In recent times, demand for light weight and high strength materials fabricated from natural fibres has increased tremendously. The use of natural fibres has rapidly increased due to their high availability, low density, and renewable capability over synthetic fibre. Natural leaf fibres are easy to extract from the plant (retting process is easy), which offers high stiffness, less energy consumption, less health risk, environment friendly, and better insulation property than the synthetic fibre-based composite. Natural leaf fibre composites have low machining wear with low cost and excellent performance in engineering applications, and hence established as superior reinforcing materials compared to other plant fibres. In this review, the physical and mechanical properties of different natural leaf fibre-based composites are addressed. The influences of fibre loading and fibre length on mechanical properties are discussed for different matrices-based composite materials. The surface modifications of natural fibre also play a crucial role in improving physical and mechanical properties regarding composite materials due to improved fibre/matrix adhesion. Additionally, the present review also deals with the effect of silane-treated leaf fibre-reinforced thermoset composite, which play an important role in enhancing the mechanical and physical properties of the composites.

Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

In this investigation, the extensive wear behaviour of materials was studied using SiC reinforced magnesium alloy composites fabricated through the stir casting process. The wear properties of AZ91 alloy composites with a small variation (i.e., 3%, 6%, 9% and 12%) of SiC particulates were evaluated by varying the normal load with sliding velocity and sliding distance. The worn surfaces were examined by scanning electron microscope to predict the different wear mechanisms on the pin while sliding on the hard disk in the dry sliding wear test condition. The microhardness of the SiC reinforced AZ91 composites was found to be more than the un-reinforced AZ91 alloy. Pins tested at load 19.62 N, and 2.6 m/s exhibited a series of short cracks nearly perpendicular to the sliding direction. At higher speed and load, the oxidation and delamination were observed to be fully converted into adhesion wear. Abrasion, oxidation, and delamination wear mechanisms were generally dominant in lower sliding velocity and lower load region, while adhesion and thermal softening/melting were dominant in higher sliding velocity and loads. The wear rate and coefficient of friction of the SiC reinforced composites were lower than that of the unreinforced alloy. This is due to the fact of higher hardness exhibited by the composites. The wear behaviour at the velocity of 1.39 m/s was dominated by oxidation and delamination wear, whereas at the velocity of 2.6 m/s the wear behaviour was dominated by abrasion and adhesion wear. It was also found that the plastic deformation and smearing occurred at higher load and sliding velocity.