Multipurpose deep learning-powered UAV for forest fire prevention and emergency response.

This paper presents a customized UAV designed for rescue and safety purposes in the forest sector. The UAV features a durable F450 frame quadcopter with four 1000KV brushless motors and a KK2.1 Flight Control Board for stability and manoeuvrability with a runtime of 90 min. It incorporates a Raspberry Pi camera for real-time video streaming, enabling efficient identification of individuals in need of assistance. The GSM module allows contactless communication, ensuring streamlined and safe interaction. A motor controls the lid of the customizable first aid kit box, facilitating efficient aid delivery. The Neo-6 M GPS module provides accurate localization of the drone and individuals in distress with a horizontal position accuracy of 2.5 m. The UAV collects temperature and humidity data using the DHT 11 sensor having +/- 2 degreesC and +- 5% accuracy respectively. This sensor employs advanced deep learning models, including artificial neural networks (ANN) and generative adversarial networks (GANs), for real-time forest fire prediction with an accuracy of 90.7 % The integration of GANs enhances accuracy through synthetic data generation. Moreover, all these components are interfaced using a Raspberry Pi4 and a GUI, providing a smooth user control experience and end-to-end information for quick and effective emergency response.

Exudation of microplastics from commonly used face masks in COVID-19 pandemic.

The COVID-19 pandemic forced use of face masks up to billions of masks per day globally. Though an important and necessary measure for control of the pandemic, use of masks also poses some inherent risks. One of those risks is inhalation of microplastics released from the mask materials. Since most of the mask materials are made from plastic/polymers, they always have the potential to expose the user to fragmented microplastics. To estimate the amount of inhalable microplastic exuded from masks, an experiment simulating real-life scenario of mask usage was performed. The study included collection of microplastics oozed out from the masks on to a filter paper followed by staining and fluorescence detection of the total number of microplastics using a microscope. Both used and new masks were studied. Based on the emission wavelength, the microplastics were found to be belonging to three different categories, namely blue, green and red emitting microplastics respectively. The number of microplastic particles emitted per mask over a period of usage of 8 h was about 5000 to 9000 for new masks and about 6500 to 15,000 for used masks respectively. The estimation of polymer type of plastic in the mask fabrics was also carried out using Raman and FTIR spectroscopy.