Predictive reliability, prognostics and risk assessment for power modules

This paper describes a framework that is being developed for the prediction and analysis of electronics power module reliability both for qualification testing and in-service lifetime prediction. Physics of failure (PoF) reliability methodology using multi-physics high-fidelity and reduced order computer modelling, as well as numerical optimization techniques, are integrated in a dedicated computer modelling environment to meet the needs of the power module designers and manufacturers as well as end-users for both design and maintenance purposes. An example of lifetime prediction for a power module solder interconnect structure is described. Another example is the lifetime prediction of a power module for a railway traction control application. Also in the paper a combined physics of failure and data trending prognostic methodology for the health monitoring of power modules is discussed.

Hazard and risk assessment from Hurricane Ivan (2004) in Grenada using Geographical Information Systems and remote sensing

Hurricanes are destructive storms with strong winds, intense storm surges, and heavy rainfall. The resulting impact from a hurricane can include structural damage to buildings and infrastructure, flooding, and ultimately loss of human life. This paper seeks to identify the impact of Hurricane Ivan on the aected population of Grenada, one of the Caribbean islands. Hurricane Ivan made landfall on 7th September 2004 and resulted in 80% of the population being adversely aected. The methods that were used to model these impacts involved performing hazard and risk assessments using GIS and remote sensing techniques. Spatial analyses were used to create a hazard and a risk map. Hazards were identied initially as those caused by storm surges, severe winds speeds, and flooding events related to Hurricane Ivan. These estimated hazards were then used to create a risk map. An innovative approach was adopted, including the use of hillshading to assess the damage caused by high wind speeds. This paper explains in detail the methodology used and the results produced.

Assessing the environmental consequences of CO2 leakage from geological CCS: Generating evidence to support environmental risk assessment

At the start of the industrial revolution (circa 1750) the atmospheric concentration of carbon dioxide (CO2) was around 280 ppm. Since that time the burning of fossil fuel, together with other industrial processes such as cement manufacture and changing land use, has increased this value to 400 ppm, for the first time in over 3 million years. With CO2 being a potent greenhouse gas, the consequence of this rise for global temperatures has been dramatic, and not only for air temperatures. Global Sea Surface Temperature (SST) has warmed by 0.4–0.8 °C during the last century, although regional differences are evident (IPCC, 2007). This rise in atmospheric CO2 levels and the resulting global warming to some extent has been ameliorated by the oceanic uptake of around one quarter of the anthropogenic CO2 emissions (Sabine et al., 2004). Initially this was thought to be having little or no impact on ocean chemistry due to the capacity of the ocean’s carbonate buffering system to neutralise the acidity caused when CO2 dissolves in seawater. However, this assumption was challenged by Caldeira and Wickett (2005) who used model predictions to show that the rate at which carbonate buffering can act was far too slow to moderate significant changes to oceanic chemistry over the next few centuries. Their model predicted that since pre-industrial times, ocean surface water pH had fallen by 0.1 pH unit, indicating a 30% increase in the concentration of H+ ions. Their model also showed that the pH of surface waters could fall by up to 0.4 units before 2100, driven by continued and unabated utilisation of fossil fuels. Alongside increasing levels of dissolved CO2 and H+ (reduced pH) an increase in bicarbonate ions together with a decrease in carbonate ions occurs. These chemical changes are now collectively recognised as “ocean acidification”. Concern now stems from the knowledge that concentrations of H+, CO2, bicarbonate and carbonate ions impact upon many important physiological processes vital to maintaining health and function in marine organisms. Additionally, species have evolved under conditions where the carbonate system has remained relatively stable for millions of years, rendering them with potentially reduced capacity to adapt to this rapid change. Evidence suggests that, whilst the impact of ocean acidification is complex, when considered alongside ocean warming the net effect on the health and productivity of the oceans will be detrimental.

Dietary glycaemic index, glycaemic load and endometrial and ovarian cancer risk: a systematic review and meta-analysis.

Long-term consumption of a high glycaemic index (GI) or glycaemic load (GL) diet may lead to chronic hyperinsulinaemia, which is a potential risk factor for cancer. To date, many studies have examined the association between GI, GL and cancer risk, although results have been inconsistent, therefore our objective was to conduct a systematic review of the literature. Medline and Embase were systematically searched using terms for GI, GL and cancer to identify studies published before December 2007. Random effects meta-analyses were performed for endometrial cancer, combining maximally adjusted results that compared risk for those in the highest versus the lowest category of intake. Separate analysis examined risk by body mass index categories. Five studies examining GI and/or GL intake and endometrial cancer risk were identified. Pooled effect estimates for endometrial cancer showed an increased risk for high GL consumers (RR 1.20; 95% CI: 1.06-1.37), further elevated in obese women (RR 1.54; 95% CI: 1.18-2.03). No significant associations were observed for GI. Only two studies examined ovarian cancer and therefore no meta-analysis was performed, but results indicate positive associations for GL also. A high GL, but not a high GI, diet is positively associated with the risk of endometrial cancer, particularly among obese women. © 2008 Cancer Research UK All rights reserved.

Knowledge-based risk assessment under uncertainty in engineering projects

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e., the first half of our reasoning process. Then we combine the prior mass and the evidence from the different rules; i.e., the second half of the reasoning process. Finally, belief intervals are calculated to help in identifying the risks. We apply our evidential-reasoner on an engineering project and the results demonstrate the feasibility and applicability of this system in this environment.

Risk assessment of E-Commerce projects using evidential reasoning

The purpose of this study is to develop a decision making system to evaluate the risks in E-Commerce (EC) projects. Competitive software businesses have the critical task of assessing the risk in the software system development life cycle. This can be conducted on the basis of conventional probabilities, but limited appropriate information is available and so a complete set of probabilities is not available. In such problems, where the analysis is highly subjective and related to vague, incomplete, uncertain or inexact information, the Dempster-Shafer (DS) theory of evidence offers a potential advantage. We use a direct way of reasoning in a single step (i.e., extended DS theory) to develop a decision making system to evaluate the risk in EC projects. This consists of five stages 1) establishing knowledge base and setting rule strengths, 2) collecting evidence and data, 3) determining evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process, 4) combining prior mass and different rules; i.e., the second half of the single step reasoning process, 5) finally, evaluating the belief interval for the best support decision of EC project. We test the system by using potential risk factors associated with EC development and the results indicate that the system is promising way of assisting an EC project manager in identifying potential risk factors and the corresponding project risks.

Dietary glycaemic index, glycaemic load and breast cancer risk: a systematic review and meta-analysis

This systematic review aimed to examine if an association exists between dietary glycaemic index (GI) and glycaemic load (GL) intake and breast cancer risk. A systematic search was conducted in Medline and Embase and identified 14 relevant studies up to May 2008. Adjusted relative risk estimates comparing breast cancer risk for the highest versus the lowest category of GI/GL intake were extracted from relevant studies and combined in meta-analyses using a random-effects model. Combined estimates from six cohort studies show non-significant increased breast cancer risks for premenopausal women (relative risk (RR) 1.14, 95% CI 0.95-1.38) and postmenopausal women (RR 1.11, 95% CI 0.99-1.25) consuming the highest versus the lowest category of GI intake. Evidence of heterogeneity hindered analyses of GL and premenopausal risk, although most studies did not observe any significant association. Pooled cohort study results indicated no association between postmenopausal risk and GL intake (RR 1.03, 95% CI 0.94-1.12). Our findings do not provide strong support of an association between dietary GI and GL and breast cancer risk. © 2008 Cancer Research UK.


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