Search Results (69)

The ocean encompasses the majority of the Earth’s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues of subpar image quality and low recognition accuracy. The precise measures are enumerated as follows: initially, to address the issue of model parameters, we optimized the ninth convolutional layer by substituting certain conventional convolutions with adaptive deformable convolution DCN v4. This modification aims to more effectively capture the deformation and intricate features of underwater targets, while simultaneously decreasing the parameter count and enhancing the model’s ability to manage the deformation challenges presented by underwater images. Furthermore, the Triplet Attention module is implemented to augment the model’s capacity for detecting multi-scale targets. The integration of low-level superficial features with high-level semantic features enhances the feature expression capability. The original CIoU loss function was ultimately substituted with Shape IoU, enhancing the model’s performance. In the underwater robot grasping experiment, the system shows particular robustness in handling radial symmetry in marine organisms and reflection symmetry in artificial structures. The enhanced algorithm attained a mean Average Precision (mAP) of 87.6%, surpassing the original YOLOv8s model by 3.4%, resulting in a marked enhancement of the object detection model’s performance and fulfilling the real-time detection criteria for underwater robots. Full article

Robust and efficient contactless human–machine interaction is critical for integrated sensor systems in clinical settings, demanding low-power solutions adaptable to edge computing platforms. This paper presents a real-time hand gesture recognition system using a low-power Frequency-Modulated Continuous Wave (FMCW) radar sensor, featuring a novel Multiple Feature Fusion (MFF) framework optimized for deployment on edge devices. The proposed system integrates velocity profiles, angular variations, and spatial-temporal features through a dual-stage processing architecture: an adaptive energy thresholding detector segments gestures, followed by an attention-enhanced neural classifier. Innovations include dynamic clutter suppression and multi-path cancellation optimized for complex clinical environments. Experimental validation demonstrates high performance, achieving 98% detection recall and 93.87% classification accuracy under LOSO cross-validation. On embedded hardware, the system processes at 28 FPS, showing higher robustness against environmental noise and lower computational overhead compared with existing methods. This low-power, edge-based solution is highly suitable for applications like sterile medical control and patient monitoring, advancing contactless interaction in healthcare by addressing efficiency and robustness challenges in radar sensing for edge computing. Full article

Waterborne polyurethane (WPU) coatings have gained significant attention in the industry due to their low environmental impact and excellent properties. Furthermore, the UV-curing system reduces energy costs and enhances curing efficiency. Hence, exploring the UV-curable WPU system is essential for advancing the next generation of coatings. In this study, a 2K WPU system was developed by functionalizing isocyanate-terminated polyurethane with thiol and vinyl groups. The coating was cured under UV light through a thiol-ene click reaction, and the effects of photoinitiator content on the coating performance were investigated. The feasibility of sunlight curing for this WPU coating was also assessed. The results showed that while photoinitiator content had a slight impact on UV-cured WPU coatings, it significantly affected sunlight-cured WPU. Also, with the appropriate photoinitiator content, sunlight-cured WPU could achieve comparable performance to UV-curable ones. Full article

Alternating current (AC)-induced corrosion poses a significant threat to the integrity of underground pipelines, making it a critical concern for pipeline infrastructure. This study investigated the corrosion behavior of protected pipelines subjected to AC interference and developed practical tools to determine the required cathodic protection (CP) measures. Numerical models were developed to simulate current distributions in pipelines, incorporating the effects of metal types, CP levels, and varying AC interferences. These models were rigorously validated and calibrated using laboratory experiments to ensure their reliability. The models were then employed to analyze the corrosion behavior of pipeline metals under diverse CP and AC conditions. The results revealed the critical role of AC interference in aggravating corrosion rates and the diminishing effectiveness of CP at higher AC levels. Additionally, the study highlights the material-dependent nature of corrosion, with X60 pipe steel demonstrating superior resistance compared to C1018 pipe steel. Under the same AC and CP levels, the corrosion rate for C1018 pipe steel is 1.85 to 3.65 times higher than that for X60 pipe steel. Based on numerical analysis, empirical equations were developed to optimize the control of corrosion rates for both C1018 and X60 pipe steels under varying AC and CP conditions. The CP current density required to mitigate corrosion under an AC interference of 50 A/m² is 1.45 A/m² for C1018 pipe steel and 0.18 A/m² for X60 pipe steel. As AC interference increases to 500 A/m², the required CP current density rises to 2.98 A/m² for C1018 pipe steel and 2.2 A/m² for X60 pipe steel, highlighting the increasing demand for cathodic protection at higher AC levels. Full article

Casing deformation (CD) is generally believed to be caused by the slip of fractures in the strata and its shear effects on horizontal wells. However, the casing deformation mode based on this theory cannot fully match the measurement data, and the differential deformation characteristics and the mechanism behind this phenomenon are not completely clear. To elucidate the mechanisms of CD and enhance prevention and control measures, the CD modes in Shunan Block were identified and deformation mechanisms of these modes were comprehensively investigated. Our research shows the following: (1) Under the mechanism of penetrating fracture shear deformation, CD exhibit obvious shear deformation, and the natural fractures near the intersection point with the wellbore are prone to form a higher risk of deformation. (2) Natural fractures with tips approaching the wellbore experience intense stress concentration (1.6 times higher than shear stress) during activation, resulting in compression and asymmetrical CD. (3) The shear deformation induced by penetrating fractures is 15.52 mm, while the fracture tip-induced compression deformation demonstrates a substantially greater magnitude at 44.17 mm. This compressive deformation exceeds the shear deformation by a factor of approximately 2.85. (4) The stress concentration at the fracture tip is highly sensitive to the injection rate. Hence, adherence to the “avoiding stress concentration” principle is crucial in hydraulic fracturing operations. The conclusion indicates that in addition to penetrating fracture shear deformation, fracture tip compression deformation is another significant mechanism that causes CD. This research finding can offer theoretical guidance for developing effective measures to prevent and control CD in the exploitation of deep shale gas. Full article

A new molecular theory of slag suggests that complex oxides in the phase diagram are also present in liquid slag. In contrast to the ion‒molecule coexistence theory, basic oxides (CaO, MgO, MnO, FeO, etc.) in slag are considered to agglomerate in the liquid state due to their strong mutual attraction, although they are ionized (M²⁺ and O²⁻). The predicted slag structure agrees with the experimental results, and when the model is applied to the CaO-SiO₂, CaO-Al₂O₃, and CaO-SiO₂-Al₂O₃ slag systems, the calculated molar fractions of CaO, SiO₂, and Al₂O₃ ($N_{\mathrm{CaO}},N_{{\mathrm{SiO}}_2},N_{{\mathrm{Al}}_2{\mathrm{O}}_3}$) are close to the measured activities (${\alpha }_{\mathrm{CaO}},a_{{\mathrm{SiO}}_2} \mathrm{and} a_{{\mathrm{Al}}_2{\mathrm{O}}_3}$) reported by different researchers. In the CaO-Al₂O₃ slag system, the results based on the new molecular theory are closer to the experimental values than the results of other theoretical calculations. In the practical application of the new molecular theory, the maximum concentration of each complex molecule is consistent with the position of the melting point of the same solid‒liquid components in the phase diagram, indicating that complex molecules have a strong influence on the melting point of slag. In addition, it is believed that the formation and decomposition of different complex molecules are responsible for changes in component activity in the CaO-SiO₂ and CaO-Al₂O₃ slag systems, and it is further deduced that 3CaO-SiO₂ is formed in two steps. Full article

Wire-driven robotic fish can effectively simulate the movement of real fish, but research on high-frequency wire-driven robotic fish is limited. This paper introduces the development of wire-driven robotic fish based on a double-sine mechanism. The appearance of the fish body is designed based on the morphology of tuna, and a mechanism that can support the high-frequency movement of the wire-driven mechanism is designed. The swimming speed and turning performance of the robotic fish are experimentally tested at various swing frequencies. The experimental results show that within the range of 1 to 4 Hz, the swimming speed of the robotic fish with different tail stiffness increases as the frequency increases. However, when the frequency exceeds 4 Hz, the swimming speed decreases. The tail joint with lower stiffness performs better at low frequencies, but as frequency increases, higher stiffness results in better swimming performance. Experimental tests show that the turning radius increases with higher swing frequencies and lower stiffness, resulting in a larger turning radius. This experiment will help to improve the application of high-frequency wire-driven mechanisms in the study of robot fish movement and carry out more in-depth bionic research in the future. Full article

Interfacial properties significantly influence emulsifying and foaming stability. We here explore the interfacial behavior of egg yolk granules (EYGs) combined with various Span emulsifiers (Span 20, 40, 60, 80) to assess their solution properties, interface dynamics, and effects on emulsifying and foaming stability. The results unveiled that as the Span concentration increased, particle size decreased from 7028 to 1200 nm, absolute zeta potential increased from 4.86 to 9.26 mv, and the structure became increasingly loosened. This loose structure of EYGs-Span complexes resulted in reduced interfacial tension (γ), higher adsorption rate (Kd), and improved interfacial composite modulus (E) compared with native EYGs. These effects were more pronounced with shorter hydrophobic chain Spans but diminished with longer chain lengths. Enhanced interfacial properties contributed to better emulsification and foaming stability, with EYGs-Span complexes displaying increased emulsifying ability and stability compared with natural EYGs. Emulsifying and foaming stability improved in the order of Span 20 > Span 40 > Span 60 > Span 80 as the Span concentration increased. The correlation analysis (p > 0.05) indicated that emulsifying stability was positively associated with interfacial composite modulus and negatively correlated with particle size. Consequently, EYGs-Span composites demonstrate considerable potential for use as effective emulsifiers in food industry applications. Full article

In recent years, frequent floods caused by heavy rainfall and persistent precipitation have greatly affected changes in groundwater levels. This has not only caused huge economic losses and human casualties, but also had a significant impact on the ecological environment. The aim of this study is to explore the effectiveness of the new method based on Long Short-Term Memory networks (LSTM) and its optimization model in groundwater level prediction compared with the traditional method, to evaluate the prediction accuracy of the different models, and to identify the main factors affecting the changes in groundwater level. Taking Chaoyang City in Liaoning Province as an example, four assessment indicators, R², MAE, RMSE, and MAPE, were used. The results of this study show that the optimized LSTM model outperforms both the traditional method and the underlying LSTM model in all assessment metrics, with the GWO-LSTM model performing the best. It was also found that high water-table anomalies are mainly caused by heavy rainfall or heavy storms. Changes in the water table can negatively affect the ecological environment such as vegetation growth, soil salinization, and geological hazards. The accurate prediction of groundwater levels is of significant scientific importance for the development of sustainable cities and communities, as well as the good health and well-being of human beings. Full article

Docetaxel has exhibited excellent therapeutic effects in cancer treatment; however, its hydrophobicity, short blood circulation time, and high blood toxicity restrict its clinical application. The use of mPEG-PLA micelles to deliver docetaxel into the body has been verified as an effective approach to enhance its therapeutic efficacy. However, mPEG-PLA micelles are easily disassembled in the bloodstream, which can easily lead to premature drug release. To broaden the application scenarios of mPEG PLA micelles, we utilized the π–π stacking effect as an intermolecular force to design a novel mPEG-PLA-Lys(Fmoc) micelle to enhance the blood stability and permeability of drug-loaded micelles. The result showed that drug-loaded micelles for injection did not alter the tissue selectivity of docetaxel. Intravenous injection of the micelles in nude mice showed better antitumor efficacy than docetaxel injection and tumor recurrence rate is 0%, which is significantly lower than that of docetaxel injection (100%). The micelles designed by this research institute are anticipated to improve the clinical therapeutic effect of docetaxel. Full article

Green technology innovations propel both economic development and environmental sustainability. Exploring the contributing factors to green technology innovations carries important policy implications, but research from the perspective of supply chain relationships has been rare. This paper examines the impact of corporate customer concentration on green technology innovations and explores its influencing mechanisms using the data of Chinese A-share listed companies. The results show that a high customer concentration inhibits the quantity and quality of green technology innovations, a finding that is robust when endogeneity is addressed and when alternative measures and an alternative estimation model are employed. Financing constraints and social responsibility play intermediary roles in the impact of customer concentration on green technology innovations. A high customer concentration tends to increase corporate financing constraints and reduce corporate social responsibility performance, which hinder green technology innovations. The heterogeneity analysis reveals that the inhibitory effect of customer concentration on green technology innovations is less severe in digitally transformed enterprises, mature enterprises, or enterprises with a high level of market power. As this study provides a novel perspective on the contributing factors to corporate green innovations, it offers important policy recommendations. Full article

To meet the demand of consumers for chicken products, poultry breeders have made improvements to chickens. However, this has led to a new problem in the modern poultry industry, namely excessive fat deposition. This study aims to understand the effects of dietary iron supplementation on fat deposition and gut microbiota in chickens. In this study, we investigated the effects of iron on the growth performance, fat deposition, and gut microbiota of silky fowl black-bone chickens. A total of 75 7-week-old silky fowl black-bone chickens were randomly divided into three groups (five replicates per group, five chickens per replicate) and fed them for 28 days using a growing diet (control group), a growing diet + 10% tallow (high-fat diet group, HFD group), and a growing diet + 10% tallow + 500 mg/kg iron (HFDFe500 group), respectively. We detected the effects of iron on the growth performance, fat deposition, and gut microbiota of silky fowl black-bone chickens using the growth performance index test, oil red O staining, and HE staining, and found that the high-fat diet significantly increased liver and serum fat deposition and liver injury, while the addition of iron to the diet could reduce the fat deposition caused by the high-fat diet and alleviate liver injury. In addition, 16S rDNA sequencing was used to compare the relative abundance of gut microbiota in the cecal contents in different feeding groups. The results showed that the high-fat diet could induce gut microbiota imbalance in chickens, while the high-iron diet reversed the gut microbiota imbalance. PICRUSt functional prediction analysis showed that dietary iron supplementation affected amino acid metabolism, energy metabolism, cofactors, and vitamin metabolism pathways. In addition, correlation analysis showed that TG was significantly associated with Firmicutes and Actinobacteriota (p < 0.05). Overall, these results revealed high dietary iron (500 mg/kg) could reduce fat deposition and affect the gut microbiota of silky fowl black-bone chickens, suggesting that iron may regulate fat deposition by influencing the gut microbiota of chickens and provides a potential avenue that prevents excessive fat deposition in chickens by adding iron to the diet. Full article

Nb₃Sn is emerging as one of the focal points in superconducting radio frequency (SRF) research, owing to its excellent superconducting properties. These properties hold significant possibilities for cost reduction and the miniaturization of accelerators. In this paper, we report the recent efforts of the Shanghai Advanced Research Institute (SARI) in fabricating high-performance Nb₃Sn superconducting cavities using the vapor diffusion method. This includes the construction of a Nb₃Sn coating system with dual evaporators and the test results of 1.3 GHz single-cell coated cavities. The coated samples were characterized, and the growth state of the Nb₃Sn films was analyzed. The first coated superconducting cavity was tested at both 4.4 K and 2 K, with different cooldown rates passing through the Nb₃Sn critical temperatures. The causes of Sn droplet spot defect formation on the surface of the first cavity were analyzed, and such defects were eliminated in the coating of the second cavity by controlling the evaporation rate. This study provides a reference for the preparation of high-performance Nb₃Sn-coated cavities using the vapor diffusion method, including the setup of the coating system, the comprehension of the vapor diffusion process, and the test conditions. Full article

Soil moisture critically influences crop growth, especially in dryland environments. Precise agricultural management requires real-time monitoring of stratified soil moisture and assessment of crops’ daily water needs. We aim to provide low-cost, high-throughput information acquisition services for dryland regions with underdeveloped infrastructure and offer scientific support for sustainable water resource management. We used UAVs (Unmanned Aerial Vehicles) with multi-spectral sensors for routine maize monitoring, capturing leaf reflectance. Constructing vegetation indices, we quantified the relationship between leaf water content and surface soil moisture, using the Biswas model to predict deep soil moisture distribution. We used UVAs to monitor crop height and calculated the daily water demand for the entire growth period of corn using the Penman Montes equation. We found an R² of 0.8603, RMSE of 2.455%, and MAE of 2.099% between NDVI and canopy leaf water content. A strong linear correlation (R² = 0.7510) between canopy leaf water content and soil moisture was observed in the top 20 cm of soil. Deep soil moisture inversion from the top 20 cm soil moisture showed an R² of 0.9984, with RE mostly under 10%, but exceeding 20% at 120 cm depth. We also constructed a maize height model aligning with a sigmoidal growth curve (R² = 0.9724). Maize’s daily water demand varied from 0.7121 to 9.4263 mm, exhibiting a downward-opening parabolic trend. Integration of rainfall and soil water data allowed for dynamic irrigation adjustments, mitigating drought and water stress effects on crops. We highlighted UAV multi-spectral imaging’s effectiveness in monitoring crop water needs, facilitating quick daily water requirement estimations. Our work offers a scientific foundation for managing maize cultivation in drylands, enhancing water resource utilization. Full article

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas. Full article