Structural, Optical, Electrical and Hall effect studies of Spray pyrolysised MgSnO 3 Thin films

Abstract: The aim of the present work was to find out the suitable substitute material for ITO. Because the production of ITO (Indium tin oxide) is low and the cost is very high. So MgSnO 3 can be considered as suitable alternative. In this work, MgSnO 3 is prepared by spray pyrolysis method using suitable molar concentration of magnesium acetate and stannic chloride (0.1:0.05, 0.1: 0.1, and 0.1:0.15) and isopropanal as the solvent at constant temperature (T s ) of 350°C.The structural analysis were taken. The prepared film at 0.1:0.05 molar concentrations shows the peak corresponding to MgO, SnO 2 and MgSnO 3 and film prepared at 0.1:0.15 molar concentration shows MgO, SnO 2. The peak position corresponding to SnO 2, MgSnO 3 and MgSnO 4 were observed for the films prepared with the concentration 0.1:0.15. From the results it is concluded that by varying the Ts and the concentration, peak corresponding to MgSnO 3 alone can be obtained, so that it can be used as a perfect TCO. The transmission studies carried out in the UV-Vis range shows that it has 60-84% transmittance and it has the optical bandgap varies from 3.65 -3.8eV. Hall effect measurements shows that MgSnO 3 shows ntype conductivity. The hall co-efficient, carrier concentration and the hall mobility were also observed

Caste and dynamics of Iyothee Thass

The message of history is that society and its dynamics have been subject to change over time. One of them is caste-based activities. The word "Satyam" is indelible all over India. There has been no change in the view of “caste discrimination” in civilization, education, and even in the developing world. In the early days, people were segregated on the basis of land and occupation. Then they became racist due to the arrival of Vanderis (disguised Brahmins). Racial discrimination sought to keep a large number of people in a state of disgrace. This situation continued for a long time. However, with the advent of British colonial rule in India, "caste discrimination" may have taken a turn for the worse. The missionaries' aim was to seize wealth and spread their religion. Only when we are all united can we restore our self. They said they could be released. Who pioneered the second stage. C. Iyothee Thass Pandit. He has publicly recorded the progress of his people based on Buddhism. This can be seen in the dominance of his views on literature

Prediction of Ionospheric TEC during the Annular and Total Solar Eclipses that Occurred over Indonesia by Using OKSM and FFNN

People across the world have been fascinated by solar eclipses for thousands of years. Solar eclipses are not only fascinating to observe but also provide opportunities for scientific research. During a solar eclipse, the quantity of solar energy reaching the Earth’s surface is reduced as the Moon passes in front of the Sun. This reduction in solar energy can have an effect on the Total Electron Content of the Earth’s ionosphere. In this paper, prediction and analysis of TEC variations in the Ionosphere during the solar eclipses happened on 26.12.2019 between 04:51 to 7:34 hours (UTC) and 09.03.2016 between 12:18 to 1:02 hours (UTC) over the Indonesia region were done by using two models: Ordinary Kriging based Surrogate Model (OKSM) and Feed-Forward Neural Network (FFNN). During the eclipse period, the TEC values were predicted by the OKSM and FFNN models and it is validated using literature. For this study, the GPS data belonging to the BAKO station situated in Indonesia were collected from IONOLAB servers and the input parameters were collected from the OMNIWEB servers. Forty days prior TEC data and input parameters were used to predict the TEC values. The credibility of the predicted results is assessed using statistical factors such as RMSE, CC, MAE, MAPE, sMAPE and R-Square. The statistical results show OKSM has performed well when compared to the FFNN model over the annular and total solar eclipse period. The study suggests that combining multiple modelling methods, such as OKSM and FFNN can improve our understanding of ionospheric variability during solar eclipses and provide more accurate predictions of TEC variations. This has important implications for satellite communications and navigation systems that rely on accurate TEC measurements for positioning and timing.</p

Prediction of Ionospheric TEC during the Annular and Total Solar Eclipses that Occurred over Indonesia by Using OKSM and FFNN

People across the world have been fascinated by solar eclipses for thousands of years. Solar eclipses are not only fascinating to observe but also provide opportunities for scientific research. During a solar eclipse, the quantity of solar energy reaching the Earth’s surface is reduced as the Moon passes in front of the Sun. This reduction in solar energy can have an effect on the Total Electron Content of the Earth’s ionosphere. In this paper, prediction and analysis of TEC variations in the Ionosphere during the solar eclipses happened on 26.12.2019 between 04:51 to 7:34 hours (UTC) and 09.03.2016 between 12:18 to 1:02 hours (UTC) over the Indonesia region were done by using two models: Ordinary Kriging based Surrogate Model (OKSM) and Feed-Forward Neural Network (FFNN). During the eclipse period, the TEC values were predicted by the OKSM and FFNN models and it is validated using literature. For this study, the GPS data belonging to the BAKO station situated in Indonesia were collected from IONOLAB servers and the input parameters were collected from the OMNIWEB servers. Forty days prior TEC data and input parameters were used to predict the TEC values. The credibility of the predicted results is assessed using statistical factors such as RMSE, CC, MAE, MAPE, sMAPE and R-Square. The statistical results show OKSM has performed well when compared to the FFNN model over the annular and total solar eclipse period. The study suggests that combining multiple modelling methods, such as OKSM and FFNN can improve our understanding of ionospheric variability during solar eclipses and provide more accurate predictions of TEC variations. This has important implications for satellite communications and navigation systems that rely on accurate TEC measurements for positioning and timing.</p

Reduced Graphene Oxide Embedded V2O5 Nanorods and Porous Honey Carbon as High Performance Electrodes for Hybrid Sodium-ion Supercapacitors

Attaining high energy density and power density in a single energy storage device is still a major challenge for electrochemical energy storage research community. Sodium-ion hybrid supercapacitor is a sustainable energy storage system which accomplishes the gap between battery and supercapacitor comprises of high energy density-battery type faradaic anode and high power density-supercapacitor type non-faradaic cathode. Here we have reported high surface area (1554 m2 g�1 ) activated porous carbon obtained from naturally occurring viscous liquid honey as a cathode and sol-gel derived, V2O5 nanorods anchored reduced graphene oxide (rGO) nanocomposite as an anode for non- aqueous sodiumion capacitor. When explored honey derived carbon as a non-faradaic cathode, it exhibits a higher specific capacitance of 224 F g�1 and V2O5@rGO anode delivers the maximum capacitance of 289 F g�1 at 0.01 A g�1 vs Na/Na+ . The prepared V2O5@rGO anode has long stable cycle life (V2O5 nanorods@rGO retains 85% of the initial capacitance (112.2 F g�1 ) at the current density of 0.06 A g�1 after 1000 cycles). The assembled sodium-ion capacitor (NIC) using honey derived activated carbon (AC) and V2O5@rGO anode delivers the energy density of 65Wh kg�1 and power density of 72W kg�1 at 0.03 A g�1. The capacity retention is 74% after 1000 cycles at the current density of 0.06 Ag�1. The assembled sodium-ion hybrid capacitor delivers maximum energy and power density and exhibits very long stable cycle lif

Prediction of ionospheric TEC by LSTM and OKSM during M class solar flares occurred during the year 2023

Advancements in space weather forecasting have become crucial for understanding and mitigating the impacts of solar activity on Earth’s ionosphere. This research focuses on the prediction of Total Electron Content (TEC) during M-class solar flare events in 2023. TEC is a vital parameter for satellite communications and navigation, making accurate forecasting imperative. Two prediction models, Long Short-Term Memory (LSTM) neural networks and Surrogate Models based on Ordinary Kriging (OKSM), are employed. LSTM, known for capturing temporal dependencies, is contrasted with OKSM, a geostatistical interpolation technique capturing spatial autocorrelation. The study utilizes TEC measurements from the Hyderabad (HYDE) GPS station for model training and evaluation along with solar and geomagnetic parameters. The performance metrics for both models across various solar flare dates are measured using Root Mean Square Error (RMSE), Normalized RMSE, Correlation Coefficient (CC), and Symmetric Mean Absolute Percentage Error(sMAPE). The research interprets the results, highlighting the strengths and limitations of each model. Notable findings include LSTM’s proficiency in capturing temporal variations and OKSM’s unique spatial perspective. Different solar flare intensities are analyzed separately, demonstrating the model’s adaptability to varying space weather conditions. The average performance metrics during M 4.65 SF events for the OKSM model, in terms of Root Mean Square Error is 5.61, Normalized RMSE is 0.14, Correlation Coefficient is 0.9813, and Symmetric Mean Absolute Percentage Error is 14.90. Similarly, for LSTM, the corresponding averages are 10.03, 0.24, 0.9313, and 28.64. The research contributes valuable insights into the predictive capabilities of LSTM and OKSM for TEC during solar flare events. The outcomes aid in understanding the applicability of machine learning and geostatistical techniques in space weather prediction. As society’s reliance on technology susceptible to space weather effects grows, this research is pivotal for enhancing space weather forecasts and ensuring the robustness of critical technological infrastructure on Earth.</p

Prediction of Ionospheric TEC Using RNN During the Indonesia Earthquakes Based on GPS Data and Comparison with the IRI Model

Total electron content (TEC) is a significant descriptive measure for the ionosphere of the earth. Due to either the sun’s activity like solar flare or the positive hall effect caused during earthquake (EQ), the oxygen atoms of the ionosphere split into oxygen ions and electrons increasing the electron content in the ionosphere which causes a rise in the TEC value, thus causing the delay in the signals coming from the satellite to the earth. TEC is associated with the Sun’s parameter and geomagnetic indices. In this research, parameters such as planetary K and A-index (Kp and Ap), Radio flux at 10.7 cm (F10.7), Sunspot number (SSN), and IONOLAB true TEC values were collected for the BAKO IGS network station situated in Indonesia (− 6.45° N, 106.85° E) for predicting TEC variations during EQ days occurred in the years 2004 and 2012. A total of three months of TEC data from the BAKO station during the years 2004 and 2012 were used for the developed Recurrent Neural Network (RNN) model in order to predict the TEC before and after the EQ days. For the year 2004, the model has an average Root Mean Square Error (RMSE) and Correlation Coefficient (CC) of 6.79 and 0.90. Also, for the year 2012, during April it has the average RMSE and CC of 8.90 and 0.94. For the same year in August month, the model has the average RMSE and CC of 8.70 and 0.94. The performance of the model is also evaluated using linear regression scatter plot. The Pearson’s R value calculated from the scatter plot is 0.92, shows that the model has good correlation with the true TEC.</p