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The hydrodynamic model, based on the strict conservation of momentum and continuity equations, can accurately simulate the distribution of a flow field. However, significant computing time and storage space requirements limit real-time prediction. Machine learning is well known for its fast computing speed and powerful learning ability, but its accuracy depends on an abundance of training data, hindering its wider use in locations without sufficient measurements. Application restrictions in data-deficient areas can be addressed through transfer learning, provided that two areas share common characteristics. In this study, a machine learning method based on a deep super-resolution convolutional neural network (DSRCNN) and transfer learning is proposed, validated, and applied to model two bend flows and one realistic test case. Firstly, the hydrodynamic model was established and validated against measured data. The validated model was considered to have the ability to generate real data and was used to generate a comprehensive data set for training and validating the machine learning model. Three different methods were compared and tested, with Realizable k-ε performing better than the others in predicting the outer bank flow distribution. DSRCNN was compared to a plain SRCNN (PSRCNN), as well as Bilinear, Nearest, and Bicubic methods, and the results showed that DSRCNN had the best performance. We compared Raw, RT, and TL methods, finding that the TL method performed the best overall. Therefore, the research results showed that the developed super-resolution convolutional neural network can provide more reliable predications and serve as an ideal tool for simulating flow field distribution in bends. [ABSTRACT FROM AUTHOR]

The consumer price index (CPI) is an important indicator to measure inflation or deflation, which is closely related to residents' lives and affects the direction of national macroeconomic policy formulation. It is a common method to discuss CPI from the perspective of economic analysis, but the statistical principles and influencing factors related to CPI are often ignored. Thus, the impact of different types of CPI on China's overall CPI was discussed from three aspects: statistical simulation, machine learning prediction and correlation analysis of various types of influencing factors and CPI in this study. Realistic data from the National Bureau of Statistics from 2010 to 2022 were selected as the analysis object. The Statistical analysis showed that in 2015 and 2020, CPI had a fluctuating trend due to the impact of education and transportation. Four types of statistical models including Gauss, Lorentz, Extreme and Pearson were compared. It was determined that the R2 fitted by Extreme model was higher (R2 = 0.81), and the optimal year of simulation was around 2019, which was close to reality. To accurately predict the CPI, the results of Support Vector Machine, Regression decision tree and Gaussian regression (GPR) were compared, and the GPR was determined to be the optimal model (R2 = 0.99). In addition, Spearman matrix analyzed the correlation between CPI and various influencing factors. Herein, this study provided a new method to determine and predict the changing trend of CPI by using big data analysis. [ABSTRACT FROM AUTHOR]

N-doped carbon Co/CoOx with laccase-like activity was directionally designed by pyrolyzing Co-coordination polymer and applied to detect epinephrine, which revealed a new preparation strategy for laccase mimics. The formation mechanism of the N-doped carbon Co/CoOx nanozyme was reconnoitered by a thermogravimetric-mass spectrometry system (TG-MS). N-doped carbon Co/CoOx exhibited outstanding laccase-like activity, and the Michaelis–Menten constant and maximum initial velocity were calculated to be 0.087 mM and 0.0089 μM s–1, respectively. Based on this principle, a simple colorimetric sensing platform was developed for the quantitative detection of epinephrine, which can be used to diagnose pheochromocytoma. In addition, the visual platform for detecting epinephrine exhibited a linear range of 3 to 20 μg mL−1 and a calculated detection limit of 0.42 μg mL−1. Therefore, the proposed colorimetric sensing platform is a promising candidate to be applied in precise early pheochromocytoma diagnosis. [ABSTRACT FROM AUTHOR]

Aerosols have great uncertainty owing to the complex changes in their composition in different regions. The radiation properties of different aerosol types differ considerably and are vital in studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. Hence, we aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. An optical database was built using Mie scattering and a complex refractive index was used as a baseline to identify different aerosol types by applying a random forest algorithm to train the aerosol optical parameters obtained from the Aerosol Robotic Network sites. The consistency rates of the new model with the traditional Gaussian density cluster method were 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse, mixed-fine, urban/industrial, and biomass burning aerosols, respectively. The corresponding precision of the new hybrid algorithm (F-score and accuracy scores) was 95 %, 89 %, 91 %, and 89 %. Lastly, a global map of aerosol types was generated using the new model to characterize aerosol types across the five continents. This study utilizing a novel approach for the classification of aerosol will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution. [ABSTRACT FROM AUTHOR]

The cycling stability of SnO2 anode as lithium-ion battery is poor due to volume expansion. Polyimide coatings can effectively confine the expansion of SnO2. However, linear polyimides are easily dissolved in ester electrolytes and their carbonyls is not fully utilized during charging/discharging process. Herein, the SnO2 enclosed with anthraquinone-based polyimide/reduced graphene oxide composite was prepared by self-assembly. Carbonyls from the anthraquinone unit provide fully available active sites to react with Li+, improving the utilization of carbonyl in the polyimide. More exposed carbonyl active sites promote the conversion of Sn to SnO2 with electrode gradual activation, leading to an increase in reversible capacity during the charge/discharge cycle. In addition, the introduction of reduced graphene oxide cannot only improve the stability of polyimide in the electrolyte, but also build fast ion and electron transport channels for composite electrodes. Due to the multiple effects of anthraquinone-based polyimide and the synergistic effect of reducing graphene oxide, the composite anode exhibits a maximum reversible capacity of 1266 mAh·g−1 at 0.25 A·g−1, and maintains an excellent specific capacity of 983 mAh·g−1 after 200 cycles. This work provides a new strategy for the synergistic modification of SnO2. [ABSTRACT FROM AUTHOR]