bluuebunny/crossref_metadata_2025 huggingface dataset:
Transcription Factors
Authors: Hauschild KE , Carlson CD , Donato LJ , Moretti R , Ansari AZ
Published: February 2008

The genome of an organism is deciphered by transcriptional processes to generate RNA and protein molecules that determine cellular fate and perform all cellular functions. The transcriptional machinery itself displays limited specificity and is involved in transcribing all genes in the genome. The exquisite specificity with which gene networks are expressed is mediated by regulatory proteins called transcription factors. Transcription factors bind to gene‐specific regulatory sites in the genome and help assemble the complex multi‐subunit machinery that transcribes the target gene(s). Spatio‐temporal regulation of gene expression permits selective expression of a subset of genes within the genome and thus governs the diversity of cell types and cellular function in response to physiologic signals. The central role of transcription factors in regulation of specific genes and networks is underscored by the fact that their malfunction is linked to the onset of a wide array of diseases, including developmental disorders, cancer, and diabetes. Thus, a major goal at the interface of chemistry, biology, and molecular medicine is the ability to generate synthetic molecules that function as transcription factors. The key requirement in the creation of these artificial transcription factors is the ability to define the properties of natural transcription factors fully. Here we discuss the common properties of transcription factors, summarize the alluring value of targeting gene expression with small molecules, and summarize the current advances toward regulating the expression of desired genes and gene networks with artificial transcription factors.
Xenobiotic Metabolism
Authors: Ioannides Costas
Published: February 2008

Humans are exposed continuously and unavoidably to a myriad of potentially toxic chemicals that are inherently lipophilic and, consequently, very difficult to excrete. To effect their elimination, the human body has developed appropriate enzyme systems that can transform metabolically these chemicals to hydrophilic, readily excretable, metabolites. This biotransformation process occurs in two distinct phases, Phase I and Phase II, and involves several enzyme systems, the most important being the cytochromes P450. The expression of these enzyme systems is regulated genetically but can be modulated also other factors, such as exposure to chemicals that can either increase or impair activity. Paradoxically, the same xenobiotic‐metabolizing enzyme systems also can convert biologically inactive chemicals to highly reactive intermediates that interact with vital cellular macromolecules and elicit various forms of toxicity. Thus, xenobiotic metabolism does not always lead to deactivation but can result also in metabolic activation with deleterious consequences.
Synthetic Chemistry: Formation of the Amide Bond
Authors: Montalbetti Christian A. G. N. , Falque Virginie M.
Published: February 2008

Amide bonds are found ubiquitously in natural or synthetic molecules of biologic interest. Since the early days of synthetic organic chemistry, methods for the formation of amides have been described. More recently, with the development of solid‐phase chemistry and automated peptide synthesis, new strategies and reagents have been devised to overcome typical problems such as low conversion and racemization. This article provides an overview of the methodology that is available today. Depending on the nature of the synthetic target and the associated synthetic challenges, different approaches can be envisaged. Methods range from the rather straightforward use of acyl halides, anhydrides, and carbodiimides, to the more elaborate, low‐racemization inducing methods that use phosphonium/uronium‐based reagents. New amide bond‐mediated ligation methodologies now offer new convergent strategies for the synthesis of highly functionalized molecules of biologic interest.
Water in Biological Recognition Processes
Authors: Chaplin Martin F.
Published: February 2008

Water is central to biology, providing much more than the means to allow the movement of biologic molecules. It is an excellent solvent, a substrate and facilitator for reactions, and it provides a means for ionizations and extends the intricate three‐dimensional surface geometry and complex charge distribution of biomolecules out towards the bulk solution. Water's hydrogen bonds and dipoles are strong enough to present structural information concerning surfaces to distant diffusing molecules and to fine tune their orientation to match potential recognition sites, but they are weak enough to break to make way for direct binding processes. Water occupies potential interaction sites until they are recognized by suitable molecules, with the release of the water providing free energy to help compensate for the entropy loss on binding. Also, water molecules may determine the conformation of the biological molecule and directly assist and take part in the recognition process. Among the techniques available, X‐ray crystallography has provided the most information on these roles for water.
NMR‐Based Metabolomics Technology in Biomarker Research
Authors: Pan Zhengzheng , Gu Haiwei , Raftery Daniel
Published: February 2008

The methods and applications of NMR‐based metabolomics are introduced in this article. As important tools for systems biology research, metabolomics, metabonomics, and metabolite profiling have been applied successfully for biomarker exploration at the molecular level. In addition to the detection and identification of putative disease biomarkers, NMR‐based metabolomics also can be performed in many areas including nutritional science, disease monitoring, and drug toxicology studies. In the following sections, basic and advanced NMR technologies associated with metabolomics are summarized. Also, to process the complex spectral data and obtain useful information, pattern recognition techniques or multivariate statistical methods are necessary. These methods successfully simplify the multivariable data set, and their improvement may enhance the development of metabolomics. Finally, several important applications of NMR‐based metabolomics are summarized, and examples are described.
Phosphate Mimics: Cyclic Compounds
Authors: Nottbohm Amanda C. , Hergenrother Paul J.
Published: February 2008

Compounds that contain phosphate and diphosphate moieties are not ideal biologic probes. Not only does their ionic character inhibit cell membrane permeability, but also, once inside a cell, the ester and the anhydride functionalities are likely targets for enzymatic cleavage. Thus, replacements for the phosphate motif are important as enzyme inhibitors, DNA or RNA analogs, phospholipid mimics, and phosphorylated metabolite analogs. To date, several classes of phosphate mimics have been developed that have been grouped into four categories: phosphorus‐containing, sulfur‐containing, dicarboxylates, and the novel cyclic mimics, which will be the focus of this review.
Pharmacokinetics of Drug Candidates
Authors: White Ronald E.
Published: February 2008

Pharmacokinetics is a mathematically based discipline that describes the time course of uptake, distribution, and elimination of a drug in an organism. This article explains the physiologic basis and clinical interpretation of the various pharmacokinetic parameters, such as half‐life, clearance, and oral bioavailability. Because of the central importance of pharmacokinetics to the clinical use of drugs, the determination of pharmacokinetics is required at several points in the research and development cycle of new drug candidates. The rationale and methods of determination are discussed.
Microarrays in Chemical Biology
Authors: Koehler Angela N.
Published: February 2008

Advances in both genomics and proteomics have provided researchers with access to large collections of biomolecules, including DNA, proteins, and metabolites. High‐throughput methods are needed to study the function and regulation of these biomolecules within complex systems. Microarrays have emerged as a common platform to study biomolecular interactions that involve nucleic acids, proteins, and small molecules. DNA microarrays have revolutionized genomic research by allowing researchers to study gene expression, sequence variation, and transcription factor binding sites on a whole‐genome scale. Protein microarrays can be used to study interactions with other proteins, DNA, RNA, and small molecules, including lipids, carbohydrates, and drugs. Protein microarrays can also be used as analytical tools to profile complex protein mixtures, such as fractionated cell lysates, in an effort to study antibody specificity, measure changes in protein abundance, or characterize disease states. Small‐molecule microarrays are useful tools for ligand discovery, comparing inhibitor specificity across enzyme classes, high‐throughput, cell‐based phenotypic screens, and as diagnostic tools for pathogen detection. The microarray approach has been extended to transfected cell microarrays, RNAi living cell microarrays, virus microarrays, and tissue microarrays. This article reviews chemical strategies for making microarrays and applications of microarrays in chemical biology.
Hemozoin: A Paradigm for Biominerals in Disease
Authors: Carter Melissa D. , Hoang Anh N. , Wright David W.
Published: February 2008

Biomineralization is the formation of organic–inorganic composites by organisms. Originally evolved as a protective mechanism, this complex process has also become a recognized contributor to several disease states, which range from kidney stone disease (nephrolithiasis) to parasitic diseases like malaria. The characteristic three‐step process for the formation of biominerals is defined by the supramolecular preorganization of a nucleating template, the interfacial molecular recognition of crystal nuclei and the cellular processing of resultant aggregates. Hemozoin formed in the heme detoxification pathway used by the malarial parasite
Plasmodium falciparum
represents a paradigm for pathogenic biominerals. Current research indicates that a supramolecular lipid template organizes heme released previously during hemoglobin catabolism. Nucleation and growth of the heme aggregate serves to protect the parasite from the toxic effects of free heme. Given the mechanisms of biomineralization, it is not surprising to discover that century‐old antimalarial compounds function by disrupting key interactions between the heme substrate and template. Subsequently, the heme‐aggregate is released into the host vasculature and deposits in patients' brains, spleens, and livers where it disrupts host innate immune response. The underlying basis of this immunomodulating activity seems to result from hemozoin mediated lipid peroxidation. Understanding the relationships between hemozoin formation and its pathogenic activity with the host immune response represents a significant challenge to chemical biology.
Lipids: Organization and Aggregation
Authors: Vattulainen Ilpo
Published: February 2008

Lipids constitute one of the main classes of molecules in biological systems. They are involved in numerous cellular functions either as individual molecules or as lipid aggregates with varying sizes and morphologies. For example, lipids are a crucial component of cellular membranes that surround and protect cells. Lipids comprise membrane domains that provide membrane proteins with a well‐defined environment to carry out their functions. Lipids also interact specifically with some proteins, which render their functions possible. Lipids play the role of drugs and enzymes, and our skin and lung surfactant lining lung epithelial cells are composed largely of lipids. What is more, lipids are used in delivery vehicles to encompass drugs and other molecules. These functions and many others develop in part from lipids' specific properties relevant on molecular scales and also from the assembly of lipids as fascinating structures observed over a multitude of scales beyond molecular size. Here, we discuss the functions of lipids and lipid structures together with their structural and dynamic properties, including examples and highlights of recent studies.
Analysis of Variance
Authors: Bingham Derek
Published: February 2008

The analysis of variance is, perhaps, the most used of all data analysis techniques. Indeed, it is the standard approach for the analysis of most industrial experiments. The aim of the analysis of the variance is to determine if there exists a relationship between a response variable and one or more experimental factors. This is achieved by partitioning the variability in the response into variability due to random error and variability due to changes in the settings of the experiment factors. A statistically significant relationship is identified by the appropriate use of an
F
‐test. This article provides an overview of the analysis of variance and its use in industrial experiments.
Foldover Designs
Authors: Li William
Published: February 2008

Foldover is a commonly used follow‐up technique. A foldover design is constructed by reversing the signs of several columns of the initial design. We demonstrate how a foldover design can be used to untangle aliased effects, especially main effects and two‐factor interactions, found from the analysis of the initial design. We then discuss several methods for constructing foldover designs and introduce optimal foldover plans for both regular and nonregular factorial designs. The main advantage and disadvantage of the foldover method are also discussed.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies
Authors: Vardeman Stephen B.
Published: February 2008

This article discusses basic design considerations for so‐called gauge repeatability and reproducibility studies. These common industrial measurement studies are used to investigate the precision of measurement systems that employ more than one operator to make measurements with a single gauge/measurement device. Data from them are typically used to estimate functions of variance components in random‐effects models that quantify both “within part‐by‐operator combination” and “between‐operator” variation in measurement.
Half‐Normal Plot
Authors: Taam Winson
Published: February 2008

A half‐normal plot is a graphical tool to discern active contributors from inert ones in a factorial experiment. It is designed for two‐level factorial experiments but can be adapted for experiments with other levels. Plotting absolute effect estimates and their corresponding half‐normal scores provides a graphical display that compares the relative strength of various effects in the experiment. Its ease of use, visual appeal, and simplicity are reasons for its popularity.
Block Designs, Incomplete
Authors: Prescott Philip
Published: February 2008

In experimental situations, incomplete blocks occur when there is insufficient homogeneous material within a block for all treatments to be applied. Balanced incomplete block designs are such that all treatments are equally represented within the design and every pair of treatments occurs together within a block the same number of times. We give examples of unreduced and reduced balanced incomplete block designs, and briefly discuss several methods of constructing such designs. The intrablock analysis of variance for the general case is given in detail, and this is extended to the simplified analysis which results when the design is balanced.
Latin Hypercube Designs
Authors: Tang Boxin
Published: February 2008

Latin hypercubes are a rich class of designs that are suitable for computer experiments and numerical integration. They are easy to generate and achieve maximum stratification in each of the univariate margins of the design region. This article introduces Latin hypercubes, explains how they can be used in computer experiments and numerical integration, and discusses their strengths and weaknesses. A design may not perform well in terms of other criteria such as those of space filling and orthogonality, simply because it is a Latin hypercube. The article concludes with a discussion on some of the methods for constructing Latin hypercubes that have better space‐filling or orthogonality properties.
Latin Squares and Related Experimental Designs
Authors: Morgan J. P.
Published: February 2008

Latin squares and their generalizations are explored for their utility as experimental designs. The popularity of Latin square designs is due to their parsimonious use of experimental material while providing orthogonal estimation of treatment factors. As a design with twov‐level blocking factors, av × vLatin square can evaluate levels of onev‐level treatment factor orthogonally to both of the blocking factors while requiring onlyv2experimental units (one for each combination of the blocking factors). Generalizations include: designs with more rows and columns (F‐squares and rectangles, Youden designs), squares with more than one experimental unit per row‐column combination (semi‐Latin squares), and designs based on multiple squares. Mutually orthogonal Latin squares allow more treatment factors to be orthogonally accommodated with the same number of experimental units as a single Latin square. This concept is explored in the wider context of the generalizations listed above.
Main Effect Designs
Authors: Jacroux Mike
Published: February 2008

In this article we consider experimental situations where a screening experiment is to be run and primary interest is in the identification of variables which have an influential effect on the process being studied. In such cases, main effects designs are often used. Main effects designs typically allow for the efficient estimation of main effects parameters and are particularly useful when the initial number of experimental variables being studied is large compared to the number of runs in the screening experiment. Here we describe a number of the orthogonal and nonorthogonal main effects designs available to practitioners along with some of their individual advantages and disadvantages.
Optimal Design
Authors: Atkinson Anthony C.
Published: February 2008

The article focuses on the use of the methods of optimal experimental design to solve a number of nonstandard problems arising in quality and reliability. Examples include response‐surface designs for an arbitrary number of observations. Because some combinations of factors may produce unstable results, designs are found over irregular design regions. The method of dividing such designs into blocks is also shown. Mixture designs, again over constrained regions, are mentioned. The theory underlying the construction of the designs is integrated using a general equivalence theorem which leads to algorithms for the construction of designs and to the assessment of the efficiency of designs. References are given to recent work.
Orthogonal Arrays
Authors: Notz William I.
Published: February 2008

In this article we use a simple example to introduce the notion of orthogonality as a desirable characteristic of an experimental design. In particular, we show that a lack of orthogonality prevents us from distinguishing the effects of some of the factors. We then formally define a class of designs, known asorthogonal arrays, which possess attractive orthogonality properties. Some examples of orthogonal arrays are given. These are followed by comments about the construction of orthogonal arrays and sources where one can find tables of orthogonal arrays. Next, we discuss the use of orthogonal arrays in several applications. These include fractional factorial experiments for controlling aliasing, with special attention to designs in which no main effects are aliased; screening experiments in which orthogonal arrays have special projection properties; response surface modeling in which orthogonal arrays yield optimal designs; robust parameter designs in which orthogonal arrays are the basis for cross arrays; and in computer experiments where orthogonal arrays produce designs that space points evenly on lower‐dimensional subspaces. We conclude with an example.
Projectivity in Experimental Designs
Authors: Tyssedal John
Published: February 2008

Projectivity concerns the properties of a factorial design when it is restricted to a subset of experimental factors. In this article we stress the importance of taking projective properties into account when a screening experiment is performed. We present results that can be used to decide on the projectivity of two‐level designs, show by an example how these properties can be taken advantage of in order to identify active subspaces of factors and give various techniques that can be used to construct designs with good projective properties for various experimental situations.
Screening Designs
Authors: Jacroux Mike
Published: February 2008

In this article, we consider experimental situations where the initial number of predictor variables to be studied in an experimental process is large and it is desired to identify which variables actually have a significant influence on the process being investigated. Screening designs are used primarily for this purpose. Here we describe some of the two‐level screening designs that are commonly used along with some of their individual advantages and disadvantages.
Supersaturated Designs
Authors: Lin Dennis K.J.
Published: February 2008

In the early stages of industrial experimentation, one often has a large set of candidate factors believed to have a possibly significant influence on the response of interest, although it is reasonable to assume that only a small fraction are influential, a condition known aseffect sparsity. A design of an experiment with the property that the number of potential effects exceeds the number of experimental runs (observations) is called asupersaturated design. These designs are useful as screening tools. This article explains what these designs are, why you would want to use them, and what are their pros and cons relative to other designs. Both design and analysis issues will be discussed.
Box–Behnken Designs
Authors: Robinson Timothy J.
Published: February 2008

Designs for second‐order response surface models play an important role in response surface methodologies. The central composite design and Box‐Behnken designs have served as popular choices for designs for second‐order models when design economy and precise prediction variance are desired. The Box‐Behnken design has been widely used in completely randomized experiments, split‐plot experiments, and within the robust parameter design setting. This article provides a historical background for the Box‐Behnken design, describes its construction, and reviews various contributions in the literature regarding the use of these designs.
Sequential Experimentation
Authors: Gunter Bert
Published: February 2008

Sequential experimentation is the application of statistical experimental design methods to improving processes when many experimental factors must be studied. It emphasizes the sequential use of small two‐level designs and steepest ascent to identify critical factors and improved settings, and the sequential assembly of second‐order designs to elucidate the nature of the response surface in the improved operational region when necessary.
Product Array Designs
Authors: Zhu Yu
Published: February 2008

This article is a review of product arrays proposed by Taguchi for robust design experiments. The motivation and advantage of using product arrays in robust design are discussed. Furthermore, alternative modeling approaches such as the location‐dispersion modeling approach and the response modeling approach are introduced. As an extension of product arrays, compound orthogonal arrays are defined. This article also discusses the optimal selection of product arrays.
Robust Design
Authors: Ankenman Bruce E.
Published: February 2008

Robust design is a methodology that seeks to set the controllable factors of a system in order to achieve a consistent and desirable response from the system. The defining characteristic of robust design is the emphasis on achieving a consistently desirable response, not just a response that is desirable on average. The robust design procedure involves designing, conducting, and analyzing experiments. In this article, robust design methodology is explored for two typical cases. The first case is when the variability in the system is due to replication error. The second is when there are certain factors, callednoise factors, which can be controlled during an experiment, but will vary randomly when the system is put into its standard operating mode. Models for the response, data analysis techniques, and experimental design techniques will all be discussed and demonstrated through a simple manufacturing example.
Signal‐to‐Noise Ratios for Robust Design
Authors: Murphy Terrence E. , Tsui Kwok‐Leung , McShane‐Vaughn Mary
Published: February 2008

Taguchi introduced a collection of performance measures called
signal‐to‐noise ratios
(SNRs) as a core component of the robust design methods he introduced to US corporations in the 1980s. The SNR is the most controversial part of Taguchi's methods because it summarizes the quality information from the entire experiment in a single entity. While the use of a single performance measure provided a conceptual simplicity attractive to engineers, it also introduced certain statistical controversies. We review the strengths and limitations of this performance measure and the evolution of related research. Our discussion includes the use of the SNR for both static and dynamic robust design (
see
Robust Design
).
Tolerance Design
Authors: Joseph V. Roshan
Published: February 2008

The output variations can be reduced by tightening the tolerances of the input parameters, thus improving the quality. However, a product with a tighter tolerance is more difficult to manufacture and thus it increases the manufacturing cost. Therefore the tolerances should be decided carefully by balancing cost with quality. This chapter explains how to do this.
Generalized Linear Models
Authors: McGrath Richard N.
Published: February 2008

When analyzing experimental data, we often use regression or analysis of variance techniques to estimate and perform statistical hypothesis tests about parameters of the model. Under the standard linear regression model, we assume that the error terms in the model follow a normal distribution with constant variance. Unfortunately, many experimental responses are not normally distributed and some accommodation to the model must be made, perhaps by transforming the observations. In generalized linear models, we are not restricted to the normal distribution as the distributional form, we can use any member of the exponential family of distributions. This family includes the binomial and Poisson for count data as well as the gamma distribution and others for continuous data. After providing some background information on generalized linear models, we will discuss application of these models in designed experiments in three situations: with a integer response, when the mean response is of interest, and when both the mean and the variance are to be modeled.
Harvey's Method for Dispersion Effects
Authors: Brenneman William A.
Published: February 2008

There has been considerable interest in the use of unreplicated designed experiments to identify both location and dispersion effects. This article takes a look at the two‐step approach which incorporates a noniterative identification step followed by an iterative final estimation step. This two‐step approach eliminates some of the bias present in the noniterative methods.
Risk Processes
Authors: Zacks Shelemyahu
Published: February 2008

The article presents stochastic processes, which may lead to catastrophic events, like ruin of an insurance company or failure of systems due to cumulative damage. We focus attention on compound renewal processes, and in particular on compound Poisson processes. Distribution of stopping times associated with these risk processes are derived.
Brownian Motion
Authors: Çinlar Erhan
Published: February 2008

This article is a survey of the elementary properties of Brownian motion and its connections to martingales, potential theory, and partial differential equations. Hitting times, the maximum process, the zero set, and local times are mentioned, connections with stable Lévy processes are pointed out, and infinitesimal generator is tied to the Laplacian.
Renewal Theory
Authors: Stadje Wolfgang
Published: February 2008

This article gives a survey of the most important results of renewal theory.
Software Reliability Modeling and Analysis
Authors: Xie Min , Yang Bo
Published: February 2008

Computer systems can be found almost everywhere in our daily life and they are also extensively used in areas such as defense and finance. Unlike hardware reliability which is a well‐developed area, software reliability is relatively new and is generally more difficult to insure. In order to improve the reliability of a software product, the software has to be tested extensively prior to its release. Software reliability models are commonly used to monitor the testing process and to measure and predict future reliability of the software. Such study is also useful to determine the release time of the software product. During the past three decades there have been many software reliability models proposed and studied. This chapter reviews some of the models and discusses the applications of the models for decision making in software development.
Stochastic Deterioration
Authors: Van Noortwijk Jan M. , Kallen Maarten‐Jan
Published: February 2008

To ensure that a system or structure performs its required functions (including safety), maintenance should be performed on a regular basis. To optimize maintenance, information about the time to failure and/or the deteriorating condition is needed. Unfortunately, there is often a lot of uncertainty involved in the applied deterioration process. Mathematical models can be used to account for the stochastic nature of deterioration over time. This article reviews two approaches in modeling deterioration: the actuarial approach based on the notion of failure rate and the physical approach based on the notion of failure due to the stress exceeding the strength. Candidates for stochastic deterioration models are finite‐state Markov processes and Markov processes with independent increments (e.g., gamma process, compound Poisson process, and Brownian motion with drift).
Aging and Positive Dependence
Authors: Spizzichino Fabio
Published: February 2008

Multivariate survival models for vectors of dependent lifetimes typically manifest also a phenomena of aging. This chapter presents a discussion about the connections existing between these types of properties and point out some aspects that they have in common, from a conceptual point of view. For this purpose some basic notions of positive dependence and some notions of multivariate aging are being recalled.
Stochastic Orders and Aging
Authors: Belzunce Félix , Shaked Moshe
Published: February 2008

Many reliability systems or components of such systems have random lifetimes that indicate aging properties of these systems or components. Most of these aging properties can be characterized by various stochastic orders. Such characterizations are useful for the identification of the corresponding aging properties and for a better understanding of the meaning of these properties. In this article we describe such characterizations.
Characterization in Reliability
Authors: Klebanov Lev B.
Published: February 2008

We describe the problems of characterization of probability distributions arising in reliability theory. These characterizations are mostly based on the property of aging of technical devices or systems. Among them are the classical lack of memory property and its generalizations, functioning properties of redundant systems, relations between the reliabilities of two systems, and reliability of hierarchical structures. Typical results on the characterizations and their stability inside some popular classes of distributions with the special character of the hazard function are also given.
Random Evolutions toward Applications
Authors: Koroliuk Vladimir S. , Limnios Nikolaos
Published: February 2008

This article gives a short and elementary presentation of random evolutions toward applications in reliability and quality engineering. At first, an example concerning the dynamical stochastic system in a two state Markov media is presented. A dynamical system in continuous time is presented since nowadays they are widely used in dynamic reliability modeling. Limit theorems in averaging and diffusion approximation scheme for ergodic and nonergodic media are also presented. Remarks on bibliography on further topics related to random evolutions are given.
Damage Processes
Authors: Singpurwalla Nozer D.
Published: February 2008

The point of view that we adopt here is that damage is an abstract notion that conveys an intuitive import. Specifically, an item fails when its damage exceeds a threshold. Whereas damage cannot be observed and measured, the surrogates that it spawns such as crack growth, CD4 cell counts, and wear, can be. These surrogates are known as
markers
. With the above in mind, we offer here a probabilistic architecture that endeavors to make the notion of damage precise and still retain its intuitive import. Our architecture looks at damage as a cumulative hazard and describes its evolution
via
a nondecreasing stochastic process. The observable markers associated with damage are also modeled by a stochastic process that is cross‐correlated with the damage process. Thus a bivariate stochastic process with one component that is nondecreasing and the other that may fluctuate around some mean could be a suitable model for encapsulating the phenomenon of damage and its markers. The latent nondecreasing process leads to the fluctuating observable processes, and an item fails when the former hits a threshold. The second feature of our architecture pertains to the threshold. We argue that the threshold needs to be random and has an exponential distribution with scale one; we call such a threshold the
hazard potential
of an item. To conclude, our perspective on what constitutes a damage process is starkly different from that which is prevalent in the reliability and the survival analysis literatures. Hopefully, it offers a platform for describing an ill‐defined but much‐discussed phenomenon.
System Signatures
Authors: Samaniego Francisco J.
Published: February 2008

The basics of structural reliability theory for engineered systems are briefly reviewed. Some of the literature on system signatures, defined in the context in which a system's components have independent and identically distributed (i.i.d.) lifetimes, is discussed. In brief, the signature of ann‐component system is a probability vector whoseith element is the probability that the ith component failure is fatal to the system. Representations are given for the survival function, density, and failure rate of systems with i.i.d. component lifetimes in terms of the system's signature vector and the common distribution of its components. The utility of signatures in the comparative study of system performance is illustrated through several “preservation” theorems. The occurrence of signatures in a variety of reliability contexts is noted. These include the study of closure theorems for the IFR class, comparisons between simple and indirect majority systems, system comparisons via stochastic precedence and via the information and the dispersive orderings, hazard rate ordering between independently drawn order statistics, optimization problems in reliability economics, and comparisons between communication networks.
B
ayesian Reliability Demonstration
Authors: Coolen Frank P.A. , Coolen‐Schrijner Pauline
Published: February 2008

This article presents several main aspects of Bayesian reliability demonstration, together with a concise discussion of key contributions to this topic.
Software Reliability:Bayesian Analysis
Authors: Wiper Michael P.
Published: February 2008

In this article, the use of Bayesian methods in software reliability predictions is reviewed. In particular, the article studies software reliability models for interfailure times or for the numbers of failures in a given time period. After a general introduction to the use of the Bayesian approach to inference in the software reliability field, in particular, commenting on the use of the prequential likelihood for model checking, the article concentrates on reviewing Bayesian analyses of some of the most well known software reliability models, outlining the concatenated failure rate model of Jelinski and Moranda. The article ends with a real data example where the goodness of fit and reliability predictions of various models are compared.
B
ayesian Reliability Analysis
Authors: Martz Harry F.
Published: February 2008

Bayesian reliability methods permit the formal incorporation of pertinent supplementary information about the parameters of interest in a statistical analysis beyond that contained in the sample data. This additional information is contained in the prior distribution of the parameters. Bayes' theorem is used to combine the prior and sampling distributions to form the posterior distribution of the parameters. Then all the desired inferences are obtained from this joint posterior. In most practical applications, Markov chain Monte Carlo sampling techniques are used to numerically perform the required calculations. A real‐world reliability example that illustrates the Bayesian approach is presented.
Empirical
B
ayes Estimation of Reliability
Authors: Pensky Marianna
Published: February 2008

Empirical Bayes decision procedures are decision theoretic procedures that utilize past data as a means for bypassing the necessity of identifying a completely unknown prior distribution. The article reviews the parametric and nonparametric empirical Bayes techniques and their application to estimation of the mean lifetime, the reliability function, and the failure intensity function.
Maintenance andMarkov Decision Models
Authors: Dekker Rommert , Nicolai Robin P. , Kallenberg Lodewijk C.M.
Published: February 2008

In this chapter we first give an introduction to Markov decision theory. We state the main optimality criteria and solution approaches. Next we sketch how it can be applied in maintenance theory. In particular, we deal with the civil infrastructure sector and show what kind of results it brings. Finally we also indicate which problems arise in applications.
Prior Distribution Elicitation
Authors: Quigley John , Bedford Tim , Walls Lesley
Published: February 2008

An overview of key issues associated with the elicitation of a prior probability distribution is provided. The nature of subjective probability is discussed, and the main causes of biased assessments are identified. Evidence is presented suggesting that people can improve their probability assessments under the correct conditions. The ways in which the elicitation of a prior distribution can be embedded within a process to minimize the impact of bias are examined. More advanced issues, namely validation of a prior distribution and reducing the cognitive burden on experts, are described.
B
ayesian Control Charts
Authors: Colosimo Bianca M.
Published: February 2008

Control charts are widely used in industrial practice to check for process stability. The basic procedure of control charting is due to Shewhart and consists in periodically inspecting a sample of quality characteristics to decide whether the process is “out of control”. The out‐of‐control state is characterized by deteriorated performances and should be detected as soon as possible to bring the process back to its nominal or “in‐control” state. With reference to this objective, different Bayesian control charts have been proposed in the literature; the term “Bayesian” is used here to denote any approach that adopts Bayes' theorem within the monitoring procedure. In particular, the literature on Bayesian control charting is characterized by two streams of research. The first assumes that all the information required to detect the degraded state is enclosed in the posterior probability that the process is out of control. The second stream of research adopts a more classical perspective, which bases the out‐of‐control rule on detecting changes of the process parameters. In this case, Bayes' theorem allows one to compute the posterior distribution of the parameter of interest. The article reviews approaches proposed in both these streams of research, focusing mainly on the first type of Bayesian procedure.
Degradation Processes
Authors: Lehmann Axel
Published: February 2008

Many failure mechanisms can be traced to an underlying process and stochastically changing covariates. The degradation process may represent wear, fatigue crack growth, corrosion, or material fatigue in engineering settings, and markers of health status and quality of life data in medical settings. Degradation data can provide much more reliability information than traditional failure‐time data and are particularly useful when the application of traditional reliability models is limited due to rare failures of highly reliable items or due to items operating in dynamic environments. A class of reliability models is considered in which failure is related to degradation due to the competing risks of hard and soft failure.
HierarchicalMarkov ChainMonteCarlo (MCMC) forBayesian System Reliability
Authors: Wilson Alyson G.
Published: February 2008

Hierarchical models are one of the central tools of Bayesian analysis. They offer many advantages, including the ability to borrow strength to estimate individual parameters and the ability to specify complex models that reflect engineering and physical realities. Markov chain Monte Carlo (MCMC) is a set of algorithms that allow Bayesian inference in a variety of models. We illustrate hierarchical models and MCMC in a Bayesian system reliability example.
Warranty Claims and Costs: Statistical Analysis of
Authors: Robinson Jeffrey A.
Published: February 2008

This article summarizes approaches to the modeling and analysis of warranty data with an emphasis on the special characteristics of the data that distinguish it from reliability data in general. The special characteristics include the existence of costs and data limitations caused by warranty coverage limits. Practical considerations are emphasized as opposed to theory.
Warranty: Usage and Wear Process for
Authors: Wilson Simon P.
Published: February 2008

Many products, for which warranties are offered, are subject to wear and this can be a major factor in when the product fails. The process by which wear accumulates is best modeled as a stochastic process. In this article, I describe some stochastic processes for wear. Each process is defined, the ease with which its probability distribution can be computed is described, and its advantages and disadvantages as a wear process is discussed. I briefly discuss how these models may be applied to practical warranty problems such as for products whose warranty is defined in terms of time and some measure of usage (like kilometers traveled) to failure.
Multiattribute Warranty Policies
Authors: Chun Young H.
Published: February 2008

Warranties have been important elements in business planning and decision making for manufacturers of products and producers of services, and this will continue to be the case in years to come. In this article, we introduce various types of single‐attribute and multiattribute warranty policies, and review different modeling approaches with an emphasis on two‐attribute warranty plans. Some suggestions for future research are also provided.
Inspection Policies for Reliability
Authors: Baker Rose D.
Published: February 2008

The rôle of inspections in improving reliability is discussed, and a distinction is drawn between inspections of standby equipment, that “waits to work”, and inspections of equipment such as a production line that is in continuous use. Simple examples of both situations are given, and some of the complexities of the various models used are discussed and referenced. It is stressed that models are valueless for practical purposes without checking that they are appropriate, and without estimation of their parameters.
Group Maintenance Policies
Authors: Wilson John G. , Benmerzouga Ali , Anderson Chris K.
Published: February 2008

This paper introduces a new group replacement policy for
n
independent identical machines operating in parallel. This policy is a generalization of the classes of
m
‐failure, (
m
,
T
) and
T
‐failure policies. The adaptive nature of this policy is its most attractive feature: the replacement rule varies according to the state of the system. Once the rule has been found, implementation is no harder than for other standard policies which do not incorporate this adaptive feature. An attractive feature of this approach is that no other policy that uses only the times of operations of the machines and the number of machines that have failed can do better. It is demonstrated that the classes of
m
‐failure, (
m
,
T
) and
T
‐failure policies are not generally the optimal.
Multivariate Age and Multivariate Renewal Replacement
Authors: Ebrahimi Nader
Published: February 2008

A large part of reliability theory deals with replacement of single‐component systems. Two of the popular replacement policies are (a) age‐replacement and (b) block‐replacement policies. As systems become more complicated and require new technologies and methodologies, more sophisticated maintenance policies are needed to solve the maintenance problems. For systems consisting of
k
related components, a legitimate question is when to replace the components. Obviously, the classical age‐and block‐replacement policies are unsuitable for such situations. This article deals with multivariate versions of age replacement policies that may be used for replacing components. Also, for planning purposes, the article describes several optimal age replacement policies.
Nonparametric Methods for Analysis of Repair Data
Authors: Hollander Myles , Samaniego Francisco J. , Sethuraman Jayaram
Published: February 2008

Repair models specify the joint distribution of the failure times of a system subject to repair. Prominent models include the minimal repair model, the Brown–Proschan model, the Block–Borges–Savits model, the Kijima models, and the general Dorado–Hollander–Sethuraman model. For such models, failure‐time data and data on the types of repairs performed allow inference for the distribution
F
of the time to first failure of the system and other features of the system that can be defined in terms of
F
. This article surveys some nonparametric inference procedures for repair data, including frequentist and Bayesian estimation of
F
, simultaneous confidence bands for
F
, two‐sample tests, tests of the minimal repair assumption, and goodness‐of‐fit tests.
Analysis of Recurrent Events from Repairable Systems
Authors: Agustin Ma. Zenia N. , Agustin Marcus A. , Peña Edsel A.
Published: February 2008

This article provides a review of recently developed dynamic stochastic models for recurrent events, in particular, recurrent events arising from monitoring of repairable systems. Inference methods such as the estimation of model parameters and goodness‐of‐fit testing are also discussed. The procedures described are demonstrated using a reliability data from load–haul–dump machines.
Nonparametric and SemiparametricBayesian Reliability Analysis
Authors: Ghosh Kaushik , Tiwari Ram C.
Published: February 2008

In this article, we first provide an overview of some nonparametric Bayesian methods of inference using life‐history data. These methods include those that use the Dirichlet process, the gamma process, and the beta process as the prior. We then present a semiparametric Bayesian method for estimating the reliability of a component in a multicomponent system using lifetime data from several systems. This method assumes that the component lifetimes have a parametric distribution with a Dirichlet process prior on the distribution of the parameters.