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Nutrient Stress Monitoring in Menthol Mint (Mentha Arvensis) Using UAV Based Multispectral Imagery

2026-08-07T12:27:03Z

Title: Nutrient Stress Monitoring in Menthol Mint (Mentha Arvensis) Using UAV Based Multispectral Imagery Authors: Jain, Sakshi; Khati, Unmesh Abstract: Mentha, a widely cultivated crop for essential oil production is one of the major cash crop for the Indian subcontinent. Monitoring nutrient stress in mentha is the need of an hour to control the soil pollution, cost of fertilization and quality of oil production. UAV based multispectral imagery is a widely used technology for crop monitoring. Mentha crop was dozed with different amount of nitrogen termed as under doze (N50), recommend doze (N100) and an overdoze (N150). NDRE, C Ig, C Ir e showed a good distinction of nitrogen stress in mentha crop. © 2025 IEEE.

Deep far-UV observations of the ELAIS N1 field using AstroSat: source catalogue, spectral energy distribution modelling, and star formation

2026-08-07T12:27:03Z

Title: Deep far-UV observations of the ELAIS N1 field using AstroSat: source catalogue, spectral energy distribution modelling, and star formation Authors: Chaturvedi, Pranjal; Datta, Abhirup Abstract: We present a far-ultraviolet (FUV) photometric study of the ELAIS N1 (European Large-Area ISO Survey-North 1) deep field using the UVIT (Ultra-Violet Imaging Telescope) onboard AstroSat, observed in the F154W filter ((Formula presented) Å) with a total on-source exposure time of 30 ks. Level 1 data were reduced using ccdlab v3.0, yielding source catalogues of 1637 objects at (Formula presented) and 458 objects at (Formula presented), with limiting magnitudes of (Formula presented) and (Formula presented), respectively. FUV positions are cross-matched against multiwavelength catalogues spanning optical and infrared wavelengths, with redshifts drawn from spectroscopic and photometric sources. Active galactic nuclei (AGNs) are identified and excluded via established multiwavelength criteria, leaving a clean sample of star-forming galaxies. Spectral energy distribution modelling is performed using cigale, employing a delayed star formation history with an optional late burst, Bruzual & Charlot stellar population synthesis, Calzetti dust attenuation, and the skirtor AGN module. From the best-fitting models, we derive star formation rates (SFRs), total stellar masses, and young stellar masses as a function of redshift. The SFR increases monotonically with redshift, consistent with the evolution of the star formation main sequence. The ratio of young-to-total stellar mass remains approximately constant across (Formula presented), confirming that the sample consists predominantly of secularly evolving systems undergoing steady, self-regulated star formation rather than starburst-driven episodes. © The Author(s) 2026. Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Probing the Large-scale Structure with 21 cm-Galaxy Cross-bispectrum: Estimates from Simulations and Forecasts for Upcoming Cosmological Surveys

2026-07-27T06:30:11Z

Title: Probing the Large-scale Structure with 21 cm-Galaxy Cross-bispectrum: Estimates from Simulations and Forecasts for Upcoming Cosmological Surveys Authors: Noble, Leon; Majumdar, Suman Abstract: The redshifted 21 cm signal from the post-reionization epoch is highly non-Gaussian; thus, higher-order statistics, such as the bispectrum, are required to extract this non-Gaussian information. However, high signal-to-noise ratio (SNR) detection of the 21 cm auto-bispectrum will be hindered by the presence of residual systematics. Cross-correlating the 21 cm signal with galaxies offers a promising path to suppress this uncertainty from residual systematics and potentially increase the SNR. We present a comprehensive analysis of the H I-galaxy cross-bispectrum using the predictions of theoretical galaxy evolution models defined on large cosmological volumes. Our analysis includes the cross-bispectrum for different triangle sizes and shapes, as well as for different combinations of the H I and galaxy fields. We forecast the detectability of the 21 cm-galaxy cross-bispectrum at redshift z ≈ 1 with the Euclid-like galaxy survey and SKA-Mid observations in both the interferometric and single-dish modes of the survey. We find that the 21 cm-galaxy cross-bispectrum shows enhanced detectability compared to the 21 cm auto-bispectrum for all unique triangles in the interferometric mode of observations. We forecast a 10σ detection of the cross-bispectrum for squeezed-limit triangles and a 100σ detection for all shapes combined for scales of 0.2 Mpc−1 ≤ k1 ≤ 0.9 Mpc−1, with 100 hr of SKA-Mid observations per pointing. However, the detectability of the cross-bispectrum for large scales (k1 < 0.1 Mpc−1), which is accessible with the single-dish mode of the survey, is limited by cosmic variance. Additionally, the signal loss due to foreground removal further suppresses the detectability. Our analysis presents a first step toward an end-to-end analysis pipeline for the future 21 cm-galaxy cross-bispectrum observations. © 2026. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Artificial Neural Network–based prediction of D-region electron density variations during solar flares using ground-based VLF observations over low-latitude Indian region

2026-07-27T06:30:10Z

Title: Artificial Neural Network–based prediction of D-region electron density variations during solar flares using ground-based VLF observations over low-latitude Indian region Authors: Datta, Abhirup Abstract: The ionospheric D-region (60–90 km) critically controls very low frequency (VLF) radio wave propagation and responds rapidly to solar flare radiation. Accurate prediction of D-region electron density is therefore essential for reliable space weather nowcasting. We develop an artificial neural network (ANN) framework to predict flare-induced electron density enhancements using ground-based VLF observations from Dehradun and Indore, India (2020–2025). A total of 244 solar flares (157 C-class, 87 M-class) were analyzed. Seven physically motivated inputs: time of day, day of year, VLF amplitude perturbation, reflection height (H′), sharpness factor (β), F10.7 flux, and GOES X-ray flux were used to train a feed-forward backpropagation network. The model achieved excellent skill for C-class flares (R = 0.98; MSE = 8.735 × 10−5; RMSE = 0.0295; and MAE = 0.0182) and moderate performance for M-class flares (R = 0.74; MSE = 4.06 × 10−1; RMSE = 0.63; MAE = 0.47), reflecting increased ionospheric variability under stronger solar forcing. These results demonstrate the robustness of ANN-based approaches for real-time D-region electron density estimation and operational space weather forecasting. © The Author(s), under exclusive licence to Springer Nature B.V. 2026.