Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Mòdul per a Moodle que possibilita la utilització de mapes conceptuals als creadors de cursos.

Actualment, i amb la ràpida expansió en el món educatiu de les plataformes d'aprenentatge virtual, s'experimenta una demanda de funcionalitats que puguen ser usades des d'aquestes plataformes i que donen resposta als reptes educatius dins d'aquest nou paradigma d'aprenentatge.En aquest treball s'ha fet una tasca d'integració d'eines que possibiliten la creació, modificació, visualització i magatzematge de mapes conceptuals dins d'una plataforma d'aprenentatge molt usada actualment, tant en ensenyament secundari com universitari. La plataforma moodle.L'eina per construir mapes conceptuals triada ha segut l'anomenada VUE, desenvolupada en la universitat de Tufts.Ambdues eines moodle i VUE s'han desenvolupat amb codi obert i baix llicències de programari lliure, així es poden tindre tots els avantatges que aquestes llicències proporcionen.El resultat és una integració que possibilita la utilització de mapes conceptuals dins de la plataforma moodle.

Wissensvermehrung durch Konzeptentwicklung? Das Beispiel Vergänglichkeit [The contibution of concept development to nursing knowledge? The example of Transitoriness].

To provide nursing practice with evidence, it is important to understand nursing phenomena in detail. Therefore, good descriptions including the identification of characteristics and attributes of nursing phenomena on various levels of abstraction, i. e., concepts, are needed. In this article the significance of concept development for nursing science will be demonstrated by drawing on the example of 'transitoriness'. The evolutionary concept analysis proposed by Rodgers (2000) is introduced in more detail. Drawing on transitoriness, the phenomenon is presented with the help of the evolutionary concept analysis by Rodgers (2000). The phenomenon's characteristics and attributes are identified, as well as potential areas of application. Moreover, areas are outlined, in which interventions for nursing practice can be developed, implemented and evaluated. Thus, nursing practice is updated to include new findings and innovation. Through concept analysis nursing phenomena can be described in more detail, enhanced or broadened for use in nursing practice. Such structured processes as concept analysis can be employed successfully for other nursing phenomena. Concept analyses can lead to the identification of tasks for the respective scientific discipline and professionals. Thus, concept analyses can lead to the concretisation of tasks in nursing.

Combination of enteral and parenteral nutrition in intensive care patients: A new concept of optimization

Undernutrition is a widespread problem in intensive care unit and is associated with a worse clinical outcome. A state of negative energy balance increases stress catabolism and is associated with increased morbidity and mortality in ICU patients. Undernutrition-related increased morbidity is correlated with an increase in the length of hospital stay and health care costs. Enteral nutrition is the recommended feeding route in critically ill patients, but it is often insufficient to cover the nutritional needs. The initiation of supplemental parenteral nutrition, when enteral nutrition is insufficient, could optimize the nutritional therapy by preventing the onset of early energy deficiency, and thus, could allow to reduce morbidity, length of stay and costs, shorten recovery period and, finally, improve quality of life. (C) 2009 Elsevier Masson SAS. All rights reserved.

Adult Learning Services

Evidence Review 4 - Adult learning services Briefing 4 - Adult learning services This pair of documents, commissioned by Public Health England, and written by the UCL Institute of Health Equity, address the role of participation in learning as an adult in improving health. There is evidence that involvement in adult learning has both direct and indirect links with health, for example because it increases employability. There is some evidence that those who are lower down the social gradient benefit most, in health terms, from adult learning. However, there is a gradient both in participation in adult learning and skill level, whereby the more someone would benefit from adult learning, the less likely they are to participate, and the lower their literacy and numeracy skills are likely to be. This is due to a range of barriers, including prohibitively high costs, lack of personal confidence, or lack of availability and access. These papers also show that there are a number of actions local authorities can take to increase access to adult learning, improve quality of provision and increase the extent to which it is delivered and targeted proportionate to need. The full evidence review and a shorter summary briefing are available to download above. This document is part of a series. An overview document which provides an introduction to this and other documents in the series, and links to the other topic areas, is available on the ‘Local Action on health inequalities’ project page. A video of Michael Marmot introducing the work is also available on our videos page.

Substance use behaviours of young people with a moderate learning disability: a longitudinal analysis.

Substance use behaviors of young people attending a special school are reported over a 4-year period from the age of 12-16 years. The article investigated these behaviors by surveying a cohort of young people with a statement for moderate learning disabilities annually during the last 4 years of compulsory schooling. The findings show that these young people consistently reported lower levels of tobacco, alcohol, and cannabis use compared with those attending mainstream school. No other illicit drug use was reported. The potential implications of these findings are discussed in relation to the context and timing of targeted substance education and prevention initiatives for young people with moderate learning disability attending a special school.This resource was contributed by The National Documentation Centre on Drug Use.

Learning collaboration in General Internal Medicine: a pilot project for undergraduate students in nursing, physiotherapy and medicine

Background: A form of education called Interprofessional Education (IPE) occurs when two or more professions learn with, from and about each other. The purpose of IPE is to improve collaboration and the quality of care. Today, IPE is considered as a key educational approach for students in the health professions. IPE is highly effective when delivered in active patient care, such as in clinical placements. General internal medicine (GIM) is a core discipline where hospital-based clinical placements are mandatory for students in many health professions. However, few interprofessional (IP) clinical placements in GIM have been implemented. We designed such a placement. Placement design: The placement took place in the Department of Internal Medicine at the CHUV. It involved students from nursing, physiotherapy and medicine. The students were in their last year before graduation. Students formed teams consisting of one student from each profession. Each team worked in the same unit and had to take care of the same patient. The placement lasted three weeks. It included formal IP sessions, the most important being facilitated discussions or "briefings" (3x/w) during which the students discussed patient care and management. Four teams of students eventually took part in this project. Method: We performed a type of evaluation research called formative evaluation. This aimed at (1) understanding the educational experience and (2) assessing the impact of the placement on student learning. We collected quantitative data with pre-post clerkship questionnaires. We also collected qualitative data with two Focus Groups (FG) discussions at the end of the placement. The FG were audiotaped and transcribed. A thematic analysis was then performed. Results: We focused on the qualitative data, since the quantitative data lacked of statistical power due to the small numbers of students (N = 11). Five themes emerged from the FG analysis: (1) Learning of others' roles, (2) Learning collaborative competences, (3) Striking a balance between acquiring one's own professional competences and interprofessional competences, (4) Barriers to apply learnt IP competences in the future and (5) Advantages and disadvantages of IP briefings. Conclusions: Our IP clinical placement in GIM appeared to help students learn other professionals' roles and collaborative skills. Some challenges (e.g. finding the same patient for each team) were identified and will require adjustments.