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31 pages, 18795 KiB  
Review
Timber Architecture for Sustainable Futures: A Critical Review of Design and Research Challenges in the Era of Environmental and Social Transition
by Agnieszka Starzyk, Nuno D. Cortiços, Carlos C. Duarte and Przemysław Łacek
Buildings 2025, 15(15), 2774; https://doi.org/10.3390/buildings15152774 - 6 Aug 2025
Abstract
This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, [...] Read more.
This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, and spatial practice. The study adopts a conceptual, problem-oriented approach, eschewing the conventional systematic aggregation of existing data. The objective of this study is to identify, interpret and categorise the key issues that are shaping the evolving discourse on timber architecture. The analysis is based on peer-reviewed literature published between 2020 and 2025, sourced from the Scopus and Web of Science Core Collection databases. Fifteen thematic challenges have been identified and classified according to their recognition level in academic and design contexts. The subjects under discussion include well-established topics, such as life cycle assessment and carbon storage, as well as less commonly explored areas, such as symbolic durability, social acceptance, traceability, and the upcycling of low-grade wood. The review under consideration places significant emphasis on the importance of integrating technical, cultural, and perceptual dimensions when evaluating timber architecture. The article proposes an interpretive framework combining design thinking and transdisciplinary insights. This framework aims to bridge disciplinary gaps and provide a coherent structure for understanding the complexity of timber-related challenges. The framework under discussion here encourages a broader understanding of wood as not only a sustainable building material but also a vehicle for systemic transformation in architectural culture and practice. The study’s insights may support designers, educators, and policymakers in identifying strategic priorities for the development of future-proof timber-based design practices. Full article
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23 pages, 10836 KiB  
Article
Potential Utilization of End-of-Life Vehicle Carpet Waste in Subfloor Mortars: Incorporation into Portland Cement Matrices
by Núbia dos Santos Coimbra, Ângela de Moura Ferreira Danilevicz, Daniel Tregnago Pagnussat and Thiago Gonçalves Fernandes
Materials 2025, 18(15), 3680; https://doi.org/10.3390/ma18153680 - 5 Aug 2025
Abstract
The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of [...] Read more.
The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of a circular economy strategy. In this context, ELV waste emerges as a valuable source of secondary raw materials, enabling the development of sustainable innovations that capitalize on its physical and mechanical properties. This paper aims to develop and evaluate construction industry composites incorporating waste from ELV carpets, with a focus on maintaining or enhancing performance compared to conventional materials. To achieve this, an experimental program was designed to assess cementitious composites, specifically subfloor mortars, incorporating automotive carpet waste (ACW). The results demonstrate that, beyond the physical and mechanical properties of the developed composites, the dynamic stiffness significantly improved across all tested waste incorporation levels. This finding highlights the potential of these composites as an alternative material for impact noise insulation in flooring systems. From an academic perspective, this research advances knowledge on the application of ACW in cement-based composites for construction. In terms of managerial contributions, two key market opportunities emerge: (1) the commercial exploitation of composites produced with ELV carpet waste and (2) the development of a network of environmental service providers to ensure a stable waste supply chain for innovative and sustainable products. Both strategies contribute to reducing landfill disposal and mitigating the environmental impact of ELV waste, reinforcing the principles of the circular economy. Full article
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23 pages, 4260 KiB  
Article
Priority Control of Intelligent Connected Dedicated Bus Corridor Based on Deep Deterministic Policy Gradient
by Chunlin Shang, Fenghua Zhu, Yancai Xu, Guiqing Zhu and Xin Tong
Sensors 2025, 25(15), 4802; https://doi.org/10.3390/s25154802 - 4 Aug 2025
Abstract
To address the substantial disparities in operational characteristics between social vehicles and dedicated bus lanes, as well as the sub-optimal coordination control effects, a comprehensive approach is proposed. This approach integrates social vehicle arterial coordination with bus priority control in dedicated bus lanes. [...] Read more.
To address the substantial disparities in operational characteristics between social vehicles and dedicated bus lanes, as well as the sub-optimal coordination control effects, a comprehensive approach is proposed. This approach integrates social vehicle arterial coordination with bus priority control in dedicated bus lanes. Initially, an analysis of the differences in travel time distribution on both types of roads is conducted. The likelihood of buses passing through upstream and downstream intersections without stopping is also assessed. This analysis aids in determining the correlated traffic states and the corresponding signal adjustment strategies for arterial coordination. Subsequently, an incentive mechanism is established by quantitatively analyzing vehicle delay losses and bus priority benefits based on the signal adjustment strategy. Finally, a deep reinforcement learning framework is proposed to solve, in real-time, the optimal signal adjustment strategy. Simulation experiments indicate that, in comparison to the arterial coordination of social vehicles and dedicated bus arterial coordination control, this method significantly reduces the average per capita delay by 38.63% and 27.43%, respectively, under conventional traffic flow scenarios. This is in contrast to the separate arterial coordination for social vehicles and dedicated bus lanes. Furthermore, it leads to a reduction of 52.17% in the number of bus stops at intersections when compared solely with the arterial coordination of social vehicles. In saturated traffic flow scenarios, this method achieves a reduction in average per capita delay by 29.7% and 9.6%, respectively, while also decreasing the number of bus stops at intersections by 39.5% and 8.7%, respectively. Full article
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27 pages, 14083 KiB  
Article
Numerical Investigations and Hydrodynamic Analysis of a Screw Propulsor for Underwater Benthic Vehicles
by Yan Kai, Pengfei Xu, Meijie Cao and Lei Yang
J. Mar. Sci. Eng. 2025, 13(8), 1500; https://doi.org/10.3390/jmse13081500 - 4 Aug 2025
Abstract
Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, [...] Read more.
Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, steady-state simulations under various advance coefficients were conducted to evaluate the influence of key geometric parameters on propulsion performance. Based on these results, a representative configuration was then selected for transient analysis to capture unsteady flow features. In the second stage, a Detached Eddy Simulation approach was employed to capture unsteady flow features under three rotational speeds. The flow field information was analyzed, and the mechanisms of vortex generation, instability, and dissipation were comprehensively studied. The results reveal that the propulsion process is dominated by the formation and evolution of tip vortices, root vortices, and cylindrical wake vortices. As rotation speed increases, vortex structures exhibit a transition from ordered spiral wakes to chaotic turbulence, primarily driven by centrifugal instability and nonlinear vortex interactions. Vortex breakdown and energy dissipation are intensified downstream, especially under high-speed conditions, where vortex integrity is rapidly lost due to strong shear and radial expansion. This hydrodynamic behavior highlights the fundamental difference from conventional propellers, and these findings provide theoretical insight into the flow mechanisms of screw propulsion. Full article
(This article belongs to the Section Ocean Engineering)
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14 pages, 1329 KiB  
Article
Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management
by Quantao Yang, Peikun Li, Fei Yang and Wenbo Lu
Sustainability 2025, 17(15), 7061; https://doi.org/10.3390/su17157061 - 4 Aug 2025
Abstract
This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an [...] Read more.
This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article
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22 pages, 5136 KiB  
Article
Application of UAVs to Support Blast Design for Flyrock Mitigation: A Case Study from a Basalt Quarry
by Józef Pyra and Tomasz Żołądek
Appl. Sci. 2025, 15(15), 8614; https://doi.org/10.3390/app15158614 (registering DOI) - 4 Aug 2025
Viewed by 74
Abstract
Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in [...] Read more.
Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in relation to controlling burden values and reducing flyrock. The research was conducted in a basalt quarry in Lower Silesia, where high rock fracturing complicated conventional blast planning. A DJI Mavic 3 Enterprise UAV was used to capture high-resolution aerial imagery, and 3D models were created using Strayos software. These models enabled precise analysis of bench face geometry and burden distribution with centimeter-level accuracy. The results showed a significant improvement in identifying zones with improper burden values and allowed for real-time corrections in blasthole design. Despite a ten-fold reduction in the number of images used, no loss in model quality was observed. UAV-based surveys followed software-recommended flight paths, and the application of this methodology reduced the flyrock range by an average of 42% near sensitive areas. This approach demonstrates the operational benefits and enhanced safety potential of integrating UAV-based photogrammetry into blasting design workflows. Full article
(This article belongs to the Special Issue Advanced Blasting Technology for Mining)
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34 pages, 5777 KiB  
Article
ACNet: An Attention–Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving
by Qiliang Zhang, Kaiwen Hua, Zi Zhang, Yiwei Zhao and Pengpeng Chen
Sensors 2025, 25(15), 4776; https://doi.org/10.3390/s25154776 - 3 Aug 2025
Viewed by 167
Abstract
In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in [...] Read more.
In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
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24 pages, 1964 KiB  
Article
Data-Driven Symmetry and Asymmetry Investigation of Vehicle Emissions Using Machine Learning: A Case Study in Spain
by Fei Wu, Jinfu Zhu, Hufang Yang, Xiang He and Qiao Peng
Symmetry 2025, 17(8), 1223; https://doi.org/10.3390/sym17081223 - 2 Aug 2025
Viewed by 231
Abstract
Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and [...] Read more.
Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO2 emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article
(This article belongs to the Section Engineering and Materials)
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20 pages, 1907 KiB  
Article
Multi-Innovation-Based Parameter Identification for Vertical Dynamic Modeling of AUV Under High Maneuverability and Large Attitude Variations
by Jianping Yuan, Zhixun Luo, Lei Wan, Cenan Wang, Chi Zhang and Qingdong Chen
J. Mar. Sci. Eng. 2025, 13(8), 1489; https://doi.org/10.3390/jmse13081489 - 1 Aug 2025
Viewed by 215
Abstract
The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it [...] Read more.
The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article
(This article belongs to the Section Ocean Engineering)
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27 pages, 21019 KiB  
Article
A UWB-AOA/IMU Integrated Navigation System for 6-DoF Indoor UAV Localization
by Pengyu Zhao, Hengchuan Zhang, Gang Liu, Xiaowei Cui and Mingquan Lu
Drones 2025, 9(8), 546; https://doi.org/10.3390/drones9080546 - 1 Aug 2025
Viewed by 239
Abstract
With the increasing deployment of unmanned aerial vehicles (UAVs) in indoor environments, the demand for high-precision six-degrees-of-freedom (6-DoF) localization has grown significantly. Ultra-wideband (UWB) technology has emerged as a key enabler for indoor UAV navigation due to its robustness against multipath effects and [...] Read more.
With the increasing deployment of unmanned aerial vehicles (UAVs) in indoor environments, the demand for high-precision six-degrees-of-freedom (6-DoF) localization has grown significantly. Ultra-wideband (UWB) technology has emerged as a key enabler for indoor UAV navigation due to its robustness against multipath effects and high-accuracy ranging capabilities. However, conventional UWB-based systems primarily rely on range measurements, operate at low measurement frequencies, and are incapable of providing attitude information. This paper proposes a tightly coupled error-state extended Kalman filter (TC–ESKF)-based UWB/inertial measurement unit (IMU) fusion framework. To address the challenge of initial state acquisition, a weighted nonlinear least squares (WNLS)-based initialization algorithm is proposed to rapidly estimate the UAV’s initial position and attitude under static conditions. During dynamic navigation, the system integrates time-difference-of-arrival (TDOA) and angle-of-arrival (AOA) measurements obtained from the UWB module to refine the state estimates, thereby enhancing both positioning accuracy and attitude stability. The proposed system is evaluated through simulations and real-world indoor flight experiments. Experimental results show that the proposed algorithm outperforms representative fusion algorithms in 3D positioning and yaw estimation accuracy. Full article
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14 pages, 4979 KiB  
Article
Oxygen Vacancy-Engineered Ni:Co3O4/Attapulgite Photothermal Catalyst from Recycled Spent Lithium-Ion Batteries for Efficient CO2 Reduction
by Jian Shi, Yao Xiao, Menghan Yu and Xiazhang Li
Catalysts 2025, 15(8), 732; https://doi.org/10.3390/catal15080732 - 1 Aug 2025
Viewed by 245
Abstract
Accelerated industrialization and surging energy demands have led to continuously rising atmospheric CO2 concentrations. Developing sustainable methods to reduce atmospheric CO2 levels is crucial for achieving carbon neutrality. Concurrently, the rapid development of new energy vehicles has driven a significant increase [...] Read more.
Accelerated industrialization and surging energy demands have led to continuously rising atmospheric CO2 concentrations. Developing sustainable methods to reduce atmospheric CO2 levels is crucial for achieving carbon neutrality. Concurrently, the rapid development of new energy vehicles has driven a significant increase in demand for lithium-ion batteries (LIBs), which are now approaching an end-of-life peak. Efficient recycling of valuable metals from spent LIBs represents a critical challenge. This study employs conventional hydrometallurgical processing to recover valuable metals from spent LIBs. Subsequently, Ni-doped Co3O4 (Ni:Co3O4) supported on the natural mineral attapulgite (ATP) was synthesized via a sol–gel method. The incorporation of a small amount of Ni into the Co3O4 lattice generates oxygen vacancies, inducing a localized surface plasmon resonance (LSPR) effect, which significantly enhances charge carrier transport and separation efficiency. During the photocatalytic reduction of CO2, the primary product CO generated by the Ni:Co3O4/ATP composite achieved a high production rate of 30.1 μmol·g−1·h−1. Furthermore, the composite maintains robust catalytic activity even after five consecutive reaction cycles. Full article
(This article belongs to the Special Issue Heterogeneous Catalysis in Air Pollution Control)
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20 pages, 6694 KiB  
Article
Spatiotemporal Assessment of Benzene Exposure Characteristics in a Petrochemical Industrial Area Using Mobile-Extraction Differential Optical Absorption Spectroscopy (Me-DOAS)
by Dong keun Lee, Jung-min Park, Jong-hee Jang, Joon-sig Jung, Min-kyeong Kim, Jaeseok Heo and Duckshin Park
Toxics 2025, 13(8), 655; https://doi.org/10.3390/toxics13080655 - 31 Jul 2025
Viewed by 233
Abstract
Petrochemical complexes are spatially expansive and host diverse emission sources, making accurate monitoring of volatile organic compounds (VOCs) challenging using conventional two-dimensional methods. This study introduces Mobile-extraction Differential Optical Absorption Spectroscopy (Me-DOAS), a real-time, three-dimensional remote sensing technique for assessing benzene emissions in [...] Read more.
Petrochemical complexes are spatially expansive and host diverse emission sources, making accurate monitoring of volatile organic compounds (VOCs) challenging using conventional two-dimensional methods. This study introduces Mobile-extraction Differential Optical Absorption Spectroscopy (Me-DOAS), a real-time, three-dimensional remote sensing technique for assessing benzene emissions in the Ulsan petrochemical complex, South Korea. A vehicle-mounted Me-DOAS system conducted monthly measurements throughout 2024, capturing data during four daily intervals to evaluate diurnal variation. Routes included perimeter loops and grid-based transects within core industrial zones. The highest benzene concentrations were observed in February (mean: 64.28 ± 194.69 µg/m3; geometric mean: 5.13 µg/m3), with exceedances of the national annual standard (5 µg/m3) in several months. Notably, nighttime and early morning sessions showed elevated levels, suggesting contributions from nocturnal operations and meteorological conditions such as atmospheric inversion. A total of 179 exceedances (≥30 µg/m3) were identified, predominantly in zones with benzene-handling activities. Correlation analysis revealed a significant relationship between high concentrations and specific emission sources. These results demonstrate the utility of Me-DOAS in capturing spatiotemporal emission dynamics and support its application in exposure risk assessment and industrial emission control. The findings provide a robust framework for targeted management strategies and call for integration with source apportionment and dispersion modeling tools. Full article
(This article belongs to the Section Air Pollution and Health)
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29 pages, 6079 KiB  
Article
A Highly Robust Terrain-Aided Navigation Framework Based on an Improved Marine Predators Algorithm and Depth-First Search
by Tian Lan, Ding Li, Qixin Lou, Chao Liu, Huiping Li, Yi Zhang and Xudong Yu
Drones 2025, 9(8), 543; https://doi.org/10.3390/drones9080543 - 31 Jul 2025
Viewed by 253
Abstract
Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. [...] Read more.
Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. To overcome these challenges, we propose a novel terrain-aided navigation framework integrating an Improved Marine Predators Algorithm with Depth-First Search optimization (DFS-IMPA-TAN). This framework maintains positioning precision in partially self-similar terrains through two synergistic mechanisms: (1) IMPA-driven optimization based on the hunger-inspired adaptive exploitation to determine optimal trajectory transformations, cascaded with Kalman filtering for navigation state correction; (2) a Robust Tree (RT) hypothesis manager that maintains potential trajectory candidates in graph-structured memory, employing Depth-First Search for ambiguity resolution in feature matching. Experimental validation through simulations and in-vehicle testing demonstrates the framework’s distinctive advantages: (1) consistent terrain association in partially self-similar topographies; (2) inherent error resilience against ambiguous feature measurements; and (3) long-term navigation stability. In all experimental groups, the root mean squared error of the framework remained around 60 m. Under adverse conditions, its navigation accuracy improved by over 30% compared to other traditional batch processing TAN methods. Comparative analysis confirms superior performance over conventional methods under challenging conditions, establishing DFS-IMPA-TAN as a robust navigation solution for AUVs in complex underwater environments. Full article
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20 pages, 2321 KiB  
Article
Electric Vehicle Energy Management Under Unknown Disturbances from Undefined Power Demand: Online Co-State Estimation via Reinforcement Learning
by C. Treesatayapun, A. J. Munoz-Vazquez, S. K. Korkua, B. Srikarun and C. Pochaiya
Energies 2025, 18(15), 4062; https://doi.org/10.3390/en18154062 - 31 Jul 2025
Viewed by 263
Abstract
This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of [...] Read more.
This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article
(This article belongs to the Special Issue Forecasting and Optimization in Transport Energy Management Systems)
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18 pages, 5328 KiB  
Article
Theoretical and Experimental Investigation of Dynamic Characteristics in Propulsion Shafting Support System with Integrated Squeeze Film Damper
by Qilin Liu, Wu Ouyang, Gao Wan and Gaohui Xiao
Lubricants 2025, 13(8), 335; https://doi.org/10.3390/lubricants13080335 - 30 Jul 2025
Viewed by 156
Abstract
The lateral vibration of propulsion shafting is a critical factor affecting the acoustic stealth performance of underwater vehicles. As the main vibration isolation component in transmitting vibrational energy, the damping efficiency of the propulsion shafting support system (PSSS) holds particular significance. This study [...] Read more.
The lateral vibration of propulsion shafting is a critical factor affecting the acoustic stealth performance of underwater vehicles. As the main vibration isolation component in transmitting vibrational energy, the damping efficiency of the propulsion shafting support system (PSSS) holds particular significance. This study investigates the dynamic characteristics of the PSSS with the integral squeeze film damper (ISFD). A dynamic model of ISFD–PSSS is developed to systematically analyze the effects of shaft speed and external load on its dynamic behavior. Three test bearings (conventional, 1S, and 3S structure) are designed and manufactured to study the influence of damping structure layout scheme, damping fluid viscosity, unbalanced load, and shaft speed on the vibration reduction ability of ISFD–PSSS through axis orbit and vibration velocity. The results show that the damping effects of ISFD–PSSS are observed across all test conditions, presenting distinct nonlinear patterns. Suppression effectiveness is more pronounced in the vertical direction compared to the horizontal direction. The 3S structure bearing has better vibration reduction and structural stability than other schemes. The research results provide a reference for the vibration control method of rotating machinery. Full article
(This article belongs to the Special Issue Water Lubricated Bearings)
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