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

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy ( V c m a x , 0 25 ), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of V c m a x , 0 25 and GPP estimation. CNN methods achieved the best V c m a x , 0 25 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to V c m a x , 0 25 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle. [ABSTRACT FROM AUTHOR]

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]