Location of Things (LoT) is an Internet of Things paradigm for mobility analytics. In LoT, massive mobility data is being gathered, processed and transmitted among heterogeneous data nodes in a decentralized architecture. Traditional centralized data quality management techniques cannot cope with such characteristics of LoT, making the management of data quality for LoT a prominent challenge. In the project MALOT, the researcher aims at designing a set of new techniques that are particularly adaptive to the decentralized and heterogeneous LoT architecture for assessing and enhancing mobility data quality. Specifically, the research actions of MALOT include (1) a core model for assessing mobility data quality at decentralized and dynamic data nodes; (2) effective quality-aware data enhancement algorithms to handle the heterogeneity and inconsistency of LoT mobility data; (3) a mechanism for scheduling quality management tasks among relevant nodes in an efficiency-optimal fashion. With the research actions dedicated to decentralized modelling, heterogeneous data integration, and mobile task planning, MALOT will firmly strengthen the researcher's scientific skills and innovative competences. Through many inter-sectoral training and communication activities planned for the project, the researcher will have great opportunities to diversify his skillsets and enhance his future career prospects. A two-way knowledge transfer is guaranteed since MALOT combines the researcher's expertise in mobility analytics and the participating organizations' expertise in big data management and decentralized information systems. Committed to the mobility data quality management for IoT-like architecture, MALOT is not only expected to benefit the academic development of the host and the researcher but will contribute to Europe's IoT innovation and applications.

Parasitic nematode infections are a major threat to human, animal and plant health. Infection prevention or control depends heavily on chemical treatment, but resistance is becoming widespread, and the compounds used pollute surface- and groundwater. To develop new mitigation strategies, it is important to understand host-parasite interactions and fundamental mechanisms of parasitism, but parasites of vertebrates are difficult to study. Entomopathogenic nematodes (EPNs) and their hosts offer great potential in this context. EPNs are microscopic nematodes that prey on larval stages of many insects and naturally help regulate insect populations. EPNs are commercially available to target a range of soil-dwelling plant pests, but efficiency depends on the environment and the targeted pest. EPNs have also been used to study immunological responses of insect hosts. In these studies, a fraction of the hosts survives the infection. The aim of research proposed here is to select the surviving hosts and establish a model system of the EPN-host complex to study host-parasite interactions through experimental evolution of parasitism. Traits including life span and stress responses, as well as genomic and transcription changes of the host and the EPN will be studied. The downstream application of this model is the optimization of biocontrol agents of plant and animal pathogens by selecting EPNs that are resistant to environmental stressors like heat, desiccation and UV radiation, and that prey on new host species. The proposed research uses the EPN Heterorhabditis bacteriophora, its symbiont Photorhabdus luminescens, and the host Drosophila melanogaster. The expertise of the supervisor in Drosophila and evolutionary research combined with the Experienced Researcher’s (ER) empirical and computational skills provides a perfect match for the proposed project. Additionally, the project will integrate the ER’s multidisciplinary skillset for a future career as an independent academic.

The research proposal addresses the design challenges in the power conversion and the energy storage systems in the electric aircraft used for urban air mobility (UAM). The success of UAM as an alternate transportation system is strongly dependent on designing the overall system to be safe, efficient and reliable. This proposal focuses on improving the power conversion efficiency and designing a smart wireless battery management system (BMS) with accurate battery state-of-charge (SoC) and state-of-health (SoH) estimations. Another desirable aspect in the UAM aircraft is improving the overall payload capacity, which is impacted by the weight of the batteries, interconnection wiring and power conversion efficiency. The proposal aims to improve it by increasing the voltage of the Li-ion battery packs above the current state-of-the-art, which would reduce the current rating and cable weight, while identifying a power converter topology to maximize the overall efficiency. The design optimisation will consider the impacts of higher insulation requirement with higher voltages and overall cost. The power converter topology and the accompanying filters are optimised to reduce electromagnetic interference that can affect the sensitive electronics on the aircraft. The proposal explores data-driven machine-learning based methods to improve the accuracy of the SoC and SoH estimations and reduce the gap between peak error and the root-mean-square error (RMSE). A reduction in the gap between peak and RMSE will provide a reliable upper bound unlike for the case when estimation methods show a lower RMSE but a wide variation in the peak error. The wireless BMS will provide the advantage of easier maintenance and elimination of the conventional wiring weight. This is a timely and innovative project that will help in novel technology development for UAM industry. It will help the applicant gain additional technical and managerial skills that would ensure a successful research career.

African Swine Fever is a notifiable devastating hemorrhagic fever with high mortality rates in pigs. It affects all members of the Suidae family and is one of the most important pig diseases due to its severe socio-economic consequences for affected countries, the difficulty of preventing spread across country boundaries, and the lack of vaccine and therapeutic control measures. We will use genome-wide DNA technologies to understand the Sus scrofa genomic response to the infection. Specifically, we will compare data on both healthy and infected individuals to (1) identify the possible presence of regions under selection. We will also assess (2) the hybridization rate and (3) the interaction strategies between the domestic pig and the wild boar to (4) identify the possible transmission routes in pig diseases.

Oxide glasses are one of the most important material families owing to their unique features, such as transparency, tunable properties, and formability. Emerging solutions to major global challenges related to energy, health, and electronics require new scientific breakthroughs in glass chemistry, mechanics, and processing. The realization of these goals is severely restricted by the main drawback of glass, namely high brittleness. Furthermore, new glass compositions are today developed through time-consuming trial-and-error experimentation due to their inherent non-equilibrium nature and disordered structure. A major task is therefore to initiate a paradigm shift within the field of glass science and technology, going from empirical to model-based approaches for the design of new glass compositions and microstructures with improved fracture resistance. This requires the development of computational approaches, from ab initio calculations to artificial intelligence, to integrate structural descriptors and glass chemistry with advanced processing and mechanical properties into holistic tools. NewGLASS challenges the current glass design strategies in order to create such tools. For this purpose, an interdisciplinary approach is proposed, in which structural descriptors at the short- and medium-range length scales are first identified and quantified based on emergent statistical mechanics and persistent homology techniques. Guided by these results, high-throughput simulations at various length scales are combined with machine learning algorithms to design novel glass compositions, tailored deformation mechanisms, and 3D-printed microstructures to achieve superior fracture resistance. By having experiments and modelling complement and advance each other reciprocally, NewGLASS will find order in disorder and provide the scientific breakthroughs for the accelerated design of glasses with outstanding mechanical performance, thus opening up for many new applications.