Nonlinear wave-activity conservation laws and Hamiltonian structure for the two-dimensional anelastic equations

Exact, finite-amplitude, local wave-activity conservation laws are derived for disturbances to steady flows in the context of the two-dimensional anelastic equations. The conservation laws are expressed entirely in terms of Eulerian quantities, and have the property that, in the limit of a small-amplitude, slowly varying, monochromatic wave train, the wave-activity density A and flux F, when averaged over phase, satisfy F = cgA where cg is the group velocity of the waves. For nonparallel steady flows, the only conserved wave activity is a form of disturbance pseudoenergy; when the steady flow is parallel, there is in addition a conservation law for the disturbance pseudomomentum. The above results are obtained not only for isentropic background states (which give the so-called “deep form” of the anelastic equations), but also for arbitrary background potential-temperature profiles θ0(z) so long as the variation in θ0(z) over the depth of the fluid is small compared with θ0 itself. The Hamiltonian structure of the equations is established in both cases, and its symmetry properties discussed. An expression for available potential energy is also derived that, for the case of a stably stratified background state (i.e., dθ0/dz > 0), is locally positive definite; the expression is valid for fully three-dimensional flow. The counterparts to results for the two-dimensional Boussinesq equations are also noted.

A dynamic causal model of the coupling between pulse stimulation and neural activity

We present a dynamic causal model that can explain context-dependent changes in neural responses, in the rat barrel cortex, to an electrical whisker stimulation at different frequencies. Neural responses were measured in terms of local field potentials. These were converted into current source density (CSD) data, and the time series of the CSD sink was extracted to provide a time series response train. The model structure consists of three layers (approximating the responses from the brain stem to the thalamus and then the barrel cortex), and the latter two layers contain nonlinearly coupled modules of linear second-order dynamic systems. The interaction of these modules forms a nonlinear regulatory system that determines the temporal structure of the neural response amplitude for the thalamic and cortical layers. The model is based on the measured population dynamics of neurons rather than the dynamics of a single neuron and was evaluated against CSD data from experiments with varying stimulation frequency (1–40 Hz), random pulse trains, and awake and anesthetized animals. The model parameters obtained by optimization for different physiological conditions (anesthetized or awake) were significantly different. Following Friston, Mechelli, Turner, and Price (2000), this work is part of a formal mathematical system currently being developed (Zheng et al., 2005) that links stimulation to the blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal through neural activity and hemodynamic variables. The importance of the model described here is that it can be used to invert the hemodynamic measurements of changes in blood flow to estimate the underlying neural activity.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Prevalence and Correlates of Cognitive Impairment among the Elderly in a General Hospital

Aims: To estimate the prevalence and correlates of cognitive impairment (CI) among the elderly in a general hospital. Methods: A cross-sectional study, including 200 Brazilian inpatients aged 60 years or over, using the Mini Mental State Examination to evaluate CI (dependent variable), and the Geriatric Depression Scale and the Katz and Lawton Index to evaluate basic (BADL) and instrumental activities of daily living (IADL). Results: 56% were women, 29% were dependent for BADL and 77.5% for IADL. The prevalence of CI was 29% and, in the logistic regression, it remained associated with higher age (1 74 years old), number of previous hospitalizations (1 3) and dependency for BADL (being dependent raised the odds of being cognitively impaired). Conclusions: It is essential to train the hospital staff to properly assist these patients, and to orient and support their caregivers. Copyright (C) 2009 S. Karger AG, Basel

An application of neural network technique to correct the dome temperature effects on pyrgeometer measurements

This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

Trainer for Kids (T4K), um único aparelho para o tratamento de dois problemas verticais: mordida aberta anterior e mordida profunda

The Trainer for Kids is a prefabricated funcitonal appliance indicated for early treatment of the Class I malocclusion, crowding, anterior open bite, deep bite and oral habits (finger and/or sucking, atypic deglutition, tongue interposition and oral breathing). The aim of this paper is show two case reports treated with the T4K appliance being an anterior open bite and a deep bite.

A project to improve the information seeking skills and increase the use of evidence-based research in public health practice

The ability of public health practitioners (PHPs) to work efficiently and effectively is negatively impacted by their lack of knowledge of the broad range of evidence-based practice information resources and tools that can be utilized to guide them in their development of health policies and programs. This project, a three-hour continuing education hands-on workshop with supporting resources, was designed to increase knowledge and skills of these resources. The workshop was presented as a pre-conference continuing education program for the Texas Public Health Association (TPHA) 2008 Annual Conference. Topics included: identification of evidence-based practice resources to aid in the development of policies and programs; identification of sources of publicly available data; utilization of data for community assessments; and accessing and searching the literature through a collection of databases available to all citizens of Texas. Supplemental resources included a blog that served as a gateway to the resources explored during the presentation, a community assessment workbook that incorporates both Healthy People 2010 objectives and links to reliable sources of data, and handouts providing additional instruction on the use of the resources covered during the workshop.^ Before- and after-workshop surveys based on Kirkpatrick's 4-level model of evaluation and the Theory of Planned Behavior were administered. Of the questions related to the trainer, the workshop, and the usefulness of the workshop, participants gave "Good" to "Excellent" responses to all one question. Confidence levels overall increased a statistically significant amount; measurements of attitude, social norms, and control showed no significant differences before and after the workshop. Lastly, participants indicated they were likely to use resources shown during the workshop within a one to three month time period on average. ^ The workshop and creation of supplemental resources served as a pilot for a funded project that will be continued with the development and delivery of four 4-week long webinar-based training sessions to be completed by December 2008. ^

Adaptive neuro-fuzzy inference systems (ANFIS) for supervised classification of competence in MIS based on psychomotor skills

INTRODUCTION: Objective assessment of motor skills has become an important challenge in minimally invasive surgery (MIS) training.Currently, there is no gold standard defining and determining the residents' surgical competence.To aid in the decision process, we analyze the validity of a supervised classifier to determine the degree of MIS competence based on assessment of psychomotor skills METHODOLOGY: The ANFIS is trained to classify performance in a box trainer peg transfer task performed by two groups (expert/non expert). There were 42 participants included in the study: the non-expert group consisted of 16 medical students and 8 residents (< 10 MIS procedures performed), whereas the expert group consisted of 14 residents (> 10 MIS procedures performed) and 4 experienced surgeons. Instrument movements were captured by means of the Endoscopic Video Analysis (EVA) tracking system. Nine motion analysis parameters (MAPs) were analyzed, including time, path length, depth, average speed, average acceleration, economy of area, economy of volume, idle time and motion smoothness. Data reduction was performed by means of principal component analysis, and then used to train the ANFIS net. Performance was measured by leave one out cross validation. RESULTS: The ANFIS presented an accuracy of 80.95%, where 13 experts and 21 non-experts were correctly classified. Total root mean square error was 0.88, while the area under the classifiers' ROC curve (AUC) was measured at 0.81. DISCUSSION: We have shown the usefulness of ANFIS for classification of MIS competence in a simple box trainer exercise. The main advantage of using ANFIS resides in its continuous output, which allows fine discrimination of surgical competence. There are, however, challenges that must be taken into account when considering use of ANFIS (e.g. training time, architecture modeling). Despite this, we have shown discriminative power of ANFIS for a low-difficulty box trainer task, regardless of the individual significances between MAPs. Future studies are required to confirm the findings, inclusion of new tasks, conditions and sample population.

Evaluation of the Magnetic Field Generated by the Inverter of an Electric Vehicle

In hybrid and electric vehicles, passengers sit very close to an electric system of significant power, which means that they may be subjected to high electromagnetic fields. The hazards of long-term exposure to these fields must be taken into account when designing electric vehicles and their components. Among all the electric devices present in the power train, the electronic converter is the most difficult to analyze, given that it works with different frequencies. In this paper, a methodology to evaluate the magnetic field created by a power electronics converter is proposed. After a brief overview of the recommendations of electromagnetic fields exposure, the magnetic field produced by an inverter is analyzed using finite element techniques. The results obtained are compared to laboratory measurements, taken from a real inverter, in order to validate the model. Finally, results are used to draw some conclusions regarding vehicle design criteria and magnetic shielding efficiency.

Integrating Evidence-Based Practice in the Training Programme Bachelor’s Degree in Nursing, University Jaume I

The Evidence-Based Practice (EBP) aims to combine a form methodological process of professional experience in health with the most current information on the clinical situation. The professional novice can make better decisions despite lacking sufficient years in clinical practice. We then train the student in correct habits within the methodological process by which you can strengthen both their knowledge and their attitude and ability, allowing secure customs, where all of your work is based on PBE.

Formação continuada em professores de educação física da Secretaria Municipal de Educação de Maceió-Alagoas

Enquadramento:A formação continuada é entendida como a formação recebida por for-mandos já profissionalizados e com uma vida ativa, tendo como base a adaptação contínua para a mudança de conhecimentos, técnicas e condições de trabalho, melhorando as quali-ficações profissionais e, por conseguinte, a sua promoção profissional e social. Objetivos: O objetivo geral foi avaliar o impacto do programa de formação continuada, oferecido pela Secretaria Municipal de Educação de Maceió-AL aos professores de educa-ção física, no período de 2005 a 2011. Os objetivos específicos foram:caracterizar o perfil do grupo pesquisado, compreender os itinerários formativos e identificar os principais fato-res profissionais e sociais na formação realizada. Métodos: Realizamos um estudo transversal descritivo de natureza quantitativa, com a seleção dos participantes elaborada com base no rol das unidades escolares que atendem a educação básica. A amostra final foi de 48 professores licenciados e concursados na área de educação física, por amostragem baseada em agrupamento e com aplicação de questio-nários de perguntas fechadas e abertas para coleta de dados, revelando: qual a modalidade de formação preferida, a participação do professor no programa de formação, principais motivações para participação, principais efeitos alcançados e qual a satisfação na formação realizada. Resultados: Entre as modalidades de formação analisadas a preferida foi a ‘oficina’com uma percentagem de 42,35%, seguida pelo ‘círculos de estudo’ com 24,71%. Os motivos referidos como muito importante pelos professores para a participação na formação foram os ‘emancipatórios’ com 66,67% e logo após motivos ‘pedagógicos’em 54,17%. Os três principais aspectos positivos na formação, mencionados pelos participantes foram a quali-dade do formador (16,60%), partilhar experiências (15,35%) e conviver/recordar colegas (14,11%). Quanto aos aspectos negativos indicados apontam duração da formação (22,58%), horário da formação (20,43%) e local da formação (15,05%). O grau de satisfa-ção quanto as dimensões exploradas (instrução, gestão/organização, clima relacional e dis-ciplina) na formação os professores encontram-se satisfeitos variando entre 43,75% e 66,67% para os diferentes itens das dimensões. Conclusão: Considerações finais apontam a frequência maior do género feminino, os pro-fessores com experiência profissional de 15 a 29 anos de atuação no magistério, e vincula-ção expressiva com outra instituição de ensino. Quanto aos itinerários formativos elegeram a modalidade formativa oficina como a preferida; quanto aos aspectos positivos valoriza-ram a qualidade do formador e negativo a duração da formação. Principalmente nas dimensões de instrução e gestão/organização da prática docente. A formação continuada da SEMED contribuiu satisfatoriamente para a prática educativa dos professores de educação física ao longo deste período. Palavras-chave: Formação continuada, educação física, professor.

Evaluation of the quality and promotion of open learning materials

In the UK, Open Learning has been used in industrial training for at least the last decade. Trainers and Open Learning practitioners have been concerned about the quality of the products and services being delivered. The argument put forward in this thesis is that there is ambiguity amongst industrialists over the meanings of `Open Learning' and `Quality in Open Learning'. For clarity, a new definition of Open Learning is proposed which challenges the traditional learner-centred approach favoured by educationalists. It introduces the concept that there are benefits afforded to the trainer/employer/teacher as well as to the learner. This enables a focussed view of what quality in Open Learning really means. Having discussed these issues, a new quantitative method of evaluating Open Learning is proposed. This is based upon an assessment of the degree of compliance with which products meet Parts 1 & 2 of the Open Learning Code of Practice. The vehicle for these research studies has been a commercial contract commissioned by the Training Agency for the Engineering Industry Training Board (EITB) to examine the quality of Open Learning products supplied to the engineering industry. A major part of this research has been the application of the evaluation technique to a range of 67 Open Learning products (in eight subject areas). The findings were that good quality products can be found right across the price range - so can average and poor quality ones. The study also shows quite convincingly that there are good quality products to be found at less than 50. Finally the majority (24 out of 34) of the good quality products were text based.

Trainer talk:structures of interaction in teacher training classrooms

The subject of this research is interaction and language use in an institutional context, the teacher training classroom. Trainer talk is an interactional accomplishment and the research question is: what structures of talk-in-interaction characterise trainer talk in this institutional setting? While there has been research into other kinds of classroom and into other kinds of institutional talk, this study is the first on trainer discourse. The study takes a Conversation Analysis approach to studying institutional interaction and aims to identify the main structures of sequential organization that characterize teacher trainer talk as well as the tasks and identities that are accomplished in it. The research identifies three main interactional contexts in which trainer talk is done: expository, exploratory and experiential. It describes the main characteristics of each and how they relate to each other. Expository sequences are the predominant interactional contexts for trainer talk. But the research findings show that these contexts are flexible and open to the embedding of the other two contexts. All three contexts contribute to the main institutional goal of teaching teachers how to teach. Trainer identity is related to the different sequential contexts. Three main forms of identity in interaction are evidenced in the interactional contexts: the trainer as trainer, the trainer as teacher and the trainer as colleague. Each of them play an important role in teacher trainer pedagogy. The main features of trainer talk as a form of institutional talk are characterised by the following interactional properties: 1. Professional discourse is both the vehicle and object of instruction - the articulation of reflection on experience. 2. There is a reflexive relationship between pedagogy and interaction. 3. The professional discourse that is produced by trainees is not evaluated by trainers but, rather, reformulated to give it relevant precision in terms of accuracy and appropriacy.

A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences. © 2008. Licensed under the Creative Commons.

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.