Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method.

Uncoordinated random-access protocols are attractive for underwater acoustic (UWA) networks due to their simplicity and low overhead, especially for data collection applications in scuba diving. However, the performance is limited by severe collisions and the challenging UWA channel, including rich multipath and time-varying channel (caused by Doppler effects and user movements). Existing UWA physical-layer waveforms struggle to resolve collisions while maintaining high data rates. This paper presents ZCMod, a Zadoff-Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to mitigate interference and encodes multiple bits via cyclic shifts for high data rates. To address UWA-specific challenges, ZCMod introduces two key designs: 1) shape-based demodulation, which tracks channel response shifts to combat multipath effects; 2) auxiliary modulation, where each symbol is modulated with two ZC sequences—one for channel estimation and the other for data transmission—to handle fast time-varying channels. Experiments and simulations demonstrate that a) ZCMod achieves more robust BER performance and eliminates error floors compared to state-of-the-art (SOTA) methods in slight time-varying channels; and b) ZCMod maintains stable throughput in fast time-varying channels, while SOTA approaches suffer significant degradation.

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.

Close-quarters encounter scenarios, characterized by their potentially severe consequences, represent one of the most formidable challenges in ship collision avoidance decision-making and trajectory planning. An efficient collision avoidance framework must simultaneously satisfy critical requirements for real-time decision-making, accurate trajectory planning, and effective vessel maneuvering, all while maintaining rigorous compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). This framework serves as the critical safeguard for the navigation of maritime autonomous surface ships. To design an efficient framework, this study proposes an innovative method for real-time partitioning of collision-free navigable space, termed “adaptive spatio-temporal voxels”, which accounts for the ship’s maneuverability by incorporating the velocity obstacle concept. Furthermore, a spatio-temporal graph, incorporating COLREGs compliance, yields an optimal collision avoidance decision represented as a sequence of voxels. These voxel sequences subsequently serve as constraints within a model predictive control (MPC) framework to optimize and plan a safe, navigable trajectory. This framework has undergone rigorous testing in a variety of close-quarters encounter scenarios. Its computational performance, with both decision-making and trajectory optimization completed within 1s, effectively meets the demands of real-time maritime collision avoidance. The results demonstrate its ability to generate efficient collision avoidance trajectories in complex environments, significantly enhancing overall collision avoidance performance.

This paper is concerned with event-triggered H∞ filtering for networked systems. A novel event-triggering scheme is proposed by taking network dynamics into account simultaneously. First, an information dispatching middleware is constructed to establish a novel framework for networked systems, where two modules namely information selection module and congestion avoidance module are introduced. The information selection module aims to regulate the transmission of the sampled data in terms of a predefined event-triggering condition. The congestion avoidance module is used to schedule those sampled data released by the information selection module to the filter. Second, the on-line scheduling strategy is proposed under this framework. Then the filtering error system based on network dynamics is formulated as a system with an interval time-varying delay. Third, Lyapunov-Krasovskii functional approach is employed to formulate a new sufficient condition to ensure the stability and to guarantee a prescribed H∞ noise attenuation performance for the filtering error system. Based on this condition, H∞ filtering parameters, network dynamic controllers and event-triggering parameters can be co-designed provided that a set of linear matrix inequalities are feasible. Finally, an example is given to illustrate the merits and effectiveness of the method proposed in this paper.

Hybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic–environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO2 emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development.

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.

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.

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.

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.