Economic production quantity with maintenance interruptions under random and correlated yields

This paper considers an economic production quantity with imperfect items where the quality of items produced within the same production run is correlated. Production and scheduled maintenance policies for a correlated binomial production system are investigated. We study the impact of correlation on the system performance measures and draw insights in terms of the effect of correlation on the production and maintenance policies. We also illustrate that the popular and commonly used interrupted geometric production systems can be analysed by an equivalent correlated binomial production model.     

Accident prediction models with random corridor parameters

Recent research advocates the use of count models with random parameters as an alternative method for analyzing accident frequencies. In this paper a dataset composed of urban arterials in Vancouver, British Columbia, is considered where the 392 segments were clustered into 58 corridors. The main objective is to assess the corridor effects with alternate specifications. The proposed models were estimated in a Full Bayes context via Markov Chain Monte Carlo (MCMC) simulation and were compared in terms of their goodness of fit and inference. A variety of covariates were found to significantly influence accident frequencies. However, these covariates resulted in random parameters and thereby their effects on accident frequency were found to vary significantly across corridors. Further, a Poisson-lognormal (PLN) model with random parameters for each corridor provided the best fit. Apart from the improvement in goodness of fit, such an approach is useful in gaining new insights into how accident frequencies are influenced by the covariates, and in accounting for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The inclusion of corridor effects in the mean function could also explain enough variation that some of the model covariates would be rendered non-significant and thereby affecting model inference.     

Preventive maintenance models with random maintenance quality

In real-world environments it is usually difficult to specify the quality of a preventive maintenance (PM) action precisely. This uncertainty makes it problematic to optimise maintenance policy. This problem is tackled in this paper by assuming that the quality of a PM action is a random variable following a probability distribution. Two frequently studied PM models, a failure rate PM model and an age reduction PM model, are investigated. The optimal PM policies are presented and optimised. Numerical examples are also given.     

Pull and push contracts in a decentralised assembly system with random component yields

This study investigates how random component yields can influence pricing and production decisions under pull and push contracts. We consider a decentralised assembly system where a manufacturer procures complementary components from two suppliers with random yields. We first characterise the centralised equilibrium decision as a benchmark and then analyse the equilibrium solutions in a decentralised assembly system under each contract. We find that neither contract is always superior to the other in terms of system profit. Under a push contract, suppliers always achieve the first mover advantage with higher payoff. However, the first mover advantage does not hold for the manufacturer under a pull contract. We further conduct sensitivity analysis to study the impact of random component yields and retail price on equilibrium solutions under each contract. Interestingly, the wholesale prices charged by suppliers always increase with supply yield uncertainty under a pull contract, but decrease under a push contract. In contrast with the centralised solution, the equilibrium quantities in the decentralised solution decrease with supply yield uncertainty under both pull and push contracts. We then extend our model to a general case with multiple suppliers. The system payoff decreases with the number of suppliers, and the main results derived in two suppliers setting still hold in the system with multiple suppliers.     

Regression models with correlated errors based on functional random design

This paper deals with the study of the estimation of the functional regression operator when the explanatory variable takes its values in some abstract space of functions. The main goal of this paper is to establish the exact rate of convergence of the mean squared error of the functional version of the Nadaraya–Watson kernel estimator when the errors come from a stationary process under long or short memory and based on random functional data. Moreover, these theoretical results are checked through some simulations with regular (smooth) and irregular curves and then with real data.     

Multivariate Spartan spatial random field models

This paper introduces a family of stationary multivariate spatial random fields with D scalar components that extend the scalar model of Gibbs random fields with local interactions (i.e., Spartan spatial random fields). We derive permissibility conditions for Spartan multivariate spatial random fields with a specific structure of local interactions. We also present explicit expressions for the respective matrix covariance functions obtained at the limit of infinite spectral cutoff in one, two and three spatial dimensions. Finally, we illustrate the proposed covariance models by means of simulated bivariate time series and two-dimensional random fields.     

Constructing models of random processes with specified properties in the frequency domain

Problems of constructing models of random processes with specified statistical properties are considered. Examples are given of the construction of such models in the frequency domain. __________ Translated from Izmeritel’naya Tekhnika, No. 8, pp. 47–50, August, 2006.     

Stochastic reduced order models for random vectors: Application to random eigenvalue problems

An improved optimization algorithm is presented to construct accurate reduced order models for random vectors. The stochastic reduced order models (SROMs) are simple random elements that have a finite number of outcomes of unequal probabilities. The defining SROM parameters, samples and corresponding probabilities, are chosen through an optimization problem where the objective function quantifies the discrepancy between the statistics of the SROM and the random vector being modeled. The optimization algorithm proposed shows a substantial improvement in model accuracy and significantly reduces the computational time needed to form SROMs, as verified through numerical comparisons with the existing approach. SROMs formed using the new approach are applied to efficiently solve random eigenvalue problems, which arise in the modal analysis of structural systems with uncertain properties. Analytical bounds are established on the discrepancy between exact and SROM-based solutions for these problems. The ability of SROMs to approximate the natural frequencies and modes of uncertain systems as well as to estimate their dynamics in time is illustrated through comparison with Monte Carlo simulation in numerical examples.     

Polymer-derived microcellular SiOC foams with magnetic functionality

SiOC microcellular ceramic foams possessing soft-ferromagnetic properties were produced from a pre-ceramic polymer, poly-methyl-methacrylate microbeads (PMMA) (used as sacrificial pore formers) and iron silicide micro-powders (as functional filler). The interactions between the matrix and the filler were studied as a function of the amount of powders introduced and the pyrolysis temperature. Magnetic and mechanical properties were also investigated.     

Fatigue lifetime under uniaxial random loading with different mean values according to some selected models

In the paper, the experimental fatigue lives of plane specimens made of 10HNAP steel have been compared with the calculated lives for random tension loading with non-zero mean value. The cycle amplitude transformation methods proposed by Goodman and Gerber, the Van Dang criterion, averaging with the Svenson–Lipp method and the strain energy density parameter was used. Cycles were counted by the rain flow algorithm; in the case of the Svenson–Lipp method the fatigue cycles were counted by the rain flow algorithm and the level crossing algorithm. Damages were accumulated according to the Palmgren–Miner hypothesis.     

Application of Poisson random effect models for highway network screening

In recent years, Bayesian random effect models that account for the temporal and spatial correlations of crash data became popular in traffic safety research. This study employs random effect Poisson Log-Normal models for crash risk hotspot identification. Both the temporal and spatial correlations of crash data were considered. Potential for Safety Improvement (PSI) were adopted as a measure of the crash risk. Using the fatal and injury crashes that occurred on urban 4-lane divided arterials from 2006 to 2009 in the Central Florida area, the random effect approaches were compared to the traditional Empirical Bayesian (EB) method and the conventional Bayesian Poisson Log-Normal model. A series of method examination tests were conducted to evaluate the performance of different approaches. These tests include the previously developed site consistence test, method consistence test, total rank difference test, and the modified total score test, as well as the newly proposed total safety performance measure difference test. Results show that the Bayesian Poisson model accounting for both temporal and spatial random effects (PTSRE) outperforms the model that with only temporal random effect, and both are superior to the conventional Poisson Log-Normal model (PLN) and the EB model in the fitting of crash data. Additionally, the method evaluation tests indicate that the PTSRE model is significantly superior to the PLN model and the EB model in consistently identifying hotspots during successive time periods. The results suggest that the PTSRE model is a superior alternative for road site crash risk hotspot identification.     

Linear random vibration by stochastic reduced‐order models

A practical method is developed for calculating statistics of the states of linear dynamic systems with deterministic properties subjected to non‐Gaussian noise and systems with uncertain properties subjected to Gaussian and non‐Gaussian noise. These classes of problems are relevant as most systems have uncertain properties, physical noise is rarely Gaussian, and the classical theory of linear random vibration applies to deterministic systems and can only deliver the first two moments of a system state if the noise is non‐Gaussian. The method (1) is based on approximate representations of all or some of the random elements in the definition of linear random vibration problems by stochastic reduced‐order models (SROMs), that is, simple random elements having a finite number of outcomes of unequal probabilities, (2) can be used to calculate statistics of a system state beyond its first two moments, and (3) establishes bounds on the discrepancy between exact and SROM‐based solutions of linear random vibration problems. The implementation of the method has required to integrate existing and new numerical algorithms. Examples are presented to illustrate the application of the proposed method and assess its accuracy. Copyright © 2009 John Wiley & Sons, Ltd.     

Porous Janus materials with unique asymmetries and functionality

Porous Janus materials have attracted widespread attention due to their asymmetry in wettability, charge, pore size or structure, thermal/electrical conductivity, and chemical activity. Multifunction integration and unique directional manipulation of liquid, ion, or gas within porous Janus materials enable rapid progress in diverse applications such as fog collection, personal moisture and healthcare management, energy conversion, water purification, sensor devices, and biomedical applications. Compared with conventional homogeneous materials, porous Janus materials not only showcase superior performance and energetic potential but also open up new applications by virtue of the synergistic or independent effects of asymmetry. This comprehensive review systematically analyzes the research progress of porous Janus materials, highlighting fabrication strategies, synergistic/independent work mechanisms, and emerging advanced applications. Finally, ongoing challenges and outlook for the future research of porous Janus materials are presented.     

Polymeric active coatings with functionality in vascular applications

Copolymers containing functional groups with activity as antiaggregating agents for platelets, based on random chains of metacryloyloxyethyl [2-(acetyloxy)-4-(trifluoromethyl)]benzoate, TH, and 2-acrylamido-2-metylpropanesulfonic acid, AMPS, with AMPS molar fractions ranging from 0.1 to 0.4, have been prepared. The spectroscopical characterization and the in vitro swelling behavior have been studied, as well as the surface free energy, showing the copolymers an appropriate surface properties from a haemocompatible point of view. Preliminary in vitro tests using human blood have shown a promising antiaggregating behavior.     

Constructing models of random processes with specified properties by the Wiener method for identifying dynamic systems

It is shown that the use of the proposed models of random processes with specified statistical properties solves fundamental problems of identifying dynamic systems by Wiener’s method using testing actions of special form. Examples of the construction of these models in the time domain are presented. __________ Translated from Izmeritel’naya Tekhnika, No. 8, pp. 43–46, August, 2006.     

Linear models for non-Gaussian processes and applications to linear random vibration

Linear models are finite sums of specified deterministic, continuous functions of time with random coefficients. It is shown that linear models provide (i) accurate approximations for real-valued non-Gaussian processes with continuous samples defined on bounded time intervals, (ii) simple solutions for linear random vibration problems with non-Gaussian input, and (iii) efficient techniques for selecting optimal designs from collections of proposed alternatives. Theoretical arguments and numerical examples are presented to establish properties of linear models, illustrate the construction of linear models, solve linear random vibration with non-Gaussian input, and propose an approach for optimal design of linear dynamic systems. It is shown that the proposed linear model provides an efficient tool for analyzing linear systems in non-Gaussian environment.     

Gap surface plasmon waveguides with enhanced integration and functionality

We propose and investigate theoretically and experimentally L-shaped gap surface plasmon waveguides (L-GSPWs) formed by a dielectric film (strip) partially enclosed between two metal films. The proposed L-GSPWs combine the benefits of strong plasmon localization in a nanogap, significant propagation distance, low cross-talk between two neighboring waveguides, high transmission through a sharp 90° bend, and simplicity of fabrication by means of the standard lithography combined with the thin film deposition.     

Sputtering yields

This paper gives a short summary of a new survey about experimental and calculated sputtering yields. A comparison of both data sets show a reasonable agreement in most cases.     

Estimation of and testing for random effects in dynamic panel data models

In this article, estimation of moments up to the fourth order of random effects and errors is first investigated for dynamic panel data models. Using the QR decomposition of a matrix, the moments of random individual effects and errors are estimated without affecting each other so that the estimation procedure is simple to implement, and the asymptotic behavior of estimation is derived. On the basis of these estimations, we construct a test for the existence of individual effects. This test is asymptotically normally distributed under the null hypothesis without any distributional assumptions on the individual effects and errors other than moments. A power study shows that our test is able to detect local alternatives that are distinct from the null at a parametric rate. Monte Carlo simulations are carried out for illustration.