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Nowadays there is the need to avoid the excessive consumption of liquid lubricant oils, as they are harmful to the environment and hard to disposal. Self-lubricant films have been seen as the sustainable solution to achieve a long-term lubrication under high temperature-cycling conditions. In this manuscript, multilayer Mo2N-Ag/SiNx films with a fixed modulation ratio (thickness of Mo2N-Ag to SiNx) of 3:1, with changing modulation period (Λ, thickness of Mo2N-Ag and SiNx) from 8 to 200 nm were produced to achieve an effective balance between the lubricious phase diffusion control and the adequate formation of the low friction tribo-layers. Results showed that a dual-phase of fcc-Mo2N and fcc-Ag co-existed in Mo2N-Ag layers, while the SiNx layer exhibited an amorphous character. Both room temperature (RT) lubricant and wear-resistance properties of the films were improved by increasing Λ from 8 to 64 nm, while a further increase of Λ degraded the wear-resistance properties. The multilayer film at Λ = 64 nm exhibited an excellent RT-500 °C temperature-cycling tribological properties. Mechanical properties and the synergistic effect of both modulation layers were the cause for the improvement of the tribological properties., Supported by the National Natural Science Foundation of China, China (51801081, 52071159, 52171071, 52172090); Portugal National Funds through FCT project, Portugal (2021.04115); Outstanding University Young Teachers of ‘‘Qing Lan Project” of Jiangsu Province, China; Excellent Talents of ‘‘Shenlan Project” of Jiangsu University of Science and Technology, China; China Merchants Marine Scientific Research and Innovation Fund, China. Filipe Fernandes acknowledges the funding received in the aim of the projects: MCTool21 - ref. ‘‘POCI-01-0247- FEDER-045940”, CEMMPRE – ref. ‘‘UIDB/00285/2020” and SMARTLUB – ref. ‘‘POCI- 01-0145-FEDER-031807”.

Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros Transition metal nitride (TMN) films occupy an important station in some areas such as cutting and die tools due to their excellent mechanical and tribological properties. The investigation of TMN is mainly focused on the IVB, VB and VIB groups. The design of composite and smart TMN films, which exhibits excellent tribological property at the wide temperature range, is the development trend in order to meet the demands of high productivity machining, high temperature machining. The TiN in IVB group, Nb-N in VB group and Mo-N in VIB group were chose in my thesis with a purpose of improvement of the tribological property at wide temperature range.

This paper focuses on Leaf Area index(LAI) inversion,using EO-1 Hyperion data for Huangfengqiao forest farm,YouXian County,Hunan Province.First,LAI was acquired using a LAI-2000 canopy analyzer at 130 sample plots(60 m × 60 m),with a Global Positioning System(Trimble GPS Geo XT).Second,atmospheric correction was applied to Hyperion data using the ground-synchronous canopy observation data.Third,effective vegetation indexes were selected to estimate LAI,according to research on correlation between LAI,bands and vegetation indexes derived from Hyperion imagery.Finally,an optimal estimation model of LAI was built by curve estimation,stepwise regression,and a partial least-squares regression algorithm.Results show that sensitivity of ratio vegetation index(RVI) was highest among all model factors,followed by SARVI0.1,NDVI705,NDVI,SARVI0.1,and SARVI0.25.Among all fit models,the effect of the partial least-squares regression was best,with R2 coefficient 0.84,whereas the curve estimation effect was worst,with R2 coefficient 0.64.Model precision analysis shows that it is reliable to build the model using 5 to 6 independent variables,and prediction accuracy of the partial least-square regression was the greatest.

A technique platform was developed for adapting to forest intensive management by applying a variety of visual simulation techniques. Firstly, thirteen stand management relevant activities were customized and expressed graphically by using the WF. On the basis of the decision making activity of interactive stand management methods proposed in this study, the flow model for visual simulation of decision making of interactive stand management methods was customized by human-computer interaction. Secondly, statistics of stand structure characteristics and the stand state were simulated in 2D by using the GDI+ drawing technique. The stand scene was simulated in 3D by using the MOGRE technique. Finally, a case study of accretion cutting in a Chinese fir plantation was carried out to verify this method. The results showed that this method with operability, flexibility and visibility could be used to create stand management flow models, and that this method could simulate the decision making of methods in order to select a better method from a variety of methods according to the decision making criterion. This method can be applied in stand management practices, can run the better method by considering the specific stand management target, and can significantly improve the stand scientific management ability.

The aim of this study was to investigate the utility of datasets composed of texture measures and other features for the classification of vegetation cover, specifically wetlands. QUEST decision tree classifier was applied to a SPOT-5 image sub-scene covering the typical wetlands area in Three River Sources region in Qinghai province, China. The dataset used for the classification comprised of: (1) spectral data and the components of principal component analysis; (2) texture measures derived from pixel basis; (3) DEM and other ancillary data covering the research area. Image textures is an important characteristic of remote sensing images; it can represent spatial variations with spectral brightness in digital numbers. When the spectral information is not enough to separate the different land covers, the texture information can be used to increase the classification accuracy. The texture measures used in this study were calculated from GLCM (Gray level Co-occurrence Matrix); eight frequently used measures were chosen to conduct the classification procedure. The results showed that variance, mean and entropy calculated by GLCM with a 9*9 size window were effective in distinguishing different vegetation types in wetlands zone. The overall accuracy of this method was 84.19% and the Kappa coefficient was 0.8261. The result indicated that the introduction of texture measures can improve the overall accuracy by 12.05% and the overall kappa coefficient by 0.1407 compared with the result using spectral and ancillary data.