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Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%. Full article

(This article belongs to the Section Ocean Engineering)

25 pages, 2680 KiB

Open AccessArticle

Performance Restoration of Chemically Recycled Carbon Fibres Through Surface Modification with Sizing

by Dionisis Semitekolos, Sofia Terzopoulou, Silvia Zecchi, Dimitrios Marinis, Ergina Farsari, Eleftherios Amanatides, Marcin Sajdak, Szymon Sobek, Weronika Smok, Tomasz Tański, Sebastian Werle, Alberto Tagliaferro and Costas Charitidis

Polymers 2025, 17(1), 33; https://doi.org/10.3390/polym17010033 (registering DOI) - 26 Dec 2024

Abstract

The recycling of Carbon Fibre-Reinforced Polymers (CFRPs) is becoming increasingly crucial due to the growing demand for sustainability in high-performance industries such as automotive and aerospace. This study investigates the impact of two chemical recycling techniques, chemically assisted solvolysis and plasma-enhanced solvolysis, on the morphology and properties of carbon fibres (CFs) recovered from end-of-life automotive parts. In addition, the effects of fibre sizing are explored to enhance the performance of the recycled carbon fibres (rCFs). The surface morphology of the fibres was characterised using Scanning Electron Microscopy (SEM), and their structural integrity was assessed through Thermogravimetric Analysis (TGA) and Raman spectroscopy. An automatic analysis method based on optical microscopy images was also developed to quantify filament loss during the recycling process. Mechanical testing of single fibres and yarns showed that although rCFs from both recycling methods exhibited a ~20% reduction in tensile strength compared to reference fibres, the application of sizing significantly mitigated these effects (~10% reduction). X-ray Photoelectron Spectroscopy (XPS) further confirmed the introduction of functional oxygen-containing groups on the fibre surface, which improved fibre-matrix adhesion. Overall, the results demonstrate that plasma-enhanced solvolysis was more effective at fully decomposing the resin, while the subsequent application of sizing enhanced the mechanical performance of rCFs, restoring their properties closer to those of virgin fibres. Full article

(This article belongs to the Special Issue Carbon Fiber Reinforced Polymer Composites: Advancements in Recycling and Sustainability)

21 pages, 10029 KiB

Open AccessArticle

An Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s

by Zhiguo Liu, Enzheng Zhang, Qian Ding, Weijie Liao and Zixiang Wu

Sensors 2025, 25(1), 85; https://doi.org/10.3390/s25010085 (registering DOI) - 26 Dec 2024

Abstract

Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the neck network to improve the model’s tracking speed for dynamic objects. By mapping the motion of dynamic objects across frames, the proposed method achieves accurate trajectory tracking. This paper provides a detailed explanation of the improvements made to YOLOv8s for enhancing detection accuracy and speed in dynamic object detection tasks. Comparative experiments on the MS-COCO dataset and the custom dataset demonstrate that the proposed method has a clear advantage in terms of detection accuracy and processing speed. The dynamic object detection experiments further validate the effectiveness of the proposed method for detecting and tracking objects at different speeds. The proposed method offers a valuable reference for the field of dynamic object detection, providing actionable insights for applications such as robotic skill learning, generalization, and artificial intelligence-driven robotics. Full article

(This article belongs to the Section Sensors and Robotics)

18 pages, 789 KiB

Open AccessArticle

Enteric Coated Pellets with Lactoferrin for Oral Delivery: Improved Shelf Life of the Product

by Nika Kržišnik, Blaž Grilc and Robert Roškar

Pharmaceutics 2025, 17(1), 23; https://doi.org/10.3390/pharmaceutics17010023 (registering DOI) - 26 Dec 2024

Abstract

Background/Objectives: Lactoferrin (Lf), a multifunctional iron-binding protein, has considerable potential for use as an active ingredient in food supplements due to its numerous positive effects on health. As Lf is prone to degradation, we aimed to develop a formulation that would ensure sufficient stability of Lf in the gastrointestinal tract and during product storage. Methods: A simple, efficient, and well-established technology that has potential for industrial production was used for the double-coating of neutral pellet cores with an Lf layer and a protective enteric coating. Results: The encapsulation efficiency was 85%, which is among the highest compared to other reported Lf formulations. The results of the dissolution tests performed indicated effective protection of Lf from gastric digestion. A comprehensive stability study showed that the stability was similar regardless of the neutral pellet core used, while a significant influence of temperature, moisture, product composition, and packaging on the stability of Lf were observed, and were therefore considered in the development of the final product. The experimentally determined shelf life is extended from 15 to almost 30 months if the product is stored in a refrigerator instead of at room temperature, which ensures the commercial applicability of the product. Conclusion: We successfully transferred a technology commonly used for small molecules to a protein-containing product, effectively protected it from the destructive effects of gastric juice, and achieved an acceptable shelf life. Full article

(This article belongs to the Special Issue Research Progress of Pharmaceutical Coating Technology)

16 pages, 1170 KiB

Open AccessArticle

Laser Welding of Micro-Wire Stent Electrode as a Minimally Invasive Endovascular Neural Interface

by Bo Wen, Liang Shen and Xiaoyang Kang

Micromachines 2025, 16(1), 21; https://doi.org/10.3390/mi16010021 (registering DOI) - 26 Dec 2024

Abstract

Minimally invasive endovascular stent electrodes are an emerging technology in neural engineering, designed to minimize the damage to neural tissue. However, conventional stent electrodes often rely on resistive welding and are relatively bulky, restricting their use primarily to large animals or thick blood vessels. In this study, the feasibility is explored of fabricating a laser welding stent electrode as small as 300 . A high-precision laser welding technique was developed to join micro-wire electrodes without compromising structural integrity or performance. To ensure consistent results, a novel micro-wire welding with platinum pad method was introduced during the welding process. The fabricated electrodes were integrated with stent structures and subjected to detailed electrochemical performance testing to evaluate their potential as neural interface components. The laser-welded endovascular stent electrodes exhibited excellent electrochemical properties, including low impedance and stable charge transfer capabilities. At the same time, in this study, a simulation is conducted of the electrode distribution and arrangement on the stent structure, optimizing the utilization of the available surface area for enhanced functionality. These results demonstrate the potential of the fabricated electrodes for high-performance neural interfacing in endovascular applications. The approach provided a promising solution for advancing endovascular neural engineering technologies, particularly in applications requiring compact electrode designs. Full article

(This article belongs to the Section B：Biology and Biomedicine)

27 pages, 7594 KiB

Open AccessFeature PaperArticle

A Network-Based Clustering Method to Ensure Homogeneity in Regional Frequency Analysis of Extreme Rainfall

by Marios Billios and Lampros Vasiliades

Water 2025, 17(1), 38; https://doi.org/10.3390/w17010038 (registering DOI) - 26 Dec 2024

Abstract

The social impacts of extreme rainfall events are expected to intensify with climate change, making reliable statistical analyses essential. High quantile estimation requires substantial data; however, available records are sometimes limited. Additionally, finite data and variability across statistical models introduce uncertainties in the final estimates. This study addresses the uncertainty that arises when selecting parameters in Regional Frequency Analysis (RFA) by proposing a method to objectively identify statistically homogeneous regions. Station coordinates, elevation, annual mean rainfall, maximum annual rainfall, and l-skewness from 55 meteorological stations are selected to study annual maximum daily rainfall. These covariates are employed to investigate the interdependency of the covariates in Principal Component Analysis (PCA) as a preprocessing step in cluster analysis. Network theory, implemented through an iterative clustering process, is used in network creation where stations are linked based on the frequency of their co-occurrence in clusters. Communities are formed by maximizing the modularity index after creating a network of stations. RFA is performed in the final communities using L-moment theory to estimate regional and InSite quantiles. Quantile uncertainty is calculated through parametric bootstrapping. The application of PCA has a negligible effect on network creation in the study area. The results show that the iterative clustering approach with network theory ensures statistically created homogeneous regions, as demonstrated in Thessaly’s complex terrain for regionalisation of extreme rainfall. Full article

(This article belongs to the Section Hydrology)

18 pages, 1747 KiB

Open AccessArticle

Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission

by Yufei He, Ruiqi Hu, Kewei Liang, Yonghong Liu and Zhiyuan Zhou

Mathematics 2025, 13(1), 46; https://doi.org/10.3390/math13010046 (registering DOI) - 26 Dec 2024

Abstract

The optimization of information transmission in unmanned aerial vehicles (UAVs) is essential for enhancing their operational efficiency across various applications. This issue is framed as a mixed-integer nonconvex optimization challenge, which traditional optimization algorithms and reinforcement learning (RL) methods often struggle to address effectively. In this paper, we propose a novel deep reinforcement learning algorithm that utilizes a hybrid discrete–continuous action space. To address the long-term dependency issues inherent in UAV operations, we incorporate a long short-term memory (LSTM) network. Our approach accounts for the specific flight constraints of fixed-wing UAVs and employs a continuous policy network to facilitate real-time flight path planning. A non-sparse reward function is designed to maximize data collection from internet of things (IoT) devices, thus guiding the UAV to optimize its operational efficiency. Experimental results demonstrate that the proposed algorithm yields near-optimal flight paths and significantly improves data collection capabilities, compared to conventional heuristic methods, achieving an improvement of up to 10.76%. Validation through simulations confirms the effectiveness and practicality of the proposed approach in real-world scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence: Large Language Models and Big Data Analysis)

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53 pages, 2887 KiB

Open AccessReview

A Comprehensive Review of Flamelet Methods: Future Directions and Emerging Challenges

by Mohammed Niyasdeen Nejaamtheen and Jeong-Yeol Choi

Energies 2025, 18(1), 45; https://doi.org/10.3390/en18010045 (registering DOI) - 26 Dec 2024

Abstract

Understanding and accurately modeling combustion processes in engines across a wide range of operating conditions is critical for advancing both subsonic and supersonic propulsion technologies. These engines, characterized by highly complex flow fields, varying degrees of compressibility, and intricate chemical reaction mechanisms, present unique challenges for computational combustion models. Among the various approaches, flamelet models have gained prominence due to their efficiency and intuitive nature. However, traditional flamelet models, which often assume fixed boundary conditions, face significant difficulties. This review article provides a comprehensive overview of the current state of incompressible flamelet modeling, with a focus on recent advancements and their implications for turbulent combustion simulations. The discussion extends to advanced topics such as the modeling of partially premixed combustion, the definition of reaction progress variables, efficient temperature computation, and the handling of mixture fraction variance. Despite the inherent challenges and limitations of flamelet modeling, particularly in 1D applications, the approach remains an attractive option due to its computational efficiency and applicability across a wide range of combustion scenarios. The review also highlights ongoing debates within the research community regarding the validity of the flamelet approach, particularly in high-speed flows, and suggests that while alternative methods may offer more detailed modeling, they often come with prohibitive computational costs. By synthesizing historical context, recent developments, and future directions, this article serves as a valuable resource for both novice and experienced combustion modelers. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

18 pages, 2111 KiB

Open AccessArticle

BGI-YOLO: Background Image-Assisted Object Detection for Stationary Cameras

by Youn Joo Lee, Ho Gi Jung and Jae Kyu Suhr

Electronics 2025, 14(1), 60; https://doi.org/10.3390/electronics14010060 (registering DOI) - 26 Dec 2024

Abstract

This paper proposes a method enhancing the accuracy of object detectors by utilizing background images for stationary camera systems. Object detection with stationary cameras is highly valuable across various applications, such as traffic control, crime prevention, and abnormal behavior detection. Deep learning-based object detectors, which are mainly used in such cases, are developed for general purposes and do not take advantage of stationary cameras at all. Previously, cascade-based object detection methods utilizing background have been studied for stationary camera systems. These methods typically consist of two stages: background subtraction followed by object classification. However, their object detection performance is highly dependent on the accuracy of the background subtraction results, and numerous parameters must be adjusted during background subtraction to adapt to varying conditions. This paper proposes an end-to-end object detection method named BGI-YOLO, which uses a background image simply by combining it with an input image before feeding it into the object detection network. In our experiments, the following five methods are compared: three candidate methods of combining input and background images, baseline YOLOv7, and a traditional cascade method. BGI-YOLO, which combines input and background images at image level, showed a detection performance (mAP) improvement compared to baseline YOLOv7, with an increase of 5.6%p on the WITHROBOT S1 dataset and 2.5%p on the LLVIP dataset. In terms of computational cost (GFLOPs), the proposed method showed a slight increase of 0.19% compared to baseline YOLOv7. The experimental results demonstrated that the proposed method is highly effective for improving detection accuracy without increasing computational cost. Full article

(This article belongs to the Special Issue Applications and Challenges of Image Processing in Smart Environment)

20 pages, 5477 KiB

Open AccessArticle

Control of Vibrio parahaemolyticus in Seafood Using the Combination of Lytic Phages and Citric Acid

by Xiaoshuang Zheng, Lu Gao, Lei Yuan, Caowei Chen and Zhenquan Yang

Foods 2025, 14(1), 37; https://doi.org/10.3390/foods14010037 (registering DOI) - 26 Dec 2024

Abstract

Vibrio parahaemolyticus is a key foodborne pathogen in seafood that poses health risks to consumers. The application of phages and organic acids is considered an alternative strategy for controlling bacterial contamination in foods. In the present study, the genome features of five previously isolated virulent V. parahaemolyticus phages (VPpYZU64, VPpYZU68, VPpYZU81, VPpYZU92, and VPpYZU110) were characterized, and their bacteriostatic effects in combination with citric acid were analyzed. Genome sequencing of the five phages showed a total genome length of 76,153–144,768 bp. No virulent or drug-resistant genes were detected in the five phages. Bacterial inhibition testing of salmon fillets stored at 25 °C for 12 h showed that the number of V. parahaemolyticus decreased by 2.02 and 3.84 log CFU/g after treatment with a phage mixture, VPpMIx, and the combination of phage mixture VPpMIx and citric acid. In addition, phage VPpYZU64 combined with 600 μg/mL citric acid exhibited the highest biofilm reduction rate for V. parahaemolyticus. Collectively, our results show that combining phages and citric acid is a natural and efficient method of controlling V. parahaemolyticus growth in seafood. Full article

(This article belongs to the Special Issue Novel Detection Approaches of Biological and Non-biological Risk Factors in Foods)

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15 pages, 2875 KiB

Open AccessArticle

Genome-Wide Identification and Characterization of HSP70 Gene Family in Tausch’s Goatgrass (Aegilops tauschii)

by Yongmei Xu, Yue Liu, Yanjun Yi and Jiajia Liu

Genes 2025, 16(1), 19; https://doi.org/10.3390/genes16010019 (registering DOI) - 26 Dec 2024

Abstract

Background: Aegilops tauschii, a winter annual grass weed native to Eastern Europe and Western Asia, has become a widespread invasive species in the wheat-growing regions of China due to its high environmental adaptability. This study aims to explore the molecular mechanisms underlying the stress resistance of Tausch’s goatgrass, focusing on the HSP70 gene family. Methods: A genome-wide analysis was conducted to identify and characterize the HSP70 gene family in A. tauschii. Afterward, their physicochemical properties, phylogenetic relationships, gene structures, and chromosomal distributions were analyzed. Additionally, cis-acting regulatory elements were predicted to understand their potential role in stress resistance. Results: A total of 19 identified HSP70 family genes were classified into four subfamilies and distributed across all chromosomes. The syntenic analysis revealed extensive homology between Tausch’s goatgrass and wheat HSP70 genes. Segmental duplication was found to play a crucial role in the expansion of the HSP70 gene family. The prediction of cis-acting elements suggested that these genes are involved in stress resistance to various environmental conditions. Conclusions: This study provides a comprehensive overview of the HSP70 gene family in A. tauschii, offering insights into their role in stress resistance and their potential application in understanding invasive species behavior and improving wheat resilience. Further research is needed to validate their functional roles in stress adaptation. Full article

(This article belongs to the Section Plant Genetics and Genomics)

18 pages, 1438 KiB

Open AccessArticle

Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings

by Chuanxiao Zhi, Jingyu Zhai, Baogang Wen and Ning Li

Appl. Sci. 2025, 15(1), 98; https://doi.org/10.3390/app15010098 (registering DOI) - 26 Dec 2024

Abstract

Integrated bearings, characterized by their unique structure, feature an inner ring that is seamlessly integrated with the shaft. This study is based on the theoretical framework of rolling bearing dynamics and considers bearing friction, lubrication, and Hertz elastic contact theory. A dynamic simulation model considering the interaction between the components of the rolling bearing is established. Additionally, a subroutine for calculating the interaction forces between the bearing components was written in C and compiled into a dynamic link library, which was then integrated with the dynamic simulation software. To solve and simulate the dynamics of the integrated bearing model, a sophisticated combination of a refined integration method and the predictor-corrector Adams–Bashforth–Moulton multistep technique was employed. The theoretical analysis offers insights into the vibration characteristics of the integrated bearings across different structural and operational parameters. Results indicate that a judicious selection of parameters, such as the curvature radius ratio of the inner and outer grooves and the gap of the cage pockets, can significantly enhance the bearings’ vibration and noise reduction capabilities. Furthermore, the application of an appropriate axial preload effectively reduces bearing vibrations, and there exists an optimal range of rotational speeds that minimizes these vibrations. Full article

(This article belongs to the Section Acoustics and Vibrations)

28 pages, 16945 KiB

Open AccessReview

Review on Repair Technologies for Underwater Concrete Structure Damage of Infrastructures

by Zhaogeng Wang, Jijian Lian, Hui Liu, Chao Liang, Kaifang Zou, Liang Chen, Suiling Wang, Nan Shao and Ye Yao

Water 2025, 17(1), 35; https://doi.org/10.3390/w17010035 (registering DOI) - 26 Dec 2024

Abstract

This paper comprehensively summarizes and discusses the latest research progress in the underwater concrete structure damage repair technology of infrastructures. The prompt application of underwater concrete structure repair technology can effectively deal with the damaged parts of underwater concrete structures, and it can ensure the safe and stable operation of infrastructure and extend its service life. Firstly, this study uses bibliometric methods to analyze the characteristics of the literature on research into underwater concrete repair in the past 30 years (1993–2023), and expounds the research status and hotspots of this field. Then, we conduct a comprehensive classification and discussion of the underwater concrete structure damage repair technologies at the current stage. This technology can be divided into two major types: direct underwater type and dry environment type. Further, the development history of these technologies is systematically sorted out and, combined with practical engineering application cases, the operation processes, applicability, limitations, and economy of these technologies are analyzed. Finally, the challenges and future development trends of the current underwater concrete structure damage repair technology are pointed out, which provides a direction for future research on the intelligent maintenance of underwater concrete structures. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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19 pages, 1089 KiB

Open AccessArticle

Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation

by Wei-Jong Yang, Chih-Chen Wu and Jar-Ferr Yang

Sensors 2025, 25(1), 80; https://doi.org/10.3390/s25010080 (registering DOI) - 26 Dec 2024

Abstract

Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human–computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps. In the encoder, we construct a multi-scale feature extractor by mixing residual configurations of vision transformers to enhance both local and global information. In the adaptive fusion decoder, we introduce adaptive fusion modules to effectively merge the features of the encoder and the decoder together. Lastly, the model is trained using a loss function that aligns with human perception to enable it to focus on the depth values of foreground objects. The experimental results demonstrate the effective prediction of the depth map from a single-view color image by the proposed autoencoder, which increases the first accuracy rate about 28% and reduces the root mean square error about 27% compared to an existing method in the NYU dataset Full article

(This article belongs to the Special Issue Challenges and Future Trends of 3D Image Sensing, Visualization, and Processing)

18 pages, 12861 KiB

Open AccessArticle

Synergistic Approach of High-Precision 3D Printing and Low Cell Adhesion for Enhanced Self-Assembled Spheroid Formation

by Chunxiang Lu, Aoxiang Jin, Chuang Gao, Hao Qiao, Huazhen Liu, Yi Zhang, Wenbin Sun, Shih-Mo Yang and Yuanyuan Liu

Biosensors 2025, 15(1), 7; https://doi.org/10.3390/bios15010007 (registering DOI) - 26 Dec 2024

Abstract

Spheroids, as three-dimensional (3D) cell aggregates, can be prepared using various methods, including hanging drops, microwells, microfluidics, magnetic manipulation, and bioreactors. However, current spheroid manufacturing techniques face challenges such as complex workflows, the need for specialized personnel, and poor batch reproducibility. In this study, we designed a support-free, 3D-printed microwell chip and developed a compatible low-cell-adhesion process. Through simulation and experimental validation, we rapidly optimized microwell size and the coating process. We successfully formed three types of spheroids—human immortalized epidermal cells (HaCaTs), umbilical cord mesenchymal stem cells (UC-MSCs), and human osteosarcoma cells (MG63s)—on the chip. Fluorescent viability staining confirmed the biocompatibility and reliability of the chip. Finally, drug response experiments were conducted using the chip. Compared to traditional methods, our proposed strategy enables high-throughput production of size-controlled spheroids with excellent shape retention, while enhanced gas exchange during culture improves differentiation marker expression. This platform provides an efficient and cost-effective solution for biosensing applications, such as drug screening, disease modeling, and personalized therapy monitoring. Furthermore, the chip shows significant potential for real-time in vitro monitoring of cellular viability, reaction kinetics, and drug sensitivity, offering valuable advancements in biosensor technology for life sciences and medical applications. Full article

(This article belongs to the Section Nano- and Micro-Technologies in Biosensors)

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