Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

AMOLED displays with transfer‐printed integrated circuits

Abstract— Small integrated circuits of crystalline silicon (chiplets) transfer‐printed onto a flat‐panel‐display substrate provide greatly improved electrical performance and uniformity in active‐matrix organic light‐emitting‐diode (OLED) displays. The integrated circuits are formed in high‐performance crystalline silicon using conventional photolithographic processes and then transfer‐printed onto a substrate using a stamp that transfers hundreds or thousands of chiplets at once. The chiplets are connected to an external controller and to pixel elements using conventional photolithographic substrate processing methods. Active‐matrix OLED (AMOLED) displays using transfer‐printed chiplets have good yields, excellent uniformity, and electrical performance and are thermally robust.     

A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system

Recommender systems usually suggest items by exploiting all the previous interactions of the users with a system (e.g., in order to decide the movies to recommend to a user, all the movies she previously purchased are considered). This canonical approach sometimes could lead to wrong results due to several factors, such as a change in user preferences over time, or the use of her account by third parties. This kind of incoherence in the user profiles defines a lower bound on the error the recommender systems may achieve when they generate suggestions for a user, an aspect known in literature as magic barrier. This paper proposes a novel dynamic coherence-based approach to define the user profile used in the recommendation process. The main aim is to identify and remove, from the previously evaluated items, those not semantically adherent to the others, in order to make a user profile as close as possible to the user’s real preferences, solving the aforementioned problems. Moreover, reshaping the user profile in such a way leads to great advantages in terms of computational complexity, since the number of items considered during the recommendation process is highly reduced. The performed experiments show the effectiveness of our approach to remove the incoherent items from a user profile, increasing the recommendation accuracy.     

Micromixing with spark-generated cavitation bubbles

The intrinsic laminarity of microfluidic devices impedes the mixing of multiple fluids over short temporal or spatial scales. Despite the existence of several mixers capable of stirring and stretching the flows to promote mixing, most approaches sacrifice temporal or spatial control, portability, or flexibility in terms of operating flow rates. Here, we report a novel method for rapid micromixing based on the generation of cavitation bubbles. By using a portable battery-powered electric circuit, we induce a localized electric spark between two tip electrodes perpendicular to the flow channel that results in several cavitation events. As a result, a vigorous stirring mechanism is induced. We investigate the spatiotemporal dynamics of the spark-generated cavitation bubbles and quantify the created flow disturbance. We demonstrate rapid (in the millisecond timescale) and efficient micromixing (up to 98%) within a length scale of only 200 µm and over a flow rate ranging from 5 to 40 µL/min.     

Estimation of predictive accuracy in survival analysis using R and S-PLUS

When the purpose of a survival regression model is to predict future outcomes, the predictive accuracy of the model needs to be evaluated before practical application. Various measures of predictive accuracy have been proposed for survival data, none of which has been adopted as a standard, and their inclusion in statistical software is disregarded. We developed the surev library for R and S-PLUS, which includes functions for evaluating the predictive accuracy measures proposed by Schemper and Henderson. The library evaluates the predictive accuracy of parametric regression models and of Cox models. The predictive accuracy of the Cox model can be obtained also when time-dependent covariates are included because of non-proportional hazards or when using Bayesian model averaging. The use of the library is illustrated with examples based on a real data set.     

Using the Meteorological Information for the Regional Rainfall Frequency Analysis: An Application to Sicily

Regional frequency analysis is a useful tool for accurate estimation of precipitation quantiles than at-site frequency analysis, especially in the case of regions with a short rainfall time series. The use of meteorological information, combined with rainfall data analysis, could improve the selection of homogeneous regions. Starting from 1958, 198 meteorological configurations, related to extreme events, have been identified throughout the national territory of Italy. The reanalyzed meteorological data of the 40 Year Re-analysis Archive (ERA-40) of the European Centre for Medium-Range Weather Forecast (ECMWF) have been analyzed to identify homogeneous regions with respect to the Convective Available Potential Energy (CAPE), the Q vector Divergence (QD) and the Vertically Integrated Moisture Flux (VIMF). The latter index appears to be the better candidate for finding regional homogeneity inside areas where high frequency values of CAPE or QD are present. The paper presents an application based on the delimitation of homogeneous regions using climatic indexes for the island of Sicily. By applying the proposed methodology, seven homogeneous areas over Sicily were found. The consistency of the final results has been validated by using a coupled approach based on the Valuation of Floods in Italy procedure (VAPI) and on the heterogeneity test of Hosking and Wallis (Water Resour Res 29:271–281, 1993, 1997).     

Finite element shakedown analysis of two‐dimensional structures

In the present paper, the formulation proposed by Casciaro and Garcea (Comput. Meth. Appl. Mech. Eng., 2002; 191 :5761–5792) and applied to the shakedown analysis of plane frames, is extended to the analysis of two‐dimensional flat structures in both the cases of plane‐stress and plane‐strain. The discrete formulation is obtained using a mixed finite element in which both stress and displacement fields are interpolated. The material is assumed to be elasto‐plastic and a linearization of the elastic domain is performed. The result is a versatile iterative scheme well suited to implementation in general purpose FEM codes. An extensive series of numerical tests is presented showing the reliability of the proposed formulation. Copyright © 2005 John Wiley & Sons, Ltd.     

Development and Preliminary Validation of the Physiological Hyperarousal Scale for Children.

Considerable empirical support exists for the positive affect and negative affect components of the tripartite model of anxiety and depression proposed by L. A. Clark and D. Watson (1991); however, less attention has been paid to the physiological hyperarousal component of the model. The development of the Physiological Hyperarousal Scale for Children (PH-C; J. Laurent, S. J. Catanzaro, & T. E. Joiner Jr., 1995) is described. The psychometric properties of items are examined using students in Grades 6-12 (N = 398). Initial scale validation includes a joint factor analysis with the Positive and Negative Affect Scale for Children (PANAS-C; J. Laurent et al., 1999; J. Laurent, K. Potter, & S. J. Catanzaro, 1994). The relationship between the PH-C and existing measures that tap related constructs is examined. Together, the PH-C and PANAS-C provide a means to assess tripartite model constructs useful in differentiating anxiety and depression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)