A significant population of India suffers from chronic pain, and their QoL is affected leading to disability. A proportion of respondents receiving pain treatment were taking nonprescription medications with a majority of respondents on NSAIDs. A very few were consulting pain management specialists.

SARS CoV-2 (COVID-19) coronavirus has been causing enormous suffering, death, and economic losses worldwide. There are rigorous containment measures on industries, non-essential business, transportation, and citizen mobility to check the spread. The lockdowns may have an advantageous impact on reducing the atmospheric pollutants. This study has analyzed the change in atmospheric pollutants, based on the Sentinel–5Ps and ground-station observed data during partial to complete lockdown period in 2020. Results revealed that the mean tropospheric NO 2 concentration substantially dropped in 2020 due to lockdown against the same period in 2019 by 18–40% over the major urban areas located in Europe (i.e. Madrid, Milan, Paris) and the USA (i.e. New York, Boston, and Springfield). Conversely, urban areas with partial to no lockdown measures (i.e. Warsaw, Pierre, Bismarck, and Lincoln) exhibited a relatively lower dropdown in mean NO 2 concentration (3 to 7.5%). The role of meteorological variability was found to be negligible. Nevertheless, the reduced levels of atmospheric pollutants were primarily attributed to the shutdown of vehicles, power plants, and industrial emissions. Improvement in air quality during COVID-19 may be temporary, but regulatory bodies should learn to reduce air pollution on a long-term basis concerning the trade-offs between the environment, society, and economic growth. The intersection of urban design, health, and environment should be addressed by policy-makers to protect public health and sustainable urban policies could be adopted to build urban resilience against any future emergencies.

Long-term studies of both functional and radiological outcomes are needed to determine the indications for fixed- versus mobile-bearing prostheses.

The rapid advancement of remote sensing and availability of polarimetric SAR (PolSAR) data have facilitated to monitor the land use land cover (LULC) dynamics. In the recent past, polarimetric decomposition theorems are applied widely to perform LULC classification with the help of machine learning techniques. In this study, we utilized ALOS PALSAR-1 L-band quad polarimetric data for performing polarimetric decomposition, textural information extraction, and to generate LULC maps over the western part of Mizoram state, northeast India. The study area comprises three districts, namely Mamit, Lunglei, and Lawngtlai. We adopted two representative full-polarimetric decomposition models: classical model-based Freeman-Durden and Yamaguchi decomposition. These methods decompose the coherency matrix of PolSAR images into surface, double-bounce, and volume scattering. Textural measures, such as variance, contrast, entropy, homogeneity, dissimilarity, and uniformity are also retrieved using grey-level co-occurrence matrix (GLCM) for LULC classification. For LULC classification, we employed a support vector machine classifier and calculated the area statistics of LULC. The outcomes were checked with the help of confusion matrix derived for six classes, such as built-up, deciduous forest, evergreen forest, scrubland, bareland, and waterbody. Each LULC class is separated using the scattering properties of PolSAR images. Results exhibited that Yamaguchi four-component decomposition (overall accuracy 90% and kappa coefficient 0.88) gives relatively better LULC classification results than the Freeman-Durden three-component decomposition (overall accuracy 87% and kappa coefficient 0.84). Use of textural images of GLCM has supported the classification accuracy at par with the Yamaguchi model. Integration of polarimetric information offers a new dimension in LULC classification and produces high accuracy maps. This approach overcomes the limitations of optical data in cloud covering areas, and furthermore, it provides better classification accuracy.

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