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.

Square piles play a crucial role as load-bearing components in transmission tower foundations, impacting the safety of power line systems under complex loading conditions due to their superior lateral resistance performance. However, existing research has predominantly focused on circular piles, with limited systematic investigation into the lateral bearing mechanisms of square piles. This study utilizes finite element analysis to develop a comprehensive full-scale model incorporating pile-soil interactions and soil spatial effects. The study systematically analyzes the effects of pile side length and embedment ratio on load-displacement curves, horizontal ultimate stage, and overturning rotation center. The results indicate that (1) Base on load-displacement curve evolution, plastic strain distribution and experimental specifications, under horizontal ultimate state in sandy soil, the critical displacement threshold at the pile head of square piles is 10 mm. (2) An increase in ultimate lateral bearing capacity by a factor of 2.4 with the square pile side length increasing from 1.2 m to 2.0 m, and an increase in bearing capacity by 0.9 times with the embedment ratio rising from 1.0 to 2.0. (3) The overturning rotation center is located at the bottom central axis position of the square pile. (4) Under horizontal ultimate loading conditions, square piles induce lateral earth pressure in sandy soil that follows a parabolic distribution along the embedding depth, with peak stress occurring at 0.5 times the pile's embedding depth below the soil surface. The results provide theoretical and practical references for optimizing the seismic and disaster-resistant design of transmission infrastructure.

This research presents the development of a new Hybrid Operational Strategy model for energy management optimization designed to evaluate the feasibility of implementing hybrid renewable energy modules in ports, aiming to improve their efficiency, sustainability, and innovation. The proposed system integrates photovoltaic, wind, and hydrokinetic energy sources, incorporating electronic components and assessing two energy storage technologies—Pump-as-Turbine (PAT) and battery systems—to determine the most viable solution for practical deployment. The optimization algorithm allows a concurrent refinement process for the power generation data of each renewable source. Four scenarios were analyzed within this optimization framework: two assessing the performance of single modules employing each storage technology individually, and two exploring configurations with multiple modules operating in parallel, either with independent storage units or a single centralized system. Battery storage was identified as the most feasible option based on the optimization outcomes. Considering the demand characteristics and generation capacity of the hybrid module, the configuration yielding the best overall performance consisted of a single module incorporating battery storage, achieving 90% demand coverage and demonstrating economic viability with a Net Present Value (NPV) of 9182.79 € and an Internal Rate of Return (IRR) of 10.88%.

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.

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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.

This article focuses on the cooperative tracking problem for a group of mobile robots (MRs) under modeling uncertainties, malicious packet losses (MPLs), and network-induced delays. First, to cope with MPLs on communications, a data packet analyzer is developed such that the intermittent packet arrival instants under MPLs can be well detected and recorded. Then, a cooperative learning (CL)-based tracking control scheme containing three control layers is designed for the group of MRs to achieve cooperative tracking. Specifically, at the kinematic layer, two networked cooperative tracking guidance laws are developed to coordinate MRs’ movements and prescribe commanded guidance signals of velocities. At the learning layer, by virtue of the designed online CL laws, modeling uncertainties are approximated in a cooperative manner. At the kinetic layer, two resilient dynamic control laws are constructed to regulate the action of involved MRs. It is demonstrated that, under the designed control scheme, the resulting cooperative tracking error dynamics is uniformly ultimately bounded. Finally, simulation and experiment examples are elaborated to verify the effectiveness and merits of the designed control scheme.

Background: Epidermal growth factor receptor (EGFR) exon 20 insertion (ex20ins)-mutant non-small cell lung cancer (NSCLC) is characterized by limited sensitivity to standard-dose EGFR tyrosine kinase inhibitors (EGFR-TKIs) and historically poor clinical outcomes. Although agents such as amivantamab and other targeted therapies have expanded treatment options, access barriers and marked variant-specific heterogeneity remain major challenges. Emerging evidence suggests that dose-escalated third-generation EGFR-TKIs may provide clinical benefit in selected ex20ins subtypes, yet real-world data are scarce. Case presentation: , with low PD-L1 expression. In the setting of limited access to amivantamab at diagnosis and preliminary evidence supporting intensified EGFR inhibition in certain "near-loop" ex20ins variants, the patient received off-label high-dose osimertinib 160 mg once daily as first-line therapy. She achieved a durable partial response with manageable Grade 1 skin and nail toxicities and no dose reductions. Following disease progression, and after multidisciplinary discussion and informed consent, she was switched to off-label furmonertinib 240 mg once daily, resulting in additional disease control. Sequential high-dose osimertinib followed by high-dose furmonertinib yielded an overall survival of approximately 37 months. Literature and trend overview: To contextualize this case, we conducted a targeted narrative review and a descriptive bibliometric overview using CiteSpace (2000-2023) based on Web of Science Core Collection records. This analysis demonstrated a growing global research focus on third-generation EGFR-TKIs and variant-specific treatment strategies for EGFR ex20ins-mutant NSCLC, supporting the rationale underpinning the therapeutic approach adopted in this report. Conclusion: Sequential high-dose third-generation EGFR-TKIs may offer clinically meaningful benefit in selected EGFR ex20ins cases; however, this strategy remains non-standard and should be regarded as hypothesis-generating, warranting further prospective evaluation.

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R2) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools.