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Ti-Ni films with various Ni contents (16.5, 22.0, 33.5 at. %) were deposited on Al alloy substrates using DC magnetron co-sputtering. The effects of Ni target power and substrate bias (−10, −70, −110 V) on morphologies, crystallography, nanomechanical properties and scratch behavior of films were studied. All the deposited Ti-Ni films exhibited a BCC structure of β-Ti (Ni). The Ti-Ni films grew with a normal columnar structure with good bonding to substrates. When increasing the Ni target power and substrate bias, the grain size grew larger and the surface became denser. The as-deposited Ti-Ni films significantly improved the hardness (>4 GPa) of the Al alloy substrate. With the increase of bias voltage, the hardness and modulus of the film increased. The hardness and modulus of the Ti-22.0Ni film prepared at −70 V bias were 5.17 GPa and 97.6 GPa, respectively, and it had good adhesion to the substrate.

The net ecosystem CO2 exchange (NEE) and water and energy fluxes at the alpine ecosystem level were obtained through the eddy covariance technique in an alpine wetland of the Longbao Region, Qinghai–Tibet Plateau. Our research used the NEE as the research object combined with meteorological factors. The NEE prediction model was constructed using Reddyproc and machine learning. Moreover, the effects of the data and features on the models and the selection of the model parameters were discussed. The results revealed the following information: (1) After removing the NEE outliers according to the friction wind speed thresholds of the different seasons, the NEE interpolation accuracy (R2) reached 0.65. Additionally, the NEE data dispersion decreased after removing the outliers, and the data quality improved effectively. (2) The decision coefficients (R2) of the eight kinds of combined machine learning algorithm models varied from 0.22 to 0.62, and the root mean square error (RMSE) ranged from 2.10 to 2.99 μmol s−1 m−2. Additionally, the multilayer perceptron (MLP) model had the best stability and the best interpolation effect. (3) There was a seasonal difference between the estimated values of Reddyproc and the estimated values of MLP. The monthly mean values of January, February, March, and October were lower than the monthly mean values of the latter, while the monthly mean values from April to September were higher than the monthly mean values of the latter, indicating that the prediction of the machine learning algorithm tends towards the carbon source in the cold season (nongrowing season) and tends towards the carbon sink in the warm season (growing season). (4) Reddyproc detected the outliers through the relationship between the night NEE and frictional wind speed, which made it possible to accurately estimate the nighttime flux under the condition of determining the threshold of the night frictional wind speed, thus obtaining a better NEE estimate with fewer input parameters. Before the training and prediction of the MLP model, the NEE was detected for the time series outliers, and the prediction accuracy was significantly improved, indicating that the elimination of the time series outliers is essential for NEE model training and further indicating that the understanding of the potential mechanism of the NEE is of great significance for the prediction model.

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