scholarly journals On the Fly Testing of Regular Patterns in Distributed Computations

1994 ◽  
Author(s):  
Eddy Fromentin ◽  
Michel Raynal ◽  
Vijay Garg ◽  
Alex Tomlinson
Keyword(s):  
Distributed Computations ◽  
Regular Patterns

Bonafide Substructure in the Core of Ferritin

1985 ◽  
Vol 43 ◽  
pp. 322-323
Author(s):  
William H. Massover
Keyword(s):  
Phase Contrast ◽  
Central Cavity ◽  
Amplitude Image ◽  
Ultrastructural Studies ◽  
The Core ◽  
Hydrated Ferric Oxide ◽  
Imaging Artifact ◽  
Contrast Image ◽  
Protein Shell ◽  
Regular Patterns

Each molecule of ferritin (d = 130Å) contains a core of iron surrounded by a 24-subunit protein shell. The amount of iron stored is variable and is present within the central cavity (d = 80Å) as a hydrated ferric oxide equivalent to the mineral, ferrihydrite. Many early ultrastructural studies of ferritin detected regular patterns of a multiparticulate substructure in the iron-rich core [e.g., 3,4], Each small particle was termed a “micelle“; a theory became widely accepted that a core consisted of up to six micelles positioned at the vertices of an octahedron. Other workers recognized that the apparent micelles were smaller or even disappeared if images were recorded closer to exact focus [e.g., 5]. In 1969, Haydon clearly established that the observed substructure was really an imaging artifact; each apparent micelle was only a dot in the underfocused phase contrast image of the supporting film superimposed on the amplitude image of the strongly scattering metal.

The buckling of thin EM specimens

1990 ◽  
Vol 48 (4) ◽  
pp. 850-851
Author(s):  
A.R. Thölén
Keyword(s):  
Electron Microscope ◽  
Elastic Properties ◽  
Crystal Surface ◽  
Specimen Surface ◽  
Radius Of Curvature ◽  
Thin Foils ◽  
Thin Electron ◽  
Regular Patterns

Thin electron microscope specimens often contain irregular bend contours (Figs. 1-3). Very regular bend patterns have, however, been observed around holes in some ion-milled specimens. The purpose of this investigation is twofold. Firstly, to find the geometry of bent specimens and the elastic properties of extremely thin foils and secondly, to obtain more information about the background to the observed regular patterns.The specimen surface is described by z = f(x,y,p), where p is a parameter, eg. the radius of curvature of a sphere. The beam is entering along the z—direction, which coincides with the foil normal, FN, of the undisturbed crystal surface (z = 0). We have here used FN = [001]. Furthermore some low indexed reflections are chosen around the pole FN and in our fcc crystal the following g-vectors are selected:

scholarly journals Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1169
Author(s):  
Juan Bógalo ◽  
Pilar Poncela ◽  
Eva Senra
Keyword(s):  
Business Cycles ◽  
Real Time ◽  
Parametric Method ◽  
Singular Spectrum Analysis ◽  
The State ◽  
Signal Extraction ◽  
Alternative Procedure ◽  
State Of The Economy ◽  
Regular Patterns ◽  
Non Parametric

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.

scholarly journals Developmental stochasticity and variation in floral phyllotaxis

2021 ◽  
Author(s):  
Miho Stephanie Kitazawa
Keyword(s):  
Pattern Formation ◽  
Gene Networks ◽  
Recent Progress ◽  
Phyllotactic Pattern ◽  
Floral Diversity ◽  
Core Eudicots ◽  
Common Process ◽  
The Mean ◽  
Floral Phyllotaxis ◽  
Regular Patterns

AbstractFloral phyllotaxis is a relatively robust phenotype; trimerous and pentamerous arrangements are widely observed in monocots and core eudicots. Conversely, it also shows variability in some angiosperm clades such as ‘ANA’ grade (Amborellales, Nymphaeales, and Austrobaileyales), magnoliids, and Ranunculales. Regardless of the phylogenetic relationship, however, phyllotactic pattern formation appears to be a common process. What are the causes of the variability in floral phyllotaxis and how has the variation of floral phyllotaxis contributed to floral diversity? In this review, I summarize recent progress in studies on two related fields to develop answers to these questions. First, it is known that molecular and cellular stochasticity are inevitably found in biological systems, including plant development. Organisms deal with molecular stochasticity in several ways, such as dampening noise through gene networks or maintaining function through cellular redundancy. Recent studies on molecular and cellular stochasticity suggest that stochasticity is not always detrimental to plants and that it is also essential in development. Second, studies on vegetative and inflorescence phyllotaxis have shown that plants often exhibit variability and flexibility in phenotypes. Three types of phyllotaxis variations are observed, namely, fluctuation around the mean, transition between regular patterns, and a transient irregular organ arrangement called permutation. Computer models have demonstrated that stochasticity in the phyllotactic pattern formation plays a role in pattern transitions and irregularities. Variations are also found in the number and positioning of floral organs, although it is not known whether such variations provide any functional advantages. Two ways of diversification may be involved in angiosperm floral evolution: precise regulation of organ position and identity that leads to further specialization of organs and organ redundancy that leads to flexibility in floral phyllotaxis.

scholarly journals P-modes in rapidly rotating stars: Looking for regular patterns in synthetic asymptotic spectra

2010 ◽  
Vol 331 (9-10) ◽  
pp. 1053-1056 ◽  
Author(s):  
F. Lignières ◽  
B. Georgeot ◽  
J. Ballot
Keyword(s):  
Rotating Stars ◽  
Rapidly Rotating Stars ◽  
Regular Patterns

Unit Mobility Ratio Displacement Calculations for Pattern Floods in Homogeneous Medium

1966 ◽  
Vol 6 (03) ◽  
pp. 217-227 ◽  
Author(s):  
Hubert J. Morel-Seytoux
Keyword(s):  
Oil Recovery ◽  
Numerical Solutions ◽  
Mobility Ratio ◽  
Finite Difference Approximation ◽  
Difference Approximation ◽  
Numerical Techniques ◽  
Sweep Efficiency ◽  
Closed Form Solutions ◽  
Regular Patterns ◽  
Quantities Of Interest

Abstract The influence of pattern geometry on assisted oil recovery for a particular displacement mechanism is the object of investigation in this paper. The displacement is assumed to be of unit mobility ratio and piston-like. Fluids are assumed incompressible and gravity and capillary effects are neglected. With these assumptions it is possible to calculate by analytical methods the quantities of interest to the reservoir engineer for a great variety of patterns. Specifically, this paper presentsvery briefly, the methods and mathematical derivations required to obtain the results of engineering concern, andtypical results in the form of graphs or formulae that can be used readily without prior study of the methods. Results of this work provide checks for solutions obtained from programmed numerical techniques. They also reveal the effect of pattern geometry and, even though the assumptions of piston-like displacement and of unit mobility ratio are restrictive, they can nevertheless be used for rather crude but quick, cheap estimates. These estimates can be refined to account for non-unit mobility ratio and two-phase flow by correlating analytical results in the case M=1 and the numerical results for non-Piston, non-unit mobility ratio displacements. In an earlier paper1 it was also shown that from the knowledge of closed form solutions for unit mobility ratio, quantities called "scale factors" could be readily calculated, increasing considerably the flexibility of the numerical techniques. Many new closed form solutions are given in this paper. INTRODUCTION BACKGROUND Pattern geometry is a major factor in making water-flood recovery predictions. For this reason many numerical schemes have been devised to predict oil recovery in either regular patterns or arbitrary configurations. The numerical solutions, based on the method of finite difference approximation, are subject to errors often difficult to evaluate. An estimate of the error is possible by comparison with exact solutions. To provide a variety of checks on numerical solutions, a thorough study of the unit mobility ratio displacement process was undertaken. To calculate quantities of interest to the reservoir engineer (oil recovery, sweep efficiency, etc.), it is necessary to first know the pressure distribution in the pattern. Then analytical procedures are used to calculate, in order of increasing difficulty: injectivity, breakthrough areal sweep efficiency, normalized oil recovery and water-oil ratio as a function of normalized PV injected. BACKGROUND Pattern geometry is a major factor in making water-flood recovery predictions. For this reason many numerical schemes have been devised to predict oil recovery in either regular patterns or arbitrary configurations. The numerical solutions, based on the method of finite difference approximation, are subject to errors often difficult to evaluate. An estimate of the error is possible by comparison with exact solutions. To provide a variety of checks on numerical solutions, a thorough study of the unit mobility ratio displacement process was undertaken. To calculate quantities of interest to the reservoir engineer (oil recovery, sweep efficiency, etc.), it is necessary to first know the pressure distribution in the pattern. Then analytical procedures are used to calculate, in order of increasing difficulty: injectivity, breakthrough areal sweep efficiency, normalized oil recovery and water-oil ratio as a function of normalized PV injected.

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