Search results

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration.

In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate.

As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.

This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

The COVID-19 pandemic has impacted the functioning of education systems in a multitude of ways. In Ireland schools closed on March 12th and remained closed for the remainder of the academic year. During this time educators engaged with students, families and colleagues in new and diverse ways. The purpose of this study was to explore educators' experiences during the closures, particularly regarding the impact of the pandemic on the wellbeing of students, school staff and wider school communities.

A series of one-to-one interviews, lasting approximately one hour, were conducted in July 2020 with 15 education professionals online via Zoom or Microsoft Teams. Participants occupied various roles (classroom teacher, school leader, special educational needs coordinator, etc.) and worked in a diverse range of communities in Ireland. Qualitative data from interviews were transcribed and emergent themes identified through an inductive followed by deductive analytic approach.

The interviews highlighted the central role that schools play in supporting their local communities and the value teachers place on their relationships with students and families. Many teachers and school leaders found themselves grappling with new identities and professional boundaries as they worked to support, care for and connect with the students and families they serve. There was considerable concern expressed regarding the plight of vulnerable or marginalised students for whom the school ordinarily offered a place of safety and security.

The findings reveal how COVID-19 has exacerbated pre-existing inequalities and the central role of schools in promoting the health and wellbeing of all its members.

The purpose of this study is to describe the design and validation of a three-dimensional (3D)-printed phantom of a uterus to support the development of uterine balloon tamponade devices conceived to stop post-partum haemorrhages (PPHs).

The phantom 3D model is generated by analysing the main requirements for validating uterine balloon tamponade devices. A modular approach is implemented to guarantee that the phantom allows testing these devices under multiple working conditions. Once finalised the design, the phantom effectiveness is validated experimentally.

The modular phantom allows performing the required measurements for testing the performance of devices designed to stop PPH.

PPH is the leading obstetric cause of maternal death worldwide, mainly in low- and middle-income countries. The proposed phantom could speed up and optimise the design and validation of devices for PPH treatment, reducing the maternal mortality ratio.

To the best of the authors’ knowledge, the 3D-printed phantom represents the first example of a modular, flexible and transparent uterus model. It can be used to validate and perform usability tests of medical devices.