We propose a novel in-silico model for computing drug release from multi-layer capsules. The diffusion problem in such heterogeneous layer-by-layer composite medium is described by a system of coupled partial differential equations, which we solve analytically using separation of variables. In addition to the conventional partitioning and mass transfer interlayer conditions, we consider also the case of finite mass transfer resistance, which corresponds to the case of a coated capsule.

The use of population dynamics models is essential to provide assessment of the fish abundance and advice on management and strategies for the fisheries. The stock-recruitment curve define the relationship between the spawning stock and the subsequent recruitment, describing nature's regulation of population size, whether or not the populations are being exploited. The two classical relations, established by Ricker and Shepherd, are: R (S) = b1S e-b2S , b1, b2 > 0 (4) R (S) = S b1 + b2Sb3 , b1, b2, b3 > 0 where S is the spawning stock and R is the recruitment, i.e.

Biological systems tend to display complex behaviour with a power-law (1/f - like) distribution. In the brain, this translates into neural activity that exhibits scale-free, temporal or spatial, properties (He, 2014). Scaleinvariance has been observed across different neuroimaging modalities and conditions (Linkenkaer-Hansen, 2001; He, 2014; Ciuciu et al. 2012). Beyond previously used features, recent electrophysiology studies have shown the presence of long-range temporal correlations (LRTCs) in the amplitude dynamics of alpha and beta oscillations (Nikulin et al. 2012).

Introduction : Neuroimaging studies have shown that in adults, the motor learning induced alterations of the functional connectivity assessed during Resting State Networks (RSN) is age-dependent (Mary et al., 2017). Motor learning relies on the build-up of new sensori-motor representations, which has been studied using the bar-man task in adults (Barlaam, Vaugoyeau, Fortin, Assaiante, & Schmitz, 2016; Paulignan, Dufossé, Hugon, & Massion, 1989) and in children (Schmitz et al, 2002).. The aim of this study was to investigate the modulations of functional connectivity after a motor learn

Background. With the exponential increase in data dimension and methodological complexities, brain networks analysis with MEG and EEG has become an increasingly challenging and time-consuming endeavor. To date, performing all the data processing steps that are required for a complete MEG/EEG analysis pipeline often require the use of a multitude of software packages and in-house or custom tools (e.g. MRI segmentation, pre-processing, source reconstruction, graph theoretical analysis, statistics).