The Effects of Interruption on Takeover Performance in Automated Vehicles

As autonomous driving technology developing, the role of human driver becomes a passive passenger in an automated vehicle. Drivers would perform non-driving related tasks instead of driving, especially multitasking.. However, most of studies did not considered multitasking as NDRTs. In this study, experiment considering self-interruption is conducted and investigate effects of interruption on takeover performance. To investigate effects of interruption on takeover performance, experiment using driving simulator was conducted. Watching a short video was selected as a NDRT and there were three conditions of NDRT according to self-interruption: ‘Baseline’, ‘Monitoring condition’, and ‘Smartphone condition’. Takeover performance was measured by eyes-on time and deactivation time. There was no statistically significant difference for eyes-on time depending on interruption. However, interruption has a significant effect on deactivation time. Also, it was more effective to use a smartphone during interruption.

Improving baseline and follow-up physical health monitoring when commencing oral antipsychotics

AimsNICE guidelines suggest baseline physical health monitoring be performed prior to commencing antipsychotics, in addition to follow-up monitoring for adverse effects for at least 12 months. ‘Shared Care Guidelines’ were adapted from NICE guidance for local use in North East Lincolnshire. Nevertheless, a local audit published in 2018 reported low compliance with baseline monitoring in community mental health teams (CMHTs) compared to inpatient teams. The parameter most infrequently performed overall was the Glasgow Antipsychotic Side Effect Scale (GASS) questionnaire.This study aimed to assess whether compliance with baseline physical health monitoring had improved in line with the previous audit's recommendations. Additionally, it aimed to expand on previous findings by adding compliance data for follow-up physical health checks and produce further recommendations to optimise performance.MethodA retrospective re-audit was performed in NAViGO Health and Social Care to assess compliance with the guidelines for physical health monitoring when commencing antipsychotics in previously antipsychotic-naïve patients. Patient records were examined for which recommended physical health checks were performed at baseline, and at 1-, 3- and 6- months from commencing antipsychotics.Result15 eligible patients were identified to have been commenced on antipsychotics, 8 patients under a CMHT and 7 under an inpatient team. The average overall compliance at baseline for checking 16 parameters was 50%. For the CMHT, compliance was 60%, compared to 38% for the inpatient team. Across both teams, baseline compliance was highest for renal function tests, liver function tests, and blood pressure and pulse (80%). For 1-, 3-, and 6- month checks, overall compliance for checking recommended parameters were 33%, 29% and 29% respectively. GASS monitoring compliance was 7% at baseline, 0% at 1- and 3-months, 7% at 6-months.ConclusionThe CMHT performed better than the inpatient team at baseline monitoring. This may reflect action on the previous audit's recommendations to increase provision of community ‘Wellbeing Health Improvement Service’ (WHISe) clinics. However, performance of the GASS questionnaire at baseline was consistent with the previous audit, with similar performance at follow-up extending these findings.In response, the first recommendation is for Quality Improvement Activities to help improve compliance with the ‘Shared Care Guidelines’. This may include CQUINs and further provision of community clinics to improve compliance with both baseline and follow-up checks. Secondly, it is proposed that GASS questionnaires be sent to patients prior to appointments to be completed in advance to avoid further risk of GASS being incomplete.

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

Volcanoes have dormancy periods that may last decades to centuries meaning that eruptions at volcanoes with no historical records of eruptions are common. Baseline monitoring to detect the early stages of reawakening is therefore important even in regions with little recent volcanic activity. Satellite techniques, such as InSAR, are ideally suited for routinely surveying large and inaccessible regions, but the large datasets typically require expert interpretation. Here we focus on Turkey where there are 10 Holocene volcanic systems, but no eruptions since 1855 and consequently little ground-based monitoring. We analyse data from the first five years of the European Space Agency Sentinel-1 mission which collects data over Turkey every 6 days on both ascending and descending passes. The high relief edifices of Turkey’s volcanoes cause two challenges: 1) snow cover during the winter months causes a loss of coherence and 2) topographically-correlated atmospheric artefacts could be misinterpreted as deformation. We propose mitigation strategies for both. The raw time series at Hasan Dag volcano shows uplift of ~ 10 cm between September 2017 and July 2018, but atmospheric corrections based on global weather models demonstrate that this is an artefact and reduce the scatter in the data to < 1 cm. We develop two image classification schemes for dealing with the large datasets: one is an easy to follow flowchart designed for non-specialist monitoring staff, and the other is an automated flagging system using a deep learning approach. We apply the deep learning scheme to a dataset of ~ 5000 images over the 10 Turkish volcanoes and find 4 possible signals, all of which are false positives. We conclude that there has been no cm-scale volcano deformation in Turkey in 2015–2020, but further analysis would be required to rule out slower rates of deformation (< 1 cm/yr). This study has demonstrated that InSAR techniques can be used for baseline monitoring in regions with few historical eruptions or little reported deformation.

Can Conversation-Based Intervention Using Speech-Generating Devices Improve Language in Children With Partially Intelligible Speech?

Children with partially intelligible speech and language difficulties are often provided with speech-generating devices (SGDs) to support their spoken communication. However, little is known about how SGDs can be used best to facilitate language development. This study evaluated the effects of a conversation-based intervention using SGDs, on the rate of expressive clause production in children with speech that is partially intelligible (SPI). A multiple probe across participants design was used to evaluate intervention with four children aged 8 to 10 years. Following baseline monitoring, the children received language-focused intervention involving adult recasting, SGD modeling, and prompting to repair. Participants showed increases in their rate, complexity and length of clause use. Gains were sustained and generalized. Intervention using conversation, recasting, and SGD modeling can increase clause production and grammaticality of children with SPI.