Subsoil compaction due to wheel traffic

Selostus: Raskaan peltoliikenteen aiheuttama pitkäaikainen maan tiivistyminen

Langevin equations with multiplicative noise: Application to domain growth

Langevin Equations of Ginzburg-Landau form, with multiplicative noise, are proposed to study the effects of fluctuations in domain growth. These equations are derived from a coarse-grained methodology. The Cahn-Hiliard-Cook linear stability analysis predicts some effects in the transitory regime. We also derive numerical algorithms for the computer simulation of these equations. The numerical results corroborate the analytical predictions of the linear analysis. We also present simulation results for spinodal decomposition at large times.

Readout schemes for low noise single-photon avalanche diodes fabricated in conventional HV-CMOS technologies

Three different pixels based on single-photon avalanche diodes for triggered applications, such as fluorescence lifetime measurements and high energy physics experiments, are presented. Each pixel consists of a 20µm x 100µm (width x length) single photon avalanche diode and a monolithically integrated readout circuit. The sensors are operated in the gated mode of acquisition to reduce the probability to detect noise counts interferring with real radiation events. Each pixel includes a different readout circuit that allows to use low reverse bias overvoltages. Experimental results demonstrate that the three pixels present a similar behaviour. The pixels get rid of afterpulses and present a reduced dark count probability by applying the gated operation. Noise figures are further improved by using low reverse bias overvoltages. The detectors exhibit an input dynamic range of 13.35 bits with short gated"on" periods of 10ns and a reverse bias overvoltage of 0.5V. The three pixels have been fabricated in a standard HV-CMOS process.

Tolerance and Relative Utility: Two Proposed Indices for Comparing Change in Clinical Measurement Noise Between Different Populations (Repeatability) or Measurement Methods (Agreement).

Reaching a consensus in terms of interchangeability and utility (i.e., disease detection/monitoring) of a medical device is the eventual aim of repeatability and agreement studies. The aim of the tolerance and relative utility indices described in this report is to provide a methodology to compare change in clinical measurement noise between different populations (repeatability) or measurement methods (agreement), so as to highlight problematic areas. No longitudinal data are required to calculate these indices. Both indices establish a metric of least to most effected across all parameters to facilitate comparison. If validated, these indices may prove useful tools when combining reports and forming the consensus required in the validation process for software updates and new medical devices.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context

Asymmetric stochastic switching driven by intrinsic molecular noise

Low-copy-number molecules are involved in many functions in cells. The intrinsic fluctuations of these numbers can enable stochastic switching between multiple steady states, inducing phenotypic variability. Herein we present a theoretical and computational study based on Master Equations and Fokker-Planck and Langevin descriptions of stochastic switching for a genetic circuit of autoactivation. We show that in this circuit the intrinsic fluctuations arising from low-copy numbers, which are inherently state-dependent, drive asymmetric switching. These theoretical results are consistent with experimental data that have been reported for the bistable system of the gallactose signaling network in yeast. Our study unravels that intrinsic fluctuations, while not required to describe bistability, are fundamental to understand stochastic switching and the dynamical relative stability of multiple states.

Physiological noise in human cerebellar fMRI.

OBJECTIVES: To compare physiological noise contributions in cerebellar and cerebral regions of interest in high-resolution functional magnetic resonance imaging (fMRI) data acquired at 7T, to estimate the need for physiological noise removal in cerebellar fMRI. MATERIALS AND METHODS: Signal fluctuations in high resolution (1 mm isotropic) 7T fMRI data were attributed to one of the following categories: task-induced BOLD changes, slow drift, signal changes correlated with the cardiac and respiratory cycles, signal changes related to the cardiac rate and respiratory volume per unit of time or other. [Formula: see text] values for all categories were compared across regions of interest. RESULTS: In this high-resolution data, signal fluctuations related to the phase of the cardiac cycle and cardiac rate were shown to be significant, but comparable between cerebellar and cerebral regions of interest. However, respiratory related signal fluctuations were increased in the cerebellar regions, with explained variances that were up to 80 % higher than for the primary motor cortex region. CONCLUSION: Even at a millimetre spatial resolution, significant correlations with both cardiac and respiratory RETROICOR components were found in all healthy volunteer data. Therefore, physiological noise correction is highly likely to improve the temporal signal-to-noise ratio (SNR) for cerebellar fMRI at 7T, even at high spatial resolution.

Are traffic violators criminals? Searching for answers in experiences of European countries

The connection between road traffic safety and criminal behavior has recently become a topic of interest in the literature, although little emphasis placed on the relationship with road accidents. Evidence worldwide shows that people who commit other offences characteristic of antisocial attitudes, are more prone to suffer road traffic accidents and infringe traffic laws. Here we examine the records of the 28 current member states of the European Union over the period 1999 - 2010. Our aim is to test the hypothesis that crime rates (and specifically, motor vehicle-related crimes) may be considered as predictors of fatal road traffic accidents. If they may, this could justify, at least prima facie, the tendency in several countries to consider traffic offences as crimes in their penal codes and to toughen the punishment imposed on those who commit them. We also analyze the effect of the severity of the legal system applied to traffic offences. Our results reveal that road traffic fatality rates are higher in countries whose inhabitants have more aggressive behavior, while the rates are lower in countries with more severe penal systems.

Are traffic violators criminals? Searching for answers in experiences of European countries

The connection between road traffic safety and criminal behavior has recently become a topic of interest in the literature, although little emphasis placed on the relationship with road accidents. Evidence worldwide shows that people who commit other offences characteristic of antisocial attitudes, are more prone to suffer road traffic accidents and infringe traffic laws. Here we examine the records of the 28 current member states of the European Union over the period 1999 - 2010. Our aim is to test the hypothesis that crime rates (and specifically, motor vehicle-related crimes) may be considered as predictors of fatal road traffic accidents. If they may, this could justify, at least prima facie, the tendency in several countries to consider traffic offences as crimes in their penal codes and to toughen the punishment imposed on those who commit them. We also analyze the effect of the severity of the legal system applied to traffic offences. Our results reveal that road traffic fatality rates are higher in countries whose inhabitants have more aggressive behavior, while the rates are lower in countries with more severe penal systems.

A comprehensive model for quantum noise characterization in digital mammography.

NlmCategory="UNASSIGNED">A version of cascaded systems analysis was developed specifically with the aim of studying quantum noise propagation in x-ray detectors. Signal and quantum noise propagation was then modelled in four types of x-ray detectors used for digital mammography: four flat panel systems, one computed radiography and one slot-scan silicon wafer based photon counting device. As required inputs to the model, the two dimensional (2D) modulation transfer function (MTF), noise power spectra (NPS) and detective quantum efficiency (DQE) were measured for six mammography systems that utilized these different detectors. A new method to reconstruct anisotropic 2D presampling MTF matrices from 1D radial MTFs measured along different angular directions across the detector is described; an image of a sharp, circular disc was used for this purpose. The effective pixel fill factor for the FP systems was determined from the axial 1D presampling MTFs measured with a square sharp edge along the two orthogonal directions of the pixel lattice. Expectation MTFs were then calculated by averaging the radial MTFs over all possible phases and the 2D EMTF formed with the same reconstruction technique used for the 2D presampling MTF. The quantum NPS was then established by noise decomposition from homogenous images acquired as a function of detector air kerma. This was further decomposed into the correlated and uncorrelated quantum components by fitting the radially averaged quantum NPS with the radially averaged EMTF(2). This whole procedure allowed a detailed analysis of the influence of aliasing, signal and noise decorrelation, x-ray capture efficiency and global secondary gain on NPS and detector DQE. The influence of noise statistics, pixel fill factor and additional electronic and fixed pattern noises on the DQE was also studied. The 2D cascaded model and decompositions performed on the acquired images also enlightened the observed quantum NPS and DQE anisotropy.

Present and future methodology for the implementation of decision support systems for traffic management

Real-time predictions are an indispensable requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. The combination of predicting the state of the network and the evaluation of different traffic management strategies in the short term future allows system managers to anticipate the effects of traffic control strategies ahead of time in order to mitigate the effect of congestion. This paper presents the current framework of decision support systems for traffic management based on short and medium-term predictions and includes some reflections on their likely evolution, based on current scientific research and the evolution of the availability of new types of data and their associated methodologies.

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network.