Search Results (743)

Sea buckthorn, a homologue of medicine and food, contains a host of bioactives that can prevent many diseases, especially cardiovascular diseases. The association between oxidative stress (OS) and cardiovascular diseases (CVDs) has been well-established, with OS ultimately leading to CVDs through lipid peroxidation and other mechanisms. In this study, antioxidant components were isolated from sea buckthorn by polyamide medium-pressure chromatography coupled with an HPLC-DPPH activity screening system. Two potential compounds were isolated and identified as Tetrahydroharmol and Isorhamnetin3-O-(6-O-E-sinapoyl)-β-D-glucopyranosyl-(1-2)-β-D-glucopyranoside-7-O-α-L-rhamnopyranoside. Molecular docking technology was used to explore the binding ability of two antioxidant active components to target proteins (LDH, SOD, Nrf2, iNOS, and eNOS). In addition, the antioxidant capacity was determined by EA.hy926 human umbilical vein endothelial fusion cell experiments. The results demonstrate the efficacy of this method for isolating high-purity antioxidants from sea buckthorn. These two activity compounds exhibit potential effects against cardiovascular diseases through antioxidant mechanisms. Full article

Additive manufacturing has emerged as a powerful approach for producing architected materials with tailored mechanical properties and enhanced energy absorption capabilities. By enabling precise control over geometry, relative density, and hierarchical topology, additive manufacturing facilitates the design of lightweight cellular structures with superior crashworthiness compared to conventional energy-absorbing materials. This review provides a comprehensive overview of recent advances in additively manufactured energy-absorbing structures, with particular emphasis on the interplay between structural architecture, fabrication technologies, and mechanical performance. Key additive manufacturing processes, including fused deposition modeling, stereolithography, selective laser sintering, and multi-jet fusion, are evaluated in terms of their fabrication capabilities, material compatibility, and inherent limitations. Special attention is given to the mechanical behavior of representative architectures, including two-dimensional cellular structures, three-dimensional lattice geometries, sandwich systems, and emerging four-dimensional programmable materials. Depending on topology and material system, additively manufactured lattices can achieve specific energy absorption values exceeding 20–40 J g⁻¹, significantly outperforming many conventional foams. Finally, current challenges, such as process-induced defects, anisotropic mechanical behavior, and the lack of standardized testing methodologies, are discussed, along with future research directions, including multi-material printing, functionally graded architectures, and adaptive metamaterials for next-generation impact mitigation systems. Full article

With the continuous advancement of polarization spectral sensing technology, multi-band polarization image fusion has emerged as a novel approach to image fusion. By integrating spectral and polarization information, this method overcomes the limitations of relying on a single information source and significantly improves overall image quality. To address this, this paper proposes a new polarization spectral fusion algorithm. First, feature matching is employed to achieve pixel-level spatial alignment of multi-band polarization images. Then, a fusion strategy based on multi-scale decomposition and singular value decomposition is adopted to preserve structural information and fine details. Subsequently, fractional-order processing and guided filtering are applied to enhance details and suppress noise. Finally, a progressive reconstruction from low to high scales is performed to ensure hierarchical consistency and information integrity throughout the fusion process. In addition, spectral information is utilized for color restoration, enabling the final image to achieve high spatial resolution while maintaining natural and rich color representation.Experimental results demonstrate that the proposed method effectively integrates features from different spectral bands and polarization information while preserving maximum similarity, leading to significant improvements in both image quality and detail representation. Full article

Ceramics are widely evaluated for their extreme hardness, high-temperature stability, and corrosion resistance, which enable applications in harsh service environments. However, these same properties, high melting points, brittleness, and low thermal shock resistance, make conventional manufacturing of complex ceramic components difficult and expensive. Traditional processes often require costly diamond tooling or energy-intensive sintering and tend to produce only simple geometries, with significant waste material and risk of defects. Additive manufacturing (AM) has recently emerged as a promising route to fabricate intricate, near-net-shape ceramic parts without these drawbacks. By building components layer by layer, AM reduces the need for extensive machining and enables the fabrication of geometrically complex, near-net-shape ceramic structures with reduced material waste, although challenges such as porosity, interlayer defects, and cracking during post-processing remain. Nonetheless, ceramic AM technologies lag behind their metal and polymer counterparts, and significant challenges remain in achieving fully dense parts with reliable mechanical properties. This review provides an in-depth overview of the state of the art in ceramics and ceramic composite additive manufacturing. We detail the most widely used AM processes (stereolithography, binder jetting, material extrusion, powder bed fusion, inkjet printing, and direct energy deposition) and typical feedstock formulations for each technique. We examine the resulting mechanical properties (strength, toughness, hardness, wear resistance) and functional properties (thermal stability, dielectric behavior, biocompatibility) of additively manufactured ceramics, and discuss their current and potential engineering applications in the aerospace, defense, automotive, biomedical, and energy sectors. Persistent challenges, including porosity, shrinkage and cracking during sintering, achieving uniform microstructures, high process costs, and scalability issues, are analyzed, and we highlight promising future directions such as multi-material grading, integration of machine learning for process optimization, and sustainable manufacturing approaches. Despite significant progress, challenges remain in achieving fully dense structures, improving process reliability, and scaling ceramic AM for industrial applications, highlighting the need for further research in process optimization, material design, and multi-material integration. Full article

High-strength bolts are critical structural components that are highly susceptible to corrosion in complex environments, posing significant threats to structural safety and reliability. Although acoustic emission (AE) technology has been widely applied in structural health monitoring, existing studies mainly focus on damage mode identification or source localization, while the identification of corrosion evolution stages based on AE signals remains insufficient. This study develops an intelligent corrosion diagnosis framework for high-strength bolts by integrating multimodal feature fusion and optimized machine learning. AE signals are first collected from the near-end and far-end of bolts using a wireless sensor network and then transformed into time–frequency representations via continuous wavelet transform (CWT). The resulting time–frequency images are fed into a modified ResNet-18 network to extract deep features, while statistical features are simultaneously extracted from the raw signals to preserve global information. These heterogeneous features are subsequently fused to form a comprehensive representation of corrosion characteristics. Furthermore, an artificial protozoa optimizer (APO) is introduced to adaptively optimize the hyperparameters of the XGBoost model. The results demonstrate that AE signals generated by hammering bolts with different corrosion levels can be successfully distinguished. The proposed method achieves high accuracy in corrosion stage classification and outperforms conventional approaches. Even when evaluated on an additional M30 bolt dataset, the proposed method maintains robust performance, demonstrating excellent generalization capability across different bolt sizes. These results demonstrate the practical potential of the proposed method for intelligent bolt corrosion diagnosis. Full article

Laser Powder Bed Fusion (PBF-LB) is currently one of the most versatile and adopted additive manufacturing technologies for printing metals. To take new PBF-LB machines into service, a thorough characterization and calibration is often necessary to get the desired output. This is commonly achieved empirically; however, data-driven methods have become more and more available over the last few years. This research explores the use of transfer learning (TL) to transfer process knowledge from an already-established source machine (Nikon SLM 500) to a target machine (Trumpf TruPrint 5000) with different hardware specifications. To predict the tensile properties of AlSi10Mg0.5 utilizing a minimal data set of merely 25 training samples, eight TL model variants, determined by their degrees of training freedom, were investigated. The results showed that TL is effective in transferring machine learning (ML)-based process models. High prediction accuracy was achieved on the target machine, with coefficient of determination ($R^2$) values reaching 75.5% for yield strength, 82.1% for ultimate tensile strength, and up to 92.0% for elongation at break in testing. Additionally, a weighted mean model ensemble of all eight single models was developed, including all eight TL variants, to enable higher prediction robustness. Validation trials for three different use cases confirmed the capability of the approach to optimize processing conditions, like increasing hatch scan speed by 167% to 292% while maintaining high mechanical performance. Additional microstructure analysis was given to support the findings. The results demonstrate a time- and resource-efficient approach for rapid industrialization of PBF-LB machines, combining ML-based process modeling with machine-specific data. Full article

Additive manufacturing (AM) represents a quite interesting technology for manufacturing components of nuclear reactors. This work investigated the microstructural stability of 316 L steel fabricated via Laser Powder Bed Fusion (L-PBF) from room temperature to 650 °C. Despite the reduced susceptibility of the material to sensitization owing to its low carbon content, temperature variations may induce deleterious effects in nuclear safety-critical components. In as-printed condition, the microstructure is not stable and undergoes significant changes induced by thermal cycling up to 650 °C in Mechanical Spectroscopy (MS) tests: the typical melt-pool pattern disappears, a population of equiaxed grains substitutes the original ones elongated in the build direction, the average size of the cells forming a finer sub-structure inside the grains increases, texture changes, and the excess of vacancies induced by the rapid cooling is recovered. Although the current literature reports that the microstructure is stable up to 500 °C, MS results indicate that the aforesaid irreversible phenomena start at a lower temperature (~230 °C). The present results suggest that the microstructure of the printed material must be stabilized through suitable heat treatments before its application in structural components for nuclear reactors. Full article

Synthetic Aperture Radar (SAR) and optical image fusion technology plays a crucial role in remote sensing applications. It effectively combines the high spatial resolution and rich spectral information of optical images with the all-weather and penetrating observation advantages of SAR images, thereby significantly enhancing image interpretation accuracy and task execution capabilities. This paper systematically reviews deep learning-based fusion methods for SAR and optical images, with a particular focus on recent advances in deep learning models. Furthermore, it summarizes commonly used evaluation metrics for assessing fusion image quality, providing a basis for comparing and analyzing the performance of different methods. In addition, commonly used SAR-optical fusion datasets are briefly reviewed to highlight their roles in algorithm development and performance evaluation. Unlike conventional review articles, this paper further analyzes the guidance and supporting role of fusion algorithms from the perspective of typical and specific applications. Finally, it identifies key challenges and issues faced by current fusion methods, including data registration, model lightweight design, and multimodal feature alignment, and offers perspectives on future research directions. This review aims to provide routes and references for the development of SAR and optical image fusion technology. Full article

Distracted driving is one of the primary causes of road traffic accidents. Behavior recognition technology based on machine vision has emerged as a research hotspot due to its non-contact and high-efficiency nature. To address the challenges of complex lighting conditions in the driver’s cabin, low detection accuracy for small-scale keypoints, and the difficulty in effectively characterizing behavioral features, this paper proposes a distracted driving behavior recognition method based on an improved YOLOv8n-Pose model and multi-feature fusion. First, the original YOLOv8n-Pose model is optimized. A P2 detection layer is added to enhance the feature extraction capabilities for small-scale human keypoints, and the SE attention module is incorporated to improve the model’s robustness under complex lighting conditions. In addition, the loss function is replaced with focal loss to tackle the class imbalance problem, thus forming the YOLOv8n-PSF-Pose keypoint detection network. Subsequently, based on the coordinates of 12 human keypoints extracted by this network, a multi-dimensional feature vector is constructed, which takes joint angles as the core and integrates the relative distances between keypoints and the number of valid keypoints. Finally, a BP neural network is adopted to classify the constructed feature vectors, enabling the accurate recognition of six typical distracted driving behaviors (normal driving, drinking or eating, making phone calls, using mobile phones, operating vehicle infotainment systems, and turning around to fetch items). The experimental results show that the improved YOLOv8n-PSF-Pose model achieves an mAP50 of 93.8% in keypoint detection, which is 6.7 percentage points higher than the original model; the BP classification model based on multi-feature fusion achieves an F1-score of 97.7% in the behavior recognition task, which is significantly better than traditional classifiers such as SVM and random forest, and the image processing speed on the NVIDIA RTX 3090TI reaches a high throughput of 45 FPS. This proves that the proposed method achieves an excellent balance between accuracy and speed. This study provides an effective solution for the real-time and accurate recognition of distracted driving behaviors. Full article

The breeding blanket is a key component of tokamaks, primarily responsible for extracting heat from fusion reactions and for tritium breeding, which is essential to ensure a fusion reactor’s fuel self-sufficiency. Recent technological advancements have led to the development of Dual-Cooled Lead–Lithium (DCLL) breeding blankets, which employ a liquid metal (specifically a Lead–Lithium eutectic alloy) as a heat transfer medium and tritium breeder, while helium gas is used to cool the structural components of the reactor. The interaction between the moving electrically conducting fluid and the strong magnetic field in the tokamak environment leads to magnetohydrodynamic (MHD) effects. The latter are characterized by the induction of eddy currents within the fluid and resulting Lorentz forces generated by their interaction with the magnetic field, which cause additional pressure losses and reduce heat transfer efficiency. This work investigates the pressure drop experienced by a Lead–Lithium flow within a rectangular section conduit under the action of an external, uniform magnetic field of different intensities. An analytical model was developed to estimate the total MHD-induced pressure losses along the channel for different values of the external magnetic field intensity and then benchmarked against relative computational fluid dynamics (CFD) simulations carried out using COMSOL Multiphysics. This comparison allowed the validation of the analytical predictions as well as a better understanding of the influence of the applied magnetic field intensity on the overall pressure drop. Therefore, the aim of the analytical model is to provide analytical tools for reasonably accurate estimations of MHD pressure losses suitable for future preliminary design purposes. Full article

As autonomous driving technology progresses, efficient and accurate object detectors are able to detect pedestrians, vehicles, road signs, and obstacles in real time, thereby enhancing driving safety and serving as a part of autonomous driving. However, the performance of such object detectors is limited and cannot be leveraged to satisfy modern autonomous driving systems. To address this issue, we develop an object detection network for autonomous driving scenarios, SST-YOLO, which is based on YOLOv8. First, we propose a Sobel Convolution & Convolution (SCC) module to enhance the backbone, which incorporates a SobelConv branch to explicitly model gradient-based edge information and improve structural feature representation. In addition, we replace the original path aggregation feature pyramid network (PAFPN) with a Small Object Augmentation Pyramid Network (SOAPN), which integrates SPDConv and CSP-OmniKernel modules to strengthen multi-scale feature fusion and enhance small object representation. Finally, a Task-Adaptive Decomposition & Alignment Head (TADAHead) is designed, which employs task decomposition, dynamic deformable convolution, and classification-aware modulation to decouple tasks and achieve adaptive spatial alignment, thereby improving detection accuracy and robustness in complex scenarios. Experiments on the public autonomous driving dataset KITTI show that our proposed method outperforms the baseline YOLOv8 model. Compared with the baseline results, mAP@0.5:0.95 ranges from 65.1% to 69.2%, which indicates that the proposed SST-YOLO network can achieve object detection for autonomous cars. Full article

Laser powder bed fusion (LPBF) technology has now reached a significant level of commercial maturity, offering some of the most reliable solutions in the additive manufacturing (AM) field. However, AM processes may introduce defects that result in high variability of mechanical properties and low reproducibility. This entails the need to thoroughly understand the behavior of the materials used, studying their response to the different types of stresses typical of real-world applications. The research activity presented consists of the analysis of the mechanical properties of the aluminum alloy AlSi10Mg, which is widely used due to its good strength-to-density ratio. Focus is put on the response to axial, torsional, and combined axial-torsional static and fatigue strength, comparing as-built T6 heat-treated conditions. Full article

Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research articles was systematically analysed to compare the capabilities, limitations and integration challenges of sensing technologies used in autonomous vehicles. In addition to these primary studies, further references were included to provide background information and describe emerging developments in autonomous sensing systems. The review shows that no single sensor technology can provide reliable perception under all environmental conditions. Camera systems offer rich visual information but are sensitive to lighting and weather conditions, while LiDAR provides highly accurate three-dimensional geometry but suffers from signal attenuation in rain and fog. Radar sensors demonstrate superior robustness in adverse weather and enable direct velocity measurement, although their spatial resolution remains limited compared to optical sensors. As a result, modern autonomous vehicles rely on multi-sensor fusion architectures that combine complementary sensing modalities to improve reliability and safety. The analysis also identifies several key research gaps in the current literature. In particular, there is a lack of systematic evaluation of trade-offs between sensor performance, computational requirements and vehicle energy consumption. Furthermore, the safety certification of artificial intelligence-based perception systems and the integration of emerging technologies such as FMCW LiDAR and terahertz radar remain open research challenges. Overall, the results suggest that the future of autonomous vehicle perception will depend not only on improvements in individual sensors but also on robust sensor fusion architectures, safety-certified AI models and energy-efficient sensor processing platforms. These findings provide guidance for researchers and engineers developing next-generation sensing systems for autonomous driving. Full article

Traditional sensory evaluation of traditional Chinese medicine (TCM) and medicinal and food homologous products has long relied on human observation of appearance, color, aroma, and taste. However, this approach is highly subjective, difficult to quantify, and often lacks reproducibility across evaluators. Intelligent sensory systems, including the electronic nose, electronic tongue, and machine vision, provide objective and digitized sensory information for TCM quality evaluation. Nevertheless, these platforms generate high-dimensional and heterogeneous datasets, creating a strong demand for efficient artificial intelligence (AI)-based analytical tools. This review summarizes recent advances in the application of machine learning and deep learning methods, such as support vector machine, random forest, convolutional neural network, and long short-term memory networks, for intelligent sensory evaluation of TCM. Particular emphasis is placed on how AI supports feature extraction, pattern recognition, classification, regression, and multisource data fusion across electronic nose, electronic tongue, and machine vision systems. Representative applications in raw material authentication, geographical origin discrimination, processing monitoring, and quality grading are also discussed. In addition, the current challenges related to data standardization, sensor drift, model robustness, and interpretability are highlighted. Overall, this review provides an integrated overview of AI-enabled intelligent sensory technologies and clarifies their potential to advance TCM quality evaluation toward a more objective, efficient, and holistic framework. Full article

Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems. Full article