The use of simulation to prepare and improve responses to infectious disease outbreaks like COVID-19: practical tips and resources from Norway, Denmark, and the UK.

In this paper, we describe the potential of simulation to improve hospital responses to the COVID-19 crisis. We provide tools which can be used to analyse the current needs of the situation, explain how simulation can help to improve responses to the crisis, what the key issues are with integrating simulation into organisations, and what to focus on when conducting simulations. We provide an overview of helpful resources and a collection of scenarios and support for centre-based and in situ simulations

Boosting and Taming Wave Breakup in Second Harmonic Generation

Modulation instability is a universal phenomenon that can be found in a wide variety of nonlinear systems where, in the presence of a noise seed, peaks of random intensities can be generated. Several dynamical systems admit exact solutions in the form of breathers or solitons on a finite background. The vast majority of soliton studies has been restricted so far to one-dimensional systems. In contrast, the occurrences of localized structures in fully spatiotemporal systems has been only sporadically explored. In this work, we experimentally study the conditions for the wave-breaking of spatially extended optical beams in the process of second harmonic generation. Whenever the pump energy of the picosecond-long fundamental beam reaches a critical level, the beam shape at the second harmonic in a KTP crystal breaks into small filaments. These filaments exhibit extreme local intensity peaks, and their statistical distribution can be modified by the input energy of the fundamental beam. Moreover, by analyzing similar wave-breaking dynamics in a PPLN crystal in the presence of a higher nonlinear quadratic response, we observe that the spatial beam breaking may even gradually vanish as the laser intensity grows larger, leading to a spatial reshaping into a smooth and wider beam, accompanied by a substantial broadening of its temporal spectrum

Multimedia quality of experience

This chapter introduces the overall concept of multimedia Quality of Experience (QoE) over the Internet

Self-referenced multiplex CARS imaging using beam self-cleaning in GRIN multimode fiber

Spatial beam self-cleaning process in graded-index multimode optical fiber was successfully applied for self-referenced multiplex coherent anti-Stokes Raman Scattering (M-CARS) based imaging. M-CARS spectroscopy of methanol and imaging of polystyrene bead are reported through a simplified scheme

Interdisciplinary research in artificial intelligence: challenges and opportunities

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes