A direct determination of the World Ocean barotropic circulation

The time-mean Argo float displacements and the World Ocean Atlas 2009 temperature–salinity climatology are used to obtain the total, top to bottom, mass transports. Outside of an equatorial band, the total transports are the sum of the vertical integrals of geostrophic- and wind-driven Ekman currents. However, these transports are generally divergent, and to obtain a mass conserving circulation, a Poisson equation is solved for the streamfunction with Dirichlet boundary conditions at solid boundaries. The value of the streamfunction on islands is also part of the unknowns. This study presents and discusses an energetic circulation in three basins: the North Atlantic, the North Pacific, and the Southern Ocean. This global method leads to new estimations of the time-mean western Eulerian boundary current transports maxima of 97 Sverdrups (Sv; 1 Sv ≡ 106 m3 s−1) at 60°W for the Gulf Stream, 84 Sv at 157°E for the Kuroshio, 80 Sv for the Agulhas Current between 32° and 36°S, and finally 175 Sv for the Antarctic Circumpolar Current at Drake Passage. Although the large-scale structure and boundary of the interior gyres is well predicted by the Sverdrup relation, the transports derived from the wind stress curl are lower than the observed transports in the interior by roughly a factor of 2, suggesting an important contribution of the bottom torques. With additional Argo displacement data, the errors caused by the presence of remaining transient terms at the 1000-db reference level will continue to decrease, allowing this method to produce increasingly accurate results in the future.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Mapeamento projetivo, associação de palavras e escala hedônica como instrumentos de avaliação da percepção de produtos de pescado por crianças da rede pública de ensino

Brazil is a country that is characterized by its low consumption of fish. With consumption records of 10.6 kg/ inhabitant/ year, it is lower than the recommended by the UN, that is 12 kg/ inhabitant/ year. The regular consumption of fish provides health gain for people and their introduction into the school feeding is an important strategy for the insertion of this food consumption habits in a population. In this context, the objective of this study was to understand the perception of fish with children from the public school system through the technical Projective Mapping (MP) and Association of Words (AP); and evaluate the acceptability of fish derivative in school meals. In the first instance with the intention to better understand the perception of children from different ages about the fish-based products, Projective Mapping techniques were applied through the use of food figures and word association. A total of 149 children from three public schools from Pato Branco, Paraná State, Brazil, took part in this study. Three groups of children aged 5-6, 7-8 and 9-10 years old were interviewed individually by six monitors experienced in applied sensory methods. Ten figures with healthy foods drawings (sushi, salad, fruit, fish, chicken), and less healthy foods (pizza, pudding, cake, hamburger, fries) were distributed to the children, who were asked to paste the figures in A3 sheet, so that the products they considered similar stayed near each other, and the ones considered very different stayed apart. After this, the children described the images and the image groups (Ultra Flash Profile). The results revealed that the MP technique was easily operated and understood by all the children and the use of images made its implementation easier. The results analysis also revealed different perceptions came from children from different ages and hedonic perceptions regarding the fish-based products had a greater weight in the percentage from older children. AP technique proved to be an important tool to understand the perception of fish by children, and strengthened the results previously obtained by the MP. In a second step it was evaluated the acceptance of fish burger (tilapia) in school meals. For this task, the school cooks were trained to prepare the hamburgers. For the evaluation of acceptance, the hedonic scale was used with 5 facial ratings (1 = disliked very much to 5 = liked a lot). Students from both genders, between 5 to 10 years old (n = 142) proved the burgers at lunchtime, representing the protein portion of the meal. The tilapia derivative products shown to be foods with important nutritional value and low calorie value. For the application of the multinomial logistic regression analysis there was no significant effect from the age and gender variation in the acceptance by children. However, statistical significance was determined in the interaction between these two variables. With 87 % acceptance rate there was potential for consumption of fish burgers in school meals.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Incidence of Chronic and Other Knee Pain in Relation to Occupational Risk Factors in a Large Working Population.

International audience

L’automédication en République Démocratique du Congo : choix ou contrainte ?

Fondé sur l’analyse des données produites par l’enquête « 1-2-3 » de 2012 en République Démocratique du Congo, cet article propose une approche quantitative de l’automédication. Il fait apparaître, le caractère relativement circonscrit de cette pratique dans les déclarations des individus confrontés à un épisode de maladie et tente de rendre compte des choix qui les guident : consulter un professionnel de santé, affirmer recourir à l’automédication, s’abstenir de se soigner ou recourir à l’automédication par défaut. La construction d’un modèle logistique multinomial non-ordonné permet à cet égard de comparer les déterminants de ces décisions, considérées sous la forme d’une double alternative : consulter ou recourir à l’automédication, et, pour ceux qui ne sollicitent pas un professionnel de santé, s’automédiquer ou s’abstenir de toute démarche thérapeutique. L’article pointe ainsi les contraintes (économiques, géographiques, sociales et culturelles) qui pèsent sur ces choix tout en soulignant comment les individus cherchent à s’en affranchir.

Factors Influencing Children’s Full Immunization in Ethiopia

Child morbidity and mortality in Ethiopia is mainly due to vaccine preventable diseases. Although numerous interventions have been made since the 1980’s to increase vaccination coverage, the level of full immunization is low in the country. This study examines factors influencing children’s full immunization based on data on 1927 children aged 12-23 months extracted from the 2011 Ethiopian Demographic and Health Survey. Multinomial logistic regression model was fitted to identify predictors of full immunization. The result shows that only 24.3% of the children were fully immunized. There was significant difference between regions in immunization coverage in which Tigray, Dire Dawa, and Addis Ababa performed well. In Oromia, Afar, Somali, Benishangul-Gumuz, and Gambela regions, the likelihood of children’s full immunization was significantly lower. Children born to mothers living in households with better socio-economic status, with frequent access to media, and who visit health facilities for antenatal care were more likely to be fully immunized. The results imply the importance of narrowing regional differences, improving women’s socio-economic status and utilization of antenatal care services, and strengthening culture-sensitive media campaign as a means of achieving full immunization of all children

Work stress, smoking status and smoking intensity: an observational study of 46 190 employees

Study objective: To examine the relationship between work stress, as indicated by the job strain model and the effort-reward imbalance model, and smoking. Setting: Ten municipalities and 21 hospitals in Finland. Design and Participants: Binary logistic regression models for the prevalence of smoking were related to survey responses of 37 309 female and 8881 male Finnish public sector employees aged 17-65. Separate multinomial logistic regression models were calculated for smoking intensity for 8130 smokers. In addition, binary logistic regression models for ex-smoking were fitted among 16 277 former and current smokers. In all analyses, adjustments were made for age, basic education, occupational status, type of employment and marital status. Main results: Respondents with high effort-reward imbalance or lower rewards were more likely to be smokers. Among smokers, an increased likelihood of higher intensity of smoking was associated with higher job strain and higher effort-reward imbalance and their components such as low job control and low rewards. Smoking intensity was also higher in active jobs in women, in passive jobs and among employees with low effort expenditure. Among former and current smokers, high job strain, high effort-reward imbalance and high job demands were associated with a higher likelihood of being a current smoker. Lower effort was associated with a higher likelihood of ex-smoking. Conclusions: This evidence suggests an association between work stress and smoking and implies that smoking cessation programs may benefit from the taking into account the modification of stressful features of work environment. Key words: effort-reward imbalance; job strain; smoking. Abbreviations: OR, odds ratio; CI, confidence interval; SES, socioeconomic status

Congestión vehicular en Medellín: una posible solución desde la Economía

Según la teoría económica el mecanismo de precios es una herramienta adecuada para solucionar el problema de congestión vehicular. El objetivo de este artículo es diagnosticar el grado de congestión vehicular de la ciudad de Medellín (Colombia) y proponer alternativas que den solución a dicho problema desde la óptica de la teoría económica. A diferencia de otros estudios, esta investigación analizó la relación entre el gasto de las familias en transporte y la elección de transporte (público o privado) a través la metodología de elasticidades. Se encontró evidencia a favor de la hipótesis de los precios como mecanismos para desincentivar el uso del automóvil privado, pues a medida que aumenta el nivel de gasto en transporte (asociado a un supuesto peaje urbano), la probabilidad de usar transporte privado disminuye, mientras que la probabilidad de utilizar transporte público aumenta.

Effort-reward imbalance at work and the co-occurrence of lifestyle risk factors: cross-sectional survey in a sample of 36,127 public sector employees

Background In occupational life, a mismatch between high expenditure of effort and receiving few rewards may promote the co-occurrence of lifestyle risk factors, however, there is insufficient evidence to support or refute this hypothesis. The aim of this study is to examine the extent to which the dimensions of the Effort-Reward Imbalance (ERI) model – effort, rewards and ERI – are associated with the co-occurrence of lifestyle risk factors. Methods Based on data from the Finnish Public Sector Study, cross-sectional analyses were performed for 28,894 women and 7233 men. ERI was conceptualized as a ratio of effort and rewards. To control for individual differences in response styles, such as a personal disposition to answer negatively to questionnaires, occupational and organizational -level ecological ERI scores were constructed in addition to individual-level ERI scores. Risk factors included current smoking, heavy drinking, body mass index ≥25 kg/m2, and physical inactivity. Multinomial logistic regression models were used to estimate the likelihood of having one risk factor, two risk factors, and three or four risk factors. The associations between ERI and single risk factors were explored using binary logistic regression models. Results After adjustment for age, socioeconomic position, marital status, and type of job contract, women and men with high ecological ERI were 40% more likely to have simultaneously ≥3 lifestyle risk factors (vs. 0 risk factors) compared with their counterparts with low ERI. When examined separately, both low ecological effort and low ecological rewards were also associated with an elevated prevalence of risk factor co-occurrence. The results obtained with the individual-level scores were in the same direction. The associations of ecological ERI with single risk factors were generally less marked than the associations with the co-occurrence of risk factors. Conclusion This study suggests that a high ratio of occupational efforts relative to rewards may be associated with an elevated risk of having multiple lifestyle risk factors. However, an unexpected association between low effort and a higher likelihood of risk factor co-occurrence as well as the absence of data on overcommitment (and thereby a lack of full test of the ERI model) warrant caution in regard to the extent to which the entire ERI model is supported by our evidence.

Unemployed, Now What? The Effect of Immigration on Unemployment Transitions of Native-born Workers in the United States

Although one would expect the unemployed to be the population most likely affected by immigration, most of the studies have concentrated on investigating the effects immigration has on the employed population. Little is known of the effects of immigration on labor market transitions out of unemployment. Using the basic monthly Current Population Survey from 2001 and 2013 we match data for individuals who were interviewed in two consecutive months and identify workers who transition out of unemployment. We employ a multinomial model to examine the effects of immigration on the transition out of unemployment, using state-level immigration statistics. The results suggest that immigration does not affect the probabilities of native-born workers finding a job. Instead, we find that immigration is associated with smaller probabilities of remaining unemployed, but it is also associated with higher probabilities of workers leaving the labor force. This effect impacts mostly young and less educated people.

Women empowerment and the current use of long acting and permanent contraceptive: Evidence from 2010 Malawi Demographic and Health Survey

Background Both contraceptive use and fertility rates are high fertility in Malawi. Status of women remains low and is believed to affect reproductive health decisions including use of Long Acting and Permanent Contraceptives Method (LAPCM). Objective This study seeks to examine the relationship between women empowerment and LAPCM. A measure of women’s empowerment is derived from the women’s responses to questions on the number of household decisions in which the respondent participates, employment status, type of earnings, women’s control over cash earnings and level of education. Methods The study is based on a sub sample of 5,948 married women from the 2010 Malawi Demographic and Health Survey. Data was analysed using descriptive statistics, Chi-square and multinomial logistic regression models (α=5%). Results The prevalence of current use of LAPCM was 20.0% and increases with increasing empowerment level (p<0.001). Mean age and empowerment score of women who are currently using LAPCM were 38.53±6.2 years and 6.80±2.9 respectively. Urban women (22.2%) were more currently using LAPCM than rural women (19.4%) p<0.001. Women who belong to Seven Day Adventists/Baptist were 1.51(C.I=1.058-2.153; p=0.023) more likely and Muslims were 0.58(C.I=0.410-0.809; p=0.001) less likely to currently use LAPCM than Catholic women. Being in the richest wealth quintile (OR=1.91; C.I=1.362-2.665; p<0.001) promotes current use of LAPCM than poorest. The likelihood of currently using LAPCM was higher among women who have access to FP programmes on media and increases consistently with increasing women empowerment level even when other potential confounding variables were used as control. Conclusion In Malawi, LAPCM is still underutilized and more than half of the women are not adequately empowered. Women empowerment, wealth quintile and access to FP programmes are key factors influencing the use of LAPCM. Programmes that address these determinants are urgently needed in Malawi.

On the suboptimality of the p-version interior penalty discontinuous Galerkin method

We address the question of the rates of convergence of the p-version interior penalty discontinuous Galerkin method (p-IPDG) for second order elliptic problems with non-homogeneous Dirichlet boundary conditions. It is known that the p-IPDG method admits slightly suboptimal a-priori bounds with respect to the polynomial degree (in the Hilbertian Sobolev space setting). An example for which the suboptimal rate of convergence with respect to the polynomial degree is both proven theoretically and validated in practice through numerical experiments is presented. Moreover, the performance of p- IPDG on the related problem of p-approximation of corner singularities is assessed both theoretically and numerically, witnessing an almost doubling of the convergence rate of the p-IPDG method.

Second-order elliptic PDE with discontinuous boundary data

We shall consider the weak formulation of a linear elliptic model problem with discontinuous Dirichlet boundary conditions. Since such problems are typically not well-defined in the standard H^1-H^1 setting, we will introduce a suitable saddle point formulation in terms of weighted Sobolev spaces. Furthermore, we will discuss the numerical solution of such problems. Specifically, we employ an hp-discontinuous Galerkin method and derive an L^2-norm a posteriori error estimate. Numerical experiments demonstrate the effectiveness of the proposed error indicator in both the h- and hp-version setting. Indeed, in the latter case exponential convergence of the error is attained as the mesh is adaptively refined.