This study focuses on the comparison of hybrid methods of estimation of biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of vegetation cover (FVC), and canopy chlorophyll content (CCC) from Sentinel-2 satellite data. Different machine learning algorithms were trained with simulated spectra generated by the physically-based radiative transfer model PROSAIL and subsequently applied to Sentinel-2 reflectance spectra. The algorithms were assessed against a standard operational approach, i.e., the European Space Agency (ESA) Sentinel Application Platform (SNAP) toolbox, based on neural networks. Since kernel-based algorithms have a heavy computational cost when trained with large datasets, an active learning (AL) strategy was explored to try to alleviate this issue. Validation was carried out using ground data from two study sites: one in Shunyi (China) and the other in Maccarese (Italy). In general, the performance of the algorithms was consistent for the two study sites, though a different level of accuracy was found between the two sites, possibly due to slightly different ground sampling protocols and the range and variability of the values of the biophysical variables in the two ground datasets. For LAI estimation, the best ground validation results were obtained for both sites using least squares linear regression (LSLR) and partial least squares regression, with the best performances values of R2 of 0.78, rott mean squared error (RMSE) of 0.68 m2 m−2 and a relative RMSE (RRMSE) of 19.48% obtained in the Maccarese site with LSLR. The best results for LCC were obtained using Random Forest Tree Bagger (RFTB) and Bagging Trees (BagT) with the best performances obtained in Maccarese using RFTB (R2 = 0.26, RMSE = 8.88 μg cm−2, RRMSE = 17.43%). Gaussian Process Regression (GPR) was the best algorithm for all variables only in the cross-validation phase, but not in the ground validation, where it ranked as the best only for FVC in Maccarese (R2 = 0.90, RMSE = 0.08, RRMSE = 9.86%). It was found that the AL strategy was more efficient than the random selection of samples for training the GPR algorithm.
Cobalt ferrite one-dimensional nanostructures (nanoribbons and nanofibers) were prepared by electrospinning combined with sol−gel technology. The nanoribbons and nanofibers were formed through assembling magnetic nanoparticles with poly(vinyl pyrrolidone) (PVP) as the structure-directing template. Nanoribbons and nanofibers were obtained after calcining the precursor nanoribbons at different temperatures. Successive Ostwald ripening processes occur during the formation of CoFe2O4 nanoribbons and nanofibers. The sizes of nanoparticles varied with calcination temperatures, which leads to different one-dimensional structures and variable magnetic properties. These novel magnetic one-dimensional structures can potentially be used in nanoelectronic devices, magnetic sensors, and flexible magnets.
Graphene, a truly two-dimensional (2D) and fully π-conjugated honeycomb network, exhibits many unique physical and chemical properties that are interesting in a wide range of areas. Since its discovery in 2004, graphene has been extensively studied in many different fields including nano-electronics, composite materials, energy research, catalysis and so on. Based on the fascinating action of members in the carbon family, notably zero dimensional (0D) fullerenes and one dimensional (1D) carbon nanotubes (CNTs) in biomedical areas, increasing number of reports have explored the potential of graphene for different biomedical and biotechnical applications since 2008. This manuscript aims to provide a summary of current research progress of graphene-based carbon materials in biosensing, drug (gene) delivery and tissue engineering, and discusses the opportunities and challenges in this rapidly growing field.
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