We use multifractal detrended fluctuation analysis (MF-DFA), to See query 1 study sunspot number fluctuations. The result of the MF-DFA shows that there are three crossover timescales in the fluctuation function. We discuss how the existence of the crossover timescales is related to a sinusoidal trend. Using Fourier detrended fluctuation analysis, the sinusoidal trend is eliminated. The Hurst exponent of the time series without the sinusoidal trend is 0.12 ± 0.01. Also we find that these fluctuations have multifractal nature. Comparing the MF-DFA results for the remaining data set to those for shuffled and surrogate series, we conclude that its multifractal nature is almost entirely due to long range correlations.

We introduce a directionality index for a time series based on a comparison of neighboring values. It can distinguish unidirectional from bidirectional coupling, as well as reveal and quantify asymmetry in bidirectional coupling. It is tested on a numerical model of coupled van der Pol oscillators, and applied to cardiorespiratory data from healthy subjects. There is no need for preprocessing and fine-tuning the parameters, which makes the method very simple, computationally fast and robust.

We investigate Markov property of rough surfaces. Using stochastic analysis we characterize the complexity of the surface roughness by means of a Fokker-Planck or Langevin equation. The obtained Langevin equation enables us to regenerate surfaces with similar statistical properties compared with the observed morphology by atomic force microscopy.Studying the growth, formation and morphology of interfaces has been one of the recent interesting fields of study because of its high technical and rich theoretical advantages [1]. One of the main problems in this area is the scaling behaviour of the moments of height difference ∆h = h(x 1 ) − h(x 2 ) and the evolution of the probability density function (PDF) of ∆h, i.e. P (∆h, ∆x) in terms of the length scale ∆x. Recently Friedrich and Peinke have been able to obtain a Fokker-Planck equation describing the evolution of the probability distribution function in terms of the length scale, by analyzing some stochastic phenomena, such as turbulent free jet, etc. [2][3][4]. They noticed that the conditional probability density of field increments (velocity field, etc.) satisfies the ChapmanKolmogorov equation. Mathematically this is a necessary condition for the fluctuating data to be a Markovian process in the length scales [5].In this letter using the method proposed by Friedrich and Peinke, we measure the Kramers-Moyal's (KM) coefficients for the fluctuating fields ∆h and h(x) of a deposited copper film. It is shown that the first and second KM's coefficients have well-defined values, while the third and fourth order coefficients tend to zero. Therefore, by addressing the implications dictated by the theorem [5] a Fokker-Planck evolution operator has been found. The Fokker-Planck equation for P (∆h, ∆x) is used to give information on changing the shape of PDF as a function of the length scale ∆x. By using this strategy the information of the observed intermittency of the height fluctuation is verified [6]. The first and second KM's coefficients for the fluctuations of h(x), enables us to write a Langevin equation for the evolution of height with respect to x. Using this equation we regenerate the surface with similar statistical properties, compared with the observed morphology by atomic force microscopy. The regeneration of a surface is known as the inverse method. There are other inverse method approaches introduced in the literature [13]. In the previous attempts, to regenerate the surface, an evolution equation for h(x, t) vs t has been evaluated. Here we do this by an evolution equation for h(x) vs x, for a certain time.For this purpose, a copper film was deposited on a polished Si(100) substrate by the resistive evaporation method in a high vacuum chamber. The pressure during evaporation was 10 −6 Torr. The thickness of the growing films was measured in situ by a quartz crystal thickness monitor. We performed all depositions at room temperature, with a deposition rate about 20 − 30nm/min. The substrate temperature was determined using a chromel/alumel thermocouple ...

In this study, thermo-mechanical vibration analyzes of functionally graded (FG) beams made of porous material subjected various thermal loadings are carried out by presenting a Navier type solution and employing a semi analytical differential transform method (DTM) for the first time. Three types of thermal loadings, namely, uniform, linear and nonlinear temperature rises through the thickness direction are considered. Thermo-mechanical material properties of FGM beam are supposed to vary continuously along the thickness direction according to the powerlaw form, which is modified to approximate the material properties with the porosity phases. The material properties of FG porous beam are assumed to be temperature-dependent. The governing equations of motion are derived through Hamilton's principle and they are solved applying DTM. According to the numerical results, it is revealed that the proposed modeling and semi analytical approach can provide accurate frequency results of the FG beams as compared to analytical results and also some cases in the literature. The detailed mathematical derivations are presented and numerical investigations are performed while the emphasis is placed on investigating the effect of the several parameters such as thermal effect, porosity volume fraction, material distribution profile, mode number and boundary conditions on the natural frequencies of the temperature-dependent FG beams in detail. It is explicitly shown that the vibration behaviour of porous FGM beams is significantly influenced by these effects. Numerical results are presented to serve as benchmarks for future analyses of FGM beams with porosity phases.

We describe a general method for analyzing a nonstationary stochastic process X(t) which, unlike many of the previous analysis methods, does not require X(t) to have any scaling feature. The method is used to study the fluctuations in the daily price of oil. It is shown that the returns time series, y(t)=ln[X(t+1)X(t)] , is a stationary and Markov process, characterized by a Markov time scale t_{M} . The coefficients of the Kramers-Moyal expansion for the probability density function P(y,tmid R:y_{0},t_{0}) are computed. P(y,tmid R:,y_{0},t_{0}) satisfies a Fokker-Planck equation, which is equivalent to a Langevin equation for y(t) that provides quantitative predictions for the oil price over times that are of the order of t_{M}. Also studied is the average frequency of positive-slope crossings, nu_{alpha};{+}=P(y_{i}>alpha,y_{i-1}

Despite the effectiveness of the standard regimen of magnesium sulfate in the treatment and prevention of eclamptic seizures, it can not provide the proposed therapeutic level of magnesium in all patients. With respect to the lack of correlation between ionized and total magnesium, further studies are necessary to investigate the superiority of measurement of ionized, rather than total magnesium, for titration of therapeutic magnesium sulfate infusion.

Multi-target drugs against particular multiple targets get better protection, resistance profiles and curative influence by cooperative rules of a key beneficial target with resistance behavior and compensatory elements. Computational techniques can assist us in the efforts to design novel drugs (ligands) with a preferred bioactivity outline and alternative bioactive molecules at an early stage. A number of in silico methods have been explored extensively in order to facilitate the investigation of individual target agents and to propose a selective drug. A different, progressively more significant field which is used to predict the bioactivity of chemical compounds is the data mining method. Some of the previously mentioned methods have been investigated for multi-target drug design (MTDD) to find drug leads interact simultaneously with multiple targets. Several cheminformatics methods and structure-based approaches try to extract information from units working cooperatively in a biomolecular system to fulfill their task. To dominate the difficulties of the experimental specification of ligand-target structures, rational methods, namely molecular docking, SAR and QSAR are vital substitutes to obtain knowledge for each structure in atomic insight. These procedures are logically successful for the prediction of binding affinity and have shown promising potential in facilitating MTDD. Here, we review some of the important features of the multi-target therapeutics discoveries using the computational approach, highlighting the SAR, QSAR, docking and pharmacophore methods to discover interactions between drug-target that could be leveraged for curative benefits. A summary of each, followed by examples of its applications in drug design has been provided. Computational efficiency of each method has been represented according to its main strengths and limitations.

Abstract. We describe a method for analyzing the stochasticity in non-stationary data for the beat-tobeat fluctuations in the heart rates of healthy subjects, as well as those with congestive heart failure. The method analyzes the return time series of the data as a Markov process, and computes the Markov time scale, i.e., the time scale over which the data are a Markov process. We also construct an effective stochastic continuum equation for the return series. We show that the drift and diffusion coefficients, as well as the amplitude of the return time series for healthy subjects are distinct from those with CHF. Thus, the method may potentially provide a diagnostic tool for distinguishing healthy subjects from those with congestive heart failure, as it can distinguish small differences between the data for the two classes of subjects in terms of well-defined and physically-motivated quantities.