bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural Reliability
Authors: Stewart Mark G. 
Published: September 2008

Abstract
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.
Failure Modes and Effects Analysis
Authors: Price Chris 
Published: September 2008

AbstractFailure 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.
Extreme Values in Reliability
Authors: Ferreira Ana 
Published: September 2008

Abstract
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.
Expert Elicitation for Risk Assessment
Authors: Wiedlea Andrew C.K. 
Published: September 2008

AbstractExpert 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

Abstract
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

Abstract
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

AbstractModeling 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

Abstract
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

Abstract
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

Abstract
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.
Experience Feedback
Authors: Kjellén Urban 
Published: September 2008

AbstractExperience feedback is the process by which information on the results of an activity is fed back to decision makers as new input to modify and improve subsequent activities. The main source of experience used in risk assessments is historic industrial systems. The experience emanates from incidents, failures, and normal operation, as well as from earlier risk assessments. Laboratory and field experiments provide complementary experience. Experience‐based knowledge from these activities is directed to the risk assessment team through different channels. The article distinguishes between channels for transfer of explicit and tacit knowledge. Experience databases and experience carriers play an important role in the first case, where experience carriers represent a higher degree of synthesis. If properly designed, the risk assessment team will constitute an arena for tacit knowledge exchange between risk analysts, personnel with operational experience, and systems designers. The article describes the processes for update of experience databases and experience carriers with new information. The need for documenting the experience basis in the risk assessment report is highlighted. An example from environmental risk assessments illustrates how experience feedback is achieved in practice.
Reliability Optimization
Authors: Coit David W. 
Published: September 2008

Abstract
Reliability optimization involves the simultaneous evaluation and selection of components and a system‐level design configuration that collectively meets all design constraints, and at the same time, optimizes some objective function, often system reliability or availability. Often these problems are very constrained because of physical or budgetary limitations. There are several variations of reliability optimization and there have been many attempts to determine optimal design configurations for different problem formulations. Applicable optimization methods include dynamic programming, integer programming, mixed integer and nonlinear programming, and metaheuristics, e.g., genetic algorithms (GA). Notable extensions to this problem include multistate reliability optimization and multiple‐objective system reliability optimization.
Persistent Organic Pollutants
Authors: Ross Peter S. , Grant Paul B.C. 
Published: September 2008

AbstractPersistent organic pollutants (POPs) comprise a wide range of chemicals, which for the most part have either been intentionally manufactured or created as an unintended consequence of human activities. Some have been used in closed systems (e.g., polychlorinated biphenyls, PCBs, as dielectric fluids), while others are intentionally applied to ecosystem compartments (e.g., organochlorine pesticides). Many POPs are classified as being persistent, bioaccumulative, and toxic: properties that shape their movement and fate in the environment, and dictate their impact on human and animal health. Accidental exposures in humans or widespread ecological impacts in the past led to a variety of national regulations and international treaties, such that some of the more notorious chemical classes are no longer widely used. Such “legacy” POPs contrast the continued use of POPs for which the evidence is less clear and the emergence of new chemical products for which a limited understanding exists (e.g., polybrominated diphenylethers, PBDEs). The extent to which experience from past failures and subsequently developed risk assessment paradigms will protect the biosphere from impacts in the future remains open to debate. Unfortunately, the witnesses are often those far removed from the source of the chemical and represent constituent groups with little influence on regulations, such as the highly POP‐contaminated subsistence‐oriented Inuit people of Arctic Canada, or the killer whales of the NE Pacific Ocean.
Mobile Phones and Health Risks
Authors: Blettner Maria , Samkange‐Zeeb Florence 
Published: September 2008

Abstract
Epidemiological studies which have been conducted up to date have found little evidence linking short‐term mobile phone use to an increased risk of brain tumors. Some studies have shown slight increases in risk for long‐term mobile phone users (>10 years). These results are however based on small numbers and need confirmation.
Cumulative Risk Assessment for Environmental Hazards
Authors: Sexton Ken 
Published: September 2008

Abstract
Humans and ecosystems face combined threats from exposures to numerous biological, chemical, physical, and psychosocial stressors in the environment. Cumulative risk assessment is a tool for organizing and analyzing information about combined adverse effects from collective exposure to multiple environmental stressors across all relevant sources, pathways, and routes. The process involves three interrelated phases: planning, scoping, and problem formulation; data analysis; and interpretation and risk characterization. Although in its infancy, cumulative risk assessment promises, over time, to provide decision makers with more realistic answers to pressing environmental questions.
B
ayes' Theorem and Updating of Belief
Authors: Lee Peter M. 
Published: September 2008

Abstract

The conditional probability of an event
A
given an event
B
is defined as Pr(
A
∩
B
)/Pr(
B
). If you start with hypotheses
H

i

for which your initial assessments of probability (your
prior probabilities
) are Pr(
H

i

) and you then observe data, the probability of which is dependent on which of the hypotheses is true (supposing them to be exclusive and exhaustive, so that one and only one of them is true), then
Bayes' theorem
allows you to update your assessments of probability to
posterior probabilities

Sometimes we have an unknown parameter that can take any value in a continuous range and the observation we are thinking of can similarly take values from a continuous range. In this case, we get Bayes' theorem for random variables in the form

These formulae can be used sequentially, taking the posterior form after one or more observations as the prior for the next.
Decision Conferencing/Facilitated Workshops
Authors: Phillips Lawrence D. 
Published: September 2008

Abstract
Decision conferencing provides a structured group approach for assessing the risks associated with several options, with expert judgment providing some or all of the inputs and models based on decision theory used to combine the inputs into an overall assessment. A facilitated workshop brings together experts about a specific risk criterion, with an impartial facilitator taking the group through the process of discussing the meaning of each criterion, establishing measurement scales, and appraising options on the scales. At a subsequent decision conference attended by the key players, the facilitator guides participants through the process of making the final judgments about inputs and trade‐offs between criteria that allow the decision theory model to bring together all the inputs to provide an overall ordering of the options. This approach allows the collective experience of specialists and key players to be brought to bear on issues of risk assessment, and creates committed alignment backed by the authority of the entire group.
Multiattribute Utility Functions
Authors: Stewart Theodor J. 
Published: September 2008

Abstract
The multiattribute utility function is a means of systematically analyzing preferences in the context of multiattribute or multicriteria decision making under conditions of uncertain or risky outcomes, and thus integrates concepts from multiattribute value measurement and expected utility theory. The process starts by eliciting utility functions for each individual attribute in turn, which then have to be aggregated in some sense in order to form the overall multiattribute utility function. A weighted additive aggregation is simple, but is demonstrated to force certain preference properties that may not be justifiable in certain decision contexts. For this reason, weaker sets of assumptions have been developed, which lead either to multiplicative or multilinear aggregation rules. Unfortunately, the weaker the sets of assumptions made, the more the decision maker is required to make cognitively challenging judgments between multivariate lotteries.
Societal Decision Making
Authors: French Simon 
Published: September 2008

Abstract
The context of decision making in the public sector is somewhat different from that in business and industry. Decision making may not be confined to a small management team or board of directors; but rather involves many politicians, officials, and different political institutions. There is a need to address many stakeholder concerns and be seen to do so, particularly when the decision involves major technological, health, environmental, or other risks. The issues addressed tend to be more complex and involve many factors and uncertainties. Here, we discuss how decision and risk analyses fit into such contexts and the differences between their approach and that of cost–benefit analysis.
Geographic Disease Risk
Authors: Wakefield Jon 
Published: September 2008

Abstract
The examination of disease risk summaries across space has a long history in epidemiology and may be performed for disease mapping, cluster detection, examination of clustering, or spatial regression. Often these exercises are carried out to gain clues to etiology with respect to putative environmental exposures. The spatial nature of the data has positive and negative aspects. For disease mapping, the spatial aspect can be exploited, since one expects near‐by areas to be more similar in terms of their risk. For spatial regression, the analysis is complicated by the spatial aspect because the usual independence assumption of conventional analysis methods is violated, and there may also be “confounding by location”. As in all epidemiological investigations, interpretation depends crucially on issues of data quality, in particular, errors in population and disease counts and exposure misclassification. Ecological bias, which arises when aggregate associations are assumed to apply at the level of the individual, is also an issue that requires serious consideration.
Assessment of Risk Association with Beryllium Exposure
Authors: Cavender Finis 
Published: September 2008

AbstractBeryllium is the lightest metal and has an excellent strength to weight ratio. It is used as a hardening agent in nonsparking tools, ceramics, computers, and aerospace industries. Early reports of health effects of beryllium salts were thought to be due to the anion rather than beryllium. Numerous health effects have been noted in occupational settings and in the populace surrounding industrial plants. Acute beryllium disease consists of pulmonary edema and severe pneumonitis caused by short‐term, massive exposures to soluble salts. Usually, workers who survive the edema are disease free for 1 year, but may develop lung cancer in later years. When hygienic standards were implemented in 1949, no new cases of acute beryllium disease were seen except following industrial accidents. Chronic beryllium disease (CBD) is an immune‐mediated disease starting with sensitization and progressing to a debilitating granulomatous disease; however, CBD can be successfully treated. In 2006, the ACGIH recommended lowering the TLV to 0.05 µg m−3as an 8‐h time‐weighted average along with a sensitization and a skin notation. When this TLV is fully implemented, it will virtually eliminate CBD.
Linkage Analysis
Authors: Shao Yongzhao 
Published: September 2008

Abstract
Linkage analysis is an important initial step in localizing and identifying genes in the chromosomes that underlie many human diseases and other traits of interest. Frequently, linkage analysis proceeds by comparing the inheritance pattern of the trait and that of the genetic markers to establish linkage between the trait and marker loci. This article provides a brief overview of commonly used methods for linkage analysis, which include model‐free and model‐based methods for mapping qualitative‐ and quantitative‐trait loci. Issues on the design of linkage studies are also briefly discussed.
Microarray Analysis
Authors: Kafadar Karen , Bjork Kathe E. 
Published: September 2008

Abstract
Microarray‐based experiments querying tens of thousands of genes, peptides, and other cellular components of varying organisms have evolved over the past several years to become a cornerstone screening method for numerous diseases and conditions, especially those with known genetic etiologies. They are also rapidly becoming a standard tool among researchers investigating reactions within and between hosts and agents relative to shifting environments and environmental conditions, and for quantitatively assessing risk to the health of humans, animals, and plants. The most commonly used microarray technologies for investigating gene expression are described herein, as well as examples of current and future research areas in public health.
Risk Characterization
Authors: Rai Shesh N. 
Published: September 2008

Abstract
Probabilistic risk characterization is now commonly used to estimate and predict risk caused by exposure to unwanted substances. We characterize risk as a function of risk factors and discuss two examples: one is based on a multiplicative model of contaminated drinking water exposure and the other is based on a general model of radon exposure. The risks associated with contaminated drinking water are easy to characterize and understand; however, those associated with radon exposure are not as well understood as the former. Radon exposure is an established risk factor for human lung cancer, and these data are used to project radon‐associated lung cancer risks to the general population.