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Accurate estimates of plant transpiration (T sf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (r c), the Penman-Monteith (PM) model is commonly used in T sf modeling. However, the r c is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the r c of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (R n), and air temperature (T a), and of soil water content (θ a) on the accuracy of JA sub-models to calculate apple tree r c and T sf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate r c. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of T sf estimates with five empirical r c models, combined with the PM model. The results showed that sub-model JA 3322 , i.e., the third constraint for VPD, the third constraint for R n , the second constraint for T a , and the second constraint for θ a attained the best estimate of r c with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m-1 and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m-1 and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of r c estimation, where the median R2 and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of r c , followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree T sf , and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously. • Jarvis was superior to other empirical models for canopy resistance estimation. • Jarvis segmented by canopy growth stage gave a more accurate estimation. • Finding the most appropriate Jarvis model for apple tree canopy resistance. • Intelligence optimization algorithms improved the accuracy of the Jarvis models. [ABSTRACT FROM AUTHOR]

Quantitatively characterizing and accurately predicting plant transpiration are of great significance, but directly measuring transpiration is impractical, time-consuming, and labor-intensive. This study compared the transpiration estimation performance of multiple linear regression (MLR), modified Jarvis–Stewart (MJS), and Shuttleworth–Wallace (S-W) with deep belief network (DBN), long short-term memory recurrent neural network (LSTM-RNN), and LSTM-RNN improved with multiple restricted Boltzmann machines (R- L -RNN) using 31 input combinations comprising complete subsets of Vapor pressure deficit (VPD), Net solar radiation (R n), Average air temperature (T a), Soil water content (SWC), and Leaf area index (LAI) observations collected at Wuwei, Changwu and Taigu stations on the Loess Plateau in China. The results showed that R- L -RNN obtained the most accurate estimations in the partial canopy stage, dense canopy stage, and whole growth stage, compared to MLR, MJS, S-W, DBN, and LSTM-RNN. The accuracy of the deep learning models (DNN) increased exponentially as the number of input variables increased, and the importance of the input variables followed the orders of: LAI > VPD > R n > T a > SWC in the partial and whole canopy stage, and VPD > R n > T a > LAI > SWC in the dense canopy stage. The apple tree transpiration models were more accurate in the partial and dense canopy stages than the whole growth stage. The coefficient of determination and Nash-Sutcliffe efficiency coefficient for the R- L -RNN model increased by 8.1–13.1% and 11.2–25.4% in the partial canopy stage, respectively, and by 2.6–6.9% and 14.7–20.1% in the dense canopy stage, whereas the relative root mean square error decreased by 8.7–28.6% and 17.3–38.2%. Overall, R- L -RNN is the most recommended model for estimating the apple tree transpiration, because it is such a simple method that agricultural water managers can easily determine the water consumption of apple trees using limited accessible observational data. • A hybrid deep learning model provided most accurate apple tree transpiration model. • Importance of different inputs to deep learning models differed with growth stages. • Accuracy of deep learning models improved exponentially with more inputs. • The transpiration models were more accurate in the partial and dense canopy stages. [ABSTRACT FROM AUTHOR]

In this work, we report a facile strategy for the preparation of polypyrrole/platinum nanocomposite (PPy/Pt nanocomposite), namely small and surface-clean Pt nanoparticles (Pt NPs) densely dispersed on polypyrrole nanoparticles (PPy NPs) by a simple, ultrafast, and microwave-assisted polyol process. The as-made nanocomposite was demonstrated to possess a good electrocatalytic activity toward the reduction of H 2 O 2 by obviously enhancing the detection sensitivity and decreasing H 2 O 2 reduction overpotential at −0.175 V (vs. Ag/AgCl). Accordingly, a selective, stable, repeatable and reproducible non-enzymatic electrochemical sensor of H 2 O 2 based on the nanocomposite was constructed. [ABSTRACT FROM AUTHOR]

• Hierarchical Co-embedded porous carbon microspheres were controllably synthesized. • The Co-based catalyst showed superior performance beyond noble-metal-based catalysts. • The partially uncovered and totally in-built Co species were likely the active sites. The direct pyrolysis of metal organic frameworks (MOFs) has recently provided an effective method to prepare various metal-incorporated composite carbon materials. In this work, hierarchical Co-embedded N-doped porous carbon microspheres were synthesized by controlled pyrolysis of bimetallic Zn-Co Prussian blue analogues (PBAs), in which the metallic Zn component served as thermally removable template and ligand [Co(CN) 6 ]3− served as both Co and N sources. When devoted to catalyzing the reduction of 4-nitrophenol (4-NPh) into 4-aminophenol (4-APh), the optimized catalyst exhibited a large apparent rate constant of 1.35 min−1 and high turnover frequency (TOF) of 0.0204 s−1, which was superior to the majority of noble-metal-based catalysts. Besides, a home-made continuous flow catalysis system was readily constructed. Control experiments confirmed that both partially uncovered and totally in-built active metallic Co species acted as catalytically active sites. This work might provide a further understanding of the identification of active sites within metal-incorporated carbon-based catalysts. [ABSTRACT FROM AUTHOR]

Accurate prediction of global solar radiation (R s) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting R s in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in R s estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R2) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R2) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R2) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting R s in various regions of China. •For standalone models, the SVM had higher prediction accuracy than DT and BP. •Three hybrid models (ACO-SVM, CS-SVM and GWO-SVM) were proposed to predict daily R s in different climatic zones of China. •The accuracy of hybrid models was higher than the standalone SVM model. •The GWO-SVM model performed better than the CS-SVM model, and the accuracy of both were better than ACO-SVM model. [ABSTRACT FROM AUTHOR]

Hierarchical nanostructure with hollow feature can greatly promote electrocatalytic activity by facilitating the diffusion of active species and accelerating the contact between catalyst and electrolyte. In this paper, we report the preparation of hierarchical hollow NiFe hydroxide nanospheres assembled from ultrathin nanosheets as the OER electrocatalyst via a simple and gentle self-templating strategy, which includes two steps: the synthesis of Ni(OH) 2 nanospheres precursor and its further transformation to the final hollow-spheres after the introduction of Fe3+ via a mild hydrothermal method. The hollow NiFe hydroxide nanospheres were proven to be composed of ultrathin nanosheets which formed porous walls. This unique structure achieved numerous mass transfer channels for electrolytes, ideal pathways for ions and electrons, and a high specific surface area, leading to the improved performance for the product during the electrocatalytic reaction. In addition, the charge transfer rate and structural stability of the architecture have been greatly improved after the addition of Fe3+, which guaranteed high electrochemical performance toward OER. The tafel plot for NiFe-190 without any conductive agents is 60 mV dec−1 and the overpotential needed to reach a current density of 10 mA cm−2 is a 270 mV. [ABSTRACT FROM AUTHOR]