Improving Real-Time Feedback During Cochlear Implantation: The Auditory Nerve Neurophonic/Cochlear Microphonic Ratio.

Objectives: Real-time monitoring of cochlear function to predict the loss of residual hearing after cochlear implantation is now possible. Current approaches monitor the cochlear microphonic (CM) during implantation from the electrode at the tip of the implant. A drop in CM response of >30% is associated with poorer hearing outcomes.

Evaluating the use of systems thinking methods in healthcare: a RE-AIM analysis of AcciMap and Net-HARMS.

There are increasing calls for the application of systems ergonomics methods in healthcare, although evidence for their utility and uptake is limited. In this study, 67 Australian healthcare workers participated in a six-month longitudinal study where they were trained to apply the AcciMap adverse event analysis and Net-HARMS risk assessment methods. Data were gathered in line with the RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) evaluation framework, including rates of organisational uptake and method validity, perceived workload, usability, and barriers and facilitators to use in practice.

Over a decade of UAV incidents: A human factors analysis of causal factors.

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014.

Effect of reducing electrical stimulation rate on hearing performance of Nucleus cochlear implant recipients.

Objective: To evaluate whether a 500 pulses per second per channel (pps/ch) rate would provide non-inferior hearing performance compared to the 900 pps/ch rate in the Advanced Combination Encoder (ACE™) sound coding strategy.

Design: A repeated measures single-subject design was employed, wherein each subject served as their own control. All except one subject used 900 pps/ch at enrolment.

Resilient interactions between cyclists and drivers, and what does this mean for automated vehicles?

The road transport system is a complex sociotechnical system that relies on a number of formal and informal rules of the road to ensure safety and resilience. Interactions between vulnerable road users and drivers often includes informal communication channels that are tightly linked to social norms, user expectations and the environmental context. Automated vehicles have a challenge in being able to communicate and respond to these informal rules of the road, therefore additional technologies are required to better support vulnerable road users.

Leptonic Model for Neutrino Emission from Active Galactic Nuclei.

It is often stated that the observation of high-energy neutrinos from an astrophysical source would constitute indisputable proof for the acceleration of hadronic cosmic rays. Here, we point out that there exists a purely leptonic mechanism to produce TeV-scale neutrinos in astrophysical environments. In particular, very high-energy synchrotron photons can scatter with x rays, exceeding the threshold for muon-antimuon pair production.

Medical practitioner perspectives on AI in emergency triage.

Introduction: A proposed Diagnostic AI System for Robot-Assisted Triage ("DAISY") is under development to support Emergency Department ("ED") triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England ("NHS") but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload. The objective of this study was to explore NHS health practitioners' perspectives and attitudes towards the future use of AI-supported technologies in ED triage.

Characteristics of pain and their relationship to disease activity in UK patients with Behçet's syndrome: a prospective cohort study.

Background: Behçet's syndrome (BS) is a rare multi-systemic vasculitis of unknown aetiology. Fibromyalgia syndrome (FMS) is more prevalent in rheumatological conditions such-as BS, than the general population. However, there is limited research into the aetiology and characteristics of pain in BS.

Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning.

Background: Exposing a healthy wound bed for skin grafting is an important step during burn surgery to ensure graft take and maintain good functional outcomes. Currently, the removal of non-viable tissue in the burn wound bed during excision is determined by expert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artificial intelligence to aid clinician judgment for the excision of non-viable tissue.

Exploring improvisations in road safety in a low-income setting.

Road collision types repeat themselves, especially in low- and middle-income countries (LMICs), where countermeasures are often improvised and implemented with little planning. At the Shahbag intersection in Dhaka, Bangladesh, speed bumps were quickly constructed at the exit of the intersection as an improvised road safety measure following the occurrence of a fatal collision, which eventually contributed to another collision between a truck and a car. The events influencing the improvisation decision, and that action's consequences, have been analysed using the Impromap methodology, a variation of the Accimap approach that focusses specifically on improvisation.

Training for the safe activation of Automated Vehicles matters: Revealing the benefits of online training to creating glaringly better mental models and behaviour.

Automated Vehicle (AV) systems are expected to reduce the frequency and severity of on-road collisions. Unless drivers have an appropriate mental model for the capabilities and limitations of the automation, they may not activate the automation safely or appropriately on the road, potentially leading to a collision. As such, a training package (L4DTP) was developed to improve drivers' decisions and behaviour when activating an AV system and this was evaluated in a between-subjects simulator experiment.

A new approach for Training Needs Analysis: A case study using an Automated Vehicle.

Considerable resources are invested each year into training to ensure trainees have the required competencies to safely and effectively perform their tasks/jobs. As such, it is important to develop effective training programmes which target those required competencies. One method that can be used at the start of the training lifecycle to establish the tasks and competencies that are required for a task/job and is considered an important activity to perform when developing a training programme is a Training Needs Analysis (TNA).

Clinical Investigation of a Rapid Non-invasive Multispectral Imaging Device Utilizing an Artificial Intelligence Algorithm for Improved Burn Assessment.

Currently, the incorrect judgment of burn depth remains common even among experienced surgeons. Contributing to this problem are change in burn appearance throughout the first week requiring periodic evaluation until a confident diagnosis can be made. To overcome these issues, we investigated the feasibility of an artificial intelligence algorithm trained with multispectral images of burn injuries to predict burn depth rapidly and accurately, including burns of indeterminate depth.

Therapeutic targeting of vascular malformation in a zebrafish model of hereditary haemorrhagic telangiectasia.

Hereditary haemorrhagic telangiectasia (HHT) causes arteriovenous malformations (AVMs) in multiple organs to cause bleeding, neurological and other complications. HHT is caused by mutations in the BMP co-receptor endoglin. We characterised a range of vascular phenotypes in embryonic and adult endoglin mutant zebrafish and the effect of inhibiting different pathways downstream of Vegf signalling.

Monitoring Cochlear Health With Intracochlear Electrocochleography During Cochlear Implantation: Findings From an International Clinical Investigation.

Objectives: Electrocochleography (ECochG) is emerging as a tool for monitoring cochlear function during cochlear implant (CI) surgery. ECochG may be recorded directly from electrodes on the implant array intraoperatively. For low-frequency stimulation, its amplitude tends to rise or may plateau as the electrode is inserted.

Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g.

Internalized Homonegativity and Mental Health Among Chinese Gay and Bisexual Cisgender Men: Exploring the Moderating Influences of Psychosocial and Sexual Identity Factors.

Much research on Chinese gay and bisexual men has focused on sexual health and, in particular, HIV-related issues. However, relatively little research has considered psychosocial aspects of being a gay or bisexual man in China. The present study was designed to address this gap by examining psychosocial predictors of mental health from a minority stress perspective.

Resilience engineering on the road: Using operator event sequence diagrams and system failure analysis to enhance cyclist and vehicle interactions.

Future visions of transport systems include both a drive towards automated vehicles and the need for sustainable, active, modes of travel. The combination of these requirements needs careful consideration to ensure the integration of automated vehicles does not compromise vulnerable road users. Transport networks need to be resilient to automation integration, which requires foresight of possible challenges in their interaction with other road users.

Predicting and mitigating failures on the flight deck: an aircraft engine bird strike scenario.

Engine damage as a consequence of foreign object debris (FOD) during flight is frequently caused by birds. One approach to minimising disruption caused by this damage is to provide flight crew with accurate information relating to the continuing operational status of the aircraft's engines. Before designing such avionic systems however, understanding of current procedures is needed.

Zebrafish vascular quantification: a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis.

Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into three-dimensional vascular development in vivo. However, quantification of the zebrafish cerebral vasculature in 3D remains highly challenging. Here, we describe and test an image analysis workflow for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification (ZVQ).

Thinking aloud on the road: Thematic differences in the experiences of drivers, cyclists, and motorcyclists.

This study takes a qualitative approach to exploring the experiences (and differences therein) of individuals using either their car, bicycle, or motorcycle to navigate a ∼10.5 km urban route in a provincial UK city, with the aim of contributing to our understanding of the needs and requirements of different road users. Forty-six individuals provided concurrent verbal reports, using the 'think aloud' method, whilst using their vehicle to navigate the test route, the transcripts of which were subjected to a theory-agnostic, inductive, thematic analysis.

Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size.

While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual's hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance.

Taking a mixed-methods approach to collision investigation: AcciMap, STAMP-CAST and PCM.

Recently, ergonomics and safety researchers have turned their attention towards applying combinations of sociotechnical methods rather than using single methods in isolation. In the current research, a mixed-method approach combining two systems-based methods, Accimaps and the Systems Theoretic Accident Model and Process - Causal Analysis using Systems Theory (STAMP-CAST), and one cognitive approach, the Perceptual Cycle Model (PCM), were employed in analysing a rail-level crossing incident in Bangladesh. Each method was applied individually to investigate the collision, and interventions were proposed corresponding to incident events at different risk management framework levels.