Effective Hamiltonians in quantum optics: a systematic approach

A general and systematic method for obtaining eiective Hamiltonians that describe diierent nonlinear optical processes is discussed. The method exploits the existence of a nonlinear deformation of the usual su(2) algebra that arises as the dynamical symmetry of the original model. When some physical parameter, dictated by the process under consideration, becomes small, a diagonal eiective Hamiltonian is obtained immediately, that correctly represents the dynamics for arbitrary states and long times. The technique is extended to su(3) and su(N), finding the corresponding eiective Hamiltonians when some resonance conditions are fulfilled.     

Entrepreneurial cognition and socially situated approach: a systematic and bibliometric analysis

Research on entrepreneurial cognition (EC) has evolved in recent years, and the aim of this contribution is to offer a clear, systematic, and bibliometric review of EC as a field of study from a more dynamic perspective, building on Socially Situated Cognition theory (SSC). Based on a data set that covers 18 years of research, from 1998 to 2016, the present study analyzes all of the 151 papers available in the Web of Science Core Collection and 15 editorials, book chapters, and books directly referring to entrepreneurial cognition. Building on our results, we divided EC studies into two stages, namely the “emerging” (1998–2007) and the “mature” (2008–2016). In addition, with this study we suggest three main topics that should be investigated in future researches: entrepreneurial action should be considered endogenous in the entrepreneurship process and the studies in the field of embodied and distributed cognition should be expanded.     

Model validation: Correlation for updating

In this paper, a review is presented of the various methods which are available for the purpose of performing a systematic comparison and correlation between two sets of vibration data. In the present case, the application of interest is in conducting this correlation process as a prelude to model correction or updating activity.     

Model development and validation of a solar cooling plant

This paper describes the dynamic model of a solar cooling plant that has been built for demonstration purposes using market-available technology and has been successfully operational since 2001. The plant uses hot water coming from a field of solar flat collectors which feed a single-effect absorption chiller of 35 kW nominal cooling capacity. The work includes model development based on first principles and model validation with a set of experiments carried out on the real plant. The simulation model has been done in a modular way, and can be adapted to other solar cooling-plants since the main modules (solar field, absorption machine, accumulators and auxiliary heater) can be easily replaced. This simulator is a powerful tool for solar cooling systems both during the design phase, when it can be used for component selection, and also for the development and testing of control strategies.     

Stochastic modelling and simulation of production lines: a computer-based approach towards model validation

This study employs a simulation-based design methodology to investigate the performance of two models of manufacturing systems. In the first model, the dynamic behaviour of a single parallel-machine stage with unreliable work stations is modelled as a Markov process. A similar analytical method for evaluating the performance of a buffered production line is presented in the second model. A simple approach towards coding and simulating the models is presented, and numerical examples based on these simulation models indicate that the approach is viable.     

Interfacial roughness and related scatter in ultraviolet optical coatings: a systematic experimental approach

For a variety of UV optical coatings, surface roughness was measured by use of an atomic-force microscope (AFM) to study its dependence on the film material and thickness, coating design, and deposition process. After analyzing the corresponding power spectral density functions, we propose a simple classification model for coatings according to the contributions of substrate roughness and intrinsic film roughness to the scattering. Results of scattering measurements on different types of coatings are presented and are found to be in good agreement with predictions based on the AFM data. Consequences for a scatter reduction strategy are discussed.     

Sampling bias in blending validation and a different approach to homogeneity assessment

Sampling of batches studied for validation is reported. A thief particularly suited for granules, rather than cohesive powders, was used in the study. It is shown, as has been demonstrated in the past, that traditional 1× to 3× thief sampling of a blend is biased, and that the bias decreases as the sample size increases. It is shown that taking 50 samples of tablets after blending and testing this subpopulation for normality is a discriminating manner of testing for homogeneity. As a criterion, it is better than sampling at mixer or drum stage would be even if an unbiased sampling device were available.     

A structural approach to assessing postponement strategies: construct development and validation

This paper endeavours to introduce and validate constructs and measured variables for postponement strategies. Although empirical researchers have examined postponement, a consistent set of valid, reliable factors has not been developed and used. The lack of valid constructs is a barrier to hypothesis testing and meta-analysis on postponement. Using confirmatory factor analysis (CFA), the validity and reliability of the proposed postponement constructs are examined. This is performed through a pilot study and a large scale survey on a sample of 219 manufacturing firms which represent a wide range of manufacturing operations. The outcomes of this paper establish a set of variables which can measure shipment, manufacturing, purchasing and design postponements.     

Model validation under epistemic uncertainty

This paper develops a methodology to assess the validity of computational models when some quantities may be affected by epistemic uncertainty. Three types of epistemic uncertainty regarding input random variables - interval data, sparse point data, and probability distributions with parameter uncertainty - are considered. When the model inputs are described using sparse point data and/or interval data, a likelihood-based methodology is used to represent these variables as probability distributions. Two approaches - a parametric approach and a non-parametric approach - are pursued for this purpose. While the parametric approach leads to a family of distributions due to distribution parameter uncertainty, the principles of conditional probability and total probability can be used to integrate the family of distributions into a single distribution. The non-parametric approach directly yields a single probability distribution. The probabilistic model predictions are compared against experimental observations, which may again be point data or interval data. A generalized likelihood function is constructed for Bayesian updating, and the posterior distribution of the model output is estimated. The Bayes factor metric is extended to assess the validity of the model under both aleatory and epistemic uncertainty and to estimate the confidence in the model prediction. The proposed method is illustrated using a numerical example.     

A systematic approach to preliminary polymer process development: modeling and design

A general and complete methodology is presented to facilitate systematic modeling and design of polymer processes during the early development period. To capture and handle the subjective type of uncertainty, embedded in the preliminary process development, fuzzy theories are used as a basis to model and design the process in the presence of ambiguity and vagueness. Physical membership functions are developed for mapping the relation between process variables and the associated fuzzy uncertainties. Based on the qualitative results generated using our previously proposed “linguistic based preliminary design method,” the process modeling can be followed even in the absence of any process governing equations. The modeling is carried out by establishing an appropriate fuzzy reasoning system which provides a specific functional mapping that relates input process variables to one (or more than one) output performance parameter(s). A reduced yet feasible domain is generated by our qualitative design scheme to constrain the process variables. Now, any optimization routine can then be employed to search for a proper process design. We demonstrate the effectiveness of the proposed methodology by its application to a typical compression molding process.     

Model order reduction in hyperelasticity: a proper generalized decomposition approach

This paper deals with the extension of proper generalized decomposition methods to non‐linear problems, in particular, to hyperelasticity. Among the different approaches that can be considered for the linearization of the doubly weak form of the problem, we have implemented a new one that uses asymptotic numerical methods in conjunction with proper generalized decomposition to avoid complex consistent linearization schemes necessary in Newton strategies. This approach results in an approximation of the problem solution in the form of a series expansion. Each term of the series is expressed as a finite sum of separated functions. The advantage of this approach is the presence of only one tangent operator, identical for every term in the series. The resulting approach has proved to render very accurate results that can be stored in the form of a meta‐model in a very compact format. This opens the possibility to use these results in real‐time, reaching kHz feedback rates, or to be used in deployed, handheld devices such as smartphones and tablets. Copyright © 2013 John Wiley & Sons, Ltd.     

An approach to systematic materials selection

An approach to systematic materials selection is believed to be suitable for both manual and computer aided materials selection. The procedure is divided into two stages; the discriminating and the optimising parts. In the former, all requirements on properties which do not influence the final sizing of the component in question are considered. From this stage a number of materials survives which are used in the optimisation stage. To rank the materials, merit parameters are introduced. This is exemplified for pressurized containers. With the help of the merit parameters it is possible to decide which material is the most suitable in a given situation. A procedure is also proposed from which the influence of the design parameters on the selection can be evaluated. This is exemplified by analysing how the pressure in the containers affects the choice of optimum material.     

An external validation study of a classification of mixed connective tissue disease and systemic lupus erythematosus patients

Mixed Connective Tissue Disease (MCTD) and Systemic Lupus Erythematosus (SLE) are autoimmune rheumatic diseases that are difficult for physicians to diagnose and to distinguish for a variety of reasons. The correct classification of these two diseases is a crucial issue for clinicians who treat autoimmune rheumatic diseases. In prior research, medical risk factors represented by instrument or laboratory measures and physician judgments (12 key features for MCTD and 12 key features for SLE) were parameterized with a one parameter logistic function in a Rasch model. Those results identified separate diagnostic dimensions for MCTD and SLE. This procedure was replicated in the present research with a sample of largely African American and Hispanic patients. Results verified separate dimensions for MCTD and SLE, which suggests MCTD is a separate disease from SLE.     

Internal validation of near-crashes in naturalistic driving studies: A continuous and multivariate approach

Large naturalistic driving studies give extremely detailed insight into how traffic accidents happen and what causes them. However, even in very large studies there are only relatively few crashes. Hence one additionally selects and studies crash surrogates, so called “near-crashes”, i.e. situations when a crash almost happened. The selection procedures invariably entail severe risks of causing bias. In this paper we use extreme value statistics to develop two methods to study the extent and form of this bias. The methods are applied to a large naturalistic driving study, the 100-car study. Both methods identified a severe discrepancy between the rear-striking near-crashes and the rear-striking crashes. Perhaps surprisingly, one conclusion is that, for rear-striking and in this study, the crashes have little relevance for increasing traffic safety. We believe substantial efforts should be made to develop statistical methods for using near-crashes and crashes in future large naturalistic driving studies (such as the SHRP2 study).     

Beam-particle approach to model cracking and energy dissipation in concrete: Identification strategy and validation

This paper focuses on the application of a beam-particle model to study the failure of concrete under complex loading. The formulation of the model is based both on lattice models and discrete elements models in order to capture cohesion, failure and frictional contact of the crack surfaces. To correctly describe the elastic phase, the peak load and the post-peak phase, the failure criteria is discussed and heterogeneities are introduced. The calibration of this model is detailed and illustrated. Finally, several test cases are analysed in order to validate the model.     

The Datum Flow Chain: A systematic approach to assembly design and modeling

Current CAD systems are part-centric and do not capture the underlying logic of an assembly at an abstract level. We need to make CAD systems assembly-centric. To be able to lay out, analyze, outsource, assemble and debug complex assemblies, we need ways to capture their fundamental structure in a top-down design process, including the designer's strategy for constraining the parts kinematically and locating them accurately with respect to each other. We describe a concept called the Datum Flow Chain to capture this logic. Most assembly problems occur due to ineffective datum logic or the choice of assembly procedures that are not consistent with the datum logic, if any, that was used to design the parts. The DFC relates the datum logic explicitly to the product's key characteristics, assembly sequences, and choice of mating features, and provides the information needed for tolerance analyses. Two types of assemblies are addressed: Type-1, where the assembly process puts parts together at their pre-fabricated mating features, and Type-2, where the assembly process can incorporate in-process adjustments to redistribute variation. Two types of assembly joints are defined: mates that pass dimensional constraint from part to part, and contacts that merely provide support. The scope of DFC in assembly planning is presented using several examples. Analysis tools to evaluate different DFCs and select the ones of interest are also presented.     

Model calibration: A hierarchical Bayesian approach

Many geotechnical engineering models are empirical and calibrated based on data gathered from various sites/projects, using optimisation algorithms with criteria like least squared errors or minimising the coefficient of variation of method bias with the constraint of mean bias equal to unity. This paper discusses the use of hierarchical Bayesian regression models for the same purpose. A database of axial capacity of piles in predominantly clay sites and a CPT-based design model, compiled and developed as part of a Joint Industry Project (JIP) led by the Norwegian Geotechnical Institute (NGI), is used for demonstration. The analyses focus on two related areas that the traditional approaches overlook: (i) quantification of uncertainty in the estimated parameters of the model, and (ii) modelling site-dependency of the model parameters (i.e., between-group variation). The former is important in the context of reliability-based design and contributes to establishing confidence in estimated reliability indices, particularly when only limited data are available. The latter expands our understanding regarding the domain of applicability of a model; that is, if a model is broadly applicable or highly site-dependent. The benefits of the proposed Bayesian approach are highlighted with a prediction exercise where the calibrated models are used in conjunction with limited site or project-specific data.     

Xenon adsorbed in zeolite Na-Y: A systematic molecular dynamics study with a flexible framework approach

Summary Molecular dynamics simulations are performed in order to study systematically the behaviour of xenon in zeolite Na-Y over a wide range of temperatures and loadings. For these investigations a full flexible framework approach is used and the polarizability of xenon is taken into account. The good agreement between the results of the simulations and available experimental data indicates that the applied interaction model is a realistic approach. It is found that the energy of activation for the diffusion process strongly depends on the concentration of the sorbate atoms. Furthermore, the influence of the framework flexibility on the properties of the adsorbed species is studied by comparative simulations with a fixed framework approach. The results show that the main energetic properties, as well as the mobility of xenon, are hardly influenced by the vibrations of the host lattice under the conditions studied here. However, significant differences in the pair distribution functions of the xenon atoms are observed.     

Model validation of simple-graph representations of metabolism

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules.