In this paper, we propose two models describing the dynamics of heavy and light vehicles on a road network, taking into account the interactions between the two classes. The models are tailored for two-lane highways where heavy vehicles cannot overtake. This means that heavy vehicles cannot saturate the whole road space, while light vehicles can. In these conditions, the creeping phenomenon can appear, i.e., one class of vehicles can proceed even if the other class has reached the maximal density.

Over the last decade, significant attention has been devoted to Closed-Loop Supply Chain (CLSC) design problems. As such, this review aims at assessing whether the current modelling approaches for CLSC problems can support the transition towards a Circular Economy at a supply chain level. The paper comprehensively assesses the extent to which existing modelling approaches evaluate the performance of supply chains across the complete spectrum of sustainability dimensions.

Network-based ranking methods (e.g. centrality analysis) have found extensive use in systems medicine for the prediction of essential proteins, for the prioritization of drug targets candidates in the treatment of several pathologies and in biomarker discovery, and for human disease genes identification. Here we propose to use critical nodes as defined by the Critical Node Problem for the analysis of key physiological and pathophysiological signaling pathways, as target candidates for treatment and management of several cancer types, neurologic and inflammatory dysfunctions, among others.

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix's symmetry and the graphs' joint learning.

The post-Minkowskian approach to gravitationally interacting binary systems (i.e., perturbation theory in G, without assuming small velocities) is extended to the computation of the dynamical effects induced by the tidal deformations of two extended bodies, such as neutron stars. Our derivation applies general properties of perturbed actions to the effective field theory description of tidally interacting bodies. We compute several tidal invariants (notably the integrated quadrupolar and octupolar actions) at the fast post-Minkowskian order.

A recently proposed mesoscale approach for the simulation of multicomponent flows with near-contact interactions is employed to investigate the early stage formation and clustering statistics of soft flowing crystals in microfluidic channels. Specifically, we first demonstrate the ability of the aforementioned mesoscale model to accurately reproduce main mechanisms leading to the formation of two basic droplet patterns (triangular and hexagonal), in close agreement with experimental evidence.

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes' fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise.

We present a new multistage method to study the N-Methyl-D-Aspartate (NMDA) neuroreceptor starting from the reconstruction of its crystallographic structure. Thanks to the combination of Homology Modelling, Molecular Dynamics and Lattice Boltzmann simulations, we analyse the allosteric transition of NDMA upon ligand binding and compute the receptor response to ionic passage across the membrane.

We establish versions for fractional Orlicz-Sobolev seminorms, built upon Young functions, of the Bourgain-Brezis-Mironescu theorem on the limit as s ->1^-, and of the Maz'ya-Shaposhnikova theorem on the limit as s->0^-, dealing with classical fractional Sobolev spaces. As regards the limit as s ->1^-, Young functions with an asymptotic linear growth are also considered in connection with the space of functions of bounded variation. Concerning the limit as s->0^+, Young functions fulfilling the \Delta_2-condition are admissible.

Mathematical models for subsoil decontamination by biological techniques