University of Oviedo

About: University of Oviedo is a education organization based out in Oviedo, Spain. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 13423 authors who have published 31649 publications receiving 844799 citations. The organization is also known as: Universidá d'Uviéu & Universidad de Oviedo.

Characterization of binding sites in molecularly imprinted polymers

Abstract: In recent years interest has grown in a deeper understanding of the heterogeneity nature of binding sites in molecularly imprinted polymers, purposively designed artificial receptor materials. This article reviews the progress of the understanding of the molecular recognition binding sites beyond the usual description of adsorption isotherms. Analytical and numerical methods used for calculating the adsorption isotherms and the adsorption energy distribution, as a quantitative measure of the imprinted polymer heterogeneity, are included. Advantages and limitations of the different approaches are also discussed.

Planck early results - XI. Calibration of the local galaxy cluster Sunyaev-Zeldovich scaling relations

Abstract: We present precise Sunyaev-Zeldovich (SZ) effect measurements in the direction of 62 nearby galaxy clusters (z < 0.5) detected at high signal-tonoise in the first Planck all-sky data set. The sample spans approximately a decade in total mass, 2 × 10 14 M� < M500 < 2 × 10 15 M� ,w hereM500 is the mass corresponding to a total density contrast of 500. Combining these high quality Planck measurements with deep XMM-Newton X-ray data, we investigate the relations between D 2 Y500, the integrated Compton parameter due to the SZ effect, and the X-ray-derived gas mass Mg,500, temperature TX, luminosity LX,500, SZ signal analogue YX,500 = Mg,500 ×TX, and total mass M500. After correction for the effect of selection bias on the scaling relations, we find results that are in excellent agreement with both X-ray predictions and recently-published ground-based data derived from smaller samples. The present data yield an exceptionally robust, high-quality local reference, and illustrate Planck’s unique capabilities for all-sky statistical studies of galaxy clusters.

Multiple goals, motivation and academic learning.

Abstract: Background: The type of academic goals pursued by students is one of the most important variables in motivational research in educational contexts. Although motivational theory and research have emphasised the somewhat exclusive nature of two types of goal orientation (learning goals versus performance goals), some studies (Meece, 1994; Seifert, 1995, 1996) have shown that the two kinds of goals are relatively complementary and that it is possible for students to have multiple goals simultaneously, which guarantees some flexibility to adapt more efficaciously to various contexts and learning situations. Aim: The principal aim of this study is to determine the academic goals pursued by university students and to analyse the differences in several very significant variables related to motivation and academic learning. Sample: Participants were 609 university students (74% women and 26% men) who filled in several questionnaires about the variables under study. Method: We used cluster analysis ('quick cluster analysis' method) to establish the different groups or clusters of individuals as a function of the three types of goals (learning goals, performance goals, and social reinforcement goals). By means of MANOVA, we determined whether the groups or clusters identified were significantly different in the variables that are relevant to motivation and academic learning. Lastly, we performed ANOVA on the variables that revealed significant effects in the previous analysis. Results: Using cluster analysis, three groups of students with different motivational orientations were identified: a group with predominance of performance goals (Group PG: n = 230), a group with predominance of multiple goals (Group MG: n = 238), and a group with predominance of learning goals (Group LG: n = 141). Conclusions: Groups MG and LG attributed their success more to ability, they had higher perceived ability, they took task characteristics into account when planning which strategies to use in the learning process, they showed higher persistence, and used more deep learning strategies than did the students with predominance of performance goals (Group PG). On the other hand, Groups MG and PG took the evaluation criteria more into account when deciding which strategies to use in order to learn, and they attributed their failures more to luck than did Group LG. Students from Group MG attributed their success more to effort than did the other two groups and they attained higher achievement than Group PG. Group LG tended to attribute their failures more to lack of effort than did the other two groups.

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

Abstract: In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.