Uniqueness and multiplicity criteria for an nth order chemical reaction

New and very strong criteria are presented for a priori prediction of the conditions for which the steady-state lumped parameter model of an nth order chemical reaction (n ≥ 0) in an adiabatic CSTR has either a unique or multiple solutions. The criteria show that the higher the order of the reaction the smaller is the region in the parameters space for which multiplicity can occur.New uniqueness and multiplicity criteria are developed also for an nth order reaction in a porous catalyst using a model, which accounts for intraparticle concentration gradients, while assuming a uniform intraparticle temperature different from the ambient one. The region in the parameters space for which steady state multiplicity can occur for this model is smaller than that for a corresponding lumped model, which ignores the intraparticle concentration gradients.     

Gewinnung von Brennstoff für Kraftwerke aus Klärschlamm

Production of a combustible for coal-fired power stations based on sewage sludge . This article describes various means of achieving more extensive mechanical dewatering of sewage sludges. The heat content of the sludge solids can become available for combustion processes. The sludge filter cakes obtained by the “CarboSed” process can be disposed of with minimum energy consumption by fluidized bed combustion or be used after appropriate work-up as a combustible in coal-fired power stations with full exploitation of their heat content for generation of steam and electric current. Partial substitution of primary energy sources such as coal and fuel oil by sludge filter cake is feasible in all cases. The processes thus make a contribution to the saving of energy resources and the disposal of sludge.     

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.