Dual-Based Local Search for Deterministic, Stochastic and Robust Variants of the Connected Facility Location Problem

In this dissertation, we propose the study of a family of network design problems that arise in a wide range of practical settings ranging from telecommunications to data management. We investigate the use of heuristic search procedures coupled with lower bounding mechanisms to obtain high quality solutions for deterministic, stochastic and robust variants of these problems. We extend the use of well-known methods such as the sample average approximation for stochastic optimization and the Bertsimas and Sim approach for robust optimization with heuristics and lower bounding mechanisms. This is particular important for NP-complete problems where even deterministic and small instances are difficult to solve to optimality. Our extensions provide a novel way of applying these techniques while using heuristics; which from a practical perspective increases their usefulness

Diagnostic statistics of daily rainfall variability in an evolving climate

International audienceTo investigate the character of daily rainfall variability under present and future climate described via global warming a suite of diagnostic statistics was used. The rainfall was modeled as a stochastic process coupled with atmospheric circulation. In this study we used an automated objective classification of daily patterns based on optimized fuzzy rules. This kind of classification method provided circulation patterns suitable for downscaling of General Circulation Model (GCM)-generated precipitation. The precipitation diagnostics included first and second order moments, wet and dry-day renewal process probabilities and spell lengths as well as low-frequency variability via the standard deviation of monthly totals. These descriptors were applied to nine elevation zones and entire area of the Mesochora mountainous catchment in Central Greece for observed, 1×CO2 and 2×CO2 downscaled precipitation. The statistics' comparison revealed significant differences in the most of the daily diagnostics (e.g. mean wet-day amount, 95th percentile of wet-day amount, dry to wet probability), spell statistics (e.g. mean wet/dry spell length), and low-frequency diagnostic (standard deviation of monthly precipitation total) between warm (2×CO2) and observed scenario in a progressive rate from lower to upper zone. The differences were very greater for the catchment area. In the light of these results, an increase in rainfall occurrence with diminished rainfall amount and a sequence of less consecutive dry days could describe the behaviour of a possible future climate on the examined catchment

PROPHETIC: Prospective Identification of Pneumonia in Hospitalized Patients in the Intensive Care Unit

BACKGROUND: Pneumonia is the leading infection-related cause of death. Using simple clinical criteria and contemporary epidemiology to identify patients at high risk of nosocomial pneumonia should enhance prevention efforts and facilitate development of new treatments in clinical trials. RESEARCH QUESTION: What are the clinical criteria and contemporary epidemiology trends helpful in identifying patients at high risk of nosocomial pneumonia? STUDY DESIGN AND METHODS: Within the intensive care units of 28 United States hospitals, we conducted a prospective cohort study among adults hospitalized more than 48 hours and considered high risk for pneumonia (defined as treatment with invasive or noninvasive ventilatory support or high levels of supplemental oxygen). We estimated the proportion of high-risk patients developing nosocomial pneumonia. Using multivariable logistic regression, we identified patient characteristics and treatment exposures associated with increased risk of pneumonia development during the intensive care unit admission. RESULTS: Between February 6, 2016 and October 7, 2016, 4613 high-risk patients were enrolled. Among 1464/4613 (32%) high-risk patients treated for possible nosocomial pneumonia, 537/1464 (37%) met the study pneumonia definition. Among high-risk patients, a multivariable logistic model was developed to identify key patient characteristics and treatment exposures associated with increased risk of nosocomial pneumonia development (c-statistic 0.709, 95% confidence interval 0.686 to 0.731). Key factors associated with increased odds of nosocomial pneumonia included an admission diagnosis of trauma or cerebrovascular accident, receipt of enteral nutrition, documented aspiration risk, and receipt of systemic antibacterials within the preceding 90 days. INTERPRETATION: Treatment for nosocomial pneumonia is common among intensive care unit patients receiving high levels of respiratory support, yet more than half of patients treated do not fulfill standard diagnostic criteria for pneumonia. Application of simple clinical criteria may improve the feasibility of clinical trials of pneumonia prevention and treatment by facilitating prospective identification of patients at highest risk

Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion