Confronto fra valutazioni del run-up fatte con un modello matematico e una formula empirica con misure di campo

La posizione planimetrica della linea di riva, soli tamente, viene determinata attraverso l\u2019uso di immagini aeree ed utilizzata pe r la ricostruzione dell\u2019evoluzione storica dei litorali. Tuttavia, le informazioni est ratte da tali immagini, descrivendo il confine istantaneo acqua-terra, consentono l\u2019indivi duazione della linea di riva esclusivamente come limite asciutto-bagnato proprio nel momento della ripresa. Per una pi\uf9 corretta localizzazione della linea di riva , \ue8 quindi necessario quantificare, oltre agli effetti di marea e di trasporto solido, gli effetti prodotti dal moto ondoso su tale posizione e in particolare il cosiddetto ru n-up. Nel presente lavoro si studia il run-up in una spia ggia naturale a debole pendenza ricadente nella Sicilia occidentale. Lo studio geom orfologico del sito precede lo studio idraulico, che partendo dalle misure di onde al largo, attraverso la loro trasposizione e la propagazione simulata con un mod ello matematico, porta alla stima delle onde sotto costa. Queste ultime sono ut ilizzate per valutare il run-up sia mediante una nota formula empirica sia utilizzando un modello numerico alla Boussinesq con una nuova condizione al contorno per la linea di riva. Il confronto dei risultati con le misure di campo mostra che i r isultati migliori si ottengono con la formula empirica, nella quale \ue8 tuttavia necessa rio calibrare i coefficienti con misure in situ

Zaltoprofen/4,4′-Bipyridine: A Case Study to Demonstrate the Potential of Differential Scanning Calorimetry (DSC) in the Pharmaceutical Field

The Zaltoprofen/4,4′-Bipyridine system gives rise to two co-crystals of different compositions both endowed - in water and in buffer solution at pH 4.5 - with considerably higher solubility and dissolution rate than the pure drug. The qualitative and quantitative analysis of the DSC measurements, carried out on samples made up of mixtures prepared according to different methodologies, allows us to elaborate and propose an accurate thermodynamic model that fully takes into account the qualitative aspects of the complex experimental framework and which provides quantitative predictions (reaction enthalpies and compositions of the co-crystals) in excellent agreement with the experimental results. Co-crystal formation and cocrystal compositions were confirmed by X-ray diffraction measurements as well as by FT-IR and NMR spectroscopy measurements. The quantitative processing of DSC measurements rationalizes and deepens the scientific aspects underlying the so-called Tammann's triangle and constitutes a model of general validity. The work shows that DSC has enormous potential, which however can be fully exploited only by paying adequate attention to the experimental aspects and the quantitative processing of the measurements

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Genome wide analysis of gene expression changes in skin from patients with type 2 diabetes

Non-healing chronic ulcers are a serious complication of diabetes and are a major healthcare problem. While a host of treatments have been explored to heal or prevent these ulcers from forming, these treatments have not been found to be consistently effective in clinical trials. An understanding of the changes in gene expression in the skin of diabetic patients may provide insight into the processes and mechanisms that precede the formation of non-healing ulcers. In this study, we investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic). This information may be useful in identifying the causal factors and potential therapeutic targets for the prevention and treatment of diabetic related diseases

Maintaining extensivity in evolutionary multiplex networks

In this paper, we explore the role of network topology on maintaining the extensive property of entropy. We study analytically and numerically how the topology contributes to maintaining extensivity of entropy in multiplex networks, i.e. networks of subnetworks (layers), by means of the sum of the positive Lyapunov exponents, HKS, a quantity related to entropy. We show that extensivity relies not only on the interplay between the coupling strengths of the dynamics associated to the intra (short-range) and inter (long-range) interactions, but also on the sum of the intra-degrees of the nodes of the layers. For the analytically treated networks of size N, among several other results, we show that if the sum of the intra-degrees (and the sum of inter-degrees) scales as N?+1, ? > 0, extensivity can be maintained if the intra-coupling (and the inter-coupling) strength scales as N??, when evolution is driven by the maximisation of HKS. We then verify our analytical results by performing numerical simulations in multiplex networks formed by electrically and chemically coupled neurons