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Objective: This study aimed to explore the composition and the functional bioactivity of tea polyphenols from cocoa tea. Methods: Chemical components of crude green tea polyphenol extracts from cocoa tea were analyzed and identified using high resolution mass spectrometry. Additionally, their potential functional effects were predicted by network pharmacological analysis. Results: The in vitro hyperglycemic potential of crude green tea polyphenol from cocoa tea was preliminarily confirmed based on their strong inhibition on α-glucosidase and α-amylase. Six key targets including GAPDH and AKT and multiple pathways such as PI3K-AKT were predicted to be involved in the underying mechanisms behind the hyperglycemic effects of crude green tea polyphenol from cocoa tea through the GO enrichment and KEGG enrichment analysis. Ten polyphenol components including myricetin, anthocyanin, etc. were suggested to play a key role in the hyperglycemic activities of crude green tea polyphenol from cocoa tea via the construction of a “component-target-pathway” network combined with the molecular docking between these components and starch hydrolase. Conclusion: Crude green tea polyphenols from cocoa tea exerted a high hypoglycemic effect by regulating multi-targets through multi-pathways via multi-components.

The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process. [ABSTRACT FROM AUTHOR]

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu's method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology. [ABSTRACT FROM AUTHOR]

In 20th-century science fiction, robotic images have already had "gender" characteristics. Through the in-depth analysis of Asimov's story collection I, Robot, this paper reveals the gendered process and identity construction mechanism of robots in its narrative. The fixed mode of human-machine relationship leads to the fact that there is a power relationship between men, women and robots, which is embodied in the two gender schemas: human "Adam" - Robot "Eve" and "Masquerade" woman - Robot "Bride". With the displacement of the power subject in different situations, the gender of the robot also changes, showing the characteristics of fluidity, discontinuity and paradox. It should be emphasized that the gender writing of sci-fi narratives is not intended to promote a discourse of power in the paradigm of anthropocentrism, but rather as a metaphor or an allegory for reflecting on humanity. [ABSTRACT FROM AUTHOR]