Atomic ordering in nano-layered FePt

Monte Carlo simulation of chemical ordering kinetics in nano-layered L1₀ AB binary intermetallics was performed. The study addressed FePt thin layers considered as a material for ultra-high-density magnetic storage media and revealed metastability of the L1₀ c-variant superstructure with monoatomic planes parallel to the surface and off-plane easy magnetization. The layers, originally perfectly ordered in a c-variant of the L1₀ superstructure, showed homogeneous disordering running in parallel with a spontaneous re-orientation of the monoatomic planes leading to a mosaic microstructure composed of a- and b-L1₀-variant domains. The domains nucleated heterogeneously on the surface of the layer and grew discontinuously inwards its volume. Finally, the domains relaxed towards an equilibrium microstructure of the system. Two “atomistic-scale” processes: (i) homogeneous disordering and (ii) nucleation of the a- and b-L1₀-variant domains showed characteristic time scales. The same was observed for the domain microstructure relaxation. The discontinuous domain growth showed no definite driving force and proceeded due to thermal fluctuations. The above complex structural evolution has recently been observed experimentally in epitaxially deposited thin films of FePt.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

NON-GAUSSIAN STATISTICS OF OIL PRICING TIME-SERIES: A CASE STUDY

The stochastic nature of oil price fluctuations is investigated over a twelve-year period, borrowing feedback from an existing database (USA Energy Information Administration database, available online). We evaluate the scaling exponents of the fluctuations by employing different statistical analysis methods, namely rescaled range analysis (R/S), scale windowed variance analysis (SWV) and the generalized Hurst exponent (GH) method. Relying on the scaling exponents obtained, we apply a rescaling procedure to investigate the complex characteristics of the probability density functions (PDFs) dominating oil price fluctuations. It is found that PDFs exhibit scale invariance, and in fact collapse onto a single curve when increments are measured over microscales (typically less than 30 days). The time evolution of the distributions is well fitted by a Levy-type stable distribution. The relevance of a Levy distribution is made plausible by a simple model of nonlinear transfer. Our results also exhibit a degree of multifractality as the PDFs change and converge toward to a Gaussian distribution at the macroscales.

The impact of a PCR assay for candidemia on antifungal drug prescribing in critical care: an interrupted time series pilot study.

Objective To evaluate the feasibility of conducting a definitive study to assess the impact of introducing a rapid PCR-based test for candidemia on antifungal drug prescribing. Method Prospective, single centre, interrupted time series study consisting of three periods of six months' duration. The assay was available during the second period, during which the PCR assay was available for routine use by physicians Monday–Friday with guaranteed 24-h turnaround time. For each period total antifungal drug use, expressed as treatment-days, was recorded and an adjustment was made to exclude estimated use for proven candidemia. Also, during the intervention period, antifungal prescribing decisions for up to 72 h after each PCR result became available were recorded as either concordant or discordant with that result. Results While overall antifungal use remained relatively stable throughout, after adjustment for candidemia, there was a 38% reduction in use following introduction of the PCR test; however, this was nonsignificant at the 95% level. During the intervention period overall concordance between the PCR result and prescribing decisions was 84%. Conclusions The PCR assay for candidemia was requested, prescribing decisions were generally concordant with the results produced and there was an apparent decrease in antifungal prescription, although this was sustained even after withdrawal of the intervention; these findings should be more thoroughly evaluated in a larger trial.

Pointwise universal trigonometric series

A series $S_a=\sum\limits_{n=-\infty}^\infty a_nz^n$ is called a {\it pointwise universal trigonometric series} if for any $f\in C(\T)$, there exists a strictly increasing sequence $\{n_k\}_{k\in\N}$ of positive integers such that $\sum\limits_{j=-n_k}^{n_k} a_jz^j$ converges to $f(z)$ pointwise on $\T$. We find growth conditions on coefficients allowing and forbidding the existence of a pointwise universal trigonometric series. For instance, if $|a_n|=O(\e^{\,|n|\ln^{-1-\epsilon}\!|n|})$ as $|n|\to\infty$ for some $\epsilon>0$, then the series $S_a$ can not be pointwise universal. On the other hand, there exists a pointwise universal trigonometric series $S_a$ with $|a_n|=O(\e^{\,|n|\ln^{-1}\!|n|})$ as $|n|\to\infty$.