Research Question, Study Design and Continuous Research Education and Training Exercises (CREATE) Program.

The clinical research project starts with identifying the optimal research question, one that is ethical, impactful, feasible, scientifically sound, novel, relevant, and interesting. The project continues with the design of the study to answer the research question. Such design should be consistent with ethical and methodological principles, and make optimal use of resources in order to have the best chances of identifying a meaningful answer to the research question. Physicians and other healthcare providers are optimally positioned to identify meaningful research questions the answer to which could make significant impact on healthcare delivery. The typical medical education curriculum, however, lacks solid training in clinical research. We propose CREATE (Continuous Research Education And Training Exercises) as a peer- and group-based, interactive, analytical, customized, and accrediting program with didactic, training, mentoring, administrative, and professional support to enhance clinical research knowledge and skills among healthcare professionals, promote the generation of original research projects, increase the chances of their successful completion and potential for meaningful impact. The key features of the program are successive intra- and inter-group discussions and confrontational thematic challenges among participating peers aimed at capitalizing on the groups' collective knowledge, experience and skills, and combined intellectual processing capabilities to optimize choice of research project elements and stakeholder decision-making.

Cognitive training: the effects of working memory training

Gemstone Team Cognitive Training

Elementary Principals' Perceptions of a Leadership Development Training Program

Research indicates that school leaders are crucial to improving instruction and raising student achievement (Council of Chief State School Officers, 2008). As such, educational reforms such as the No Child Left Behind Act (2001) and Race to the Top (2009) have sparked an accountability movement where principals are being held accountable for students' academic achievement and educational outcomes. The shift towards greater accountability has placed new attention on the ways principals are trained. Researchers have noted that organized professional development programs have not adequately prepared school principals to meet the priority demands of the 21st century (Hale & Moorman, 2003; Murphy, 1994). Murphy (1994) stated, "Traditional preparation programs - usually pre-service programs based in colleges or universities, that awarded certification and advanced degrees - rarely concentrated on the leadership challenges that principals actually face in real schools" (p. 4). As a result, many school districts are seeking ways to develop leadership development training programs that will prepare principals for their job responsibilities as a school leader. In spite of the additional training principals receive, researchers suggests that there is an obvious gap between the readiness of administrators to be instructional leaders and the demands for accountability that school administrators face (Hale & Moorman, 2003). This quantitative study examined elementary school principals' perceptions of their leadership development training program. Guided by four research questions, the study examined principals' perceptions of their overall training and how well their training prepared them to deal with school and classroom practices that contribute to student achievement; to work with teachers and others to design and implement a system for continuous student achievement; and to provide necessary support to carry out sound school, curriculum, and instructional practices. Data for this study was collected by way of survey responses from a total of 46 elementary school principals. The results from the study revealed that more than half (58.7%) of participants perceived their training as excellent. While principals' perceived that their training adequately prepared them to work collaboratively in teams, set clear visions and goals, and to use data to improve students achievement, many respondents reported a lack of training in being informed and focused on student achievement. Principals also suggested that they were not effectively trained in finding effective ways to obtain support from central office or community members.

Data collection in support of the modelling of naval vessels

Evacuation analysis of passenger and commercial shipping can be undertaken using computer-based simulation tools such as maritimeEXODUS. These tools emulate human shipboard behaviour during emergency scenarios; however it is largely based around the behaviour of civilian passengers and fixtures and fittings of merchant vessels. If these tools and procedures are to be applied to naval vessels there is a clear requirement to understand the behaviour of well-trained naval personnel interacting with the fixtures and fittings that are exclusive to warships. Human factor trials using Royal Navy training facilities were recently undertaken to collect data to improve our understanding of the performance of naval personnel in warship environments. The trials were designed and conducted by staff from the Fire Safety Engineering Group (FSEG) of the University of Greenwich on behalf of the Sea Technology Group (STG), Defence Procurement Agency. The trials involved a selection of RN volunteers with sea-going experience in warships, operating and traversing structural components under different angles of heel. This paper describes the trials and some of the collected data.

Can the use of supplementary video and audio materials support student understanding of 3D animation? Some results, analysis and conclusions

A project within a computing department at the University of Greenwich, has been carried out to identify whether podcasting can be used to help understanding and learning of a subject (3D Animation). We know that the benefits of podcasting in education (HE) can be justified, [1]; [2]; [3]; [4]; [5]; [6] and that some success has been proven, but this paper aims to report the results of a term-long project that provided podcast materials for students to help support their learning using Xserve and Podcast Producer technology. Findings in a previous study [6] identified podcasting as a way to diversify learning and provde a more personalised learning experience for students, as well as being able to provide access to a greater mix of learning styles [7]. Finally this paper aims to present the method of capture and distribution, the methodologies of the study, analysis of results, and conclusions that relate to podcasting and enhanced supported learning.

Habit reversal training as a treatment for refractory OCD - A case study

Nearly 4 million American men and women from all geographic, ethnic, or economic backgrounds are diagnosed with obsessive-compulsive disorder (OCD). While a combination of cognitive behaviour therapy (CBT) and psycho-pharmaca seems successful for 50% to 60% of patients, for intractable cases the typical recommendation is more medication or more CBT, however there is little evidence that the intensified treatment regimen is successful. In this paper, habit reversal training, including awareness training, competing/other response training, self-monitoring, social support, and generalisation, was implemented with a long-term treatment-refractory OCD patient. Treatment gains and long-term maintenance indicate the potential of habit reversal procedures with these patients.

Approved Social Work Training in Northern Ireland: Using Research to Examine Competence-Based Learning and Influence Policy Change

This article reports on how research activity helped describe and analyse ASW (Approved Social Worker) learning experience as well as acting as a catalyst for change and development in policy and practice in Northern Ireland. The paper contextualizes the study by outlining the legislation, the main features of the ASW role and the approach to ASW training in Northern Ireland, and by reviewing the literature on the efficacy and value of competence-based learning. While the findings do not provide conclusive evidence that a competence-based approach is inherently more effective than previous courses, they do indicate that candidates who were trained in this way were moderately more satisfied than those who had participated in non-competence based programmes. The research also highlights the importance of the interrelationship between training, practice experience and support in developing and sustaining competence. The paper concludes with a review of the recommendations arising from the study and an analysis of the developments in training and regulations relating to practice experience and re-approval of ASWs since publication of the research. The study is of contemporary interest given the proposed changes to the role of ASWs/Mental Health Officers in the context of the reviews of UK mental health law.

The ABC for Life Programme - Teaching Basic Life Support in Schools

Background Less than 1% of the general public know how to assess or manage someone who has collapsed. It has been estimated that if 15–20% of the population were capable of performing cardiopulmonary resuscitation (CPR), mortality of out of hospital cardiac arrest could be decreased significantly. Training basic life support (BLS) skills to school children would be the most cost effective way of achieving this goal and ensuring that a large proportion of the population acquire basic life saving skills. Aims To assess retention of knowledge of basic life support 6 months after a single course of instruction in cardiopulmonary resuscitation designed specifically for school children. Setting School pupils in a rural location in one region of the United Kingdom. Methods A course of instruction in cardiopulmonary resuscitation – the ‘ABC for life’ programme – specifically designed to teach 10–12-year-old school children basic life support skills. The training session was given to school pupils in a rural location in Northern Ireland. A 22 point questionnaire was used to assess acquisition and retention of basic life support knowledge. Results Children instructed in cardiopulmonary resuscitation showed a highly significant increase in level of knowledge following the training session. While their level of knowledge decreased over a period of 6 months it remained significantly higher than that of a comparable group of children who had never been trained. Conclusion A training programme designed and taught as part of the school curriculum would have a significant impact on public health.

Lifelong learning through digital storytelling in corporate training

In this paper we discuss collaborative learning strategies based on the use of digital stories in corporate training and lifelong learning. The text starts with a concise review on theoretical and technical foundations about the use of digital technologies in collaborative strategies in lifelong learning. We will also discuss if the corporate training may be improved by the use of individual audio-visual experience in learning process. Careful planning, scripting and production of audio-visual digital stories can help in the construction of collaborative learning spaces in which adults are in the context of vocational training throughout life. Our analysis concludes emphasizing on the need to experience the routing performance of digital stories in the context of corporate training, following the reference levels mentioned here, so we can have in a future more theoretical and empirical elements for the validation and conceptualization in the use of digital stories in the context of corporate training. Ultimately we believe that lifelong learning can be improved with the use of strategies that promote the production of personal audio-visual for those involved in teaching and learning process in organizational context.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.

SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

N-gram analysis is an approach that investigates the structure of a program using bytes, characters, or text strings. A key issue with N-gram analysis is feature selection amidst the explosion of features that occurs when N is increased. The experiments within this paper represent programs as operational code (opcode) density histograms gained through dynamic analysis. A support vector machine is used to create a reference model, which is used to evaluate two methods of feature reduction, which are 'area of intersect' and 'subspace analysis using eigenvectors.' The findings show that the relationships between features are complex and simple statistics filtering approaches do not provide a viable approach. However, eigenvector subspace analysis produces a suitable filter.

Ethics in neuroscience graduate training programs:Views and models from Canada

Consideration of the ethical, social, and policy implications of research has become increasingly important to scientists and scholars whose work focuses on brain and mind, but limited empirical data exist on the education in ethics available to them. We examined the current landscape of ethics training in neuroscience programs, beginning with the Canadian context specifically, to elucidate the perceived needs of mentors and trainees and offer recommendations for resource development to meet those needs. We surveyed neuroscientists at all training levels and interviewed directors of neuroscience programs and training grants. A total of 88% of survey respondents reported general interest in ethics, and 96% indicated a desire for more ethics content as it applies to brain research and clinical translation. Expert interviews revealed formal ethics education in over half of programs and in 90% of grants-based programs. Lack of time, resources, and expertise, however, are major barriers to expanding ethics content in neuroscience education. We conclude with an initial set of recommendations to address these barriers which includes the development of flexible, tailored ethics education tools, increased financial support for ethics training, and strategies for fostering collaboration between ethics experts, neuroscience program directors, and funding agencies. © 2010 the Authors. Journal Compilation © 2010 International Mind, Brain, and Education Society and Blackwell Publishing, Inc.

Integrating geophysical data and upscaling techniques to support regional groundwater flow modelling: A practical example of the Lagan Valley, Northern Ireland

When studying heterogeneous aquifer systems, especially at regional scale, a degree of generalization is anticipated. This can be due to sparse sampling regimes, complex depositional environments or lack of accessibility to measure the subsurface. This can lead to an inaccurate conceptualization which can be detrimental when applied to groundwater flow models. It is important that numerical models are based on observed and accurate geological information and do not rely on the distribution of artificial aquifer properties. This can still be problematic as data will be modelled at a different scale to which it was collected. It is proposed here that integrating geophysics and upscaling techniques can assist in a more realistic and deterministic groundwater flow model. In this study, the sedimentary aquifer of the Lagan Valley in Northern Ireland is chosen due to intruding sub-vertical dolerite dykes. These dykes are of a lower permeability than the sandstone aquifer. The use of airborne magnetics allows the delineation of heterogeneities, confirmed by field analysis. Permeability measured at the field scale is then upscaled to different levels using a correlation with the geophysical data, creating equivalent parameters that can be directly imported into numerical groundwater flow models. These parameters include directional equivalent permeabilities and anisotropy. Several stages of upscaling are modelled in finite element. Initial modelling is providing promising results, especially at the intermediate scale, suggesting an accurate distribution of aquifer properties. This deterministic based methodology is being expanded to include stochastic methods of obtaining heterogeneity location based on airborne geophysical data. This is through the Direct Sample method of Multiple-Point Statistics (MPS). This method uses the magnetics as a training image to computationally determine a probabilistic occurrence of heterogeneity. There is also a need to apply the method to alternate geological contexts where the heterogeneity is of a higher permeability than the host rock.