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On the other hand, the accessible multiple oxidation states of reactive Cu elements (Cu SP 0 sp , Cu SP I sp , Cu SP II sp , and Cu SP III sp ) within Cu-based catalysts can enable targeted catalytic reactions that proceed via both single- and multi-electron transfer. Dang et al. [[5]] synthesized self-supported Cu/Cu SB 3 sb P nanoarrays through a low-temperature phosphidation process, which were directly grown on the surfaces of Cu nanosheets from Cu/CuO nanoarrays precursor. Compared with the Cu(111) surface, the Cu-skin Cu SB 3 sb Au(111) surface showed higher stability of O SB 2 sb adsorption and a lower dissociation energy barrier of the adsorbed O SB 2 sb . [Extracted from the article]

Currently, green hydrogen harvesting from electrochemical water splitting is overly reliant on noble metals with high catalytic activity but low natural abundance and high cost. As one of potential earth‐abundant alternatives, copper, however, displays a poor hydrogen evolving capability, mainly due to inherently a weak hydrogen binding energy (HBE). It poses a substantial restriction to metallic Cu alone serving as primary active sites for high‐performance hydrogen evolution. Thanks to the presence of hydrogen spillover that was often overlooked during the electrocatalysis, the hydrogen evolution in a nonacidic electrolyte can be accelerated by the recombination desorption of adsorbed hydrogen over Cu with a weak HBE. Here, we concisely highlighted this promotion effect of secondary active Cu on electrocatalytic hydrogen evolution and eagerly expected to extend hydrogen spillover effects to the advance of multi‐site electrocatalytic systems. [ABSTRACT FROM AUTHOR]

The intrinsic activity of single-atom catalysts is influenced by the local electronic structures of metal centers, with existing modulation strategies limited to adjacent atomic coordination. However, the impacts of support surface geometry on local bonding environment, and thus electronic structures of single-atom centers have rarely been known. Here, we prepared highly curved B,N co-doped carbon-supported ruthenium catalyst with an ultra-low Ru loading of 0.4 wt%, which exhibited an ultrahigh turnover frequency (TOF) of 10 H 2 s−1 (38 times that of Pt/C) and superior stability in alkaline hydrogen evolution reaction (HER). We found that curving support surface induced the strain, resulting in 1.5% compressed Ru-N and 4% stretched Ru-B bonds, leading to the accumulation of positive charge and quenching of spin polarization at Ru sites, thereby achieving the optimal binding of H* and enhanced performance for HER. This work highlights the significant support effects upon the structural design of active sites. [Display omitted] • Two types of B, N co-doped carbon-supported Ru site catalysts with and without support surface curvature were synthesized. • Curving the support surface induces strain and thereby changes the local bond length at the Ru sites. • Regulating bond length increases the aggregation of positive charges and suppresses spin polarization at the Ru sites. • The B, N co-doped carbon-supported Ru site catalyst with curvature achieves optimal H* binding strength for HER. • The turnover frequency of Ru sites with curvature is significantly higher than those without surface curvature. [ABSTRACT FROM AUTHOR]

Accurate evapotranspiration (ET) estimation is paramount for effective agricultural water management. As the key parameter of surface resistance (r s)-based ET models, the direct acquisition of canopy resistance (r c) remains challenge. Moreover, the applicability of r c models and r s -based ET models has not been fully validated on kiwifruit. To address above problems, this study investigates tree growth patterns, water consumption change, and soil water content (SWC) dynamics under different irrigation strategies based on a two-year experiment in Northwest China. Subsequently, the Whale Algorithm (WOA), Quantum Whale Algorithm (QWOA), and Differential Whale Algorithm (DWOA) were employed to optimize Jarvis-type r c models (JA) constructed by different constraint functions of SWC. After internal comparison within Jarvis-type models and external comparison with Kelliher-Perrier (KP), Faria (FA), and Stannard (ST) model, the recommended r c model was integrated into Penman-Monteith (PM), Shuttleworth-Wallace (SW), and Clumping (CL) to select most suitable kiwifruit ET model. The results indicated that the water stress coefficient is suitable for JA model as a constraint function of SWC, achieving R2, NSE, RMSE, and MAPE of 0.782–0.805, 0.672–0.737, 654.191–3396.594 s m−1, and 0.102–0.183 under sufficient irrigation strategy, respectively. Under deficit irrigation strategies, the corresponding R2, NSE, RMSE, and MAPE ranged 0.886–0.917, 0.828–0.885, 325.736–3251.434 s m−1, and 0.111–0.166, respectively. Besides, DWOA is the best algorithm during whole growth period, which improved original JA by 13.2–32.9% for R2, 23.0–60.2% for NSE, 26.8–36.8% for RMSE, and 17.5–36.2%, respectively. Regarding r s -based ET models, the CL performed best under various irrigation conditions, with values of R2, NSE, RMSE, and MAPE ranging 0.908–0.961, 0.842–0.947, 0.311–0.808 mm, and 0.088–0.197, respectively. Overall, the CL integrated with DWOA-JA3 is most recommended for estimating kiwifruit ET under different irrigation strategies, which is helpful for orchardists to enhance agricultural water utilization efficiency and promote sustainable kiwifruit production. • Soil water deficit coefficient was accepted as constraint function to develop canopy resistance model. • Jarvis-type models were optimized using Quantum and Differential Whale Optimization Algorithm. • The accuracy of canopy resistance model was evaluated under different irrigation strategies. • Surface resistance-based evapotranspiration models were compared for kiwifruit in Northwest China. [ABSTRACT FROM AUTHOR]

Accurate reference crop evapotranspiration (ET 0) estimation is essential for agricultural water management, crop productivity, and irrigation systems. As the standard ET 0 estimation method, the Penman-Monteith equation has been widely recommended worldwide. However, its application is still restricted to comprehensive meteorological data deficiency, making the exploration of alternative simpler models for acceptable ET 0 estimation highly meaningful. Concerning the aforementioned requirement, this study developed the novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, and regression component, to estimate ET 0 based on radiation-based (R n -based), humidity-based (RH-based), and temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal and external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), and empirical models, the result indicated that MA-CNN-BiLSTM achieved superior precision, with values of Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 mm d−1 for internal strategy and 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 mm d−1, 0.294–0.699 mm d−1 for external strategy. Specifically, R n -based MA-CNN-BiLSTM excelled in the temperate continental zone (TCZ) and mountain plateau zone (MPZ), while RH-based MA-CNN-BiLSTM yielded best precision in others. Furthermore, the internal strategy was superior to external strategy by 2.74–106.04% for R2, 1.11–120.49% for NSE, 1.41–40.27% for RRMSE, 1.68–45.53% for RMSE, and 1.21–38.87% for MAE, respectively. In summary, the main contribution of the present study is the proposal of a novel LSTM-type ET 0 model (MA-CNN-BiLSTM) to cope with various data-missing scenarios throughout China, which can provide effective support for decision-making in regional agriculture water management. • MA-CNN-BiLSTM model provides the highest accuracy for reference crop evapotranspiration estimation across China. • Machine learning and empirical models performed better with internal strategy than external strategy. • Radiation-based models excelled in temperate continental and mountain plateau zone, while humidity-based excelled in others. • Multi-head attention improved Long Short-Term Memory network type model as intricate relationship processor. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives of the Penman - Monteith equation, empirical methods have been widely recommended worldwide. However, its application is still limited to parameters localization varied with geographical and climatic conditions, therefore developing an excellent optimization algorithm for calibrating parameters is very necessary. Regarding the above requirement, the present study developed a novel improved Grey Wolf Algorithm (MDSL-GWA) to optimize the most recommended ones among three types of ETo methods. After the optimization performance comparison among Least Square Method (LSM), Genetic Algorithm (GA), Grey Wolf Algorithm (GWA), and MDSL-GWA in four climatic regions of China, this study found that the Priestley-Taylor (PT) method was the best radiation-based (R n -based) method and achieved better performance in temperate continental region (TCR), mountain plateau region (MPR), and temperate monsoon region (TMR) than other types. While the temperature-based (T-based) Hargreaves-Samani (HS) method performed best in subtropical monsoon region (SMR), further attaching better performance among the same type in TMR and TCR, while the Oudin method was the best T-based method in MPR. Moreover, the Romanenko method was better humidity-based (RH-based) in TCR and MPR, whereas the Brockamp-Wenner method exhibited higher in SMR and TMR. Furthermore, despite intelligence optimization algorithms significantly enhancing original ETo methods, the MDSL-GWA achieved best performance and outperformed other algorithms by 4.5–29.6% in determination coefficient (R2), 4.7–27.3% in nash-sutcliffe efficient (NSE), 3.7–44.4% in relative root mean square error (RRMSE), and 3.1–56.2% in mean absolute error (MAE), respectively. After optimization, the MDSL-GWA-PT was the most recommended ETo method in TMR, TCR, and MPR, and the median values of R2, NSE, RRMSE, and MAE ranged 0.907–0.958, 0.887–0.925, 0.083–0.103, and 0.115–0.162 mm, respectively. In SMR, the MDSL-GWA-HS produced the best ETo estimates, with median values of R2, NSE, RRMSE, and MAE being 0.876, 0.843, 0.112, and 0.146 mm, respectively. In summary, this study recommended the best ETo method and algorithms using accessible data in four climatic regions of China, which is helpful for decision-making in effective management and utilization of regional agricultural water resources. • Hargreaves-Samani excelled in subtropical monsoon, but Priestley-Taylor was best elsewhere. • Brockamp-Wenner performed better as RH-based in eastern China, but vice versa for Romanenko. • Intelligence optimization algorithms improved the original version of empirical ET O methods. • Improved Grey Wolf Algorithm provided most suitable parameters for empirical ET O methods. [ABSTRACT FROM AUTHOR]

• LSTM-type models outperformed empirical methods and classical machine learning models. • BiLSTM was recommended for SPEI-3, and CNN-LSTM was suitable for SPEI-6 and SPEI-12. • Rn-based models performed best for estimating multiscale SPEI throughout China. • RH-based was superior to T-based ML models, but empirical methods differed with zones. Although the accurate prediction of the Standardized Precipitation Evapotranspiration Index (SPEI) is considered meaningful in reducing drought losses, its wide applications are limited to substantial meteorological data requirements. Considering Long Short-Term Memory network (LSTM) has proved its potential in estimating drought index, the concern is justified regarding the performance of its variants for estimating SPEI using limited meteorological input at the national level. Therefore, this study established the SPEI models using empirical methods, SVM, RNN, LSTM, BiLSTM, and CNN-LSTM, respectively, to cope with different data-missing scenarios. Based on a comprehensive comparison among different methods for multiscale SPEI estimation at four climatic zones of China, the results showed that BiLSTM was the most recommended model for estimating SPEI at 3-month timescale, with R2, NSE, and RMSE ranging 0.916–0.997, 0.907–0.997, and 0.143–0.353, respectively. Whereas CNN-LSTM was more suitable for other timescales, with R2, NSE, and RMSE being 0.904–0.999, 0.858–0.989, and 0.145–0.365 for estimating SPEI at 6-month timescale, respectively, and 0.858–0.998, 0.795–0.991, and 0.081–0.568 for estimating SPEI at 12-month timescale. Generally, the accuracy performance of SPEI methods can be ranked from best to worst as LSTM-type models, SVM, RNN, and empirical methods. The exception was that H-S exceeded RNN3 in TCZ and MPZ by 4.1–30.0 % for R2, 4.1–65.0 % for NSE, and 2.3–19.6 % for RMSE, respectively. Moreover, this study found that the accuracy performance of machine learning models for SPEI estimation got worse with the number of independent variables decreased. Overall, the variants of LSTM exerted excellent performance for multiscale SPEI estimation, which provided the most accurate prediction of meteorological, agroecological, and hydrological droughts throughout China. [ABSTRACT FROM AUTHOR]

Predicting daily solar radiation using a novel hybrid Long Short-Term Memory network across four climate regions of China. [Display omitted] • MEA-DBN-LSTM was the most recommended R s estimated model across China. • DBN improved complex-based LSTM models but without impact on other input types. • MEA was significantly effective in optimizing the hyperparameters of DBN-LSTM. • Complex-based were superior to the sunshine-based and temperature-based R s models. • Temperature is the critical input for SMR, but sunshine is more important for other regions. Accurate daily solar radiation (R s) data is crucial for the growth and development of crops, but its direct observation is relatively scarce in most regions around the world. Therefore, accurately estimating daily R s is of great significance. Regarding the above practical requirement, this study developed a novel hybrid deep learning model (MEA-DBN-LSTM), which combined the Long Short-Term Memory model (LSTM), Deep Belief Network (DBN), and Mind Evolutionary Algorithm (MEA), to estimate the R s across four different climate regions of China using five easily accessible meteorological input combination (sunshine-based, temperature-based and three complex-based). The results showed that MEA-DBN-LSTM was proved to be the most recommended R s estimated model across four different climate regions of China, and the corresponding median R2, NSE, RMSE, MAE, and MAPE values ranged 0.805–0.999, 0.656–0.954, 1.069–4.289 MJ m−2 d–1, 0.889–3.532 MJ m−2 d–1, and 0.055–0.296 respectively. Correspondingly, compared to the empirical R s model, the improvement values of MEA-DBN-LSTM were 12.33–52.13 % for R2, 14.80–69.38 % for NSE, 33.75–69.77 % for RMSE, 36.03–62.70 % for MAE, and 42.40–66.67 % for MAPE, respectively. Moreover, the MEA-DBN-LSTM exhibits improvements in R2, NSE, RMSE, MAE, and MAPE, ranging 1.40–13.50 %, 1.27–24.79 %, 1.81–61.18 %, 1.90–61.30 %, and 2.31–57.55 %, respectively, compared to other forms of LSTM-type models. Specifically, this study found the DBN modules significantly improved the complex-based single LSTM model, but without impact on sunshine-based and temperature-based single LSTM models. Regarding the MEA modules, it was effective in the hyperparameter optimization compared to DBN-LSTM. Furthermore, significant accuracy discrepancies exist among R s models using different input combinations across four different climate regions, and the complex-based was generally superior to other input combinations. Overall, our findings can provide accurate daily R s across four different climate regions of China using easily accessible meteorological data, which is of great significance for crop water consumption estimation, agricultural water resources management, crop growth conditions optimization, and sustainable agricultural development. [ABSTRACT FROM AUTHOR]

• Solar radiation model is sensitive to environments, structure, and input variables. • Optimal algorithm selection should consider its characteristic and model structure. • Gravitational search algorithm suitable for Ögelman, Fan, and Chen to estimate ET 0. • Mind evolution algorithm suitable for Angstrom, Hunt, and El–Sebaii to estimate ET 0. • PM GSA–Fan is the most recommended hybrid PM for ET 0 estimates throughout China. Accurate estimates of reference evapotranspiration (ET 0) are of great significance to water resources planning and management, but the actual solar radiation (R s), as the primary parameter for ET 0 estimation, is difficult to obtain directly in most areas. Thus, studying the impacts of locally calibrated empirical solar radiation (R s) models to improve the accuracy of Penman-Monteith (PM) is significant. Meanwhile, swarm intelligence algorithms have proved their potential in the domains of agriculture and hydrology, but few studies applied them in optimizing R s models to improve ET 0 estimation. This study used the particle swarm optimization (PSO), the gravitational search algorithm (GSA), and the mind evolutionary algorithm (MEA), respectively, to optimize the nine most common empirical R s models, comprising three sunshine-based models (Angstrom, Ögelman, Bahel), three temperature-based models (Hargreaves, Bristow-Campbell, Hunt), and three combined-based models (Fan, Chen, El-Sebaii), and then integrated them into the PM for ET 0 estimation at four climatic zones of China. The results showed that the Fan model obtained the most accurate R s estimates in china, while the sunshine-based and temperature-based exerted significantly different applicability at different climatic zones. Regarding optimization algorithm, this study found that GSA performed better for the Ögelman model, Fan model, and Chen model when integrating into the PM equation for ET 0 estimation, whereas MEA performed better for the Angstrom model, Hunt model, and El-Sebaii model. After optimization, PM GSA–Fan obtained the most accurate estimates of ET 0 at four climatic zones, with a regional spatial gradient in estimates accuracy of R s from north to south of China. In terms of sunshine-based models, the PM MEA–Angstrom performed better in TMZ, TCZ, and MPZ, whereas the PM GSA–Ögelman performed better in SMZ, respectively; in terms of temperature-based models, the PM MEA–Hunt performed better in TMZ and SMZ, whereas the PM LSM–Hargreaves performed better in TCZ and MPZ, respectively. Overall, this study identifies the optimal R s estimation model and optimization algorithm for ET 0 estimation at four climatic zones, which provide regionally and nationally accurate water consumption information without actual measured R s in China. [ABSTRACT FROM AUTHOR]

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]

• Hybrid deep learning model achieved the best performance for estimating daily ET 0. • Rn– were superior to RH– and T–based ET 0 models on the Loess Plateau. • RH– were superior to T–based deep learning model, but vice versa for empirical model. • Significant spatial accuracy discrepancies exist in ET 0 models on the Loess Plateau. Reference evapotranspiration (ET 0) is usually employed for estimating actual crop ET together with crop coefficients (K c). However, it is necessary to explore an alternative model to estimate ET 0 concisely because of numerous limitations in the Penman–Monteith method. To improve the ET 0 estimation accuracy using limited meteorological data, this study developed a novel hybrid deep learning model (d –LSTM) based on the meteorological data during 1961–2020 observed at fifty stations on the Loess Plateau, which used the Deep Belief Network (DBN) module to extract features from meteorological data and the Long Short–Term Memory (LSTM) module to expand time features and process data information with sequential features, respectively. Based on the comparative evaluation of ET 0 estimation accuracy between the d –LSTM, DBN, LSTM, and nine empirical models, the results drawn from this study demonstrated that the d –LSTM model manifested the best performance as RH–based, Rn–based, and T–based ET 0 estimating models. For local strategy, the value of R2, NSE, RMSE, MAPE, and GPI ranging 0.941 ± 0.020, 0.940 ± 0.032, 0.436 ± 0.457 mm d–1, 0.150 ± 0.016, and 1.611 ± 0.180 for D–LSTM1 (RH–based), 0.944 ± 0.030, 0.943 ± 0.037, 0.423 ± 0.313 mm d–1, 0.119 ± 0.013, and 1.917 ± 0.155 for D–LSTM2 (Rn–based), and 0.902 ± 0.091, 0.891 ± 0.094, 0.558 ± 0.319 mm d–1, 0.181 ± 0.058, and 1.440 ± 0.550 for D–LSTM3 (T–based). For external strategy, the average value of R2, NSE, RMSE, MAPE, and GPI were 0.874, 0.872, 0.651 mm d–1, 0.159, and 1.837 for D–LSTM1, 0.894, 0.892, 0.591 mm d–1, 0.138, and 2.000 for D–LSTM2, and 0.839, 0.827, 0.768 mm d–1, 0.212, and 1.482 for D–LSTM3. Following, the LSTM performed better than DBN for local strategy, but vice versa for external strategy. Despite DL models outperforming RH–based and Rn–based empirical models, H S outperformed the DBN3 for local strategy, and was superior to LSTM3 for external strategy. Under limited meteorological data, the Rn–based ET 0 estimating models are superior to RH–based and T–based models, and RH–based achieved better accuracy than T–based for DL models, but vice versa for empirical models. There is significant spatial variability in the accuracy of daily ET 0 models, but the high precision of the d –LSTM was stable on the Loess Plateau. Overall, the d –LSTM model, which combines the advantages of DBN and LSTM, is the most recommended ET 0 model using incomplete meteorological data on the Loess Plateau, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling. [ABSTRACT FROM AUTHOR]

• Hybrid deep learning model achieved the best performance for estimating daily ET 0. • Rn– were superior to RH– and T–based ET 0 models on the Loess Plateau. • RH– were superior to T–based deep learning model, but vice versa for empirical model. • Significant spatial accuracy discrepancies exist in ET 0 models on the Loess Plateau. Reference evapotranspiration (ET 0) is usually employed for estimating actual crop ET together with crop coefficients (K c). However, it is necessary to explore an alternative model to estimate ET 0 concisely because of numerous limitations in the Penman–Monteith method. To improve the ET 0 estimation accuracy using limited meteorological data, this study developed a novel hybrid deep learning model (d –LSTM) based on the meteorological data during 1961–2020 observed at fifty stations on the Loess Plateau, which used the Deep Belief Network (DBN) module to extract features from meteorological data and the Long Short–Term Memory (LSTM) module to expand time features and process data information with sequential features, respectively. Based on the comparative evaluation of ET 0 estimation accuracy between the d –LSTM, DBN, LSTM, and nine empirical models, the results drawn from this study demonstrated that the d –LSTM model manifested the best performance as RH–based, Rn–based, and T–based ET 0 estimating models. For local strategy, the value of R2, NSE, RMSE, MAPE, and GPI ranging 0.941 ± 0.020, 0.940 ± 0.032, 0.436 ± 0.457 mm d–1, 0.150 ± 0.016, and 1.611 ± 0.180 for D–LSTM1 (RH–based), 0.944 ± 0.030, 0.943 ± 0.037, 0.423 ± 0.313 mm d–1, 0.119 ± 0.013, and 1.917 ± 0.155 for D–LSTM2 (Rn–based), and 0.902 ± 0.091, 0.891 ± 0.094, 0.558 ± 0.319 mm d–1, 0.181 ± 0.058, and 1.440 ± 0.550 for D–LSTM3 (T–based). For external strategy, the average value of R2, NSE, RMSE, MAPE, and GPI were 0.874, 0.872, 0.651 mm d–1, 0.159, and 1.837 for D–LSTM1, 0.894, 0.892, 0.591 mm d–1, 0.138, and 2.000 for D–LSTM2, and 0.839, 0.827, 0.768 mm d–1, 0.212, and 1.482 for D–LSTM3. Following, the LSTM performed better than DBN for local strategy, but vice versa for external strategy. Despite DL models outperforming RH–based and Rn–based empirical models, H S outperformed the DBN3 for local strategy, and was superior to LSTM3 for external strategy. Under limited meteorological data, the Rn–based ET 0 estimating models are superior to RH–based and T–based models, and RH–based achieved better accuracy than T–based for DL models, but vice versa for empirical models. There is significant spatial variability in the accuracy of daily ET 0 models, but the high precision of the d –LSTM was stable on the Loess Plateau. Overall, the d –LSTM model, which combines the advantages of DBN and LSTM, is the most recommended ET 0 model using incomplete meteorological data on the Loess Plateau, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling. [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]

We describe the synthesis of a hybrid nanocomposite consisting of MoS and reduced graphene oxide (MoS/rGO) by a one-pot hydrothermal process, and the use of the material in an amperometric sensor. Scanning electron microscopy and nitrogen adsorption-desorption tests revealed that the nanocomposite possesses a porous and flower-like structure and a large specific surface. Cyclic voltammetry and differential pulse voltammetry demonstrated the nanocomposite to perform excellently in the electrochemical oxidation of ascorbic acid (AA), dopamine (DA), and uric acid (UA), respectively. The oxidation potentials are well separated (oxidation peaks at −64, 168 and 320 mV vs. Ag/AgCl) for AA, DA, and UA, and the respective detection sensitivities are strongly enhanced (0.12, 4.11 and 1.59 μA μM cm). The sensor was applied to the detection of the three species in spiked human serum samples where it gave recoveries ranging from 98.9 to 104.1 % (for n = 3). [Figure not available: see fulltext.] [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]

A unique network of a-NiCo-MOFNWs-P is developed by a two-step pyrolysis strategy, which exhibits superior activity and long-term durability in methanol electrooxidation reactions. [Display omitted] Developing high-performance and low-cost electrocatalysts toward methanol oxidation reaction (MOR) is essential for fuel cell applications. Herein, we report a defect engineering strategy integrating amorphization and phosphorization to construct directly interconnected networks of amorphous NiCo-based metal–organic framework nanowires (a-NiCo-MOFNWs) with phosphorus (P) doping. The resulting P-doped a-NiCo-MOFNWs (a-NiCo-MOFNWs-P) network displays superior MOR efficiency and long-term durability over 1000 cyclic voltammetry (CV) measurements. The special structure of directly interconnected networks and the synergistic effect between the amorphous MOFs and dispersed phosphorus species give rise to abundant exposed active sites, accelerated electron transport, and increased porosity for mass transfer, thus boosting the reaction kinetics of MOR. This work provides additional insights into the network assembly and structural evolution of one-dimensional (1D) MOFs, and also opens up new avenues for the design of highly reactive and robust non-precious metal-based electrocatalysts. [ABSTRACT FROM AUTHOR]

• A new hybrid PSO-LSTM model was proposed for soil moisture estimation. • The hybrid PSO-LSTM model had strong stability, universality and high prediction accuracy. • The prediction accuracy of the hybrid PSO-LSTM model was better than the LSTM model. • The prediction accuracy of models with vertical polarization was higher than that with cross-polarization. Soil moisture content is a vital variable in agricultural, hydrological, ecological and climatological processes. However, susceptible to soil type, soil structure, topography, vegetation and human activities, soil moisture content exhibits strong spatial heterogeneity in spatial distribution, which makes it difficult to accurately estimate the soil moisture content distribution information at a large scale using conventional methods. To solve the problem, this study proposed a novel hybrid model (PSO-LSTM) based on the particle swarm optimization (PSO) and long short-term memory (LSTM) network model to accurately predict soil moisture content at a large scale. Five different input combinations were constructed based on the vertical polarization (VV) and cross-polarization (VH) of multi-phase Sentinel-1A data, and the soil moisture content at depths of 5 cm, 10 cm, 20 cm and 40 cm in citrus orchards were estimated using the standalone LSTM and hybrid PSO-LSTM models. The results showed that the estimation accuracy of the hybrid PSO-LSTM model was greater than that of the standalone LSTM model at different depths, with the normalized root mean square error (NRMSE) of 4.568–11.023 % and 18.056–30.156 %, respectively. With the VV polarization as the only inputs, the PSO-LSTM model obtained high prediction accuracy, with the normalized root mean square error (NRMSE) of 5.458–10.125 %, respectively. Therefore, the PSO-LSTM model with VV polarization input was recommended to estimate the soil moisture content at different depths in citrus orchards, which provides important data for decision-making on distributed precision irrigation at a large scale. [ABSTRACT FROM AUTHOR]

• We conducted a meta-analysis with 1834 observations from 72 published studies. • Water productivity and nitrogen use efficiency of global apple production were analyzed. • Optimizing water and nitrogen input greatly increased apple yield and quality by 5.2–25.5 %. • Water saving and nitrogen saving potentials for apple production were 20.4 % and 37 %. Inappropriate strategies in water use and nitrogen (N) fertilizer application in apple production can decrease apple yield, water productivity (WP), nitrogen use efficiency (NUE), and quality, leading to severe environmental pollution. However, there is a lack of systematic analysis on the effects of water use and N fertilizer inputs on apple yield, quality, WP, and NUE at the global scale. Therefore, we conducted a comprehensive meta-analysis with 1834 observations from 72 published studies across 18 countries to assess the effects of water use and N inputs on apple yield, quality, WP, and NUE. We aim to quantify the potential for water-saving and N-saving for global apple production. Our results showed that the global average apple yield, organic acids (OA), total soluble solids (TSS), vitamin C (VC), firmness (FN), WP, and NUE were 34.7 t ha−1, 0.51 %, 13.9 %, 4.7 mg 100 g−1, 7.4 kg cm−2, 6.8 kg m−3, and 214.5 kg kg−1, respectively. Reducing above-optimal water input to optimal water input may increase apple yield by 19.4 %, WP by 36.1 %, OA by 12.5 %, TSS by 8.4 %, VC by 17.5 % and FN by 9.8 %, respectively. Reducing above-optimal N inputs to optimal N inputs may increase apple yield by 8.9 %, NUE by 48.1 %, OA by 10.7 %, TSS by 6.1 %, VC by 13.3 % and FN by 5.2 %, respectively. The overall global mean water-saving and N-saving potentials for apple production were 20.4 % (136 mm less water irrigated) and 37 % (203 kg ha−1 less N fertilizer used), respectively. Our findings provide guidance for water use and N fertilizer management of apple production and can thus help to improve/ maintain yield and quality for greatly saving water and N fertilizer. [ABSTRACT FROM AUTHOR]

Deficit drip irrigation is an advanced agricultural technology widely promoted and applied around the world to maintain/increase yield and quality while simultaneously minimizing water consumption. However, there is still a lack of comprehensive research on the effects of deficit drip irrigation at different stages on apple yield, water productivity, and quality. The aim was to investigate the effects of deficit drip irrigation treatments (DDITs) on apple yield, quality, and water productivity (WP), and optimize the deficit drip irrigation strategy for apples in the Loess Plateau of China. a two-year field experiment was conducted with 17 irrigation treatments, including a control treatment (CK, full irrigation (FI)) and four DDITs (D15%, D30%, D45%, D60%) during the bud burst to leafing stage (I), flowering to fruit set stage (II), fruit expansion stage (III), and fruit maturation stage (IV). The optimal irrigation management was determined using the fuzzy Borda combined model, which was based on four different single evaluation models of the TOPSIS method, principal component analysis (PCA), grey correlation analysis (GCA), and membership function analysis (MFA). The DDITs at stage IV decreased midday leaf water potential (Ψs) by 2.57–20.00% compared with CK, and the difference of Ψs between IV-D30%, IV-D45%, IV-D60%, and CK were significant in two years (P<0.05). DDITs at stages I, II, and IV improved fruit weight (FW), fruit shape index (FSI), and fruit firmness (FN), but did not significantly affect fruit water content (FWC). The DDITs at stages III and IV significantly enhanced total soluble solids (TSS), soluble sugars (SS), and vitamin C (V c) in 2021 and 2022, but a notable decrease in titratable acidity (TA) was observed. DDITs at stage IV exhibited the highest comprehensive benefit of apple, followed by DDITs at stages I and II, and DDITs at stage III had the lowest values. The DDITs of D15%, D30%, CK, and D15% at stages I, II, III, and IV were identified as the optimal irrigation strategy to improve apple yield, quality, and WP. Our findings can guide the development of appropriate irrigation strategies for apple cultivation in Loess Plateau of China, promoting efficient and sustainable apple production. • Deficit drip irrigation treatments (DDITs) greatly decreased midday leaf water potential. • Proper DDITs at fruit maturation stage improved apples physical/ chemical quality. • Proper DDITs during the early growth stage greatly improved WP without yield loss. • The optimal irrigation strategy was determined using the fuzzy Borda combined model. [ABSTRACT FROM AUTHOR]

The synergistic effects between Co NPs and pyridinic nitrogen of Co/CN-x catalysts was the key to realize the base-free oxidation of mercaptans to their corresponding disulfides by decreasing the energy barrier for the deprotonation of mercaptans. [Display omitted] The oxidation of mercaptans under mild and base-free conditions is of vital importance in terms of economy and environment for petroleum processing industry. Here, we developed a series of MOF-derived cobalt-based nitrogen-doped (N -doped) carbon (Co/CN-x) catalysts for the base-free catalytic oxidation of mercaptans. The optimal Co/CN-900 showed excellent catalytic activity for the oxidation of mercaptans under base-free conditions, yielding complete conversion of various mercaptans and > 99.0% selectivity of disulfides. The high performance can be contributed to the advantages of hierarchical pore structure for the diffusion and migration of substrates, self-carrying alkalinity for the formation of mercaptide anion, abundant active Co sites for catalytic oxidation of mercaptans as well as the synergistic effects between the Co nanoparticles (NPs) and N -doped carbon supports. Furthermore, a possible mechanism for base-free catalytic oxidation of mercaptans over Co/CN-x catalysts is proposed based on a set of control experiments and density functional theory (DFT) calculations. [ABSTRACT FROM AUTHOR]

Fruit trees provide significant economic and ecological benefits and have become an important source of income for many rural households. However, orchards are known to be hotspots for reactive nitrogen loss, including N 2 O emissions and NH 3 volatilization. Despite this, there is a lack of quantitative understanding regarding N 2 O emissions and NH 3 volatilization associated with different nitrogen fertilizer (NF) management practices (fertilizer source, fertilizer method, fertilizer frequency, and fertilizer rate), as well as the key influencing factors. Therefore, we conducted a comprehensive meta-analysis of 285 observations from 42 published articles. Our results showed that NF significantly increased N 2 O emissions and NH 3 volatilization in orchards by 236.1% and 233.7%, respectively, compared to no N fertilizer (NNF). The most important influencing factors were management practices (especially NF rate), climate factors (especially mean annual temperature and mean annual precipitation), and soil properties (soil bulk density and soil organic matter). We also found a linear positive correlation between NF rates and N 2 O emissions and NH 3 volatilization. The responses of different fruit species (apple, citrus, grape, olive, peach, and banana) orchards to N 2 O emissions and NH 3 volatilization varied under NF, and the risk of N 2 O emissions and NH 3 volatilization in olive and grape orchards under NF was smaller. Soil with <1.4 g cm−3 bulk density and ≤ 10 g kg–1 organic matter is beneficial for mitigating the risk of N 2 O emissions and NH 3 volatilization under NF. It was recommended that one-time application of organic manure be used for fruit cultivation in regions with a mean annual temperature (MAT) ranging from 10 to 15 ℃ and mean annual precipitation (MAP) ranging from 0 to 600 mm, particularly under irrigated conditions. Our findings provide guidance for optimizing NF management in orchards to improve N use efficiency and mitigate environmental pollution risks. • We conducted a meta-analysis of 285 observations from 42 published articles. • Nitrogen fertilizer (NF) increased N 2 O and NH 3 emissions by 236.12% and 233.69%. • There is a linear correlation between NF rates and the N 2 O and NH 3 emissions. • The most important influencing factors were NF rate and soil bulk density. [ABSTRACT FROM AUTHOR]

The great challenges of food security, climate change and environmental degradation resulting from unreasonable nitrogen fertilizer (NF) inputs necessitate the optimization of fertilizer application to ensure fruit production and environmental sustainability. However, there is a lack of systematic analysis on the effects of NF inputs on apple yield and reactive nitrogen losses (N 2 O emission, NH 3 volatilization, and nitrate leaching) at the global scale. Therefore, we used a meta-analysis of 159 observations from 31 published studies to evaluate the response of apple yield and reactive nitrogen losses to NF. We aim to identify suitable NF rates, environmental conditions, and planting factors that improve apple yield while minimizing reactive nitrogen losses. Our results showed that NF significantly increased apple yield, N 2 O emission, NH 3 volatilization, and nitrate leaching by 17.1%, 255.7%, 236.4%, and 68.7% compared with no nitrogen fertilizer (NNF), respectively. The effects of NF rates and environmental factors on the response of N 2 O emission, NH 3 volatilization, and nitrate leaching to NF were prior to those of planting factors, but the result of apple yield was the opposite. Apple production under NF in regions with MAT (mean annual air temperature) ≥ 10 °C and MAP (mean annual precipitation) < 600 mm declined the risk of reactive nitrogen losses and significantly increased apple yield by 24.4%−28%. NF application was more beneficial in soil with pH ≥ 7 and SOM < 15 g kg−1 for apple production. We found that the suitable NF rate was 250–450 kg ha−1, which can increase apple yield by 0.8%−21.3% while significantly reducing reactive nitrogen losses compared with lower NF rate (< 250 kg ha−1) and higher NF rate (> 450 kg ha−1). Our findings provide guidance for optimizing NF management in apple orchard to achieve high crop yields, reduce emissions, and mitigate pollution. • Nitrogen (N) fertilizer significantly boosted apple yield and reactive nitrogen losses. • N fertilizer application was more beneficial in soil with pH ≥ 7 and SOM < 15 g kg−1. • Recommended N fertilizer rate was 250–450 kg ha−1, which can increase apple yield while minimizing reactive nitrogen losses. [ABSTRACT FROM AUTHOR]

Encapsulation of TiO 2 nanobelts into crystalline and porous covalent organic frameworks materials to form a core-shell structure can enhance reactivity, selectivity and durability of photocatalysts distinctly. In this work, a range of TiO 2 nanobelts@COF nanocomposites with different thicknesses of COF shell were fabricated via a novel and facile seed growth method. The results of photooxidation of benzyl alcohol under visible light (> 420 nm) showed that TiO 2 @COF-3 with COF shell thickness of around 15 nm showed the highest conversion (92.5%) with the maximum rate constant (6.73 × 10−2 h−1), which is approximately 10.1 times as that of TiO 2 and 12.9 times as that of COF. The enhanced photocatalytic activity of hybrid materials was mainly owing to the improved photon adsorption ability and charge carriers transfer from COF to TiO 2. In addition, TiO 2 @COF-3 still showed relatively high stability after 5 cycles under the same reaction condition. Therefore, this type of hybrid photocatalysts possesses great potentials in future green organic synthesis. • Core-shell TiO 2 @COF hybrids are synthesized by a seed growth method. • The COFs materials show as an excellent light-harvest materials. • The covalent-bond connection promotes the charge-carrier transfer and separation. • TiO 2 @COF-3 shows high activity and stability in photooxidation of aromatic alcohols. • TiO 2 @COF hybrids are firstly applied to the photooxidation of aromatic alcohol. [ABSTRACT FROM AUTHOR]

Deficit irrigation (DI) is widely recognized as an irrigation method to save water and increase/maintain yield. The objective of this study was to evaluate the effects of DI on yield and water productivity (WP, the ratio of yield divided by evapotranspiration (ET)) of woody fruits (apple, citrus, pear, peach), herbaceous fruits (strawberry, watermelon) and vine fruit (grape), and to identify the optimal irrigation management strategy for different fruit species groups. For this, we conducted a comprehensive meta-analysis with 591 observations from 56 peer-reviewed papers. Results showed that DI reduced the yield of woody, herbaceous, and vine fruits by 13.74%, 20.51%, and 9.03%, and increased WP by 13.34%, − 2.08%, and 9.89% compared with full irrigation (FI), respectively. Herbaceous fruits were more vulnerable to yield reduction than woody and vine fruits under DI. As for woody fruits, compared with FI, low degree (80%−100% irrigation amount of FI) DI performed better, increasing yield and WP by 0.87% and 9.77%. Woody fruits are suitable for DI in stage I and stage II (bud burst to leafing stage and flowering to fruit set stage), which can reduce the risk of yield reduction and significantly increase WP by 1.86%− 9.28%. Among irrigation methods, surge-root irrigation and sprinkler irrigation under DI performed better for woody fruits, increasing yield and WP by 1.81% and 11.89%, − 5.85% and 43.91%, respectively. In terms of herbaceous fruits, compared with FI, mild degree (60%−80% FI) DI declined the risk of yield reduction and significantly increased WP by 2.25%. DI at stage IV (fruit maturation stage) performed better, which can decrease the risk of herbaceous fruit yield reduction and improve WP by 0.37%. Among irrigation methods, furrow irrigation under DI performed better for herbaceous fruits, increasing yield and WP by − 0.66% and 2.29%. In terms of vine fruits, compared with FI, moderate degree (40%−60% FI) DI performed better, which can significantly increase yield and WP by − 8.05% and 13.87%. Vine fruits are suitable for DI in stage I, increasing yield and WP by 5.38% and 22.13%. For woody fruits, DI is suitable for higher seasonal precipitation (SP > 400 mm) and annual average temperature (AAT ≥ 10 °C). In contrast, for vine fruits and herbaceous fruits, DI is suitable for lower SP (< 200 mm) and AAT (< 10 °C). Our findings provide guidance for precise water deficit management of woody, herbaceous, and vine fruits. • As for vine fruits, moderate degree DI (deficit irrigation) at stage I (bud burst to leafing stage) was recommended. • As for woody fruits, low degree DI at stage I or II (flowering to fruit set stage) performed better. • As for herbaceous fruits, mild degree DI at stage IV (fruit maturation stage) can save water and increase yield. [ABSTRACT FROM AUTHOR]

• Construct four classical RF, SVM, ANN and ELM models. • The ELM and SVM performed slightly better than RF and ANN. • The RF and ANN models can also simulate accurately with less parameter input. • Meteorological factors and crop coefficient have significant effects on maize ET c in different growth stages. The accurate estimation of crop evapotranspiration (ET c) is essential for precision irrigation, optimal allocation of regional water resources, and efficiency improvement of agricultural water resources. This study developed Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) models for maize ET c estimation in northwest China. The meteorological data and crop data from 2011 to 2012 were used to train the RF, SVM, ANN and ELM. The models' simulation accuracy was verified by using the data of 2013 under six different input combinations. The input combinations included daily data for crop coefficient (K c), global solar radiation (R s), wind speed (u 2), maximum and minimum air temperatures (T max and T min), and maximum and minimum relative humidity (RH max and RH min). The results showed that the SVM model achieved the highest simulation accuracy at the seedling emergence to jointing stage and at the grouting to harvest stage of summer maize, with the coefficient of determination (R2) ranging 0.701–0.895 and 0.637–0.841, mean absolute error (MAE) ranging 0.310–0.654 and 0.468–0.743 mm/d, and mean square error (MSE) ranging 0.227–0.722 and 0.513–1.227 mm/d, respectively. The ELM model achieved the highest simulation accuracy at the booting to silking stage and during the whole growth period, the coefficient of determination (R2) ranging 0.601–0.828 and 0.891–0.954, mean absolute error (MAE) ranging 0.418–1.194 and 0.285–0.530 mm/d, and mean square error (MSE) ranging 0.887–2.515 and 0.182–0.587 mm/d, respectively. Considering the accessibility and simulation accuracy of input parameters, the SVM Ⅰ-2 , ELM Ⅱ-5 , SVM III-4 , and ELM IV-2 models were recommended for simulating ET c at the seedling emergence to jointing stage, at the booting to silking stage, at the grouting to harvest stage, and during the whole growth period, with the coefficient of determination (R2) of 0.796, 0.879, 0.800 and 0.896, mean absolute error (MAE) of 0.416, 0.418, 0.553 and 0.328 mm/d, and mean square error (MSE) of 0.327, 0.887, 0.655 and 0.190 mm/d, respectively. In conclusion, machine learning models can accurately simulate the daily evapotranspiration of maize in northwest China. [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]

• The accuracy of the temperature, radiation and hybrid models improved by 12.05 %, 11.06% and 10.46% after being optimized by WOA. • The estimation accuracy of the temperature, radiation and hybrid models optimized by the whale algorithm were higher than the prediction result of the ELM model. • The empirical model with more input parameters has higher estimation accuracy than the empirical model with fewer parameters. The accurate estimation of reference crop evapotranspiration (ET 0) is of great significance to improve agricultural water use efficiency and optimize regional water resources management. At present, the applicability evaluation system of ET 0 models is still lacking in several climate regions in China, leading to the confusion in application of the ET 0 model in some specific regions. In this study, the daily meteorological data of 84 representative stations in four climate regions of China during the past 30 years (1991–2019) were selected to evaluate the ET 0 simulation results of twelve empirical models (four temperature models, five radiation models, and three hybrid models) on the daily scale, and the optimal models suitable for each climate region were screened. Whale optimization algorithm (WOA) was used to optimize the optimal model to improve the simulation accuracy, and the ET 0 results were compared with those predicted by extreme learning machine (ELM). The results showed that the estimation accuracy of the hybrid model was the best throughout China, followed by the radiation model, and the temperature model was relatively poor, with R2 ranges of 0.77–0.88, 0.60–0.86, and 0.58–0.82, respectively. Among the temperature-based models, Hargreaves-Samani and Improve Baier-Robertson model had the highest accuracy, with R2 of 0.80 and 0.79. Among the radiation-based models, Priestley-Taylor and Jensen-Haise models had the best accuracy, with R2 of 0.82 and 0.79. Among the hybrid models, Penman model had the highest accuracy, with R2 of 0.84. The accuracy of Hargreaves-Samani and Improve Baier-Robertson model in SMZ climate region was higher than TCZ, TMZ, and MPZ, and the accuracy of Jensen-Haise model in TCZ was the highest. The estimation accuracy of Priestley-Taylor and Penman models was similar in SMZ, TCZ, TMZ and MPZ. Using WOA to optimize the optimal temperature, radiation, and hybrid models, the prediction accuracy was improved by 12.05 %, 11.06 %, and 10.46 %, which were higher than the result of ELM model, with R2 of 0.90, 0.91, 0.95 and 0.90, respectively. Therefore, it is recommended to adopt WOA to optimize the empirical model to estimate the ET 0 all over China. [ABSTRACT FROM AUTHOR]

• Latent heat flux consumed 78–88% of R n during the growing seasons of the kiwi orchard. • Canopy transpiration accounted for 41%–46% of evapotranspiration (ET) in the orchard. • Allowing individual estimation of water interception is one of the merits of PT-JPL for ET simulation in this humid site. • S-W model performed slightly better in estimating ET components. Understanding energy and water flux cycling and quantifying their components are essential steps to elucidate the hydrological process and sustainable water resources management in agricultural ecosystems. The challenge of partitioning energy and evapotranspiration (ET) components (canopy transpiration T c and surface evaporation E s) was addressed by the combination of eddy covariance (EC) technique, micro-lysimeters, and sap flow methods in a humid region kiwifruit orchard of Southwest China. The variations of energy and water flux and their controls were assessed, then the measured ET and its component were compared with the simulated values by Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), Shuttleworth-Wallace (S-W), and FAO-56 dual crop coefficient models (Dual-FAO). The results showed that latent heat flux (LE) dominated the consumption of net radiation energy (R n), with the averaged ratio (EF = LE/R n) above 0.70 during the growing seasons. More radiation energy is released in the form of LE with increased surface moisture and vegetation cover (total leaf area index, LAI t). T c accounted for 41%–46% ET. The increase of canopy leaf area index (LAI c) strongly affected ET partitioning through the early growing seasons, while atmospheric evaporation demand dominated the T c /ET and even caused the energy to be redistributed since high atmospheric evaporation demand caused a decrease of T c while an increase of E s during the active growing seasons (LAI c > 2.5 m2 m−2). All the ET models performed well in estimating ET (R2 = 0.84–0.92) and its components (T c , R2 = 0.70–0.74; E s , R2 = 0.63–0.69), allowing individual estimation of water interception by the canopy is one of the merits of the PT-JPL model for ET simulation in this humid site, with R2 of 0.92, RMSE of 0.55 mm d−1, while the S-W model performed slightly better in ET component simulations (T c , R2 = 0.74, RMSE = 0.35 mm d−1; E s , R2 = 0.69, RMSE = 0.38 mm d−1). The results of our study may have much referential significance for eco-hydrological process analysis and water flux estimation in regions with similar environmental conditions. [ABSTRACT FROM AUTHOR]

• The canopy resistance was significantly correlated with R n , while it was correlated with LAI, θ and VPD. • The accuracy of estimated maize canopy resistance was improved by considering LAI threshold segmentation method. • The performance of Differential Evolution Algorithm was better than Genetic Algorithm. Accurate estimation of maize evapotranspiration (ET) is of great significance for the improvement of crop water use efficiency and precision irrigation. The Penman-Monteith model (P-M) has been widely used to simulate crop ET. In the P-M model, the estimation accuracy of canopy resistance (r c) has a direct impact on ET. In this study, based on the eddy covariance system, large-scale lysimeter and meteorological station data from three sites (Yucheng, Yangling and Shangqiu) in semi-humid regions of northern China, the P-M model was applied to obtain canopy resistance (r c-PM) and correlation significances between r c-PM and different impact factors (R n net radiation, T temperature, VPD saturated vapor pressure difference, θ soil moisture content, LAI leaf area index) were analysed. The whole growth period of maize was divided according to different LAI thresholds (0.1, 0.5, 1.0, 1.5, 2.0 and 3.0 m2 m−2). The Genetic Algorithms (GA) and Differential Evolution (DE) algorithms were used to optimize the empirical parameters of the Jarvis model, and the P-M model was applied to estimate ET under different LAI thresholds at the three stations. The correlation significances of r c-PM with different influencing factors followed the order R n > LAI > θ >VPD >T, and it was extremely significant with R n (P < 0.01) and significant with LAI and θ (P < 0.05). The GA and DE algorithm optimization results showed that the calculation accuracy of r c was highest when LAI=0.5 m2 m−2 at Yucheng station, with R2 of 0.80 and 0.81, respectively, and when LAI=1.0 m2 m−2, and the accuracy of r c was highest at Yangling station, with R2 of 0.87 and 0.89, respectively, and when LAI = 1.0 m2 m−2, and the accuracy of r c was highest at Shangqiu station, with R2 of 0.84 and 0.84, respectively. Combined with P-M model to calculate maize ET under different LAI thresholds, the simulation accuracy of ET was best when LAI = 0.5 m2 m−2 at Yucheng station, with averege R2 of 0.85, the order of simulation ET accuracy was: 0.5 >1.0 >1.5 >2.0 >3.0 >0.1 m2 m−2. When LAI = 1.0 m2 m−2, and the accuracy of maize ET was highest at Yangling and Shangqiu stations, with averege R2 of 0.83 and 0.85, respectively, the order of simulation ET accuracy was: 1.0>0.5>1.5>2.0>3.0>0.1 m2 m−2. ET accuracy calculated by the DE optimization algorithm was better than that of the GA optimization algorithm, with R2 of 0.40–0.84 and 0.58–0.86, respectively. This study suggests that the algorithm is of great importance to optimize the empirical parameters of the Jarvis model, of which DE optimization algorithm is recommended to simulate maize ET in semi-humid regions of northern China. [ABSTRACT FROM AUTHOR]

• Hybrid ELM models (PSO-ELM, GA-ELM and ABC-ELM) were proposed for estimating ET 0 in different climate zones of China. • PSO-ELM model had the highest accuracy, followed by GA-ELM and ABC-ELM. • Hybrid ELM models outperformed standalone ELM and empirical models in different climate zones. • PSO-ELM model with T max , T min and RH obtained accurate ET 0 estimates in TCZ, SMZ and TMZ. • PSO-ELM model with only T max and T min was better performance on ET 0 estimates in MPZ. Accurate prediction of reference crop evapotranspiration (ET 0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET 0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R 2 = 0.973–0.999) all performed better than the standalone ELM model (R 2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R 2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T max , T min and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R 2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T max and T min data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET 0 with fewer meteorological parameters in different climatic zones of China. [ABSTRACT FROM AUTHOR]

As a critical hydrological cycle parameter, reference evapotranspiration (ET 0) plays a vital role in guiding agricultural water management. Analyzing the multifractal nature of ET 0 can clarify the complex fluctuations and obtain insights into the evolutionary mechanism and the regular patterns of the time series. The present study used the Multifractal Detrended Fluctuation Analysis (MFDFA) method to explore the multifractality of daily ET 0 series from 1961 to 2017, which filtered the seasonal component at 719 weather stations in seven sub-regions of China. The results indicated that the ET 0 series exhibited persistence and presented a decreasing trend in all sub-regions as the value of h (2) was exceeded 0.5 and the right-skewed was displayed in the multifractal spectrum. Multifractality existed and showed heterogeneity among various sub-regions, where NE was characterized by the strongest multifractality, followed by NW, IM, CSC, QT, SC, and NC. Both long-range correlation and broad probability density function contributed to the multifractality, and the long-range correlation played a dominant role in multifractality. This study is helpful to understand the complex fluctuation of ET 0 series, and provides new ideas for accurately predicting agricultural water demand and formulating scientific irrigation schedules in different climate zones of China. • Comprehensive multifractal analysis of daily E T 0 series. • E T 0 series exhibited persistence and would persist in a decreasing trend in the future. • Multifractality existed in and differed from sub-regions with the strongest in NE. • Long-range correlation was the dominant source of multifractality. • Visualization of the spatial distribution of persistence and multifractality in China. [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]

• Highly flexible CNTs based monolithic support was constructed via a directional freezing method. • Monolithic CNTs based composite PCM exhibits superior mechanical and thermal properties. • Flexible mechanism of monolithic CNTs based composite PCM is proposed. • This design strategy provides a new direction for future wearable fitting-skin temperature-controlled materials. Currently, most reported composite phase change materials (PCMs) are powdery shape, which require secondary processing for practical applications. Although some monolithic composite PCMs have been developed, their flexibility usually undergoes a remarkable reduction or even complete disappearance when supporting material s are infiltrated with PCMs. To solve this problem, we fabricated a flexible supporting material with a folded layer-bridge network structure by dispersing carbon nanotubes (CNTs) in acetic acid solution of chitosan (CS) with poly(vinyl alcohol) (PVA) using a directional freezing method. Then CS/PVA/CNTs (CPC) scaffold was infiltrated with polyethylene glycol (PEG) to prepare PEG@CPC composite PCM. The resulting flexible composite PCM displays excellent mechanical properties, such as high tensile strength of 2.42 MPa and bending resistance of >100 cycles. Moreover, it displays outstanding thermal properties, such as high crystallinity of close to 100% and encapsulation ratio of 92.6 wt%. This work provides a simple method for preparation of flexible monolithic composite PCMs for many potential applications, such as wearable fitting-skin temperature-controlled materials. [ABSTRACT FROM AUTHOR]