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Exploring the impact of different monitoring angles from unmanned aerial vehicle (UAV) on the monitoring accuracy of soil moisture content (SMC) is crucial for precision irrigation. To this end, experiment was conducted to monitor the SMC of winter wheat at different growth stages under different irrigation treatments in Yangling, Shaanxi, China. At a solar zenith angle of 45°, multispectral remote sensing data from a UAV were collected at thirteen different monitoring zenith angles. Simultaneously, the SMC in the wheat root zone was measured. From the UAV multispectral images, spectral reflectance was extracted for the construction of vegetation indices. Then the correlation between the vegetation indices and the measured SMC was analyzed. With the vegetation indices as input variables, SMC monitoring models were constructed using Extreme Learning Machine (ELM), Random Forest (RF), and Back Propagation Neural Network (BPNN). The study also examined the effect of specific angles (hotspot and dark spot angles) on the estimation accuracy of the SMC at the nadir angle. The results indicated that different monitoring angles significantly impact the SMC estimation accuracy. Band reflectance and vegetation indices exhibited significant peak values and angular effects at the monitoring zenith angle of 45°. The models achieved the optimal inversion accuracy at the hotspot angle (at a monitoring zenith angle of 45° in the solar principal plane), and the accuracy was ranked as follows: BPNN (R2 = 0.71; RMSE = 1.69) > ELM (R2 = 0.52; RMSE = 1.94) > RF (R2 = 0.48; RMSE = 2.10). By eliminating shadows at the nadir angle through the threshold of dark spot, inversion accuracy similar to the hotspot direction is achieved. This study provides a basis for appropriate selection of UAV flight angles for the monitoring of SMC in the root zone of winter wheat. [ABSTRACT FROM AUTHOR]

The pursuit of fabricating high‐performance graphene films has aroused considerable attention due to their potential for practical applications. However, developing both stretchable and tough graphene films remains a formidable challenge. To address this issue, we herein introduce mechanical bond to comprehensively improve the mechanical properties of graphene films, utilizing [2]rotaxane as the bridging unit. Under external force, the [2]rotaxane cross‐link undergoes intramolecular motion, releasing hidden chain and increasing the interlayer slip distance between graphene nanosheets. Compared with graphene films without [2]rotaxane cross‐linking, the presence of mechanical bond not only boosted the strength of graphene films (247.3 vs 74.8 MPa) but also markedly promoted the tensile strain (23.6 vs 10.2 %) and toughness (23.9 vs 4.0 MJ/m3). Notably, the achieved tensile strain sets a record high and the toughness surpasses most reported results, rendering the graphene films suitable for applications as flexible electrodes. Even when the films were stretched within a 20 % strain and repeatedly bent vertically, the light‐emitting diodes maintained an on‐state with little changes in brightness. Additionally, the film electrodes effectively actuated mechanical joints, enabling uninterrupted grasping movements. Therefore, the study holds promise for expanding the application of graphene films and simultaneously inspiring the development of other high‐performance two‐dimensional films. [ABSTRACT FROM AUTHOR]

Continuous time-series spectral data are important for inversion of crop or soil information. UAV remote sensing is usually selected under clear and windless weather conditions, but it is not possible to have such weather every day, which results in the UAV not collecting continuous daily spectral information. To explore this issue, we focused on summer maize with four irrigation levels as the research subject. A UAV platform with a multispectral sensor was used to acquire measured spectra of the maize canopy. The solar zenith angle was calculated and substituted into the Ambrals kernel-driven model to obtain simulated spectral data for the maize canopy, and the time-series UAV spectral data were complemented. Then, four vegetation indices (VIs) were established using simulated and measured spectral data, respectively, and the accuracy of the simulated VIs was evaluated. Finally, the simulated and measured VIs were used to monitor and evaluate variations in soil surface moisture content, respectively, and provide irrigation warning. The results demonstrated that Ambrals kernel-driven model can be used to simulate the reflectance of maize canopy collected by UAV. The simulated reflectance can complement time-series UAV spectral data and be used to establish VIs, among which Structure Intensive Pigment Index (SIPI) was established with the highest accuracy (R = 0.729). The VIs established by simulated reflectance can be used to monitor soil surface moisture content, and the monitoring effect is similar to the measured data (R2 = 0.642, RMSE = 0.42). It can evaluate the soil moisture a few days after irrigation and ensure the continuity and timeliness of soil moisture data, so as to improve the crop irrigation system and carry out irrigation warning. These results have certain reference for the supplementation of time-series spectral data and farmland irrigation using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]

The capture and conversion of carbon dioxide (CO2) into valuable chemical products under mild conditions is an important and challenging approach for contemporary industry. Carboxylic acid ligands are widely used in the development of functionalized metal organic framework materials due to their excellent stability. Herein, a novel mixed-metal organic framework Cu-TCPP(Fe) was assembled from iron-(Fe)-porphyrin ligands, which can efficiently catalyze the reaction of propargylic amines and CO2 to synthesize 2-oxazolidinones. [ABSTRACT FROM AUTHOR]

When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44–70, 32–44, 42–58, and 76–97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions. [ABSTRACT FROM AUTHOR]

The Arrhenius equation is generally adopted for the application of accelerated aging in laboratory tests, and activation energy is one of the important parameters in the equation. In most cases, activation energy was determined by obtaining a series of viscosity data at different temperature, which was a complex process. Therefore, this study aimed to analyze the correlation between activation energy for viscous flow (Eη) and molecular characteristics as well as rheological properties and obtain easily measurable properties that can be used to predict Eη. In this study, nine base asphalt binders were subjected to accelerated aging, and their viscosity, rheological properties, and molecular weight distribution were tested. Small molecule size (SMS) content was the most strongly correlated parameter with Eη for unaged asphalt binder compared with medium molecule size (MMS) content and large molecule size (LMS) content from the result of gray relation analysis. The correlation further weakened after considering aging, which indicated that the change in molecular distribution of asphalt binder may not be the decisive parameter in determining the change in Eη of asphalt binder. Single regression analysis showed a significant correlation between rheological performance parameters and Eη. It was feasible to predict Eη based on the failure temperature and obtain the modified Arrhenius equation. The modified equation could be used to calculate the accelerated aging factor and obtain the aging asphalt and accurate aging status of asphalt binders. By verification, the average error in predicting the Eη of the asphalt binder using the modified equation was less than 5%. [ABSTRACT FROM AUTHOR]

The single-column model, with its advantages of low computational cost and fast execution speed, can assist users in gaining a more intuitive understanding of the impact of parameters on the simulated results of climate models. It plays a crucial role in the study of parameterization schemes, allowing for a more direct exploration of the influence of parameters on climate model simulations. In this paper, we employed various methods to conduct sensitivity analyses on the 11 parameters of the Single Column Atmospheric Model (SCAM). We explored their impact on output variables such as precipitation, temperature, humidity, and cloud cover, among others, across five test cases. To further expedite experimentation, we utilized machine learning methods to train surrogate models for the aforementioned cases. Additionally, three-parameter joint perturbation experiments were conducted based on these surrogate models to validate the combined parameter effects on the results. Subsequently, targeting the sensitive parameter combinations identified from the aforementioned experiments, we further conducted parameter tuning for the corresponding test cases to minimize the discrepancy between the results of SCAM and observational data. Our proposed method not only enhances model performance but also expedites parameter tuning speed, demonstrating good generality at the same time. [ABSTRACT FROM AUTHOR]

In this paper, we study the nonlinear Klein–Gordon systems arising from relativistic physics and quantum field theories