Decentralized air tra ffic management systems o ff er a scalable alternative to centralized control, but often assume high levels of cooperation. In practice, such assumptions frequently break down since airspace sectors operate independently and prioritize local objectives. We address the problem of sector overload in decentralized air tra ffic management by proposing a mechanism that models self-interested behaviors based on best response dynamics. Each sector adjusts the departure times of flights under its control to reduce its own congestion, without any shared decision making. A tunable cooperativeness factor models the degree to which each sector is willing to reduce overload in other sectors. We prove that the proposed mechanism satisfies a potential game structure, ensuring that best response dynamics converge to a pure Nash equilibrium, under a mild restriction. In addition, we identify a su fficient condition under which an overload-free solution corresponds to a global minimizer of the potential function. Numerical experiments using 24 hours of European flight data demonstrate that the proposed algorithm substantially reduces overload even with only minimal cooperation between sectors, while maintaining scalability and matching the solution quality of centralized solvers. Keywords: Air Tra ffi c Management, Noncooperative Coordination, Game Theory, Potential Game, Decentralized System, Sector Overload 1. Introduction Decentralized approaches to air tra ffic management (A TM) are gaining attention as scalable alternatives to centralized control [1, 2, 3, 4, 5], due to the increasing complexity of air tra ffic.

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), offer powerful capabilities for interpreting the complex data landscape in healthcare. In this paper, we present a comprehensive overview of the capabilities, requirements and applications of GenAI for deriving clinical insights and improving clinical efficiency. We first provide some background on the forms and sources of patient data, namely real-time Remote Patient Monitoring (RPM) streams and traditional Electronic Health Records (EHRs). The sheer volume and heterogeneity of this combined data present significant challenges to clinicians and contribute to information overload. In addition, we explore the potential of LLM-powered applications for improving clinical efficiency. These applications can enhance navigation of longitudinal patient data and provide actionable clinical decision support through natural language dialogue. We discuss the opportunities this presents for streamlining clinician workflows and personalizing care, alongside critical challenges such as data integration complexity, ensuring data quality and RPM data reliability, maintaining patient privacy, validating AI outputs for clinical safety, mitigating bias, and ensuring clinical acceptance. We believe this work represents the first summarization of GenAI techniques for managing clinician data overload due to combined RPM / EHR data complexities.

Social media platforms such as Twitter and Facebook have become deeply embedded in our everyday life, offering a dynamic stream of localized news and personal experiences. The ubiquity of these platforms position them as valuable resources for identifying estate-related issues, especially in the context of growing urban populations. In this work, we present a language model-based system for the detection and classification of estate-related events from social media content. Our system employs a hierarchical classification framework to first filter relevant posts and then categorize them into actionable estate-related topics. Additionally, for posts lacking explicit geotags, we apply a transformer-based geolocation module to infer posting locations at the point-of-interest level. This integrated approach supports timely, data-driven insights for urban management, operational response and situational awareness.

Compliant mechanisms have significant potential in precision applications due to their ability to guide motion without contact. However, an inherent vulnerability to fatigue and mechanical failure has hindered the translation of compliant mechanisms to real-world applications. This is particularly challenging in service environments where loading is complex and uncertain, and the cost of failure is high. In such cases, mechanical hard stops are critical to prevent yielding and buckling. Conventional hard-stop designs, which rely on stacking single-DOF limits, must be overly restrictive in multi-DOF space to guarantee safety in the presence of unknown loads. In this study, we present a systematic design synthesis method to guarantee overload protection in compliant mechanisms by integrating coupled multi-DOF motion limits within a single pair of compact hard-stop surfaces. Specifically, we introduce a theoretical and practical framework for optimizing the contact surface geometry to maximize the mechanism's multi-DOF working space while still ensuring that the mechanism remains within its elastic regime. We apply this synthesis method to a case study of a caged-hinge mechanism for orthopaedic implants, and provide numerical and experimental validation that the derived design offers reliable protection against fatigue, yielding, and buckling. This work establishes a foundation for precision hard-stop design in compliant systems operating under uncertain loads, which is a crucial step toward enabling the application of compliant mechanisms in real-world systems.

This paper presents R-CAGE (Rhythmic Control Architecture for Guarding Ego), a theoretical framework for restructuring emotional output in long-term human-AI interaction. While prior affective computing approaches emphasized expressiveness, immersion, and responsiveness, they often neglected the cognitive and structural consequences of repeated emotional engagement. R-CAGE instead conceptualizes emotional output not as reactive expression but as ethical design structure requiring architectural intervention. The model is grounded in experiential observations of subtle affective symptoms such as localized head tension, interpretive fixation, and emotional lag arising from prolonged interaction with affective AI systems. These indicate a mismatch between system-driven emotion and user interpretation that cannot be fully explained by biometric data or observable behavior. R-CAGE adopts a user-centered stance prioritizing psychological recovery, interpretive autonomy, and identity continuity. The framework consists of four control blocks: (1) Control of Rhythmic Expression regulates output pacing to reduce fatigue; (2) Architecture of Sensory Structuring adjusts intensity and timing of affective stimuli; (3) Guarding of Cognitive Framing reduces semantic pressure to allow flexible interpretation; (4) Ego-Aligned Response Design supports self-reference recovery during interpretive lag. By structurally regulating emotional rhythm, sensory intensity, and interpretive affordances, R-CAGE frames emotion not as performative output but as sustainable design unit. The goal is to protect users from oversaturation and cognitive overload while sustaining long-term interpretive agency in AI-mediated environments.

Societal cognitive overload, driven by the deluge of inform ation and complexity in the AI age, poses a critical challenge to human well-being an d societal resilience. This paper argues that mitigating cognitive overload is not only essential for improving present-day life but also a crucial prerequisite fo r navigating the potential risks of advanced AI, including existential threats. W e exa mine how AI exacerbates cognitive overload through various mechanisms, incl uding information proliferation, algorithmic manipulation, automation anxiet ies, deregulation, and the erosion of meaning. The paper reframes the AI safety debate t o center on cognitive overload, highlighting its role as a bridge between near-te rm harms and long-term risks. It concludes by discussing potential institutional adaptations, research directions, and policy considerations that arise from adopti ng an overload-resilient perspective on human-AI alignment, suggesting pathways fo r future exploration rather than prescribing definitive solutions. W e stand at a precipice. Human societies are increasingly st ruggling to process the sheer volume and complexity of information in the digital age, a conditio n dramatically amplified by the rapid proliferation of artificial intelligence (AI). While Toffle r (1970) foresaw "future shock" from accelerating change and Eppler & Mengis (2004); Bawden & Robin son (2009) analyzed individual information overload, Byung-Chul Han, in his critique of ne oliberalism and technological domination (Han, 2017), argues that contemporary society faces a regime of technological domination that exploits and overwhelms the psyche. This exploitation and overwhelming of the psyche, now dramatically amplified by AI-driven information and comple xity, elevates information overload to a systemic crisis: societal cognitive overload .

The rapid electrification of transportation, driven by stringent decarbonization targets and supportive policies, poses significant challenges for distribution system operators (DSOs). When numerous electric vehicles (EVs) charge concurrently, local transformers risk overloading - a problem that current tariff-based strategies do not adequately address. This paper introduces an aggregator-based coordination mechanism that shifts EV charging from congested to underutilized periods using a rule-based scheduling algorithm. Unlike conventional methods that depend on complex real-time pricing signals or optimization-heavy solutions, the aggregator approach uses a simple yet effective "laxity" measure to prioritize charging flexibility. To assess technical and economic viability, a multi-agent simulation was developed to replicate residential user behavior and DSO constraints under the use of a 400 kVA low-voltage transformer. The results indicate that overloads are completely eliminated with minimal inconvenience to users, whose increased charging costs are offset by the aggregator at an annual total of under DKK 6000 - significantly lower than the cost of infrastructure reinforcement. This study contributes by (i) quantifying the compensation needed to prevent large-scale overloads, (ii) presenting a replicable, computationally feasible, rule-based aggregator model for DSOs, and (iii) comparing aggregator solutions to costly transformer upgrades, underscoring the aggregator's role as a viable tool for future distribution systems.

It is a significant requirement for a quadrotor trajectory planner to simultaneously guarantee trajectory quality and system lightweight. Many researchers focus on this problem, but there's still a gap between their performance and our common wish. In this paper, we propose an ultra lightweight quadrotor planner with time-optimal primitives. Firstly, a novel motion primitive library is proposed to generate time-optimal and dynamical feasible trajectories offline. Secondly, we propose a fast collision checking method with a deterministic time consumption, independent of the sampling resolution of the primitives. Finally, we select the minimum cost trajectory to execute among the safe primitives based on user-defined requirements. The propsed transformation relation between the local trajectories ensures the smoothness of the global trajectory. The planner reduces unnecessary online computing power consumption as much as possible, while ensuring a high-quality trajectory. Benchmark comparisons show that our method can generate the shortest flight time and distance of trajectory with the lowest computation overload. Challenging real-world experiments validate the robustness of our method.

This paper addresses the critical integration of electric vehicles (EVs) into the electricity grid, essential for achieving carbon neutrality by 2050. The rapid increase in EV adoption poses significant challenges to the existing grid infrastructure, particularly in managing the increasing electricity demand and mitigating the risk of grid overloads. Centralized EV charging strategies are investigated due to their potential to optimize grid stability and efficiency, compared to decentralized approaches that may exacerbate grid stress. Utilizing a multi-agent based simulation model, the study provides a realistic representation of the electric vehicle home charging ecosystem in a case study of Strib, Denmark. The findings show that the Earliest-deadline-first and Round Robin performs best with 100% EV adoption in terms of EV user satisfaction. The simulation considers a realistic adoption curve, EV charging strategies, EV models, and driving patterns to capture the full ecosystem dynamics over a long-term period with high resolution (hourly). Additionally, the study offers detailed load profiles for future distribution grids, demonstrating how centralized charging strategies can efficiently manage grid loads and prevent overloads. Keywords: multi-agent based simulation, multi-agent systems, agent-based modeling, electric vehicle, charging strategies, charging algorithms.

The growing shift towards a Smart Grid involves integrating numerous new digital energy solutions into the energy ecosystems to address problems arising from the transition to carbon neutrality, particularly in linking the electricity and transportation sectors. Yet, this shift brings challenges due to mass electric vehicle adoption and the lack of methods to adequately assess various EV charging algorithms and their ecosystem impacts. This paper introduces a multi-agent based simulation model, validated through a case study of a Danish radial distribution network serving 126 households. The study reveals that traditional charging leads to grid overload by 2031 at 67% EV penetration, while decentralized strategies like Real-Time Pricing could cause overloads as early as 2028. The developed multi-agent based simulation demonstrates its ability to offer detailed, hourly analysis of future load profiles in distribution grids, and therefore, can be applied to other prospective scenarios in similar energy systems. Keywords: multi-agent based simulation, multi-agent systems, agent-based modeling, electric vehicle, charging strategies.