Systemic risk approach to mitigate delay cascading in railway networks

In public railway systems, minor disruptions can trigger cascading events that lead to delays in the entire system. Typically, delays originate and propagate because the equipment is blocking ways, operational units are unavailable, or at the wrong place at the needed time. The specific understanding of the origins and processes involved in delay-spreading is still a challenge, even though large-scale simulations of national railway systems are becoming available on a highly detailed scale. Without this understanding, efficient management of delay propagation, a growing concern in some Western countries, will remain impossible. Here, we present a systemic risk-based approach to manage daily delay cascading on national scales. We compute the {\em systemic impact} of every train as the maximum of all delays it could possibly cause due to its interactions with other trains, infrastructure, and operational units. To compute it, we design an effective impact network where nodes are train services and links represent interactions that could cause delays. Our results are not only consistent with highly detailed and computationally intensive agent-based railway simulations but also allow us to pinpoint and identify the causes of delay cascades in detail. The systemic approach reveals structural weaknesses in railway systems whenever shared resources are involved. We use the systemic impact to optimally allocate additional shared resources to the system to reduce delays with minimal costs and effort. The method offers a practical and intuitive solution for delay management by optimizing the effective impact network through the introduction of new cheap local train services.Comment: 27 pages, 14 figure

HUMAN-ROBOT INTERACTION: INVESTIGATING CONTROL PARADIGMS FOR END-EFFECTOR BASED UPPER LIMB REHABILITATION

Ph.DDOCTOR OF PHILOSOPHY (NUSGS

Interactive robot assistance for upper-limb training

Burdet E, Li Y, Kager S, Chua Sui Geok K, Hussain A, Campolo D. Interactive robot assistance for upper-limb training. In: Colombo R, Sanguineti V, eds. Rehabilitation Robotics. Technology and application. London: Elsevier; 2018: 137-148

Proprioceptive assessment in clinical settings: Evaluation of joint position sense in upper limb post-stroke using a robotic manipulator

<div><p>Proprioception is a critical component for motor functions and directly affects motor learning after neurological injuries. Conventional methods for its assessment are generally ordinal in nature and hence lack sensitivity. Robotic devices designed to promote sensorimotor learning can potentially provide quantitative precise, accurate, and reliable assessments of sensory impairments. In this paper, we investigate the clinical applicability and validity of using a planar 2 degrees of freedom robot to quantitatively assess proprioceptive deficits in post-stroke participants. Nine stroke survivors and nine healthy subjects participated in the study. Participants’ hand was passively moved to the target position guided by the H-Man robot (Criterion movement) and were asked to indicate during a second passive movement towards the same target (Matching movement) when they felt that they matched the target position. The assessment was carried out on a planar surface for movements in the forward and oblique directions in the contralateral and ipsilateral sides of the tested arm. The matching performance was evaluated in terms of error magnitude (absolute and signed) and its variability. Stroke patients showed higher variability in the estimation of the target position compared to the healthy participants. Further, an effect of target was found, with lower absolute errors in the contralateral side. Pairwise comparison between individual stroke participant and control participants showed significant proprioceptive deficits in two patients. The proposed assessment of passive joint position sense was inherently simple and all participants, regardless of motor impairment level, could complete it in less than 10 minutes. Therefore, the method can potentially be carried out to detect changes in proprioceptive deficits in clinical settings.</p></div

Apparatus and procedure.

<p>(A) Participant (blindfolded) holding the handle of H-Man, the robotic device employed in the study. (B) Experimental procedure: starting from the initial position (1), H-Man placed the handle on the target position and held it there for 2 seconds (2), after which the handle was returned to the initial position to start the new movement towards the same target (3), which was stopped via a hand-held button by the investigator when the participant verbally indicated that the position of the handle matched the target (4). The handle was then returned to the initial position for the following trial.</p

Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform

ISSN:1664-229

Pairwise comparisons of signed errors between each subject and the control group in the 3 directions.

<p>Mean differences are evaluated as Control-S. Significant differences are marked with an asterisk.</p

Variability.

<p>(A) Box-plot of variability for the two groups. (B) Mean variability in the 3 directions for the two groups, where the gray squares represents data from the control group and black circles represent stroke patients.</p

Pairwise comparisons of absolute errors between each subject and the control group in the 3 directions.

<p>Mean differences are evaluated as Control-S. Significant differences are marked with an asterisk.</p

Absolute errors.

<p>(A) Box-plot of absolute errors for the two groups. (B) Mean absolute errors in the 3 directions for the two groups, where the gray squares represent data from the control group and black circles represent stroke patients. (C) Absolute errors for each patient and mean value of the control subjects.</p