Development and implementation of a hybrid scale up model for a batch high shear wet granulation operation

The high shear wet granulation (HSWG) operation consists of several rate processes influenced by the raw material properties, process operational parameters, and equipment design. Their combined effect determines the granule attributes. In literature, these rate processes have been modeled using different dimensionless numbers and their correlations. Each of these dimensionless numbers represent only certain rate processes. Since many of these rate processes occur at the same time, it is necessary to simultaneously model them to account for all the important degrees of freedom. Most of the HSWG scale up approaches in literature calculate scale up conditions based on a single rate process or operating parameter of interest that can lead to sub-optimal process design. We present the development of a hybrid HSWG scale up model accounting simultaneously for all the rate processes. The approach was successfully implemented to scale up the HSWG operation across laboratory, pilot, and commercial scales.     

Biofilm Responses to Flow Regulation by Dams in Mediterranean Rivers

Dams regulate downstream hydrology and modify water quality, which in turn can impinge on the biota, especially in rivers naturally subject to large hydrological variability, such as those under Mediterranean climate. The effect of dams on biofilms was analysed in three tributaries (Cinca, Siurana and Montsant) of the Ebro River (NE Spain). We hypothesized that flow regulation would lead to lower spatial variability of biofilms on the streambed and to a decrease in their metabolic rate per unit biomass, especially during low flow periods. Biofilm characteristics were studied in five transects evenly spaced along river reaches upstream (control) and downstream (impact) of dams in each river, along with riverbed granulometry, hydraulics and water chemistry. Chlorophyll‐a, respiratory activity, photosynthetic capacity and efficiency, and extracellular enzymatic activities (β‐d ‐glucosidase, alkaline phosphatase and leucine‐amino‐peptidase) of epilithic biofilms were measured in different seasons. Spatial variability of chemical and biological variables was reduced downstream of the dams. Chlorophyll‐a concentration, photosynthetic efficiency and respiration capacity were higher in impact than in control reaches, but generally, low inorganic phosphorus concentrations resulted in comparable phosphatase activities downstream and upstream of dams. On the other hand, β‐d ‐glucosidase and leucine‐amino‐peptidase activities were higher at impact reaches. Biofilms were thicker and metabolically more active at the impact reaches, with higher ability to transform dissolved organic matter. Overall, results from this study provide evidence that dams can largely affect the structure and activity of river biofilms, with foreseeable important consequences for river ecosystem functioning. Copyright © 2014 John Wiley & Sons, Ltd.     

Landmark-based music recognition system optimisation using genetic algorithms

Audio fingerprinting allows us to label an unidentified music fragment within a previously generated database. The use of spectral landmarks aims to obtain a robustness that lets a certain level of noise be present in the audio query. This group of audio identification algorithms holds several configuration parameters whose values are usually chosen based upon the researcher’s knowledge, previous published experimentation or just trial and error methods. In this paper we describe the whole optimisation process of a Landmark-based Music Recognition System using genetic algorithms. We define the actual structure of the algorithm as a chromosome by transforming its high relevant parameters into various genes and building up an appropriate fitness evaluation method. The optimised output parameters are used to set up a complete system that is compared with a non-optimised one by designing an unbiased evaluation model.     

Socio-Cultural Connections and Ruptures: Social Roles of Older Adults in Chile

This research focused on determining the sociocultural factors that build the social perception of the social roles of senior citizens in Chile in the 2002–2012 period. For this purpose, public opinion was studied in newspapers El Mercurio and La Cuarta. Specialized opinion included the Senior Citizen Program of Pontificia Universidad Católica de Chile, the National Service for Senior Citizens, the Social Observatory for Aging and Old Age of Universidad de Chile, and the Society of Geriatrics and Gerontology of Chile. A comprehensive methodology was used, along with a bibliographical four-stage design. The following ten sociocultural factors were identified: Health and Quality of Life, Studies and Data, Social Images of Aging, Gerontological Concepts and Sociopolitical Participation, Gerontological Policies, Plans and Principles, Aging, Production and Gender, Institutionalization, Demographic Growth, Gerontological Training, and Exclusion and Old Age. This work suggests modifying the conceptual premise that emphasizes the inexistence of a social role for elderly people with a vision centered on the heterogeneity of social roles under permanent sociocultural construction.     

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.     

Efficient wavelet-based ECG processing for single-lead FHR extraction

This paper describes a novel model for fetal heart rate (FHR) monitoring from single-lead mother?s abdomen ECG (AECG) measurements. This novel method is divided in two stages: the first step consists on a one-step wavelet-based preprocessing for simultaneous baseline and high-frequency noise suppression, while the second stage efficiently detects fetal QRS complexes allowing FHR monitoring. The presented structure has been simplified as much as possible, in order to reduce computational cost and thus enable possible custom hardware implementations. Moreover, the proposed scheme and its fixed-point modeling have been tested using real abdominal ECG signals, which allow the validation of the presented approach and provide high accuracy.     

Cartogram visualization for nonlinear manifold learning models

Real-world applications of multivariate data analysis often stumble upon the barrier of interpretability. Simple data analysis methods are usually easy to interpret, but they risk providing poor data models. More involved methods may instead yield faithful data models, but limited interpretability. This is the case of linear and nonlinear methods for multivariate data visualization through dimensionality reduction. Even though the latter have provided some of the most exciting visualization developments, their practicality is hindered by the difficulty of explaining them in an intuitive manner. The interpretability, and therefore the practical applicability, of data visualization through nonlinear dimensionality reduction (NLDR) methods would improve if, first, we could accurately calculate the distortion introduced by these methods in the visual representation and, second, if we could faithfully reintroduce this distortion into such representation. In this paper, we describe a technique for the reintroduction of the distortion into the visualization space of NLDR models. It is based on the concept of density-equalizing maps, or cartograms, recently developed for the representation of geographic information. We illustrate it using Generative Topographic Mapping (GTM), a nonlinear manifold learning method that can provide both multivariate data visualization and a measure of the local distortion that the model generates. Although illustrated here with GTM, it could easily be extended to other NLDR visualization methods, provided a local distortion measure could be calculated. It could also serve as a guiding tool for interactive data visualization.