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Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field. [ABSTRACT FROM AUTHOR]

Crop nitrogen content inversion based on UAV (Unmanned Aerial Vehicle) hyperspectral data is vital for addressing global food supply challenges. Many studies have estimated nitrogen content via simple linear regression using a single vegetation index, multiple vegetation indices, hyperspectral representation, and simple structural transformation. Utilizing UAV hyperspectral data of the winter wheat canopy and leveraging the benefits of fractional differential processing to enhance spectral details, traditional hyperspectral vegetation indices were established, and characteristic parameters derived from spectral position and canopy area were extracted. Then, winter wheat plant nitrogen content models with different spectral information characteristics were highlighted, optimally selected and verified. Results reveal that when the original canopy spectrum is processed using the fractional differential, the association between hyperspectral reflectance and nitrogen levels in winter wheat plants can be effectively enhanced. Fractional differential spectra represent outstanding effects on refining spectral details. The findings provide valuable insights into the potential of hyperspectral fractional differential spectra to enhance the precision of nitrogen estimation in winter wheat. [ABSTRACT FROM AUTHOR]

Hydrogen generation from rapid hydrolysis of aluminum in sodium fluoride solution was investigated through a hydrolysis experiment. Rapid and instant hydrogen yield were observed using sodium fluoride as additive. The experimental results demonstrate that the increase of temperature and the amount of additives in a certain range will boost the hydrogen production. The amount of additives outside the range only has an effect on the rapid hydrolysis of the aluminum during the initial stage, but the total amount of hydrogen produced doesn’t increased significantly. Theoretical analysis of the effects of the mixing ratio and the temperature on the hydrogen production rates were performed using the shrinking core model and the kinetic model. The shrinking core model parameter a and k indicate the film change degree of porosity and thickness and the effect of time on the diffusion coefficient. the kinetic model is verified and the activation energy confirming hydrogen yield control by a molecular diffusion process. Correspondingly, mechanisms of Al corrosion in NaF solutions under low and high alkalinity were proposed, respectively.

In this paper, the thermal calculation and thermal-structural coupling analysis of the vertical machining center are carried out to obtain the theoretical values of thermal generation and thermal displacement. Combined with the thermal structure analysis of vertical machining center, sensors are placed at the key positions with large thermal displacement to measure and analyze the thermal error data, and the law of temperature and thermal deformation of machining center is found out. This research provides theoretical basis for thermal error compensation.

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]

Changes in distribution density, morphology and secretory content of endocrine cells in the gastro-entero-pancreatic system of black-spotted frogs Rana nigromaculata before and after fasting were investigated using immunohistochemistry and antisera to six gut hormones. Six types of endocrine cells were detected in the digestive tract of Rana nigromaculata, including 5-hydroxytryptamine (5-HT), gastrin (GAS), somatostatin (SOM), glucagon (GLU), pancreatic polypeptide (PP) and vasoactive intestinal polypeptide (VIP) cells. After fasting, the density of 5-HT cells in the esophagus, cardia and fundus, GAS cells in fundus and pylorus, PP cells in the fundus decreased significantly(P 0.05), while SOM, GAS, GLU and PP cells increased significantly (P