Machine learning and mixed reality for smart aviation: applications and challenges

The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

Towards a Legal end Ethical Framework for Personal Care Robots. Analysis of Person Carrier, Physical Assistant and Mobile Servant Robots.

Technology is rapidly developing, and regulators and robot creators inevitably have to come to terms with new and unexpected scenarios. A thorough analysis of this new and continuosuly evolving reality could be useful to better understand the current situation and pave the way to the future creation of a legal and ethical framework. This is clearly a wide and complex goal, considering the variety of new technologies available today and those under development. Therefore, this thesis focuses on the evaluation of the impacts of personal care robots. In particular, it analyzes how roboticists adjust their creations to the existing regulatory framework for legal compliance purposes. By carrying out an impact assessment analysis, existing regulatory gaps and lack of regulatory clarity can be highlighted. These gaps should of course be considered further on by lawmakers for a future legal framework for personal care robot. This assessment should be made first against regulations. If the creators of the robot do not encounter any limitations, they can then proceed with its development. On the contrary, if there are some limitations, robot creators will either (1) adjust the robot to comply with the existing regulatory framework; (2) start a negotiation with the regulators to change the law; or (3) carry out the original plan and risk to be non-compliant. The regulator can discuss existing (or lacking) regulations with robot developers and give a legal response accordingly. In an ideal world, robots are clear of impacts and therefore threats can be responded in terms of prevention and opportunities in form of facilitation. In reality, the impacts of robots are often uncertain and less clear, especially when they are inserted in care applications. Therefore, regulators will have to address uncertain risks, ambiguous impacts and yet unkown effects

Service Quality Factors Influencing the Use of Artificial Intelligent Security Technology in UAE

This study assessed various factors related to service quality in influencing artificial intelligence security technology. It was carried out through quantitative approach where the data of respondents perceptions of UAE citizens towards the enhancing service quality in the UAE's AI security sector was derived through queationnaire survey. The factors were categorized in four groups which are performance expectancy group with seven factors;  effort expectancy group with seven factors; social influence group with four factors; and facilitates condition group with five factors. With these factors, the respondents were requested to gauge the degree of influence of each factor using 5-points likert scale. The questionnaire survey managed to secure 359 completed questionnaire forms. The data from these forms were analysed using descriptive statistic. It was found that facilitates condition group of factor is the most influencing category on service quality to adopt AI technology. It can be concluded that the overall results indicate that facilitates condition, effort expectancy and performance expectancy groups are reported having very high influencing categories while social influence group having high influencing ategory. It was also found that AI technologies are very frequently used technologies by the UAE citizens. Based on the study, it is deduced that service quality is a basic consideration as reported by the UAE personnel for adoption of any technology

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Biorefarmeries: Milking ethanol from algae for the mobility of tomorrow

The idea of this project is to fully exploit microalgae to the best of its potential, possibly proposing a sort of fourth generation fuel based on a continuous milking of macro- and microorganisms (as cows in a milk farm), which produce fuel by photosynthetic reactions. This project proposes a new transportation concept supported by a new socio-economic approach, in which biofuel production is based on biorefarmeries delivering fourth generation fuels which also have decarbonization capabilities, potential negative CO2 emissions plus positive impacts on mobility, the automotive Industry, health and environment and the econom

Handing over the keys: a qualitative study of the experience of automation in driving

This article presents a qualitative driving simulator study designed to understand the experience of giving up control to automated processes in semiautonomous driving systems. The study employed an experience prototyping methodology, with 12 drivers (4 female) completing 2 sessions in a high-fidelity driving simulator. Condition A simulated a normally functioning car, while Condition B simulated a semiautonomous system that monitors driver behavior and takes evasive action when danger is detected. The simulator experience was used to ground wider discussion of automation and the experience of driving, which was explored through a semistructured interview. Results identify design challenges for autonomous driving systems; the loss of user agency and confidence, and handling the change between manual and automated control. Opportunities were identified; in augmenting rather than removing human abilities, and in providing new learning opportunities for drivers