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The Jinbaoshan Pt-Pd deposit is China’s largest independent PGM deposit. However, the deposit has not been utilized until now because of the low grade of precious metals, the complex mineral composition, and, notably, the presence of precious metals in the microgranular material disseminated to other minerals. Its high MgO content, in particular, is regarded as a challenge for efficiently recovering precious metals via mature pyrometallurgical methods. In this research, the feasibility of a smelting process to recover precious metals from Jinbaoshan Pt-Pd concentrates at a conventional smelting temperature (1350 °C) with the addition of iron ore as a metal collector and SiO₂ and CaO as fluxes was verified on the basis of thermodynamic slag design and experimental analyses. Under the optimal conditions of 100 g of the Pt-Pd concentrates, 32.5 g of SiO₂, 7.5 g of CaO, and 30 g of iron ore at 1350 °C for 1 h, the extraction efficiencies of Au, Pt, and Pd were 94.66%, 96.75%, and 97.28%, respectively. This strategy enables the rapid collection of PGMs from Jinbaoshan Pt-Pd concentrates at the conventional temperature within a short time and minimizes the use of fluxes and collectors, contributing to energy and cost conservation. Full article

(This article belongs to the Special Issue Advances in Precious and Critical Mineral Beneficiation and Extraction)

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20 pages, 7127 KiB

Open AccessArticle

An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy

by Shenghui Fu, Naixu Ren, Shuangxi Liu, Mingxi Shao, Yuanmao Jiang, Yuefeng Du, Hongjian Zhang, Linlin Sun and Wen Zhang

Agronomy 2025, 15(8), 1867; https://doi.org/10.3390/agronomy15081867 (registering DOI) - 1 Aug 2025

Abstract

In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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23 pages, 1178 KiB

Open AccessArticle

A Qualitative Analysis and Discussion of a New Model for Optimizing Obesity and Associated Comorbidities

by Mohamed I. Youssef, Robert M. Maina, Duncan K. Gathungu and Amr Radwan

Symmetry 2025, 17(8), 1216; https://doi.org/10.3390/sym17081216 (registering DOI) - 1 Aug 2025

Abstract

This paper addresses the problem of optimizing obesity, which has been a challenging issue in the last decade based on recent data revealed in 2024 by the World Health Organization (WHO). The current work introduces a new mathematical model of the dynamics of weight over time with embedded control parameters to optimize the number of obese, overweight, and comorbidity populations. The mathematical formulation of the model is developed under certain sufficient conditions that guarantee the positivity and boundedness of solutions over time. The model structure exhibits inherent symmetry in population group transitions, particularly around the equilibrium state, which allows the application of analytical tools such as the Routh–Hurwitz and Metzler criteria. Then, the analysis of local and global stability of the obesity-free equilibrium state is discussed based on these criteria. Based on the Pontryagin maximum principle (PMP), the deviation from the obesity-free equilibrium state is controlled. The model’s effectiveness is demonstrated through simulation using the Forward–Backward Sweeping algorithm with parameters derived from recent research in human health. Incorporating symmetry considerations in the model enhances the understanding of system behavior and supports balanced intervention strategies. Results suggest that the model can effectively inform strategies to mitigate obesity prevalence and associated health risks. Full article

(This article belongs to the Special Issue Mathematical Modeling of the Infectious Diseases and Their Controls)

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16 pages, 4272 KiB

Open AccessArticle

Prediction Analysis of Integrative Quality Zones for Corydalis yanhusuo W. T. Wang Under Climate Change: A Rare Medicinal Plant Endemic to China

by Huiming Wang, Bin Huang, Lei Xu and Ting Chen

Biology 2025, 14(8), 972; https://doi.org/10.3390/biology14080972 (registering DOI) - 1 Aug 2025

Abstract

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

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17 pages, 902 KiB

Open AccessReview

Cancer Stem Cells in Melanoma: Drivers of Tumor Plasticity and Emerging Therapeutic Strategies

by Adrian-Horațiu Sabău, Andreea-Cătălina Tinca, Raluca Niculescu, Iuliu Gabriel Cocuz, Andreea Raluca Cozac-Szöke, Bianca Andreea Lazar, Diana Maria Chiorean, Corina Eugenia Budin and Ovidiu Simion Cotoi

Int. J. Mol. Sci. 2025, 26(15), 7419; https://doi.org/10.3390/ijms26157419 (registering DOI) - 1 Aug 2025

Abstract

Cutaneous malignant melanoma is an extraordinarily aggressive and heterogeneous cancer that contains a small subpopulation of tumor stem cells (CSCs) responsible for tumor initiation, metastasis, and recurrence. Identification and characterization of CSCs in melanoma is challenging due to tumor heterogeneity and the lack of specific markers (CD271, ABCB5, ALDH, Nanog) and the ability of cells to dynamically change their phenotype. Phenotype-maintaining signaling pathways (Wnt/β-catenin, Notch, Hedgehog, HIF-1) promote self-renewal, treatment resistance, and epithelial–mesenchymal transitions. Tumor plasticity reflects the ability of differentiated cells to acquire stem-like traits and phenotypic flexibility under stress conditions. The interaction of CSCs with the tumor microenvironment accelerates disease progression: they induce the formation of cancer-associated fibroblasts (CAFs) and neo-angiogenesis, extracellular matrix remodeling, and recruitment of immunosuppressive cells, facilitating immune evasion. Emerging therapeutic strategies include immunotherapy (immune checkpoint inhibitors), epigenetic inhibitors, and nanotechnologies (targeted nanoparticles) for delivery of chemotherapeutic agents. Understanding the role of CSCs and tumor plasticity paves the way for more effective innovative therapies against melanoma. Full article

(This article belongs to the Special Issue Mechanisms of Resistance to Melanoma Immunotherapy)

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11 pages, 1692 KiB

Open AccessCommunication

Nanogel Loaded with Perilla frutescens Leaf-Derived Exosome-like Nanovesicles and Indomethacin for the Treatment of Inflammatory Arthritis

by Xianqiang Li, Fei Wang, Rui Wang, Yanjie Cheng, Jinhuan Liu and Wanhe Luo

Biology 2025, 14(8), 970; https://doi.org/10.3390/biology14080970 (registering DOI) - 1 Aug 2025

Abstract

Inflammatory arthritis (IA) is a chronic condition marked by joint dysfunction and pain, posing significant challenges for effective drug delivery. This study separated Perilla frutescens leaf-derived exosome-like nanovesicles (PFE) to effectively penetrate the stratum corneum barrier. These nanovesicles and indomethacin (IND) were subsequently developed into a nanogel designed for topical drug delivery systems (PFE-IND-GEL). PFE exhibited a typical vesicular structure with a mean diameter of 98.4 ± 1.3 nm. The hydrodynamic size and zeta potential of PFE-IND-GEL were 129.6 ± 5.9 nm and −17.4 ± 1.9 mV, respectively. Mechanistic investigations in HaCaT keratinocytes showed that PFE significantly downregulated tight junction proteins (ZO-1 and Occludin, p < 0.01) via modulation of the IL-17 signaling pathway, as evidenced by transcriptomic analysis. In a sodium urea crystal-induced rat IA model, the topical application of PFE-IND-GEL significantly reduced joint swelling (p < 0.05) and serum levels of inflammatory cytokines (IL-6, IL-1α, TNF-α) compared to control groups. Histopathological analysis confirmed the marked attenuation of synovial inflammation and cartilage preservation in treated animals. These findings underscore the dual role of PFE as both a topical permeation enhancer and an anti-inflammatory agent, presenting a promising strategy for managing IA. Full article

(This article belongs to the Section Biochemistry and Molecular Biology)

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19 pages, 3489 KiB

Open AccessArticle

Impact of Nitrogen Fertilisation and Inoculation on Soybean Nodulation, Nitrogen Status, and Yield in a Central European Climate

by Waldemar Helios, Magdalena Serafin-Andrzejewska, Marcin Kozak and Sylwia Lewandowska

Agriculture 2025, 15(15), 1654; https://doi.org/10.3390/agriculture15151654 (registering DOI) - 1 Aug 2025

Abstract

Soybean (Glycine max [L.] Merr.) cultivation is expanding in Central Europe due to the development of early-maturing cultivars and growing demand for plant-based protein produced without the use of genetically modified organisms. However, nitrogen (N) management remains a major challenge in temperate climates, where variable weather conditions can significantly affect nodulation and yield. This study evaluated the effects of three nitrogen fertilisation doses (0, 30, and 60 kg N·ha⁻¹), applied in the form of ammonium nitrate (34% N) and two commercial rhizobial inoculants—HiStick Soy (containing Bradyrhizobium japonicum strain 532C) and Nitragina (including a Polish strain of B. japonicum)—on nodulation, nitrogen uptake, and seed yield. A three-year field experiment (2017–2019) was conducted in southwestern Poland using a two-factor randomized complete block design. Nodulation varied significantly across years, with the highest values recorded under favourable early-season moisture and reduced during drought. In the first year, inoculation with HiStick Soy significantly increased nodule number and seed yield compared to Nitragina and the uninoculated control. Nitrogen fertilisation consistently improved seed yield, although it had no significant effect on nodulation. The highest nitrogen use efficiency was observed with moderate nitrogen input (30 kg N·ha⁻¹) combined with inoculation. These findings highlight the importance of integrating effective rhizobial inoculants with optimized nitrogen fertilisation to improve soybean productivity and nitrogen efficiency under variable temperate climate conditions. Full article

(This article belongs to the Special Issue Strategies to Enhance Nutrient Use Efficiency and Crop Nutrition)

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19 pages, 2547 KiB

Open AccessArticle

Artificial Intelligence Optimization of Polyaluminum Chloride (PAC) Dosage in Drinking Water Treatment: A Hybrid Genetic Algorithm–Neural Network Approach

by Darío Fernando Guamán-Lozada, Lenin Santiago Orozco Cantos, Guido Patricio Santillán Lima and Fabian Arias Arias

Computation 2025, 13(8), 179; https://doi.org/10.3390/computation13080179 (registering DOI) - 1 Aug 2025

Abstract

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

(This article belongs to the Section Computational Engineering)

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40 pages, 1548 KiB

Open AccessArticle

Real-Time Service Migration in Edge Networks: A Survey

by Yutong Zhang, Ke Zhao, Yihong Yang and Zhangbing Zhou

J. Sens. Actuator Netw. 2025, 14(4), 79; https://doi.org/10.3390/jsan14040079 (registering DOI) - 1 Aug 2025

Abstract

With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks. Full article

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21 pages, 645 KiB

Open AccessReview

Vernalization of Winter Crops Increases Photosynthetic Energy Conversion Efficiency and Seed Yield

by Norman P. A. Hüner, Alexander G. Ivanov, Beth Szyszka-Mroz, Leon A. Bravo, Leonid V. Savitch and Marianna Krol

Plants 2025, 14(15), 2357; https://doi.org/10.3390/plants14152357 (registering DOI) - 31 Jul 2025

Abstract

We summarize our present knowledge of the regulation of photostasis and photosynthetic performance versus photoprotection in response to vernalization and conclude that the enhanced photosynthetic performance of winter crops is due to an inherent increase in photosynthetic energy conversion efficiency induced by vernalization which translates into high seed yield in the field as well as under controlled environment conditions. This is consistent with the published data for enhanced photosynthetic performance of the only two extant terrestrial angiosperms, Colobanthus quitensis and Deschampsia antarctica, native to the frigid conditions of terrestrial Antarctica. The Cold Binding factor family of transcription factors (CBFs/DREBs) governs the enhanced photosynthetic performance of winter cereals as well as the Antarctic angiosperms. In contrast to winter crops, spring varieties survive cold environments by stimulating photoprotection at the expense of photosynthetic performance like that observed for green algae and cyanobacteria. Consequently, this minimizes the photosynthetic energy conversion efficiency of spring varieties and limits their seed yield upon cold acclimation. This review provides critical insights into the regulation of photostasis and the balance between photosynthetic performance and photoprotection in plants and how vernalization has enhanced photosynthetic energy conversion, which is essential for understanding plant adaptation to cold environments and optimizing agricultural productivity for improving crop resilience and yield in challenging climates. Full article

(This article belongs to the Special Issue Exploring Photosynthesis and Physiological Adaptations for Sustainable Growth)

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27 pages, 1628 KiB

Open AccessArticle

Reliability Evaluation and Optimization of System with Fractional-Order Damping and Negative Stiffness Device

by Mingzhi Lin, Wei Li, Dongmei Huang and Natasa Trisovic

Fractal Fract. 2025, 9(8), 504; https://doi.org/10.3390/fractalfract9080504 (registering DOI) - 31 Jul 2025

Abstract

Research on reliability control for enhancing power systems under random loads holds significant and undeniable importance in maintaining system stability, performance, and safety. The primary challenge lies in determining the reliability index while optimizing system parameters. To effectively address this challenge, we developed a novel intelligent algorithm and conducted an optimal reliability assessment for a Negative Stiffness Device (NSD) seismic isolation structure incorporating fractional-order damping. This algorithm combines the Gaussian Radial Basis Function Neural Network (GRBFNN) with the Particle Swarm Optimization (PSO) algorithm. It takes the reliability function with unknown parameters as the objective function, while using the Backward Kolmogorov (BK) equation, which governs the reliability function and is accompanied by boundary and initial conditions, as the constraint condition. During the operation of this algorithm, the neural network is employed to solve the BK equation, thereby deriving the fitness function in each iteration of the PSO algorithm. Then the PSO algorithm is utilized to obtain the optimal parameters. The unique advantage of this algorithm is its ability to simultaneously achieve the optimization of implicit objectives and the solution of time-dependent BK equations.To evaluate the performance of the proposed algorithm, this study compared it with the algorithm combines the GRBFNN with Genetic Algorithm (GA-GRBFNN)across multiple dimensions, including performance and operational efficiency. The effectiveness of the proposed algorithm has been validated through numerical comparisons and Monte Carlo simulations. The control strategy presented in this paper provides a solid theoretical foundation for improving the reliability performance of mechanical engineering systems and demonstrates significant potential for practical applications. Full article

(This article belongs to the Special Issue Stochastic Dynamical Systems with Fractional Derivative: Theoretical Analysis and Numerical Simulation)

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15 pages, 490 KiB

Open AccessArticle

The Labour Conditions and Health of Migrant Agricultural Workers in Spain: A Qualitative Study

by Vanesa Villa-Cordero, Amalia Sillero Sillero, María del Mar Pastor-Bravo, Iratxe Pérez-Urdiales, María del Mar Jiménez-Lasserrotte and Erica Briones-Vozmediano

Healthcare 2025, 13(15), 1877; https://doi.org/10.3390/healthcare13151877 (registering DOI) - 31 Jul 2025

Abstract

Background/Objectives: Agricultural workers in Spain with a migratory background face challenging working and living conditions that significantly affect their health. This study aimed to explore how professionals in healthcare, social services, civil society organisations, and labour institutions perceive that the working conditions affect the physical health of this population. Methods: A qualitative descriptive study was conducted through 92 semi-structured interviews with professionals from six provinces in Spain. Data were analysed using thematic analysis following Braun and Clarke’s six-phase framework. Rigour was ensured through triangulation, independent coding, and interdisciplinary consensus. Results: Two overarching themes were identified: (1) the health consequences of workplace demands and environmental hazards, and (2) navigating health services such as sick leave and disability permits. These findings highlight how the impact of precarious working conditions and limited access to healthcare affect the physical health of migrant agricultural workers. Conclusions: The professionals interviewed described and relate precarious working conditions with adverse health outcomes among migrant agricultural workers. Their insights reveal the need for systemic reforms to enforce labour rights, ensure access to health services, and address the structural factors that contribute to exclusion and vulnerability. Full article

(This article belongs to the Special Issue Improving the Health and Well-Being of Groups of Workers Experiencing Work Disparities)

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20 pages, 4135 KiB

Open AccessArticle

Climate-Induced Water Management Challenges for Cabbage and Carrot in Southern Poland

by Stanisław Rolbiecki, Barbara Jagosz, Roman Rolbiecki and Renata Kuśmierek-Tomaszewska

Sustainability 2025, 17(15), 6975; https://doi.org/10.3390/su17156975 (registering DOI) - 31 Jul 2025

Abstract

Climate warming poses significant challenges for the sustainable management of natural water resources, making efficient planning and usage essential. This study evaluates the water requirements, irrigation demand, and rainfall deficits for two key vegetable crops, carrot and white cabbage, under projected climate scenarios RCP 4.5 and RCP 8.5 for the period 2031–2100. The analysis was conducted for Kraków and Rzeszów Counties in southern Poland using projected monthly temperature and precipitation data from the Klimada 2.0 portal. Potential evapotranspiration (ETp) during the growing season (May–October) was estimated using Treder’s empirical model and the crop coefficient method adapted for Polish conditions. The reference period for comparison was 1951–2020. The results reveal a significant upward trend in water demand for both crops, with the highest increases under the RCP 8.5 scenario–seasonal ETp values reaching up to 517 mm for cabbage and 497 mm for carrot. Rainfall deficits are projected to intensify, especially during July and August, with greater shortages in Rzeszów County compared to Kraków County. Irrigation demand varies depending on soil type and drought severity, becoming critical in medium and very dry years. These findings underscore the necessity of adapting irrigation strategies and water resource management to ensure sustainable vegetable production under changing climate conditions. The data provide valuable guidance for farmers, advisors, and policymakers in planning effective irrigation infrastructure and optimizing water-use efficiency in southern Poland. Full article

(This article belongs to the Special Issue Sustainable Management and Regulation of Agricultural Water Resources in the Context of Global Climate Change, 2nd Edition)

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29 pages, 482 KiB

Open AccessReview

AI in Maritime Security: Applications, Challenges, Future Directions, and Key Data Sources

by Kashif Talpur, Raza Hasan, Ismet Gocer, Shakeel Ahmad and Zakirul Bhuiyan

Information 2025, 16(8), 658; https://doi.org/10.3390/info16080658 (registering DOI) - 31 Jul 2025

Abstract

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Information Systems)

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18 pages, 1643 KiB

Open AccessArticle

Precise Tracking Control of Unmanned Surface Vehicles for Maritime Sports Course Teaching Assistance

by Wanting Tan, Lei Liu and Jiabao Zhou

J. Mar. Sci. Eng. 2025, 13(8), 1482; https://doi.org/10.3390/jmse13081482 (registering DOI) - 31 Jul 2025

Abstract

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents a novel high-precision trajectory tracking control algorithm designed to ensure stable navigation of the USVs along predefined competition boundaries, thereby facilitating the reliable execution of buoy placement and escort missions. First, the paper proposes an improved adaptive fractional-order nonsingular fast terminal sliding mode control (AFONFTSMC) algorithm to achieve precise trajectory tracking of the reference path. To address the challenges posed by unknown environmental disturbances and unmodeled dynamics in marine environments, a nonlinear lumped disturbance observer (NLDO) with exponential convergence properties is proposed, ensuring robust and continuous navigation performance. Additionally, an artificial potential field (APF) method is integrated to dynamically mitigate collision risks from both static and dynamic obstacles during trajectory tracking. The efficacy and practical applicability of the proposed control framework are rigorously validated through comprehensive numerical simulations. Experimental results demonstrate that the developed algorithm achieves superior trajectory tracking accuracy under complex sea conditions, thereby offering a reliable and efficient solution for maritime sports education and related applications. Full article

(This article belongs to the Section Ocean Engineering)

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