Improvement of modal scaling factors using mass additive technique

A general investigation into the improvement of modal scaling factors of an experimental modal model using additive technique is discussed. Data base required by the proposed method consists of an experimental modal model (a set of complex eigenvalues and eigenvectors) of the original structure and a corresponding set of complex eigenvalues of the mass-added structure. Three analytical methods,i.e., first order and second order perturbation methods, and local eigenvalue modification technique, are proposed to predict the improved modal scaling factors. Difficulties encountered in scaling closely spaced modes are discussed. Methods to compute the necessary rotational modal vectors at the mass additive points are also proposed to increase the accuracy of the analytical prediction

A new method to real-normalize measured complex modes

A time domain subspace iteration technique is presented to compute a set of normal modes from the measured complex modes. By using the proposed method, a large number of physical coordinates are reduced to a smaller number of model or principal coordinates. Subspace free decay time responses are computed using properly scaled complex modal vectors. Companion matrix for the general case of nonproportional damping is then derived in the selected vector subspace. Subspace normal modes are obtained through eigenvalue solution of the (M sub N) sup -1 (K sub N) matrix and transformed back to the physical coordinates to get a set of normal modes. A numerical example is presented to demonstrate the outlined theory

Multimodal estimation of distribution algorithms

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima

The Research of Sequential Images: Rebuilding of Gray (Position) ~ Time Function on Direction Lines and Their Applications

Contrasted with other information carriers, such as speech and text, images contains larger amount of information, especially in sequential images, that is waiting to be exploited, in particular the dynamic information of correlation, difference, and temporal relationship between different frames. This dynamic information contributes a great deal in analysis of 4D images. This paper proposes a method for detecting dynamic information from sequential images, based on the rebuilding of their gray (position)~time function on direction lines, an approach that has been analyzed and studied extensively on the setting of various direction lines. This method is based on motion that is presented on sequential images. In particular, the method, Omni directional M-mode Echocardiography system, which we have studied extensively, will be described leading to a robust way of diagnosing heart diseases

Multifractal detrending moving average cross-correlation analysis

There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. The multifractal detrended cross-correlation analysis (MF-DCCA) approaches can be used to quantify such cross-correlations, such as the MF-DCCA based on detrended fluctuation analysis (MF-X-DFA) method. We develop in this work a class of MF-DCCA algorithms based on the detrending moving average analysis, called MF-X-DMA. The performances of the MF-X-DMA algorithms are compared with the MF-X-DFA method by extensive numerical experiments on pairs of time series generated from bivariate fractional Brownian motions, two-component autoregressive fractionally integrated moving average processes and binomial measures, which have theoretical expressions of the multifractal nature. In all cases, the scaling exponents $h_{xy}$ extracted from the MF-X-DMA and MF-X-DFA algorithms are very close to the theoretical values. For bivariate fractional Brownian motions, the scaling exponent of the cross-correlation is independent of the cross-correlation coefficient between two time series and the MF-X-DFA and centered MF-X-DMA algorithms have comparative performance, which outperform the forward and backward MF-X-DMA algorithms. We apply these algorithms to the return time series of two stock market indexes and to their volatilities. For the returns, the centered MF-X-DMA algorithm gives the best estimates of $h_{xy}(q)$ since its $h_{xy}(2)$ is closest to 0.5 as expected, and the MF-X-DFA algorithm has the second best performance. For the volatilities, the forward and backward MF-X-DMA algorithms give similar results, while the centered MF-X-DMA and the MF-X-DFA algorithms fails to extract rational multifractal nature.Comment: 15 pages, 4 figures, 2 matlab codes for MF-X-DMA and MF-X-DF

Residue-specific structures and membrane locations of pH-low insertion peptide by solid-state nuclear magnetic resonance

The pH-low insertion peptide (pHLIP) binds to a membrane at pH 7.4 unstructured but folds across the bilayer as a transmembrane helix at pH similar to 6. Despite their promising applications as imaging probes and drug carriers that target cancer cells for cytoplasmic cargo delivery, the mechanism of pH modulation on pHLIP-membrane interactions has not been completely understood. Here, we show the first study on membrane-associated pHLIP using solid-state NMR spectroscopy. Data on residue-specific conformation and membrane location describe pHLIP in various surface-bound and membrane-inserted states at pH 7.4, 6.4 and 5.3. The critical membrane-adsorbed state is more complex than previously envisioned. At pH 6.4, for the major unstructured population, the peptide sinks deeper into the membrane in a state II\u27 that is distinct from the adsorbed state II observed at pH 7.4, which may enable pHLIP to sense slight change in acidity even before insertion

Quenched Fe Moment in the Collapsed Tetragonal Phase of Ca1−x_{1-x}Prx_{x}Fe2_2As2_2

We report $^{75}$As NMR studies on single crystals of rare-earth doped iron pnictides superconductor Ca$_{1-x}$Pr$_{x}$Fe$_{2}$As$_{2}$ ($x$=0.075 and 0.15). The $^{75}$As spectra show a chemical pressure effect with doping and a first order structure transition to the collapsed tetragonal phase upon cooling. A sharp drop of the Knight shift is seen below the structural transition, whereas $1/T_1$ is strongly enhanced at low-temperatures. These evidences indicate quenching of Fe local magnetism and short-range ordering of Pr$^{3+}$ moment in the collapsed tetragonal phase. The quenched Fe moment through structure collapse suggests a strong interplay of structure and magnetism, which is important for understanding the nature of the collapsed tetragonal phase.Comment: 5 pages, 5 figure