Various issues relating to computational fluid dynamics simulations of carotid bifurcation flow based on models reconstructed from three-dimensional ultrasound images

Computational fluid dynamics (CFD) flow simulation techniques have the potential to enhance understanding of how haemodynamic factors are involved in atherosclerosis. Recently, three-dimensional ultrasound has emerged as an alternative to other three-dimensional imaging techniques, such as magnetic resonance angiography (MRA). The method can be used to generate accurate vascular geometry suitable for CFD simulations and can be coupled with Doppler ultrasound to provide physiologically realistic flow boundary conditions. However, there are various ways to utilize the flow data acquired, possibly leading to different results regarding both flow and wall shear stress patterns. A disadvantage of three-dimensional ultrasound for imaging the carotid bifurcation has been established as being the scanning limitation of the jawbone position. This may make artificial extensions of the internal and/or external carotid arteries necessary, which in turn may influence the predicted flow patterns. Flow simulations were carried out for three outflow calculation schemes as well as four geometries with different extensions to the carotid daughter vessels. It was found that variation of flow patterns was more strongly influenced by the outflow conditions than by the extensions of the daughter vessels. Consequently, it is recommended that for future CFD simulations of carotid flow using three-dimensional ultrasound data, the outflow boundary conditions should rely on the most accurate measurement available, and flow data recorded in the common and internal carotid are considered more reliable than data from the external carotid. Even though the extended lengths of the daughter vessels have insignificant effects on the predicted haemodynamic parameters, it would be a safer option to extend the internal carotid by approximately three times the diameter of the common carotid artery.     

A dynamical model for generating synthetic electrocardiogram signals

A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. The operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In particular, both respiratory sinus arrhythmia at the high frequencies (HFs) and Mayer waves at the low frequencies (LFs) together with the LF/HF ratio are incorporated in the model. Much of the beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation are shown to result. This model may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.     

Modeling Human Judgment of Digital Imagery for Multimedia Retrieval

The application of machine learning techniques to image and video search has been shown to boost the performance of multimedia retrieval systems, and promises to lead to more generalized semantic search approaches. In particular, the availability of large training collections allows model-driven search using a substantial number of semantic concepts. The training collections are obtained in a manual annotation process where human raters review images and assign predefined semantic concept labels. Besides being prone to human error, manual image annotation is biased by the view of the individual annotator because visual information almost always leaves room for ambiguity. Ideally, several independent judgments are obtained per image, and the inter-rater agreement is assessed. While disagreement between ratings bears valuable information on the annotation quality, it complicates the task of clearly classifying rated images based on multiple judgments. In the absence of a gold standard, evaluating multiple judgments and resolving disagreement between raters is not trivial. In this paper, we present an approach using latent structure analysis to solve this problem. We apply latent class modeling to the annotation data collected during the TRECVID 2005 Annotation Forum, and demonstrate how to use this statistic to clearly classify each image on the basis of varying numbers of ratings. We use latent class modeling to quantify the annotation quality and discuss the results in comparison with the well-known Kappa inter-rater agreement measure.     

Institutional stability and industry renewal: diverging trajectories in the Cognac beverage cluster

Adding to approaches highlighting network dynamics as a basis for regional economic development, increased attention is paid to institutions as contextual factors contributing to explaining how and why economies change. Research has shown that firms tend to react differently to the same institutional configurations, with the main explanatory factors being their sectoral backgrounds and intra-firm characteristics. This study adds to these insights by examining a regional economy in France, that of Cognac, in which 300 firms are operating under homogeneous institutional preconditions. Despite these similarities, we identify different development trajectories from the 1990s onwards. Our observations illustrate how firms’ responses to external change diverge and bring them on different trajectories due to different positions in the industry hierarchy and different experiences and capabilities among individuals within firms. The study contributes to the better understanding of mechanisms of path dependence, which have gained wide recognition in the literature in the recent decades.     

Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model

The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area, is used in many numerical weather prediction (NWP) models as an indicator of the vegetation development state, which is of paramount importance to characterize land evaporation, photosynthesis, and carbon-uptake processes. LAI is often simply represented by lookup tables, dependent on the vegetation type and seasons. However, global LAI datasets derived from remote sensing observations have more recently become available. These products are based on sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically 3 days and with a spatial resolution of the order of 1 km.

We examine the meteorological impact of satellite-derived LAI products on near-surface air temperature and humidity, which comes both from the stomatal transpiration of leaves and from the intercepted water on the surface of leaves, re-evaporating into the atmosphere.

Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface (screen-level) air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.     

Modular design of mechatronic systems with function modeling

This paper develops a modularization scheme based on the functional model of a system. The modularization approach makes use of the function–behavior–state (FBS) model of the system to derive the entity relations. The design structure matrix (DSM) is automatically constructed based on the FBS model. In this way, the tedious work of filling the DSM entries based on expert knowledge is avoided. The approach makes use of k-means clustering algorithm to allow the user to try different number of clusters in a fast way. The k-means clustering is adopted for DSM based modularization by defining a proper entity representation, relation measure and objective function. Two modularization schemes are performed, one based on the immediate relations and one on the deeper behavioral relations between the components. Considering the application on the shifting system of the Delft University of Technology (DUT) Formula Student car, the latter modularization resulted in more mechatronic behavior based modules, while the former resulted in modules based on mere disciplinary and spatial closeness.     

Posture and muscular behaviour in emergency braking: An experimental approach

In the field of numerical crash simulations in road safety research, there is a need to accurately define the initial conditions of a frontal impact for the car occupant. In particular, human models used to simulate such impacts barely take into account muscular contracting effects. This study aims to quantify drivers’ behaviour in terms of posture and muscular activity just before a frontal impact.Experiments on volunteers were performed in order to define these conditions, both on a driving simulator and on a real moving car. Brake pedal loads, lower limbs kinematics and muscle activation were recorded.Coupling instantaneous data from both experimental protocols (simulator versus Real car), a standard emergency braking configuration could be defined as (1) joint flexion angles of 96°, 56° and 13° for the right hip, knee and ankle respectively; (2) a maximum brake pedal load of 780 N; (3) a muscular activation of 55% for the anterior thigh, 26% for the posterior thigh, 18% for the anterior leg and 43% for the posterior leg.The first application of this research is the implementation of muscle tone in human models designed to evaluate new safety systems.