Melt processing of nanocomposites of cellulose nanocrystals with biobased thermoplastic polyurethane

Nanocomposites of thermoplastic polyurethane (TPU) with cellulose nanocrystals (CNC) without and with surface treatment are obtained by melt processing. Nanocomposites are obtained with nanofiller weight content near of the theoretical percolation threshold (3.9 wt%). Visual observation of CNC agglomerates is sufficient to prove the inefficiency of the mixing in systems with untreated CNC. The crystallization kinetics of the TPU changes with the addition of CNC and this is confirmed by differential scanning calorimetry analysis. Thermogravimetric analysis prove that the addition of CNC increases the thermal stability of the TPU. From the rheological analysis it is possible to verify the absence of percolation and an intermediate state of sol–gel transition in the nanocomposites. CNC/TPU nanocomposites with 5 wt% of treated CNC present better mechanical performance than de neat TPU and the other processed nanocomposites and display around 130% increase in Young's modulus while retaining significant values of toughness, tensile strength and elongation at break.     

<Emphasis Type="Italic">Melodie di pietra</Emphasis> by Adriana Rossi

In Melodie di pietre, Adriana Rossi has produced an in-depth analysis of the cloister of San Cugat on the basis of musical notions and symbolism. This provides us with an occasion for some brief reflections on the historic relationships between music and architecture.     

Hybridized electronic states between CdSe nanoparticles and conjugated organic ligands: A theoretical and ultrafast photo-excited carrier dynamics study

Formation of densely packed thin films of semiconductor nanocrystals is advantageous for the exploitation of their unique optoelectronic properties for real-world applications. Here we investigate the fundamental role of the structure of the bridging ligand on the optoelectronic properties of the resulting hybrid film. In particular, we considered hybrid films formed using the same CdSe nanocrystals and two organic ligands that have the same bidentate dithiocarbamate binding moiety, but differ in their bridging structures, one bridged by ethylene, the other by phenylene that exhibits conjugation. Based on the results of photo-excited carrier dynamics experiments combined with theoretical calculations on the electronic states of bridged CdSe layers, we show that only the phenylene-based ligand presents a strong hybridization of the molecular HOMO state with CdSe layers, that is a marker of formation of an effective bridge. We argue that this hybridization spread favors the hopping of photo-excited carriers between nanocrystals, which may explain the reported larger photo-currents in phenylene-based hybrid films than those observed in ethylene-based ones.

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Preface

Preface

Preface     

Robust invariant sets for constrained storage systems

Several items are produced and stored into n buffers in order to supply an external demand without interruptions. We consider the classical problem of determining control laws and smallest buffer levels guaranteeing that an unknown bounded demand is always satisfied. A simple model with n decoupled integrators and n additive bounded disturbances is employed. The coupling arises from bounds on the total production capacity and on the total demand. Invariant set theory for linear and switched linear systems is exploited to compute robust positive invariant sets and controlled robust invariant sets for two commonly adopted scheduling policies. This paper provides the explicit expression of the invariant sets for any arbitrary n.     

A multi-matcher system based on knuckle-based features

We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero equal error rate).

Machine learning multi-classifiers for peptide classification

In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier. Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously published in the literature.

Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.     

Mixture of KL subspaces for relevance feedback

Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user’s feedback to a single search session, performing a short-term learning. In this paper we present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term learning, the feedback history of all the users is exploited to create and update a representation of the images which is adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.