Search Results (313)

To address the issues of hydrogen embrittlement, creep, and fatigue that commonly occur in the welds of hydrogen production reactor operating under hydrogen exposure, high temperature and high pressure, this study proposes adding Si and Mo as reinforcing elements to the welding materials to enhance weld performance. Given the varying performance requirements of different weld layers in the stacked weld, a gradient performance optimization method for the stacked weld of hydrogen production reactors based on the response surface methodology (RSM)–genetic algorithm (GA) is proposed. Using tensile strength, the hydrogen embrittlement sensitivity index, fatigue strain strength, creep rate and weld performance evaluation indices, a high-precision regression model for Si and Mo contents and weld performance indices was established through RSM and analysis of variance (ANOVA). A multi-objective optimization mathematical model for gradient improvement of the stacked weld was also established. This model was solved using a GA to obtain the optimal element content combination added to the welding wire and the optimal weld thickness for each weld layer. Finally, submerged arc welding experiments of the stacked weld were conducted according to the optimization results. The results show that the tensile strength of the base layer, filling layer and cover layer of the stacked weld increased by 5.60%, 6.16% and 4.53%, respectively. Hydrogen embrittlement resistance increased by 70.56%, 52.40% and 45.16%, respectively. The fatigue and creep resistance were also improved. The experimental results validate the feasibility and accuracy of the proposed optimization method. Full article

Communication delay, as a key factor restricting the rapid and accurate transmission of data in the smart grid, will affect the collaborative operation of power electronic devices represented by the Distributed Power Flow Controller (DPFC), and further affect the construction and safe and stable operation of the new power system. Aiming at the problem of DPFC communication delay prediction, this paper proposes a new SAR-ARIMA-LSTM hybrid prediction model. This model introduces the spatial autoregressive model (SAR) on the basis of the traditional ARIMA-LSTM model to extract the spatial correlation between devices caused by geographical location and communication load, and then combines ARIMA-LSTM prediction. The experimental structure shows that compared with the traditional ARIMA-LSTM model, the model proposed in this paper predicts that RMSE decreases from 1.59 to 1.2791 and MAE decreases from 1.27 to 1.0811, with a reduction of more than 14%. The method proposed in this paper can effectively reduce the communication delay prediction data of DPFC at different spatial positions, has a stronger ability to handle high-delay fluctuations, and provides a new technical approach for improving the reliability of the power grid communication network. Full article

Ammonium (NH₄⁺) toxicity impairs plant growth, but nitrate (NO₃⁻) can mitigate this effect through unresolved mechanisms. Using leaf δ¹³C values (photosynthetic capacity) and a bidirectional ¹⁵N tracer (NH₄⁺ assimilation efficiency and source utilization), this study investigated these mechanisms in 35-day-old Orychophragmus violaceus plantlets grown in modified Murashige and Skoog media under varying NH₄⁺:NO₃⁻ ratios. ¹⁵N isotope fractionation during NH₄⁺ (same fixed 20 mM NH₄Cl) assimilation decreased with increasing NO₃⁻ supply (10, 20, and 40 mM NaNO₃). Under 20 mM NH₄⁺ (δ¹⁵N = −2.64‰) at two ¹⁵NO₃⁻-labels (δ¹⁵N-NO₃⁻ = 8.08‰, low ¹⁵N, L) and (δ¹⁵N-NO₃⁻ = 22.67‰, high ¹⁵N, H), increasing NO₃⁻ concentrations enhanced NO₃⁻ assimilation, alleviating acidic stress from NH₄⁺ and improving photosynthesis. Higher NO₃⁻ levels also increased NH₄⁺ utilization efficiency, reducing futile NH₄⁺ cycling and decreasing associated ¹⁵N fractionation during assimilation. Our results demonstrate that NO₃⁻ alleviates NH₄⁺ toxicity primarily by enhancing photosynthetic performance and optimizing NH₄⁺ utilization efficiency. Full article

Papaya ripeness identification is a key task in orchard management. To achieve efficient deployment of this task on edge computing devices, this paper proposes a lightweight detection model, ABD-YOLO-ting, based on YOLOv8. First, the width factor of YOLOv8n is adjusted to construct a lightweight backbone network, YOLO-Ting. Second, a low-computation ADown module is introduced to replace the standard downsampling structure, aiming to enhance feature extraction efficiency. Third, an enhanced BiFPN is integrated into the neck structure to achieve efficient multi-scale feature fusion. Finally, to strengthen the model’s capability in identifying small objects, the dynamic detection head DyHead is introduced to improve ripeness recognition accuracy. On a self-constructed Japanese quince orchard dataset, ABD-YOLO-ting achieves a mAP50 of 94.7% and a mAP50–95 of 77.4%, with only 1.47 M parameters and 5.4 G FLOPs, significantly outperforming mainstream models such as YOLOv5, YOLOv8, and YOLOv11. On edge devices, the model achieves a well-balanced trade-off between detection speed and accuracy, demonstrating strong potential for practical applications in intelligent harvesting and orchard management. Full article

Fogging on glass poses a severe challenge in daily life, potentially even becoming life-threatening during driving and surgery; therefore there is a need for antifog surface structures. Fabricating superhydrophilic surfaces has been one of the major solutions to the challenge. Conventional direct thermal annealing glass in a furnace at 900 K for 2 h led to superhydrophicity but failed to produce superhydrophilicity on quartz. Meanwhile, it degraded transmission and was low throughput. This study developed a programmed fast plasma treatment of planar soda-lime glass and quartz in air, applied for only a few seconds, that was able to fabricate superhydrophilic surfaces. The process led to a 0° contact angle without sacrificing transmission, a result unreported before. The plasma treatment covered a whole 30 × 30 cm² substrate in only approximately 5 s, resulting in superhydrophilicity, which has rarely been reported before. This simple yet controllable process has great potential for further scale-up and practical applications. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

A comprehensive diagnosis of eddy–mean flow interaction in the Kuroshio Current (KC) region and the associated energy conversion pathway is conducted employing a state-of-the-art high-resolution global ocean–sea ice coupled model. The spatial distributions of the energy reservoirs and their conversions exhibit significant complexity. The cross-stream variation is found in the energy conversion pattern in the along-coast region, whereas a mixed positive–negative conversion pattern is observed in the off-coast region. Considering the area-integrated conversion rates between energy reservoirs, barotropic and baroclinic instabilities dominate the energy transferring from the mean flow to eddy field in the KC region. When the KC separates from the coast, it becomes highly unstable and the energy conversion rates intensify visibly; moreover, the local variations of the energy conversion are significantly influenced by the topography in the KC extension region. The mean available potential energy is the total energetic source to drive the barotropic and baroclinic energy pathway in the whole KC region, while the mean kinetic energy supplies the total energy in the extension region. For the whole KC region, the mean current transfers 84.9 GW of kinetic energy and 37.3 GW of available potential energy to the eddy field. The eddy kinetic energy is generated by mixed barotropic and baroclinic processes, amounting to 84.9 GW and 15.03 GW, respectively, indicating that topography dominates the generation of mesoscale eddy. Mean kinetic energy amounts to 11.08 GW of power from the mean available potential energy and subsequently supplies the barotropic pathway. Full article

In view of the bottleneck problems of serious spectral peak cross-interference, redundant data dimensions, and inefficient traditional dimensionality reduction methods in the infrared spectral analysis of mixed gases, this paper studies a joint dimensionality reduction strategy combining stacked self encoder (SAE) and partial least squares (PLS) and constructs an XGBoost regression model for quantitative detection. The experimental data are from the real infrared spectrum dataset of the National Institute of Standards and Technology (NIST) database, covering key industrial gases such as CO, CH₄, etc. Compared with the traditional principal component analysis (PCA), which relies on the variance contribution rate and leads to dimensional redundancy, and the calculation efficiency of dimension parameters that need to be cross-verified for PLS dimension reduction alone, the SAE-PLS joint strategy has two advantages: first, the optimal dimension reduction is automatically determined by SAE’s nonlinear compression mechanism, which effectively overcomes the limitations of linear methods in spectral nonlinear feature extraction; and second, the feature selection is carried out by combining the variable importance projection index of PLS. Compared with SAE, the compression efficiency is significantly improved. The XGBoost model was selected because of its adaptability to high-dimensional sparse data. Its regularization term and feature importance weighting mechanism can suppress the interference of spectral noise. The experimental results show that the mean square error (MSE) on the test set is reduced to 0.012% (71.4% lower than that of random forest), and the correlation coefficient (R²) is 0.987. By integrating deep feature optimization and ensemble learning, this method provides a new solution with high efficiency and high precision for industrial process gas monitoring. Full article

We synthesized QM-AgNPs (Dianthus superbus L.-AgNPs, Qu Mai-AgNPs) by an economical and environmentally friendly method using Dianthus superbus L. extract as a reducing and stabilizing agent. The resulting QM-AgNPs were comprehensively characterized and evaluated for their antioxidant, cytotoxic, and antibacterial activities. Herein, TEM analysis revealed that the QM-AgNPs were predominantly spherical, polydisperse, and exhibited a core particle size ranging from 11 to 18 nm. In contrast, DLS analysis showed a larger hydrodynamic diameter (primarily 60–87 nm), reflecting the hydrated shell and surface biomolecular corona. The crystalline nature of QM-AgNPs was confirmed by XRD and SAED spectra while FTIR spectroscopy indicated the presence of functional groups from the plant extract that may contribute to nanoparticle stabilization. Functional assessments demonstrated that QM-AgNPs exhibited strong antioxidant activity, with efficient DPPH radical scavenging, and selective cytotoxicity against A549 cancer cells while sparing normal cells. Moreover, QM-AgNPs showed significant antibacterial activity against both Staphylococcus aureus (Gram-positive) and Escherichia coli (Gram-negative), likely due to membrane disruption and the leakage of intracellular contents. To explore practical applications, we developed a GEL@AgNPs coating system for the postharvest preservation of grapes. As a result, the reduced weight loss and decay rate suggest a potential role for QM-AgNPs in extending fruit freshness. Comprehensive shelf-life studies are planned to further substantiate the potential of QM-AgNPs as an effective material for active food packaging applications. Full article

The coal reservoir in the Ordos Mizhi block is buried at a depth of over 2000 m. This study aims to obtain the characteristics of the coal reservoir in the Mizhi block through various experimental methods and combine the gas-bearing characteristics obtained from on-site desorption experiments to analyze the gas content and logging response characteristics of the study area. On this basis, a reservoir parameter interpretation model for the study area is established. This provides a reference for the exploration and development of coal-rock gas in the Mizhi block. The research results show that: (1) The study area is characterized by the development of the No. 8 coal reservoirs of the Benxi Formation, with a thickness ranging from 2 to 11.6 m, averaging 7.2 m. The thicker coal reservoirs provide favorable conditions for the formation and storage of coal-rock gas. The lithotypes are mainly semi-bright and semi-dark. The coal maceral is dominated by the content of the vitrinite, followed by the inertinite, and the exinite is the least. The degree of metamorphism is high, making it a high-grade coal. In the proximate analysis, the moisture ranges from 0.36 to 1.09%, averaging 0.65%. The ash ranges from 2.34 to 42.17%, averaging 16.57%. The volatile ranges from 9.18 to 15.7%, averaging 11.50%. The fixed carbon ranges from 45.24 to 87.51%, averaging 71.28%. (2) According to the results of scanning electron microscopy (SEM), the coal samples in the Mizhi block have developed fractures and pores. Based on the results of the carbon dioxide adsorption experiment, the micropore adsorption capacity is 7.8728–20.3395 cm³/g, with an average of 15.2621 cm³/g. The pore volume is 0.02492–0.063 cm³/g, with an average of 0.04799 cm³/g. The specific surface area of micropores is 79.514–202.3744 m²/g, with an average of 153.5118 m²/g. The micropore parameters are of great significance for the occurrence of coal-rock gas. Based on the results of the desorption experiment, the gas content of the coal rock samples in the study area is 12.97–33.96 m³/t, with an average of 21.8229 m³/t, which is relatively high. (3) Through the correlation analysis of the logging parameters of the coal reservoir, the main logging response parameters of the reservoir are obtained. Based on the results of the logging sensitivity analysis of the coal reservoir, the interpretation model of the reservoir parameters is constructed and verified. Logging interpretation models for parameters such as industrial components, microscopic components, micropore pore parameters, and gas content are obtained. The interpretation models have interpretation effects on the reservoir parameters in the study area. Full article

Increased global drought severity threatens crop yield and quality. Fulvic acid (FA), a humic acid compound, enhances crop stress tolerance. This study investigated FA application on drought-stressed tomato ‘Provence’ during the seedling and fruiting stages. Seedling-stage drought severely inhibited growth, physiology, biochemistry, and photosynthesis, reducing seedling quality. Subsequent fruiting-stage drought further significantly decreased photosynthetic efficiency and assimilate synthesis, drastically lowering fruit yield and quality. FA application mitigated drought damage, with 400 mg·L⁻¹ being optimal. At this concentration, under seedling drought, Seedling strength index (Si), Photosynthetic efficiency (Pn), and Instantaneous water use efficiency (IWUE) increased significantly by 76.54%, 67.46%, and 36.97%, respectively, with no adverse morphological effects by flowering. Post-drought FA spraying later significantly enhanced leaf photosynthetic enzyme activity and WUE (by 89.16%, 98.48%, 42.20%, and 40%), boosting Pn, promoting assimilate accumulation and transport to fruits. This resulted in significantly improved fruit yield and comprehensive quality. In conclusion, spraying 400 mg·L⁻¹ FA significantly enhances tomato drought tolerance and water use efficiency in arid/semi-arid regions, offering an effective strategy for saving irrigation water and improving crop productivity in water-scarce areas. Full article

This study examines freeze–thaw deterioration patterns and predicts the service life of wet-sprayed concrete with composite cementitious materials in cold-region tunnels. The microstructure and particle size distribution of four materials (cement, fly ash, silica fume, and mineral powder) were analyzed. Subsequent tests evaluated the rebound rate, mechanical properties, and durability of wet-sprayed concrete with various compositions and proportions of cementitious materials, emphasizing freeze–thaw resistance under cyclic freezing and thawing. A freeze–thaw deterioration equation was developed using damage mechanics theory to predict the service life of early-stage wet-sprayed concrete in tunnels. The results indicate that proportionally combining cementitious materials with different particle sizes and gradations can enhance concrete compactness. Adding mineral admixtures increases concrete viscosity, effectively reducing rebound rates and dust generation during wet spraying. Concrete incorporating binary and ternary mineral admixtures shows reduced early-age strength but significantly enhanced later-age strength. Its frost resistance is also improved to varying degrees. The ternary composite binder fills voids between cement particles and at the interface between paste and aggregate, resulting in a dense microstructure due to a ‘composite superposition effect.’ This significantly enhances the frost resistance of wet-mixed shotcrete, enabling it to withstand up to 200 freeze–thaw cycles, compared to failure after 75 cycles in plain cement concrete. The relative dynamic modulus of elasticity of wet-shotcrete follows a parabolic deterioration trend with increasing freeze–thaw cycles. Except for specimen P5 (R² = 0.89), the correlation coefficients of deterioration models exceed 0.94, supporting their use in durability prediction. Simulation results indicate that, across all regions of China, the service life of wet-shotcrete with ternary admixtures can exceed 100 years, while that of plain cement concrete remains below 41 years. Full article

Proton exchange membrane fuel cells (PEMFCs) have been widely studied as an efficient and environmentally friendly energy conversion technology in recent years. However, the high cost, easy poisoning and complex synthesis methods of noble metal catalysts have hindered their commercialization. Therefore, in this paper, a non-noble metal composite catalyst CuFeCo/C for the oxygen reduction reaction (ORR) was prepared by using a facile liquid-phase reduction method. The ORR kinetic performance of CuFeCo/C was evaluated by cyclic voltammetry (CV), linear sweep voltammetry (LSV) and rotating ring-disk electrode (RRDE) tests. The results show that the oxygen reduction peak of CuFeCo/C appears at about 0.64 V, the half-wave potential is about 0.73 V, the limiting current density is about −16.51 A·m⁻², and the Tafel slope is about −0.08. The 10,800 s chronoamperometry test shows that the catalyst has a very good long-term cycle stability. This indicates that the CuFeCo/C composite catalyst has strong stability, good conductivity and ORR catalytic activity under alkaline conditions, which can promote the large-scale commercial application of PEMFCs. Full article

Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, respectively, implementing a multi-gradient fertilization design with 39 plots and 810 sampling grids. Multispectral imagery was acquired by unmanned aerial vehicles (UAVs) during five critical growth stages: mid-tillering (T1), late-tillering (T2), mid-elongation (T3), late-elongation (T4), and maturation (T5). Following rigorous image preprocessing (including stitching, geometric correction, and radiometric correction), 16 VIs were extracted. To identify yield-sensitive vegetation indices (VIs), a spectral feature selection criterion combining gray relational analysis and correlation analysis (GRD-r) was proposed. Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. Results demonstrated that multi-stage models consistently outperformed their single-stage counterparts. Among the single-stage models, the RF model using T3-stage features achieved the highest accuracy (R² = 0.78, RMSE_V = 7.47 t/hm²). The best performance among multi-stage models was obtained using a GBDT model constructed from a combination of DVI (T1), NDVI (T2), TDVI (T3), NDVI (T4), and SRPI (T5), yielding R² = 0.83 and RMSE_V = 6.63 t/hm². This study highlights the advantages of integrating multi-temporal spectral features and advanced machine learning techniques for improving sugarcane yield prediction, providing a theoretical foundation and practical guidance for precision agriculture and harvest logistics. Full article

Accumulating epidemiological evidence has indicated that arsenic exposure can lead to kidney injury. However, the underlying mechanisms of arsenic-induced nephrotoxicity have not been fully elucidated. In this study, the effect of sodium arsenite on the cell viability of HEK-293 cells was studied using a CCK-8 assay. Metabolomic and transcriptomic analyses were applied to identify differential metabolites (DMs) and differentially expressed genes (DEGs) in human renal cells exposed to arsenite, respectively. The results showed that the IC₅₀ of arsenite on HEK-293 cells was 25 μM. A total of 621 DMs were identified in arsenic-treated cells (VIP > 1, p < 0.05). The results of the metabolome analysis revealed that purine metabolism was the major affected pathway, with 21 DMs enriched within this pathway. Additionally, 9831 DEGs were obtained after arsenic exposure (|log₂FC| > 1, Padj < 0.05). The results of the transcriptome analysis showed that ECM–receptor interaction and cell adhesion molecules were the major altered KEGG pathways, with 54 and 70 enriched DEGs, respectively. Integrated metabolomics and transcriptomics analyses revealed that the predominant mechanisms underlying arsenic-induced nephrotoxicity were associated with the perturbations of lipid metabolism and purine metabolism. Overall, the present study provided comprehensive insights into the metabolic and transcriptional alterations in human renal cells in response to arsenic exposure, providing a referable scientific basis for subsequent arsenic-induced nephrotoxicity studies. Full article