Short-time dynamics of percolation observables

We consider the critical short-time evolution of magnetic and droplet-percolation order parameters for the Ising model in two and three dimensions, through Monte Carlo simulations with the (local) heat-bath method. We find qualitatively different dynamic behaviors for the two types of order parameters. More precisely, we find that the percolation order parameter does not have a power-law behavior as encountered for the magnetization, but develops a scale (related to the relaxation time to equilibrium) in the Monte Carlo time. We argue that this difference is due to the difficulty in forming large clusters at the early stages of the evolution. Our results show that, although the descriptions in terms of magnetic and percolation order parameters may be equivalent in the equilibrium regime, greater care must be taken to interpret percolation observables at short times. In particular, this concerns the attempts to describe the dynamics of the deconfinement phase transition in QCD using cluster observables.

Short-time dynamics of colloidal suspensions in confined geometries

We analyze the short-time dynamical behavior of a colloidal suspension in a confined geometry. We analyze the relevant dynamical response of the solvent, and derive the temporal behavior of the velocity autocorrelation function, which exhibits an asymptotic negative algebraic decay. We are able to compare quantitatively with theoretical expressions, and analyze the effects of confinement on the diffusive behavior of the suspension.

Dynamic behavior of cluster observables for the 2d Ising model

We present results of our numerical study of the critical dynamics of percolation observables for the two-dimensional Ising model. We consider the (Monte Carlo) short-time evolution of the system with small initial magnetization and heat-bath dynamics. We find qualitatively different dynamic behaviors for the magnetization M and for Ω, the so-called strength of the percolating cluster, which is the order parameter of the percolation transition. More precisely, we obtain a (leading) exponential form for Ω as a function of the Monte Carlo time t, to be compared with the power-law increase encountered for M at short times. Our results suggest that, although the descriptions in terms of magnetic or percolation order parameters may be equivalent in the equilibrium regime, greater care must be taken to interpret percolation observables at short times.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales

This paper examines two hydrochemical time-series derived from stream samples taken in the Upper Hafren catchment, Plynlimon, Wales. One time-series comprises data collected at 7-hour intervals over 22 months (Neal et al., submitted, this issue), while the other is based on weekly sampling over 20 years. A subset of determinands: aluminium, calcium, chloride, conductivity, dissolved organic carbon, iron, nitrate, pH, silicon and sulphate are examined within a framework of non-stationary time-series analysis to identify determinand trends, seasonality and short-term dynamics. The results demonstrate that both long-term and high-frequency monitoring provide valuable and unique insights into the hydrochemistry of a catchment. The long-term data allowed analysis of long-termtrends, demonstrating continued increases in DOC concentrations accompanied by declining SO4 concentrations within the stream, and provided new insights into the changing amplitude and phase of the seasonality of the determinands such as DOC and Al. Additionally, these data proved invaluable for placing the short-term variability demonstrated within the high-frequency data within context. The 7-hour data highlighted complex diurnal cycles for NO3, Ca and Fe with cycles displaying changes in phase and amplitude on a seasonal basis. The high-frequency data also demonstrated the need to consider the impact that the time of sample collection can have on the summary statistics of the data and also that sampling during the hours of darkness provides additional hydrochemical information for determinands which exhibit pronounced diurnal variability. Moving forward, this research demonstrates the need for both long-term and high-frequency monitoring to facilitate a full and accurate understanding of catchment hydrochemical dynamics.

A study of the errors of the averaged models in the restricted three-body problem in a short time scale

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

Flavour retention during high temperature short time extrusion cooking process: a review

Research on the stability of flavours during high temperature extrusion cooking is reviewed. The important factors that affect flavour and aroma retention during the process of extrusion are illustrated. A substantial number of flavour volatiles which are incorporated prior to extrusion are normally lost during expansion, this is because of steam distillation. Therefore, a general practice has been to introduce a flavour mix after the extrusion process. This extra operation requires a binding agent (normally oil), and may also result in a non-uniform distribution of the flavour and low oxidative stability of the flavours exposed on the surface. Therefore, the importance of encapsulated flavours, particularly the beta -cyclodextrin-flavour complex, is highlighted in this paper.

Robotic manipulators with vibrations: short time fourier transform of fractional spectra

This paper analyzes the signals captured during impacts and vibrations of a mechanical manipulator. In order to acquire and study the signals an experimental setup is implemented. The signals are treated through signal processing tools such as the fast Fourier transform and the short time Fourier transform. The results show that the Fourier spectrum of several signals presents a non integer behavior. The experimental study provides valuable results that can assist in the design of a control system to deal with the unwanted effects of vibrations.

Using Typical Consumption Profiles to Stablish the Consumption Level for Short Time Periods

The current practices in the consumption metering by electricity utilities is currently largely based on monthly consumption reading. The consumption metering device is always calculating the cumulative consumption. Then, it is possible to calculate the difference between the actual and the previous consumption evaluation in order to estimate the monthly consumption. The power systems planning needs in many aspects to handle consumption data obtained for shorter periods, namely in the Demand Response programs planning. The work presented in this paper is based on the application of typical consumption profiles that are previously defined for a certain power system area. Such profiles are then used in order to estimate the 15 minutes consumption for a certain consumer or consumer type.

Firm Size and Short-Term Dynamics in Aggregate Entry and Exit

Much of the research on industry dynamics focuses on the interdependence between the sectorial rates of entry and exit. This paper argues that the size of firms and the reaction-adjustment period are important conditions missed in this literature. I illustrate the effects of this omission using data from the Spanish manufacturing industries between 1994 and 2001. Estimates from systems of equations models provide evidence of a conical revolving door phenomenon and of partial adjustments in the replacement-displacement of large firms. KEYWORDS: aggregation, industry dynamics, panel data, symmetry, simultaneity. JEL CLASSIFICATION: C33, C52, L60, L11

Influence of short time exposure to an insect growth regulator, hexaflumuron, on mortality and adult emergence of vector mosquitoes

Hexaflumuron, an insect growth regulator (IGR), was found to greatly affect the development of immatures and emergence of adults of three species of vector mosquitoes, Culex quinquefasciatus, Aedes aegypti and Anopheles stephensi, when larvae were subjected to short time exposure of < or = 1h. This IGR could completely prevent adult emergence even at a minimum exposure time of 10 min at 0.001, 0.01 and 0.1 mg/l. On treatment, larval and pupal mortality as well as varying degrees of morphogenetic abnormalities were induced in immatures and adults of the three species. Four weeks of control achieved in a slow moving sullage canal breeding Culex quinquefasciatus indicates that this IGR can be of use in such breeding habitats.

On the closed-form approximation of short-time random strike options

In this paper we propose a general technique to develop first and second order closed-form approximation formulas for short-time options withrandom strikes. Our method is based on Malliavin calculus techniques andallows us to obtain simple closed-form approximation formulas dependingon the derivative operator. The numerical analysis shows that these formulas are extremely accurate and improve some previous approaches ontwo-assets and three-assets spread options as Kirk's formula or the decomposition mehod presented in Alòs, Eydeland and Laurence (2011).

A Hull and White formula for a general stochastic volatility jump-diffusion model with applications to the study of the short-time behavior of the implied volatility

In this paper, generalizing results in Alòs, León and Vives (2007b), we see that the dependence of jumps in the volatility under a jump-diffusion stochastic volatility model, has no effect on the short-time behaviour of the at-the-money implied volatility skew, although the corresponding Hull and White formula depends on the jumps. Towards this end, we use Malliavin calculus techniques for Lévy processes based on Løkka (2004), Petrou (2006), and Solé, Utzet and Vives (2007).

On the short-time behavior of the implied volatility for jump-diffusion models with stochastic volatility

In this paper we use Malliavin calculus techniques to obtain an expression for the short-time behavior of the at-the-money implied volatility skew for a generalization of the Bates model, where the volatility does not need to be neither a difussion, nor a Markov process as the examples in section 7 show. This expression depends on the derivative of the volatility in the sense of Malliavin calculus.