A Termination Condition of Unfolding Loop for Generalized Partial Computation

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The effect of sequential dual-gas testing on laser-induced breakdown spectroscopy-based discrimination: Application to brass samples and bacterial strains

Four Cu–Zn brass alloys with different stoichiometries and compositions have been analyzed by laser-induced breakdown spectroscopy (LIBS) using nanosecond laser pulses. The intensities of 15 emission lines of copper, zinc, lead, carbon, and aluminum (as well as the environmental contaminants sodium and calcium) were normalized and analyzed with a discriminant function analysis (DFA) to rapidly categorize the samples by alloy. The alloys were tested sequentially in two different noble gases (argon and helium) to enhance discrimination between them. When emission intensities from samples tested sequentially in both gases were combined to form a single 30-spectral line “fingerprint” of the alloy, an overall 100% correct identification was achieved. This was a modest improvement over using emission intensities acquired in argon gas alone. A similar study was performed to demonstrate an enhanced discrimination between two strains of Escherichia coli (a Gram-negative bacterium) and a Gram-positive bacterium. When emission intensities from bacteria sequentially ablated in two different gas environments were combined, the DFA achieved a 100% categorization accuracy. This result showed the benefit of sequentially testing highly similar samples in two different ambient gases to enhance discrimination between the samples.     

DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity

In this paper we propose, analyze and also quantify cross-correlations between climatological data. For this purpose we adopt the DCCA cross-correlation coefficient ρ_DCCA. In order to accomplish this goal, we calculate the cross-correlation between time series of air temperature and relative humidity. This analysis was performed taking into account several stations (cities) around the world. The results found here, depending on the station location, may exhibit one of the following behaviors, i.e., negative, positive, or null cross-correlations. It is noteworthy that, the level of cross-correlation between air temperature and relative humidity is quantified in these cases. Finally, DCCA cross-correlation coefficients show that, in general, the data are influenced by seasonal components.     

Limb dominance changes in walking evolution explored by asymmetric correlations in gait dynamics

Fluctuations in the stride interval time series of unconstrained walking are not random but seem to exhibit long-range correlations that decay as a power law (Hausdorff et al. (1995) [35]). Here, we examine whether asymmetries are present in the long-range correlations of different gait parameters (stride, swing and stance intervals) for the left and right limbs. Gait dynamics corresponding to 16 healthy subjects were obtained from the Physionet database, which contains stride, stance and swing intervals for both left and right limbs. Detrended Fluctuation Analysis (DFA) revealed the presence of asymmetric long-range correlations in all gait cycle variables investigated. A rich variety of scaling exponent dynamics was found, with the presence of synchronicity, decreased correlations and dominant correlations. The results are discussed in terms of the hypothesis that reduced strength of long-range correlations reflect both enhanced stability and adaptability.     

Preservation of long range temporal correlations under extreme random dilution

Many natural time series exhibit long range temporal correlations that may be characterized by power-law scaling exponents. However, in many cases, the time series have uneven time intervals due to, for example, missing data points, noisy data, and outliers. Here we study the effect of randomly missing data points on the power-law scaling exponents of time series that are long range temporally correlated. The Fourier transform and detrended fluctuation analysis (DFA) techniques are used for scaling exponent estimation. We find that even under extreme dilution of more than 50%, the value of the scaling exponent remains almost unaffected. Random dilution is also applied on heart interbeat interval time series. It is found that dilution of 70%-80% of the data points leads to a reduction of only 8% in the scaling exponent; it is also found that it is possible to discriminate between healthy and heart failure subjects even under extreme dilution of more than 90%.     

一种压缩DFA的高效FPGA实现

目前,面向网络流实时处理的正则表达式匹配技术面临两方面的挑战:一方面,复杂或大规模规则集会导致DFA存储空间爆炸的问题;另一方面,传统计算机的串行DFA匹配技术很难满足对高速主干网的线速深度包检测。本文提出了一个基于改进游程编码的DFA压缩算法,并在FPGA上高效实现了该压缩DFA的匹配引擎。测试结果表明规则集的单个DFA的吞吐率均大于800Mbps,在FPGA块内存最大利用率情况下的理论最大吞吐率达到49.5Gbps。     

Breathing of voltage dependent anion channel as revealed by the fractal property of its gating

The gating of voltage dependent anion channel (VDAC) depends on the movement of voltage sensors in the transmembrane region, but the actual mechanism is still not well understood. With a view to understand the phenomenon we have analyzed the current recordings of VDAC in lipid bilayer membrane (BLM) and found that the data show self-similarity and fractal characteristics. We look for the microscopic and molecular basis of fractal behavior of gating of VDAC. A model describing the oscillatory dynamics of voltage sensors of VDAC in the transmembrane region under applied potential has been proposed which gives rise to the aforesaid fractal behavior.     

Merging two traditional methods: the Hartree–Fock and the Heitler–London and adding density functional correlation corrections

The variational combination of the Hartree– Fock (HF) with the Heitler–London (HL) methods, yielding the Hartree–Fock–Heitler–London (HF–HL) method is analyzed for diatomic hydrides and homonuclear molecules of the first and second row atoms. This recent quantum chemistry development is considered in the light of the continuous evolution of hypotheses and corresponding verifications occurring in quantum chemistry. The correlation energy correction needed in HF and HL computations is reduced in the HF–HL method to its dynamic component, since the non-dynamical correlation is accounted by explicitly considering near degeneracy and state crossing. The dynamical correlation is computed either (1) via expansion with multi-configuration of HF and HL functions or (2) including ionic HL structures, yielding a large fraction of the molecular extra correlation energy or (3) using the Coulomb hole density functional. With the latter the computed total and binding energies for all internuclear separations are in excellent agreement with laboratory data. Contribution to the Serafin Fraga Memorial Issue.     

ToF-SIMS studies of Bacillus using multivariate analysis with possible identification and taxonomic applications

In this paper we discuss the application of ToF-SIMS with an Au₃⁺ primary ion beam, combined with principal components analysis (PCA) and discriminant function analysis (DFA) for the identification of individual strains of two Bacillus species. The ToF-SIMS PC-DFA methodology is capable of distinguishing bacteria at the strain level based on analysis of surface chemical species. By classifying the data using hierarchical cluster analysis (HCA) we are able to show quantitative separation of species and of these strains. This has taxonomic implications in the areas of rapid identification of pathogenic microbes isolated from the clinic, food and environment.     

Real-time fractal signal processing in the time domain

Fractal analysis has proven useful for the quantitative characterization of complex time series by scale-free statistical measures in various applications. The analysis has commonly been done offline with the signal being resident in memory in full length, and the processing carried out in several distinct passes. However, in many relevant applications, such as monitoring or forecasting, algorithms are needed to capture changes in the fractal measure real-time. Here we introduce real-time variants of the Detrended Fluctuation Analysis (DFA) and the closely related Signal Summation Conversion (SSC) methods, which are suitable to estimate the fractal exponent in one pass. Compared to offline algorithms, the precision is the same, the memory requirement is significantly lower, and the execution time depends on the same factors but with different rates. Our tests show that dynamic changes in the fractal parameter can be efficiently detected. We demonstrate the applicability of our real-time methods on signals of cerebral hemodynamics acquired during open-heart surgery.