Stabilized dense emulsions display a rich phenomenology connecting microstructure and rheology. In this work we study how an emulsion with a finite yield stress can be built via large-scale stirring. By gradually increasing the volume fraction of the dispersed minority phase, under the constant action of a stirring force, we are able to achieve volume fractions close to 80%. Despite the fact that our system is highly concentrated and not yet turbulent we observe a droplet size distribution consistent with the -10/3 scaling, often associated to inertial range droplets breakup.

We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled microorganisms in complex biological flows.

This paper describes a signal processing application an embedded hardware platform, based on Hilbert transform method for demodulation of digital signals.

Neural oscillations contribute to speech parsing via cortical tracking of hierarchical linguistic structures, including syllable rate. While the properties of neural entrainment have been largely probed with speech stimuli at either normal or artificially accelerated rates, the important case of natural fast speech has been largely overlooked. Using magnetoencephalography, we found that listening to naturally-produced speech was associated with cortico-acoustic coupling, both at normal (~6 syllables/s) and fast (~9 syllables/s) rates, with a corresponding shift in peak entrainment frequency.

We consider a core-radius approach to nonlocal perimeters governed by isotropic kernels having critical and supercritical exponents, extending the nowadays classical notion of s-fractional perimeter, defined for 0<s<1, to the case s>=1. We show that, as the core-radius vanishes, such core-radius regularized s-fractional perimeters, suitably scaled, ?-converge to the standard Euclidean perimeter.

We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guest dynamics, unlocking comfort- and safety-driven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign.

This paper deals with the elastic energy induced by systems of straight edge dislocations in the framework of linearized plane elasticity. The dislocations are introduced as point topological defects of the displacement-gradient fields. Following the core radius approach, we introduce a parameter ? > 0 representing the lattice spacing of the crystal, we remove a disc of radius ? around each dislocation and compute the elastic energy stored outside the union of such discs, namely outside the core region. Then, we analyze the asymptotic behaviour of the elastic energy as ?

In Cosmology and in Fundamental Physics there is a crucial question like: where the elusive substance that we call Dark Matter is hidden in the Universe and what is it made of? that, even after 40 years from the Vera Rubin seminal discovery [1] does not have a proper answer.