This paper is concerned with overlapping modedependent H∞ control for a discrete-time Markov jump linear system in the presence of overlapping local operation modes and incomplete mode transition probabilities. By developing a randomly overlapping decomposition method, the system with deficient global operation modes is reformulated by a set of locally overlapping switched groups with accessible group and local modes. An overlapping group- And local-mode-dependent state feedback controller is delicately constructed. Unlike some existing controllers proposed in the literature, we do not require complete global mode information, but take full advantage of the knowledge of group and local modes of the reformulated system. Moreover, overlapping local modes are allowed to be existed in the formed groups. The stability analysis and control design procedures are developed based on the stochastic Lyapunov functional approach. The proposed framework is shown to be more general, which covers the traditional Markovian jump linear system with completely available global modes as a special case. In the case of only one local group, it is also shown that some existing results from the literature can be regarded as a special case of our derived results. A simulation example is finally presented to show the effectiveness and merits of the proposed method. © 2013 IEEE.

How do nations decide whether to intervene militarily to prevent or stop genocide? How do military and intelligence officers determine the severity of physical or psychological harm to inflict on terrorism suspects? Should a nation escalate troop deployments during an armed conflict that is assessed to be unwinnable? Public officials confronting policy challenges like these must decide among many interests related to transnational security, morality, politics, and the rule of law. Historical evidence and findings from decision research suggest that officials will often decide in favor of security, even when that choice contradicts stated values and otherwise leads to suboptimal welfare outcomes. This Article explores opportunities for lawyers advising the U.S. president and other national security officials to change those outcomes. The prominence effect describes challenges in making quantitative tradeoffs among competing attributes and the likelihood that individuals will decide in favor of the more inherently important, defensible – i.e., prominent – attribute. This Article presents prominence as an impediment to faithful application of transnational humanitarian law because security considerations are more defensible than humanitarian considerations for decision makers and their advisors. Part I describes the research behind prominence and instances when it has been observed or hypothesized in global crises. Part II provides an overview of the national security attorney’s roles in a variety of U.S. national security settings.Part III proposes ways for those attorneys to help policy makers overcome prominence. Part IV discusses other opportunities to mitigate prominence in strategic decision-making processes.

<div class="section abstract"><div class="htmlview paragraph">The requirement on high energy density Li-ion batteries demands high energy chemistry system, this rise concerns on batteries’ safety issue. Battery non-active components, including current collectors and separator play important role in improving battery safety. Composite current collectors, which are consisted of a polymer layer between two plated thin metal layers, are widely treated as a solution to reduce safety concerns caused by high nickel layered cathode materials, e.g. LiNi<sub>1-x-y</sub>Co<sub>x</sub>Mn<sub>y</sub>O<sub>2</sub>, LiNi<sub>1-x-y</sub>Co<sub>x</sub>Al<sub>y</sub>O<sub>2</sub> and LiNi<sub>1-x-y-z</sub>Co<sub>x</sub>Mn<sub>y</sub>Al<sub>z</sub>O<sub>2</sub> with Ni content higher than 0.8. In the meantime, composite current collectors can reduce most weight of current collectors and improve the cell’s gravimetric energy density without replacing cathode or anode materials. Moreover, high thermal stable separator could effectively prevent internal short circuit for it melts in higher temperature. In this work, we came up with a cell design which contains composite current collectors as positive/negative current collector and high thermal stable separator with aramid coating layers. This design improved separator breaking point by 84 °C while reduced current collector melting point by 900 °C, thereby it makes current collector shrinks earlier than separator break, this avoids internal short circuit by detaching cathode and anode coating layer when the separator is still in place. The design was applied in high nickel LiNi<sub>0.91</sub>Co<sub>0.03</sub>Mn<sub>0.05</sub>Al<sub>0.01</sub>O<sub>2</sub> cathode and graphite anode chemistry system with a thick coated electrode (4 mAh cm<sup>-2</sup>, 21 mg cm<sup>-2</sup> per coating side). Pouch cell with 5 Ah nominal capacity was fabricated in this cell design. The electrochemical benefits and drawbacks by adopting positive or negative current collectors or both were evaluated, including the affection in cycling stability, cell resistance and rate performance. Nail penetration and thermal ramping was also adopted to evaluate the safety benefit of the design. The cell shows comparable electrochemical performance and improved cell safety after composite current collector and high thermal stable separator adoption.</div></div>

Reliable protocol knowledge is often difficult to obtain in industrial networks, as industrial communications come with limited documentation, vendor-specific encodings, and opaque payloads. This lack of transparency hinders message interpretation and protocol analysis. To recover this missing protocol knowledge, network-trace-based protocol reverse engineering (PRE) infers message structure, field roles, and interaction logic directly from recorded traces. This enables protocol-aware intrusion detection, process monitoring, and protocol testing and fuzzing without access to device internals. Although PRE has advanced rapidly, existing techniques are developed under diverse objectives and assumptions. As a result, it is often unclear how isolated results relate to an end-to-end reverse-engineering workflow, and how evaluation outcomes should be compared across tasks and protocols. In this article, we cast reverse engineering of industrial protocols from network traces as a task-driven pipeline and articulate a unified task decomposition spanning message type identification, protocol syntax and semantic inference, payload pattern recognition and semantic inference, and protocol state machine reconstruction. For each task, we describe key methodological themes, common evaluation practices, and practical limitations that affect robustness and deployability in industrial settings. We further discuss security, privacy, and ethical risks that accompany increasingly capable PRE, and identify promising research directions toward more systematic, dependable, and deployment-oriented PRE methodologies.

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.

With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFMs). This survey provides a systematic review and comparative analysis of classical MASs (CMASs) and LFM-based MASs (LMASs). First, within a closed-loop coordination framework, CMASs are reviewed across four fundamental dimensions: perception, communication, decision-making, and control. Beyond this framework, LMASs integrate LFMs to lift collaboration from low-level state exchanges to semantic-level reasoning, enabling more flexible coordination and improved adaptability across diverse scenarios. Then, a comparative analysis is conducted to contrast CMASs and LMASs across architecture, operating mechanism, adaptability, and application. Finally, future perspectives on MASs are presented, summarizing open challenges and potential research opportunities.

This study addresses the problem of event-triggered and privacy-preserved platooning control of connected automated vehicles with finite communication resources and data privacy constraints. To efficiently use the communication resources, an asynchronous edge-based dynamic event-triggered mechanism that features adaptive edge-related triggering parameters is designed. Such a design allows for dynamic scheduling of the inter-vehicle communication on a per-edge basis while avoiding the Zeno behavior. Privacy of transmitted vehicular data is then protected through a novel hybrid privacy-preserving strategy that combines output masking with matrix transformation. Subsequently, a set of event-triggered adaptive distributed estimators with guaranteed privacy is developed to facilitate each follower vehicle’s accurate estimation of the full leader motion state. The state estimates are then employed in the design of neural adaptive platoon controllers such that each follower vehicle in the platoon follows the leader with synchronized speed and acceleration under a refined constant time headway spacing policy. Tractable design criteria for admissible estimator and controller gains as well as triggering and learning parameters, are further derived. Finally, co-simulations using CarSim and MATLAB/Simulink are performed to validate the effectiveness of the derived results.

Abstract The rapid growth of the low-altitude economy, including unmanned aerial vehicles (UAVs) and urban air mobility (UAM), is reshaping industries from transportation to emergency response. Powered by advances in fifth-generation (5G) and 5G-advanced (5.5G) connectivity, artificial intelligence (AI), and new energy systems, these platforms are becoming increasingly autonomous and capable. However, their growing software complexity introduces critical cybersecurity risks. Vulnerabilities in communication protocols, onboard firmware, and AI systems can be exploited to hijack UAVs, disrupt operations, or leak sensitive data. While research has addressed isolated aspects, a unified security perspective is still lacking. This work presents a systematic review of software-level security challenges and defenses in low-altitude UAV/UAM systems. We first categorize major attack surfaces across communication, firmware, and AI layers. Furthermore, we survey defense mechanisms suited to real-time, resource-constrained aerial platforms. Finally, we propose future directions, including quantum-resistant communication protocols, hardware-software cosecurity, and edge-AI-driven architectures. Our work aims to inform researchers, practitioners, and regulators in developing integrated, resilient security strategies for the evolving low-altitude ecosystem.

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.

Multi-agent systems (MASs) have demonstrated significant achievements in a wide range of tasks, leveraging their capacity for coordination and adaptation within complex environments. Moreover, the enhancement of their intelligent functionalities is crucial for tackling increasingly challenging tasks. This goal resonates with a paradigm shift within the artificial intelligence (AI) community, from “internet AI” to “embodied AI”, and the MASs with embodied AI are referred to as embodied multi-agent systems (EMASs). An EMAS has the potential to acquire generalized competencies through interactions with environments, enabling it to effectively address a variety of tasks and thereby make a substantial contribution to the quest for artificial general intelligence. Despite the burgeoning interest in this domain, a comprehensive review of EMAS has been lacking. This paper offers analysis and synthesis for EMASs from a control perspective, conceptualizing each embodied agent as an entity equipped with a “brain” for decision and a “body” for environmental interaction. System designs are classified into open-loop, closed-loop, and double-loop categories, and EMAS implementations are discussed. Additionally, the current applications and challenges faced by EMASs are summarized and potential avenues for future research in this field are provided.

Urban environments in developing countries remain affected by legacy asbestos-containing materials, yet integrated assessments of multi-pathway asbestos release and environmental mobilization integrated with demographic distribution remain limited. This study aimed to develop a spatially explicit framework to assess environmental deterioration and asbestos-related environmental hazard where multiple asbestos release pathways converge in a post-ban urban setting, using Cartagena, Colombia, as a case study. A multi-pathway approach was implemented, combining source characterization of asbestos-cement (AC) roofs through microvacuum sampling, analysis of roof runoff and drinking water, spatial distribution of AC pipelines, and demographic data at the neighborhood scale. A total of 72 roof surface samples were collected, of which 92% showed detectable asbestos fibers, with concentrations reaching up to 326 × 106 structures/cm2. Runoff water analysis indicated 85% detection, with average concentrations of 3.5 ± 3.14 million fibers per liter (MFL). Drinking water samples showed 11% positivity, with lower concentrations (mean 1.01 ± 1.59 MFL). Spatial analysis revealed that approximately 9.5% of the urban area exhibited high airborne release potential and 3.1% exhibited high runoff-related hazard, while integrated spatial prioritization identified 5.59% of the city as high priority for intervention. Results indicated that less deteriorated roofs exhibited higher surface fiber availability, suggesting that emission potential is not directly proportional to visible degradation. The integration of environmental and demographic data supported the identification of critical hotspots where multiple asbestos release pathways converge. The proposed methodology provides a novel framework for multi-pathway asbestos spatial prioritization in urban environments and highlights the need for source-based monitoring approaches. These findings support the development of targeted mitigation strategies in cities with widespread legacy asbestos infrastructure.

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything (V2X) cooperative perception (CP), which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This article provides a comprehensive survey of recent developments in V2X CP, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. This article concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in V2X CP.