On the tractability of the piecewise-linear approximation for general discrete-choice network revenue management

The choice network revenue management (RM) model incorporates customer purchase behavioras customers purchasing products with certain probabilities that are a function of the offeredassortment of products, and is the appropriate model for airline and hotel network revenuemanagement, dynamic sales of bundles, and dynamic assortment optimization. The underlyingstochastic dynamic program is intractable and even its certainty-equivalence approximation, inthe form of a linear program called Choice Deterministic Linear Program (CDLP) is difficultto solve in most cases. The separation problem for CDLP is NP-complete for MNL with justtwo segments when their consideration sets overlap; the affine approximation of the dynamicprogram is NP-complete for even a single-segment MNL. This is in contrast to the independentclass(perfect-segmentation) case where even the piecewise-linear approximation has been shownto be tractable. In this paper we investigate the piecewise-linear approximation for network RMunder a general discrete-choice model of demand. We show that the gap between the CDLP andthe piecewise-linear bounds is within a factor of at most 2. We then show that the piecewiselinearapproximation is polynomially-time solvable for a fixed consideration set size, bringing itinto the realm of tractability for small consideration sets; small consideration sets are a reasonablemodeling tradeoff in many practical applications. Our solution relies on showing that forany discrete-choice model the separation problem for the linear program of the piecewise-linearapproximation can be solved exactly by a Lagrangian relaxation. We give modeling extensionsand show by numerical experiments the improvements from using piecewise-linear approximationfunctions.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium.

Ethical issues in nutrition support of severely disabled elderly persons: a guide for health professionals.

Providing or withholding nutrition in severely disabled elderly persons is a challenging dilemma for families, health professionals, and institutions. Despite limited evidence that nutrition support improves functional status in vulnerable older persons, especially those suffering from dementia, the issue of nutrition support in this population is strongly debated. Nutrition might be considered a basic need that not only sustains life but provides comfort as well by patients and their families. Consequently, the decision to provide or withhold nutrition support during medical care is often complex and involves clinical, legal, and ethical considerations. This article proposes a guide for health professionals to appraise ethical issues related to nutrition support in severely disabled older persons. This guide is based on an 8-step process to identify the components of a situation, analyze conflicting values that result in the ethical dilemma, and eventually reach a consensus for the most relevant plan of care to implement in a specific clinical situation. A vignette is presented to illustrate the use of this guide when analyzing a clinical situation.

Guidelines for specialized nutritional and metabolic support in the critically-ill patient. Update. Consensus SEMICYUC-SENPE: Oncohematological patient

Patients with cancer, irrespective of the stage of their disease, can require admission to the intensive care unit as a result of the complications of their underlying process or the surgical or pharmacological treatment provided. The cancer itself, as well as the critical status that can result from the complications of the disease, frequently lead to a high degree of hypermetabolism and inadequate energy intake, causing a high incidence of malnutrition in these patients. Moreover, cancer causes anomalous use of nutritional substrates and therefore the route of administration and proportion and intake of nutrients may differ in these patients from those in noncancer patients.

Issue review : Instructional support program funding inequities.

School districts may receive funding for the instructional support program subject to school board or voter approval. Program funding is based on a formula that includes a local funding provision, property tax and income surtax and a state aid component. When initially implemented, state aid was distributed through a formula designed to provide property tax equity and equalize the property tax burden between school districts. Since the initial year of the program, the state aid portion has not been fully funded and in fiscal year 2012, no state dollars were appropriated for the program. The result of underfunding the state-aid portion of the program has led to an inequity in the amount of funds school districts receive from the program. In fiscal year 2012, the portion of actual program funding for school districts ranged from a low of 52.6 percent to a high of 92.8 percent. This issue review examines the inequity in more detail.

Prediction of Childhood Asthma Using Conditional Probability and Discrete Event Simulation

Abstract: Asthma prevalence in children and adolescents in Spain is 10-17%. It is the most common chronic illness during childhood. Prevalence has been increasing over the last 40 years and there is considerable evidence that, among other factors, continued exposure to cigarette smoke results in asthma in children. No statistical or simulation model exist to forecast the evolution of childhood asthma in Europe. Such a model needs to incorporate the main risk factors that can be managed by medical authorities, such as tobacco (OR = 1.44), to establish how they affect the present generation of children. A simulation model using conditional probability and discrete event simulation for childhood asthma was developed and validated by simulating realistic scenario. The parameters used for the model (input data) were those found in the bibliography, especially those related to the incidence of smoking in Spain. We also used data from a panel of experts from the Hospital del Mar (Barcelona) related to actual evolution and asthma phenotypes. The results obtained from the simulation established a threshold of a 15-20% smoking population for a reduction in the prevalence of asthma. This is still far from the current level in Spain, where 24% of people smoke. We conclude that more effort must be made to combat smoking and other childhood asthma risk factors, in order to significantly reduce the number of cases. Once completed, this simulation methodology can realistically be used to forecast the evolution of childhood asthma as a function of variation in different risk factors.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

Religious coping in schizophrenia patients : spiritual support in medical care and pastoral counselling

Following an introduction focusing on the role of religion in the treatment of psychosis, the first part of this paper describes an initial study in which the role of spirituality and religiosity was assessed in 115 patients with schizophrenia in Geneva (Switzerland) and 126 in Trois-Rivières (Quebec). These themes have been shown to be highly prevalent for these patients, though their clinicians are often unaware of this prevalence. The following part of the paper presents a second study where religious supervision was offered to clinicians in Geneva. Comparison between forty patients who received spiritual assessment and opportunities to work on religious topics with their clinicians was made with thirty patients without religious intervention. In the supervisory sessions, six different types of religious interventions were suggested. Outcomes at three months show that patients of the intervention group maintain their interest for help in religious matters while clinicians' interest in integrating religious topics in discussions with their patients has decreased. The third and main part of the paper is devoted to an analysis of the suggested interventions from the viewpoint of the study of religions. Five aspects of religion are distinguished, and explanations of the reasons some of them are easier to manage for clinicians are proposed. The paper concludes with proposals for the education of clinicians to help them to differentiate different kinds of religious coping and to recognize when it could be helpful to refer the patient to a pastoral counsellor.

Classification of very high spatial resolution imagery using mathematical morphology and support vector machines

We investigate the relevance of morphological operators for the classification of land use in urban scenes using submetric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.