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Institution

Open University of Catalonia

EducationBarcelona, Spain
About: Open University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 1943 authors who have published 4646 publications receiving 64200 citations. The organization is also known as: Universitat Oberta de Catalunya & UOC.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors present the case of Catalonia, one of the main tourist destinations in Europe, and show how the combination of these theories can be especially practical for constructing a global model that groups tourism development by phases with its paradigmatic changes.

134 citations

Journal ArticleDOI
TL;DR: P. aeruginosa multi-drug resistance independently predicted higher hospital costs with a more than 70% increase per admission compared with non-resistant strains.
Abstract: We aimed to assess the hospital economic costs of nosocomial multi-drug resistant Pseudomonas aeruginosa acquisition. A retrospective study of all hospital admissions between January 1, 2005, and December 31, 2006 was carried out in a 420-bed, urban, tertiary-care teaching hospital in Barcelona (Spain). All patients with a first positive clinical culture for P. aeruginosa more than 48 h after admission were included. Patient and hospitalization characteristics were collected from hospital and microbiology laboratory computerized records. According to antibiotic susceptibility, isolates were classified as non-resistant, resistant and multi-drug resistant. Cost estimation was based on a full-costing cost accounting system and on the criteria of clinical Activity-Based Costing methods. Multivariate analyses were performed using generalized linear models of log-transformed costs. Cost estimations were available for 402 nosocomial incident P. aeruginosa positive cultures. Their distribution by antibiotic susceptibility pattern was 37.1% non-resistant, 29.6% resistant and 33.3% multi-drug resistant. The total mean economic cost per admission of patients with multi-drug resistant P. aeruginosa strains was higher than that for non-resistant strains (15,265 vs. 4,933 Euros). In multivariate analysis, resistant and multi-drug resistant strains were independently predictive of an increased hospital total cost in compared with non-resistant strains (the incremental increase in total hospital cost was more than 1.37-fold and 1.77-fold that for non-resistant strains, respectively). P. aeruginosa multi-drug resistance independently predicted higher hospital costs with a more than 70% increase per admission compared with non-resistant strains. Prevention of the nosocomial emergence and spread of antimicrobial resistant microorganisms is essential to limit the strong economic impact.

133 citations

Journal ArticleDOI
TL;DR: In this article, the effects of FOMC announcements of federal funds target rate decisions on individual stock returns, volatilities and correlations at the intraday level were studied.
Abstract: We study the effects of FOMC announcements of federal funds target rate decisions on individual stock returns, volatilities and correlations at the intraday level. For all three characteristics we find that the stock market responds differently to positive and negative target rate surprises. First, the average response to positive surprises (that is, bad news for stocks) is larger. Second, in case of bad news the mere occurrence of a surprise matters most, whereas for good news its magnitude is more important. These new insights are possible due to the use of high-frequency intraday data.

133 citations

Journal ArticleDOI
TL;DR: The novel meta-heuristic algorithm called Black Widow Optimization (BWO) is introduced to find the best threshold configuration using Otsu or Kapur as objective function and is found to be most promising for multi-level image segmentation problem over other segmentation approaches that are currently used in the literature.
Abstract: Segmentation is a crucial step in image processing applications. This process separates pixels of the image into multiple classes that permits the analysis of the objects contained in the scene. Multilevel thresholding is a method that easily performs this task, the problem is to find the best set of thresholds that properly segment each image. Techniques as Otsu’s between class variance or Kapur’s entropy helps to find the best thresholds but they are computationally expensive for more than two thresholds. To overcome such problem this paper introduces the use of the novel meta-heuristic algorithm called Black Widow Optimization (BWO) to find the best threshold configuration using Otsu or Kapur as objective function. To evaluate the performance and effectiveness of the BWO-based method, it has been considered the use of a variety of benchmark images, and compared against six well-known meta-heuristic algorithms including; the Gray Wolf Optimization (GWO), Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Sine–Cosine Algorithm (SCA), Slap Swarm Algorithm (SSA), and Equilibrium Optimization (EO). The experimental results have revealed that the proposed BWO-based method outperform the competitor algorithms in terms of the fitness values as well as the others performance measures such as PSNR, SSIM and FSIM. The statistical analysis manifests that the BWO-based method achieves efficient and reliable results in comparison with the other methods. Therefore, BWO-based method was found to be most promising for multi-level image segmentation problem over other segmentation approaches that are currently used in the literature.

132 citations

Journal ArticleDOI
TL;DR: In this paper, the authors propose a principled framework for the study and analysis of group interaction and group scaffolding which is built by combining different aspects and issues of collaboration, learning and evaluation.
Abstract: Evaluating on-line collaborative learning interactions is a complex task due to the variety of elements and factors that take place and intervene in the way a group of students comes together to collaborate in order to achieve a learning goal. The aim of this paper is to provide a better understanding of group interaction and determine how to best support the collaborative learning process. To that end, we propose a principled framework for the study and analysis of group interaction and group scaffolding which is built by combining different aspects and issues of collaboration, learning and evaluation. In particular, we define learning activity indicators at several levels of description which prompt to the application of a mixed interaction analysis scheme and the use of different data types and specific tools. At an initial layer, the basis of the approach is set by applying a qualitative process for evaluating the individual and group task performance as well as the group functioning and scaffolding. The interaction analysis process is completed by defining and applying two more layers: a social network analysis of the group activity and participation behaviour and a quantitative analysis of group effectiveness as regards task achievement and active interaction involvement. Our work defines a grounded and holistic conceptual model that describes on-line collaborative learning interactions sufficiently and applies it in a real, web-based, complex and long-term collaborative learning situation. An in-depth empirical evaluation of the conceptual model is fully discussed, which demonstrates the usefulness and value of the approach.

132 citations


Authors

Showing all 2008 results

NameH-indexPapersCitations
Andrea Saltelli6518431540
Jose A. Rodriguez6359717218
Cristina Botella5540413075
Fatos Xhafa5269210379
Jaime Kulisevsky4821015066
William H. Dutton432777048
Angel A. Juan412845040
Aditya Khosla396150417
Jordi Cabot381065022
Jordi Cortadella382265736
Antoni Valero-Cabré37996091
Berta Pascual-Sedano34874377
Josep Lladós332714243
Carlo Gelmetti331593912
Juan V. Luciano331062931
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202328
202286
2021503
2020505
2019401
2018343