Time-domain Analysis of Laboratory Experiments on the Transient Pressure Damping in a Leaky Polymeric Pipe

Water Resources Management - The pressure damping occurring in pressurized pipes with a leak during transients has been examined as a diagnostic tool – the so-called transient damping method...     

pH sensitive phosphate crosslinked films of starch-carboxymethyl cellulose

This work aims to develop hydrogel films of starch and carboxymethyl cellulose (CMC) crosslinked with sodium trimetaphosphate (STMP) and to characterize some of their properties. Starch and STMP (S/T), starch and CMC (S/C), and mixed (S/T/C) films were prepared by casting. The degree of substitution, morphology, swelling degree, FTIR, mechanical properties, and sorption isotherms were studied. Reticulated samples (S/T and S/T/C) showed the same degree of substitution (0.050 ± 0.001). All films presented homogeneous morphology, but the mixed film showed greater roughness. Crosslinking increased the swelling capacity of the mixed hydrogel at pH 7, although it remained decreased concerning the S/T hydrogel. However, this property was sensitive to pH variations. The mixed film (S/T/C) showed greater mechanical resistance. The casting process was efficient to produce hydrogel films of starch/CMC crosslinked with STMP and the general results demonstrated the advantages of the mixed hydrogel.     

3D MRI of explanted sheep hearts with submillimeter isotropic spatial resolution: comparison between diffusion tensor and structure tensor imaging

Magnetic Resonance Materials in Physics, Biology and Medicine - The aim of the study is to compare structure tensor imaging (STI) with diffusion tensor imaging (DTI) of the sheep heart...     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

A multivariate fuzzy c-means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.     

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.     

Simple tests for multi-criteria optimality

In this paper we present two tests which can decide whether a given pointx ₀ ^N is locally efficient or not with respect to a given finite set of real valued continuously differentiable functions defined on ^N . Examples indicate that the tests may fail on a nowhere dense set.     

A class of high performance Maekawa-type algorithms for distributed systems under heavy demand

Summary Distributed Mutual Exclusion algorithms have been mainly compared using the number of messages exchanged per critical section execution. In such algorithms, no attention has been paid to the serialization order of the requests. Indeed, they adopt FCFS discipline. Conversely, the insertion of priority serialization disciplines, such as Short-Job-First, Head-Of-Line, Shortest-Remaining-Job-First etc., can be useful in many applications to optimize some performance indices. However, such priority disciplines are prone to starvation. The goal of this paper is to investigate and evaluate the impact of the insertion of a priority discipline in Maekawa-type algorithms. Priority serialization disciplines will be inserted by means of agated batch mechanism which avoids starvation. In a distributed algorithm, such a mechanism needs synchronizations among the processes. In order to highlight the usefulness of the priority based serialization discipline, we show how it can be used to improve theaverage response time compared to the FCFS discipline. The gated batch approach exhibits other advantages: algorithms are inherently deadlock-free and messages do not need to piggyback timestamps. We also show that, under heavy demand, algorithms using gated batch exchange less messages than Maekawa-type algorithms per critical section excution. Roberto Baldoni was born in Rome on February 1, 1965. He received the Laurea degree in electronic engineering in 1990 from the University of Rome La Sapienza and the Ph.D. degree in Computer Science from the University of Rome La Sapienza in 1994. Currently, he is a researcher in computer science at IRISA, Rennes (France). His research interests include operating systems, distributed algorithms, network protocols and real-time multimedia applications. Bruno Ciciani received the Laurea degree in electronic engineering in 1980 from the University of Rome La Sapienza. From 1983 to 1991 he has been a researcher at the University of Rome Tor Vergata. He is currently full professor in Computer Science at the University of Rome La Sapienza. His research activities include distributed computer systems, fault-tolerant computing, languages for parallel processing, and computer system performance and reliability evaluation. He has published in IEEE Trans. on Computers, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Software Engineering and IEEE Trans. on Reliability. He is the author of a book titled Manufactoring Yield Evaluation of VLSI/WSI Systems to be published by IEEE Computer Society Press.This research was supported in part by the Consiglio Nazionale delle Ricerche under grant 93.02294.CT12This author is also supported by a grant of the Human Capital and Mobility project of the European Community under contract No. 3702 CABERNET     

Chiral Se Nanobrooms with Wavelength and Polarization Sensitive Scattering

High-index dielectric nanostructures offer strong magnetic and electric resonances in the visible range and low optical losses, stimulating research interest in their use for light manipulation technologies. Lithographic fabrication of dielectric nanostructures, while providing precise control over the pattern dimensions, limits the scalability of this approach for practical applications due to an inefficient fabrication process and limited production quantity. Here, the colloidal synthesis of high-index chiral dielectric nanostructures with a broom-like geometry made from trigonal Se is demonstrated. The anisotropic morphology and crystal structure of Se nanobrooms enable both linearly and circularly polarized scattering, as well as spectrum variation along the particle axis, which is, to the authors’ knowledge, the first observation of such behavior from dielectric colloidal nanostructures. To show the versatility of the highly scattering Se NB suspensions, 2D and 3D printing of Se NB inks are demonstrated as a proof of concept. This approach provides a way to manipulate light using aqueous dispersions of high-index dielectric nanostructures, unlocking their potential to fit in various morphologies and dimensions in 2D and 3D for broad applications.