Earthquakes in India occur in the plate-boundary region of the Himalayas as well as in the intraplate region of peninsular India (PI). Devastating events have occurred in PI in the recent past, which is a warning about the possibility of such earthquakes in the future. But very limited recorded data are available about ground motion in PI for engineers to rely upon. The present paper, after a review of available data, develops an attenuation relationship based on a statistically simulated seismological model. The proposed equation for peak ground acceleration (PGA), under bed rock conditions, is of the form ln(PGA/g) = c 1 +c2(M-6)+s 6) 2 -In(R) -c4R + lne, (1) with c 1 --1.6858, c 2 =0.9241, c3 =-0.0760, c4= 0.0057, and o'(lnt3) -0.4648. Correction factors for other site conditions are also computed. In the absence of a robust database of strong-motion records, seismological modeling is a rational alternative until sufficient instrumental records become available in PI. It is observed that attenuation of strong motion in PI is similar to that in other intraplate regions of the world.

Peninsular India (PI), which lies south of 24 • N latitude, has experienced several devastating earthquakes in the past. However, very few strong motion records are available for developing attenuation relations for ground acceleration, required by engineers to arrive at rational design response spectra for construction sites and cities in PI. Based on a well-known seismological model, the present paper statistically simulates ground motion in PI to arrive at an empirical relation for estimating 5% damped response spectra, as a function of magnitude and source to site distance, covering bedrock and soil conditions. The standard error in the proposed relationship is reported as a function of the frequency, for further use of the results in probabilistic seismic hazard analysis.

Indian monsoon rainfall data is shown to be decomposable into six empirical time series, called intrinsic mode functions. This helps one to identify the first empirical mode as a nonlinear part and the remaining as the linear part of the data. The nonlinear part is handled by artificial neural network (ANN) techniques, whereas the linear part is amenable for modeling through simple regression concepts. It is found that the proposed model explains between 75 to 80% of the interannual variability (IAV) of eight regional rainfall series considered here. The model is efficient in statistical forecasting of rainfall as verified on an independent subset of the data series. It is demonstrated that the model is capable of foreshadowing the drought of 2002, with the help of only antecedent data. The statistical forecast of All India rainfall for the year of 2004 is 80.34 cms with a standard deviation of 3.3 cms. This expected value is 94.25% of the longterm climatic average.

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COVID-19 has disrupted education for millions of children across the globe. The education community is re-imagining and redesigning to build back better. This Viewpoint takes the principles behind UNESCO's Futures of Education initiative to highlight their importance in post-COVID-19 recovery. The pandemic has shown how communities can come together to educate children. The article argues that, post-COVID-19, education systems should recognize community-driven support systems, use technology to overcome the digital divide in learning, and focus more on SDG 4.7 and its links to climate crises.