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28 pages, 1396 KB  
Article
Scenario-Based Sensor Selection for Autonomous Maritime Systems: A Multi-Criteria Analysis of Sensor Configurations for Situational Awareness
by Florian Hoehner, Vincent Langenohl, Ould el Moctar and Thomas E. Schellin
J. Mar. Sci. Eng. 2025, 13(10), 2008; https://doi.org/10.3390/jmse13102008 (registering DOI) - 19 Oct 2025
Abstract
Effective operation of autonomous maritime systems requires sensor architectures tailored to mission-specific requirements, as key performance criteria like accuracy and energy consumption vary significantly by operational context. Against this background, this study develops a dual-stage, multi-criteria procedure to evaluate and assess individual sensors [...] Read more.
Effective operation of autonomous maritime systems requires sensor architectures tailored to mission-specific requirements, as key performance criteria like accuracy and energy consumption vary significantly by operational context. Against this background, this study develops a dual-stage, multi-criteria procedure to evaluate and assess individual sensors accounting for scenario-based requirements, using the TOPSIS algorithm as its core method. The first stage individually assesses sensors against scenario-specific requirements to generate context-aware weighting factors (αis). In the second stage, these factors are used to evaluate the overall performance of seven predefined sensor suites across five distinct operational scenarios (e.g., ‘Coastal Surveillance’ or ‘Protection of Critical Infrastructure’). The procedure is complemented by an architectural robustness assessment that systematically captures the impact of component failures. This flexible approach serves as a generic decision framework for designing unmanned maritime systems across different mission profiles. By integrating key performance metrics and failure scenarios within a context of prioritized operational requirements, the dual-stage multi-criteria procedure enables more than just selecting an optimal configuration. It reveals the fundamental architectural design principles. Our results demonstrate that for precision-focused tasks such as ‘Coastal Surveillance’, specialized sensor suites combining electro-optical and laser rangefinder achieves the highest performance score (0.84). Conversely, for scenarios with balanced requirements like ‘Protection of Critical Infrastructure’, architectures based on functional complementarity (e.g., electro-optical and Radar, score (0.64)) prove most effective. A key finding is that maximizing sensor quantity does not guarantee optimal performance, as targeted, mission-specific configurations often outperform fully integrated systems. The significance of this study lies in providing a systematic framework that shifts the design paradigm from a ‘more is better’ approach to an intelligent, context-aware composition, enabling the development of truly robust and efficient sensor architectures for autonomous maritime systems. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)
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15 pages, 3808 KB  
Article
Formation of Nanocompounds of TiO2 Using PVA-HAp Nanofibers by Sol-Gel Technique
by Marvin Elco Estrada Macias, Humberto Alejandro Monreal Romero, Guillermo Martínez Mata, Rosaura Pacheco Santiesteban, Claudia López Meléndez, Héctor Alfredo López Aguilar, Oscar Chávez Acosta, Carlos A. Martínez-Pérez, Caleb Carreño-Gallardo and José Guadalupe Chacón-Nava
Polymers 2025, 17(20), 2796; https://doi.org/10.3390/polym17202796 (registering DOI) - 19 Oct 2025
Abstract
The use of hydroxyapatite (HAp) nanofibers in combination with titanium dioxide (TiO2) emerges as a method for the design and improvement of materials at the biomedical, architectonic, and industrial levels. In this research, TiO2 nanocomposites were developed using HAp nanofibers [...] Read more.
The use of hydroxyapatite (HAp) nanofibers in combination with titanium dioxide (TiO2) emerges as a method for the design and improvement of materials at the biomedical, architectonic, and industrial levels. In this research, TiO2 nanocomposites were developed using HAp nanofibers through the sol-gel technique. The molecular assembly strategy reveals the formation of nanocomposites with sizes of 100–500 nm at 700 °C. EDS analysis shows the presence of Ca and P, indicating that HAp nanofibers have been integrated into the nanocomposites. The crystalline phases corresponding to rutile and anatase were detected by X-ray spectroscopy analysis. The morphology of the composites was analyzed by surface segmentation analysis, scanning electron microscope, and scanning tunneling microscope. Full article
(This article belongs to the Section Polymer Fibers)
23 pages, 3661 KB  
Article
The Establishment of a Geofencing Model for Automated Data Collection in Soybean Trial Plots
by Jiaxin Liang, Bo Zhang, Changhai Chen, Haoyu Cui, Yongcai Ma and Bin Chen
Agriculture 2025, 15(20), 2169; https://doi.org/10.3390/agriculture15202169 (registering DOI) - 19 Oct 2025
Abstract
Collecting crop growth data in field environments is crucial for breeding research. The team’s current autonomous soybean phenotyping system requires manual control to start and stop data collection. To address the aforementioned issues, this study innovatively proposes an elliptical calibration rotating geofencing technique. [...] Read more.
Collecting crop growth data in field environments is crucial for breeding research. The team’s current autonomous soybean phenotyping system requires manual control to start and stop data collection. To address the aforementioned issues, this study innovatively proposes an elliptical calibration rotating geofencing technique. Preprocess coordinates using Z-scores and mean fitting perform global error calibration via weighted least squares, calculate the inclination angle between the row direction and the relative standard direction by fitting a straight line to the same row of data, and establish a rotation model based on geometric feature alignment. Results show that the system achieves an average response time of 0.115 s for geofence entry, with perfect accuracy and Recall rates of 1, meeting the requirements for starting and stopping geographic fencing in soybean ridge trial plots. This technology provides the critical theoretical foundation for enabling a dynamic, on-demand automatic start–stop functionality in smart data collection devices for soybean field trial zones within precision agriculture. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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13 pages, 334 KB  
Article
The Prevalence of Second Neoplasms in Patients with Non-Aldosterone Producing Adrenocortical Lesions
by Paraskevi Tripolitsioti, Ariadni Spyroglou, Odysseas Violetis, Panagiota Konstantakou, Eleni Chouliara, Grigoria Betsi, Konstantinos Iliakopoulos, Eleni Memi, Konstantinos Bramis, Denise Kolomodi, Paraskevi Xekouki, Manousos Konstadoulakis, George Mastorakos and Krystallenia I Alexandraki
Int. J. Mol. Sci. 2025, 26(20), 10167; https://doi.org/10.3390/ijms262010167 (registering DOI) - 19 Oct 2025
Abstract
Over the last few decades, due to improvement in imaging techniques, the increased detection of adrenal incidentalomas is observed. Non-aldosterone producing adrenal adenomas (NAPACAs) often co-exist with second benign or malignant lesions. In the present study, we aimed to assess the presence of [...] Read more.
Over the last few decades, due to improvement in imaging techniques, the increased detection of adrenal incidentalomas is observed. Non-aldosterone producing adrenal adenomas (NAPACAs) often co-exist with second benign or malignant lesions. In the present study, we aimed to assess the presence of second neoplasms, both benign and malignant, in patients with NAPACAs, and to investigate possible correlations with clinical parameters, hormonal characteristics and the emergence of comorbidities. A total of 130 NAPACA patients were included in this single-center retrospective study. In this cohort, 35.4% of NAPACA patients carried any second neoplasm (either benign or malignant) whereas, 26.9% had a second malignant neoplasm. Cortisol levels after 1 mg overnight dexamethasone suppression test (F-ODS) were significantly higher in patients without a second neoplasm (p = 0.02), and this finding was consistent even when categorizing patients with and without malignancies (p = 0.02). In line with this observation, ACTH/F-ODS levels were significantly higher in patients with second malignancies (p < 0.05). Interestingly, the presence of mild autonomous cortisol secretion tended to be lower in patients with second malignancies (p = 0.08). No remarkable differences in the comorbidities of NAPACA patients with and without a second neoplasm were documented. Further prospective studies will be needed to elucidate the role of mild hypercortisolemia on the development of these second tumors in NAPACA patients. Full article
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24 pages, 6262 KB  
Article
Selective Removal of Arsenic and Antimony by Alkaline Leaching with Sodium Sulfide: Remediation of Metalloids-Contaminated Concentrates from Zimapán, Hidalgo, Mexico
by Gabriel Cisneros, Julio C. Juárez, Iván A. Reyes, Martín Reyes, Gustavo Urbano, Jesús I. Martínez, Aislinn M. Teja and Mizraim U. Flores
Processes 2025, 13(10), 3347; https://doi.org/10.3390/pr13103347 (registering DOI) - 19 Oct 2025
Abstract
Selective alkaline leaching was evaluated to remove arsenic (As) and antimony (Sb) from a polymetallic copper concentrate from Zimapán, Mexico, where these metalloids cause environmental risk and smelter penalties. Batch tests used sodium sulfide (Na2S) in alkaline media, varying reagent concentrations [...] Read more.
Selective alkaline leaching was evaluated to remove arsenic (As) and antimony (Sb) from a polymetallic copper concentrate from Zimapán, Mexico, where these metalloids cause environmental risk and smelter penalties. Batch tests used sodium sulfide (Na2S) in alkaline media, varying reagent concentrations and temperature; kinetic modeling identified the rate-controlling step, and X-ray diffraction (XRD) plus scanning electron microscopy/energy-dispersive spectroscopy (SEM–EDS) assessed phase changes. The kinetic analysis indicated chemical control with a higher reaction order for Na2S than for NaOH. A quadratic regression described the process and identified Na2S concentration and temperature as the dominant factors. Maximum extractions reached 91.9% for As and 72.1% for Sb while limiting dissolution of value-bearing sulfides, as supported by XRD and SEM–EDS. Environmental indices (Igeo, EF) classified As and Sb as highly contaminating and geochemically enriched in the feed, underscoring the need for selective removal. Overall, alkaline leaching with Na2S provides a technically feasible and environmentally favorable route to remediate metalloids and upgrade polymetallic concentrates. Full article
18 pages, 5635 KB  
Article
Multi-Soliton Propagation and Interaction in Λ-Type EIT Media: An Integrable Approach
by Ramesh Kumar Vaduganathan, Prasanta K. Panigrahi and Boris A. Malomed
Photonics 2025, 12(10), 1034; https://doi.org/10.3390/photonics12101034 (registering DOI) - 19 Oct 2025
Abstract
Electromagnetically induced transparency (EIT) is well known as a quantum optical phenomenon that permits a normally opaque medium to become transparent due to the quantum interference between transition pathways. This work addresses multi-soliton dynamics in an EIT system modeled by the integrable Maxwell–Bloch [...] Read more.
Electromagnetically induced transparency (EIT) is well known as a quantum optical phenomenon that permits a normally opaque medium to become transparent due to the quantum interference between transition pathways. This work addresses multi-soliton dynamics in an EIT system modeled by the integrable Maxwell–Bloch (MB) equations for a three-level Λ-type atomic configuration. By employing a generalized gauge transformation, we systematically construct explicit N-soliton solutions from the corresponding Lax pair. Explicit forms of one-, two-, three-, and four-soliton solutions are derived and analyzed. The resulting pulse structures reveal various nonlinear phenomena, such as temporal asymmetry, energy trapping, and soliton interactions. They also highlight coherent propagation, elastic collisions, and partial storage of pulses, which have potential implications for the design of quantum memory, slow light, and photonic data transport in EIT media. In addition, the conservation of fundamental physical quantities, such as the excitation norm and Hamiltonian, is used to provide direct evidence of the integrability and stability of the constructed soliton solutions. Full article
(This article belongs to the Section Quantum Photonics and Technologies)
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17 pages, 10634 KB  
Article
Hybrid Convolutional Transformer with Dynamic Prompting for Adaptive Image Restoration
by Jinmei Zhang, Guorong Chen, Junliang Yang, Qingru Zhang, Shaofeng Liu and Weijie Zhang
Mathematics 2025, 13(20), 3329; https://doi.org/10.3390/math13203329 (registering DOI) - 19 Oct 2025
Abstract
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for [...] Read more.
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for them to adaptively handle complex and diverse restoration scenarios. To address this issue, we propose a novel adaptive image restoration framework named Hybrid Convolutional Transformer with Dynamic Prompting (HCTDP). Our approach introduces two key architectural innovations: a Spatially Aware Dynamic Prompt Head Attention (SADPHA) module, which performs fine-grained local restoration by generating spatially variant prompts through real-time analysis of image content and a Gated Skip-Connection (GSC) module that refines multi-scale feature flow using efficient channel attention. To guide the network in generating more visually plausible results, the framework is optimized with a hybrid objective function that combines a pixel-wise L1 loss and a feature-level perceptual loss. Extensive experiments on multiple public benchmarks, including image deraining, dehazing, and denoising, demonstrate that our proposed HCTDP exhibits superior performance in both quantitative and qualitative evaluations, validating the effectiveness of the adaptive restoration framework while utilizing fewer parameters than key competitors. Full article
(This article belongs to the Special Issue Intelligent Mathematics and Applications)
20 pages, 6299 KB  
Article
Quality and Maturity Detection of Korla Fragrant Pears via Integrating Hyperspectral Imaging with Multiscale CNN–LSTM
by Zhengbao Long, Tongzhao Wang, Zhijuan Zhang and Yuanyuan Liu
Foods 2025, 14(20), 3561; https://doi.org/10.3390/foods14203561 (registering DOI) - 19 Oct 2025
Abstract
To address the limitations of single indices in comprehensively evaluating the quality of Korla fragrant pears, this study proposes the firmness–soluble solids ratio (FSR), defined as the ratio of average firmness (FI) to soluble solid content (SSC) for each individual fruit, as a [...] Read more.
To address the limitations of single indices in comprehensively evaluating the quality of Korla fragrant pears, this study proposes the firmness–soluble solids ratio (FSR), defined as the ratio of average firmness (FI) to soluble solid content (SSC) for each individual fruit, as a novel index. Using 600 samples from five maturity stages with hyperspectral imaging (950–1650 nm), the dataset was split 4:1 by the SPXY algorithm. The findings demonstrated that FSR’s effectiveness in quantifying the dynamic relationship between FI and SSC during maturation. The developed multiscale convolutional neural network–long short-term memory (MSCNN–LSTM) model achieved high prediction accuracy with determination coefficients of 0.8934 (FI), 0.8731 (SSC), and 0.8610 (FSR), and root mean square errors of 0.9001 N, 0.7976%, and 0.1676, respectively. All residual prediction deviation values exceeded 2.5, confirming model robustness. The MSCNN–LSTM showed superior performance compared to other benchmark models. Furthermore, the integration of prediction models with visualization techniques successfully mapped the spatial distribution of quality indices. For maturity discrimination, hyperspectral-based partial least squares discriminant analysis and linear discriminant analysis models achieved perfect classification accuracy (100%) under five-fold cross-validation across all five maturity stages. This work provides both a theoretical basis and a technical framework for non-destructive evaluation of comprehensive quality and maturity in Korla fragrant pears. Full article
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34 pages, 2571 KB  
Review
Nondestructive Quality Detection of Characteristic Fruits Based on Vis/NIR Spectroscopy: Principles, Systems, and Applications
by Chen Wang, Xiaonan Li, Zijuan Zhang, Xuan Luo, Jianrong Cai and Aichen Wang
Agriculture 2025, 15(20), 2167; https://doi.org/10.3390/agriculture15202167 (registering DOI) - 18 Oct 2025
Abstract
Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive [...] Read more.
Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive multi-attribute quantification through molecular overtone vibrations. This review examines recent advancements in Vis/NIR-based fruit quality detection, encompassing fundamental principles, system configurations, and detection strategies calibrated to fruit biophysical properties. Firstly, optical mechanisms and system architectures (portable, online, vehicle-mounted) are compared, emphasizing their compatibility with fruit structural complexity. Then, critical challenges arising from fruit-specific characteristics—such as rind thickness, pit interference, and spatial heterogeneity—are analyzed, highlighting their impact on spectral accuracy. Applications across diverse fruit categories (pitted, thin-rinded, and thick-rinded) are systematically reviewed, with case studies demonstrating the robust prediction of key quality indices. Subsequently, considerations in model development and validation are presented. Finally, persistent limitations in model transferability and environmental adaptability are discussed, proposing future research directions centered on integrating hyperspectral imaging, AI-driven calibration transfer, standardized spectral databases, and miniaturized, field-deployable sensors. Collectively, these methodological breakthroughs will pave the way for autonomous, next-generation quality assessment platforms, revolutionizing postharvest management for characteristic fruits. Full article
17 pages, 1775 KB  
Article
AI-Driven Analysis for Real-Time Detection of Unstained Microscopic Cell Culture Images
by Kathrin Hildebrand, Tatiana Mögele, Dennis Raith, Maria Kling, Anna Rubeck, Stefan Schiele, Eelco Meerdink, Avani Sapre, Jonas Bermeitinger, Martin Trepel and Rainer Claus
AI 2025, 6(10), 271; https://doi.org/10.3390/ai6100271 (registering DOI) - 18 Oct 2025
Abstract
Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for [...] Read more.
Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article
(This article belongs to the Special Issue AI in Bio and Healthcare Informatics)
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21 pages, 5067 KB  
Article
Rectal Microbiomes and Serum Metabolomics Reveal Changes in Serum Antioxidant Status and Immune Responses of Dezhou Donkeys in Late Gestation to Parturition
by Fang Hui, Yanli Zhao, Zaccheaus Pazamilala Akonyani, Yongmei Guo, Xiaoyu Guo, Qingyue Zhang, Fanzhu Meng, Li Li, Binlin Shi and Sumei Yan
Antioxidants 2025, 14(10), 1253; https://doi.org/10.3390/antiox14101253 (registering DOI) - 18 Oct 2025
Abstract
Parturition is a critical event in the reproductive cycle of dairy animals, accompanied by multiple physiological changes in sex hormones, metabolism, antioxidant capacity, and immune function. However, the changes in the rectal microbiota and metabolic products of Jennies from late gestation to parturition [...] Read more.
Parturition is a critical event in the reproductive cycle of dairy animals, accompanied by multiple physiological changes in sex hormones, metabolism, antioxidant capacity, and immune function. However, the changes in the rectal microbiota and metabolic products of Jennies from late gestation to parturition affect serum antioxidant capacity and anti-inflammatory responses, but it is still unclear. The present study aimed to investigate the serum antioxidant capacity and anti-inflammatory responses of Dezhou donkeys from late gestation to parturition by analyzing rectal microbiomes and serum metabolomics. Nine pregnant multiparous Dezhou Jennies, aged 6.0 ± 0.1 years, with a body weight of 292 ± 33 kg, an average parity number of 2.7 ± 0.1, and similar expected dates of confinement (35 ± 4 days), were selected for this study. The study investigates the changes in antioxidant capacity and inflammatory responses, as well as the alterations in rectal microbiota structure and serum metabolites, in Jennies at 35 days prepartum (B1), 7 days prepartum (B2), and at 0 h postpartum (B3). The results showed that from groups B1 to B2, serum activity of GSH-Px, IL-10, and GLU concentrations were decreased significantly. In contrast, the concentrations of MDA, IgG, LF, IL-1β, IL-2, IL-6, TNF-α, and ROS increased significantly. From groups B2 to B3, serum activities of GSH-Px, CAT, SOD, and T-AOC, as well as the concentrations of MDA, IgG, IL-2, AST, ALP, and BHBA, were significantly increased, whereas the concentrations of IL-4, IL-10, and CRE decreased considerably. Therefore, from 35 days prepartum to parturition, Jennies experienced a gradually intensifying oxidative stress and inflammatory states, with the inflammatory response being the most severe at parturition, and with enhanced antioxidant capacity corresponding to increased oxidative damage. Microbiome analysis revealed that the group B1 significantly increased the relative abundance of Prevotella and Fibrobacteres. Group B2 significantly increased the relative abundance of Prevotellaceae_UCG-001, Streptococcus, and Acetitomaculum. Group B3 showed a significant upregulation of the relative abundance of Norank_f__F082, Lachnospiraceae_UCG-009, and Prevotellaceae_UCG-004. At the same time, metabolomics analysis revealed that, compared with group B1, group B3 may alleviate inflammation and enhance the body’s antioxidant function by upregulating the tryptophan and arginine metabolic pathways and enriching the differential metabolites (L-tryptophan, L-kynurenine, 3-Indoleacetonitrile, N-acetylglutamic acid). Concurrently, the elevation of these differential metabolites may be associated with the relative abundance of the beneficial bacterium Lachnospiraceae_UCG-009. However, the increase in LysoPC, a fatty acid oxidation product in glycerophospholipid metabolism, as well as the correlation between the sucrose content in the galactose metabolic pathway and the abundance of Paracoccus, indicates the reason why the Jennies are in a state of oxidative stress. Furthermore, group B1 may enhance the serum anti-inflammatory response in Jennies during late gestation by increasing the levels of estrogen in the steroid hormone biosynthesis metabolic pathway. These results could provide useful information for improving the health levels at the specific physiological stages and processes in Dezhou donkeys. Full article
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18 pages, 3666 KB  
Article
Reinforcement Learning Enabled Intelligent Process Monitoring and Control of Wire Arc Additive Manufacturing
by Allen Love, Saeed Behseresht and Young Ho Park
J. Manuf. Mater. Process. 2025, 9(10), 340; https://doi.org/10.3390/jmmp9100340 (registering DOI) - 18 Oct 2025
Abstract
Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such [...] Read more.
Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article
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21 pages, 5019 KB  
Article
Real-Time Parking Space Detection Based on Deep Learning and Panoramic Images
by Wu Wei, Hongyang Chen, Jiayuan Gong, Kai Che, Wenbo Ren and Bin Zhang
Sensors 2025, 25(20), 6449; https://doi.org/10.3390/s25206449 (registering DOI) - 18 Oct 2025
Abstract
In the domain of automatic parking systems, parking space detection and localization represent fundamental challenges that must be addressed. As a core research focus within the field of intelligent automatic parking, they constitute the essential prerequisite for the realization of fully autonomous parking. [...] Read more.
In the domain of automatic parking systems, parking space detection and localization represent fundamental challenges that must be addressed. As a core research focus within the field of intelligent automatic parking, they constitute the essential prerequisite for the realization of fully autonomous parking. Accurate and effective detection of parking spaces is still the core problem that needs to be solved in automatic parking systems. In this study, building upon existing public parking space datasets, a comprehensive panoramic parking space dataset named PSEX (Parking Slot Extended) with complex environmental diversity was constructed by integrating the concept of GAN (Generative Adversarial Network)-based image style transfer. Meanwhile, an improved algorithm based on PP-Yoloe (Paddle-Paddle Yoloe) is used to detect the state (free or occupied) and angle (T-shaped or L-shaped) of the parking space in real-time. For the many and small labels of the parking space, the ResSpp in it is replaced by the ResSimSppf module, the SimSppf structure is introduced at the neck end, and Silu is replaced by Relu in the basic structure of the CBS (Conv-BN-SiLU), and finally an auxiliary detector head is added at the prediction head. Experimental results show that the proposed SimSppf_mepre-Yoloe model achieves an average improvement of 4.5% in mAP50 and 2.95% in mAP50:95 over the baseline PP-Yoloe across various parking space detection tasks. In terms of efficiency, the model maintains comparable inference latency with the baseline, reaching up to 33.7 FPS on the Jetson AGX Xavier platform under TensorRT optimization. And the improved enhancement algorithm can greatly enrich the diversity of parking space data. These results demonstrate that the proposed model achieves a better balance between detection accuracy and real-time performance, making it suitable for deployment in intelligent vehicle and robotic perception systems. Full article
(This article belongs to the Special Issue Robot Swarm Collaboration in the Unstructured Environment)
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26 pages, 18261 KB  
Article
Fully Autonomous Real-Time Defect Detection for Power Distribution Towers: A Small Target Defect Detection Method Based on YOLOv11n
by Jingtao Zhang, Siwen Chen, Wei Wang and Qi Wang
Sensors 2025, 25(20), 6445; https://doi.org/10.3390/s25206445 (registering DOI) - 18 Oct 2025
Abstract
Drones offer a promising solution for automating distribution tower inspection, but real-time defect detection remains challenging due to limited computational resources and the small size of critical defects. This paper proposes TDD-YOLO, an optimized model based on YOLOv11n, which enhances small defect detection [...] Read more.
Drones offer a promising solution for automating distribution tower inspection, but real-time defect detection remains challenging due to limited computational resources and the small size of critical defects. This paper proposes TDD-YOLO, an optimized model based on YOLOv11n, which enhances small defect detection through four key improvements: (1) SPD-Conv preserves fine-grained details, (2) CBAM amplifies defect salience, (3) BiFPN enables efficient multi-scale fusion, and (4) a dedicated high-resolution detection head improves localization precision. Evaluated on a custom dataset, TDD-YOLO achieves an mAP@0.5 of 0.873, outperforming the baseline by 3.9%. When deployed on a Jetson Orin Nano at 640 × 640 resolution, the system achieves an average frame rate of 28 FPS, demonstrating its practical viability for real-time autonomous inspection. Full article
(This article belongs to the Section Electronic Sensors)
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24 pages, 14492 KB  
Article
Design and Control of a Bionic Underwater Collector Based on the Mouth Mechanism of Stomiidae
by Zexing Mo, Ping Ren, Lei Zhang, Jisheng Zhou, Yaru Li, Bowei Cui and Luze Wang
J. Mar. Sci. Eng. 2025, 13(10), 2001; https://doi.org/10.3390/jmse13102001 (registering DOI) - 18 Oct 2025
Abstract
Deep-sea mining has gradually emerged as a core domain in global resource exploitation. Underwater autonomous robots, characterized by low cost, high flexibility, and lightweight properties, demonstrate significant advantages in deep-sea mineral development. To address the limitations of traditional deep-sea mining equipment, such as [...] Read more.
Deep-sea mining has gradually emerged as a core domain in global resource exploitation. Underwater autonomous robots, characterized by low cost, high flexibility, and lightweight properties, demonstrate significant advantages in deep-sea mineral development. To address the limitations of traditional deep-sea mining equipment, such as large volume, high energy consumption, and insufficient flexibility, this paper proposes an innovative Underwater Vehicle Collector System (UVCS). Integrating bionic design with autonomous robotic technology, this system features a collection device mimicking the large opening–closing kinematics of the mouth of deep-sea dragonfish (Stomiidae). A dual-rocker mechanism is employed to realize the mouth opening-closing function, and the collection process is driven by the pitching motion of the vehicle without the need for additional motors, thus achieving the advantages of high flexibility, low energy consumption, and light weight. The system is capable of collecting seabed polymetallic nodules with diameters ranging from 1 to 12 cm, thus providing a new solution for sustainable deep-sea mining. Based on the dynamics of UVCS, this paper verifies its attitude stability and collection efficiency in planar motions through single-cycle and multi-cycle simulation analyses. The simulation results indicate that the system operates stably with reliable collection actions. Furthermore, water tank testings demonstrate the opening and closing functions of the UVCS collection device, fully confirming its design feasibility and application potential. In conclusion, the UVCS system, through the integration of bionic design, opens up a new path for practical applications in deep-sea resource exploitation. Full article
(This article belongs to the Section Ocean Engineering)
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