National Institute of Technology Calicut

ABSTRACT The redshifted 21-cm signal from the dark ages offers a powerful probe of cosmological models and the underlying dark matter (DM) microphysics. We investigate deviations from the standard $\Lambda$ cold dark matter ($\Lambda$CDM) prediction, an absorption trough of approximately $-40.6\, \mathrm{mK}$ at redshift $z\simeq 85.6$, in the context of co-SIMP (strongly interacting massive particle) DM. The co-SIMP interaction strength is encoded by the parameter $C_{\rm int}$, incorporating the masses of DM and standard model particles, the interaction cross-section, and the amount of heat exchange between the two sectors. Increasing $C_{\rm int}$ deepens the absorption feature and shifts the trough to higher redshifts in the global signal. For $C_{\rm int}=1.0$, the minimum brightness temperature reaches $-50.6\,\mathrm{mK}$ at $z\simeq 86.2$. The 21-cm power spectrum increases with $C_{\rm int}$ in addition to the global signal. We assess the detectability of these signatures using signal-to-noise ratio (SNR) and Fisher forecasts. The maximum SNR reaches ${\sim} 15.7$ for $C_{\rm int}=1.0$ for the global signal. Fisher forecasts for 1000 h of integration time show that this model can be distinguished from a null-signal at $4.3\sigma$ and a mild $1.6\sigma$ from $\Lambda$CDM, improving by an order of magnitude for 100 000 h. For the 21-cm power spectrum, a $5\,\mathrm{km}^2$ array with 1000 h yields a $4.63\sigma$ detection and is mildly separated from the standard scenario at $1.78\sigma$. These findings highlight the potential of the 21-cm cosmology to probe the properties of DM and demonstrate that upcoming dark ages experiments, particularly space-based and lunar observations, can offer a promising avenue to test co-SIMP models.

High temperatures and heatwaves are among the most significant climate extremes, with well-documented effects on human health, including increased mortality rates. Heatwave impacts may arise from a single extreme variable, such as air temperature, or from compound conditions in which multiple variables jointly contribute to heat stress without all being individually extreme. In humid tropical regions, the co-occurrence of high temperature and humidity substantially amplifies physiological heat stress. India is a major global heatwave hotspot, particularly during the pre-monsoon season.In this study, we identify and characterize compound heatwave events over Kerala, India, using a multi-variable framework that integrates air temperature and Wet-Bulb Globe Temperature (WBGT) a humidity-sensitive indicator of physiological heat stress. Heatwaves are classified into dry and humid categories based on percentile-based thresholds and the duration of event. Dry heatwaves are defined using the 90th percentile and a minimum three-day duration of daily maximum air temperature from the India Meteorological Department, while humid heatwaves are identified using the 90th percentile and three-day persistence of WBGT.Our results indicate that compound heatwave events are increasingly frequent over Kerala, with heat stress intensifying as atmospheric humidity increases. These compound heatwaves impose a substantially higher heat stress burden than dry heatwaves, highlighting the limitations of temperature-only indices and highlighting the importance of incorporating humidity-sensitive metrics for improved heatwave monitoring, early warning, and risk assessment in humid tropical regions.

The inherent mechanism of material removal in µEDM also removes material from the tool electrode, making it difficult to achieve the required dimensional accuracy. Therefore, the current study investigates the influence of voltage and depth of the µ-holes in achieving the required depth of µ-holes on T­i–6Al–4V with tungsten carbide (WC) as the tool electrode. A full factorial experimental design was adapted to investigate the dimensional inaccuracies. In order to minimize dimensional inaccuracies, material removal rate (MRR), electrode wear rate (EWR), and surface roughness ( S a ) are investigated. A maximum of dimensional inaccuracies of 43.26% was observed while machining a µ-hole with a depth of 400 µm at a voltage of 100 V. A minimum dimensional inaccuracies of 16.27% was observed while machining a hole of 200 µm with 80 V due to the efficient flushing of the debris with maximum MRR of 34.5 × 10 5 µm 3 /min, EWR of 12.64 × 10 5 µm 3 /min and improved S a of 2.21 µm compared to other µ-holes machined. In addition, the machined µ-hole surface was examined through SEM, EDX and XRD to understand the morphology and chemical composition and understand that a significant amount of carbide, oxides, and tungsten in various phases are deposited on the surfaces. Additionally, a model for an artificial neural network was developed to predict the process output responses (MRR, EWR and S a ) in relation to the input parameters (voltage and target depth). The minimum percentage of error between experimental and predicted values for MRR, EWR and S a are 9.21%, 11.56% and 8.43%, respectively.