The United States Environmental Protection Agency announces the availability of 1996 grants for research on endocrine disruptors, role of interindividual variation in human susceptibility to cancer, risk-based decisions for contaminated sediments.

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Risk based decision support system for effective implementation of projects

Conventional project management techniques are not always sufficient to ensure time, cost and quality achievement of large-scale construction projects due to complexity in planning, design and implementation processes. The main reasons for project non-achievement are changes in scope and design, changes in government policies and regulations, unforeseen inflation, underestimation and improper estimation. Projects that are exposed to such an uncertain environment can be effectively managed with the application of risk management throughout the project's life cycle. However, the effectiveness of risk management depends on the technique through which the effects of risk factors are analysed/quantified. This study proposes the Analytic Hierarchy Process (AHP), a multiple attribute decision making technique, as a tool for risk analysis because it can handle subjective as well as objective factors in a decision model that are conflicting in nature. This provides a decision support system (DSS) to project management for making the right decision at the right time for ensuring project success in line with organisation policy, project objectives and a competitive business environment. The whole methodology is explained through a case application of a cross-country petroleum pipeline project in India and its effectiveness in project management is demonstrated.

Development of risk based treatability and engineering measures for reducing exposure to lead contaminated media in the Miami inner city, Florida

A major consequence of contamination at the local level’s population as it relates to environmental health and environmental engineering is childhood lead poisoning. Environmental contamination is one of the pressing environmental concerns facing the world today. Current approaches often focus on large contaminated industrial size sites that are designated by regulatory agencies for site remediation. Prior to this study, there were no known published studies conducted at the local and smaller scale, such as neighborhoods, where often much of the contamination is present to remediate. An environmental health study of local lead-poisoning data in Liberty City, Little Haiti and eastern Little Havana in Miami-Dade County, Florida accounted for a disproportionately high number of the county’s reported childhood lead poisoning cases. An engineering system was developed and designed for a comprehensive risk management methodology that is distinctively applicable to the geographical and environmental conditions of Miami-Dade County, Florida. Furthermore, a scientific approach for interpreting environmental health concerns, while involving detailed environmental engineering control measures and methods for site remediation in contained media was developed for implementation. Test samples were obtained from residents and sites in those specific communities in Miami-Dade County, Florida (Gasana and Chamorro 2002). Currently lead does not have an Oral Assessment, Inhalation Assessment, and Oral Slope Factor; variables that are required to run a quantitative risk assessment. However, various institutional controls from federal agencies’ standards and regulation for contaminated lead in media yield adequate maximum concentration limits (MCLs). For this study an MCL of .0015 (mg/L) was used. A risk management approach concerning contaminated media involving lead demonstrates that the linkage of environmental health and environmental engineering can yield a feasible solution.

RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES

Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV.

Metodologia para otimização da manutenção

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia da Manutenção

Analisi dei guasti di un termovalorizzatore

L’obiettivo del presente lavoro di tesi è stato quello di effettuare, tramite l’applicazione di una metodologia RBM – Risk Based Maintenance, l’analisi dei guasti avvenuti nel biennio 2012-2013 nel termovalorizzatore di rifiuti solidi urbani di Modena gestito dalla società Herambiente.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Supplier selection based on supply chain ecosystem, performance and risk criteria

A supply chain ecosystem consists of the elements of the supply chain and the entities that influence the goods, information and financial flows through the supply chain. These influences come through government regulations, human, financial and natural resources, logistics infrastructure and management, etc., and thus affect the supply chain performance. Similarly, all the ecosystem elements also contribute to the risk. The aim of this paper is to identify both performances-based and risk-based decision criteria, which are important and critical to the supply chain. A two step approach using fuzzy AHP and fuzzy technique for order of preference by similarity to ideal solution has been proposed for multi-criteria decision-making and illustrated using a numerical example. The first step does the selection without considering risks and then in the next step suppliers are ranked according to their risk profiles. Later, the two ranks are consolidated into one. In subsequent section, the method is also extended for multi-tier supplier selection. In short, we are presenting a method for the design of a resilient supply chain, in this paper.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Community center-based resistance training for the maintenance of glycemic control in adults with type 2 diabetes

OBJECTIVE - The purpose of this study was to determine whether beneficial effects on glycemic control of an initial laboratory-supervised resistance training program could be sustained through a community center-based maintenance program. RESEARCH DESIGN AND METHODS - We studied 57 overweight (BMI >= 27 kg/m(2)) sedentary men and women aged 40-80 years with established (> 6 months) type 2 diabetes. initially, all participants attended a twice-weekly 2-month supervised resistance training program conducted in the exercise laboratory. Thereafter, participants undertook a resistance training maintenance program (2 times/week) for 12 months and were randomly assigned to carry this out either in a community fitness and recreation center (center) or in their domestic environment (home). Glycemic control (HbA(1c) [A1C]) was assessed at 0, 2, and 14 months. RESULTS - Pooling data from the two groups for the 2-month supervised resistance training program showed that compared with baseline, mean A1C fell by -0.4% [95% CI -0.6 to -0.2]. Within-group comparisons showed that A I C remained lower than baseline values at 14 months in the center group (- 0.4% [-0.7 to -0.03]) but not in the home group (-0.1% [-0.4 to 0.3]). However, no between-group differences were observed at each time point. Changes in AIC during the maintenance period were positively associated with exercise adherence in the center group only. CONCLUSIONS - Center-based but not home-based resistance training was associated with the maintenance of modestly improved glycemic control from baseline, which was proportional to program adherence. Our findings emphasize the need to develop and test behavioral methods to promote healthy lifestyles including increased physical activity in adults with type 2 diabetes.