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Efficiently predicting the wake of propellers is of great importance for achieving propeller design optimization. In this work, the deep learning (DL) method called propeller wake convolutional neural networks (PWCNN) is proposed, which combines the transformer encoder and dilated convolutional block to capture the multi-scale characteristics of wakes. Computational fluid dynamics (CFD) simulations are conducted using the delayed detached eddy simulation model for the wake to generate extensive high-fidelity wake data of the propeller operating under different operating conditions required for DL. PWCNN takes the wake predicted at the previous time step to update input and iteratively predicts the wake at future time steps to achieve dynamic wake prediction. The good agreement between DL prediction and CFD simulation results, with the mean relative error of the velocity components less than 2.36% for 15 future time steps, proves that PWCNN can efficiently capture the spatiotemporal evolution characteristic of dynamic wakes. Furthermore, PWCNN can predict the wake dynamic changes with reasonable accuracy under unseen operating conditions, further confirming the generality of the proposed model in forecasting the spatiotemporal evolution of propeller wake. [ABSTRACT FROM AUTHOR]

The issue of bacterial resistance is an escalating problem due to the misuse of antibiotics worldwide. This study introduces a new antibacterial mechanism, the ferroptosis-like death (FLD) of bacteria, and an approach to creating green antibacterial nano-reactors. This innovative method leverages natural iron-containing ovotransferrin (OVT) assembled into an organic skeleton to encapsulate low-concentration adriamycin (ADM) for synthesizing eco-friendly nano-reactors. FLD utilizes the Fenton reaction of reactive oxygen species and ferrous ions to continuously produce ·OH, which can attack the bacterial cell membrane and destroy the cell structure to achieve bacteriostasis. The OVT@ADM nano-reactors are nearly spherical, with an average diameter of 247.23 nm and uniform particle sizing. Vitro simulations showed that Fe3+ in OVT@ADM was reduced to Fe2+ by glutathione in the bacterial periplasmic space, which made the structure of OVT loose, leading to a sustained slow release of ADM from OVT@ADM. The H2O2 continuously produced by ADM oxidized Fe2+ through the Fenton reaction to produce ·OH and Fe3+. The results of the antibacterial assay showed that OVT@ADM had a satisfactory antibacterial effect against S. aureus, and the inhibition rate was as high as 99.3%. The cytotoxicity results showed that the mitigation strategy significantly reduced the cytotoxicity caused by ADM. Based on the FLD mechanism, OVT@ADM nano-reactors were evaluated and applied to bacteriostasis. Therefore, the novel antibacterial mechanism and OVT@ADM by the green synthesis method have good application prospects. [ABSTRACT FROM AUTHOR]

A W-doped Pt modified graphene oxide (Pt-W-GO) electrochemical microelectrode was developed to detect hydrogen peroxide (H2O2) in real time at a subcellular scale. Interestingly, results showed that the concentration of H2O2 in the nucleus of HeLa cells was 2.68 times and 0.51 times that in the extracellular membrane and cytoplasm, respectively. [ABSTRACT FROM AUTHOR]

During production and operation, enterprises are faced with occurrences of production accidents. One of the prerequisites for enterprises to achieve sustainable development is building an intelligent emergency command platform. To establish a scientific and advanced emergency management information system and address the challenges related to managing emergency plans to ensure production safety, such as ambiguous roles and responsibilities, inefficient application processes, independent resources, and slow responses by enterprises with multiple types of operations and disasters, an intelligent emergency command platform was built for multiple types of operations and disasters, and this platform was extended to include rescue steps. The structure and digital management of emergency plans under multiple coupled disasters and multipoint cogeneration were determined. Similar emergency plans were automatically recommended by crawler technology and an SVM algorithm based on a public information data lake, and the effectiveness of the plans was evaluated via a fuzzy analytic hierarchy process to promote the preparation of more efficient and scientific emergency plans. Finally, the analysis of pipeline leakage and emergency drill scenarios proved that the system is scientific and reliable. The results are of great significance for improving the deep integration of modern emergency-related information technology and emergency management businesses, promoting institutional and mechanical innovation, to provide a reference for other multibusiness enterprises, wchih can also be integrated into methods for urban safety and rescue. [ABSTRACT FROM AUTHOR]

To investigate the inhibition effect of chloroacetic acid allyl ester (CAAE) grafting modification on water‐tree aging in crosslinked polyethylene (XLPE), CAAE grafted XLPE (XLPE‐g‐CAAE) with different CAAE contents are prepared by melt blending and grafting modification method. The accelerated water‐tree aging experiments are carried out by the water‐blade electrode method. It is found that XLPE‐g‐CAAE has a great inhibitory effect on the water‐tree aging compared to XLPE, and XLPE‐g‐CAAE with 1.0 phr CAAE has the most significant effect on the water‐tree growth in the scope of the study. The microstructure of XLPE and XLPE‐g‐CAAE is investigated by thermal elongation, gel content, and static water contact angle, respectively. The results show that the grafting of CAAE containing polar groups can enhance the polarity of XLPE and improve the hydrophilicity, which has a dispersing effect on the polar water molecules entering the polymer. Thus, the water molecules are not easy to gather, inhibiting the water‐tree growth. Meanwhile, although the mechanical properties of XLPE‐g‐CAAE decrease to a small extent, and the relative dielectric constant and dielectric loss factor increase slightly, but the performance still meet the requirements of water‐tree resistance XLPE cable insulation. Highlights: CAAE with polar groups was successfully grafted onto XLPE.The polar groups can enhance the polarity of XLPE to improve the hydrophilicity.CAAE grafting can inhibit the water‐tree growth in XLPE.The dielectric properties meet the requirements of water‐tree resistance XLPE. [ABSTRACT FROM AUTHOR]

The practical design optimization of blade structures is crucial for enhancing the power capture capability of tidal turbines. However, the significant computational costs required for directly optimizing turbine blades through numerical simulations limit the practical application of blade structure optimization. This paper proposes a framework for tidal turbine blade design optimization based on deep learning (DL) and blade element momentum (BEM). This framework employs control points to parameterize the three-dimensional geometric shape of the blades, uses convolutional neural networks to predict the hydrodynamic performance of each hydrofoil section, and couples BEM to forecast the performance of tidal turbine blades. The multi-objective non-dominated sorting genetic algorithm II is employed to optimize the geometric parameters of turbine blades to maximize the power coefficient and minimize the thrust coefficient, aiming to obtain the optimal trade-off solution. The results indicate that the prediction of the DL-BEM model agrees well with experimental data, significantly improving optimization efficiency. The optimized tidal turbine blades exhibit excellent power coefficients and reduced thrust coefficients, achieving a more balanced structural solution. The proposed optimization framework based on DL accurately and rapidly predicts the performance of tidal turbines, facilitating the design optimization of high-performance tidal turbine blades. [ABSTRACT FROM AUTHOR]

Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d -1 , unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d -1 , mean absolute errors (MAEs) of 0.81 and 0.73 mm d -1 , and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at 10.5281/zenodo.8047038 (Li et al., 2023b). [ABSTRACT FROM AUTHOR]