven model predictive control (MPC) scheme for time-delay systems with unknown dynamics as well as input and state constraints. An infinite-horizon optimization problem is first formulated, in which a data-driven system representation is employed as a predictive model, and a delay-dependent state feedback controller is designed. By introducing a Lyapunov function, the control problem is systematically reduced to a tractable form, with a sufficient condition for the controller existence derived based on linear matrix inequality techniques. Then, the recursive feasibility of the MPC optimization and the stability of the resulting closed-loop system are rigorously established. Finally, the effectiveness of the proposed method is verified through numerical simulation.

Supplementary methods, tables, and figures for the article “Distinct plasma protein profiles after long-term remission of Cushing’s disease” published in The Journal of Clinical Endocrinology & Metabolism in 2026.

Water utilities frequently perform pipeline-emptying operations for maintenance, repair, and operational management. This process involves transient flow conditions with entrapped air. It must be carefully controlled, as the expansion of air pockets can generate sub-atmospheric pressures that may lead to pipeline collapse. The mathematical modelling of emptying processes with air valves has been extensively studied in recent years; however, such approaches typically rely on complex algebraic–differential equation systems. This study advances understanding of this phenomenon by proposing a novel procedure that uses a machine learning model to approximate system behaviour while avoiding fully coupled hydraulic formulations. An experimental facility consisting of a pipeline with an internal diameter of 0.042 m and a total length of 4.6 m was used, in conjunction with a complete regulation valve manoeuvre. The system was first calibrated using experimental data and subsequently employed in Monte Carlo simulations to generate a dataset for training the machine learning model. The results demonstrate that a Rational Quadratic Gaussian Process Regression model can accurately predict the minimum sub-atmospheric pressure, achieving a coefficient of determination greater than 0.999 during validation and testing. The proposed framework is presented as a proof-of-concept and has been validated only for the specific case study analysed. While the results highlight its potential to support planning for emptying operations under varying air-admission conditions and air-pocket sizes, further validation is required before generalising to real-world water distribution systems. For practical implementation, the model must be appropriately trained for each specific installation.

This paper identifies the difficulties encountered in extending thermostatics to the description of irreversible processes in solid materials, and compares the resolutions proposed for them. The difficulties stem from the requirement of a continuum formulation of. the theory in view of the nonuniform fields accompanying processes, and also from the paradoxes which arise in a casual adaption of the theoretical framework to the description of state for inelastic behavior such as plasticity, creep, and relaxation. The discussion is in terms of the classical theory of irreversible processes. Basic concepts and results are compared with the more recent nonlinear field theory, employing memory functional representations. Finally, the role of internal variables is examined in bringing inelastic behavior within the framework of the classical theory. One noteworthy result, for a wide class of rate-dependent materials, is the existence of a potential function of stress, at each set of · internal variables, from which the inelastic strain rate may be derived. 1.