Symbionts in Mesozooplankton Communities from NE Atlantic Ocean: Ecology and Recruitment of Parasites to the Marine Trophic Web

Beca JAE-Predoctoral CISC; Proyecto LARECO CTM2011-25929

Satisfaction of clinical nurses with the formation of a university tutorial program

Degree in nursing from the Universitat Jaume I (UJI) maintains the continuity of learning with an integrated learning methodology (theory, simulated practice and clinical practice). The objective of this methodology is to achieve consistency between the knowledge, abilities and skills acquired in the classroom, laboratory and clinic to ensure skills related. Reference Nurse is a key figure in this process, you receive accredited training on Educational Methods, assessment of competence, and Evidence-Based Practice that plays the role of evaluating in conjunction with the subjects. It does not perceive economic remuneration. The main objective of this study is to determine the level of satisfaction of clinical nurses on the Nurses Training Program Reference in UJI (Castellon- Spain). A cross sectional study was performed and conducted on 150 nurses. 112 questionnaires were completed, collected and analysed at the end of training. The survey consists of 12 items measured with the Likert scale with 5 levels of response and two open questions regarding the positive and negative aspects of the course and to add in this formation. The training is always performed by the same faculty and it's used four sessions of 2012. We perform a quantitative analysis of the variables under study using measures of central tendency. The completion rate of the survey is 95.53% (n=107). Anonymity rate of 54,14% The overall satisfaction level of training was 3.65 (SD = 0.89) on 5 points. 54.2% (n = 58) of the reference nurses made a contribution in the open questions described in the overall results. The overall satisfaction level can be considered acceptable. It is considered necessary to elaborate a specific survey to detect areas of improvement of nurse training program reference and future recruitment strategies. The main objective of the present work is the selection and integration of different methodologies among those applicable within the framework of the European Higher Education Area to combine teaching methods with high implication from both lecturers and students.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.