Grant awarded: 1,965,583.75 EUR (Total EU Grant Awarded); DMU to receive 353,396.25 EUR
Funder: EU – HORIZON-SESAR-2023-DES-ER2-WA2-2
Project Leads: Raouf Hamzaoui (PI) and Feng Chen (Co-I)
The ATMACA (Air Traffic Management and Communication over ATN/IPS) project proposes an innovative solution that enables effective, seamless, interoperable air-to-ground datalink communication and digital flight monitoring and management through aeronautical telecommunication (ATN) based on the Internet Protocol Suite (IPS) within all domains of flight. ATMACA aims at supporting the “air-ground integration and autonomy” initiative in the Strategic Research and Innovation Agenda (SRIA), which presents strategic roadmaps to achieve SESAR phase D “Digital European Sky” in the European ATM Master Plan 2020 edition. ATMACA proposes a beyond the state-of-the-art IP-based datalink communication solution by introducing an application-layer mobility management for ATN and enabling commercial of-the-shelf equipment. It will also provide a next generation human-machine interface (HMI) that will process higher quality data, enable interactions with existing and future aeronautical applications and services, and meet the needs of end-users. The ATMACA solution will be validated through real-time simulations and real-time monitoring tests by considering relevant applicable SESAR key performance areas and indicators, as well as industry standards. The consortium consists of a balanced mixed of universities and industrial partners (an air navigation service provider, an airline, and a research and consultancy firm specialized in HMI design) to ensure the project meets its objectives.
The environMENTAL project will investigate how some of the greatest global environmental challenges, climate change, urbanisation and psychosocial stress caused by the COVID-19-pandemic affect mental health over the lifespan. It will identify their underlying molecular mechanisms and develop preventions and early interventions. Leveraging cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large scale behavioural neuroimaging cohorts, we will identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress and substance abuse.
By linking population and patient data via geo-location to spatiotemporal environmental data derived from remote sensing satellites, climate models, regional-socioeconomic data and digital health applications, our interdisciplinary team will develop a neurocognitive model of multimodal environmental signatures related to transdiagnostic symptom groups that are characterised by EnvironMENTAL – Reducing the impact of major environmental challenges on mental health shared brain mechanisms.
We will uncover the molecular basis underlying these mechanisms using multi-modal -omics analyses, brain organoids and virtual brain simulations, thus providing an integrated perspective for each individual across the lifespan and spectrum of functioning. The insight gained will be applied to developing risk biomarkers and stratification markers. We will then screen for pharmacological compounds targeting the molecular mechanisms discovered. We will also reduce symptom development and progression using virtual reality interventions based on the adverse environmental features developed in close collaboration withstakeholders.
Overall, this project will lead to objective biomarkers and evidence-based pharmacologic and VR-based interventions that will significantly prevent and improve outcomes of environmentally- related mental illnesses, and empower EU citizens to manage better their mental health and well-being.
The pandemic not only changed the way we work, but the way work is managed and the way human resources are managed. And as more data becomes available to human resource departments there is more potential for using leading edge technologies to analyse it and develop evidence-based decision making.
Commercial applications have become available to control entry to lifts, monitor office occupancy in real time and measure physical characteristic such as carbon dioxide and temperature. Systems can monitor remote and home working. Combined with personnel dataset these provide a rich source for analysis.
Enter People Analytics (PA) which uses artificial intelligence to analyse huge datasets. Using PA sentiment analysis can be conducted, team motivation charted, job applicants selected and resignations predicted. With PA the HR department ceases to be just a business support function and becomes a partner in developing corporate strategy.
But what are the consequences of letting a machine recruit employees, of disbanding a team based on sentiment analysis or excluding an employee from promotion because their departure is predicted by PA?
As AI becomes more and more a part of everyday life, it will determine careers and prospects through the application of PA to every spoke of the talent management cycle.
Work by Neil McBride and Mayen Cunden in the Centre for Computing and Social Responsibility and Vincent Bryce at the University, featured in People Management and the Journal of Information, Communication and Ethics in Society has begun to investigate the ethical issues associated with People Analytics and to chart the new responsibilities human resource departments shoulder due to the accelerated impact of artificial intelligence. Through understanding the ethical parameters of people analytics a framework for the responsible research and innovation of PA within HR departments will be defined.
Feedback from a talk given for the Bedfordshire and Milton Keynes branch of the Chartered Institute of Personnel Directors suggested that for HR professionals the key issues lie in understanding how AI works, and effectively using people analytics without overshadowing the tacit skills and wisdom of HR professionals. Also, HR departments need to develop the skills to evaluate the plethora of PA systems emerging post pandemic.
Most models in the current data analysis require perfect data without missing or incomplete data. However, the real-world data are in most cases are not perfect at all. Incomplete data are common in most situations. As a model dedicated to incomplete data, grey data analysis appears as a prospective model to deal with such data. This research originated from China during the 1970’s when China recovered from the cultural revolution. At that time, China faced a huge gap in available data in nearly all areas due to the significant interruption of the cultural revolution. The theory of grey systems developed quickly in China and made a significant contribution to China’s economic boom from a seriously frozen economy. However, it is still relatively unknown outside China although it has achieved significant success in China.
To further promote grey data analysis in the world, we successfully applied and secured an advanced Marie Curie International Incoming Fellowship in FP7 for Prof. Sifeng Liu to join us at DMU for 2 years during 2015-2016. In this project, the international incoming research fellow, Prof. Sifeng Liu, has spent two years at De Montfort University conducting the proposed research together with Prof. Yingjie Yang. They have published over 30 research papers in academic journals and conferences.
The research is going to have a significant impact in the development of grey systems and data mining both in China and Europe. As a developing subject, there are still gaps in grey systems both in theoretical and applied research, and they have restricted its further development in Europe. The progress made in this project has showcased the feasibility of grey systems in data mining and its great potential with limited and poor data. Given the big data-oriented research in Europe, this project fills the gap for data mining with limited and poor data and will contribute greatly to those areas with limited and poor data, such as social economic analysis, healthcare, new product development, etc. It is valuable, especially for business and corporate decision-makers, public policy makers and public system managers to obtain useful information from limited and poor information.
Prof. Liu and Prof. Yang have made a number of outreach activities to deliver visits, seminars, and training courses. For example, they have visited and delivered seminars at Napier University, South Bank University, Bucharest University of Economic Studies, Universidad Pablo de Olavide, Fuzhou University, Xiamen University, Lanzhou University, Shihezi University, Hebei University of Engineering, etc. They have also delivered several training courses at De Montfort University and Nanjing University of Aeronautics and Astronautics. Furthermore, they organized the 2015 IEEE International Conference on Grey Systems and Intelligent Services at De Montfort University. They have also initiated the establishment of the International Association of Grey Systems and Uncertainty Analysis in 2016. The results of this project will certainly help to establish a new subject in Europe and complement the existing big data initiatives.
Prof Liu has delivered a series of training events in grey systems at De Montfort University in 2015 and 2016, each training consisting of three days of intensive lectures, seminars and discussion sessions. The scientist in charge, Prof. Yingjie Yang, has also delivered equivalent training at Nanjing University of Aeronautics and Astronautics in 2015 and 2016 on uncertainty modeling. In addition to these training events, Prof. Liu has also been involved in PhD supervision at De Montfort University (Archie Singh), Prof. Liu has contributed to research meetings at DMU and attended training sessions organized by DMU. As an active member, Prof. Liu has enjoyed close integration at DMU and participated in our research life actively. Prof. Yang and Prof. Liu had jointly supervision several PhD students at Grey Data Analysis
DMU and the University of Aeronautics and Astronautics (Archie Khuman, Lifeng Wu, and Xiaojun Guo). Prof. Liu has also brought several visiting scholars from China to De Montfort University (Lifeng Wu, Chong Li and Mingli Hu). Prof. Yang and Prof. Liu have also initialized several research proposal applications involving both European partners and Chinese partners.
The project has turned out to be a huge success. In the 2017 MSCA competition, The fellow, Prof. Sifeng Liu, has been ranked as one of the top 10 in Europe for his work in this project.
Prof. Liu has also been cited as one of the few Chinese scholars who had a significant impact on the world in Merkel’s speech in China in 2019.
The great success of the project has also attracted attention from industry in the world. For example, Surbana Jurong Consultants Pte Ltd from Singapore invited Prof. Yang to carry out a consultant project with DMU in 2018 where grey systems are applied to their data mining and decision-making practice.