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1. Multiomics, virtual reality and artificial intelligence in heart failure.

Author

Gladding PA, Loader S, Smith K, Zarate E, Green S, Villas-Boas S, Shepherd P, Kakadiya P, Hewitt W, Thorstensen E, Keven C, Coe M, Nakisa B, Vuong T, Rastgoo MN, Jüllig M, Starc V, and Schlegel TT

Subjects
Artificial Intelligence, Humans, Prognosis, Stroke Volume, Ventricular Function, Left, Heart Failure diagnostic imaging, Ventricular Dysfunction, Left diagnostic imaging, Virtual Reality
Abstract

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10 -4 ) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Published
2021
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2. INDUSTRY 4.0:SOCIAL CHALLENGES AND RISKS

Author

Gilberto Marzano, Luis Ochoa Siguencia, and This paper was supported by the European project NewMetro (embeddiNg kEts and Work based learning into MEchaTROnic profile) - Project n. 600984-EPP-1-2018-1-IT-EPPKA2-SSA. This document reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained there in.

Subjects
Engineering management, Industry 4.0, Computer science, business.industry, Industrial production, Control (management), Production (economics), Virtual reality, Value chain, Industrial Revolution, business, Automation, human-centered approach Industry 4.0, Internet of Everything, irony of automation
Abstract

Industry 4.0 is a term first introduced by the German government during the Hannover Messe fair in 2011 when it launched an initiative to support German industry in tackling future challenges. It refers to the 4th industrial revolution in which disruptive digital technologies, such as the Internet of Things (IoT), Internet of Everything (IoE), robotics, virtual reality (VR), and artificial intelligence (AI), are impacting industrial production. The new industrial paradigms of Industry 4.0 demand a socio-technical evolution of the human role in production systems, in which all working activities of the value chain will be performed with smart approaches. However, the automation of processes can have unpredictable effects. Nowadays, in a smart factory, the role of human operators is often only to control and supervise the automated processes. This new condition of workers brought forth a paradox: malfunctions or irregularities in the automated production process are rare but challenging. This article discusses the challenges and risks that the 4th industrial revolution is bringing to society. It introduces the concept of the Irony of Automation. This propounds that the more reliable an automated system, the less human operators have to do and, consequently, the less attention they pay to the system while it is operating. The authors go on to discuss the human-centered approach to automation, whose purpose is not necessarily to automate previously manual functions but, rather, to enhance user effectiveness and reduce errors.

Published
2021

3. New Functionalities of a Virtual Computer Model Design and Construction

Author

Stoyanov, Stoyan, Zhelezov, Stanimir, and The research presented in this paper is partially funded by the project of Shumen University: RD- 08-96/01.02.2019.

Subjects
virtual reality, 3D–modelling, virtual scenes
Abstract

The purpose of this paper is to construct a virtual model of a computer system for student training, based on analysis of different platforms, creating virtual reality and its related 3D graphics. A classification of three-dimensional models was made. Software platforms for three-dimensional modelling and creation of virtual scenes have been analysed and selected. A virtual model has been developed.

Published
2019

4. Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Author

Sangseok You, Lionel P. Robert, Jeonghwan Kim, SangHyun Lee, Vineet R. Kamat, Okinawa Institute of Science and Technology Graduate University (OIST), Institute for Computational Engineering and Sciences [Austin] (ICES), University of Texas at Austin [Austin], and HEC Research Paper Series

Subjects
0209 industrial biotechnology, Identification, Computer science, 0211 other engineering and technologies, Theoretical models, 02 engineering and technology, Robot Acceptance Safety Model (RASM), Virtual reality, Trust, Human–robot interaction, 020901 industrial engineering & automation, Human–computer interaction, 021105 building & construction, 0502 economics and business, Set (psychology), Masonry, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Construction automation, Civil and Structural Engineering, Perceived safety, Immersive Virtual, business.industry, 05 social sciences, Robotics, Building and Construction, Environment (IVE), Human–Robot Work Collaboration (HRWC), Identification (information), Work (electrical), Control and Systems Engineering, Robot, [SHS.GESTION]Humanities and Social Sciences/Business administration, Intention to Work with Robot, Artificial intelligence, Safety, Immersive virtual environment, business, Humanoid robot, 050203 business & management, Virtual prototyping, Team
Abstract

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.

Published
2018