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A curcumin delivery system was prepared by using the pH-shift method to load curcumin into the oil phase core of soybean oil body, which has a natural oil-in-water structure. The encapsulation characteristics of the emulsion were studied in terms of its encapsulation efficiency, particle size, zeta potential, and microstructure. The structural changes were analyzed by endogenous fluorescence spectroscopy and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and the storage stability, pH stability and oxidation stability were evaluated. The results showed that curcumin was successfully encapsulated with soybean oil body by the pH-shift method. At pH 1.0, the highest encapsulation efficiency of 65.55% was observed, and the lipid droplets were regular in shape and uniform in size, with an average particle size of 590.5 nm. The oxidative stability was good, but the storage stability and pH stability were poor. Protein structure was not changed significantly compared to untreated oil body. This study may provide a reference for future research on natural oil body to carry lipophilic polyphenols such as curcumin.

In this article, I recontextualize Weber's ideal type in the philosophy of natural science and economics in terms of idealization. The goal is to promote the appreciation of this methodology in contemporary social science and improve upon some Weberians' underdeveloped interpretations. Drawing on Weber's methodological writing and study of the Protestant ethic, I argue that the philosophy of idealization and Weber's ideal type share methodological characteristics: both support the selection of a research topic by isolating a particular component of reality and then constructing an imagination based on empirical materials and thought experimentation. By using the imagination as a benchmark, investigators counterfactually and contrastively explain why a particular case was observed as it was, as opposed to other possible outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes multi-frequency inertial and visual data fusion for attitude estimation. The proposed strategy is based on the locally weighted linear regression (LWLR), multi-layer perception (MLP), and cubature Kalman filter (CKF). First, we analyze the discrepant-frequency and the attitude divergence problems. Second, we construct the filter equation for the visual and inertial data and attitude differential equation for inertial-only data, which are used to estimate the attitude in time series. Third, we employ LWLR to compute the vision discrepancies between actual vision data and fitted vision data. The vision discrepancy is used as the input of MLP training. In MLP, the discrepancy is used as weights of the sums through the activation function of the hidden layer. To address the divergence problem, which is inherent in a multi-frequency fusion, the MLP is utilized to compensate for the inertial-only data. Finally, experimental results on different environments of pseudo-physical simulations show the superior performance of the proposed method in terms of the accuracy of attitude estimation and divergence capability.

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control. [ABSTRACT FROM AUTHOR]