General practitioner teachers’ job satisfaction and their medical students' wish to join the field – a correlational study

Background There will be increasing competition for young physicians worldwide as more and more physicians retire. While enthusiasm towards GP work is important for GP teachers as role models, satisfaction within the profession has declined. This study aims to determine if medical students’ desire to become GPs is related to the job satisfaction of their teaching GPs and explore the factors tied to this job satisfaction. Methods In this cross-sectional, correlational study, teaching GPs of the University of Bern and the fourth year medical students completing internships with them filled in separate questionnaires. Results Whether or not the GP teacher is perceived by a student to be satisfied with her/his job is correlated to that student’s satisfaction with the internship, which in turn, is correlated with student’s wish to be a GP after the internship. Results show which factors are most related to GP job satisfaction and the effect of working hours and their composition. Conclusions Medical students’ perception of their GP teachers’ job satisfaction positively affect their wish to become GPs, and their satisfaction with their internships adds to this. Enhancing the positive aspects of GP work, such as recognition, and improving negative ones, such as administrative duties, are necessary to attract medical students into the GP field.

Improved solutions to dynamic and stochastic maritime pick-up and delivery problems using local search.

Heuristics for stochastic and dynamic vehicle routing problems are often kept relatively simple, in part due to the high computational burden resulting from having to consider stochastic information in some form. In this work, three existing heuristics are extended by three different local search variations: a first improvement descent using stochastic information, a tabu search using stochastic information when updating the incumbent solution, and a tabu search using stochastic information when selecting moves based on a list of moves determined through a proxy evaluation. In particular, the three local search variations are designed to utilize stochastic information in the form of sampled scenarios. The results indicate that adding local search using stochastic information to the existing heuristics can further reduce operating costs for shipping companies by 0.5–2 %. While the existing heuristics could produce structurally different solutions even when using similar stochastic information in the search, the appended local search methods seem able to make the final solutions more similar in structure.

The usability of semantic search tools:a review

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

Ranked search on data graphs

Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity—users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. The purpose of this dissertation was to develop techniques for user-friendly, high quality and efficient searching of graph structured databases. Several ranked search methods on data graphs have been studied in the recent years. Given a top-k keyword search query on a graph and some ranking criteria, a keyword proximity search finds the top-k answers where each answer is a substructure of the graph containing all query keywords, which illustrates the relationship between the keyword present in the graph. We applied keyword proximity search on the web and the page graph of web documents to find top-k answers that satisfy user’s information need and increase user satisfaction. Another effective ranking mechanism applied on data graphs is the authority flow based ranking mechanism. Given a top- k keyword search query on a graph, an authority-flow based search finds the top-k answers where each answer is a node in the graph ranked according to its relevance and importance to the query. We developed techniques that improved the authority flow based search on data graphs by creating a framework to explain and reformulate them taking in to consideration user preferences and feedback. We also applied the proposed graph search techniques for Information Discovery over biological databases. Our algorithms were experimentally evaluated for performance and quality. The quality of our method was compared to current approaches by using user surveys.

A Procedure for Semantic Querying in the Adaptive Formalism

This paper contains a new proposal for the definition of the fundamental operation of query under the Adaptive Formalism, one capable of locating functional nuclei from descriptions of their semantics. To demonstrate the method`s applicability, an implementation of the query procedure constrained to a specific class of devices is shown, and its asymptotic computational complexity is discussed.

Effects of interventions in health care settings on physical activity or cardiorespiratory fitness

Introduction: This paper reviews studies of physical activity interventions in health care settings to determine effects on physical activity and/or fitness and characteristics of successful interventions. Methods: Studies testing interventions to promote physical activity in health care settings for primary prevention (patients without disease) and secondary prevention (patients with cardiovascular disease [CVD]) were identified by computerized search methods and reference lists of reviews and articles. Inclusion criteria included assignment to intervention and control groups, physical activity or cardiorespiratory fitness outcome measures, and, for the secondary prevention studies, measurement 12 or more months after randomization. The number of studies with statistically significant effects was determined overall as well as for studies testing interventions with various characteristics. Results: Twelve studies of primary prevention were identified, seven of which were randomized. Three of four randomized studies with short-term measurement (4 weeks to 3 months after randomization), and two of five randomized studies with long-term measurement (6 months after randomization) achieved significant effects on physical activity. Twenty-four randomized studies of CVD secondary prevention were identified; 13 achieved significant effects on activity and/or fitness at twelve or more months. Studies with measurement at two time points showed decaying effects over time, particularly if the intervention were discontinued. Successful interventions contained multiple contacts, behavioral approaches, supervised exercise, provision of equipment, and/or continuing intervention. Many studies had methodologic problems such as low follow-up rates. Conclusion: Interventions in health care settings can increase physical activity for both primary and secondary prevention. Long-term effects are more likely with continuing intervention and multiple intervention components such as supervised exercise, provision of equipment, and behavioral approaches. Recommendations for additional research are given.

Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultural systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with literature and our studies identifying evolutionary algorithms (including genetic algorithms) as superior in this regard to simulated annealing, tabu search, hill-climbing, and direct-search methods. Results of a complex beef property optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultural systems. (C) 2001 Elsevier Science Ltd. All rights reserved.

Estimating and evaluating the statistics of gapped local-alignment scores

We present a novel maximum-likelihood-based algorithm for estimating the distribution of alignment scores from the scores of unrelated sequences in a database search. Using a new method for measuring the accuracy of p-values, we show that our maximum-likelihood-based algorithm is more accurate than existing regression-based and lookup table methods. We explore a more sophisticated way of modeling and estimating the score distributions (using a two-component mixture model and expectation maximization), but conclude that this does not improve significantly over simply ignoring scores with small E-values during estimation. Finally, we measure the classification accuracy of p-values estimated in different ways and observe that inaccurate p-values can, somewhat paradoxically, lead to higher classification accuracy. We explain this paradox and argue that statistical accuracy, not classification accuracy, should be the primary criterion in comparisons of similarity search methods that return p-values that adjust for target sequence length.

Direct multisearch for multiobjective optimization

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.

Prostituição feminina de rua : escutar e atuar: papel do técnico d’O Ninho na intervenção com mulheres prostitutas

O Ninho é uma Associação que procura o conhecimento real do fenómeno prostituição, do próprio meio e dos seus agentes, bem como garantir qualidade na intervenção técnica e nas respostas sociais. Assim, o trabalho desenvolvido pelos técnicos objetiva responder aos problemas identificados pelas mulheres prostitutas e colmatar as lacunas nas respostas existentes no que concerne ao fenómeno social prostituição feminina de rua. Este estudo pretende ir ao encontro da maior preocupação associada a esta problemática e, mais especificamente, no caso da prostituição feminina de rua – a preocupação com a inserção social das mulheres. Para a elaboração do presente trabalho foram utilizadas estratégias metodológicas como a observação participante e entrevistas semiestruturadas. Da análise dos resultados verificamos que a prostituição agudiza os estereótipos, preconceitos e o estigma social que tanto marginaliza as mulheres prostituídas. Assim, todo o trabalho técnico realizado n’O Ninho visa promover a autonomia das mulheres, a aquisição de regras e hábitos de trabalho, o cumprimento e definição de objetivos e a posterior procura de emprego. - ABSTRACT O Ninho is an association that seeks the real knowledge of the phenomenon prostitution, of the environment and their agents, as well as ensure quality in technical intervention and social responses. Thus, the work done by the technicians aims to respond to the problems identified by the prostitutes and fill the gaps in the existing responses, concerning to the social phenomenon female prostitution of street. This study intends to respond to the main concern associated with this issue and, more specifically, in the case of female street prostitution – the concern with social inclusion of women. For the preparation of this work, as methodological strategies, we used the participant observation and semi-structured interviews. By the analysis of the results we found that prostitution exacerbates the stereotypes, prejudices and the social stigma that marginalize women forced into prostitution. Thus, all the technical work done at O Ninho aims to promote women's autonomy, rules and work habits acquisition, setting goals and subsequent job search.

Derivative-free nonlinear optimization filter simplex

The filter method is a technique for solving nonlinear programming problems. The filter algorithm has two phases in each iteration. The first one reduces a measure of infeasibility, while in the second the objective function value is reduced. In real optimization problems, usually the objective function is not differentiable or its derivatives are unknown. In these cases it becomes essential to use optimization methods where the calculation of the derivatives or the verification of their existence is not necessary: direct search methods or derivative-free methods are examples of such techniques. In this work we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of simplex and filter methods. This method neither computes nor approximates derivatives, penalty constants or Lagrange multipliers.

Improvement of a filters method in a derivative free optimization

Constraints nonlinear optimization problems can be solved using penalty or barrier functions. This strategy, based on solving the problems without constraints obtained from the original problem, have shown to be e ective, particularly when used with direct search methods. An alternative to solve the previous problems is the lters method. The lters method introduced by Fletcher and Ley er in 2002, , has been widely used to solve problems of the type mentioned above. These methods use a strategy di erent from the barrier or penalty functions. The previous functions de ne a new one that combine the objective function and the constraints, while the lters method treat optimization problems as a bi-objective problems that minimize the objective function and a function that aggregates the constraints. Motivated by the work of Audet and Dennis in 2004, using lters method with derivative-free algorithms, the authors developed works where other direct search meth- ods were used, combining their potential with the lters method. More recently. In a new variant of these methods was presented, where it some alternative aggregation restrictions for the construction of lters were proposed. This paper presents a variant of the lters method, more robust than the previous ones, that has been implemented with a safeguard procedure where values of the function and constraints are interlinked and not treated completely independently.

Node adaptation for global collocation with radial basis functions using direct multisearch for multiobjective optimization

Meshless methods are used for their capability of producing excellent solutions without requiring a mesh, avoiding mesh related problems encountered in other numerical methods, such as finite elements. However, node placement is still an open question, specially in strong form collocation meshless methods. The number of used nodes can have a big influence on matrix size and therefore produce ill-conditioned matrices. In order to optimize node position and number, a direct multisearch technique for multiobjective optimization is used to optimize node distribution in the global collocation method using radial basis functions. The optimization method is applied to the bending of isotropic simply supported plates. Using as a starting condition a uniformly distributed grid, results show that the method is capable of reducing the number of nodes in the grid without compromising the accuracy of the solution. (C) 2013 Elsevier Ltd. All rights reserved.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Integrating protein structural information

Dissertação apresentada para obtenção de Grau de Doutor em Bioquímica,Bioquímica Estrutural, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia