Evaluation of a User-Adapted Spoken Language Dialogue System: Measuring the Relevance of the Contextual Information Sources

We present an evaluation of a spoken language dialogue system with a module for the management of userrelated information, stored as user preferences and privileges. The flexibility of our dialogue management approach, based on Bayesian Networks (BN), together with a contextual information module, which performs different strategies for handling such information, allows us to include user information as a new level into the Context Manager hierarchy. We propose a set of objective and subjective metrics to measure the relevance of the different contextual information sources. The analysis of our evaluation scenarios shows that the relevance of the short-term information (i.e. the system status) remains pretty stable throughout the dialogue, whereas the dialogue history and the user profile (i.e. the middle-term and the long-term information, respectively) play a complementary role, evolving their usefulness as the dialogue evolves.

Clean Coal Technologies Scenario and Evaluation of Present CO2 Dwindling Initiatives to Approach Zero Emission Power Stations By Coal Combustion. Deployment Situation and Evaluation Study

In the present uncertain global context of reaching an equal social stability and steady thriving economy, power demand expected to grow and global electricity generation could nearly double from 2005 to 2030. Fossil fuels will remain a significant contribution on this energy mix up to 2050, with an expected part of around 70% of global and ca. 60% of European electricity generation. Coal will remain a key player. Hence, a direct effect on the considered CO2 emissions business-as-usual scenario is expected, forecasting three times the present CO2 concentration values up to 1,200ppm by the end of this century. Kyoto protocol was the first approach to take global responsibility onto CO2 emissions monitoring and cap targets by 2012 with reference to 1990. Some of principal CO2emitters did not ratify the reduction targets. Although USA and China spur are taking its own actions and parallel reduction measures. More efficient combustion processes comprising less fuel consuming, a significant contribution from the electricity generation sector to a CO2 dwindling concentration levels, might not be sufficient. Carbon Capture and Storage (CCS) technologies have started to gain more importance from the beginning of the decade, with research and funds coming out to drive its come in useful. After first researching projects and initial scale testing, three principal capture processes came out available today with first figures showing up to 90% CO2 removal by its standard applications in coal fired power stations. Regarding last part of CO2 reduction chain, two options could be considered worthy, reusing (EOR & EGR) and storage. The study evaluates the state of the CO2 capture technology development, availability and investment cost of the different technologies, with few operation cost analysis possible at the time. Main findings and the abatement potential for coal applications are presented. DOE, NETL, MIT, European universities and research institutions, key technology enterprises and utilities, and key technology suppliers are the main sources of this study. A vision of the technology deployment is presented.

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth?s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

Quantification of soluble fibre in feedstuffs for rabbits and evaluation of the interference between the determinations of soluble fibre and intestinal mucin.

This work compared the quantification of soluble fibre in feeds using different chemical and in vitro approaches, and studied the potential interference between soluble fibre and mucin determinations. Six ingredients: sugar beet pulp (SBP), SBP pectins, insoluble SBP, wheat straw, sunflower hulls and lignocellulose, and seven rabbit diets, differing in soluble fibre content, were evaluated. In experiment 1, ingredients and diets were analyzed for total dietary fibre (TDF), insoluble dietary fibre (IDF), soluble dietary fibre (SDF), aNDFom (corrected for protein, aNDFom-cp) and 2-step pepsin/pancreatin in vitro DM indigestibility (corrected for ash and protein, ivDMi2). Soluble fibre was estimated by difference using three procedures: TDF?IDF (SDFIDF), TDF?ivDMi2 (SDFivDMi2), and TDF?aNDFom-cp (SDFaNDFom-cp). Soluble fibre determined directly (SDF) or by difference as SDFivDMi2 were not different (109 g/kg DM, on average). However, when it was calculated as SDFaNDFom-cp the value was 40% higher (153 g/kg DM, P menor que 0.05), whereas SDFIDF (124 g/kg DM) did not differ from any of the other methods. The correlation between the four methods was high (r ? 0.96; P ? 0.001; n = 13), but it decreased or even disappeared when SBP pectins and SBP were excluded and a lower and more narrow range of variation of soluble fibre was used. In experiment 2, the ivDMi2 using crucibles (reference method) were compared to those made using individual or collective ankom bags in order to simplify the determination of SDFivDMi2. The ivDMi2 was not different when using crucibles or individual or collective ankom bags. In experiment 3, the potential interference between soluble fibre and intestinal mucin determinations was studied using rabbit intestinal raw mucus, digesta and SBP pectins, lignocelluloses and a rabbit diet. An interference was observed between the determinations of soluble fibre and crude mucin, as contents of TDF and apparent crude mucin were high in SBP pectins (994 and 709 g/kg DM) and rabbit intestinal raw mucus (571 and 739 g/kg DM). After a pectinase treatment, the coefficient of apparent mucin recovery of SBP pectins was close to zero, whereas that of rabbit mucus was not modified. An estimation of the crude mucin carbohydrates retained in digesta TDF is proposed to correct TDF and soluble fibre digestibility. In conclusion, the values of soluble fibre depend on the methodology used. The contamination of crude mucin with soluble fibre is avoided using pectinase.

Desing and development of a robotic platform for the evaluation of search algorithms

Plant diseases represent a major economic and environmental problem in agriculture and forestry. Upon infection, a plant develops symptoms that affect different parts of the plant causing a significant agronomic impact. As many such diseases spread in time over the whole crop, a system for early disease detection can aid to mitigate the losses produced by the plant diseases and can further prevent their spread [1]. In recent years, several mathematical algorithms of search have been proposed [2,3] that could be used as a non-invasive, fast, reliable and cost-effective methods to localize in space infectious focus by detecting changes in the profile of volatile organic compounds. Tracking scents and locating odor sources is a major challenge in robotics, on one hand because odour plumes consists of non-uniform intermittent odour patches dispersed by the wind and on the other hand because of the lack of precise and reliable odour sensors. Notwithstanding, we have develop a simple robotic platform to study the robustness and effectiveness of different search algorithms [4], with respect to specific problems to be found in their further application in agriculture, namely errors committed in the motion and sensing and to the existence of spatial constraints due to land topology or the presence of obstacles.

Evaluation of air quality impacts with an integrated assessment model for Spain

The present paper describes the advancement and evaluation of air quality-related impacts with the Atmospheric Evaluation and Research Integrated system for Spain (AERIS). In its current version, AERIS is able to provide estimates on the impacts of air quality over human health (PM2.5 and O3), crops and vegetation (O3). The modules that allow quantifying the before mentioned impacts were modeled by applying different approaches (mostly for the European context) present in scientific literature to the conditions of the Iberian Peninsula. This application was supported by reliable data sources, as well as by the good predictive capacity of AERIS for ambient concentrations. For validation purposes, the estimates of AERIS for impacts on human health (change in the statistical life expectancy-PM2.5) and vegetation (loss of wheat crops-O3) were compared against results from the SERCA project and GAINS estimates for two emission scenarios. In general, good results evidenced by reasonable correlation coefficients were obtained, therefore confirming the adequateness of the followed modeling approaches and the quality of AERIS predictions.