Abstract
Vehicle suspension systems are usually based on passive actuators and control modes in which the damping and stiffness parameters are predefined and kept constant for all road profiles and vehicle response. A different approach is to use active systems to monitor and control the suspension motion in order to improve the vehicle handling and comfort. However, these systems have a complex design requiring a relatively high power source to operate. Semi-active systems are also capable to modify the properties of the vehicle suspension but with low power requirements making them a promising technology for demanding vibration control systems. This paper presents the findings of a numerical simulation involving a simplified model of a vehicle suspension system equipped with a MR actuator. The system is designed to improve the behavior (comfort and handling) of the vehicle compared with a traditional passive suspension system. A simple fuzzy logic controller is used to decide the control action in accordance with the measured system response. © Springer International Publishing Switzerland 2017.

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.

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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