An optimization‐based decision‐support tool for post‐disaster debris operations

Debris generated by disasters can hinder relief efforts and result in devastating economic, environmental, and health problems. In this study, we present a decision‐support tool employing analytical models to assist disaster and waste management officials with decisions regarding collection, transportation, reduction, recycling, and disposal of debris. The tool enables optimizing and balancing the financial and environmental costs, duration of the collection and disposal operations, landfill usage, and the amount of recycled materials. In addition to post‐disaster operational decisions, the tool can also support the challenging task of developing strategic plans for disaster preparedness. We illustrate the applicability and effectiveness of the tool with a disaster scenario based on Hurricane Andrew

Selection of spectral features for land cover type classification

Sophisticated sensors of satellites help researchers collect detailed maps of land surface in various image wavebands. These wavebands are processed to form spectral features identifying distinct land structures. However, depending on the structures subject to the research topic, only a portion of collected features might be sufficient for identification. Aim of this study is to present a scheme to pick most valuable spectral features derived from ASTER imagery in order to distinguish four types of tree ensembles: 'Sugi' (Japanese Cedar), 'Hinoki' (Japanese Cypress), 'Mixed deciduous', and 'Others'. Forward selection, a type of wrapper techniques, was employed with four types of classifiers in several train/test splits. Final rank of each feature was determined by Condorcet ranking after application of each classifier. Results showed that among four classifiers, artificial neural networks helped the selection process choose the most valuable features and a high accuracy value of 90.42% (with a true skill statistics score of 91.26%) was obtained using only top-ten features. For feature sets in smaller sizes, support vector machines classifier also performed well and provided an accuracy of 80.33% (with a true skill statistics score of 81.84%) using only top-three features. With help of these findings, landscape data can be represented in smaller forms with spectral features having most discriminative power. This will help reduce processing time and storage needs of expert systems. (C) 2018 Elsevier Ltd. All rights reserved

Humanitarian Supply Chain Management - An Overview

Disasters recently received the attention of the Operations Research community due to the great potential of improving disaster related operations through the use of analytical tools, and the impact on people that this implies. In this introductory article, we describe the main characteristics of disaster supply chains, and we highlight the particular issues that are faced when managing these supply chains. We illustrate how Operations Research tools can be used to make better decisions, taking debris management operations as an example, and discuss potential general research directions in this area

Pulmonary Rehabilitation in Idiopathic Pulmonary Fibrosis: A Chance for a Multidisciplinary Treatment Approach

Objectives: Idiopathic pulmonary fibrosis (IPF) is characterized by progressively worsening lung function, ventilation capacity, dyspnea, and finally reduced exercise intolerance. All of these have a significant negative impact on functional capacity and quality of life. In this study, we aim to evaluate the effects of pulmonary rehabilitation (PR) in IPF and assess the predictors of success. Methods: Data from 17 IPF patients who completed the program from the total of 27 patients who applied to PR were used in our study. We evaluated their pulmonary function tests, exercise capacity, peripheral-respiratory muscle strength, body composition, quality of life, and psychological states before and after PR. Results: Following the PR program, improvements over the minimal clinically important differences were observed in almost all parameters compared to the baseline; however, statistically significant improvements were only observed in the medical research council (P=0.020), the St. George respiratory questionnaire (P=0.002), the maximal inspiratory pressure (P=0.024), the anxiety score (P=0.001), the depression score (P=0.002), and the right quadriceps muscle strength (P=0.046). There was only a statistically significant negative correlation between the initial forced vital capacity and the forced expiratory volume in one-second value with the increase in patients’ maximal inspiratory pressure values after PR.  Discussion: After a multidisciplinary, comprehensive PR program, dyspnea sensation, exercise capacity, endurance time, quality of life, respiratory and peripheral muscle strengths, and psychological status were improved regardless of age, gender, antifibrotic treatment, and comorbidities. Therefore, patients should be referred to PR units before the deterioration in the quality of life in the early stages of the disease

The Post-Disaster Debris Clearance Problem Under Incomplete Information

Debris management is one of the most time consuming and complicated activities among post-disaster operations. Debris clearance is aimed at pushing the debris to the sides of the roads so that relief distribution and search-and-rescue operations can be maintained in a timely manner. Given the limited resources, uncertainty, and urgency during disaster response, efficient and effective planning of debris clearance to achieve connectivity between relief demand and supply is important. In this paper, we define the stochastic debris clearance problem (SDCP), which captures post-disaster situations where the limited information on the debris amounts along the roads is updated as clearance activities proceed. The main decision in SDCP is to determine a sequence of roads to clear in each period such that benefit accrued by satisfying relief demand is maximized. To solve SDCP to optimality, we develop a partially observable Markov decision process model. We then propose a heuristic based on a continuous-time approximation, and we further reduce the computational burden by applying a limited look ahead on the search tree and heuristic pruning. The performance of these approaches is tested on randomly generated instances that reflect various geographical and information settings, and instances based on a real-world earthquake scenario. The results of these experiments underline the importance of applying a stochastic approach and indicate significant improvements over heuristics that mimic the current practice for debris clearance

An Optimization-Based Decision-Support Tool for Post-Disaster Debris Operations

Debris generated by disasters can hinder relief efforts and result in devastating economic, environmental, and health problems. In this study, we present a decision-support tool employing analytical models to assist disaster and waste management officials with decisions regarding collection, transportation, reduction, recycling, and disposal of debris. The tool enables optimizing and balancing the financial and environmental costs, duration of the collection and disposal operations, landfill usage, and the amount of recycled materials. In addition to post-disaster operational decisions, the tool can also support the challenging task of developing strategic plans for disaster preparedness. We illustrate the applicability and effectiveness of the tool with a disaster scenario based on Hurricane Andrew

A decision-support tool for post-disaster debris operations

Debris generated by disasters can hinder relief efforts and result in devastating economic, environmental and health problems. In this paper, we present a decision-support tool to assist disaster and waste management officials with the collection, transportation, reduction, recycling, and disposal of debris. The tool enables optimizing and balancing the financial and environmental costs, duration of the removal operations, landfill usage, and the amount of recycled materials generated. It can support post-disaster operational decisions as well as the challenging task of developing strategic plans for disaster preparedness. (C) 2015 Published by Elsevier Lt

Influenza and pneumonia vaccination rates in patients hospitalized with acute respiratory failure

Background and method: Despite their proven effect, the rates of vaccinations are low. The aim of this study was to determine the rates and associated factors of influenza and pneumonia vaccinations in patients who were hospitalized because of acute respiratory failure. Patients hospitalized because of acute hypoxemic or hypercapnic respiratory failure were recruited for this retrospective study. A survey was conducted with 97 patients. Primary diagnoses, ages, reasons of hospitalizations, education status, vaccination rates, information resources, and thoughts about vaccinations were recorded. Results: In total 45 (46%) of the patients were female, and 52 (54%) were male. The mean age was 67 ± 12 years. The primary diagnoses were lung disorders (n = 77, 79%), cardiac disorders (n = 16, 17%), and neuromuscular disorders (n = 5, 4%). In total 72 (74%) patients had chronic obstructive pulmonary disease (COPD) with primary lung disorders. All patients were hospitalized due to acute respiratory failure. The main reason for acute respiratory failure was infection in 40 patients (42%). The overall influenza and pneumococcal vaccination rates were 26% and 15%, respectively; for patients with COPD it was 30% and 17%, respectively. The main providers of information were doctors (42%). Vaccination status was not associated with infections or other reasons of hospitalization, age, sex, educational status, and number of hospital admissions in the previous year. A total of 51 patients (52%) had no belief in the benefits of vaccinations. Conclusion: Vaccination rates were found to be low in patients who were frequently hospitalized. Vaccination status was not related with hospitalization due to infections and history of hospitalization; awareness of vaccinations should be improved both in doctors and patients