Are turbulence effects on droplet collision-coalescence a key to understanding observed rain formation in clouds?

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics-turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision-coalescence.

Coping methods among Polish students during Covid-19 pandemic.

Objectives: The Covid-19 pandemic changed daily routines and forced people to develop various coping methods. University students were a social group that suffered due to a drastic change in their daily routine. The analysis of adaptation to chronic stress may help in developing more individualized care for people affected by it.

Relative Consistency of Sample Entropy Is Not Preserved in MIX Processes.

Relative consistency is a notion related to entropic parameters, most notably to Approximate Entropy and Sample Entropy. It is a central characteristic assumed for e.g.

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics.

In the atmosphere, refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics.

Artificial intelligence outperforms human students in conducting neurosurgical audits.

Objectives: Neurosurgical audits are an important part of improving the safety, efficiency and quality of care but require considerable resources, time, and funding. To that end, the advent of the Artificial Intelligence-based algorithms offered a novel, more economically viable solution. The aim of the study was to evaluate whether the algorithm can indeed outperform humans in that task.