Miracle-dz/UniFAR-FTUs huggingface dataset:
DIFFUSION EXPERIMENTS WITH NUMERICALLY INTEGRATED ISOTROPIC TURBULENCE.

Results are presented of computer simulations of particle diffusion on a numerically integrated, decaying isotropic turbulent flow field. The diffusion of both fluid particles and “small” spherical particles is studied. Comparisons are made of the various temporal autocorrelation coefficients. It is found that for “short” times, the Lagrangian (fluid point) velocity correlation is larger than the Eulerian correlation, while for longer times the opposite is true. It is also found in the “small” spherical particle case that as the response time of the particle is increased, the velocity autocorrelation coefficient increases.
GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER

Vibration signals from a gearbox are usually noisy. As a result, it is difficult to find early symptoms of a potential failure in a gearbox. Wavelet transform is a powerful tool to disclose transient information in signals. An adaptive wavelet filter based on Morlet wavelet is introduced in this paper. The parameters in the Morlet wavelet function are optimised based on the kurtosis maximisation principle. The wavelet used is adaptive because the parameters are not fixed. The adaptive wavelet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early fatigue tooth crack. Two types of discrete wavelet transform (DWT), the decimated with DB4 wavelet and the undecimated with harmonic wavelet, are also used to analyse the same signals for comparison. No periodic impulses appear on any scale in either DWT decomposition.
Correlation modelling on the sphere using a generalized diffusion equation

An important element of a data assimilation system is the statistical model used for representing the correlations of background error. This paper describes a practical algorithm that can be used to model a large class of two‐ and three‐dimensional, univariate correlation functions on the sphere. Application of the algorithm involves a numerical integration of a generalized diffusion‐type equation (GDE). The GDE is formed by replacing the Laplacian operator in the classical diffusion equation by a polynomial in the Laplacian. The integral solution of the GDE defines, after appropriate normalization, a correlation operator on the sphere. The kernel of the correlation operator is an isotropic correlation function. The free parameters controlling the shape and length‐scale of the correlation function are the products kpT, p = 1, 2, …, where (‐1)pkp is a weighting (‘diffusion’) coefficient (kp > 0) attached to the Laplacian with exponent p, and T is the total integration ‘time’. For the classical diffusion equation (a special case of the GDE with kp = 0 for all p > 1) the correlation function is shown to be well approximated by a Gaussian with length‐scale equal to (2k1T)1/2.
Regular graph construction for semi-supervised learning

Semi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of the available data by identifying classes or groups regardless data distribution and representing submanifold in Euclidean space. Most of methods used in literature for SSL classification do not worry about graph construction. However, regular graphs can obtain better classification accuracy compared to traditional methods such as k-nearest neighbor (kNN), since kNN benefits the generation of hubs and it is not appropriate for high-dimensionality data. Nevertheless, methods commonly used for generating regular graphs have high computational cost. We tackle this problem introducing an alternative method for generation of regular graphs with better runtime performance compared to methods usually find in the area. Our technique is based on the preferential selection of vertices according some topological measures, like closeness, generating at the end of the process a regular graph. Experiments using the global and local consistency method for label propagation show that our method provides better or equal classification rate in comparison with kNN.
Discriminative distance measures for object detection

Abstract : The reliable detection of an object of interest in an input image with arbitrary background clutter and occlusion has to a large extent remained an elusive goal in computer vision. Traditional model-based approaches are inappropriate for a multi-class object detection task primarily due to difficulties in modeling arbitrary object classes. Instead, we develop a detection framework whose core component is a nearest neighbor search over object parts. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input measurements belong to different classes. This pair-wise probability is not in general a metric distance measure. Furthermore, it can out-perform any metric distance, approaching even the Bayes optimal performance. In practice, we seek a model for the optimal distance measure that combines the discriminative powers of more elementary distance measures associated with a collection of simple feature spaces that are easy and efficient to implement; in our work, we use histograms of various feature types like color, texture and local shape properties. For performing efficient nearest neighbor search over large training sets, the linear model was extended to discretized distance measures that combines distance measures associated with discriminators organized in a tree-like structure. Finally, the nearest neighbor search over object parts was integrated into a whole object detection system and evaluated against both an indoor detection task as well as a face recognition task yielding promising results.
On the interaction of internal waves and surface gravity waves

The perturbation of pre-existing surface gravity waves caused by the presence of an internal wave was studied both experimentally and analytically. An extensive series of experiments was performed, and quantitative results were obtained for the one-dimensional monochromatic interaction of internal waves and surface gravity waves. Internal wave-induced surface slope, amplitude and wavenumber modulations were measured for a wide range of interaction conditions. A complementary theoretical analysis, based on the conservation approach of Whitham (1962) and Longuet-Higgins & Stewart (1960,1961), was performed and a closed form solution obtained for the one-dimensional wave interaction. Both the theory and the experiment demonstrate that the effect increases with interaction distance. The maximum interaction effect is found to occur when the phase speed of the internal wave and the group velocity of the surface wave are matched. The phase of the internal wave at which maximum surface-wave modulation occurs is found to be a sensitive and continuous function of the relative wave speeds. The experimental data are in good agreement with the present theoretical analysis.
Statistics of X-Ray Fluctuations from Cygnus X-1: Reservoirs in the Disk?

X-rays from stellar black hole candidates, such as Cygnus X-1, are known to exhibit rapid time variations. Previously, these variations have seemed to occur at random. At a result, the “shot-noise model,” which assumes superpositions of randomly occurring identical shots, has often been utilized to interpret the observed time variabilities. Recent Ginga data of Cyg X-1 display evidence, however, which does not support a Poisson distribution in the time intervals between the shots, i.e., a random temporal distribution of the shots. The observations show that the frequencies of big shot events are lower than those expected by random distributions for the first several seconds before and/or after each big event. Moreover, this low-frequency interval, “waiting time,” is longer for the shots with larger peaks. We also find an exponential peak-intensity distribution of X-ray shots. These observational features strongly suggest the presence of numerous reservoirs with different capacities for triggering X-ray fluctuations, a key assumption of the model based on the self-organized criticality.
Heteroepitaxial growth of In-face InN on GaN (0001) by plasma-assisted molecular-beam epitaxy

The thermodynamic aspects of indium-face InN growth by radio frequency plasma-assisted molecular-beam epitaxy (rf-MBE) and the nucleation of InN on gallium-face GaN (0001) surface were investigated. The rates of InN decomposition and indium desorption from the surface were measured in situ using reflected high-energy electron diffraction and the rf-MBE “growth window” of In-face InN (0001) was identified. It is shown that sustainable growth can be achieved only when the arrival rate of active nitrogen species on the surface is higher than the arrival rate of indium atoms. The maximum substrate temperature permitting InN growth as a function of the active nitrogen flux was determined. The growth mode of InN on Ga-face GaN (0001) surface was investigated by reflected high-energy electron diffraction and atomic force microscopy. It was found to be of the Volmer–Weber-type for substrate temperatures less than 350°C and of the Stranski–Krastanov for substrate temperatures between 350 and 520°C. The number of m.
Inverse approach to elasticity reconstruction in shear wave imaging

Acoustic shear waves of low frequency can be detected and measured using a phase contrast based magnetic resonance imaging technique called MR Elastography or correlation or phase measurement based echo ultrasound techniques. Spatio-temporal variations of displacements caused by the propagating waves can be used to estimate local values of the elasticity of the object being imaged. The currently employed technique for estimating the elasticity from the wave displacement maps, the local frequency estimator (LFE), has fundamental resolution limits and also has problems with shadowing and other refraction-related artifacts. These problems can be overcome with an inverse approach using Green's function integrals which directly solve the wave equation problem for the propagating wave. The complete measurements of wave displacements as a function of space and time over the object of interest obtained by the above techniques permit an iterative approach to inversion of the wave equation to obtain elasticity and attenuation maps. This article describes the inversion algorithm and evaluates the improvements over the LFE for simulated data and in-vivo breast measurements.
Rapid measurement of ultrashort-pulse amplitude and phase from a two-photon absorption sonogram trace

By measuring the sonogram of an ultrashort pulse with a two-photon detector we have demonstrated a robust and sensitive complete characterization method for 80fs pulses derived from a self-mode-locked Ti:sapphire laser. Acquisition and retrieval rates as high as 0.5 Hz are demonstrated, and the design of a rapid-acquisition configuration is described, where we discuss the use of white-light interferometry to achieve a nearly dispersionless bandpass filter. Excellent agreement is obtained between retrieved pulse data and independent experimental pulse measurements.
Numerical simulations of the formation and chemical evolution of galaxies

We present the results of a set of three-dimensional SPH-Treecode simulations which model the formation and early evolution of disc galaxies, including the generation and return of heavy elements to the interstellar medium by star formation. Starting from simple initial conditions which are given by a uniform density sphere of gas which is embedded in a dark matter halo and in solid-body rotation, we are able to form realistic disc galaxies, and find that an exponential gas disc is quickly formed. Star formation within this exponential disc naturally leads to the formation of abundance gradients which are in broad agreement with those observed, although they are slightly shallower than some observations. We investigate the systematic effects of variation of mass, rotation and star formation parameters on the abundance gradients. We find that the abundance gradients are most sensitive to changes in the star formation parameters or rotation. Including a critical-density cut-off in the star formation law causes abundance gradients to be steepened. Analysis of gas flows within the models shows radial flows which are a function of angle of azimuth around the galaxies, with alternating inward and outward flows. This motion is linked to the presence of a bar, whose strength is related to the amount of star formation in the models, and there is a gentle drift of mass inwards. The shallow abundance gradients may be linked to these radial flows.
Extremal hypotheses for river behavior

The hypotheses of minimum energy dissipation, minimum stream power, and minimum unit stream power are summarized and compared. Their derivation from analogies with laminar flow and linear thermodynamics is criticized on the grounds that these situations differ fundamentally from river flows, which are usually highly turbulent and strongly nonlinear. The authors' empirical hypothesis of maximum friction factor seems preferable to the minimization hypotheses because it is compatible with the known behavior of turbulent flows and nonlinear processes, it is applicable with a wider range of independent variables, and it is more in keeping with trends shown by experimental data under all constraints. The minimization hypotheses seem likely to give incorrect predictions when flow rate and depth are independent variables. The empirical success of the minimization hypotheses is confined to situations in which they predict similar behavior to the maximum friction factor hypothesis; they may be considered as special cases of this more general hypothesis.
Computational approaches to problems in protein structure and function

We present computational approaches to solve several problems arising in protein structure and function. In the first part of this thesis, we develop a new method for finding the lowest energy positions of side chains when given the backbone of a protein, a widely studied problem that has applications in homology modeling and protein design. We present an integer linear programming formulation of side-chain positioning and relax it to give a polynomial-time linear programming heuristic that allows us to tackle large problems. We test the integer and linear programming approach on native and homologous backbones, where we show that optimal solutions can usually be found using linear programming, and in protein redesign, where we find that instances often cannot be solved using linear programming directly, but where optimal solutions for large instances can be found using the more expensive integer programming procedure. We also present an alternative formulation of the side-chain positioning problem as a semidefinite program, which provides a tighter relaxation than the linear program. We introduce two novel rounding schemes to convert fractional solutions of the semidefinite program into choices of rotamers and provide some theoretical justifications for their effectiveness. We extensively test the semidefinite programming formulation and rounding schemes on simulated data and on the redesign of two naturally occurring protein cores and show that the approach finds good solutions. The second part of this thesis considers the problem of finding transcription factor binding sites by locating a collection of mutually similar subsequences within the upstream DNA sequences of genes. Our approach to side-chain positioning can be recast to solve this problem, and it has previously been shown that this is a promising direction to pursue. We improve the mathematical programming formulation to find binding sites up to 45 times faster. Finally, in the last part of the thesis, we investigate protein function more broadly and give extensions to the popular phylogenetic profile method for predicting shared function from cross-genomic evolutionary history. For many biological functions, our methods are better able to identify functionally linked proteins than previously introduced methods.
A COMPATIBILIDADE DOS METADADOS DISPONÍVEIS EM SISTEMAS VGI COM O PERFIL DE METADADOS EMPREGADO NA INFRAESTRUTURA NACIONAL DE DADOS ESPACIAIS DO BRASIL (INDE-BR)

As informacoes geograficas voluntarias tem sido investigadas por pesquisadores e agencias oficiais de mapeamento, os quais tentam compreender como individuos sem conhecimento formalizado em cartografia podem contribuir para a construcao das bases cartograficas oficiais. Em paises como o Brasil, onde o investimento nos servicos relativos a informacao espacial e escasso, as informacoes geograficas voluntarias tambem sao interessantes alternativas para incrementar a velocidade de atualizacao cartografica, pois sao de livre acesso e constantemente revisadas. Neste contexto, a Infraestrutura Nacional de Dados Espaciais do Brasil (INDE-BR), no seu papel de facilitar e ordenar o acesso as bases de dados espaciais oficias, tambem pode se beneficiar do uso de informacoes prestadas por usuarios voluntarios na tarefa de enriquecer o conjunto de dados disponivel em seu banco. Dessa maneira, elaborou-se o presente artigo, no qual investiga-se a compatibilidade das informacoes geograficas voluntarias com o perfil de metadados oficial do Brasil, adotado na estruturacao da INDE-BR. Para tanto, foram selecionados sistemas de informacoes geograficas voluntarias os quais tiveram seus metadados analisados e comparados aos padroes adotados no documento Perfil de Metadados Geoespaciais do Brasil. Dos resultados, pode-se dizer que os padroes adotados pelo sistema OpenStreetMap sao compativeis com o padrao adotado pelo Perfil MGB. Esse resultado aponta para a necessidade de se investigar as informacoes geograficas voluntarias para que se tenha mais uma potencial ferramenta a favorecer o crescimento social, economico e politico de uma nacao, assim como o Brasil.
Designs of solar voltaic cells based on carbon nano-tubes II

Inspired by Asian rice-paddy and Firefighter spiraling steps staircase, we employ a nano-manipulator augmented with CAD as a nano-robot water-buffalo, promised to improve by an order of the magnitude the pioneer work of GE Solar voltaic cell (SVC) made of one Carbon NanoTube (CNT) enjoyed QECNT~5%. Our CNT was made of the semiconductor at NIR wavelength EBG= 1.107 eV which can absorb any photon whose wavelength λ ≤ λNIR =1.11 μm. This EBG is chosen due to the cutoff of Pb-Crown glass which happened put us in the equal amount of solar energy spectrum as silicon p-n junction SVC. Nevertheless, the exceeding of the Shockley and Queisser efficiency limit 30% might be due to the fact that we have much compact one-dimensional building block CNT of a tiny diameter 0.66 nm. It allows us to construct 3D structure, called volume pixel, "voxel," in a much efficient spiraling steps staircase fashion to capture the solar spectral energy spreading naturally by a simple focusing lens without occlusion. For real-estate premium applications, in Space or Ocean, we designed a volume pixel (Voxel) housing a stack of 16 CNTs steps spiraling 22° each like the fire house staircase occupying the height of 16 x dCNT =16 x 0.66nm= 10.56 nm and covering over 360°. The total SVC had the size 2x2 meter2, consisting of 100×100 lenslet array. Each lens was made of Pb-Crown glass which was inexpensive simple spherical lens having the diameter of Dlens=2 cm and F#=0.7. It can focus the sunlight a millionth times stronger in a smallest possible focal spot size, λYellow=0.635 μm< λMax photons <λRed=0.73 μm, where the largest number of solar photons, 68%, according to the Plank radiation spectrum at 6000°K and the Lord Rayleigh diffraction limit. The solar panel seals individually such an array of 3D cavities of SVC enjoying theoretically from the UV 12% (wasted in passing through) visible 68% to the infrared 20% at a total of 16x5%~80% total QECNT per cell. The solar panel is made of light-weight carbon composite tolerating about 20% inactive fill factor and 10% dead pixels.
A HD-Zip I transcription factor from physic nut, JcHDZ21, confers sensitive to salinity in transgenic Arabidopsis

HD-Zip is a plant-specific transcription factor that plays an important regulatory role in plant growth and stress response. However, there have been few reports on the functions of members of the physic nut HD-Zip gene family. In this study, we cloned a HD-Zip I family gene from physic nut by RT-PCR, and named JcHDZ21. Expression pattern analysis showed that JcHDZ21 gene had the highest expression in physic nut seeds, and salt stress inhibited the expression of JcHDZ21 gene. Subcellular localization and transcriptional activity analysis showed that JcHDZ21 protein is localized in the nucleus and has transcriptional activation activity. Salt stress results indicated that JcHDZ21 transgenic plants were smaller and had more severe leaf yellowing compared to those of the wild type. Physiological indicators showed that transgenic plants had higher electrical conductivity and MDA content, and lower proline and betaine content compared with wild-type plants under salt stress. In addition, the expression of abiotic stress-related genes in JcHDZ21 transgenic plants was significantly lower than that in wild type under salt stress. Our results showed that ectopic expression of JcHDZ21 increased the sensitivity of transgenic Arabidopsis to salt stress. This study provides a theoretical basis for the future application of JcHDZ21 gene in the breeding of physic nut stress-tolerant varieties.
The Cech number of Cp(X) when X is an ordinal space

The Cech number of a space Z, C(Z), is the pseudocharacter of Z in βZ. In this article we obtain, in ZFC and assuming SCH, some upper and lower bounds of the Cech number of spaces C p (X) of realvalued continuous functions defined on an ordinal space X with the pointwise convergence topology.
Comparison between the modal identification method and the POD‐Galerkin method for model reduction in nonlinear diffusive systems

This paper presents a comparison between the modal identification method (MIM) and the proper orthogonal decomposition‐Galerkin (POD‐G) method for model reduction. An example of application on a nonlinear diffusive system is used to illustrate the study. The study shows that in both methods, the state formulation of the nonlinear diffusive equation may be similar. However, the ideas behind both methods are completely different. The considered example shows that, for both methods, reducing the order up to 99.5% gives enough accuracy to simulate the dynamic of the original system. It is also seen in this example that the reduced model given through the MIM are slightly faster and more accurate than the ones given through the POD‐G method. Copyright © 2006 John Wiley & Sons, Ltd.