Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.     

Meßtechnik zur Charakterisierung von Gas/Flüssigkeits-Reaktoren

Measuring techniques for characterizing gas/liquid reactors . The article considers measuring techniques for the characterization of the phases in gas/liquid reactors. These include the velocity profile and turbulence in the continuous phase as well as holdup, size, and velocity of the dispersed phase. Also, methods for the determination of the specific interfacial area and the volumetric mass transfer coefficients are discussed. Measuring methods have made enormous advances in recent years. In spite of this, there is a lack of reliable methods, especially for use during reactions without disturbing the state of the reactor. It is suspected that probe technique will gradually fall into disuse and more and more attention will be focussed on contractless methods. This calls for further development of ultrasonic methods and methods relying on emission of light.     

It takes two to mimic: Behavioral consequences of self-construals.

The present studies demonstrated the moderation of self-construal orientation on mimicry. Recent research has indicated that an interdependent self-construal is associated with assimilation of the other to the self whereas an independent self-construal is associated with minimizing the influence of others on the self (H. R. Markus & S. Kitayama, 1991; D. Stapel & W. Koomen, 2001). Therefore, the authors hypothesized that an interdependent self-construal would be associated with more mimicry than an independent self-construal. When self-construal orientations were experimentally primed. as in Studies 1 and 2, independent self-construals produced less nonconscious mimicry than interdependent self-construals. When self-construals were examined as cultural differences with either a chronically dominant independent (Americans) or interdependent (Japanese) construal of the self, these results were replicated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Critical Shear Stress of Bimodal Sediment in Sand-Gravel Rivers

A new model for the critical shear stress and the transport of graded sediment is presented. The model is based on the size distribution of the bed surface and can be used to compute sediment transport rates in numerical simulations with an active layer model. This model makes a distinction between unimodal and bimodal sediments. It is assumed that all size fractions of unimodal sediments have the same critical shear stress while there is selective transport for the gravel fractions of bimodal sediments. A recently published laboratory transport data set is used to calibrate our model.     

Treatment of essential arterial hypertension with hypercholesterolemia. Effects of an alpha-adrenergic blocker and an inhibitor of the angiotensin converting enzyme

BACKGROUND: Hypertension and hypercholesterolemia are frequently associated with this leading to considerable cardiovascular risk. METHODS: An open parallel randomized study was performed in which the effects of doxazosin, an alpha-adrenergic blocker and enalapril, an inhibitor of the angiotensin converting enzyme were compared in 70 patients with essential high blood pressure and plasma cholesterol levels greater than 240 mg/dl. Following 2-4 weeks of placebo administration the patients were randomly treated with one of the two drugs. When required doses were increased and hydrochlorothiazide added until blood pressure lower than 160/95 mmHg was achieved. After this period the patients were observed for a minimum of 8 weeks. The mean length of the study was of 22 weeks. RESULTS: Both drugs significantly reduced blood pressure without modifying cardiac frequency. Doxazosin tended to favorably modify the lipid profile of the plasma while enalapril significantly reduced the levels of cholesterol, lipids and high density lipoproteins (HDL). Upon termination of the study the total HDL/cholesterol index increased 8.6% in those treated with doxazosin and decreased 5.5% in those receiving enalapril (p < 0.05). CONCLUSIONS: Although doxazosin and enalapril are potent antihypertensive drugs, the effects on plasma lipid obtained with doxazosin indicate that a reduction in cardiovascular risk was achieved with this drug in the patients included in this study.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

Incidence of Lightspeed separation and the potential for bypassing

It is important for endodontic instruments to have a low fracture rate. If a fracture does occur, it would be desirable to have the ability to bypass the broken segment and complete the root canal treatment. One hundred sixty-two root canals in 52 maxillary and mandibular first molars were cleaned and shaped with Lightspeed instruments by three endodontists in their private practices. The canals were instrumented using the technique recommended by the manufacturer. All canals were instrumented to at least a size 45 at the working length. Six instruments separated during treatment. All six had been used more times than recommended by the manufacturer. Five of the six were easily bypassed and treatment completed.