Optimizing the number of stations in arrays measurements: experimental outcomes for different array geometries and the f-k method

Array measurements have become a valuable tool for site response characterization in a non-invasive way. The array design, i.e. size, geometry and number of stations, has a great influence in the quality of the obtained results. From the previous parameters, the number of available stations uses to be the main limitation for the field experiments, because of the economical and logistical constraints that it involves. Sometimes, from the initially planned array layout, carefully designed before the fieldwork campaign, one or more stations do not work properly, modifying the prearranged geometry. Whereas other times, there is not possible to set up the desired array layout, because of the lack of stations. Therefore, for a planned array layout, the number of operative stations and their arrangement in the array become a crucial point in the acquisition stage and subsequently in the dispersion curve estimation. In this paper we carry out an experimental work to analyze which is the minimum number of stations that would provide reliable dispersion curves for three prearranged array configurations (triangular, circular with central station and polygonal geometries). For the optimization study, we analyze together the theoretical array responses and the experimental dispersion curves obtained through the f-k method. In the case of the f-k method, we compare the dispersion curves obtained for the original or prearranged arrays with the ones obtained for the modified arrays, i.e. the dispersion curves obtained when a certain number of stations n is removed, each time, from the original layout of X geophones. The comparison is evaluated by means of a misfit function, which helps us to determine how constrained are the studied geometries by stations removing and which station or combination of stations affect more to the array capability when they are not available. All this information might be crucial to improve future array designs, determining when it is possible to optimize the number of arranged stations without losing the reliability of the obtained results.

Nano-biocomposite films with modified cellulose nanocrystals and synthesized silver nanoparticles

Ternary nano-biocomposite films based on poly(lactic acid) (PLA) with modified cellulose nanocrystals (s-CNC) and synthesized silver nanoparticles (Ag) have been prepared and characterized. The functionalization of the CNC surface with an acid phosphate ester of ethoxylated nonylphenol favoured its dispersion in the PLA matrix. The positive effects of the addition of cellulose and silver on the PLA barrier properties were confirmed by reductions in the water permeability (WVP) and oxygen transmission rate (OTR) of the films tested. The migration level of all nano-biocomposites in contact with food simulants were below the permitted limits in both non-polar and polar simulants. PLA nano-biocomposites showed a significant antibacterial activity influenced by the Ag content, while composting tests showed that the materials were visibly disintegrated after 15 days with the ternary systems showing the highest rate of disintegration under composting conditions.

Combined effects of cellulose nanocrystals and silver nanoparticles on the barrier and migration properties of PLA nano-biocomposites

Poly(lactic acid) (PLA)-based high performance nano-biocomposites were prepared to be used in active food packaging. Pristine (CNC) and surfactant modified cellulose nanocrystals (s-CNC) with silver (Ag) nanoparticles were used as the matrix modifiers. Binary and ternary systems were prepared. Morphological investigations revealed the good distribution of silver nanoparticles in PLA ternary systems. The combination of s-CNC and Ag nanoparticles increased the barrier effect of the produced films while the results of overall migration for the PLA nano-biocomposites revealed that none of the samples exceeded the overall migration limit, since results were well below 60 mg kg−1 of simulant.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.