Yarn strength prediction: a practical model based on Artificial Neural Networks

Yarn strength is one of the most significant parameters to be controlled during yarn spinning process. This parameter strongly depends on both the rovings' characteristics and the spinning process. On the basis of their expertise textile technicians are able to provide a raw and qualitative prediction of the yarn strength by knowing a series of fiber parameters like length, strength, and fineness. Nevertheless, they often need to perform many tests before producing a yarn with a desired strength. This paper describes a Feed Forward Back Propagation Artificial Neural Network-based model able to help the technicians in predicting the yarn strength without the need of physically spinning the yarn. The model performs a reliable prediction of the yarn strength on the basis of a series of roving parameters, commonly measured by the technicians before the yarn spinning process starts. The model has been trained with 98 training data and validated with 50 new tests. The mean error in prediction of yarn strength, using the validation set, is less than 4%. The results have been compared with the one obtained by means of a classical method: the multiple regression. Nowadays, the developed model is running in the laboratory of New Mill S.p.A., an important textile company that operates in Prato (Italy)

Monotonicity formulas for obstacle problems with Lipschitz coefficients

We prove quasi-monotonicity formulas for classical obstacle-type problems with energies being the sum of a quadratic form with Lipschitz coefficients, and a Hölder continuous linear term.With the help of those formulas we are able to carry out the full analysis of the regularity of free-boundary points following the approaches by Caffarelli (J Fourier Anal Appl 4(4–5), 383–402, 1998), Monneau (J Geom Anal 13(2), 359–389, 2003), and Weiss (Invent Math 138(1), 23–50, 1999)

Clustering methods for Mode S stations: Evaluation and perspectives

Grouping of a set of Secondary Surveillance Radar (SSR) Mode S stations into functional entities called clusters has significant operational facets; it calls for a constrained optimization, an important significant constraint being the scarcity of Interrogation Identifier (II) codes. This clustering problem can be approached by two ways, i.e., integer programming methods and heuristic approaches. The definition of a general, usable decision support tool to build up and evaluate clustering strategies in any operational airspace, e.g. the one of a nation or, even more complicated, of a system such as the European one, is a very challenging task. This paper describes some steps toward this envisaged result proposing a mathematical formulation and a heuristic approach for the problem

Invasion of the central nervous system by Cryptococcus neoformans requires a secreted fungal metalloprotease.

UnlabelledCryptococcus spp. cause life-threatening fungal infection of the central nervous system (CNS), predominantly in patients with a compromised immune system. Why Cryptococcus neoformans has this remarkable tropism for the CNS is not clear. Recent research on cerebral pathogenesis of C. neoformans revealed a predominantly transcellular migration of cryptococci across the brain endothelium; however, the identities of key fungal virulence factors that function specifically to invade the CNS remain unresolved. Here we found that a novel, secreted metalloprotease (Mpr1) that we identified in the extracellular proteome of C. neoformans (CnMpr1) is required for establishing fungal disease in the CNS. Mpr1 belongs to a poorly characterized M36 class of fungalysins that are expressed in only some fungal species. A strain of C. neoformans lacking the gene encoding Mpr1 (mpr1Δ) failed to breach the endothelium in an in vitro model of the human blood-brain barrier (BBB). A mammalian host infected with the mpr1Δ null strain demonstrated significant improvement in survival due to a reduced brain fungal burden and lacked the brain pathology commonly associated with cryptococcal disease. The in vivo studies further indicate that Mpr1 is not required for fungal dissemination and Mpr1 likely targets the brain endothelium specifically. Remarkably, the sole expression of CnMPR1 in Saccharomyces cerevisiae resulted in a robust migration of yeast cells across the brain endothelium, demonstrating Mpr1's specific activity in breaching the BBB and suggesting that Mpr1 may function independently of the hyaluronic acid-CD44 pathway. This distinct role for Mpr1 may develop into innovative treatment options and facilitate a brain-specific drug delivery platform.ImportanceCryptococcus neoformans is a medically relevant fungal pathogen causing significant morbidity and mortality, particularly in immunocompromised individuals. An intriguing feature is its strong neurotropism, and consequently the hallmark of cryptococcal disease is a brain infection, cryptococcal meningoencephalitis. For C. neoformans to penetrate the central nervous system (CNS), it first breaches the blood-brain barrier via a transcellular pathway; however, the identities of fungal factors required for this transmigration remain largely unknown. In an effort to identify extracellular fungal proteins that could mediate interactions with the brain endothelium, we undertook a proteomic analysis of the extracellular proteome and identified a secreted metalloprotease (Mpr1) belonging to the M36 class of fungalysins. Here we found that Mpr1 promotes migration of C. neoformans across the brain endothelium and into the CNS by facilitating attachment of cryptococci to the endothelium surface, thus underscoring the critical role of M36 proteases in fungal pathogenesis

Probing the statistical decay and alpha-clustering effects in 12c+12c and 14n+10b reactions

An experimental campaign has been undertaken at INFN Laboratori Nazionali di Legnaro, Italy, in order to progress in our understanding of the statistical properties of light nuclei at excitation energies above particle emission threshold, by measuring exclusive data from fusion-evaporation reactions. A first reaction 12C+12C at 7.9 AMeV beam energy has been measured, using the GARFIELD+Ring Counter experimental setup. Fusion-evaporation events have been exclusively selected. The comparison to a dedicated Hauser-Feshbach calculation allows us to give constraints on the nuclear level density at high excitation energy for light systems ranging from C up to Mg. Out-of-equilibrium emission has been evidenced and attributed both to entrance channel effects favoured by the cluster nature of reaction partners and, in more dissipative events, to the persistence of cluster correlations well above the 24Mg threshold for 6 alphas decay. The 24Mg compound nucleus has been studied with a new measurement 14N + 10B at 5.7 AMeV. The comparison between the two datasets would allow us to further constrain the level density of light nuclei. Deviations from a statistical behaviour can be analyzed to get information on nuclear clustering.Comment: 4 pages, 2 figures, Contribution to conference proceedings of the 25th International Nuclear Physics Conference (INPC 2013

Micro-beam and pulsed laser beam techniques for the micro-fabrication of diamond surface and bulk structures

Micro-fabrication in diamond is involved in a wide set of emerging technologies, exploiting the exceptional characteristics of diamond for application in bio-physics, photonics, radiation detection. Micro ion-beam irradiation and pulsed laser irradiation are complementary techniques, which permit the implementation of complex geometries, by modification and functionalization of surface and/or bulk material, modifying the optical, electrical and mechanical characteristics of the material. In this article we summarize the work done in Florence (Italy) concerning ion beam and pulsed laser beam micro-fabrication in diamond.Comment: 14 pages, 5 figure