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.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

Compressive sensing for gait recognition

Compressive Sensing (CS) is a popular signal processing technique, that can exactly reconstruct a signal given a small number of random projections of the original signal, provided that the signal is sufficiently sparse. We demonstrate the applicability of CS in the field of gait recognition as a very effective dimensionality reduction technique, using the gait energy image (GEI) as the feature extraction process. We compare the CS based approach to the principal component analysis (PCA) and show that the proposed method outperforms this baseline, particularly under situations where there are appearance changes in the subject. Applying CS to the gait features also avoids the need to train the models, by using a generalised random projection.

Advanced battery storage control for an autonomous microgrid

A new control method for battery storage to maintain acceptable voltage profile in autonomous microgrids is proposed in this article. The proposed battery control ensures that the bus voltages in the microgrid are maintained during disturbances such as load change, loss of micro-sources, or distributed generations hitting power limit. Unlike the conventional storage control based on local measurements, the proposed method is based on an advanced control technique, where the reference power is determined based on the voltage drop profile at the battery bus. An artificial neural network based controller is used to determine the reference power needed for the battery to hold the microgrid voltage within regulation limits. The pattern of drop in the local bus voltage during power imbalance is used to train the controller off-line. During normal operation, the battery floats with the local bus voltage without any power injection. The battery is charged or discharged during the transients with a high gain feedback loop. Depending on the rate of voltage fall, it is switched to power control mode to inject the reference power determined by the proposed controller. After a defined time period, the battery power injection is reduced to zero using slow reverse-droop characteristics, ensuring a slow rate of increase in power demand from the other distributed generations. The proposed control method is simulated for various operating conditions in a microgrid with both inertial and converter interfaced sources. The proposed battery control provides a quick load pick up and smooth load sharing with the other micro-sources in a disturbance. With various disturbances, maximum voltage drop over 8% with conventional energy storage is reduced within 2.5% with the proposed control method.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Calibration and validation of wearable monitors

Background Wearable monitors are increasingly being used to objectively monitor physical activity in research studies within the field of exercise science. Calibration and validation of these devices are vital to obtaining accurate data. This article is aimed primarily at the physical activity measurement specialist, although the end user who is conducting studies with these devices also may benefit from knowing about this topic. Best Practices Initially, wearable physical activity monitors should undergo unit calibration to ensure interinstrument reliability. The next step is to simultaneously collect both raw signal data (e.g., acceleration) from the wearable monitors and rates of energy expenditure, so that algorithms can be developed to convert the direct signals into energy expenditure. This process should use multiple wearable monitors and a large and diverse subject group and should include a wide range of physical activities commonly performed in daily life (from sedentary to vigorous). Future Directions New methods of calibration now use "pattern recognition" approaches to train the algorithms on various activities, and they provide estimates of energy expenditure that are much better than those previously available with the single-regression approach. Once a method of predicting energy expenditure has been established, the next step is to examine its predictive accuracy by cross-validating it in other populations. In this article, we attempt to summarize the best practices for calibration and validation of wearable physical activity monitors. Finally, we conclude with some ideas for future research ideas that will move the field of physical activity measurement forward.