Researchers have been consistently looking for new materials that can be integrated in optoelectronic and spintronic devices. In this research, we investigated the crystalline structure, Raman, and optical characteristics of Mg-doped SnO₂ compounds. The solid-state reaction technique was utilized to produce polycrystalline samples of Sn_1−xMg_xO₂ (0 ≤ x ≤ 0.10) for their potential use in optoelectronics devices. It was discovered that all the compounds were synthesized into a tetragonal rutile-type structure of SnO₂. The analysis of these samples using Raman spectroscopy provided more evidence, supporting the creation of the tetragonal rutile phase of SnO₂ and the successful integration of Mg ions in SnO₂. The measurements of the optical properties, such as absorbance and transmittance, carried out with a UV-Vis spectrophotometer demonstrated that the optical band gap widened with the increase in the magnesium doping concentration in SnO₂. In addition, it was noticed that increasing the quantity of magnesium doping concentration led to an increase in the transmittance value from 83% to 91%.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network’s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

In recent years, there has been a rising interest in using gamification to promote local intangible cultural heritage. Gamification can make cultural heritage more accessible and relatable by transforming traditional practices into interactive digital experiences. Traditional hand-woven fabrics of tribal communities of Northeast India represent their cultural expression and identity. However, industrialization and globalization have negatively affected the inheritance of the traditional weaving practice. Their traditional fabrics also carry a rich legacy of oral narratives, which are underappreciated and at risk of being lost due to lack of adequate documentation. This paper proposes a gamified system model to disseminate the oral cultural expressions associated with tribal fabrics and their textile heritage. It also integrates instructional design approach for creating a gamified learning environment for fostering cultural awareness and education among young generation of the tribal communities. This model may also offer a new approach for digitally archiving indigenous intangible cultural heritage.

This concrete study demonstrates that spectral reflectance signature is the demanding factor for identifying asphalt road surface. To achieve a reasonable degree of accuracy, spectral values obtained from ISRO Radiometer and ASD Spectro-Radiometer are compared. These reflectance factors are dependent on the spectral behavior of an object. Spectral reflectance signature is a key feature to identify material, but it also depends on the capability of sufficient spectral resolution of the sensing system. Therefore, a material can also easily distinguish from other materials by using this sufficient spectrum. The characteristics of the spectral reflectance curve help to configure intrinsic characteristics of an object. The selection of particular wavelength region(s) shows significant influence. Therefore acquired remotely sensed data is used for a particular application. The spectral response curve of asphalt road surface obtained in the study with ASD Spectro-radiometer is quite similar to a reference curve of asphalt spectral reflectance as given by the USGS (United States Geological Survey) spectral library. This proves that every material has a unique spectral response, which is the basis of multi-spectral remote sensing and thus it can be used for land use/land cover classification.