Spatial scales, patterns, and positivity trends of SARS-CoV-2 pandemics in mass rapid antigen testing in Slovakia

Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

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Determining the Long-run Equilibrium Price by the Mean Reversion Process and the Cobweb Model

Proceedings of the 2nd Annual International Conference on Computational Mathematics, Computational Geometry & Statistics ◽

10.5176/2251-1911_cmcgs13.18 ◽

2013 ◽

Author(s):

Vuong Thi Thao Binh

Keyword(s):

Mean Reversion ◽

Equilibrium Price ◽

Cobweb Model ◽

Long Run ◽

The Mean ◽

Mean Reversion Process ◽

Long Run Equilibrium

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The mirror mode: a “superconducting” space plasma analogue

Annales Geophysicae ◽

10.5194/angeo-36-1015-2018 ◽

2018 ◽

Vol 36 (4) ◽

pp. 1015-1026 ◽

Cited By ~ 2

Author(s):

Rudolf A. Treumann ◽

Wolfgang Baumjohann

Keyword(s):

Magnetic Field ◽

Correlation Length ◽

Spatial Scales ◽

Debye Length ◽

Low Frequency ◽

Space Plasma ◽

Equilibrium Structure ◽

Critical Magnetic Field ◽

Anisotropic Pressure ◽

Mirror Mode

Abstract. We examine the physics of the magnetic mirror mode in its final state of saturation, the thermodynamic equilibrium, to demonstrate that the mirror mode is the analogue of a superconducting effect in a classical anisotropic-pressure space plasma. Two different spatial scales are identified which control the behaviour of its evolution. These are the ion inertial scale λim(τ) based on the excess density Nm(τ) generated in the mirror mode, and the Debye scale λD(τ). The Debye length plays the role of the correlation length in superconductivity. Their dependence on the temperature ratio τ=T‖/T⟂<1 is given, with T⟂ the reference temperature at the critical magnetic field. The mirror-mode equilibrium structure under saturation is determined by the Landau–Ginzburg ratio κD=λim/λD, or κρ=λim/ρ, depending on whether the Debye length or the thermal-ion gyroradius ρ – or possibly also an undefined turbulent correlation length ℓturb – serve as correlation lengths. Since in all space plasmas κD≫1, plasmas with λD as the relevant correlation length always behave like type II superconductors, naturally giving rise to chains of local depletions of the magnetic field of the kind observed in the mirror mode. In this way they would provide the plasma with a short-scale magnetic bubble texture. The problem becomes more subtle when ρ is taken as correlation length. In this case the evolution of mirror modes is more restricted. Their existence as chains or trains of larger-scale mirror bubbles implies that another threshold, VA>υ⟂th, is exceeded. Finally, in case the correlation length ℓturb instead results from low-frequency magnetic/magnetohydrodynamic turbulence, the observation of mirror bubbles and the measurement of their spatial scales sets an upper limit on the turbulent correlation length. This might be important in the study of magnetic turbulence in plasmas.

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Bi-cubic B-spline fitting-based local volatility model with mean reversion process

Journal of Systems Science and Complexity ◽

10.1007/s11424-015-3066-8 ◽

2016 ◽

Vol 29 (1) ◽

pp. 119-132 ◽

Cited By ~ 1

Author(s):

Shifei Zhou ◽

Hao Wang ◽

Jerome Yen ◽

Kin Keung Lai

Keyword(s):

Mean Reversion ◽

Local Volatility ◽

B Spline ◽

Spline Fitting ◽

Local Volatility Model ◽

Volatility Model ◽

Mean Reversion Process

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A COMPARISON OF UAV AND TLS DATA FOR SOIL ROUGHNESS ASSESSMENT

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-5-145-2016 ◽

2016 ◽

Vol III-5 ◽

pp. 145-152

Author(s):

M. Milenković ◽

W. Karel ◽

C. Ressl ◽

N. Pfeifer

Keyword(s):

Correlation Length ◽

Measurement Errors ◽

Laser Scanning ◽

Spatial Scales ◽

Reference Method ◽

Bundle Adjustment ◽

Surface Model ◽

Surface Geometry ◽

Roughness Assessment ◽

Uav Images

Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

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Spatial trend analysis of gridded temperature data at varying spatial scales

Advances in Statistical Climatology Meteorology and Oceanography ◽

10.5194/ascmo-6-1-2020 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1-12

Author(s):

Ola Haug ◽

Thordis L. Thorarinsdottir ◽

Sigrunn H. Sørbye ◽

Christian L. E. Franzke

Keyword(s):

Spatial Scales ◽

Gaussian Random Field ◽

Summer Season ◽

Temperature Data ◽

Hierarchical Bayesian Model ◽

Spatial Smoothing ◽

Spatial Correlations ◽

Spatial Trend ◽

Trend Coefficient

Abstract. Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.

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Testing the correlation between mean reversion process and grey system theory for metal price forecasting

Podzemni radovi ◽

10.5937/podrad1424061g ◽

2014 ◽

Vol 22 (24) ◽

pp. 61-71

Author(s):

Zoran Gligorić ◽

Jelena Milojević ◽

Čedomir Beljić

Keyword(s):

System Theory ◽

Mean Reversion ◽

Grey System Theory ◽

Price Forecasting ◽

Grey System ◽

Mean Reversion Process

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Characteristic spatial scales of SARS-CoV-2 pandemics: lessons from mass rapid antigen testing in Slovakia

10.1101/2020.12.23.20248808 ◽

2020 ◽

Author(s):

Katarína Boďová ◽

Richard Kollár

Keyword(s):

Spatial Scales ◽

Mitigation Measures ◽

Spatial Characteristic ◽

Repeated Testing ◽

Important Data ◽

Geographical Patterns ◽

Per Capita ◽

Good Proxy ◽

Distributed Test ◽

Antigen Testing

AbstractMass antigen testing in Slovakia conducted in October and November 2020 is a source of important data. We perform its statistical analysis and study epidemic geographical patterns. We observe exponentially distributed test positivity and exponential trends in its geographical distribution, and its approximately 10 km spatial characteristic correlation length. A small correlation between positivity in two consecutive testing rounds appeared on the municipalities level but it significantly increased on the counties level. Recent 7-day PCR tests incidence per capita served as a good proxy for antigen test positivity. Positivity of non-residents was higher than of residents when mass testing was offered only in municipalities with the highest positivity in previous rounds. Reduction in positivity in repeated testing increased with the positivity in the earlier round. Our results contribute to better understanding of pandemic data, and aid an assessment of mass testing efficiency, and planning of mitigation measures.

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