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The present work focuses on studying the pyroelectric properties of chemical solution deposition‐grown Pb1−xLax(Zr0.4Ti0.6)O3 films on nickel substrate which are annealed at constant temperature of 650 °C with lanthanum concentrations ranging from x = 2–8% respectively. Structural studies reveal polycrystalline nature in all the PLZT films. The dielectric constant values rise from 497 to 1048 as La doping concentration rises from 2% to 6% at a constant frequency of 1 MHz, reaching a maximum value of 1048 for 6% La doping with little variation in dielectric loss between 0.03 and 0.05. As La doping level increases from 2% to 6%, the remnant (Pr) and saturation (Ps) polarization values increase from 12 to 47 μC cm−2 and 18 to 53 μC cm−2 respectively. The leakage current is found to be 2.68 × 10−8 A for 2%‐doped PZT film and it reduces to 2.94 × 10−10 A for PLZT (6% doping) film. The influence of lanthanum doping on the pyroelectric properties has been studied in detail. The value of pyroelectric coefficient (p) is varied from 240 × 10−3 to 980 × 10−3 Cm−2 K−1 and current responsivity is varied from 960 × 10−10 to 3920 × 10−10 mV−1 with the variation in lanthanum content from 2% to 6%, respectively. The promising pyroelectric results found in PLZT films encourage these films in energy applications field. [ABSTRACT FROM AUTHOR]

In India and throughout the world, air pollution is becoming a severe worry day by day. Governments and the general public have grown more concerned about how air pollution affects human health. Consequently, it is crucial to forecast the air quality with accuracy. In this paper, Machine learning methods SVR and RFR were used to build the hybrid forecast model to predict the concentrations of Air Quality Index in Haryana Cities. The forecast models were built using air pollutants and meteorological parameters from 2019 to 2021 and testing and validation was conducted on the air quality data for the year 2022 of Jind and Panipat city in the State of Haryana. Further, performance of hybrid forecast model was enhanced using scalar technique and performance was evaluated using various coefficient metrics and other parameters. First, the important factors affecting air quality are extracted and irregularities from the dataset are removed. Second, for forecasting AQI various approaches have been used and evaluation is carried out using performance metrics. The experimental results showed that the proposed hybrid model had a better forecast result than the standard Random forest Regression, Support Vector Regression and Multiple Linear Regression. [ABSTRACT FROM AUTHOR]

Forecast of urban air pollutant concentrations must deal with a growth in the volume of environmental monitoring data as well as complex changes in the air pollutants. This initiates the need of efficient prediction techniques to increase prediction accuracy and stop pollution causing issues. In this paper, the combination of Random Forest Regression (RFR) and Support Vector Regression (SVR) based machine learning model is proposed to predict the AQI values. Daily meteorological and air pollution data between March 2020 and June 2022 from Jind city in Haryana were used for model training and test the model’s results. First, the important factors affecting air quality are extracted and null values are replaced by their mean values to handle the irregularities in the dataset. Second, the Random Forest regression machine is used for model training and prediction of the value, the SVR model is used in correction of residual items, and finally the predicted values of AQI are obtained. The experimental results showed that the proposed prediction model of RFR-SVR had a better prediction result than the standard Random Forest Regression, support vector regression machine learning. RMSE, R2, MAPE are used as evaluation indicators to evaluate the performance of the proposed model. [ABSTRACT FROM AUTHOR]