State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Dietary advanced glycation end‐products: Perspectives linking food processing with health implications

Dietary advanced glycation end products (dAGEs) are complex and heterogeneous compounds derived from nonenzymatic glycation reactions during industrial processing and home cooking. There is mounting evidence showing that dAGEs are closely associated with various chronic diseases, where the absorbed dAGEs fuel the biological AGEs pool to exhibit noxious effects on human health. Currently, due to the uncertain bioavailability and rapid renal clearance of dAGEs, the relationship between dAGEs and biological AGEs remains debatable. In this review, we provide the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies. Available evidence demonstrating a relevance between dAGEs and food allergy is also included. AGEs are ubiquitous in foods and their contents largely depend on the reactivity of carbonyl and amino groups, along with surrounding condition mainly pH and heating procedures. Once being digested and absorbed into the circulation, two separate pathways can be involved in the deleterious effects of dAGEs: an AGE receptor‐dependent way to stimulate cell signals, and an AGE receptor‐independent way to dysregulate proteins via forming complexes. Inhibition of AGEs formation during food processing and reduction in the diet are two potent approaches to restrict health‐hazardous dAGEs. To elucidate the biological role of dAGEs toward human health, the following significant perspectives are raised: molecular size and complexity of dAGEs; interactions between unabsorbed dAGEs and gut microbiota; and roles played by concomitant compounds in the heat‐processed foods.     

Adaptive actuator failure compensation design for unknown chaotic multi‐input systems

In this paper, an adaptive control approach is designed for compensating the faults in the actuators of chaotic systems and maintaining the acceptable system stability. We propose a state‐feedback model reference adaptive control scheme for unknown chaotic multi‐input systems. Only the dimensions of the chaotic systems are required to be known. Based on Lyapunov stability theory, new adaptive control laws are synthesized to accommodate actuator failures and system nonlinearities. An illustrative example is studied. The simulation results show the effectiveness of the design method. Copyright © 2014 John Wiley & Sons, Ltd.     

Long-run urban dynamics: understanding local housing market change in London

Abstract

Recently, a literature has emerged using empirical techniques to study the evolution of international cities over many centuries; however, few studies examine long-run change within cities. Conventional models and concepts are not always appropriate and data issues make long-run neighbourhood analysis particularly problematic. This paper addresses some of these points. First, it discusses why the analysis of long-run urban change is important for modern urban policy and considers the most important concepts. Second, it constructs a novel data set at the micro level, which allows consistent comparisons of London neighbourhoods in 1881 and 2001. Third, the paper models some of the key factors that affected long-run change, including the role of housing. There is evidence that the relative social positions of local urban areas persist over time but, nevertheless, at fine spatial scales, local areas still exhibit change, arising from aggregate population dynamics, from advances in technology, and also from the effects of shocks, such as wars. In general, where small areas are considered, long-run changes are likely to be greater, because individuals are more mobile over short than long distances. Finally, the paper considers the implications for policy.     

A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

Study of the synergistic effect of boron nitride and carbon nanotubes in the improvement of thermal conductivity of epoxy composites

The enhancement of the thermal conductivity, keeping the electrical insulation, of epoxy thermosets through the addition of pristine and oxidized carbon nanotubes (CNTs) and microplatelets of boron nitride (BN) was studied. Two different epoxy resins were selected: a cycloaliphatic (ECC) epoxy resin and a glycidylic (DGEBA) epoxy resin. The characteristics of the composites prepared were evaluated and compared in terms of thermal, thermomechanical, rheological and electrical properties. Two different dispersion methods were used in the addition of pristine and oxidized CNTs depending on the type of epoxy resin used. Slight changes in the kinetics of the curing reaction were observed in the presence of the fillers. The addition of pristine CNTs led to a greater enhancement of the mechanical properties of the ECC composite whereas the oxidized CNTs presented a greater effect in the DGEBA matrix. The addition of CNTs alone led to a marked decrease of the electrical resistivity of the composites. Nevertheless, in the presence of BN, which is an electrically insulating material, it was possible to increase the proportion of pristine CNTs to 0.25 wt% in the formulation without deterioration of the electrical resistivity. A small but significant synergic effect was determined when both fillers were added together. Improvements of about 750% and 400% in thermal conductivity were obtained in comparison to the neat epoxy matrix for the ECC and DGEBA composites, respectively. © 2019 Society of Chemical Industry     

A case-deletion diagnostic for penalized calibration estimators and BLUP under linear mixed models in survey sampling

The penalized calibration technique in survey sampling combines usual calibration and soft calibration by introducing a penalty term. Certain relevant estimates in survey sampling can be considered as penalized calibration estimates obtained as particular cases from an optimization problem with a common basic structure. In this framework, a case deletion diagnostic is proposed for a class of penalized calibration estimators including both design-based and model-based estimators. The diagnostic compares finite population parameter estimates and can be calculated from quantities related to the full data set. The resulting diagnostic is a function of the residual and leverage, as other diagnostics in regression models, and of the calibration weight, a singular feature in survey sampling. Moreover, a particular case, which includes the basic unit level model for small area estimation, is considered. Both a real and an artificial example are included to illustrate the diagnostic proposed. The results obtained clearly show that the proposed diagnostic depends on the calibration and soft-calibration variables, on the penalization term, as well as on the parameter to estimate.     

A practical Equivalent Electrical Circuit model for Proton Exchange Membrane Fuel Cell (PEMFC) systems

An alternative Equivalent Electrical Circuit for Proton Exchange Membrane Fuel Cells is modelled in this study. Both I–V characteristics and H₂ consumptions corresponding to generated power under load and no-load conditions are investigated. For this purpose, H₂ consumptions and I–V characteristics of three different sized PEMFCs are tested. There is a very good harmony between the model results and measured values (relative error %0.7, %6.4 and %2.5 for FC-A, FC-B and FC-C respectively). In the proposed model current passes only on parallel resistance and not on serial resistance at no-load condition. Thus, a FC with higher parallel resistance should be preferred. Another key output of this study is that based on the proposed model, performance comparison of FCs can be performed with the parameters defined in this work. Proposals made in this study can easily be used for performance analysis of FCs under for both steady state and transient analysis.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.