Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

The present work reports the development of nonlinear time series prediction method of genetic algorithm(GA) with singular spectrum analysis(SSA) for forecasting the surface wind of a point station in the South China Sea(SCS) with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days(longer for some point station) in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.     

Unsupervised neural networks for solving Troesch’s problem

In this study, stochastic computational intelligence techniques are presented for the solution of Troesch’s boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm(GA) and pattern search(PS) techniques are used as the global search methods and the interior point method(IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM(GA-IPM) and PS hybridized with IPM(PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs.     

基于LIBS和遗传算法的畜禽粪便中钙含量研究

为实现畜禽粪便高效、安全资源化利用,快速、准确定量表征分析畜禽粪便中钙(Ca)元素含量具有重要意义。以我国畜禽养殖比较集中地区的代表性畜禽粪便样本为对象,探讨了LIBS快速、定量分析其Ca元素含量模型构建和通过GA筛选变量优化模型的可行性。采集光谱的仪器参数设置如下：采集点数、激光能量、压制样压力、光斑大小和延迟时间分别为80个,15%,20 T,400 μm和1 μs。建模结果表明：采用基于Ca元素特征波长所建三种线性模型精度较低;基于全谱的PLS所建模型中,R2_v和RPD分别为0.85和2.13;选用化学计量学方法-遗传算法进行变量筛选可显著减少建模变量信息,提高建模效率,经GA优选所得12个变量所建PLS模型中R2_v和RPD为0.90和3.04,具有较高精度和实际应用价值。研究表明,将激光诱导击穿光谱技术和遗传算法相结合可定量分析畜禽粪便中Ca元素含量。     

Respiratory noise correction using phase information

Respiratory noise is a confounding factor in functional magnetic resonance imaging (MRI) data analysis. A novel method called Respiratory noise Correction using Phase information is proposed to retrospectively correct for the respiratory noise in functional MRI (fMRI) time series. It is demonstrated that the respiratory movement and the phase of functional MRI images are highly correlated in time. The signal fluctuation due to respiratory movements can be effectively estimated from the phase variation and removed from the functional MRI time series using a Wiener filtering technique. In our experiments, this new method is compared with RETROICOR, which requires recording respiration signal simultaneously in an fMRI experiment. The two techniques show comparable performance with respect to the respiratory noise correction for fMRI time series. However, this technique is more advantageous because there is no need for monitoring the subjects’ respiration or changing functional MRI protocols. This technique is also potentially useful for correcting respiratory noise from abnormal breathing or when the respiration is not periodic.     

Monitoring deprotonation of gallic acid by Raman spectroscopy

Gallic acid (GA) is involved in many biochemical processes and reactions and is of great importance in environment thanks to its antioxidant and pro‐oxidant properties. We present in this paper a comprehensive study of GA deprotonation by means of Raman spectroscopy. In the Raman spectra, we identified the characteristic bands that were sensitive to the five pH‐dependent GA forms. From these results, we extracted the GA pKa values that are consistent with the reference values. These results permit the monitoring of GA forms for applications. Copyright © 2015 John Wiley & Sons, Ltd.     

Time series-based bifurcation diagram reconstruction

We consider the problem of reconstructing bifurcation diagrams (BDs) of maps using time series. This study goes along the same line of ideas presented by Tokunaga et al. [Physica D 79 (1994) 348] and Tokuda et al. [Physica D 95 (1996) 380]. The aim is to reconstruct the BD of a dynamical system without the knowledge of its functional form and its dependence on the parameters. Instead, time series at different parameter values, assumed to be available, are used. A three-layer fully-connected neural network is employed in the approximation of the map. The task of the network is to learn the dynamics of the system as function of the parameters from the available time series. We determine a class of maps for which one can always find a linear subspace in the weight space of the network where the network’s bifurcation structure is qualitatively the same as the bifurcation structure of the map. We discuss a scheme in locating this subspace using the time series. We further discuss how to recognize time series generated by this class of maps. Finally, we propose an algorithm in reconstructing the BDs of this class of maps using predictor functions obtained by neural network. This algorithm is flexible so that other classes of predictors, apart from neural networks, can be used in the reconstruction.     

Pathological tremors: Deterministic chaos or nonlinear stochastic oscillators?

Pathological tremors exhibit a nonlinear oscillation that is not strictly periodic. We investigate whether the deviation from periodicity is due to nonlinear deterministic chaotic dynamics or due to nonlinear stochastic dynamics. To do so, we apply various methods from linear and nonlinear time series analysis to tremor time series. The results of the different methods suggest that the considered types of pathological tremors represent nonlinear stochastic second order processes. Finally, we evaluate whether two earlier proposed features capturing nonlinear effects in the time series allow for a discrimination between two pathological forms of tremor for a much larger sample of time series than previously investigated. (c) 2000 American Institute of Physics.     

Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets

A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions.     

Investigating directed influences between activated brain areas in a motor-response task using fMRI

Localization of cognitive processes is a strength of functional neuroimaging. However, information about functional interactions between brain areas is crucial for a deeper understanding of brain function. We applied vector autoregressive modeling in the context of Granger causality as a method to analyze directed connectivity in a standard event-related fMRI study using a simple auditory-motor paradigm. The basic idea is to use temporal information in stochastic time series of a brain region in order to predict signal time courses in other brain regions. Thus, we predicted that the method should demonstrate causal influence of the auditory cortex and the supplementary motor area (SMA) on primary motor cortex. Eleven right-handed healthy female subjects were instructed to press a ball with either their left or their right hand when hearing the command "left" or "right" in the scanner. Influence to the left motor cortex was found from bilateral auditory cortex as well as from the SMA in 9 of 11 subjects. Granger causality to the right motor cortex existed from bilateral auditory cortex in 5 and from SMA in 6 subjects. Granger causality to the SMA existed from right auditory cortex in 7 subjects and from left auditory cortex in 8 subjects. Our findings in a simple task show that even under suboptimal circumstances (a relatively long TR of 2440 ms), Granger causality can be a useful tool to explore effective connectivity. Temporally optimized scanning should increase that potential.     

The Gravitating Perfect Fluid-Scalar Field Equations: Quintessence and Tachyonic Matter

The system consisting of a self gravitating perfect fluid and scalar field is considered in detail. The scalar fields considered are the quintessence and tachyonic forms which have important application in cosmology. Mathematical properties of the general system of equations are studied including the algebraic and differential identities as well as the eigenvalue structure. The Cauchy problem for both quintessence and the tachyon is presented. We discuss the initial constraint equations which must be satisfied by the initial data. A Cauchy evolution scheme is presented in the form of a Taylor series about the Cauchy surface. Finally, a simple numerical example is provided to illustrate this scheme.     

基于多算法优化SVM的粗糙面参数反演

支持向量机（SVM）是粗糙面参数反演中常用的一种反演算法,SVM反演中的惩罚参数C和核函数参数G对反演结果精度的影响较大,若参数取值不当,会使模型产生"过学习"或者"欠学习"的现象,从而降低预测精度.给出几种SVM参数C和参数G的优化算法,如K折交叉验证（K-CV）、遗传算法（GA）和粒子群算法（PSO）,并在此基础上提出一种基于K-CV和GA改进的PSO算法（GA-CV-PSO）.利用矩量法（MoM）获得的粗糙面后向散射系数构造训练集和测试集,通过不同参数反演的仿真结果对比不同优化算法的反演精度和计算时间,表明GA-CV-PSO算法克服了单一优化算法的缺陷,具有更精确的反演精度和更强的泛化能力.     

Review of scattering and extinction cross-sections, damping factors, and resonance frequencies of a spherical gas bubble

Perhaps the most familiar concepts when discussing acoustic scattering by bubbles are the resonance frequency for bubble pulsation, the bubbles' damping, and their scattering and extinction cross-sections, all of which are used routinely in oceanography, sonochemistry, and biomedicine. The apparent simplicity of these concepts is illusory: there exist multiple, sometimes contradictory definitions for their components. This paper reviews expressions and definitions in the literature for acoustical cross-sections, resonance frequencies, and damping factors of a spherically pulsating gas bubble in an infinite liquid medium, deriving two expressions for "resonance frequency" that are compared and reconciled with two others from the reviewed literature. In order to prevent errors, care is needed by researchers when combining results from different publications that might have used internally correct but mutually inconsistent definitions. Expressions are presented for acoustical cross-sections associated with forced pulsations damped by liquid shear and (oft-neglected) bulk or dilatational viscosities, gas thermal diffusivity, and acoustic re-radiation. The concept of a dimensionless "damping coefficient" is unsuitable for radiation damping because different cross-sections would require different functional forms for this parameter. Instead, terms based on the ratio of bubble radius to acoustic wavelength are included explicitly in the cross-sections where needed.     

A two-compartment gel phantom for optimization and quality assurance in clinical BOLD fMRI

Clinical applications of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) depend heavily on robust paradigms, imaging methods and analysis procedures. In this work, as a means to optimize and perform quality assurance of the entire imaging and analysis chain, a phantom that provides a well known and reproducible signal change similar to a block type fMRI experiment is presented. It consists of two gel compartments with slightly different T2 that dynamically enter and leave the imaged volume. The homogeneous gel in combination with a cylindrical geometry results in a well-defined T*2 difference causing a signal difference between the two compartments in T*2-weighted MR images. From time series data obtained with the phantom, maps of percent signal change (PSC) and t-values are calculated. As an example of image parameter optimisation, the phantom is demonstrated to be useful for accurate determination of the influence of echo time (TE) on BOLD fMRI results, taking the t-value as a measure of sensitivity. In addition, the phantom is proposed as a tool for quality assurance (QA) since reproducible time series and t-maps are obtained in a series of independent repeat experiments. The phantom is relatively simple to build and can therefore be used by any clinical fMRI center.     

Blood Oxygenation Level Dependent Signal Time Courses During Prolonged Visual Stimulation

Previous functional magnetic resonance imaging (MRI) studies using extended visual stimulation have reported disparate results. Two studies have shown that blood oxygen level dependent (BOLD) contrast decays over time which is cited as evidence of recoupling between oxygen utilisation and cerebral blood flow during stimulus presentation. These findings have serious implications for the design of functional MRI experiments because they raise the possibility that BOLD contrast may not accurately reflect neuronal activity. Another study reported no decay of BOLD contrast. These studies used different visual stimuli and imaging techniques. We have performed a series of experiments, using different MRI techniques (echo-planar imaging and fast low angle shot) and two different visual stimuli to assess which of these factors may explain the previous results. In all of our experiments the signal time course from areas of significant activation remained largely elevated throughout the duration of stimulation and this is not affected by the imaging method used. Our data, in accordance with that of Bandettini et al., suggest that recoupling between blood flow and oxygen extraction is not a general phenomenon in the human brain when visual stimuli are presented for an extended time.     

Elimination of k-space spikes in fMRI data

The subtle signal changes in functional magnetic resonance imaging (fMRI) can be easily overwhelmed by noise of various origins. Spikes in the collected fMRI raw data often arise from high-duty usage of the scanner hardware and can introduce significant noise in the image and thereby in the image time series. Consequently, the spikes will corrupt the functional data and degrade the result of functional mapping. In this work, a simple method based on processing the time course of the k-space data are introduced and implemented to remove the spikes in the acquired data. Application of the method to experimental data shows that the methods are robust and effective for eliminating of spike-related noise in fMRI time series.     

遗传算法在二维电磁成像病态方程求解中的应用研究

 病态方程的计算是电磁成像中一个难以避免但又迄今未得到有效解决的难题。遗传算法以其强大的搜索寻优功能为该难题的解决提供了一种新的途径。分析了将遗传算法应用于电磁成像反演计算中的可能性和优势，并通过两个数值计算举例，一个用遗传算法计算病态线性方程组，一个用遗传算法计算TM入射波下的二维微波成像，证明遗传算法用于电磁成像中计算病态方程是可行和有效的。     

Nitric oxide heme interactions in nitrophorin from <Emphasis Type="Italic">Cimex lectularius</Emphasis>

The nitrophorin from the bedbug Cimex lectularius (cNP) is a nitric oxide (NO) carrying protein. Like the nitrophorins (rNPs) from the kissing bug Rhodnius prolixus, cNP forms a stable heme Fe(III)-NO complex, where the NO can be stored reversibly for a long period of time. In both cases, the NPs are found in the salivary glands of blood-sucking bugs. The insects use the nitrophorins to transport the NO to the victim’s tissues, resulting in vasodilation and reduced blood coagulation. However, the structure of cNP is significantly different to those of the rNPs from Rhodnius prolixus. Furthermore, the cNP can bind a second NO molecule to the proximal heme cysteine when present at higher concentrations. High field Mössbauer spectroscopy on ⁵⁷Fe enriched cNP complexed with NO shows reduction of the heme iron and formation of a ferrous nitric oxide (Fe(II)-NO) complex. Density functional theory calculations reproduce the experimental Mössbauer parameters and confirm this observation.     

Efficient formalism for large-scale ab initio molecular dynamics based on time-dependent density functional theory

A new "on the fly" method to perform Born-Oppenheimer ab initio molecular dynamics (AIMD) simulations is presented. Inspired by Ehrenfest dynamics in time-dependent density functional theory, the electronic orbitals are evolved by a Schr?dinger-like equation, where the orbital time derivative is multiplied by a parameter. This parameter controls the time scale of the fictitious electronic motion and speeds up the calculations with respect to standard Ehrenfest dynamics. In contrast with other methods, wave function orthogonality needs not be imposed as it is automatically preserved, which is of paramount relevance for large-scale AIMD simulations.