bluuebunny/crossref_metadata_2025 huggingface dataset:
Successful Strategies for Recruitment of Emergency Medical Volunteers
Authors: Hasselmann Anne Rinchiuso
Published: February 2011

Objectives

A robust medical volunteer program is critical to ensuring a successful response to public health and medical emergencies. The New York City (NYC) Department of Health and Mental Hygiene created the NYC Medical Reserve Corps in 2003 to build a multidisciplinary team of health professionals who wish to assist NYC with response during large-scale health emergencies. This article reports on the search to determine which recruitment activities have been most successful to date, with the goal of modeling future activities upon those that worked best.

Methods

A retrospective review of effectiveness of recruitment strategies to identify and register new NYC Medical Reserve Corps volunteers was undertaken.

Results

A broad range of recruitment activities have been implemented since the program's inception, with varying degrees of success. Various recruitment modalities were tried, including direct invitations to licensed professionals by the NYC Health Commissioner and announcements through professional organization partners. The direct invitation by the NYC Health Commissioner to health professionals licensed in 1 of the 5 boroughs of NYC has proved to be the most successful recruitment tool to date.

Conclusions

The local health commissioner or other trusted community figure is an excellent messenger for recruiting emergency volunteers. It is also critical that recruitment messages reach as many potential volunteers as possible to ensure that the requisite number of volunteers and mix of professional disciplines are identified. (Disaster Med Public Health Preparedness. 2013;7:266-271)
Phenolic Resins
Authors: Hesse Wolfgang , Lang Jürgen
Published: February 2011

The article contains sections titled:1.Introduction2.Physical Properties3.Raw Materials3.1.Phenols3.2.Aldehydes3.3.Catalysts4.Production4.1.Novolacs4.2.Resols4.3.Phenolic Resins Modified by Natural Resins4.4.Phenolic Resins with Special Properties4.5.Wastewater5.Storage and Transportation6.Testing and Analysis7.Uses7.1.Novolacs7.1.1.Cross‐Linked Novolacs7.1.2.Novolacs without Cross‐Linking7.2.Resols7.2.1.Water‐Soluble Resols7.2.2.Resols in Organic Solvents7.2.3.Alkylphenol Resols7.3.Phenolic Resins Modified by Natural Resins7.4.Waterborne Paints, Phenol Ether Resins7.5.High‐Temperature Coking8.Economic Aspects9.Toxicology and Occupational Health
Tantalum and Tantalum Compounds
Authors: Albrecht Sven , Cymorek Christian , Andersson Klaus , Reichert Karlheinz , Wolf Rüdiger
Published: February 2011

The article contains sections titled:1.Introduction2.Properties2.1.Physical Properties2.2.Chemical Properties3.Occurrence4.Extraction4.1.Extraction and Processing of Ore Concentrates4.2.Obtaining Ta ‐ Nb Concentrates from Tin Slags4.3.Processing Tantalum Scrap5.Metallic Tantalum5.1.Production5.2.Semifinished Products5.3.Tantalum Powder for Capacitors5.4.Other Uses6.Tantalum Alloys7.Tantalum Compounds8.Analysis9.Economic Aspects10.Toxicology
Nondestructive Testing, 3. Ultrasonics
Authors: Ono Kanji , Erhard Anton
Published: February 2011

The article contains sections titled:1.Introduction2.Physical Basis3.Generation and Detection3.1.Conventional Probes3.2.Piezocomposite Materials for Ultrasound Generation3.3.Phased Array Technique3.3.1.Phased Array Probes3.3.2.Applications4.Inspection Methods5.Display Methods6.Interpretation7.Inspection Standards
Mutagenesis Mechanisms
Authors: Polosina Yaroslava Y , Cupples Claire G
Published: February 2011

Deoxyribonucleic acid
(DNA), the keeper of genetic information in all organisms, is constantly modified by internal and external factors during the cell's lifetime. Many types of DNA modification cause mutations. Although mutations may be harmful to individual cells or organisms, mutation in populations is valuable because it leads to genetic variation and ultimately to evolution. There are even circumstances when it is useful to increase the number of mutations in a cell. Maintaining the optimum balance between genetic stability and variability requires the cell to regulate both the frequency with which modifications to the DNA occur, and the efficiency with which the original sequence is restored by the various DNA repair pathways. One group of cellular enzymes, the
DNA polymerases
, plays a key role in both mutation occurrence and mutation avoidance. In this article we consider how mutations occur in DNA, and how cells regulate their mutation rate, with an emphasis on the role of the
DNA polymerases
.

Key Concepts:

Modification of DNA structure is the first step in the pathway to mutation.

Most structural changes in DNA are caused by normal physiological processes.

Mutation frequency depends on the frequency of DNA structural change and the efficiency of DNA repair processes.

DNA polymerase is the key enzyme of mutagenesis.

Mutations are a cause of genetic disease.

Mutations are the raw material for evolution.

To ensure survival, cells must maintain the optimal balance between genetic stability and variability.
Bornaviruses
Authors: Lipkin W Ian , Briese Thomas
Published: February 2011

Borna disease virus

(BDV) is a nonsegmented negative‐strand
ribonucleic acid
(RNA) virus that is unique among viruses of the order
Mononegavirales
in its genomic organisation, nuclear localisation for replication and transcription, splicing and neurotropism. Most reports of natural infection have described outbreaks in horses and sheep in central Europe; however, the virus appears to be distributed worldwide and has the potential to infect many, if not all, warm‐blooded hosts, causing disorders of the central, peripheral and autonomic nervous systems. In horses and sheep BDV is associated with fatal meningoencephalitis. In parrots and related exotic birds the recently characterised avian Bornavirus (ABV) may also infect the central nervous system; however, disease is typically manifest as a wasting disease due to autonomic nervous system infection and impaired peristalsis in the gastrointestinal tract. Whether bornaviruses infect humans remains controversial.

Key Concepts:

Borna disease virus is unique in its molecular biology, broad host range and neurotropism.

As the first infectious agent identified through subtractive cloning Borna disease virus is an example of the power of molecular biology in pathogen discovery.

The controversy over the role of Borna disease virus in human disease illustrates the complexity of proving causation using molecular diagnostics.
Primary Immunodeficiency Affecting the Innate Immune System
Authors: Rosenzweig Sergio D
Published: February 2011

Primary immunodeficiencies are a heterogeneous group of genetically inherited diseases quantitively or functionally affecting innate or adaptive immunity. The innate and adaptive arms of the immune system work synergistically to efficiently control and neutralise the wide range of pathogens that challenge vertebrates. Although at first glance innate immunity might appear primitive, innate immune cells can orchestrate discrete immune responses to different infections through the recognition of diverse pathogens by germline‐encoded pattern recognition receptors. Neutrophils, macrophages, dendritic cells, natural killer (NK) cells and NKT cells, in conjunction with natural antimicrobial agents, opsonins (as Complement) and cytokines, are critical innate components in neutralising infectious agents. Primary immunodeficiencies are understood as a state of primary lack of protective immunity. Different compartments of the innate immune system are analysed in this article to highlight their critical pathophysiological role in host defences and protective immunity.

Key Concepts:

The innate immune system includes epithelial and mucosal barriers, cells, natural antimicrobial product, pattern recognition receptors (PRR) and cytokines to protect the host from infections without the need of previous exposure to those microorganisms.

Neutrophil disorder should be suspected in patients with recurrent, severe, bacterial or fungal infections, especially those caused by unusual organisms.

The mononuclear phagocytes are crucial for antigen presentation and for protection against intracellular infections, especially mycobacteria.

Natural killer cell deficiency could involve quantitative and functional defects, in both cases showing increased susceptibility to viral infections.

Natural killer T (NKT) cells constitute a distinctive subpopulation of mature lymphocytes that coexpress NK (CD56) and T (CD3) cell antigens and play a central role in infectious (e.g. virus), allergic (e.g. asthma) and autoimmune diseases (e.g. hepatitis), as well as tumour control.

The complement system is a cascade system of more than 30 serum and membrane‐bound proteins that, when deficient, leads to impaired host defence and inappropriate inflammation.
Tumour Suppressor Genes
Authors: Skolnik Jeffrey M , Fisher David
Published: February 2011

Tumour suppressor genes are a group of genes whose loss contributes to malignancy. The original hypothesis surrounding tumour suppressor genes defined a ‘two‐hit’ requirement for mutagenesis; however, it is now known that deletion, silencing or downregulation of one or more genes may lead to cancer formation. There is a growing list of putative tumour suppressor genes, and recently several new candidates were added. Research into apoptosis, or programmed cell death, continues to expand. As pathways for which tumour suppressor genes are responsible become clearer, so do potential therapeutics to capitalise on these targets. A summary of the mechanisms by which tumour suppressor genes may act, the mutations which lead to tumourigenesis, and the clinical implications of these phenomena are presented.

Key Concepts:

Tumour suppressor genes prevent mutagenesis.

Perturbation of tumour suppressor gene function may lead to cancer formation.

The first tumour suppressor gene identified was the Rb gene, and mutations in the gene lead to the childhood cancer known as retinoblastoma.

Although many tumour suppressor genes require two mutations to cause a loss‐of‐function, in some cases, downregulation of a single tumour suppressor gene may lead to cancer.

The p53 gene is involved in a complex pathway of cell death and cell arrest, and mutations in p53 or other genes in the pathway can lead to the development of cancer.

There are several other modulators of tumour formation, including telomeres, adhesion molecules and microRNA, that may function like tumour suppressor genes.

Identification of tumour suppressor genes has lead to the development of anticancer therapeutic agents targeted to specific pathways that take advantage of this knowledge.
Protein Structure Design and Engineering
Authors: Berry Steven M , Lu Yi
Published: February 2011

Protein structure design and engineering is a research endeavour in which proteins with predicted structure and function are constructed in the laboratory through rational design, combinatorial selection or combination of both approaches. It is built upon our knowledge about the structure and function of proteins and can be accomplished either from scratch (de novodesign) or based on native scaffolds (redesign). The area of protein design is an exciting and rapidly growing field, advancing from the design of simple protein structures, to those that are more complicated and recently to the designs of functional proteins. Design of artificial proteins containing unnatural amino acids, backbone linkages or cofactors have also been reported, making it possible to prepare proteins with structural and functional properties beyond those of native proteins. These advances bring us closer to realising the dream of tailor‐made artificial enzymes with high catalytic efficiency and selectivity for biotechnological and pharmaceutical applications.

Key Concepts:

Protein structure design and engineering is a research endeavour in which proteins with predicted structure and function are constructed in the laboratory.

The protein design field can be organised into two complimentary approaches: rational design and combinatorial selection of the desired protein.

Rational protein design strategies can involve designing a protein from scratch (de novodesign) or redesigning a protein with native scaffolds to achieve new structures and functions.

Combinatorial approaches can involve sampling a large population of proteins to select the desired one or it can involve numerous rounds of randomised mutation followed by selection to fine‐tune properties being selected for.

Techniques are available that allow the incorporation of unnatural moieties, such as unnatural amino acids, backbone linkages or cofactors, into proteins.

The field has made a big stride recently in designing functional proteins.
Mitochondrial Genome
Authors: Saccone Cecilia
Published: February 2011

Mitochondria are cytoplasmic organelles present in all eukaryotic cells that are able to respire. They possess their own genetic system whose size and organisation are quite variable in plants, protists and fungi. A certain degree of structural variability is observed also in Metazoa, whereas mitochondrial
deoxyribonucleic acid
(mtDNA) is rather small but constant in vertebrate cells (about 17
 
kb). The genetic content and organisation of human mtDNA is very similar in all vertebrates. It consists of two ribosomal
ribonucleic acid
(RNA) genes, 13 protein‐coding genes and 22 transfer RNAs. All other mitochondrial products are coded by nuclear DNA and transported into the mitochondrion. Mitochondrial genetic system has a uni‐directional way of inheritance, and it is present in cell in a multicopy state. mtDNA replication, transcription and translation are also very peculiar. Evolutionary origin of mitochondria has been described through the endosymbiotic theory.

Key Concepts:

Mitochondria are eukaryotic cell organelles with their own genome.

Mitochondrial genome shows great structural variability in most eukaryotic taxa but are quite stable for content and organisation in Metazoans.

mtDNA has a poor gene content; mitochondrial protein genes encode for respiratory chain subunits, fundamental for cell energy production.

The mitochondrial genome has bacterial origin, with several peculiar features about its replication and expression.

mtDNA is a multicopy genome that is uni‐parentally transmitted and does not undergo recombination.

Evolution of mtDNA (within Metazoa) is lineage‐specific.
Primate Evolution: Gene Loss and Inactivation
Authors: Satta Yoko
Published: February 2011

Pseudogenes are defined as genes that have lost function. However, despite the loss of function, pseudogenes can play an important role in evolution, especially when the environment changes. Owing to some environmental changes in diet or habitat, for example, pseudogenisation may be beneficial rather than deleterious for individual fitness. The human‐specific pseudogenes are of particular interest, and more than 100 have been identified. These include olfactory receptors, immune function‐related genes and metabolic pathway genes, which seem to be sensitive to environmental conditions. In this article, examples of environment‐driven pseudogenisation are presented and the role or significance of the loss‐of‐function genes in the evolution of primates and humans is discussed.

Key Concepts:

Functional constraints maintain function of a protein and therefore maintain the conserved way of molecular evolution.

Relaxation of constraints can be achieved in two ways: gene duplication and environmental changes.

Gene duplication provides an extra copy of a functional gene, resulting in the relaxation of a functional constraint to on the gene.

The ‘less‐is‐more hypothesis’ proposed by Maynard Olson in 1999 suggests loss of function could be an advantage to organisms and also affect phenotypic or physiological changes in evolution.
Gonadotropin Hormones: Disorders
Authors: Martinez‐Aguayo Alejandro , Dattani Mehul T , Achermann John C
Published: February 2011

Abnormalities in gonadotropin hormone release and function can arise from changes in a variety of genes expressed throughout the hypothalamic–pituitary (gonadotrope)–gonadal (HPG) axis. Alterations in these genes can disrupt proteins that are involved in many different biological processes such as neuronal migration, gonadotropin‐releasing hormone (GnRH) release and action, and pituitary development, as well as affecting synthesis and function of the gonadotropin hormones (follicle‐stimulating hormone and luteinising hormone) themselves. The identification and characterisation of these factors is providing important insight into the reproductive axis in humans and may result in improved treatment and counselling for patients with infertility problems. However, the underlying cause of GnRH deficiency or altered gonadotropin action is not currently known in most cases, and it is emerging that digenic or oligogenic inheritance patterns are important in some situations. Therefore, the molecular basis of disorders of gonadotropin hormones may be more complex than originally thought.

Key Concepts:

The gonadotropins, FSH and LH, are heterodimeric glycoprotein hormones released in pulses from the pituitary gonadotrope cells.

FSH and LH secretion is regulated by hypothalamic GnRH, which itself is pulsatile.

GnRH‐secreting neurons originate in the olfactory placode and migrate to the hypothalamus during development; defects affecting neuronal migration can be associated with a lack of smell (anosmia).

Gonadotropes develop within the anterior pituitary gland, which originates from the oral ectoderm; defects affecting gonadotrope function can be associated with other pituitary hormone deficiencies.

FSH and LH act through peripheral G protein‐coupled receptors to stimulate gonad development and function.

Some factors can influence the hypothalamic–pituitary–gonadal axis at multiple levels.

More than 30 single gene disorders affecting gonadotropin function have now been described, often affecting receptor‐ligand pairs.

Phenotypic penetrance can be variable, even within families and may reflect the influence of modifier genes, epigenetic factors or environmental stimuli.

In some situations, changes in two genes can result in a clinical phenotype (digenic inheritance).

In many cases the underlying molecular basis of hypogonadism is still unknown.
Protein Interaction and Genetic Disease
Authors: Gregersen Niels , Bross Peter , Olsen Rikke KJ , Palmfeldt Johan , Corydon Thomas J
Published: February 2011

The function of proteins is dependent on other proteins. Proteins function as oligomers, complexes, super‐complexes or higher order networks, in which they interact with each other, either temporarily when they exert their function or ‘permanently’ in functional units. Genetic defects in single proteins may therefore, in addition to disturbing the specific function of the defective protein, disturb other functions that are dependent on it. In this review we will discuss how the two main types of defects in genetic disease, truncating variations (stop‐codon introductions and small out‐of‐frame deletions/insertions) and in‐frame variations (missense variations and small in‐frame deletions/insertions), may disturb normal interactions. Depending on the importance (location) of the missing or aberrant protein, the effect on the cellular pathway or interacting network may be severe or mild. Protein interactions and disturbances therein may be determined by protein mass spectrometry after immuno‐precipitation or other fractionation and separation methods.

Key Concepts:

Cellular proteins interact in oligomer and complex structures as well as in higher order networks.

Protein interactions may be permanent in the lifetime of proteins or temporary during their function.

Genetic defects may disturb interactions between proteins depending on the nature of the defect and type of interaction.

Missing proteins due to truncation, comprising big deletions, small out‐of‐frame deletions/insertions and severe splice alteration, may abolish the function of oligomers, complexes, pathways and networks.

Aberrant proteins due to missense variations and in‐frame deletions/insertions may disturb interactions with protein partners in oligomers, complexes and networks.

Missing and aberrant proteins in branch‐points (nodes) have as a role severe consequences, resulting in genotype–phenotype association.

Missing and aberrant proteins in redundant pathways have variable consequences, resulting in poor genotype–phenotype association.

Missing and aberrant proteins in molecular machines, such as the chaperonin Hsp60, may elicit pleotropic effects due to defective processing of client proteins.

Protein interactions and disturbances therein may be determined experimentally by protein mass spectrometry (MS), preceded by immunoprecipitation of target proteins, either directly or after cross‐linking, or indicated after mild solubilisation followed by extensive separating procedures and MS.

The challenge is to design experiments that can determine qualitative and quantitative disturbances of proteins interactions in cells and tissue from patients, model animals and model cells compared to interactions in normal cells and tissue.
Gain‐ and Loss‐of‐function Mutations in Trypsinogen
Authors: Chen Jian‐Min , Férec Claude
Published: February 2011

Historically, trypsinogen has been among the most extensively studied models of protein structure and function. It has received renewed attention since the identification of a gain‐of‐function missense mutation in the cationic trypsinogen gene (

PRSS1

) as a cause of hereditary pancreatitis in 1996; a finding gave strong support to the then century‐old hypothesis that pancreatitis is an autodigestive disease in which prematurely activated trypsin within the pancreas was thought to play a pivotal role. Whereas gain‐of‐function

PRSS1

missense mutations cause chronic pancreatitis through a negative effect on trypsin lysis and a positive effect on trypsinogen autoactivation, duplication and triplication copy number mutations of the trypsinogen locus cause the disease through a gene‐dosage effect. By contrast, loss‐of‐function variations in the

PRSS1

and

PRSS2

(encoding anionic trypsinogen) genes protect against chronic pancreatitis. The study of pathogenic

PRSS1

mutations also shed lights on the evolution of trypsinogen genes.

Key Concepts:

More than 100 years ago, pancreatitis was hypothesised to be an autodigestive disease in which prematurely activated trypsin within the pancreas was thought to play a pivotal role.

The century‐old hypothesis was given strong support when a gain‐of‐function missense mutation (R122H) in the cationic trypsinogen gene (
PRSS1
) was identified as a cause of hereditary pancreatitis in 1996.

Gain‐of‐function
PRSS1
missense mutations are characterised by a negative effect on trypsin lysis and a positive effect on trypsinogen autoactivation.

Duplication and triplication copy number mutations of the trypsinogen locus cause a gain of trypsin through a gene‐dosage effect.

Gain‐of‐function
PRSS1
mutations can be generated from diverse mutational mechanisms such as CpG substitution, gene conversion, nonallelic homologous recombination (NAHR) and microhomology‐mediated replication‐dependent recombination (MMRDR).

Whereas gain‐of‐function
PRSS1
mutations predispose to chronic pancreatitis, loss‐of‐function variations in the
PRSS1
and
PRSS2
(encoding anionic trypsinogen) genes protect against chronic pancreatitis.

The study of pathogenic
PRSS1
mutations also shed lights on the evolution of trypsinogen genes.
Origin of the Tuscans
Authors: Barbujani Guido , Ghirotto Silvia
Published: February 2011

Tuscany was home to the Etruscan culture, in the first millennium
ad
and is one of the few areas of Europe for which the genealogical relationships between past and present inhabitants have been thoroughly investigated at the genetic level. Analyses of deoxyribonucleic acid show that the contemporary Tuscans have affinities with people from the Near East but share very few mitochondrial haplotypes with the local Iron‐age population, that is, the Etruscans. These results do not imply that the Etruscans got extinct, but certainly that they cannot be regarded as ancestral to most modern people of Tuscany. By contrast, there is evidence of genealogical continuity between Medieval and current times, which suggests that the main demographic change(s) occurred in previous centuries.

Key Concepts:

Comparison of DNAs in ancient and modern individuals provides evidence about their genealogical relationships.

Modern methods to infer genealogical relationships from DNA data are based on extensive computer simulations.

The available evidence demonstrates that current populations are not necessarily descended from pre‐historic ancestors who dwelt in the same places, although there are a few cases of genealogical continuity across the past 2500 years.

Genetic variation in modern and ancient Tuscany shows that the Etruscans cannot be regarded as the direct ancestors of the bulk of the contemporary population. It also suggests that one or more immigration processes, probably occurring in the first millennium
ad
, have reshaped the population structure.
Block Replacement Policies
Authors: Belzunce Félix , Shaked Moshe
Published: February 2011

In this article, we describe block replacement policies and we list several results that compare block replacement policies with some other policies. Mainly, we present results that compare the number of unplanned failures in a block replacement policy in the sense of several stochastic orders, with the number of unplanned failures under the usual replacement policy and under an age replacement policy. Some other results for the increasing convex order, Laplace order, and comparisons of expectations for the arrival times are considered. We first list univariate results, and later we list some extensions of the block replacement policy and comparisons of entire processes. We conclude with a basic description of the cost analysis that is associated with block replacement policies.
Calculating Real Option Values
Authors: Dias Marco Antonio Guimarães
Published: February 2011

This article presents an overview of real option methodologies for evaluation and decision on capital investments under uncertainty. These methodologies are based on one of the two fundamental ideas: the construction of a risk‐free portfolio and the risk‐neutral probability measure, both to calculate the present value of options using risk‐free discount rate. The article discusses these ideas in discrete‐ and continuous‐time approaches, with methods like binomial lattices, stochastic differential equations, integral method, and Monte Carlo simulation. The strengths and weakness of these methods are discussed in light of applications. The article discusses different types of real options as well as the combination of real options with other disciplines like game theory, decision analysis, and genetic algorithms.
Data Classification and Prediction
Authors: Yang Eric , Androulakis Ioannis P.
Published: February 2011

In this article we focus on classification and prediction as significant problems under the broad umbrella of data mining. We explore alternative mathematical programming formulations and explore alternative solution approaches. We explore the details of decision trees, support vector machines, Bayes and nearest neighbor classifiers, and identify and discuss broad challenges.
Combinatorial Traveling Salesman Problem Algorithms
Authors: D'Ambrosio Claudia , Lodi Andrea , Martello Silvano
Published: February 2011

The
traveling salesman problem
(TSP) is a fundamental and well‐known problem in combinatorial optimization. We start by reviewing some of its ancestors, including the famous Hamiltonian cycle problem of which the TSP is the weighted version. We then introduce the most famous formulations of both the symmetric and the asymmetric TSP, and describe combinatorial approaches for both versions of the problem. We conclude with a brief discussion on the available TSP software.
Combining Exact Methods and Heuristics
Authors: Boschetti Marco A. , Maniezzo Vittorio
Published: February 2011

The combination of exact and heuristic methods is as old as mathematical programming (MP) itself, because usually exact methods cannot work properly without a good bound on the optimal cost, which helps pruning the search space. However, it is only in very recent years that the combination has also gone the other way round, permitting the use of methods originally conceived for exact problem solution also in heuristic frameworks. In this article, we revise the newly identified field of metaheuristics, which refers in particular to algorithms combining metaheuristics with MP techniques and show how current research aims at exploiting MP and metaheuristics synergies.
Dynamic Programming, Control, and Computation
Authors: Hanson Floyd B.
Published: February 2011

The presentation in this article is in the formal manner of classical applied mathematics and probability in order to focus on the methods and their implementation. In the section titled “Dynamic Programming in Continuous‐Time,” a fairly general model of stochastic dynamic programming in continuous‐time is outlined. In the section titled “Computational Reducing Canonical Forms,” canonical forms, such as a linear dynamics and quadratic cost model in control, that lead to a large reduction in computational effort are given or discussed. In the section titled “Finite Difference PDE Computational Methods,” finite difference partial differential equation (PDE) methods are given that are suitable for solving nonlinear Bellman dynamic programming equations, approximately. Alternatively, Markov chain approximation probabilistic methods which systematically justify the stability and weak convergence of the approximating Markov chain are summarized in the section titled “Probabilistic Markov Chain Approximation.” In the last section, there is a brief summary and directions to some other approaches.
Computational Methods for
DTMCs
Authors: Dayar Tuǧrul , Stewart William J.
Published: February 2011

We consider methods for computing transient and stationary distributions of ergodic discrete‐time Markov chains. We also briefly consider the computation of moments of first passage times. The computation of transient distributions seldom presents difficulties and hence the major part of this article is devoted to the computation of stationary distributions. We discuss the theory, application, and suitability of direct methods based on Gaussian elimination and two of its variants, LU factorization and the GTH algorithm. Iterative methods are the most commonly used methods for computing stationary distributions, and in this context we examine three different approaches: (i) Methods based on splittings, including the power method, (block) Jacobi, and (block) successive overrelaxation. (ii) Decompositional methods, primarily the iterative aggregation‐disaggregation method. (iii) Krylov subspace projection methods. In this context, we discuss both popular methods and the concept of preconditioning which is vital to the success of such methods.
Nonlinear Conjugate Gradient Methods
Authors: Dai Yu‐Hong
Published: February 2011

Conjugate gradient methods are a class of important methods for solving linear equations and for solving nonlinear optimization. In this article, a review on conjugate gradient methods for unconstrained optimization is given. They are divided into early conjugate gradient methods, descent conjugate gradient methods, and sufficient descent conjugate gradient methods. Two general convergence theorems are provided for the conjugate gradient method assuming the descent property of each search direction. Some research issues on conjugate gradient methods are mentioned.
Category and Inventory Management
Authors: Alan Yasin , Gaur Vishal
Published: February 2011

Category and inventory management in retailing involves many novel and challenging aspects, including shelf space allocation, effect of inventory on stimulating demand, retail competition, substitution, and transshipment. Suitably managing these aspects has required new modeling advancements in the operations research and management science literature with the potential for a large improvement in the profitability of retailers. In this introductory article, we describe the main characteristics of these models, focusing on the principles involved, the insights from optimization, and techniques or heuristics used in practice.
Control Variates
Authors: Lee Loo Hay , Jijun Shao
Published: February 2011

Control variate is a variance reduction technique, which is widely used in many applications of Monte Carlo Simulation. In this article, we present the concepts and formulations of several control variate techniques. Some of the classical results of these techniques and their generalization is also discussed.
Cooperative Game Theory with Nontransferable Utility
Authors: Oh Jungsuk
Published: February 2011

The central assumption of the cooperative game theory, that agents can impose binding agreements among themselves, is a huge step from the settings of the noncooperative game for acknowledging cooperative nature and capacity of economic agents. Yet, it is also a source of criticism for being “naive” to underestimate the complex dynamics among agents. When the cooperative game theory is utilized on top of the convenience of the transferable utility (TU) assumption, such criticism for being oversimplified and unrealistic is amplified. On the other hand, when detailed information on individual payoff scheme for various coalition structures is available, nontransferable utility (NTU) assumption can be accommodated.

This is an introductory article that surveys a few central advances in game theory on NTU cooperative game situation. First, common criticisms of TU assumptions are presented, providing a motivation for the development of the NTU theory. Next, the role of the Nash bargaining solution in the development of the NTU theory is presented. Then, a few attempts to provide effectivity criteria for NTU settings in specifying the coalitional function is presented, which allows one to specify a coalitional‐form NTU game. Subsequently, the definition of the NTU coalitional form game is presented. Then, the NTU version of the definition and the theory of the core are described. Next, a few value concepts of NTU games, primarily the Shapley NTU value, are described, utilizing a few numerical examples. Finally, the axiomatization approach that has been fruitful in characterizing and comparing various NTU solution concepts is briefly surveyed.
Cross‐Entropy Method
Authors: Kroese Dirk P.
Published: February 2011

The cross‐entropy method is a recent versatile Monte Carlo technique. This article provides a brief introduction to the cross‐entropy method and discusses how it can be used for rare‐event probability estimation and for solving combinatorial, continuous, constrained, and noisy optimization problems. A comprehensive list of references on cross‐entropy methods and applications is included.
Customer Relationship Management: Maximizing Customer Lifetime Value
Authors: Lewis Michael
Published: February 2011

An inherent difficulty in the practice of Customer Relationship Management is that the development of marketing policies that optimize the value of a firm's customer assets requires fairly sophisticated statistical and optimization techniques. This article reviews the existing academic literature focused on methods for optimizing customer lifetime value. The article covers key customer metrics and the use of dynamic programming to maximize customer value and reviews existing applications. The article also discusses limitations of the current body of research and opportunities for developing optimization models that employ rich models of consumer behavior.
D
antzig–
W
olfe Decomposition
Authors: Chung William
Published: February 2011

In this section, the development of the Danztig–Wolfe (DW) decomposition method is presented. The topics include not only the details of the DW decomposition algorithm, but also the discussions of computational efficiency, long‐tail effect, column dropping, parallel implementation, the concept of price‐directive coordination, optimal degree of decentralization, and organizational studies. Moreover, other non‐LP developments of the DW method are briefly introduced. They are the DW method of staircase LP, NLP, MIP, and variational inequality problems.

It should be noted that the DW method may be significantly less efficient than the simplex approach. Hence, the DW method should be the only algorithmic choice when the problem is too large to be solved in one piece. Another motivation of the use of the DW decomposition is to ease model development and maintenance by its solution method that links separately developed submodels only when a joint model solution is desired.

On the other hand, the recent applications of the DW method concern (i) how to generalize the DW method to integer programming's branch‐and‐price procedures and (ii) how to apply the DW method to equilibrium models.
Stochastic Mixed‐Integer Programming Algorithms: Beyond Benders' Decomposition
Authors: Sen Suvrajeet
Published: February 2011

This article is dedicated to a new generation of decomposition algorithms that are designed to solve two‐stage stochastic mixed‐integer programming problems. This class of methods, which constitute extensions of Benders' decomposition, is designed to allow integer variables within subproblems, and such situations occur naturally within stochastic mixed‐integer programming problems. In addition to providing a unified treatment of the algorithms, we also summarize some computational results.
Descriptive Models Of Decision Making
Authors: Baucells Manel , Katsikopoulos Konstantinos V.
Published: February 2011

How should people decide in situations involving risk, time, uncertainty, and multiattribute alternatives? How do people actually decide? In this article, we contrast the rational decision maker with the modal subject observed in experiments and surveys. We use a simple setup to present preference models and decision processes that are descriptively more accurate than the rational model.
Deterministic Dynamic Programming (DP) Models
Authors: Smith David K.
Published: February 2011

This section describes the principles behind models used for deterministic dynamic programming. The subject is introduced with some contemporary applications, in computer science and biology. After this, the author outlines several well‐established problem types, and explains how dynamic programming models are constructed and solved for each one.
Differential Games
Authors: Van Long Ngo
Published: February 2011

This article highlights the main features of differential games, explains various equilibrium concepts, and outlines basic solution techniques. Several examples are provided. The merits of alternative equilibrium concepts are discussed. Several extensions of differential games are reviewed.
DTMCS
with Costs and Rewards
Authors: Xu Susan
Published: February 2011

A discrete‐time Markov chain (DTMC) with an attached reward to a state occupancy or to a state transition is called a DTMC with rewards. DTMC reward models find wide applications in many stochastic systems such as inventory management and portfolio performance. This article reviews several performance measures and solution techniques commonly used in DTMC reward models.
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems
Authors: Maglaras Costis
Published: February 2011

This article reviews multiproduct dynamic pricing models for a revenue maximizing monopolist firm. The baseline model studied in this article is of a seller that owns a fixed capacity of a resource that is consumed in the production or delivery of some type of product. The seller selects a dynamic pricing strategy for the offered product so as to maximize its total expected revenues over a finite time horizon. We then review how this model can be extended to settings where the firm is selling multiple products that consume this firm's capacity, and finally highlight a connection between these dynamic pricing models and the closely related model where prices are fixed, and the seller dynamically controls how to allocate capacity to requests for the different products. Methodologically, this article reviews the dynamic programming formulations of the above problems, as well as their associated deterministic (fluid) analogues. It highlights some of the key insights and pricing heuristics that are known for these problems, and briefly mentions possible extensions and areas of current interest.
Computation and Dynamic Programming
Authors: Topaloglu Huseyin
Published: February 2011

Dynamic programming provides a structured framework for solving sequential decision‐making problems under uncertainty, but its computational appeal has traditionally been limited when dealing with problems with large state and action spaces. In this article, we describe a variety of methods that are targeted towards alleviating the computational difficulties associated with dynamic programming. The methods that we describe here are simulation based and model free methods, the linear programming approach to approximate dynamic programming, approximate policy iteration, rollout policies, and state aggregation. An important aspect of the methods that we describe in this article is that they use parsimoniously parameterized value function approximations to ensure that the approximations can be stored in a tractable fashion and they utilize simulation to avoid computing expectations explicitly.
Effective Application of Simulated Annealing
Authors: Ohlmann Jeffrey W. , Van Laarhoven Jon W.
Published: February 2011

First adapted as a solution approach for combinatorial optimization problems in the 1980s, simulated annealing and its variants continue to enjoy widespread application. The widespread application of SA is due to the relative ease of implementing its logic, its general nature and flexibility allowing use with little problem‐specific tailoring (at least for alpha versions of an implementation), and its robust performance with respect to solution quality and running time on a wide array of problems. This article describes implementation details for various features of the simulated annealing algorithm, and then illustrates the development of annealing‐based algorithms through the description of several applications.
Eliciting Subjective Probability Distributions from Groups
Authors: Daneshkhah Alireza , Revie Matthew , Bedford Tim , Walls Lesley , Quigley John
Published: February 2011

Uncertainty analysis has become an increasingly important part of risk assessments and operations research models, and the role of expert judgment in providing information for decision making has become more useful. Decision makers often have access to more than one expert, and it is common to make the decisions on the basis of the expertise of several experts, which leads to the problem of how to combine or aggregate the experts' judgments. A number of approaches have been proposed as to how to elicit, and how to synthesize, the different experts' knowledge. We discuss a variety of models that lead to specific combination methods. The output of these methods is a “combined probability distribution,” which can be viewed as representing a summary of the current state of information regarding the uncertainty of interest. We also briefly review the psychology literature relating to group decision making, which, obviously, is relevant to behavioral aggregation.
Estimating Intensity and Mean Value Function
Authors: Giorgio Massimiliano , Pulcini Gianpaolo
Published: February 2011

This article provides nonparametric and parametric estimation procedures of the intensity function and of the mean value function of point stochastic processes describing the failure process of repairable systems. Data arising both from a single repairable system and from multiple repairable systems are analyzed. A specific attention is devoted to two frequently used processes: the homogeneous Poisson process and the power law process. Two numerical examples are then given to illustrate the estimation procedures.
Estimating Survival Probability
Authors: Giorgio Massimiliano , Pulcini Gianpaolo
Published: February 2011

This article illustrates a number of classical nonparametric and parametric procedures for estimating the survival probability (often called the reliability function) of nonrepairable units. Both point and interval estimation procedures are discussed and some graphical methods are briefly presented. A specific attention is devoted to two frequently used distribution functions: the standard exponential and two‐parameter Weibull distributions. Finally, a number of examples are given to illustrate the estimation procedures.
Evaluating and Comparing Forecasting Models
Authors: Wang Shouyi , Chaovalitwongse Wanpracha Art
Published: February 2011

Forecasting is a very important aspect of any business, and has enormous social, economic, and environmental impacts. Various forecasting models have been developed to help people make right decisions against future uncertainties. However, all forecasting models have distinct advantages and limitations. Selecting appropriate forecasting methods from numerous alternatives is crucial to success. This article briefly summarizes the state‐of‐the‐art forecasting methods in terms of basic procedure, underlying assumptions, applications, and limits. And then the most popular model selection criteria and guidelines are presented.
Evolutionary Game Theory and Evolutionary Stability
Authors: Cressman Ross
Published: February 2011

Evolutionary game theory is used to predict the behavior of individuals in populations (either of humans or other species) without relying on a detailed description of how these behaviors evolve over time (e.g., the replicator equation or the best response dynamics). For instance, if these behaviors correspond to a population state that satisfies the static payoff‐comparison conditions of an evolutionarily stable strategy (ESS), then there is typically dynamic (i.e., evolutionary) stability at this state. We begin with a thorough summary of the evolutionary game theory perspective, when there is a finite set of (pure) strategies, for a symmetric two‐player game in either normal or extensive form. The article then briefly discusses generalizations of evolutionary game theory, ESS, and evolutionary stability to several other classes of games. These include symmetric population games where payoffs to pure strategies are nonlinear functions of the current population state as well as asymmetric games where players are assigned different roles (e.g., two‐role bimatrix games). Although terminology borrowed from evolutionary game theory applied to behaviors of individuals in biological species is used throughout, the concepts introduced are equally relevant in games modeling human behavior.
Fluid Models of Queueing Networks
Authors: Gamarnik David
Published: February 2011

Fluid models are a powerful approximation technique for studying important dynamic properties of queueing networks. The model is typically derived from an underlying queueing system by replacing a stochastic discrete model with a continuous deterministic model, using a certain law of large numbers type of rescaling. The obtained process reveals a lot of fundamental dynamic structures in the underlying system. This article introduces basics of fluid models and illustrates them with a lot of examples. We begin with an informal construction to be followed by rigorous definitions and basic properties. A particular focus is given to the notions of stability of fluid models and methods of stability analysis such as the Lyapunov function technique. Examples of stable fluid models are given as well as some counterexamples, illustrating subtle issues in stability analysis. The formality of connections between fluid models and underlying stochastic queueing systems is discussed at a very high level, since the underlying techniques are highly involved and fall outside of the scope of the article. Instead we point the reader to a rich literature on this subject.