Why do medical graduates choose rural careers?

Introduction: This study is based on the metaphor of the ‘rural pipeline’ into medical practice. The four stages of the rural
pipeline are: (1) contact between rural secondary schools and the medical profession; (2) selection of rural students into medical
programs; (3) rural exposure during medical training; and (4) measures to address retention of the rural medical workforce.
Methods: Using the rural pipeline template we conducted a literature review, analysed the selection methods of Australian
graduate entry medical schools and interviewed 17 interns about their medical career aspirations.
Results: Literature review: The literature was reviewed to assess the effectiveness of selection practices to predict successful
gradation and the impact of rural pipeline components on eventual rural practice. Undergraduate academic performance is the
strongest predictor of medical course academic performance. The predictive power of interviews is modest. There are limited data
on the predictive power of other measures of non-cognitive performance or the content of the undergraduate degree. Prior rural
residence is the strongest predictor of choice of a rural career but extended rural exposure during medical training also has a
significant impact. The most significant influencing factors are: professional support at national, state and local levels; career
pathway opportunities; contentedness of the practitioner’s spouse in rural communities; preparedness to adopt a rural lifestyle;
educational opportunities for children; and proximity to extended family and social circle. Analysis of selection methods: Staff
involved in student selection into 9 Australian graduate entry medical schools were interviewed. Four themes were identified:
(1) rurality as a factor in student selection; (2) rurality as a factor in student selection interviews; (3) rural representation on student
selection interview panels; (4) rural experience during the medical course. Interns’ career intentions: Three themes were identified:
(1) the efficacy of the rural pipeline; (2) community connectedness through the rural pipeline; (3) impediments to the effect of the
rural pipeline, the most significant being a partner who was not committed to rural life
Conclusion: Based on the literature review and interviews, 11 strategies are suggested to increase the number of graduates
choosing a career in rural medicine, and one strategy for maintaining practitioners in rural health settings after graduation.

Face recognition via the overlapping energy histogram

In this paper we investigate the face recognition problem via the overlapping energy histogram of the DCT coefficients. Particularly, we investigate some important issues relating to the recognition performance, such as the issue of selecting threshold and the number of bins. These selection methods utilise information obtained from the training dataset. Experimentation is conducted on the Yale face database and results indicate that the proposed parameter selection methods perform well in selecting the threshold and number of bins. Furthermore, we show that the proposed overlapping energy histogram approach outperforms the Eigenfaces, 2DPCA and energy histogram significantly.

Comparing the small sample properties of two break Lagrange Multiplier unit root tests

In this note, we examine the size and power properties and the break date estimation accuracy of the Lee and Strazicich (LS, 2003) two break endogenous unit root test, based on two different break date selection methods: minimising the test statistic and minimising the sum of squared residuals (SSR). Our results show that the performance of both Models A and C of the LS test are superior when one uses the minimising SSR procedure.

Improving telemarketing intelligence through significant proportion of target instances

In this paper we propose, develop, and test a new single-feature evaluator called Significant Proportion of Target Instances (SPTI) to handle the direct-marketing data with the class imbalance problem. The SPTI feature evaluator demonstrates its stability and outstanding performance through empirical experiments in which the real- orld customer data of an e-recruitment firm are used. This research demonstrates that the feature selection using SPTI successfully improves the classifier’s performance in terms of two practical performance metrics. Additionally, we show that it outperforms other well-known feature selection methods and state-of-the-art remedies to the class-imbalance problem. Practically, the findings, when used with the classification model, will help telemarketers to better understand their customers.

A modified Intelligent Water Drops algorithm and its application to optimization problems

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. © 2014 Elsevier Ltd. All rights reserved.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.

Stabilizing l1-norm prediction models by supervised feature grouping

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.

O conhecimento científico do poder como pré-requisito para o exercício da função gerencial nas organizações privadas

Nowadays, management of Intellectual Capital seems to be one of the most efficacious alternatives so that companies may permanently develop competitive differentials to enable their survival in a market that confers paradigma status to globalization. Managers are searching for solutions aiming to attract and maintain in their boards professionals that might be able to develop these competitive differentials and assure to their companies the expected results and the business growth. In this environment, the manager¿s functions appears as a strategic function towards the organization¿s routine and yet suggests an evaluation about the professionals performance concerning challenges (efforts) that market requires. The amount of scientific knowledge which these professionals detain about power should be measured as well as the thought that this can improve their performance regarding their capacity to convince (induce) people. This research concerns those that look for empirical evidences and scientific arguments to confirm the supposition that power knowledge may in fact provide a superior performance of those professionals that are in charge of management functions. The reason and results here presented allow to: reevaluate managers duties in private organizations, improve executive training programs so as to be more productive and appropriated, change the selection methods used to recognize such executives and finally suggest a reevaluation about the real adequation of graduation and master degree courses in regards to professionals graduation which are capable to ingress in business market and take over leadership positions.

Voleibol brasileiro : parâmetros fenotípicos e genotípicos na seleção de atletas

Objective: Identify phenotype and genotype parameters of female volleyball players at different performance levels to help in player selection. Methods: We identified characteristics of phenotype and genotype using the somatotype method (Heath Carter); anthropometry (weight, height and fat percentage); dermatoglyphics (Cummins and Midlo s method) as well as applying physical quality tests (Shuttle Run to assess agility and the Sargent Jump Test adapted for spike and block reach). The sample was composed of 179 players (54 from national teams and 125 from state teams). Results: Somatotype was similar among the performance levels in the mesomorphic component. The Height and ectomorphic component were greater in national team players as was spike and block reach. The vertical jump height for the spike was similar between the national under-17 team and the state teams observed, but in the block jump the lower level players were better. The dermatoglyphics characteristics identified were similar among the groups studied. Conclusions: The results of the variables studied show that somatotype, height, spike reach and block reach are fundamental parameters in player selection and in the specific characteristics of each game position of this sport. This paper proposes a multidisciplinary approach applicable in the fields of physical education, medicine and nutrition

Heritability estimation in early generations of two-way crosses in soybean

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Melhoramento genético da mamona visando incremento de flores femininas

O objetivo deste trabalho foi testar métodos de seleção visando ao aumento de flores femininas na população FCA-UNESP-PB de mamona (Ricinus communis L.). A seleção foi realizada no município de Botucatu (SP), na safrinha de 2007. Por meio de seleção massal, foram selecionadas plantas com racemo primário estritamente feminino. Destas plantas, as que tinham reversão sexual foram autofecundadas. As avaliações foram realizadas na safrinha de 2008 em Botucatu e São Manuel (SP), onde foram comparados os tratamentos: método de seleção massal; método de seleção massal com autofecundação e testemunha (racemos de plantas colhidos ao acaso, sem seleção). Foram avaliados: porcentagem de flores femininas do racemo primário (%), produtividade de grãos (kg ha-1) e teor de óleo das sementes (%). O delineamento experimental utilizado foi o de blocos casualizados com 30 repetições. Os dados foram submetidos à análise de variância individual para cada local e conjuntamente para os dois locais, pelo teste F a 1% de probabilidade. Mediante os resultados conclui- se que o método de seleção massal com autofecundação foi aquele que proporcionou maiores valores de porcentagem de flores femininas no racemo primário, com ganho fenotípico realizado de 18% em Botucatu e 29% em São Manuel (SP). Por meio dos métodos de seleção, notou-se comportamento diferencial em relação aos locais para a característica produtividade de grãos, e o método seleção massal com autofecundação proporcionou a menor produtividade. No teor de óleo não houve diferenças significativas entre os métodos e os locais avaliados.

Sistema inteligente para la selección de brocas de perforación de pozos

The present work begins with a review of the literature on bit selection methods for oil well drilling. A proposal for the structure and organization of a drilling database and a knowledge base, is described. Previous studies formed the principal elements in the process of selection of drills for proposed drilling. The procedure was implemented as a computer system for the selection of tricone bits. A drilling bit database for three different Brazilian sedimentary basins was obtained for several wells drilled, and knowledge was collected from drilling engineers from different fields both electronically and also by means of interviews. It can be concluded that the selection process showed good results based on tests, which were carried out.

Influência do método de separação dos espermatozóides viáveis (swim-up) na eficiência de seleção do sexo de bovinos por gradiente descontínuo de densidade e o impacto no melhoramento genético animal

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimativas do coeficiente de herdabilidade entre e dentro de famílias F5 de soja

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)