Conditions for the Optimality of Exponential Smoothing Forecast Procedures

Exponential smoothing procedures, in particular those recommended by Brown are used extensively in many areas of economics, business and engineering. It is shown in this paper that: (i) Brown's forecasting procedures are optimal in terms of achieving minimum mean square error forecasts only if the underlying stochastic process is included in a limited subclass of ARIMA (p,d,q) processes. Hence, it is shown what assumptions are made when using these procedures. (ii) The implication of point (i) is that the users of Brown's procedures tacitly assume that the stochastic processes which occur in the real world are from the particular restricted subclass of ARIMA (p,d,q) processes. No reason can be found why these particular models should occur more frequently than others. (iii) It is further shown that even if a stochastic process which would lead to Brown's model occurred, the actual methods used for making the forecasts are clumsy and much simpler procedures can be employed

Learning Curves and p-charts for a preliminary estimation of asymptotic performances of a manufacturing process

This paper presents a method for a preliminary estimation of asymptotic performances of a manufacturing process based on the knowledge of its learning curve estimated during the setting up of p-chart. The main novelties of the method are the possibility of estimating the asymptotic variability of a process and providing a simple approach for evaluating the period of revision of process control limits. An application of the method to a real example taken from the literature is also provided

Implementation of Discrete Capability into the enhanced Multipoint Approximation Method for solving mixed integer-continuous optimization problems

Multipoint approximation method (MAM) focuses on the development of metamodels for the objective and constraint functions in solving a mid-range optimization problem within a trust region. To develop an optimization technique applicable to mixed integer-continuous design optimization problems in which the objective and constraint functions are computationally expensive and could be impossible to evaluate at some combinations of design variables, a simple and efficient algorithm, coordinate search, is implemented in the MAM. This discrete optimization capability is examined by the well established benchmark problem and its effectiveness is also evaluated as the discreteness interval for discrete design variables is increased from 0.2 to 1. Furthermore, an application to the optimization of a lattice composite fuselage structure where one of design variables (number of helical ribs) is integer is also presented to demonstrate the efficiency of this capability

Transcriptomic and proteomic profiling of maize embryos exposed to camptothecin

<p>Abstract</p> <p>Background</p> <p>Camptothecin is a plant alkaloid that specifically binds topoisomerase I, inhibiting its activity and inducing double stranded breaks in DNA, activating the cell responses to DNA damage and, in response to severe treatments, triggering cell death.</p> <p>Results</p> <p>Comparative transcriptomic and proteomic analyses of maize embryos that had been exposed to camptothecin were conducted. Under the conditions used in this study, camptothecin did not induce extensive degradation in the genomic DNA but induced the transcription of genes involved in DNA repair and repressed genes involved in cell division. Camptothecin also affected the accumulation of several proteins involved in the stress response and induced the activity of certain calcium-dependent nucleases. We also detected changes in the expression and accumulation of different genes and proteins involved in post-translational regulatory processes.</p> <p>Conclusions</p> <p>This study identified several genes and proteins that participate in DNA damage responses in plants. Some of them may be involved in general responses to stress, but others are candidate genes for specific involvement in DNA repair. Our results open a number of new avenues for researching and improving plant resistance to DNA injury.</p

Detection of nano scale thin films with polarized neutron reflectometry at the presence of smooth and rough interfaces

By knowing the phase and modules of the reflection coefficient in neutron reflectometry problems, a unique result for the scattering length density (SLD) of a thin film can be determined which will lead to the exact determination of type and thickness of the film. In the past decade, several methods have been worked out to resolve the phase problem such as dwell time method, reference layer method and variation of surroundings, among which the reference method and variation of surroundings by using a magnetic substrate and polarized neutrons is of the most applicability. All of these methods are based on the solution of Schrodinger equation for a discontinuous and step-like potential at each interface. As in real sample there are some smearing and roughness at boundaries, consideration of smoothness and roughness of interfaces would affect the final output result. In this paper, we have investigated the effects of smoothness of interfaces on determination of the phase of reflection as well as the retrieval process of the SLD, by using a smooth varying function (Eckart potential). The effects of roughness of interfaces on the same parameters, have also been investigated by random variation of the interface around it mean position

Case study:shipping trend estimation and prediction via multiscale variance stabilisation

<p>Shipping and shipping services are a key industry of great importance to the economy of Cyprus and the wider European Union. Assessment, management and future steering of the industry, and its associated economy, is carried out by a range of organisations and is of direct interest to a number of stakeholders. This article presents an analysis of shipping credit flow data: an important and archetypal series whose analysis is hampered by rapid changes of variance. Our analysis uses the recently developed data-driven Haar–Fisz transformation that enables accurate trend estimation and successful prediction in these kinds of situation. Our trend estimation is augmented by bootstrap confidence bands, new in this context. The good performance of the data-driven Haar–Fisz transform contrasts with the poor performance exhibited by popular and established variance stabilisation alternatives: the Box–Cox, logarithm and square root transformations.</p