Forecasting industrial production with linear, nonlinear, and structural change models

We compare the forecasting performance of linear autoregressive models, autoregressive models with structural breaks, self-exciting threshold autoregressive models, and Markov switching autoregressive models in terms of point, interval, and density forecasts for h-month growth rates of industrial production of the G7 countries, for the period January 1960-December 2000. The results of point forecast evaluation tests support the established notion in the forecasting literature on the favorable performance of the linear AR model. By contrast, the Markov switching models render more accurate interval and density forecasts than the other models, including the linear AR model. This encouraging finding supports the idea that non-linear models may outperform linear competitors in terms of describing the uncertainty around future realizations of a time series.nonlinearity;structural change;density forecasts;forecast evaluation tests;interval forecasts

Comment on "Magnetic field effects on neutron diffraction in the antiferromagnetic phase of UPt3"

Moreno and Sauls [Phys. Rev. B 63, 024419 (2000)] have recently tried to reanalyze earlier neutron scattering studies of the antiferromagnetic order in UPt3 with a magnetic field applied in the basal plane. In their calculation of the magnetic Bragg peak intensities, they perform an average over different magnetic structures belonging to distinct symmetry representations. This is incorrect. In addition, they have mistaken the magnetic field direction in one of the experiments, hence invalidating their conclusions concerning the experimental results.Comment: Revised 5 June 2001: Added group theory analysis and modified discussion of S and K domain

A linear programming approach to Markov reward error bounds for queueing networks

In this paper, we present a numerical framework for constructing bounds on stationary performance measures of random walks in the positive orthant using the Markov reward approach. These bounds are established in terms of stationary performance measures of a perturbed random walk whose stationary distribution is known explicitly. We consider random walks in an arbitrary number of dimensions and with a transition probability structure that is defined on an arbitrary partition of the positive orthant. Within each component of this partition the transition probabilities are homogeneous. This enables us to model queueing networks with, for instance, break-downs and finite buffers. The main contribution of this paper is that we generalize the linear programming approach of [1] to this class of models

Single-mask thermal displacement sensor in MEMS

In this work we describe a one degree-of-freedom microelectromechanical thermal\ud displacement sensor integrated with an actuated stage. The system was fabricated in the device layer of a silicon-on-insulator wafer using a single-mask process. The sensor is based on the temperature dependent electrical resistivity of silicon and the heat transfer by conduction through a thin layer of air. On a measurement range of 50 μm and using a measurement bandwidth of 30 Hz, the 1-sigma noise corresponds to 3.47 nm. The power consumption of the sensor is 209 mW, almost completely independent of stage position. The drift of the sensor over a measurement period of 32 hours was 32 nm

Forecasting industrial production with linear, nonlinear, and structural change models

We compare the forecasting performance of linear autoregressive models, autoregressive models with structural breaks, self-exciting threshold autoregressive models, and Markov switching autoregressive models in terms of point, interval, and density forecasts for h-month growth rates of industrial production of the G7 countries, for the period January 1960-December 2000. The results of point forecast evaluation tests support the established notion in the forecasting literature on the favorable performance of the linear AR model. By contrast, the Markov switching models render more accurate interval and density forecasts than the other models, including the linear AR model. This encouraging finding supports the idea that non-linear models may outperform linear competitors in terms of describing the uncertainty around future realizations of a time series

Reply to ``Comment on `Magnetic field effects on neutron diffraction in the antiferromagnetic phase of UPt3UPt_3'''

Fak, van Dijk and Wills (FDW) question our interpretation of elastic neutron-scattering experiments in the antiferromagnetic phase of UPt_3. They state that our analysis is incorrect because we average over magnetic structures that are disallowed by symmetry. We disagree with FDW and reply to their criticism. FDW also point out that we have mistaken the magnetic field direction in the experiment reported by N. H. van Dijk et al. [Phys. Rev. B 58, 3186 (1998)]. We correct this error and note that our previous conclusion is also valid for the correct field orientation.Comment: 3 page

Царський і республіканський Рим в антикознавчих дослідженнях М.П. Драгоманова

У статті розглянуто особливості вивчення та рецепції римської історії VIII–I ст. до н. е. в антикознавчих студіях М. Драгоманова. Зроблено висновок, що дослідження М. Драгоманова з історії Риму вплинули на формування наукових і суспільно-політичних поглядів вченого, а також Лесі Українки та І. Франка.In the article peculiarities of study and reception of Roman history VIII–I centuries B.C. in M. Drahomanov’s classical researches are highlighted. The author concludes that M. Drahomanov’s Roman classical studies influenced on formation of scientific and political views of M. Drahomanov, Lesya Ukrainka, I. Franko

Analysis of the dosage controversy in recess-resect and Faden surgery with the Robinson computer model of eye movements

In recess-resect surgery, the dosage depends on the preoperative angle of squint and on the ratio between squint-angle reduction and dosage that the surgeon has found in previous surgery. Recommendations pertaining to this ratio vary widely among authors. Some say a recession does more than a resection, while others believe the opposite is true. Finally, most find a lower ratio at smaller preoperative angles of squint. We investigated the matter, using our modified version of the Robinson computer model of eye movements. We calculated the amounts of surgery needed to reduce 10, 15, 20, 25 and 30 degree angles of squint to zero. The increase of the ratio at large angles of squint was indeed predicted by the model. The decrease at small angles of squint, however, was not predicted by the model. We found it impossible to model the decrease of the ratio at small preoperative angles of squint. The ratios for recess and resect surgery were approximately similar. We present an inventory of the possible causes of the discrepancies. In addition, we calculated the effects of Faden surgery and found that the predictions of the computer model correspond closely to reality

STOX1: Key Player in Trophoblast Dysfunction Underlying Early Onset Preeclampsia with Growth Retardation

Currently, only two preeclampsia susceptibility genes (ACVR2A, STOX1) have been identified within confirmed regions with significant genome-wide linkage, although many genetic screens in multiple populations have been performed. In this paper, we focus on the STOX1 gene. The epigenetic status of this gene is discussed explaining the maternal transmission of the STOX1 susceptibility allele observed in preeclamptic families. The known upstream regulation and downstream effector genes of the transcription factor STOX1 are described. Finally, we propose a model in which we combine the cell type-specific and allele-specific effects of STOX1. This includes intrinsic effects (differential CpG island methylation) and extrinsic effects (regulation of effector genes)

Sampling free iterative PCE filter for state and parameter estimation of nonlinear dynamical systems

We present a novel filter for state and parameter estimation in non-linear dynamical systems, based on a generalised Kalman filter formulation. To achieve a sampling-free implementation, polynomial chaos expansion (PCE) and a Galerkin projection method are utilized for the propagation of uncertainties through the system dynamics. The non-linear dynamics of the system are then linearised by a sequence of Gauss-Newton iterations in combination with linear Kalman updates. Additionally, we introduce a new square root implementation of the PCE-based filter. The proposed filter is evaluated on the Lorenz-63 and Lorenz-84 models for the task of simultaneous state and parameter estimation and is compared with two related approaches. Finally, the computational complexity of our square-root implementation is compared against two existing square root approaches.</p