Mechanical mode dependence of bolometric back-action in an AFM microlever

Two back action (BA) processes generated by an optical cavity based detection device can deeply transform the dynamical behavior of an AFM microlever: the photothermal force or the radiation pressure. Whereas noise damping or amplifying depends on optical cavity response for radiation pressure BA, we present experimental results carried out under vacuum and at room temperature on the photothermal BA process which appears to be more complex. We show for the first time that it can simultaneously act on two vibration modes in opposite direction: noise on one mode is amplified whereas it is damped on another mode. Basic modeling of photothermal BA shows that dynamical effect on mechanical mode is laser spot position dependent with respect to mode shape. This analysis accounts for opposite behaviors of different modes as observed

Combining Optimization and Randomization Approaches for the Design of Clinical Trials

t Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and only one, of the alternative treatment arms. The main requisite is that the profiles in the alternative arms remain similar with respect to some relevant patients’ attributes (age, gender, disease, symptom severity and others). We perform numerical experiments based on a real case study and discuss how to conveniently set up perturbation parameters, in order to yield a suitable balance between optimality – the similarity among the relative frequencies of patients in the several categories for both arms, and decoupling – the absence of a tendency to allocate each pair of patients consistently to the same arm

Solar cycle variations in the growth and decay of sunspot groups

We analysed the combined Greenwich (1874-1976) and Solar Optical Observatories Network (1977-2011) data on sunspot groups. The daily rate of change of the area of a spot group is computed using the differences between the epochs of the spot group observation on any two consecutive days during its life-time and between the corrected whole spot areas of the spot group at these epochs. Positive/negative value of the daily rate of change of the area of a spot group represents the growth/decay rate of the spot group. We found that the total amounts of growth and decay of spot groups whose life times > or = 2 days in a given time interval (say one-year) well correlate to the amount of activity in the same interval. We have also found that there exists a reasonably good correlation and an approximate linear relationship between the logarithmic values of the decay rate and area of the spot group at the first day of the corresponding consecutive days, largely suggesting that a large/small area (magnetic flux) decreases in a faster/slower rate. There exists a long-term variation (about 90-year) in the slope of the linear relationship. The solar cycle variation in the decay of spot groups may have a strong relationship with the corresponding variations in solar energetic phenomena such as solar flare activity. The decay of spot groups may also substantially contribute to the coherence relationship between the total solar irradiance and the solar activity variations.Comment: 12 pages, 7 figures, Accepted for publication in Astrophysics & Space Science. arXiv admin note: substantial text overlap with arXiv:1105.106

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure

Cointegration analysis with state space models

Abstract: This paper presents and exemplifies results developed for cointegration analysis with state space models by Bauer and Wagner in a series of papers. Unit root processes, cointegration and polynomial cointegration are defined. Based upon these definitions the major part of the paper discusses how state space models, which are equivalent to VARMA models, can be fruitfully employed for cointegration analysis. By means of detailing the cases most relevant for empirical applications, the I(1), MFI(1) and I(2) cases, a canonical representation is developed and thereafter some available statistical results are briefly mentioned.