Search

In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R2) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB. [ABSTRACT FROM AUTHOR]

This article deals with closed-cell aluminum alloy LM13-cenosphere hybrid foams (ACHFs) with cell anisotropy prepared through stir casing technique. The compressive deformation behavior of hybrid foams in both transverse and longitudinal directions was measured at a strain rate of 0.01/s. The hybrid foam loaded in longitudinal direction (LD) shows higher plastic collapse stress than that in transverse direction (TD). The stress drop ratio is also observed to be higher in the LD. The plastic collapse stress and energy absorption capacity of ACHFs follow the power law relationship with relative density in both the directions. The plateau stress is higher in case of LD and densification strains are marginally higher in case of LD than that in TD. [ABSTRACT FROM AUTHOR]

Pediatric adrenocortical carcinoma and adrenocortical adenoma are two rare diseases affecting children. Molecular analyses were performed to identify commonalities in gene expression between the diseases. Differentially expressed genes were identified for the pediatric adrenocortical adenoma and carcinoma tissues, as compared with normal tissues, using the expression dataset. Protein‑protein interaction (PPI) networks were constructed for adenoma and carcinoma disease models, and common modules among the diseases were identified. A total of two common modules with 14 nodes and 20 nodes were revealed among the adenoma and carcinoma networks, respectively. Genes of the common modules were also identified to be the common hub genes of the disease models. Enrichment of the genes of the common modules suggested associations with steroid biosynthesis, the proteasome, cell cycle and metabolic pathways. Modularity, topological and functional analysis of the PPI networks revealed common modules among pediatric adenoma and carcinoma disease models, which provided insight into the underlying disease mechanisms and suggesting prospective targets for future study. [ABSTRACT FROM AUTHOR]