An integrated approach for assessing the bioreceptivity of glazed tiles to phototrophic microorganisms

A laboratory-based methodology was designed to assess the bioreceptivity of glazed tiles. The experimental set-up consisted of multiple steps: manufacturing of pristine and artificially aged glazed tiles, enrichment of phototrophic microorganisms, inoculation of phototrophs on glazed tiles, incubation under optimal conditions and quantification of biomass. In addition, tile intrinsic properties were assessed to determine which material properties contributed to tile bioreceptivity. Biofilm growth and biomass were appraised by digital image analysis, colorimetry and chlorophyll a analysis. SEM, micro-Raman and micro-particle induced X-ray emission analyses were carried out to investigate the biodeteriorating potential of phototrophic microorganisms on the glazed tiles. This practical and multidisciplinary approach showed that the accelerated colonization conditions allowed different types of tile bioreceptivity to be distinguished and to be related to precise characteristics of the material. Aged tiles showed higher bioreceptivity than pristine tiles due to their higher capillarity and permeability. Moreover, biophysical deterioration caused by chasmoendolithic growth was observed on colonized tile surfaces.

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations.