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The application potential of unmanned aerial vehicles (UAVs) in marine search and rescue is especially of concern for the ongoing advancement of visual recognition technology and image processing technology. Limited computing resources, insufficient pixel representation for small objects in high-altitude images, and challenging visibility conditions hinder UAVs’ target recognition performance in maritime search and rescue operations, highlighting the need for further optimization and enhancement. This study introduces an innovative detection framework, CFSD-UAVNet, designed to boost the accuracy of detecting minor objects within imagery captured from elevated altitudes. To improve the performance of the feature pyramid network (FPN) and path aggregation network (PAN), a newly designed PHead structure was proposed, focusing on better leveraging shallow features. Then, structural pruning was applied to refine the model and enhance its capability in detecting small objects. Moreover, to conserve computational resources, a lightweight CED module was introduced to reduce parameters and conserve the computing resources of the UAV. At the same time, in each detection layer, a lightweight CRE module was integrated, leveraging attention mechanisms and detection heads to enhance precision for small object detection. Finally, to enhance the model’s robustness, WIoUv2 loss function was employed, ensuring a balanced treatment of positive and negative samples. The CFSD-UAVNet model was evaluated on the publicly available SeaDronesSee maritime dataset and compared with other cutting-edge algorithms. The experimental results showed that the CFSD-UAVNet model achieved an mAP@50 of 80.1% with only 1.7 M parameters and a computational cost of 10.2 G, marking a 12.1% improvement over YOLOv8 and a 4.6% increase compared to DETR. The novel CFSD-UAVNet model effectively balances the limitations of scenarios and detection accuracy, demonstrating application potential and value in the field of UAV-assisted maritime search and rescue.

This study delves into rotor–stator interaction within a bidirectional bulb tubular pump under cavitation conditions. Using pressure pulsation tests on a model pump and numerical simulations performed with ANSYS CFX software, we analyzed pressure pulsation and flow field data across three distinct flow rates and multiple cavitation numbers. Both time-domain and frequency-domain analyses were conducted to examine the patterns of pressure pulsation influenced by flow rates and cavitation numbers at various monitoring locations. A numerical flow field analysis further validated the findings. The results reveal that rotor–stator interaction manifests in the vaneless spaces of the pump during cavitation. The onset of cavitation alters the amplitudes of dominant frequencies at different flow rates. Near the guide vane and impeller, the dominant frequencies shift toward the impeller frequency and guide vane frequency, respectively. Under low-flow conditions, the rotor–stator interaction effect is more conspicuous due to the deteriorated flow pattern. Pressure pulsations are more strongly influenced in the front vaneless space (FVP) than in the rear vaneless space (RVP). This difference arises because the front guide vane destabilizes rather than stabilizes the flow pattern, worsening the rotor–stator interaction. Additionally, the FVP is less affected by the impeller than the RVP, further amplifying the influence of rotor–stator interaction on pressure pulsation. These findings provide a theoretical foundation for mitigating the effects of rotor–stator interaction on the operational stability and efficiency of bidirectional bulb tubular pumps.

The convenient construction of carbon-based electrochemical immunosensors with high performance is highly desirable for the efficient detection of tumor biomarkers. In this work, an electrochemical immunosensor was fabricated by integrating a biofunctionalized mesoporous silica nanochannel film with a carbon-based electrode, which can enable the sensitive determination of carcinoembryonic antigen (CEA) in serum. The commonly used carbonaceous electrode, glassy carbon electrode (GCE), was employed as the supporting electrode and was pre-treated through electrochemical polarization to achieve the stable binding of a vertically ordered mesoporous silica film with amino groups (NH2-VMSF) without the use of any adhesive layer. To fabricate the immunorecognition interface, antibodies were covalently immobilized after the amino groups on the outer surface of NH2-VMSF was derivatized to aldehyde groups. The presence of amino sites within the high-density nanochannels of NH2-VMSF can facilitate the migration of negatively charged redox probes (Fe(CN)63-/4-) to the supporting electrode through electrostatic adsorption, leading to the generation of electrochemical signals. In the presence of CEA, the formation of immunocomplexes on the recognitive interface can reduce the electrochemical signal of Fe(CN)63-/4- on the supporting electrode. Based on this principle, the sensitive electrochemical detection of CEA was achieved. CEA can be determined to range from 0.01 ng mL−1 to 100 ng mL−1 with a limit of detection of 6.3 pg mL−1. The fabricated immunosensor exhibited high selectivity, and the detection of CEA in fetal bovine serum was achieved.

Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map’s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship’s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN’s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.

Hot compression tests of Mo–1.5 wt% Al2O3/ZrO2 molybdenum alloys were carried out using the Gleeble–1500 simulator at 0.01 s−1−5 s−1 strain rates and 1000–1500 °C deformation temperatures. The microstructural changes of the alloy at 1000–1500 °C were studied. The changes in the hot deformation process for the Mo−1.5 wt% Al2O3/ZrO2 molybdenum alloys were analyzed by means of EBSD. The ZrO2 particles had a greater effect on improving the thermal deformation resistance of molybdenum alloys than did the Al2O3 particles. The activation energy of the molybdenum alloy doped with ZrO2 (403.917 kJ/mol) was lower than that of the molybdenum alloy doped with Al2O3 (440.314 kJ/mol). Due to the occurrence of recrystallization, the intensity of {100} the texture first increased and, then, dropped down with increase in the temperature, while the change law of {111} texture was the opposite. Above 1200 °C, the higher deformation temperature made the texture more random by lowering the texture intensity. The dislocation density was sacrificed to promote recrystallization. When dynamic recrystallization occurred, the sub–grain boundaries absorbed dislocations and transformed them into high–angle grain boundaries, resulting in a decrease in dislocation density and an increase in high–angle grain boundaries at high temperatures and low strain rates. At 0.01 s−1 strain rate, the average grain size of Mo–1.5 wt% ZrO2 alloy increased from 2.38 μm to 4.67 μm, and the proportion of large angle grain boundaries increased from 59.8% to 86.6%.

Camelina sativa (camelina) seed, oil, and defatted meal are widely used for food, animal feed, and other purposes. The accurate quantification of camelina glucosinolates is critical as their functionalities are highly dose-dependent. The classic quantification of glucosinolates in camelina products involves tedious desulfation steps, toxic reagents, and a lengthy instrument time because glucosinolates are easy to degrade and subject to interference in the liquid chromatography. Thus, we developed and validated an eco-efficient UPLC-DAD method for determining glucoarabin (GS9), glucocamelinin (GS10), and homoglucocamelinin (GS11) in camelina seed, oil, and defatted meal. Glucosinolates were extracted using 80% cold methanol to denature myrosinase, and were separated by an HSS T3 column without desulfation. Glucotropaeolin was used as an internal standard to track analyte degradation and loss during sample preparation. The method has shown high precision (relative standard deviations ranging from 4.12% to 6.54%) and accuracy (>94.4% spike recovery) for GS9-11, and all validation parameters passed the industry-consensus AOAC Appendix F criteria. To our best knowledge, this is the first eco-efficient and low-cost analytical method that is validated against strict AOAC criteria for the quantification of intact camelina glucosinolates. The method is suitable to be adopted as a new industrial testing standard to assist in the quality control of camelina products.

At present, most studies focus on Cu-W composites with high W content (W content > 50 wt%), while there are only sporadic reports on Cu-W composites with low W content (W content < 50 wt%). In this work, Cu-W composites with different W contents (0, 10 wt%, 20 wt% and 30 wt%) were prepared, and the effects of W content on microstructure, density, hardness, electrical conductivity, strength and electrical contact properties were systematically studied, with the expectation of providing an experimental basis and theoretical support for expanding the application range of Cu-W composites with low W content. The results showed that, with the increase in W content, the Cu matrices divided into finer and more uniform grains; the density and electrical conductivity of Cu-W composites decreased; and the compressive yield strength and hardness gradually increased. As the content of W increased, the arc burning time of the Cu-W composite contacts began to fluctuate. There was a loss of both the cathode and the anode contacts of the pure Cu, but the mass transfer of the Cu-W composite contacts occurred as follows: the anode weight increases, while the cathode weight decreases. The addition of W particles changed the non-uniform ablation of the pure Cu, and the surface ablation of the Cu-W composite contacts remained uniform.

Particleboard surface defects have a significant impact on product quality. A surface defect detection method is essential to enhancing the quality of particleboard because the conventional defect detection method has low accuracy and efficiency. This paper proposes a YOLO v5-Seg-Lab-4 (You Only Look Once v5 Segmentation-Lab-4) model based on deep learning. The model integrates object detection and semantic segmentation, which ensures real-time performance and improves the detection accuracy of the model. Firstly, YOLO v5s is used as the object detection network, and it is added into the SELayer module to improve the adaptability of the model to receptive field. Then, the Seg-Lab v3+ model is designed on the basis of DeepLab v3+. In this model, the object detection network is utilized as the backbone network of feature extraction, and the expansion rate of atrus convolution is reduced to the computational complexity of the model. The channel attention mechanism is added onto the feature fusion module, for the purpose of enhancing the feature characterization capabilities of the network algorithm as well as realizing the rapid and accurate detection of lightweight networks and small objects. Experimental results indicate that the proposed YOLO v5-Seg-Lab-4 model has mAP (Mean Average Precision) and mIoU (Mean Intersection over Union) of 93.20% and 76.63%, with a recognition efficiency of 56.02 fps. Finally, a case study of the Huizhou particleboard factory inspection is carried out to demonstrate the tiny detection accuracy and real-time performance of this proposed method, and the missed detection rate of surface defects of particleboard is less than 1.8%.

Cu-W composite powder with various W contents (0, 10, 20, and 30 wt%) has been fabricated by spray drying, calcination, and two-stage hydrogen reduction in this work. The microstructures and phase evolution of Cu-W composite powder were studied by FESEM, HRTEM, and XRD. Results show that precursor powder is composed of Cu2(OH)3NO3 and H2WO4·H2O. Spherical CuWO4 with a particle size of about 50 nm and micron-scale flat flake CuO were obtained when the calcination temperature was 500 °C. Through chemical vapor transport (CVT) during the reduction process, volatile WO2(OH)2 continuously migrates to the copper surface and is reduced to W, resulting in a W-coated Cu structure. This coating structure can inhibit the aggregation and growth of copper particles. The particle sizes of Cu-10 wt%W, Cu-20 wt%W, and Cu-30 wt%W composite powders are 9.309 μm, 8.440 μm, and 6.290 μm, reduced by 40.51%, 46.06%, and 59.80%, respectively, compared to the particle size of pure copper powder, 15.648 μm. With increasing W content, the W-coated Cu particles gradually become denser and trend to grow from sphere to flake.

Shape memory polymers (SMPs) have gained increasing attention as intelligent morphing materials. However, due to the inherent electrical insulation and poor thermal conductivity of polymers, deformation and temperature control of SMPs usually require external heating devices, bringing about design inconveniences and fragility of interfaces. Herein, we report a shape memory composite that integrates reliable temperature and shape control functions into the interior. The composite is comprised of resin-based SMP and three-dimensional interconnected graphene foam (3DGF), exhibiting a high recovery rate and thermal/electrical conductivity. With only 0.26 wt% of graphene foam, the composite can improve electrical conductivity by 15 orders of magnitude, thermal conductivity by 180%, tensile strength by 64.8%, and shape recovery speed by 154%. Using a very simple Joule heating scheme, decimeter-sized samples of the composite deformed to their preset shapes in less than 10 s.

American elderberry (Sambucus canadensis) is a plant native to North America with anthocyanin-rich fruits. Our objective was to investigate the effects of cultivar and ripeness on the phytochemical characteristics of its fruits and the corresponding color performance. Cultivars ‘Adams’, ‘Johns’, ‘Nova’, ‘Wyldewood’, and ‘York’ were examined for their °Brix, pH, anthocyanin (pH-differential method), and phenolic content (Folin-Ciocalteau method). Extract composition were analyzed by uHPLC-PDA-MS/MS. Color and spectra were determined using a plate reader. All characteristics evaluated were significantly affected by ripeness and cultivar, except for °Brix and total phenolic content, which did not vary significantly among cultivars. Most anthocyanins (63–72%) were acylated with p-coumaric acid, with cyanidin-3-(trans)-coumaroylsambubioside-5-glucoside the most predominant. The proportion of acylated anthocyanins was the only characteristic evaluated that decreased during ripening (from 80 to 70%). Extract from fully-ripened fruits exhibited red (lvis-max ~520 nm) and blue hues (lvis-max ~600 nm) at acidic and alkaline pH, respectively. Extracts from half-ripe fruit rendered yellowish tones and overall dull color. C-18 semi-purified extracts displayed higher color saturation (smaller L* and larger C*ab) than crude extracts. The vibrant and broad color expression of fully-ripened fruit extract, especially after C-18 purification, suggests this North American native plant as a promising natural colorant source.