Lipolytic and oxidative changes in 'Chorizo' during ripening

Changes in fats during the ripening of 'chorizo'-a Spanish dry sausage- elaborated by traditional and industrial processes have been studied. Total free fatty acids (FFA), carbonyls, volatile fatty acids (acetic acid), TBA number and individual FFA were determined. A marked increase of total free fatty acids was observed, although an increase of rancidity was not detected. Except in one of the batches studied in which a rise in carbonyls was reported, these compounds remained unchanged during the ripening in the rest of the batches analysed. No significant change occurred in the proportions of different free fatty acids during ripening in both types of 'chorizo'. There was a marked increase of acetic acid during the first days of ripening followed by a slow fall in the ultimate stages.     

Thermodynamic properties of ni nitrides and phase stability in the Ni-N system

The thermodynamics of the Ni-N system is poorly known from experiments, and there is a need of information on the stability of the various nitride phases and the Ni-N phase diagram. This kind of information has been obtained by us, by combining the few measurements available with predictions, based on recently reported regularities in bonding properties and vibrational entropy of 3d transition metal compounds. A calculated Ni-N phase diagram is presented. A certain range of homogeneity for the hexagonal nitride phase is obtained, which is comparable to that of other 3d transition metal-nitrogen systems. The question of the possible existence of a stable Ni₄N phase is examined. According to our results, Ni₄N is metastable in the Ni-N system.     

Twin-image suppression in digital in-line holography based on wave-front filtering

Pattern Analysis and Applications - Digital holography is an imaging process able to recreate three-dimensional representations of objects from recording pattern interference among distinct waves....     

Recommending multimedia visiting paths in cultural heritage applications

The valorization and promotion of worldwide Cultural Heritage by the adoption of Information and Communication Technologies represent nowadays some of the most important research issues with a large variety of potential applications. This challenge is particularly perceived in the Italian scenario, where the artistic patrimony is one of the most diverse and rich of the world, able to attract millions of visitors every year to monuments, archaeological sites and museums. In this paper, we present a general recommendation framework able to uniformly manage heterogeneous multimedia data coming from several web repositories and to provide context-aware recommendation techniques supporting intelligent multimedia services for the users—i.e. dynamic visiting paths for a given environment. Specific applications of our system within the cultural heritage domain are proposed by means of real case studies in the mobile environment related both to an outdoor and indoor scenario, together with some results on user’s satisfaction and system accuracy.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

Online identification of appliances from power consumption data collected by smart meters

The efficient use of resources is a matter of great concern in today’s society, especially in the energy sector. Although the main strategy to decrease energy use has long been focused on supply, over the last few years, there has been a shift to the demand side. Under this new line of action, demand-side management networks have emerged and extended from the household level to larger installations, with the appearance of the concepts of Smart Grids and even Smart Cities. The extended use of Smart Meters for measuring residential electricity consumption facilitates the creation of such intelligent environments. In this context, this article proposes a system which extracts value from the collected consumer information to identify the appliances belonging to that smart environment by means of machine learning techniques. Considering the large amount of information that would be handled when millions of homes were sending data, big data technology has been used. An experiment to evaluate the classification method was carried out with seven devices and three different configurations. The results are also reported, achieving promising results, with recognition rates of 75 % after 1 h of training and 100 % after 4 h.     

Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior.