Automated ice-core layer-counting with strong univariate signals

We present an automated process for determining the annual layer chronology of an ice-core with a strong annual signal, utilising the hydrogen peroxide record from an Antarctic Peninsula ice-core as a test signal on which to count annual cycles and explain the methods. The signal is de-trended and normalised before being split into sections with a deterministic cycle count and those that need more attention. Possible reconstructions for the uncertain sections are determined which could be used as a visual aid for manual counting, and a simple method for assigning probability measures to each reconstruction is discussed. The robustness of this process is explored by applying it to versions of two different chemistry signals from the same stretch of the NGRIP (North Greenland Ice Core Project) ice-core, which shows more variation in annual layer thickness, with and without thinning to mimic poorer quality data. An adapted version of these methods is applied to the more challenging non-sea-salt sulphur signal from the same Antarctic Peninsula core from which the hydrogen peroxide signal was taken. These methods could readily be adapted for use on much longer datasets, thereby reducing manual effort and providing a robust automated layer-counting methodology

Hard, soft and off-the-shelf foot orthoses and their effect on the angle of the medial longitudinal arch: A biplane fluoroscopy study

Background: Foot orthoses have proven to be effective for conservative management of various pathologies. Pathologies of the lower limb can be caused by abnormal biomechanics such as abnormal foot structure and alignment, leading to inadequate support. Objectives: To compare biomechanical effects of different foot orthoses on the medial longitudinal arch (MLA) during dynamic gait using skeletal kinematics. Study Design: Prospective, cross-sectional study design. Methods: The MLA angle was measured for 12 participants among three groups: pes planus, pes cavus and normal arch. Five conditions were compared: three orthotic devices (hard custom foot orthosis (CFO), soft CFO, and off-the-shelf Barefoot Science©), barefoot and shod. An innovative method, markerless fluoroscopic radiostereometric analysis (RSA), was used to measure the MLA angle. Results: Mean MLA angles for both CFO conditions were significantly different from the barefoot and shod conditions (p0.05). Additionally, the differences between hard and soft CFOs were not statistically significant. All foot types showed an MLA angle decrease with both the hard and soft CFOs. Conclusions: These results suggest that CFOs can reduce motion of the MLA for a range of foot types during dynamic gait

The optimization of traffic count locations in multi-modal networks

In this paper we will investigate ways to optimize the placement and number of traffic counters used in multi-modal transportation analysis studies for motorized vehicles, bicycles and pedestrians. The goal is to strike a balance between using as few as possible traffic counters for economical efficiency and deploying more counters which could collect more data. By using shortest path algorithms to determine the paths between the centroids of statistical divisions, we derive from origin-destination matrices which traf

Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive l

Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research

Taste variation in environmental features of bicycle routes

In this paper we look at route choice modeling based on observational GPS traces collected by bicyclists in Amsterdam and surroundings. We consider factors influencing bicycle route choice such as distance and environmental factors such as cycle-way infrastructure, land-use environment, tree cover and the effect of noise emitting roads using data from a noise emission model. We estimate a route choice model, comparing multinomial logit, mixed logit and mixed path size logit specifications. Our results show that cyclists have a highly stochastic behavior that are likely to prefer detours to drive over cycle-way infrastructure, near greener landuse and near water, and on less busy roads. Models such as mixed logit that can estimate the stochasticity of cyclists perform best to capture this behavior

Door-to-door transit accessibility using Pareto optimal range queries

Public transit is a backbone for well-functioning cities, forming a complicated system of interconnecting lines each with their own frequency. Defining accessibility for public transit is just as complicated, as travel times can change every minute depending on location and departure time. With Pareto optima

A review of methods to model route choice behavior of bicyclists: inverse reinforcement learning in spatial context and recursive logit

Used for route choice modeling by the transportation research community, recursive logit is a form of inverse reinforcement learning, the field of learning an agent’s objective by observing it’s behavior. By solving a large-scale system of linear equations it allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of IRL models applied to real world travel trajectories and look at some of the challenges with recursive logit for modeling bicycle route choice in the city center area of Amsterdam

Mechanisms of pulsus paradoxus during resistive respiratory loading and asthma

To determine the mechanisms of pulsus paradoxus during asthma, six subjects known to have cold air bronchial hyperreactivity were studied while in a quiescent phase of their disease. All were free of significant airway obstruction at the time of study. After placement of an esophageal balloon to estimate intrathoracic pressure, the subjects were assessed during quiet breathing, resistive airway loading and then during a stable period of airway obstruction induced by cold air. Steady state left ventricular volume and performance were measured using radionuclide ventriculography; right ventricular volume was calculated from the stroke volume ratio and right ventricular ejection fraction. Cardiac cycles were segregated according to their occurrence in inspiration or expiration using a flow signal from a pneumotachograph.Combined inspiratory and expiratory resistance produced pulsus paradoxus and changes in esophageal pressure that were similar to those during asthma and significantly greater than those during quiet breathing. These changes were accompanied by decreases in left ventricular diastolic volume and stroke volume during inspiration, and increases in these variables during expiration; right ventricular volume and stroke volume demonstrated changes reciprocal to those seen in the left ventricle. These data indicate that during periods of increase in airway resistance, abnormal pulsus paradoxus results from an exaggeration in the normal inspiratory-expiratory difference in stroke volume mediated primarily by the effects of intrathoracic pressure on ventricular preload