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.

20 pages

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.

Fusarium crown rot (FCR) caused by Fusarium species adversely affects wheat production worldwide. The present study investigated the distribution and diversity of Fusarium spp. collected from wheat samples in 12 regions of Anhui Province, China, in 2020, 2022, and 2024. A total of nine Fusarium species were identified from 1,099 isolates based on morphological and molecular identification. The dominant pathogen of FCR gradually changed from F. graminearum to F. pseudograminearum with time and from regions of Anhui Province. Pathogenicity assays indicated that all Fusarium species might induce FCR in wheat seedlings; however, F. culmorum was the most pathogenic, followed by F. pseudograminearum and F. graminearum. The knowledge regarding fungicide combinations used to control FCR is largely limited. Hence, the control effects of pyraclostrobin and prothioconazole against F. pseudograminearum were evaluated, both individually and in combination. The results showed that F. pseudograminearum is sensitive to prothioconazole and pyraclostrobin, with average EC 50 values of 0.611 and 1.345 μg/ml, respectively. Additionally, the co-formulation (1:1) was more effective than prothioconazole alone. The results of the seed treatment experiment also revealed that the combination of prothioconazole and pyraclostrobin had greater control effects (80.34%) and yields (7,892.35 kg/ha) in the field than did the combination of prothioconazole or pyraclostrobin alone. Thus, the present study is the first to monitor the distribution pattern of Fusarium spp. in Anhui Province and report a novel combination of the triazole prothioconazole and pyraclostrobin for FCR control in wheat to ensure sustainable agriculture.

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.

ABSTRACT This paper is concerned with the stabilization of linear discrete‐time delay systems with unknown system matrices. The objective is to design stabilizing controllers using input and state measurements collected from experiments, which are affected by process disturbances. Assuming that these unknown disturbances are upper‐bounded, the pair of system matrices is represented as a data‐based nominal matrix plus a norm‐bounded uncertain matrix. By utilizing the Lyapunov functional approach, sufficient criteria are derived to design control gains that ensure asymptotic stability of the closed‐loop system. Simulation results validate the effectiveness of the proposed approach, demonstrating an extended allowable delay range compared to some recent methods.