^{138}Ba(d,α) Study of States in ^{136}Cs: Implications for New Physics Searches with Xenon Detectors.

We used the ^{138}Ba(d,α) reaction to carry out an in-depth study of states in ^{136}Cs, up to around 2.5 MeV. In this Letter, we place emphasis on hitherto unobserved states below the first 1^{+} level, which are important in the context of solar neutrino and fermionic dark matter (FDM) detection in large-scale xenon-based experiments.

Plausible explanation for the third COVID-19 wave in India and its implications.

Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict the observed third wave for India.

Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach.

Phenomenological and deterministic models are often used for the estimation of transmission parameters in an epidemic and for the prediction of its growth trajectory. Such analyses are usually based on single peak outbreak dynamics. In light of the present COVID-19 pandemic, there is a pressing need to better understand observed epidemic growth with multiple peak structures, preferably using first-principles methods.

A new logistic growth model applied to COVID-19 fatality data.

Background: Recent work showed that the temporal growth of the novel coronavirus disease (COVID-19) follows a sub-exponential power-law scaling whenever effective control interventions are in place. Taking this into consideration, we present a new phenomenological logistic model that is well-suited for such power-law epidemic growth.

Methods: We empirically develop the logistic growth model using simple scaling arguments, known boundary conditions and a comparison with available data from four countries, Belgium, China, Denmark and Germany, where (arguably) effective containment measures were put in place during the first wave of the pandemic.

A random walk Monte Carlo simulation study of COVID-19-like infection spread.

Recent analysis of early COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 (Maier and Brockmann, 2020). The power-law behavior was attributed to a combination of effective containment and mitigation measures employed as well as behavioral changes by the population.