We study the effects of a controlled gas flow on the dynamics of electrified jets in the electrospinning process. The main idea is to model the air drag effects of the gas flow by using a nonlinear Langevin-like approach. The model is employed to investigate the dynamics of electrified polymer jets at different conditions of air drag force, showing that a controlled gas counterflow can lead to a decrease of the average diameter of electrospun fibers, and potentially to an improvement of the quality of electrospun products.

We establish the statistical mechanics framework for a bundle of N-f living and uncrosslinked actin filaments in a supercritical solution of free monomers pressing against a mobile wall. The filaments are anchored normally to a fixed planar surface at one of their ends and, because of their limited flexibility, they grow almost parallel to each other. Their growing ends hit a moving obstacle, depicted as a second planar wall, parallel to the previous one and subjected to a harmonic compressive force.

Additive partial linear models with nonparametric additive components of heterogeneous smoothness are studied. To achieve optimal rates in large sample situations we use block wavelet penalisation techniques combined with adaptive (group) LASSO procedures for selecting the variables in the linear part and the the additive components in the nonparametric part of the models. Numerical implementations of our procedures for proximal like algorithms are discussed.

Estimates for solutions to homogeneous Dirichlet problems for a class of elliptic equations with zero order term in the form L(u) = g(x, u) + f (x),where the operator L fulfills an anisotropic elliptic condition, are established. Such estimates are obtained in terms of solutions to suitable problems with radially symmetric data, when no sign conditions on g are required.

We present the results obtained by using an evolution of our CUDA-based solution for the exploration, via a breadth first search, of large graphs. This latest version exploits at its best the features of the Kepler architecture and relies on a combination of techniques to reduce both the number of communications among the GPUs and the amount of exchanged data. The final result is a code that can visit more than 800 billion edges in a second by using a cluster equipped with 4,096 Tesla K20X GPUs.

We present the latest version of OpenCAPWAP, our open-source implementation of the Internet Engineering Task Force control and provisioning of wireless access point (CAPWAP) protocol. The CAPWAP protocol is designed to support centralized management of large-scale and heterogeneous wireless networks, with a special focus on IEEE 802.11-based networks. The implementation presented in this paper improves substantially on the previous version, adding full support for the Split MAC architecture and decoupling completely the implementation from a specific driver solution.

The large amount of work on community detection and its applications leaves unaddressed one important question: the statistical validation of the results. We present a methodology able to clearly detect the truly significance of the communities identified by some technique, permitting us to discard those that could be merely the consequence of edge positions in the network. Given a community detection method and a network of interest, our procedure examines the stability of the partition recovered against random perturbations of the original graph structure.

The phenomenologyand reported effects of meditation vary according to the technique practiced.While numerous studies have explored the cerebral mechanisms involved inmeditation, little research provides direct comparisons between the neuronalnetwork dynamics involved in different meditation techniques.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers.