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The integration of machine learning (ML) regression models in predicting the parameters of metamaterial antennas significantly reduces the design time required for optimizing antenna performance compared to traditional simulation tools. Metamaterial antennas, known for overcoming the bandwidth constraints of small antennas, benefit greatly from these advanced predictive models. This study applies and evaluates four ML regression models — Extra Trees, Random Forest, XGBoost, and CatBoost — to predict key antenna parameters such as S11, gain, and bandwidth. Each model’s performance is assessed using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared (R²), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) across different training and testing set configurations (30%, 50%, and 70%). The Extra Trees model achieves the best performance for predicting gain, with an R² of 0.9990, MAE of 0.0069, MSE of 0.0002, RMSE of 0.0145, and MAPE of 0.3106. Feature importance analysis reveals that specific features, such as pr and p0 for gain and Y a and Xa for bandwidth, are critical in the predictive models. These findings highlight the potential of ML methods to improve the efficiency and accuracy of metamaterial antenna design. [ABSTRACT FROM AUTHOR]

The synthesis and characterization of Dy3+-activated CaSi2O5 phosphor is reported. The phosphors were synthesized using a modified solid-state reaction method with variable concentrations of doping ions (0.1–2.5 mol.%) of Dy3+. The synthesized phosphors were characterized by X-ray diffraction (XRD) analysis and the field emission scanning electron microscopy (FESEM) technique. The XRD pattern revealed that the doped phosphors had a cubic structure. FESEM micrographs clearly indicated that the particles crystallized in inhomogeneous morphology on a micrometer scale with a sporadic and asymmetrical shape. Photoluminescence excitation and emission spectra were monitored for variable doping concentrations and the emission found at 485 and 576 nm (excited at 353 nm). The corresponding transitions of the doping ion and concentration quenching effect were studied in detail. The 1931 CIE (x, y) chromaticity coordinates (x = 0.30 and y = 0.31) showed the distribution of a spectral region calculated from PL emission spectra. On the basis of CIE analysis, the prepared phosphor is useful for white light emission in inorganic light-emitting diodes. As-synthesized phosphor was examined by thermoluminescence glow curve analysis with different doses of gamma and electron beam and corresponding kinetic parameters were calculated using the computerized glow curve deconvolution technique. [ABSTRACT FROM AUTHOR]

AbstractThe complex dielectric permittivities were determined using a precision LCR meter of the binary mixtures of a polar liquid (methyl ethyl ketone, or MEK) and a non-polar liquid (dimethyl silicone fluid, or DMSF) at 303.15 K temperature. After determining the complex impedance (Z* (ω)) using the complex permittivity portion (ɛ* (ω)) of polar and nonpolar liquids, the complex impedance data (Z* (ω)) was fitted to the Nyquist Plot. Our research described here investigated the impact of ionic impurities in the pure MEK, DMSF and in their mixed state. The established parameters provided information on the impact of concentration changes on the electrical characteristics of the binary mixtures. Our research delved into utilizing machine learning (ML) techniques, such as random forest (RF), LightGBM, and XGBoost regressors, to improve the material design and prediction modeling. The main objective was to assess the efficacy of various regression techniques in evaluating the material characteristics performance. Experiments spanning from 30% to 50% of the data set were carried out, with performance metrics, like, R2 score and MAE, being utilized. Notably, the RF regressor demonstrated outstanding performance in these evaluations with an R2 score of 0.9999. The simulation results indicated that the ML-based techniques offer resource and time savings, serving as effective tools for predicting material performance at intermediate frequencies. [ABSTRACT FROM AUTHOR]

Epoxy nanocomposites exhibit remarkable characteristics by synergistically integrating the characteristics of epoxy resin with the unique properties of nanoparticles. This study focuses on the incorporation of ZnO and TiO2 nanoparticles into epoxy composites and investigates their impact on dielectric properties. A three-dimensional crosslinked structure was formed using Bisphenol-A epoxy resin and amine hardener. Dielectric spectroscopic assessments of the nanoparticle-doped epoxy composites were conducted over a frequency range of 20 Hz to 2 MHz and extended to 200 MHz to 20 GHz. The research provides a detailed analysis of the influence of ZnO and TiO2 nanoparticles on pristine, undoped epoxy resin. We present Regression analysis which is a useful tool for predicting the dielectric constant of intermediate frequency because it can save time and resources. Experiment data from tests show that using the XGBoost regression analysis can result in a 60% reduction in both time and resource requirements. [ABSTRACT FROM AUTHOR]

This paper presents a three-phase induction motor (IM) drive comprising a single-phase Pulse Width Modulated (PWM) converter with an active power decoupling (APD) as an active front-end converter (AFEC) to reduce the size of a DC link capacitor. Consequently, film capacitors can be used in place of bulky electrolytic capacitors that improve the power density, reliability, and cost of AFECs. A control scheme for an APD circuit is developed to mitigate the ripple power, and to control the three-phase IM, a maximum torque per ampere (MTPA) control scheme is implemented. A Simulink model of the proposed system rated for 3.73 kW, 200 V, and 50 Hz three-phase IM drive and its equivalent prototype model are developed in a laboratory and the APD and the MTPA control schemes are implemented with the help of STM32F407VG, a 32-bit microcontroller and dSPACE 1104, respectively. This paper demonstrates the experimental verification of the concepts for APD and MTPA control. Further, a significant reduction in motor current at starting/light load conditions is observed that leads to lower energy requirements. The prospective application of these schemes includes railway traction drive, electric vehicles, and so on, where compactness, reliability, and lower energy requirements are the prime concerns. [ABSTRACT FROM AUTHOR]

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya. [ABSTRACT FROM AUTHOR]