bluuebunny/crossref_metadata_2025_split huggingface dataset:
Catastrophical Risk
Authors: Chichilnisky Graciela 
Published: December 2014

Abstract
This article analyses decision under uncertainty, motivated by problems emerging from global environmental risks. These are typically low‐probability events with major irreversible consequences. For such risks the Von Neumann–Morgenstern (NM) Axioms for decision‐making under uncertainty are not appropriate, since they are insensitive to low‐probability events. The article introduces an alternative set of axioms that require sensitivity to both low and large probability events. Through a representation theorem in functional analysis, the results characterize all the operators whose maximization leads to the fulfillment of these axioms. They involve a convex combination of expected utility and a criterion based on the desire to avoid low‐probability and potentially catastrophical events. It is shown that the new axioms help resolve the Allais paradox. Open questions about risk aversion, games under uncertainty and calculus of variations are discussed.
Fire Risk, Industrial
Authors: Thomas Ian 
Published: December 2014

Abstract
Fire represents both a benefit and a threat to society. Fire is essential and is used constructively in providing many of the materials and services essential to modern living. However, unwanted fires represent a threat that is generally immediately recognized and instils fear into those threatened. Fire generally represents a small but not insignificant risk in modern societies. It represented a greater risk in the past when open fires were common in domestic and industrial situations, but even today unwanted fires are responsible for considerable loss of life, property and services. The consequences of unwanted fires include injuries and death to people and animals involved, loss of contents and building structures and consequential damage to businesses and services. Many of these losses are not recorded, or alternatively, not made publicly available, and so the consequences associated with them are unable to be estimated accurately. However, in many countries considerable effort is expended in recording details of unwanted fires, including locations, causes and consequences. However, international collection of such information is restricted to very basic information such as the overall losses and costs of fires and the number of fire ‐related fatalities per annum.
Flood Risk Management, Quantitative Methods
Authors: Gelder P. H. A. J. M 
Published: December 2014

AbstractIn this article, quantitative methods for flood risk management are discussed. Reliability analysis of flood defense systems and the probabilistic flood risk management approach are outlined. The application of quantitative methods offers the designer a way to unify the design of engineering structures, processes, and management systems. For this reason, there is a growing interest in the use of these methods in the design and safety assessment of flood defenses. This article describes the background of probabilistic analyses, uncertainties, and systems analysis for flood risk management.
Risk Assessment, Probabilistic
Authors: Thompson Kimberly M. 
Published: December 2014

Abstract
A primary criticism of environmental risk assessment as it has been practiced in the last 20 years has been the failure of analysts to properly account for issues of variability and uncertainty. Many risk assessments focused on producing ‘point estimates’ of risks have provided risk managers and decision makers with information that is at best incomplete and at worst dangerously misleading. As the field of risk assessment has matured, techniques for better treatment of variability and uncertainty have been developed and incorporated into what is known as ‘probabilistic’ risk assessments. In contrast to the point estimate approach, where single values are assumed for each model input, the probabilistic approach quantifies the possible range of values that each input may take, as well as the probability associated with each value. Modern computational power then allows the analyst to generate a multidimensional distribution of risk that reflects both the variability of risk across different individuals in the population as well as the uncertainty associated with each estimate.
Bienyeme‐Galton‐Watson Branching Process
Authors: Athreya K.B. 
Published: December 2014

Abstract

The study of the growth and development of many species of animals, plants and other organisms over time may be approached in the following manner. At some point in time a set of individuals called ancestors or the zeroth generation is identified. These produce offspring and the collection of offspring of all the ancestors constitutes the first generation. The offspring of these first generation individuals constitute the second generation and so on. If one specifies the rules by which the offspring production takes place then one could study the behavior of the long time evolution of such a process, called a
branching process
. Questions of interest are the long term survival of such a process or its extinction, the growth rate of such a population, fluctuation of population sizes, effects of control mechanicisms etc. In this article several simple mathematical models and some results for these will be discussed. Many of the assumptions made, especially the one about independence of lines of descent, are somewhat idealistic and unrealistic. Nevertheless, the models do prove useful in answering some general questions, since many results about long term behavior deviate somewhat from the basic assumptions.
Control Theory
Authors: Young Peter C. 
Published: December 2014

Abstract
Control theory is concerned with the mathematical characterization and analysis of deterministic and stochastic dynamic systems for the purpose of identification, estimation and control. It emphasizes the importance of feedback processes, both as an integral aspect of all dynamic systems and as a means of artificially controlling or optimizing the behavior of such systems. In relation to environmetrics, the main importance of control theory relates to the impact that it has had on the analysis of environmental time series and the modeling of environmental systems. In addition, however, its powerful ability to improve our understanding of how dynamic systems behave in feedback terms can have a profound effect on how environmetric models are interpreted in physically meaningful terms.
Criticality, Self‐Organized
Authors: Li Bai‐Lian 
Published: December 2014

Abstract

The causes and possible relations between the abundance of fractal structures (
see
Fractal dimensions) and 1/
f
signals found in nature have puzzled scientists for years. Based on observations of computer simulations of the cellular automata sandpile model, the term self‐organized criticality (SOC) was invented by theoretical physicists and was used to explain a universal dynamic organizing process governing various fractal growths in space and time. SOC describes that systems organize themselves so their internal dynamics produce critical behavior over a very wide range of length scales. The term consists of two parts.
Self‐organization
has been used to describe the ability of certain nonequilibrium systems to develop structures and patterns often without explicit pressure or involvement from outside the systems.
Criticality
is a technical word used in connection with phase transitions, and has a very precise meaning, in which the system becomes critical when all members of the system influence each other and events of all sizes occur.
Cylindrical Data
Authors: Anderson‐cook Christine M. , Otieno B. Sango 
Published: December 2014

AbstractCylindrical data are a form of directional data, where two responses are considered. An angular or circular response is combined with a linear response to form observations, which can be thought of as a location in polar coordinates or on the surface of a unit cylinder. There are several alternatives for modeling the responses including circular‐linear relationships. Depending on the nature of the relationship between the two responses, some model forms can be more natural than others. Environmental examples of cylindrical data include plant and animal migration, as well as wind direction with another environmental measure such as ozone concentration or temperature.
Dynamic Modeling: Introduction
Authors: Dowd Michael 
Published: December 2014

Abstract
Dynamic modeling describes the quantitative representation of time‐dependent systems, usually as systems of differential equations or discrete versions thereof. Dynamic models may be nonlinear, high dimensional, spatially explicit, and can exhibit complex behavior. They are most commonly deterministic, but can be stochastic. They are key elements for many scientific disciplines, and the foundation for environmental prediction. Dynamic modeling in environmetrics considers uncertainty analysis, as well as inference on the state of the system and its parameters using available observations.
Ensemble Models
Authors: Fuentes Montserrat , Foley Kristen 
Published: December 2014

Abstract
An ensemble, or sample, of competing numerical models has been used in many applications to represent different predictions of the true state of a physical system. Ensembles of computer models (e.g. weather, climate, air quality, ocean models) are often used to forecast future states of a physical system and to quantify uncertainty in the numerical model predictions. Various statistical methods have been proposed to improve ensemble predictions from deterministic computer simulations based on actual measurements of the physical systems (e.g. data assimilation, Bayesian model averaging). Ensemble data mining methods have also been developed for a wide variety of applications to combine different versions of a statistical model (e.g. time series models, simple regression models, neural networks) to improve the predictive model performance. We present different statistical criteria that have been proposed to select or weight ensemble members for both numerical model‐based and statistical model‐based ensembles.
Extremes for Processes in Random Environments
Authors: Piterbarg Vladimir I. 
Published: December 2014

Abstract
We give definitions for random processes in random environment, in particular, Gaussian random processes in random environment, or, in other words, conditionally Gaussian processes. With some constrains on a random environment, we formulate the large deviation principle for Gaussian processes in the random environment. Further, we consider a class of Gaussian processes in random environment that allows exact calculations for probabilities of large extremes. This is a class of Gaussian stationary processes with smooth bounded random stationary variance. We show how the random environment affects on such probabilities.
Extremes: Spatial Models and Prediction
Authors: Stoev Stilian A. 
Published: December 2014

Abstract
Max‐stable processes and fields are reviewed as natural models of spatial extremes. Their stochastic properties, extremal integral, and de Haan spectral representations are briefly discussed. A solution to the prediction problem for a large class of spectrally discrete max‐linear models is presented and illustrated over a data set of rainfall extremes.
Fractal Dimensions
Authors: Li Bai‐Lian 
Published: December 2014

AbstractThe term ‘fractal’ (from the Latinfractus, meaning ‘broken’), introduced by Benoit Mandelbrot about 25 years ago, is used to characterize spatial and/or temporal phenomena that are continuous but not differentiable. Geometrically, a fractal is a rough or fragmented geometric shape that can be subdivided in parts, each of which is (at least approximately) a reduced‐size copy of the whole. Fractal structures do not have a single length scale, while fractal processes (e.g. time series) cannot be characterized by a single time scale. Fractal theory offers methods for describing the inherent irregularity of natural objects. In fractal analysis, the Euclidean concept of ‘length’ is viewed as a process. This process is characterized by a constant parameterDknown as the fractal (or fractional) dimension. There are many fractal dimensions introduced in mathematical and physical literature. Fractal dimensions can be positive, negative, complex, fuzzy, multifractal, etc. The two most commonly used are the Hausdorf dimension and capacity.
Gaussian Process: Theory
Authors: Davis Richard A. 
Published: December 2014

Abstract

The class of Gaussian processes is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space, or time and space. The popularity of such processes stems primarily from two essential properties. First, a Gaussian process is completely determined by its mean and covariance functions. This property facilitates model fitting as only the first‐ and second‐order moments of the process require specification. Second, solving the prediction problem is relatively straightforward. The best predictor of a Gaussian process at an unobserved location is a linear function of the
observed
values and, in many cases, these functions can be computed rather quickly using recursive formulas.
J
oint
M
odels
Authors: S. Thompson Darby J. 
Published: December 2014

Abstract
Joint modeling encompasses strategies to simultaneously model several outcomes of interest. There are three principal strategies; classical joint modeling, conditional models, and conditional independence models. Likely the most pervasive area of joint modeling is in the modeling of longitudinal and time‐to‐event data; in particular, accounting for drop‐out in longitudinal data or incorporating error‐prone, sporadically measured, longitudinal outcomes in models for event times. Conditional independence is a popular strategy, which assumes the outcomes of interest are noisy, independent measures of some underlying latent process; it is this process that induces their correlation providing a tractable assumption in many practical settings.
Markov Process, Hidden
Authors: Zucchini Walter , Macdonald Iain L. 
Published: December 2014

Abstract

Hidden Markov processes, more commonly known as hidden Markov models (HMMs), are a class of models for time series {

X
t

,
t
= 0, 1, 2, …} in which the probability distribution of

X
t

is determined by the unobserved (‘hidden’) state

C
t

of an
m
‐state Markov chain {

C
t

,
t
= 0, 1, 2, …}.
Massive Data, Models for
Authors: Zhang Hao 
Published: December 2014

Abstract
Massive data present a challenge to the use and manipulation of well‐studied inferential strategies such as maximum likelihood estimation, Bayesian methods, and best linear prediction (kriging) since these methods require the inversion of a large covariance matrix. This entry introduces statistical models that circumvent computational issues, which include local models, covariance tapering, low‐rank models, and Markovian models.
Max‐Stable Processes
Authors: Padoan Simone A. 
Published: December 2014

AbstractEnvironmental problems such as floods require statistical analysis that takes into account the complex nature of the data, namely, the observations that are sampled at different spatial points in a given region for a certain time. Thus the spatial dependence structure cannot be ignored. Extreme statistics for the design of structures for flood protection, for the study of the structural failures such as those in bridges, dams, and so on, for the prediction of heat waves, and other spatial extremes should be based on a solid theoretical framework. Max‐stable processes provide such a theory and in the last decade have emerged as fertile ground for research and have become a common tool for the statistical modeling of spatial extremes. This article provides a summary of max‐stable processes.
Point Processes, Temporal
Authors: Brillinger David R. , Guttorp Peter M. , Schoenberg Frederic Paik 
Published: December 2014

Abstract

A temporal point process is a random process whose realizations consist of the times {τ

j

}, τ

j

∈ ℝ,
j
= 0, ±1, ±2, … of isolated events scattered in time. A point process is also known as a counting process or a random scatter. The times may correspond to events of several types.
Seismological Modeling
Authors: Rathbun Stephen L. 
Published: December 2014

AbstractTemporal point process models for seismological applications are described including Poisson point process, Hawkes' self‐exciting point process for clustered events, and the stress‐release point process for regularly‐spaced events. Earthquake magnitudes may be included in marked versions of the point process models. In seismology, the self‐exciting point process model is often referred to as the epidemic‐type aftershock sequences (ETAS) model and is suitable for modeling aftershock sequences. Dependencies among seismological events may be described using Laguerre polynomials or the modified Omori function and versions of the model can be constructed under which high‐magnitude shocks produce a higher mean number of aftershocks. The ETAS model can also accommodate long‐term trends and periodic behavior. Under the stress‐release model describes the release of tectonic stress following each earthquake. Maximum likelihood estimation of model parameters and its statistical inference are briefly described. Methods are applied to a sequence of 432 California earthquakes.
B
rownian Motion
Authors: Riedi Rudolf H. 
Published: December 2014

Abstract
This article on Brownian motion starts with a short historical survey on its discovery and importance and continues with its most relevant properties. Mathematically detailed formulas are given and connections between the properties are indicated, however no derivations or proofs are provided.
The properties covered start with the mathematical modeling of Brownian motion via the Wiener process; equivalent definitions and constructions such as the Lévy characterization, the midpoint displacement, and the rescaled random walks are discussed. Basic properties given include uniqueness, symmetry, restarted Brownian motion, and time inversion. More advanced topics treated cover stopping times, the strong Markov property, the reflection principle, extreme values, escape times from a strip, and last visits. Path oscillations, stationarity, Gaussian white noise, and Karhunen‐Löwe expansions are also discussed. Brownian motion with drift, the Brownian bridge, as well as intersections, recurrence, and transience of Brownian motion in higher dimensions, are mentioned briefly.
One‐Pot Synthesis of 2‐Arylquinazolines and Tetracyclic Isoindolo[1,2‐a]quinazolines via Cyanation Followed by Rearrangement of ortho‐Substituted 2‐Halo‐N‐arylbenzamides
Authors: Gawande Sachin D. , Zanwar Manoj R. , Kavala Veerababurao , Kuo Chun‐Wei , Rajawinslin R. R. , Yao Ching‐Fa 
Published: December 2014

AbstractThe one‐pot synthesis of substituted 2‐arylquinazoline derivatives and tetracylic isoindolo[1,2‐a]quinazoline via cyanation followed by rearrangement of ortho‐substituted 2‐halo‐N‐arylbenzamides is described. Using dimethyl sulfoxide (DMSO) as the solvent, the cleavage of the tetracyclic isoindole fused quinazoline leads to the formation of 2‐arylquinazoline derivatives. When 1,4‐dioxane is used as the solvent, tetracyclic isoindole fused quinazolines are produced in good yield. A wide range of products, including 2‐phenylquinazolin‐4‐amine, 4‐methyl‐2‐phenylquinazoline and long‐chain 2‐phenyl‐4‐styrylquinazoline derivatives were produced in moderate to good yields using DMSO as the solvent. However, various tetracyclic isoindole fused quinazoline derivatives were obtained in good yields when 1,4‐dioxane was used as the solvent.magnified image
Peptide o‐Aminoanilides as Crypto‐Thioesters for Protein Chemical Synthesis
Authors: Wang Jia‐Xing , Fang Ge‐Min , He Yao , Qu Da‐Liang , Yu Min , Hong Zhang‐Yong , Liu Lei 
Published: December 2014

AbstractFully unprotected peptide o‐aminoanilides can be efficiently activated by NaNO2 in aqueous solution to furnish peptide thioesters for use in native chemical ligation. This finding enables the convergent synthesis of proteins from readily synthesizable peptide o‐aminoanilides as a new type of crypto‐thioesters. The practicality of this approach is shown by the synthesis of histone H2B from five peptide segments. Purification or solubilization tags, which are sometimes needed to improve the efficiency of protein chemical synthesis, can be incorporated into the o‐aminoanilide moiety, as demonstrated in the preparation of the cyclic protein lactocyclicin Q.
Sequential Synthesis of Coordination Polymersomes
Authors: Ohtani Ryo , Inukai Munehiro , Hijikata Yuh , Ogawa Tetsuya , Takenaka Mikihito , Ohba Masaaki , Kitagawa Susumu 
Published: December 2014

AbstractNovel organic–inorganic hybrid liposomes, so‐called coordination polymersomes (CPsomes), with artificial domains that exhibit strong lateral cohesion were prepared by a three‐step procedure that formed a coordinative interaction leading to a lipid bilayer. First, the lipophilic complex (dabco‐C18)[Mn(N)(CN)4(dabco‐C18)] (1; dabco‐C18+=1,4‐diazabicyclo[2,2,2]octane‐(CH2)17CH3 cation), was synthesized. 1 has a lipophilic alkyl tail part and a tetracyanometallate head group, which can be used for an expansion to two‐dimensional coordination networks. Second, 1 and 1,2‐dipalmitoyl‐sn‐glycero‐3‐phosphocholine were mixed to prepare the liposomes. Finally, CPsomes were obtained by the addition of transition‐metal ions (M) to form unilamellar faceted liposomes with plain CP raft domains with MnCNM linkages. The concentration of 1 influences the size of the CP raft domains and the shape of the CPsomes. The synthesis of coordination polymers in lipid bilayers is a novel approach for the construction of artificial architectures as raft domains, for example, in cell membranes.
A Sharp Thermal Transition of Fast Aromatic‐Ring Dynamics in Ubiquitin
Authors: Kasinath Vignesh , Fu Yinan , Sharp Kim A. , Wand A. Joshua 
Published: December 2014

AbstractAromatic amino acid side chains have a rich role within proteins and are often central to their structure and function. Suitable isotopic‐labelling strategies enable studies of sub‐nanosecond aromatic‐ring dynamics using solution NMR relaxation methods. Surprisingly, it was found that the three aromatic side chains in human ubiquitin show a sharp thermal dynamical transition at approximately 312 K. Hydrostatic pressure has little effect on the low‐temperature behavior, but somewhat decreases the amplitude of motion in the high‐temperature regime. Therefore, below the transition temperature, ring motion is largely librational. Above this temperature, a complete ring‐rotation process that is fully consistent with a continuous diffusion not requiring the transient creation of a large activated free volume occurs. Molecular dynamics simulations qualitatively corroborate this view and reinforce the notion that the dynamical character of the protein interior has much more liquid‐alkane‐like properties than previously appreciated.
Integrative Chemical Biology Approaches for Identification and Characterization of “Erasers” for Fatty‐Acid‐Acylated Lysine Residues within Proteins
Authors: Liu Zheng , Yang Tangpo , Li Xin , Peng Tao , Hang Howard C. , Li Xiang David 
Published: December 2014

AbstractAcylation of proteins with fatty acids is important for the regulation of membrane association, trafficking, subcellular localization, and activity of many cellular proteins. While significant progress has been made in our understanding of the two major forms of protein acylation with fatty acids, N‐myristoylation and S‐palmitoylation, studies of the acylation of lysine residues, within proteins, with fatty acids have lagged behind. Demonstrated here is the use of integrative chemical biology approaches to examine human sirtuins as de‐fatty‐acid acylases in vitro and in cells. Photo‐crosslinking chemistry is used to investigate enzymes which recognize fatty‐acid acylated lysine. Human Sirt2 was identified as a robust lysine de‐fatty‐acid acylase in vitro. The results also show that Sirt2 can regulate the acylation of lysine residues, of proteins, with fatty acids within cells.