Open University of Catalonia

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

Paradox of time in research on educational technology

Abstract: Though the concept of time is fuzzily defined in education, it embraces many key teaching and learning aspects of the educational situation from scheduling (at the macro, meso, and micro level) thr...

Enabler and inhibitor factors influencing competitive intelligence practices

Abstract: Purpose – This paper has a twofold aim. Firstly, to give some insight into competitive intelligence practices in a little‐explored area in the field of competitive intelligence: the higher education sector. Secondly, to find out more about the factors influencing competitive intelligence practices, since little research on this subject has been published.Design/methodology/approach – The investigation used a mixed‐methods approach, including face‐to‐face, semi structured interviews with 47 university managers (degree coordinators, deans and vice‐rectors), followed by a semi‐structured questionnaire carried out with 400 degree coordinators and deans and analysed quantitatively and qualitatively. The interviews informed the questionnaire design.Findings – The survey confirms the proposed framework's usefulness for analysing the enabler and inhibitor factors in an organisation for promoting efficient competitive intelligence practice and also gives some insight into which factors enable or inhibit the effica...

The political economy of water infrastructure: An introduction to financialization

Abstract: Water infrastructure has been financed by differing combinations of private and public ownership throughout history and across different geographies. In the present moment, processes of financialization suggest a radical reconfiguration of these arrangements in a number of locations, such that water infrastructure is being transformed into a wealth extraction mechanism. In this Primer Article, we introduce financialization, showing how the term describes a process through which financial actors have gained new power and in which the locus of profit making at least appears to have shifted from the “real economy” to a financial economy. In the case of water infrastructure, processes of financialization have enabled apparently fixed and stable forms such as pipes, water treatment plants, and sewers to be transformed into liquid assets, opening up new opportunities for sovereign wealth funds and pension fund investors. The super‐profits made by these financial actors are best conceptualized as forms of rent, derived in part from the monopoly ownership of a basic need. This distinctive shift needs to be positioned in relation to broader changes in the political economy of water infrastructure. We situate financialization historically in relation to the development of water utilities and networks: municipalization and nationalization during the first decades of the 20th century, privatization since the 1990s, and renewed interest in remunicipalization in some places alongside the deepening logic of financialization in others. We conclude by thinking through the likely implication of water financialization for future infrastructural arrangements.

Hippocampal profiling: Localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis.

Abstract: OBJECTIVE Hippocampal sclerosis (HS) is the most common cause of drug-resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross-sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities. METHODS T1-weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3-T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole-hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior-posterior axis, we were able to calculate hippocampal cross-sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls. RESULTS Profiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole-hippocampal measurements yield normal values. SIGNIFICANCE Hippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions.

A new multi-objective optimization algorithm combined with opposition-based learning

Abstract: The optimization problems are divided into a single objective and multi-objective. Single objective optimization has only one objective function; whereas, multi-objective optimization has multiple objective functions that generate the Pareto set; therefore, solving a multi-objective problem is a challenging problem. This paper presents a new multi-objective optimization method (called MWDEO) based on improved whale optimization algorithm (WOA) by combining the differential evolution (DE) algorithm and the opposition-based learning (OBL). The MWDEO uses the WOA to perform a global exploration, whereas DE is used to exploit the search space; while the OBL is applied to improve the exploration and exploitation by generating the opposite values. The proposed algorithm is evaluated using 32 multi-objective test problems besides a set of benchmark problems of CEC’2017. The experimental results are compared with nine state-of-the-art multi-objective methods. The analysis of the results showed that the proposed MWDEO outperformed all other algorithms in most of the test problems which indicates that the proposed MWDEO is competitive and effective in solving different types of multi-objective problems.