bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental Security
Authors: Linkov Igor
Published: September 2008

The concept of “environmental security” has emerged as a basis for understanding risks related to limited natural resources and critical infrastructure, and is a phrase that can mean a variety of things. It can signify security issues stemming from environmental concerns or conflicting needs, or it can mean that the environment is treated as a resource for the long term. These meanings lead to the question, What should be done today to preserve the quality of the environment in the future? In the same way that energy security is about ensuring access to energy for the long run, it can also mean that pressing environmental concerns create a situation where different countries and communities are forced to collaboratively design a unified response, even if cooperation is not generally in the logic of their relations. Risk assessment is an important tool that could help in managing security risks, but its application is particularly challenging due to the knowledge vacuum and constricted timeline in which security decisions must be made. Challenges of risk assessment for situations with a limited knowledge base, high uncertainty, and variability require coupling traditional risk assessment with other quantitative tools, such as multicriteria decision analysis (MCDA), to support environmental security applications.
Hotspot Geoinformatics
Authors: Patil Ganapathi P. , Joshi Sharadchandra W. , Rathbun Stephen L.
Published: September 2008

Government agencies continue to require meaningful summaries of georeferenced data to support policies and decisions involving risk maps, geographic targets, and resource allocations. So also the public with initiatives of digital governance, enabling transparency, efficiency, and efficacy in the risk assessment and management.

This article briefly introduces hotspot geoinformatics for hotspot detection and prioritization, and provides examples of societal importance involving environmental risk.
Kriging
Authors: Switzer Paul
Published: September 2008

Kriging is a method for spatial interpolation based on correlation modeling between observations. This article describes how correlation is used to assign weights to control data for interpolations, and how correlation is used to assess the uncertainty of interpolation. Additionally, the article describes how auxiliary and prior information are used in kriging interpolation and how such information constrains the weights assigned to the control data. Finally, the article describes the impact of measurement error and short‐scale variability on interpolated values and on the interpolation uncertainty. An illustration indicates how control data are weighted differently according to their relation to the interpolation location and how interpolation uncertainty would change with increasing density of the control data.
Molecular Markers and Models
Authors: Preston R. Julian
Published: September 2008

Risk‐assessment practices for a range of diseases, in general, now encourage the use of mechanistic data to enhance the ability to predict responses at low environmental exposures. In particular, the pathway from normal biology to pathologic state can be described by a set of measurable key events. These key events can be considered molecular markers of disease. Recent advances in molecular biology methodologies and knowledge of the underlying mechanisms of several diseases have allowed for the identification of truly informative biomarkers of response, especially for tumors. The cancer risk from exposures to environmental chemicals can similarly be assessed using these types of molecular biomarkers. Models of cancer have further enhanced the appreciation that tumor formation is a tissue response, and that modifications of normal biology need to be considered at a systems level rather than at the gene or single pathway levels. Molecular models that are based upon such a systems approach have been developed recently; these include models of mitosis, cell signaling, nuclear transport, and cell cycles. Abrogations of these systems can lead to disease outcomes. A very recent model based on gene–gene interactions among genes in

Caenorhabditis elegans

involved in signaling pathways that are also mutated in human diseases has identified a set of highly connected “hub” genes that can essentially enhance the effects of mutations in a range of different genes. These “hub” genes are all chromatin modulators, leading to a powerful approach for identifying key events in disease etiology. This type of molecular‐modeling systems approach marks the way forward for incorporating key events into the risk‐assessment process for a range of diseases.
Stratospheric Ozone Depletion
Authors: Solow Andrew R.
Published: September 2008

The depletion of ozone in the stratosphere by man‐made chlorine compounds poses a significant risk to human health. Although the basic chemistry of ozone depletion has been understood since the 1970s, the physical and chemical processes controlling depletion in different parts of the atmosphere are complicated. This article reviews some statistical analyses of surface and satellite observations of ozone concentrations.
Point Source Modeling
Authors: Diggle Peter J.
Published: September 2008

We consider the problem of investigating spatial variation in disease risk around one or more preidentified point sources of pollution. This topic has its origins in concerns about the health effects to the surrounding population of potentially toxic emissions from industrial sites. We describe approaches to the problem that apply the theory of spatial point processes to the analysis of data from an epidemiological case–control study. We first formulate an idealized model in which cases and controls are modeled as inhomogeneous Poisson point processes whose spatially varying intensities are linked by a relative risk function ρ(x), wherexdenotes location. Typically, ρ(·) is modeled as a function of the distance, and possibly the orientation, ofxrelative to the point source of interest. We show how likelihood‐based inference about ρ(·) can be carried out by fitting a nonlinear binary regression model to the case–control labels, conditioning on the locations of cases and controls. We describe several specific parametric models that have been suggested in the literature, and a nonparametric alternative in which ρ(·) is modeled as a smooth, but otherwise arbitrary, function of distance.
Risk from Ionizing Radiation
Authors: Cullings Harry M. , Cologne John B.
Published: September 2008

Ionizing radiation, perhaps the most studied of all environmental hazards, illustrates many important aspects of quantitative risk assessment. We discuss the nature of ionizing radiation, major sources of exposure to humans, and the major categories of resulting health effects, focusing on stochastic late effects such as increased rates of cancer. We explain the role of the fundamental exposure metric, radiation dose, in regard to issues such as the shape and magnitude of the dose–response and the important dependence of dose–response on time variables and other factors specific to the affected individual (effect modification). After explaining basic methods of radiation risk estimation, including methods for dealing with uncertainty in the doses of individuals in a study cohort, we discuss several important principles related to the application of the resulting estimates in radiation protection. Finally, we outline some of the major results of radioepidemiological studies.
Alternative Risk Transfer
Authors: Banks Erik
Published: September 2008

Active risk management is important for companies interested in maximizing enterprise value, which can be defined as the discounted value of future after‐tax profits. While established risk techniques based on retention, transfer, and hedging comprise the core of active risk management, innovative solutions from the alternative risk transfer market have proven useful in giving companies additional tools by which to manage their exposures. Growth in the alternative sector has been driven primarily by efficiencies and cost savings that allow companies to boost enterprise value.
Bonus–Malus Systems
Authors: Walhin Jean‐François
Published: September 2008

This paper describes a particular type of a posteriori rating scheme, namely bonus‐malus systems within the general framework of mixed Poisson regression. It is shown that the bonus‐malus scale is not independent of the
a priori
rating applied by the insurer. Considerations about bonus hunger, moral hazard, and signal theory are briefly introduced.
Collective Risk Models
Authors: Lin X. Sheldon , Willmot Gordon E.
Published: September 2008

This article reviews models for aggregate claims arising from an insurance portfolio when policy replacements are allowed. It presents various approaches to evaluation of aggregate claims.
Credibility Theory
Authors: Goulet Vincent
Published: September 2008

Credibility models are actuarial tools to distribute premiums fairly among a heterogeneous group of policyholders. More generally, they can be seen as prediction methods applicable in any setting in which repeated measures are made for subjects with different risk levels. The article presents the two main approaches to credibility—limited fluctuations and greatest accuracy—with emphasis on the modeling of heterogeneity using a Bayesian framework. The main results of the classical Bühlmann and Bühlmann–Straub models are interpreted. More advanced models are briefly introduced.
Dynamic Financial Analysis
Authors: D'Arcy Stephen P.
Published: September 2008

Dynamic financial analysis (DFA) is an approach used by organizations to model financial results of the entity under a variety of conditions. The modeling techniques embedded in DFA developed as advances in the computational power of business computers made running large numbers of complex simulations feasible. The models have gradually become more complex, more realistic, and more widely applicable. Companies, rating agencies, and regulators now routinely use DFA type models to evaluate the financial condition of an organization and assess operational changes. This article describes this approach, compares simulation with other financial planning methods, and provides the details of a publicly accessible DFA model that has been extensively used in research and education. DFA is most widely used by insurance companies and other financial institutions, but many nonfinancial firms utilize similar techniques.
Equity‐Linked Life Insurance
Authors: Bacinello Anna R.
Published: September 2008

Equity‐linked life insurance contracts are characterized by the fact that benefits are directly linked to the value of an investment portfolio, typically composed of units of one or more mutual funds and kept separate from the other assets of the insurance company. These contracts generally imply a rather high level of financial risk that can be totally charged to the policyholder, in
pure equity‐linked contracts
, or shared between the policyholder and the insurance company, in
guaranteed equity‐linked contracts
. In the first case the insurance company acts as a mere financial intermediary, while in the second case it has to set up suitable hedging strategies because the financial risk is of systematic nature and cannot be eliminated by sufficiently increasing the size of its portfolio. In this contribution we first describe, in general terms, the main characteristics of equity‐linked products and their advantages with respect to other types of life insurance and investment contracts. Then we pass to analyze how the investment portfolio to which benefits are linked evolves over time and, finally, to present some possible approaches that the insurance company can follow in order to hedge the liabilities arising from the issue of minimum guarantees.
Fair Value of Insurance Liabilities
Authors: Biffis Enrico , Millossovich Pietro
Published: September 2008

We outline the basic principles underlying accounting standards aimed at marking to market insurers' balance sheets. Some issues specific to insurance liabilities are examined.
Longevity Risk and Life Annuities
Authors: Milevsky Moshe A.
Published: September 2008

Longevity risk is faced by individuals, corporations, as well as society and their governments. This brief article reviews the various dimensions and aspects of longevity risk as they relate to the market for life annuities and mortality‐contingent claims. The main argument used is that regardless of one's perspective on longevity risk, this field will continue to provide fertile ground for advanced research for many, albeit uncertain, years to come.
Nonlife Loss Reserving
Authors: De Alba Enrique , Hernández Diego
Published: September 2008

This article presents a brief description of nonlife claims reserving methods. Nonlife claims reserves are financial resources, set aside from all other insurance operations, as a provision for claims not completely settled at a specific time. Since these provisions constitute the largest liabilities carried by nonlife insurers, their determination affects many aspects of the business, such as ratemaking and the assessment of the insurer's net worth. From the actuarial point of view, reserving is a problem of estimation, or more precisely, forecasting. As a consequence, reserving methods range from very simple deterministic techniques to some very sophisticated ones, based on stochastic models for claim settlement. We present, in a structured way, the main approaches in the field, and provide the interested reader with references for further study.
Ordering of Insurance Risk
Authors: Rüschendorf Ludger
Published: September 2008

Ordering of risks is a basic tool for analyzing risks and their premiums. It is an extension of the classical mean‐variance approach of Markovitz. We introduce some of the basic orderings as stochastic order and stop loss order and present some applications for the comparison of individual and collective risk models, to the diversification of risks and to reinsurance contracts. In particular, we demonstrate the dramatic influence of positive dependence in a risk portfolio on the total risk. Obtaining sharp bounds on the influence of positive dependence in a portfolio on the total risk is an actual active research area.
Reinsurance
Authors: Simões Onofre , De Lourdes Centeno Maria
Published: September 2008

We start by giving an overview of the importance of reinsurance for the management of direct insurers and introduce most of the reinsurance concepts, namely, by presenting the different forms that reinsurance contracts can assume and how they affect the retained business of the insurers. We also explain how reinsurance can be placed and finish with some considerations about designing reinsurance programmes.
Risk Classification in Nonlife Insurance
Authors: Antonio Katrien , Beirlant Jan
Published: September 2008

Within the actuarial profession, a major challenge can be found in the construction of a fair tariff structure. We discuss botha priorianda posteriorirating systems in light of the statistical techniques that are involved. The article introduces basic concepts, illustrates them with real‐life actuarial data and summarizes references to complementary literature. Examples of likelihood‐based as well as Bayesian estimation are included.
Stochastic Control for Insurance Companies
Authors: Schmidli Hanspeter
Published: September 2008

We consider some (simple) optimization problems in insurance. In particular, we want to minimize the ruin probability by dynamic decisions, maximize the value of future dividends, and keep the contribution rate and the size of a pension fund close to some predefined values. The method is stochastic control theory. We illustrate how a Hamilton–Jacobi–Bellman equation can be obtained. We also show how to solve the equation and how the optimal strategies can be obtained.
From Basel
II
to Solvency
II
—Risk Management in the Insurance Sector
Authors: Rudolf Markus , Frenkel Michael
Published: September 2008

Basel II has not only changed the risk‐management standards in the banking system dramatically, it has also affected the competitive positions of different banking systems. This is due to the fact, that Basel II has shifted banking closer toward capital markets and away from banking institutions. Consequently, we did observe an increased significance of the Anglo‐Saxon banking sector and less significance of the banking systems in Japan and Germany. In the insurance sector, a regulatory framework called Solvency II was launched in 2002. Solvency II is comparable to Basel II with regard to many aspects. The view which is advocated here is therefore, that developments similar to those which have been observed in the banking system might apply to the global insurance industry in the next decade.
Nonparametric Calibration of Derivatives Models
Authors: Wu Lixin
Published: September 2008

Model calibration is a procedure used to incorporate observed information in a derivatives market into a derivatives pricing model. We explain the motivations for model calibration, and introduce representative calibration technologies for equity and interest‐rate derivatives markets.
Professional Organizations in Risk Management
Authors: Apostolik Richard
Published: September 2008

The role of professional organizations in risk management is not a new concept. Many have been around for 50 or more years meeting the specialized needs of their members. What is new is the increasingly important role these organizations are now playing in assisting in the ongoing personal education of financial professionals and the development of the global regulatory environment within the financial services industry. This article explores the rationale behind the proliferation of professional organizations in the field of financial risk management, their historical role, what financial risk‐management professionals expect from these organizations, what the organizations can be reasonably expected to deliver, and the future role of professional risk‐management organizations of which the rapidly growing global discipline of financial risk management is a part.

Two basic issues need to be addressed before dealing with the expectations for and the delivery of products and services from a professional risk‐management association. These issues present somewhat of a “chicken‐and‐egg” problem. The first involves defining financial risk management, and the second relates to simply being able to define the organization's role in attempting to realistically meet the expectations of an increasingly broad number of varied constituencies in which probably the most dynamic discipline is the financial services industry.
Integration of Risk Types
Authors: Overbeck Ludger
Published: September 2008

To obtain an accurate estimation of the total risk, it is necessary that the different risk categories are integrated in a consistent way. Usually, the three most important risks are market (
see
Risk Measures and Economic Capital for (Re)insurers; Default Correlation; Statistical Arbitrage), credit (
see
Options and Guarantees in Life Insurance; Mathematical Models of Credit Risk), and operational risk (
see
Solvency; Simulation in Risk Management; Extreme Value Theory in Finance; Recurrent Event Data). Since all risk measures are functions of a random variable describing the potential loss, or more accurately the potential change in value of positions and transactions the financial institution holds, the integration should take place at the level of random variables. We assume that the same fixed horizon, usually 1 year, is used in the risk measurement process for all risk types. On the basis of the conceptual similarity in the model approach for risk types, they can be integrated at the level of their risk factors. At this level, integration means that the joint distribution of all risk factors should be specified. The integrated risk can then be calculated either on single transaction level, which is very challenging, or on the loss distribution in each risk type. The latter calculation can easily be achieved if the loss distribution in each risk type is a function of the underlying factors. If the financial institutions cannot identify the risk factors or their multivariate distribution, an even more robust method for integration that is based on the copula (
see
Copulas and Other Measures of Dependency) approach is applied to the loss distribution for each risk type.
Risk‐Neutral Pricing: Importance and Relevance
Authors: Basak Gopal K. , Mukherjee Diganta
Published: September 2008

This paper discusses the concept ofrisk neutralityin a general setting. Starting from the discrete‐time binomial model, it outlines the transition to a continuous‐time stochastic differential equation that epitomizes the continuous‐time asset pricing theories. Underno‐arbitrage principleand using Itô's lemma it gives a risk‐neutral derivation of the famous Black–Scholes–Merton equation. It then introduces theequivalent martingale measurethat results fromrisk neutralityand finally, briefly discusses thestochastic volatilitymodels.
Weather Derivatives
Authors: Chan Ngai H. , Wong Chun Y.
Published: September 2008

The weather derivatives market has now become one of the fastest growing markets. This article provides some historical background of weather derivatives and presents the difference between weather derivatives and “standard” derivatives. We give a brief review of existing pricing methods.
Credit Migration Matrices
Authors: Schuermann Til
Published: September 2008

This article provides a brief overview of credit migration or transition matrices, which characterize past changes in credit quality of obligors (typically firms). They are cardinal inputs to many risk management applications, including portfolio risk assessment, the pricing of bonds and credit derivatives, and the assessment of regulatory capital as is the case for the New Basel Accord. I address questions of how to estimate these matrices, how to make inference and compare them, and provide two examples of their use: the pricing of a derivative called a
yield spread option
, and the calculation of the value distribution for a portfolio of credit assets. The latter is especially useful for risk management of credit portfolios.
Intensity Modeling: TheCox Process
Authors: Lando David
Published: September 2008

We define the Cox process as a randomly time‐changed Poisson process and outline the most important specifications and some simple properties. An example from credit risk modeling is given.
What are Hazardous Materials?
Authors: Khalili Nasrin R.
Published: September 2008

A hazardous material is any biological, chemical, or physical element or compound with a potential to cause harm either by itself or through interaction with other factors. The threat to human health and the environment is due to the toxic, corrosive, ignitable, explosive, or chemically reactive nature of the material categorized as hazardous. Hazardous materials can be characterized as a carcinogen, developmental hazard, acute toxicant, chronic toxicant, and/or radioactive depending on the regulatory body involved. Depending on the dose and the route and duration of exposure, a hazardous material is capable of inducing reactions in tissues causing cell damage, sickness, or death. The quantitative likelihood that an exposure will produce damage is called a
risk
, that is, a conditional probability of experiencing harm, its likelihoods, and severity of the harm. Risk assessments can estimate the probability and magnitude of a specific hazard for specific conditions for both humans and the organisms (fish, birds, and other living things) in the environment.
Environmental Risks
Authors: Samet Jonathan M. , White Ronald H.
Published: September 2008

The term
environment
can be defined based on spatial factors that range from the individual to the global levels, as well as temporal and activity factors. Risks to human health from environmental agents can result in an increased risk of disease or injury
via
various biological or physiological processes or of premature death. Environmental pollutants are typically categorized according to media‐specific pathways that are biologically relevant, and for regulatory purposes (e.g., air, water, and foods). Pollutants are regulated through limits on acceptable levels in these media, or through limits on the emissions from sources of these pollutants
via
these media into the environment. Humans are exposed to environmental contaminants from diverse sources through various pathways including air, water, food, and soil contact. The contaminants can be transferred from the exterior to the interior of the exposed person or organism through several possible routes of exposure: inhalation (
via
air); ingestion (
via
water or food consumption); or dermal absorption (
via
water or soil contact). Health risks from environmental pollutants can be characterized at the individual and population levels. For individuals, either absolute or relative risk indicators may be applied. For populations, estimates of the proportion of the total burden of disease attributable to the environmental agent can be developed.
Occupational Risks
Authors: Gaffney Shannon H. , Roberts Jennifer D.
Published: September 2008

Occupational risk deals with the probability of injury or illness occurring as a result of hazards within the workplace. Occupational hazards can be generally categorized into six types: safety, chemical, biological, physical, ergonomic, and psychosocial. Although many of these hazards have been recognized for centuries, the modern U.S. state and federal legislation regarding workplace safety and health was initiated owing to harsh occupational conditions or catastrophes. Today, occupational hazards are anticipated, recognized, evaluated, and controlled by trained professionals using engineering controls, administrative controls, and personal protection. Occupational exposure limits are designed to minimize the risk of adverse health effects that may be caused by occupational exposure. To continue to adequately protect workers as a country continues to grow and develop economically, it is important to acknowledge the ever‐changing work environment so that occupational risks can be minimized and prevented.
Lead
Authors: Latshaw Megan Weil , Schwartz Brian S.
Published: September 2008

Perhaps more than any other chemical, the toxicity of lead has been extensively studied. As a result, there are several unique features that provide considerable advantages to a lead risk assessment. First, there are well‐validated biomarkers of lead exposure. Both blood lead levels and bone lead levels are markers of internal dose; the former represents recent dose, while the latter, if measured in tibia bone, represents cumulative dose. This is important since doses acquired over differing time periods can have different adverse effects. Second, a great deal of evidence has been collected on the relationship between external lead levels and internal dose in humans, so much of the uncertainty normally present in a risk assessment is less of a concern for lead. Additionally, there is less need to extrapolate data since lead has been studied at very low doses in humans. Finally, there are several surveillance programs in the United States that eliminate the need to estimate the size of the population at risk for lead poisoning. Despite the large amount of evidence, risk management of lead is a controversial topic. The standards for occupational exposure to lead have not changed in almost three decades, despite new technologies and new evidence that adverse effects occur at levels below those currently deemed acceptable.
Methylmercury
Authors: Bellinger David C.
Published: September 2008

Methylmercury (MeHg) is a heavy metal that is dispersed into the environment as the result of both natural and anthropogenic processes. The primary pathway of human exposure is the consumption of seafood from contaminated waters. Because of biomagnification and bioaccumulation as MeHg ascends the aquatic food chain, MeHg levels are highest in the muscles of long‐lived predatory fish. MeHg is actively transported to the brain and across the placenta. The devastating effects of high‐dose MeHg exposures were revealed by poisoning episodes in Japan and Iraq. These episodes resulted in the recognition that fetal exposure is particularly deleterious. They also provided motivation for the conduct of many epidemiological studies seeking to determine whether adverse effects result from chronic low‐dose MeHg exposure as the result of frequent consumption of seafood. For risk assessment purposes, the most important of these studies, some of which are ongoing, were conducted in New Zealand, the Faroe Islands, and the Seychelles Islands. The findings have been mixed, and different governmental agencies have made different choices in selecting the data on which to base MeHg risk assessments. The benchmark dose modeling approach used by the US Environmental Protection Agency to establish a MeHg reference dose (RfD) is described. The sensitivity of the RfD to the underlying assumptions is illustrated. All MeHg risk assessments are complicated by the fact that the consumption of seafood also provides beneficial nutrients whose health effects can be antagonistic to those of MeHg.
Asbestos
Authors: Pierce Jennifer S. , Paustenbach Dennis J.
Published: September 2008

Asbestos has been of interest to occupational health professionals for hundreds of years, and it has been a chemical that has undergone intense study for much of the past century. Owing to its resistance to combustion, it has been frequently used in high temperature applications. Inhalation of asbestos is the route of exposure which can result in an increased risk of several diseases if dose and duration are sufficient. Research has shown that there is a significant difference in potency between chrysotile and the amphiboles for increasing the risk of lung cancer and mesothelioma. This chapter reviews the chemistry of asbestos fibers, the evolution in our understanding of the toxicology and epidemiology, of asbestos some occupations that have been at historical risk, and yet unresolved aspects in the risk assessment of the various fiber types.
Radon Risk
Authors: Samet Jonathan M.
Published: September 2008

Radon is an inert gas, produced naturally from decay of radium in the decay series of uranium. It is the greatest source of population radiation exposure in the United States and it was the first environmental respiratory carcinogen identified. Many epidemiologic studies of underground miners of uranium and other ores have established exposure to radon daughters as a cause of lung cancer. Beginning in the 1970s, radon was found to be ubiquitous in the air of homes, at high levels in some, and concern quickly arose as to the associated lung cancer risk to the general population. Biological understanding supports a linear dose–response relationship without threshold at typical indoor exposures. The burden of lung cancer associated with indoor radon has been estimated by using the distribution of indoor radon concentrations from surveys and the exposure–response relationship estimated from the miner studies. Estimates show that approximately 15 000–20 000 lung cancer deaths per year in the United States are caused by radon. A substantial proportion of these premature deaths could be avoided through the use of testing programs that identify homes with unacceptably high concentrations.
Benzene
Authors: Beatty Patrick
Published: September 2008

Physical/Chemical Properties

Evolution of the Understanding of Benzene Toxicity

Animal Data

Metabolism

Regulatory History

OSHA

USEPA

IARC

ACGIH

in the 1990s

Europe and Other Nations

Unresolved Issues

Cell Type

Dose–Response and Threshold

Biomonitoring—Breath Benzene, Phenol, S‐Phenylmercapturic Acid (
SPMA
)

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Radioactive Chemicals/Radioactive Compounds
Authors: Widner Thomas E.
Published: September 2008

Some atoms have unstable nuclei that spontaneously transform to more stable forms, often with the emission of particles and energy that are called
radiation
. Over 60 radionuclides are found in nature, and hundreds more can be made by man in devices such as reactors or accelerators. Terrestrial radionuclides are found in the earth's crust, and have been there since the earth was created. Cosmogenic radionuclides are generated when cosmic rays from space interact with the earth's atmosphere and crust. Anthropogenic radioactivity comes from human activities such as nuclear weapon tests and use, reactor operation and accidents, and use of particle accelerators. Health effects from exposure to radiation fall into two categories. Deterministic effects occur only when a minimum dose or threshold is exceeded, and get more severe as the dose increases further. Examples include reddening of the skin (erythema) and formation of cataracts. With stochastic effects (such as cancer and genetic effects), the
probability
rather than the severity of the effect increases with dose. The degree of health risk from a radioactive compound varies with factors that include the concentration and strength of the radionuclide in the material, the types of radiation that are emitted, the route by which exposure occurs, how the material behaves in the human body, and the chemical toxicity of the material. Much research has been conducted in humans and animals to describe how radioactive materials are retained and transported in the human body and to describe the kinetics of transport, retention, and excretion. Detailed mathematical models have been developed to support prediction and/or reconstruction of concentrations of radionuclides in the different body tissues over time so that doses to specific organs can be estimated and health risks evaluated.
Burn‐in Testing: Its Quantification and Applications
Authors: Kececioglu Dimitri B. , Chen Xiaofang
Published: September 2008

Early life failures are usually attributed to problems in the manufacturing, packaging, and assembling phases. After making improvements to the greatest possible extent in these phases, the only way to improve the product's reliability is to eliminate defective units before they reach customers. A common approach used to weed out defective and weak parts before shipping the products to customers is burn‐in testing. Burn‐in testing has become a frequently used procedure in many industries, especially the semiconductor industry. In this article, an overview of burn‐in testing is presented and its applications are explained. Burn‐in methods are related to a variety of failure mechanisms and the conditions under which the tests are run are specified. Finally, the burn‐in time is determined through a reliability bathtub curve model to be the time after which the failure rate remains constant.
Accelerated Life Testing
Authors: Donahoe Daniel , Zhao Ke , Murray Steven , Ray Rose M.
Published: September 2008

Accelerated testing is a set of methods that attempts to replicate and predict the quantitative effect on product life of what are assumed to be the most important degradation mechanisms determining product life in actual service. In all accelerated testing approaches, one or more physical degradation methods are applied to promote failure. The history and limitations of accelerated testing and mathematical tools for modeling product lifetime as a function of acceleration mechanisms are discussed.
Software Testing and Reliability
Authors: McGettrick Andrew D.
Published: September 2008

Software, increasingly, forms an important part of many systems—engineering, medical, administrative, educational, finance, and so on. Achieving high level of reliability of the software is a prerequisite for that system being successful. This article addresses the problem of testing of software and relates it to issues about reliability.
Multistate Systems
Authors: Levitin Gregory
Published: September 2008

This article presents a general model of multistate system that can function at different levels of performances. The measures of risk associated with undesirable system behavior are defined on the basis of an acceptability function that represents the required relation between the system performance and a demand. A reliability block diagram method based on a universal generating function technique is presented as an efficient tool for evaluating the risk measures for complex multistate series–parallel systems. The risk associated with combination of internal failures and external attack is considered and a defense strategy methodology is discussed.
Structural Reliability
Authors: Stewart Mark G.
Published: September 2008

Structural systems such as buildings, bridges, water tanks, containment vessels, and communication towers are often represented as load‐resistance systems. In all cases, these structures represent substantial and expensive assets that are unique or “one‐off”. Even apparently, similar buildings have different occupancy loads, foundations, connection details, etc. Clearly, their reliability cannot be directly inferred from observation of failures or other experimental studies. In these circumstances, reliabilities need to be predicted from predictive models and probabilistic methods. As such, there is a clear recognition that uncertainty and variability are associated with many variables describing a structure's performance and that this can be accounted for explicitly by the use of probability distributions and structural reliability theory. Structural reliability theory allows the probability of failure to be predicted where failure is defined to occur when load exceeds a resistance. Not surprisingly, structural reliability theory underpins many recent advances in structural and safety engineering, including development of design and assessment codes, predicting service‐life performance, and assessment and maintenance of aging and deteriorating structures.
Multivariate Reliability Models and Methods
Authors: Hanagal David D.
Published: September 2008

We propose some multivariate reliability models with emphasis on quantitative risk assessment. We give a brief description of different exponential and Weibull models used for multicomponent systems and frailty models for multivariate survival data. We give some applications of frailty models for the multivariate survival data.
Wear
Authors: Bérenguer Christophe , Grall Antoine
Published: September 2008

This article presents an overview of probabilistic wear models based on lifetime distributions or stochastic processes and discusses their interest in wear‐dependent reliability modeling and condition‐based maintenance evaluation.
Failure Modes and Effects Analysis
Authors: Price Chris
Published: September 2008

Failure mode and effects analysis (FMEA) is a safety analysis method that evaluates the effects of lower level failures on an overall system. The output of performing an FMEA is often ranked by significance, enabling engineers to focus on the failures with the most important effects. FMEA has its origins in the automotive and aerospace industries, but is now being applied in a much broader range of domains, from medical systems to semiconductor manufacturing.
Reliability Data
Authors: Moss Thomas R.
Published: September 2008

Understanding the reliability characteristics of equipment is important when making decisions concerning the safety and productivity of an industrial plant. Good quality data is vital to this process. This article discusses methods for the analysis of the reliability data required for input to predictive models and the in‐service monitoring of plant and equipment reliability performance.
Extreme Values in Reliability
Authors: Ferreira Ana
Published: September 2008

Extreme value (EV) theory provides probabilistic and statistical tools to deal with the largest or the smallest values of a sample, hence with tails of distribution functions. Applications of EV theory in reliability analysis can be found in many fields. For instance, in corrosion analysis, where pits of larger depth are of primary interest, as pitting corrosion can lead to the failure of metal structures, or, in strength of material problems where the principle of a chain being as strong as its weakest link dominates.

In the following text, main concepts of EV theory such as the maximum domain of attraction condition and EV distributions, peaks over threshold and the generalized Pareto distributions are discussed. Estimation within these frameworks, along with applications, are exemplified.
Mathematics of Risk and Reliability: A Select History
Authors: Singpurwalla Nozer D. , Wilson Simon P.
Published: September 2008

This article is a brief description of some landmark advances in the mathematics of risk and reliability, starting with the initial developments of probability theory in the seventeenth century to the ascendancy of reliability theory during the last 60 years.
Expert Elicitation for Risk Assessment
Authors: Wiedlea Andrew C.K.
Published: September 2008

Expert elicitation—the use of structured, calibrated questions to gather qualitative risk information—is a key risk assessment tool. Particularly when there does not exist sufficient observed data to estimate the nature of a risk process, or in cases when a system risk is generated by the interactions of a complex set of component interactions, qualitative information gathered from subject matter experts may be the best, if not the only, source of relevant information. Ensuring that information gathered from subject matter experts is relatively free from bias and probability logic inconsistency is a key interest of elicitation research.
Availability and Maintainability
Authors: Kumar Uday , Akersten Per Anders
Published: September 2008

The increasing pace of mechanization and automation of industries has enhanced the business risks associated with nonfunctioning of production units and other engineering systems. In general, such units and engineering systems are expected to function round the clock without failure. However, owing to design deficiencies or the influence of operational stresses, these systems are not always able to meet the users' requirements in terms of system performance. This is often attributed to poorly designed reliability and maintainability characteristics, combined with poor maintenance and product support strategies. If the designed reliability and maintainability characteristics are poor and the maintenance organization is unable to meet the requirements, this may result in a poor availability performance. Even though high reliability is desirable to reduce the costs of maintenance and maintenance preparedness, the benefits of improved maintainability should not be ignored in achieving the required availability performance. Availability is the most commonly used measure of the state of plant and systems all over the world.

In this chapter, a brief discussion on the concepts of availability and maintainability is presented with some examples.
Warranty AnalysisThis article is a revised version of “Warranty Cost Analysis,” by D. N. Prabhakar Murthy and Wallace R. Blischke, which appeared in Encyclopedia of Statistics in Quality and Reliability, copyright John Wiley & Sons, Limited, 2006. Reprinted with permission.
Authors: Prabhakar Murthy D.N. , Blischke Wallace R.
Published: September 2008

For products sold with warranty, an important element of risk for the manufacturer is the potential cost resulting from the servicing of failures occurring under warranty. These costs depend on several factors, the most important being the reliability of the product. For planning purposes, the manufacturer must estimate (a) the expected cost of warranty per unit sold and (b) the expected cost per unit time over the product life cycle. This must be done in the product development stage. This article deals with models that may be used to obtain these cost estimates for products sold with free‐replacement and pro rata warranties.
Common Cause Failure Modeling
Authors: Vaurio Jussi K.
Published: September 2008

Modeling and quantification of simultaneous failures of multiple components due to common causes is a frequent and often crucial task in safety system reliability and risk assessments. Several models of common cause failures (CCF) are introduced. Their mutual relationships, limitations, and advantages, as well as sources of parametric values, are described. The models are presented in terms of basic multiple‐failure rates and probabilities that are independent of system testing schemes and intervals, and have direct connection to the basic event probabilities needed in system logic models.

The early CCF models such as the β factor, binomial failure rate, common load, and distributed failure probability models are all special cases of stochastic reliability analysis. Even if mathematically attractive, these models are inherently constrained by certain mutual couplings between the CCF rates or CCF probabilities.

More general α factor and multiple Greek letter models are based on the ratios of multiple‐failure rates or probabilities. In applications, both models need some additional failure rate from outside of the model to yield CCF rates and basic event probabilities.

Extensive national and international common cause failure data collection efforts are making it feasible to estimate plant‐specific multiple‐failure rates and probabilities by combining data from many similar systems with empirically oriented Bayesian methods without assuming common generic parametric values. This methodology is briefly described. It takes into account both statistical and assessment uncertainties due to limited observations, interpretations, and documentation of CCF events.
Reliability Integrated Engineering Using Physics of Failure
Authors: Wang Weiqiang , Das Diganta , Osterman Michael , Pecht Michael
Published: September 2008

Reliability is the ability of a product to perform without failure and within specified performance limits, for a specified time, in its life‐cycle application conditions. Physics of failure is the preferred methodology to design for and assess product reliability. This article presents the concept and underlying principles of physics of failure and shows how the methodology is used as an integrated product development method to develop, assess, and enhance product reliability.
Stress Screening
Authors: Newman Robert M.
Published: September 2008

Stress screening is a process used by manufacturers to minimize the risk of delivering products with latent defects that may cause failure in service. It is particularly applicable to low‐volume products with manual intervention in final assembly and where the consequences of service failure can be severe. This article explains the background to stress screening and how to go about determining the need for and establishing the test conditions for maximum benefit.
Fault Detection and Diagnosis
Authors: Gertler Janos J.
Published: September 2008

The article describes the detection and isolation (diagnosis) of faults (major equipment and sensor/actuator malfunctions) in engineering systems. The simpler, and less powerful methods do not rely on any mathematical model of the system; these include limit checking, special and multiple sensors, frequency analysis, and fault‐tree analysis. More advanced methods use mathematical models obtained from first principles or from experimental data. Such methods include on‐line parameter estimation, consistency checking, and principal component analysis. Two examples, one related to a simple electrical circuit and the other to a car‐engine subsystem, demonstrate the use of some of the methods.