Abstract
Computer Assisted Qualitative Data Analysis Software (CAQDAS) are tools that help researchers to develop qualitative research projects. These software packages help the users with tasks such as transcription analysis, coding and text interpretation, writing and annotation, content search and analysis, recursive abstraction, grounded theory methodology, discourse analysis, data mapping, and several other types of analysis. This paper focus the new paradigm of self-learning, that presents itself increasingly as a competence to support learning in a proactive way. It further analyses education and CAQDAS with emphasis on the use of CAQDAS in educational research and the self-learning of CAQDAS. The study conducted had two main goals: (1) analyse the self-learning tools of CAQDAS and (2) identify CAQDAS's users learning profile. Six software packages were selected: NVivo, Atlas.ti, Dedoose, webQDA, MAXQDA, and QDA Miner. They were reviewed, taking into account their transversality, language, (self-learning) tools, among other criteria. The results show that there is a considerable demand for information from users regarding the execution of processes in CAQDAS, and that the packages analysed do not guide users towards the self-learning tools that best fit their learning style.

Abstract
Elbow joint incongruity is recognized as an important factor in the development, treatment, and prognosis of canine elbow dysplasia. Elbow incongruity has been measured based on radiographic joint space widths, however these values can be affected by the degree of elbow joint flexion. Recent studies have reported radiographic curvature radii asmore precise measures of humeroulnar congruity in dogs. The aim of this prospective observational study was to describe radiographic curvature radii measured from flexed and extended elbow radiographs for a sample of dogs representing a medium breed (Portuguese Pointing Dog) and a large breed (Estrela Mountain Dog). The curvature radii from the ulnar trochlear notch and humeral trochlea were measured in 114 mediolateral elbow extended radiographic views (30 Portuguese Pointing Dog and 27 Estrela Mountain Dog), and 84 mediolateral flexed views (22 Portuguese Pointing Dog and 20 Estrela Mountain Dog). The sampled animals' ages ranged from 12 to 84 months (34.6 +/- 17.8 months). Good agreement was observed between curvature radii measurements for flexed vs. extended views in both breed groups. Ulnar trochlear notch curvature radii measurements were greater than humeral trochlea curvature radii measurements in both breed groups. Both curvature radii were greater in the large-breed dog group vs. the medium-breed dog group. Both breed groups had ulnar and humeral curves with similar typology. However, the large breed group had greater intermediate differences between the humeroulnar surface curvature radii. Results from this study supported the use of curvature radii as measures of humeroulnar congruity in mediolateral flexed elbow radiographs of medium and large breed dogs.

Abstract

Abstract
This work aims to help in the correct and early diagnosis of the acute kidney injury, through the application of data mining techniques. The main goal is to be implemented in Intensive Care Units (ICUs) as an alarm system, to assist health professionals in the diagnosis of this disease. These techniques will predict the future state of the patients, based on his current medical state and the type of ICU. Through the comparison of three different approaches (Markov Chain Model, Markov Chain Model ICU Specialists and Random Forest), we came to the conclusion that the best method is the Markov Chain Model ICU Specialists. © Springer International Publishing AG 2017.

Abstract

Abstract
The share of renewable energy sources (RESs) in the overall power production is on the upward trend in many power systems. Especially in recent years, considerable amounts of RES type distributed generations (DGs) are being integrated in distribution systems, albeit several challenges mainly induced by the intermittent nature of power productions using such resources. Optimal planning and efficient management of such resources is therefore highly necessary to alleviate their negative impacts, which increase with the penetration level. This paper deals with the optimal allocation (i.e. size and placement) of RES type DGs in coordination with reconfiguration of distribution systems (RDS). Moreover, the paper presents quantitative analysis with regards to the impacts of RDS on the integration level of such DGs in distribution systems. To this end, a tailor-made genetic algorithm (GA) based optimization model is developed. The proposed model is tested on a 16-node network system. Numerical results show the positive contributions of network reconfiguration on increasing the level of renewable DG penetration, and improving the overall performance of the system in terms of reduced costs and losses as well as a more stabilized voltage profile.