In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).
Graphene and its derivatives have emerged as peerless electrode materials for energy storage applications due to their exclusive electroactive properties such as high chemical stability, wettability, high electrical conductivity, and high specific surface area. However, electrodes from graphene-based composites are still facing some substantial challenges to meet current energy demands. Here, we applied one-pot facile solvothermal synthesis to produce nitrogen-doped reduced graphene oxide (N-rGO) nanoparticles using an organic solvent, ethylene glycol (EG), and introduced its application in supercapacitors. Electrochemical analysis was conducted to assess the performance using a multi-channel electrochemical workstation. The N-rGO-based electrode demonstrates the highest specific capacitance of 420 F g-1 at 1 A g-1 current density in 3 M KOH electrolyte with the value of energy (28.60 Whkg-1) and power (460 Wkg-1) densities. Furthermore, a high capacitance retention of 98.5% after 3000 charge/discharge cycles was recorded at 10 A g-1. This one-pot facile solvothermal synthetic process is expected to be an efficient technique to design electrodes rationally for next-generation supercapacitors.