Communities in complex networks

The study of complex networks has attracted the attention of the scientific community for many obvious reasons. A vast number of systems, from the brain to ecosystems, power grid, and the Internet, can be represented as large complex networks, i.e, assemblies of many interacting components with nontrivial topological properties. The link between these components can describe a global behaviour such as the Internet traffic, electricity supply service, market trend, etc. One of the most relevant topological feature of graphs representing these complex systems is community structure which aims to identify the modules and, possibly, their hierarchical organization, by only using the information encoded in the graph topology. Deciphering network community structure is not only important in order to characterize the graph topologically, but gives some information both on the formation of the network and on its functionality.

The Linear Ordering Problem Revisited

The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this problem. This paper studies a procedure to extract static information from an instance of the problem, and proposes a method to incorporate the obtained knowledge in order to improve the performance of local search-based algorithms. The procedure introduced identifies the positions where the indexes cannot generate local optima for the insert neighbourhood, and thus global optima solutions. This information is then used to propose a restricted insert neighbourhood that discards the insert operations which move indexes to positions where optimal solutions are not generated. In order to measure the efficiency of the proposed restricted insert neighbourhood system, two state-of-the-art algorithms for the LOP that include local search procedures have been modified. Conducted experiments confirm that the restricted versions of the algorithms outperform the classical designs systematically. The statistical test included in the experimentation reports significant differences in all the cases, which validates the efficiency of our proposal.

Temperature-and pH-Sensitive Nanohydrogels of Poly(N-Isopropylacrylamide) for Food Packaging Applications: Modelling the Swelling-Collapse Behaviour

Temperature-sensitive poly(N-isopropylacrylamide) (PNIPA) nanohydrogels were synthesized by nanoemulsion polymerization in water-in-oil systems. Several cross-linking degrees and the incorporation of acrylic acid as comonomer at different concentrations were tested to produce nanohydrogels with a wide range of properties. The physicochemical properties of PNIPA nanohydrogels, and their relationship with the swelling-collapse behaviour, were studied to evaluate the suitability of PNIPA nanoparticles as smart delivery systems (for active packaging). The swelling-collapse transition was analyzed by the change in the optical properties of PNIPA nanohydrogels using ultraviolet-visible spectroscopy. The thermodynamic parameters associated with the nanohydrogels collapse were calculated using a mathematical approach based on the van't Hoff analysis, assuming a two-state equilibrium (swollen to collapsed). A mathematical model is proposed to predict both the thermally induced collapse, and the collapse induced by the simultaneous action of two factors (temperature and pH, or temperature and organic solvent concentration). Finally, van't Hoff analysis was compared with differential scanning calorimetry. The results obtained allow us to solve the problem of determining the molecular weight of the structural repeating unit in cross-linked NIPA polymers, which, as we show, can be estimated from the ratio of the molar heat capacity (obtained from the van't Hoff analysis) to the specific heat capacity (obtained from calorimetric measurements).

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni's FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Temperature-and pH-Sensitive Nanohydrogels of Poly(N-Isopropylacrylamide) for Food Packaging Applications: Modelling the Swelling-Collapse Behaviour

Temperature-sensitive poly(N-isopropylacrylamide) (PNIPA) nanohydrogels were synthesized by nanoemulsion polymerization in water-in-oil systems. Several cross-linking degrees and the incorporation of acrylic acid as comonomer at different concentrations were tested to produce nanohydrogels with a wide range of properties. The physicochemical properties of PNIPA nanohydrogels, and their relationship with the swelling-collapse behaviour, were studied to evaluate the suitability of PNIPA nanoparticles as smart delivery systems (for active packaging). The swelling-collapse transition was analyzed by the change in the optical properties of PNIPA nanohydrogels using ultraviolet-visible spectroscopy. The thermodynamic parameters associated with the nanohydrogels collapse were calculated using a mathematical approach based on the van't Hoff analysis, assuming a two-state equilibrium (swollen to collapsed). A mathematical model is proposed to predict both the thermally induced collapse, and the collapse induced by the simultaneous action of two factors (temperature and pH, or temperature and organic solvent concentration). Finally, van't Hoff analysis was compared with differential scanning calorimetry. The results obtained allow us to solve the problem of determining the molecular weight of the structural repeating unit in cross-linked NIPA polymers, which, as we show, can be estimated from the ratio of the molar heat capacity (obtained from the van't Hoff analysis) to the specific heat capacity (obtained from calorimetric measurements).