The G5 geomagnetic storm of May 2024 provided a significant opportunity to investigate global ionospheric disturbances using vertical total electron content (VTEC) data derived from 422 GNSS-IGS stations and GIM. This study presents a comprehensive spatio-temporal analysis of VTEC modulation before, during, and after the storm, focusing on hemispheric asymmetries and longitudinal variations. The primary objective of this study is to analyze the spatial and temporal modulation of VTEC under extreme geomagnetic conditions, assess the hemispheric asymmetry and longitudinal disruptions, and evaluate the influence of geomagnetic indices on storm-time ionospheric variability. The indices examined reveal intense geomagnetic activity, with the dst index plunging to −412 nT, the Kp index reaching 9, and significant fluctuations in the auroral electrojet indices (AE, AL, AU), all indicative of severe space weather conditions. The results highlight storm-induced hemispheric asymmetries, with positive storm effects (VTEC enhancement) in the Northern Hemisphere and negative storm effects (VTEC depletion) in the Southern Hemisphere. These anomalies are primarily attributed to penetration electric fields, neutral wind effects, and composition changes in the ionosphere. The storm’s peak impact on DoY 132 exhibited maximum disturbances at ±90° and ±180° longitudes, emphasizing the role of geomagnetic forces in plasma redistribution. Longitudinal gradients were strongly amplified, disrupting the usual equatorial ionization anomaly structure. Post-storm recovery on DoY 136 demonstrated a gradual return to equilibrium, although lingering effects persisted at mid- and high latitudes. These findings are crucial for understanding space weather-induced ionospheric perturbations, directly impacting GNSS-based navigation, communication systems, and space weather forecasting.

In the present work, we incorporate redshift space distortion measurement to investigate the growth of large scale structure within the framework of multi-fluid cosmology in the context of f ( G ) gravity. Using 3 different f ( G ) gravity models, where f ( G ) is the function of Gauss–Bonnet invariant, we compare the predictions of f ( G ) gravity expansion history—through the Friedmann equation with Hubble data, BAO data sets and constrain models parameters such as $$\Omega _{m}$$ Ω m and $$H_{0}$$ H 0 . Within the context of multi-fluid cosmology in f ( G ) gravity, we obtain the structure growth equation. This equation is then combined with $$\sigma _{8}$$ σ 8 to get $$f\sigma _{8}$$ f σ 8 predictions—which is compared with redshift space distortion data to constrain models parameters to obtain best-fit values including $$\sigma _{8}$$ σ 8 . This involves performing a Markov Chain Monte Carlo (MCMC) analysis for these specific forms of f ( G ) models. we also perform a statistical analysis incorporating the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), to assess the goodess-of-fit of the considered models.

Bluetongue (BT) is considered endemic in the southern states of India, with sporadic incidences reported from the northern, western and central parts of India. However, the eastern and north-eastern states of India have not experienced active disease so far. In the recent past, an extensive sero-epidemiological investigation was carried out in the eastern and north-eastern Indian states. With the aim of getting updated and refined estimates of positivity rates, the sero-surveillance data were analyzed using the Markov chain Monte Carlo (MCMC) method to calculate the positivity rates of various species across different states and agro-climatic zones. The posterior positivity distribution helped in accurately estimating the seroprevalence of bluetongue virus (BTV) among different species and regions. The MCMC method was applied for the first time in a BTV seroprevalence analysis that enhanced our understanding of infection dynamics, guided targeted interventions and supported better decision-making in bluetongue disease control, prevention and disease preparedness. This exercise is quite pertinent in the context of the recent upsurge of newer BTV strains, e.g., BTV-3 and BTV-8, in the western world. In short, as a powerful computational tool, MCMC could be used for accurate seroprevalence estimation, species-specific insights, regional analysis, enhanced decision-making and epidemiological insights for bluetongue.

Solar eclipses present a valuable opportunity for controlled in situ ionosphere studies. This work explores the response of the upper atmosphere’s F-layer during the total eclipse of 8 April 2024, which was primarily visible across North and South America. Employing a multi-instrument approach, we analyze the impact on the ionosphere’s Total Electron Content (TEC) and Very Low Frequency (VLF) signals over a three-day period encompassing the eclipse (7–9 April 2024). Ground-based observations leverage data from ten International GNSS Service (IGS)/Global Positioning System (GPS) stations and four VLF stations situated along the eclipse path. We compute vertical TEC (VTEC) alongside temporal variations in the VLF signal amplitude and phase to elucidate the ionosphere’s response. Notably, the IGS station data reveal a decrease in VTEC during the partial and total solar eclipse phases, signifying a reduction in ionization. While VLF data also exhibit a general decrease, they display more prominent fluctuations. Space-based observations incorporate data from Swarm and COSMIC-2 satellites as they traverse the eclipse path. Additionally, a spatiotemporal analysis utilizes data from the Global Ionospheric Map (GIM) database and the DLR’s (The German Aerospace Center’s) database. All space-based observations consistently demonstrate a significant depletion in VTEC during the eclipse. We further investigate the correlation between the percentage change in VTEC and the degree of solar obscuration, revealing a positive relationship. The consistent findings obtained from this comprehensive observational campaign bolster our understanding of the physical mechanisms governing ionospheric variability during solar eclipses. The observed depletion in VTEC aligns with the established principle that reduced solar radiation leads to decreased ionization within the ionosphere. Finally, geomagnetic data analysis confirms that external disturbances do not significantly influence our observations.