Trickle Flow Multiplicity: The Influence of the Prewetting Procedure on Flow Hysteresis

The existence of multiple hydrodynamic studies (MHS) in trickle flow is a well-known phenomenon. It is also known that different prewetting procedures result in major differences in MHS when the hydrodynamic variables pressure drop, liquid holdup and gas–liquid mass transfer are considered. Given a certain prewetting procedure one still has the option to perform flow hysteresis cycles to achieve an even wider variety of MHS. Although numerous studies have been performed on trickle flow hysteresis, none have attempted to decouple the hysteresis behaviour from the prewetting procedure followed. Accordingly there are numerous hysteresis possibilities that have not been investigated. In this work a single liquid and gas cycle were performed for four distinct prewetting procedures described here as a dry bed, a Levec type prewetted bed, Kan prewetted bed (achieved by increasing either the liquid or the gas flow rate until the pulsing flow regime is reached) and a Super prewetted bed. Pressure drop, liquid holdup and gas–liquid mass transfer are the hydrodynamic parameters studied to quantify the various MHS. It is shown that the shape and extent of the hysteresis cycle are strongly dependant on the prewetting procedure. In terms of flow structure, similar hysteresis trends on the Kan Liquid and Super prewetting modes indicate that these modes are hydrodynamically similar. The additional measurement of the hysteresis behaviour of gas–liquid mass transfer proofs that neither holdup nor pressure drop can be used as an indicator of the distribution uniformity.     

Exploring Particle Density Effects on Partial Mobility of Steelhead Spawning Gravels

Yellowbank Creek is a small stream in coastal central California being assessed for salmonid habitat limiting factors and restoration potential. Yellowbank flows through low‐density marine mudstone bedrock, which is the gravel source for the stream. To assess the potential effects of the low‐density substrate on spawning gravels, a tracer stone study comparing the incipient motion of low‐density mudstone particles and typical density granitic particles was used to populate a logistic regression particle entrainment model. A model comparison approach was used to test the strength of the model. Results demonstrate partial mobility of both mudstone and granitic particles under boundary shear conditions ranging from 6.9 to 42.2 N m^‐2. The modelling results quantify the strong negative correlation between particle entrainment and particle density. Mudstone gravel was three times more likely to be entrained than granitic gravel, within the context of the experimental conditions. The effect of density difference on partial mobility was greater in smaller grain size fractions. This work has implications for salmonid spawning success in atypical geologic settings and may assist in prioritization of restoration efforts. Copyright © 2013 John Wiley & Sons, Ltd.     

Fibroblast protein profile analysis highlights the role of oxidative stress and vitamin K recycling in the pathogenesis of pseudoxanthoma elasticum

Pseudoxanthoma elasticum (PXE) is a genetic disorder associated to mutations in the ABCC6 gene; however, the pathogenetic mechanisms leading to elastic fibre calcifications and to clinical manifestations are still unknown. Dermal fibroblasts, directly involved in the production of the extracellular milieu, have been isolated from healthy subjects and from patients affected by PXE, cultured in vitro and characterized for their ability to produce reactive oxygen species, for structural and functional properties of their cell membranes, for changes in their protein profile. Data demonstrate that oxidative stress has profound and endurable consequences on PXE fibroblast phenotype being responsible for: reduced levels of global DNA methylation, increased amount of carbonylated proteins and of lipid peroxidation products, altered structural properties of cell membranes, modified protein expression. Data shed new light on the pathogenetic pathways in PXE, by identifying a network of proteins affecting elastic fibre calcification through inefficient vitamin K recycling, and highlight the role of differentially expressed proteins as targets for validating the efficacy of future therapeutic strategies aiming to delay and/or revert the pathologic phenotype of PXE fibroblasts. Moreover, data open new perspectives for investigating PXE-like phenotypes in the absence of ABCC6 mutations.     

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

Characterizing Strong Stability Preserving Additive Runge-Kutta Methods

Space discretization of some time dependent partial differential equations give rise to ordinary differential equations containing additive terms with different stiffness properties. In these situations, additive Runge-Kutta (additive RK) methods are used. For additive RK methods the curve of absolute monotonicity gives stepsize restrictions for monotonicity. Necessary conditions for nontrivial curves of absolute monotonicity are the nonnegativity of the additive RK coefficients and some inequalities on some incidence matrices. In this paper we characterize strong stability preserving additive Runge-Kutta methods giving some order barriers and structural properties. Research supported by the Ministerio de Educación y Ciencia, Project MTM2005-03894.     

UAVs Integration in the SWIM Based Architecture for ATM

The System Wide Information Management (SWIM) approach has been conceived to overcome the capacity and flexibility limitations of the current ATM systems. On the other hand the commercial applications of Unmanned Aerial Vehicles (UAVs) require the integration of these vehicles in the ATM. From this perspective, the unavoidable modernization of the ATM is seen as an opportunity to integrate the UAVs with the rest of the air traffic. This paper is devoted to study the feasibility and impact of the aggregation of UAVs on the future ATM supported by a SWIM inspired architecture. Departing from the existing technical documents that describe the fundamentals of SWIM we have explored the compatibility with a potential UAVs integration and also explored how the UAVs could help to improve the future ATM system. We will use the weather application as an example in both cases.     

An integrated domain analysis approach for teleoperated systems

Teleoperated systems for ship hull maintenance (TOS) are robotic systems for ship maintenance tasks, such as cleaning or painting a ship’s hull. The product line paradigm has recently been applied to TOS, and a TOS reference architecture has thus been designed. However, TOS requirements specifications have not been developed in any rigorous way with reuse in mind. We therefore believe that an opportunity exists to increase the abstraction level at which stakeholders can reason about this product line. This paper reports an experience in which this TOS domain was analyzed, including the lessons learned in the construction and use of the TOS domain model. The experience is based on the application of extensions of well-known domain analysis techniques, together with the use of quality attribute templates traced to a feature model to deal with non-functional issues. A qualitative research method (action research) was used to carry out the experience.     

Boosting k-nearest neighbor classifier by means of input space projection

The k-nearest neighbors classifier is one of the most widely used methods of classification due to several interesting features, such as good generalization and easy implementation. Although simple, it is usually able to match, and even beat, more sophisticated and complex methods. However, no successful method has been reported so far to apply boosting to k-NN. As boosting methods have proved very effective in improving the generalization capabilities of many classification algorithms, proposing an appropriate application of boosting to k-nearest neighbors is of great interest.Ensemble methods rely on the instability of the classifiers to improve their performance, as k-NN is fairly stable with respect to resampling, these methods fail in their attempt to improve the performance of k-NN classifier. On the other hand, k-NN is very sensitive to input selection. In this way, ensembles based on subspace methods are able to improve the performance of single k-NN classifiers. In this paper we make use of the sensitivity of k-NN to input space for developing two methods for boosting k-NN. The two approaches modify the view of the data that each classifier receives so that the accurate classification of difficult instances is favored.The two approaches are compared with the classifier alone and bagging and random subspace methods with a marked and significant improvement of the generalization error. The comparison is performed using a large test set of 45 problems from the UCI Machine Learning Repository. A further study on noise tolerance shows that the proposed methods are less affected by class label noise than the standard methods.