Analysis of the admissions tests for teacher training in Spain and Finland: knowledge or competences

One of the most decisive factors in the quality of education and academic performance of students is quality, preparation and dedication of the teachers. The exquisite system of selecting candidates for teacher training programs is one of the fundamentals of success of the Finnish Education System. The responsibility of choosing the best students to convert them into teachers is a challenge that involves a significant reform of university admission. Achieving this goal involves the choice of strategies and educational tools in accordance to the complexity of the demands presented by the teaching profession in the digital age. This study describes, analyzes and compares the admission tests in the University of Spain (PAU) and Finland (VAKAVA), for those who wish to become professional educators, in order to understand the possible influence of these tests to select the most suitable candidates to develop into future teaching professionals. The results showed that in Spain, the entrance test to universities is developed in a general way for all the students that aspire to any field of knowledge, while in Finland, the test is specific and particular for students aspiring to the field of education. The results of this study can guide and encourage the necessary changes that have to be done in the admission tests to Spanish university in general and to teacher education faculties in particular.

Information sharing and reporting systems in the UK and Ireland: Professional barriers to reporting child maltreatment concerns

Across the UK recent policy developments have focused on improved information sharing and inter-agency cooperation. Professional non-reporting of child maltreatment concerns has been consistently highlighted as a problem in a range of countries and the research literature indicates that this can happen for a variety of reasons. Characteristics such as the type of abuse and the threshold of evidence available are key factors, as are concerns that reporting will damage the professional-client relationship. Professional discipline can also impact on willingness to report, as can personal beliefs about abuse, attitudes towards child protection services and experiences of court processes. Research examining the role of organisational factors in information sharing and reporting emphasises the importance of training and there are some positive indications that training can increase professional awareness of reporting processes and requirements and help to increase knowledge of child abuse and its symptoms. Nonetheless, this is a complex issue and the need for training to go beyond simple awareness raising is recognised. In order to tackle non-reporting in a meaningful way, childcare professionals need access to on-going multidisciplinary training which is specifically tailored to address the range of different factors which impact on reporting attitudes and behaviours.

Development of a multi-media book for clinical supervision training in undergraduate nursing

There is an increased interest in higher education in the use of e-learning resources for students. This can be attributed to many factors including, the availability of advanced technology systems, a growing student population that is technology focused, financial implications and the recruitment of international students. However, the introduction of technology and e-learning into teaching has given rise to issues regarding quality and quantity of educational practice . The challenge now is for educationalists is to deliver an optimal learning experience that is effective and appropriate for students’ learning needs.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.

Massive MIMO with Optimal Power and Training Duration Allocation

We consider the uplink of massive multicell multiple-input multiple-output systems, where the base stations (BSs), equipped with massive arrays, serve simultaneously several terminals in the same frequency band. We assume that the BS estimates the channel from uplink training, and then uses the maximum ratio combining technique to detect the signals transmitted from all terminals in its own cell. We propose an optimal resource allocation scheme which jointly selects the training duration, training signal power, and data signal power in order to maximize the sum spectral efficiency, for a given total energy budget spent in a coherence interval. Numerical results verify the benefits of the optimal resource allocation scheme. Furthermore, we show that more training signal power should be used at low signal-to-noise ratio (SNRs), and vice versa at high SNRs. Interestingly, for the entire SNR regime, the optimal training duration is equal to the number of terminals.

Realist synthesis: Critical care rapid response systems.

Background Rapid Response Systems (RRS) consist of four interrelated and interdependent components; an event detection and trigger mechanism, a response strategy, a governance structure and process improvement system. These multiple components of the RRS pose problems in evaluation as the intervention is complex and cannot be evaluated using a traditional systematic review. Complex interventions in healthcare aimed at changing service delivery and related behaviour of health professionals require a different approach to summarising the evidence. Realist synthesis is such an approach to reviewing research evidence on complex interventions to provide an explanatory analysis of how and why an intervention works or doesn’t work in practice. The core principle is to make explicit the underlying assumptions about how an intervention is suppose to work (ie programme theory) and then use this theory to guide evaluation. Methods A realist synthesis process was used to explain those factors that enable or constrain the success of RRS programmes. Results The findings from the review include the articulation of the RRS programme theories, evaluation of whether these theories are supported or refuted by the research evidence and an evaluation of evidence to explain the underlying reasons why RRS works or doesn’t work in practice. Rival conjectured RRS programme theories were identified to explain the constraining factors regarding implementation of RRS in practice. These programme theories are presented using a logic model to highlight all the components which impact or influence the delivery of RRS programmes in the practice setting. The evidence from the realist synthesis provided the foundation for the development of hypothesis to test and refine the theories in the subsequent stages of the Realist Evaluation PhD study [1]. This information will be useful in providing evidence and direction for strategic and service planning of acute care to improve patient safety in hospital. References: McGaughey J, Blackwood B, O’Halloran P, Trinder T. J. & Porter S. (2010) Realistic Evaluation of Early Warning Systems and the Acute Life-threatening Events – Recognition and Treatment training course for early recognition and management of deteriorating ward-based patients: research protocol. Journal of Advanced Nursing 66 (4), 923-932.

Early warning systems: problems, pragmatics and potential

Symposium Chair: Dr Jennifer McGaughey

Title: Early Warning Systems: problems, pragmatics and potential

Early Warning Systems (EWS) provide a mechanism for staff to recognise, refer and manage deteriorating patients on general hospital wards. Implementation of EWS in practice has required considerable change in the delivery of critical care across hospitals. Drawing their experience of these changes the authors will demonstrate the problems and potential of using EWS to improve patient outcomes.

The first paper (Dr Jennifer McGaughey: Early Warning Systems: what works?) reviews the research evidence regarding the factors that support or constrain the implementation of Early Warning System (EWS) in practice. These findings explain those processes which impact on the successful achievement of patient outcomes. In order to improve detection and standardise practice National EWS have been implemented in the United Kingdom. The second paper (Catherine Plowright: The implementation of the National EWS in a District General Hospital) focuses on the process of implementing and auditing a National EWS. This process improvement is essential to contribute to future collaborative research and collection of robust datasets to improve patient safety as recommended by the Royal College of Physicians (RCP 2012). To successfully implement NEWS in practice requires strategic planning and staff education. The practical issues of training staff is discussed in the third paper. This paper (Collette Laws-Chapman: Simulation as a modality to embed the use of Early Warning Systems) focuses on using simulation and structured debrief to enhance learning in the early recognition and management of deteriorating patients. This session emphasises the importance of cognitive and social skills developed alongside practical skills in the simulated setting.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

virtX - Ein rechnergestütztes System zum Training des intraoperativen Einsatzes eines mobilen Bildverstärkers

Hintergrund und Fragestellung: Die korrekte intraoperative Positionierung und Einstellung eines mobilen Bildverstärkers (auch C-Bogen) kann zurzeit theoretisch mit Hilfe von Lehrbüchern erlernt, am Gerät selbst aber nur ohne visuelle Rückmeldung, d.h. ohne ein zur Ausrichtung korrespondierendes Röntgenbild, trainiert werden. Hieraus ergibt sich die Fragestellung, inwiefern das Training der Handhabung und richtigen Einstellung des C-Bogens in verschiedenen Operationsszenarien durch ein C-Bogen Simulationssystem als Teil eines CBT-Systems (Computer Based Training) unterstützt werden kann. Methoden: In Kooperation mit Ärzten aus Unfallchirurgie und Radiologie wurde das computer-basierte Trainingssystem virtX entwickelt. virtX kann dem Nutzer verschiedene Aufgaben zur Einstellung eines C-Bogens stellen und die Ausführung und das Ergebnis bewerten. Die Aufgaben können mit Hilfe eines Autorensystems erstellt und vom Trainierenden in verschiedenen Modi erfüllt werden: im rein virtuellen Modus oder im kombinierten virtuell-realen Modus. Im rein virtuellen Modus steuert der Nutzer den virtuellen C-Bogen in einem virtuellen OP-Saal mittels einer grafisch-interaktiven Benutzungsoberfläche. Im virtuell-realen Modus hingegen wird die Ausrichtung eines realen C-Bogens erfasst und auf den virtuellen C-Bogen übertragen. Während der Aufgabenerfüllung kann der Benutzer zu jeder Zeit ein realitätsnahes, virtuelles Röntgenbild erzeugen und dabei alle Parameter wie Blendenstellung, Röntgenintensität, etc. wie bei einem realen C-Bogen steuern. virtX wurde auf einem dreitägigen Kurs für OP-Personal mit 120 Teilnehmern eingesetzt und auf der Basis von Fragebögen evaluiert. Ergebnisse: Von den Teilnehmern gaben 79 einen ausgefüllten Evaluations-Fragebogen ab. Das Durchschnittsalter der 62 weiblichen und 15 männlichen Teilnehmer (zwei o.A.) lag bei 34 ± 9 Jahren, die Berufserfahrung bei 8,3 ± 7,6 Jahren. 18 Personen (23%) gaben an, gelegentlich mit einem C-Bogen zu arbeiten, 61 (77%) arbeiteten regelmäßig damit. Über 83% der befragten Teilnehmer empfanden virtX als eine sinnvolle Ergänzung zur herkömmlichen Ausbildung am C-Bogen. Das virtuelle Röntgen wurde mit einer Zustimmung von 91% der befragten Teilnehmer als besonders wichtig für das Verständnis der Arbeitsweise eines C-Bogens beurteilt. Ebenso erhielt der kombinierte virtuell-reale Modus mit 84% Zustimmung einen vergleichsweise hohen Stellenwert. Schlussfolgerung: Die Befragung zeichnet ein positives Bild der Akzeptanz des virtX-System als substanzielle Ergänzung zur herkömmlichen Ausbildung am C-Bogen.

Intelligent behavior in a Cyber-Ambient training system for control center operators

This paper deals with the application of an intelligent tutoring approach to delivery training in diagnosis procedures of a Power System. In particular, the mechanisms implemented by the training tool to support the trainees are detailed. This tool is part of an architecture conceived to integrate Power Systems tools in a Power System Control Centre, based on an Ambient Intelligent paradigm. The present work is integrated in the CITOPSY project which main goal is to achieve a better integration between operators and control room applications, considering the needs of people, customizing requirements and forecasting behaviors.

Master's in autonomous systems: an overview of the robotics curriculum and outcomes at ISEP, Portugal

Robotics research in Portugal is increasing every year, but few students embrace it as one of their first choices for study. Until recently, job offers for engineers were plentiful, and those looking for a degree in science and technology would avoid areas considered to be demanding, like robotics. At the undergraduate level, robotics programs are still competing for a place in the classical engineering graduate curricula. Innovative and dynamic Master’s programs may offer the solution to this gap. The Master’s degree in autonomous systems at the Instituto Superior de Engenharia do Porto (ISEP), Porto, Portugal, was designed to provide a solid training in robotics and has been showing interesting results, mainly due to differences in course structure and the context in which students are welcomed to study and work

Curso E-learning de sistemas multicorpo (multibody systems) para engenheiros mecânicos

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.