Evaluation of surface decarburization depth by magnetic Barkhausen noise technique

Industrially unfavorable process of steel surface decarburization was induced by annealing in air. Two methods of after-anneal surface treatment were used: an acid pickling and a sand blasting. The obtained decarburized layers were examined by optical microscope, wave dispersive spectrometer, and surface X-ray diffraction method. Magnetic Barkhausen noise technique was tested for applicability of non-destructive characterization of the decarburized layer depth. A newly introduced parameter, Barkhausen noise coercivity, was proposed for practical use due to its sensitivity to decarburization and stability to measurement conditions. Other magnetic parameters, e.g. number of Barkhausen noise counts, were found to be sensitive to the compressive residual stress caused by the sand blasting.     

First-principles study on the Ni@Pt12 Ih core-shell nanoparticles: A good catalyst for oxygen reduction reaction

The adsorption, diffusion and dissociation properties of O₂ on the icosahedron (Ih) Ni@Pt12 core-shell nanoparticle were investigated using the ab initio density functional theory calculations. It is found that, compared with the Pt(111) surface, the Ih Ni@Pt12 core-shell nanoparticle can enhance the adsorption, diffusion and dissociation of O₂, as well as the adsorption and diffusion of the atomic O (the dissociation product of O₂), and therefore serve as a good catalyst for oxygen reduction reaction. Our study gives a reasonable theoretical explanation to the high catalytic activity of the Ni@Pt core-shell nanoparticles for the oxygen reduction reaction.     

Multifractal moving average analysis and test of multifractal model with tuned correlations

We address two common major problems in the study of time series characterizing fluctuations in complex systems: multifractal analysis and multifractal modeling. Specifically, we introduce a multi-fractal centered moving average (MF-CMA) analysis, which is computationally easier but equivalently performing compared with the well-established multi-fractal detrended fluctuation analysis (MF-DFA) with linear detrending. In addition, we study in detail a generalized binomial multi-fractal model (GB-MFM) to conveniently and reliably generate multifractal surrogate data with arbitrary singularity strengths and arbitrary long-term persistence. We use the data generated by this model as well as realistic, by construction monofractal data series with crossovers and trends to test and compare the multifractal analysis methods and discuss finite-size effects as well as limitations due to spurious multifractality.     

On spurious and corrupted multifractality: The effects of additive noise, short-term memory and periodic trends

We study the performance of multifractal detrended fluctuation analysis (MF-DFA) applied to long-term correlated and multifractal data records in the presence of additive white noise, short-term memory and periodicities. Such additions and disturbances that can be typically found in the observational records of various complex systems ranging from climate dynamics to physiology, network traffic, and finance. In monofractal records, we find that (i) additive white noise hardly results in spurious multifractality, but causes underestimated generalized Hurst exponents h(q) for all q values; (ii) short-range correlations lead to pronounced crossovers in the generalized fluctuation functions F_q(s) at positions that decrease with increasing moment q, thus causing significantly overestimated h(q) for small q and spurious multifractality; (iii) periodicities like seasonal trends (with standard deviations comparable with the one of the studied process) result in spurious “reversed” multifractality where h(q) increases with increasing q (except for very short time windows). We also show that in multifractal cascades moderate additions of noise, short-range memory, or periodic trends cause flawed results for h(q) with q<2, while h(q) with q>2 remains nearly unchanged.     

Unsupervised MRI segmentation of brain tissues using a local linear model and level set

Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately position the region's boundaries; and (2) the region models are embedded in the level set framework, so that the spatial coherence of the segmentation can be controlled in a natural way. Our new method has been tested on the ground-truthed Internet Brain Segmentation Repository (IBSR) database and gave promising results, with Tanimoto indexes ranging from 0.61 to 0.79 for the classification of the white matter and from 0.72 to 0.84 for the gray matter. To our knowledge, this is the first time a region-based level set model has been used to perform the segmentation of real-world MRI brain scans with convincing results.     

A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm

This study proposes an expectation–maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.     

The relationship between BOLD signal and autonomic nervous system functions: implications for processing of "physiological noise"

Functional magnetic resonance imaging (fMRI) research has revealed not only important aspects of the neural basis of cognitive and perceptual functions, but also important information on the relation between high-level brain functions and physiology. One of the central outstanding questions, given the features of the blood oxygenation level-dependent (BOLD) signal, is whether and how autonomic nervous system (ANS) functions are related to changes in brain states as measured in the human brain. A straightforward way to address this question has been to acquire external measurements of ANS activity such as cardiac and respiratory data, and examine their relation to the BOLD signal. In this article, we describe two conceptual approaches to the treatment of ANS measures in the context of BOLD fMRI analysis. On the one hand, several research lines have treated ANS activity measures as noise, considering them as nothing but a confounding factor that reduces the power of fMRI analysis or its validity. Work in this line has developed powerful methods to remove ANS effects from the BOLD signal. On the other hand, a different line of work has made important progress in showing that ANS functions such as cardiac pulsation, heart rate variability and breathing rate could be considered as a theoretically meaningful component of the signal that is useful for understanding brain function. Work within this latter framework suggests that caution should be exercised when employing procedures to remove correlations between BOLD data and physiological measures. We discuss these two positions and the reasoning underlying them. Thereafter, we draw on the reviewed literature in presenting practical guidelines for treatment of ANS data, which are based on the premise that ANS data should be considered as theoretically meaningful information. This holds particularly when studying cortical systems involved in regulation, monitoring and/or generation of ANS activity, such as those involved in decision making, conflict resolution and the experience of emotion.     

基于多级微反射镜的傅里叶变换红外光谱仪中准直误差的分析

通过对基于多级微反射镜的空间调制傅里叶变换红外光谱仪准直误差的分析,模拟了准直误差的存在而导致的干涉图大光程差处对比度反转和光谱图边频噪声的产生。讨论了在不同的误差系数下复原光谱图的信噪比和分辨率,并且分析了干涉图大光程差处对比反转以及光谱图边频分量的产生原因。仿真结果表明,在误差系数α=0.15°.mm-1时,光谱的信噪比会下降到6 dB,同时光谱分辨率会退化到13.4cm-1。本文的结论将应用于微型光谱仪系统的光学设计与装调。     

地铁沿线建筑内噪声多维度感受影响机制

随着城市铁路系统的快速发展,铁路沿线建筑内人群对地铁运行产生噪声的抱怨逐渐增多,为控制室内地铁噪声对人群所产生的负面影响,有必要对地铁噪声产生的多维度负面情绪进行评估。采集了79名受试者对建筑内地铁噪声的多维度负面感受(压抑感,不舒适度和不满意度),基于偏最小二乘法分析了主客观影响因素对多维度负面感受的作用机制,并比较了多维度负面感受在反映地铁噪声影响上的差异。结果表明,多维度负面感受主要取决于主观响度和声学参数,活动干扰度、地铁噪声的敏感度、厌烦度和适应性的影响明显更弱;3个多维度负面感受中,相比于压抑感,不舒适度和不满意度受主观响度的影响轻微更显著,在相同的主观响度感受下,不满意度等级最高。 该文可以为地铁沿线建筑物内噪声负面感受的评价和改善提供参考。     

Persistence and extinction of a stochastic logistic model with delays and impulsive perturbation

A stochastic logistic model with delays and impulsive perturbation is proposed and investigated. Sufficient conditions for extinction are established as well as nonpersistence in the mean, weak persistence and stochastic permanence. The threshold between weak persistence and extinction is obtained. Furthermore, the theoretical analysis results are also derivated with the help of numerical simulations.