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Removal of arsenic from water is of utmost priorities on a global scenario due to its ill effects. Therefore, in the present study, aluminium oxide nano-particles (nano-alumina) were synthesised via solution combustion method, which is self-propagating and eco-friendly in nature. Synthesised nano-alumina was further employed for arsenate removal from water. Usually, pre-oxidation of arsenite is performed for better removal of arsenic in its pentavalent form. Thus, arsenate removal as a function of influencing parameters such as initial concentration, dose, pH, temperature, and competing anions was the prime objective of the present study. The speciation analysis showed that H 2 AsO4and HAsO 4 2− were co-existing anions between pH 6 and 8, as a result of which higher removal was observed. Freundlich isotherm model was well suited for data on adsorption. At optimal temperature of 298 K, maximum monolayer adsorption capacity was found as 1401.90 μg/g. The kinetic data showed film diffusion step was the controlling mechanism. In addition, competing anions like nitrate, bicarbonate, and chloride had no major effect on arsenate removal efficiency, while phosphate and sulphate significantly reduced the removal efficiency. The negative values of thermodynamic parameters ΔH°(− 23.15 kJ/mol) established the exothermic nature of adsorption, whereas the negative values of ΔG°(

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COVID-19 has made video communication one of the most important modes of information exchange. While extensive research has been conducted on the optimization of the video streaming pipeline, in particular the development of novel video codecs, further improvement in the video quality and latency is required, especially under poor network conditions. This paper proposes an alternative to the conventional codec through the implementation of a keypoint-centric encoder relying on the transmission of keypoint information from within a video feed. The decoder uses the streamed keypoints to generate a reconstruction preserving the semantic features in the input feed. Focusing on video calling applications, we detect and transmit the body pose and face mesh information through the network, which are displayed at the receiver in the form of animated puppets. Using efficient pose and face mesh detection in conjunction with skeleton-based animation, we demonstrate a prototype requiring lower than 35 kbps bandwidth, an order of magnitude reduction over typical video calling systems. The added computational latency due to the mesh extraction and animation is below 120ms on a standard laptop, showcasing the potential of this framework for real-time applications. The code for this work is available at https://github.com/shubhamchandak94/digital-puppetry/.