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.
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.
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.
Conservation and management of marine turtle populations are challenging because of their complex life history involving long-distance migrations. Consequently, marine turtles are affected by a wide range of anthropogenic threats across temporal and spatial scales, including climate change. The cumulative effect of these impacts is likely to have direct and indirect implications on these endangered species in the near future. This project aims to refine our understanding of the spatial structure of green turtles (Chelonia mydas) in the South-West Indian Ocean to understand better how populations interact and how they may be impacted by threats both at the nesting ground, at distant foraging areas, and along migratory corridors. To do so we aim to apply the power of genome-wide Single Nucleotide Polymorphisms (SNPs) combined with traditional mtDNA markers to assess fine-scale population structure and recent demographic history. In addition, the research will take a multidisciplinary approach, combining novel genetic methods with data from movement ecology (satellite telemetry) and oceanography (ocean current modelling) to provide a comprehensive analysis of connectivity between breeding and foraging areas. The results will allow conservation managers and policymakers to identify areas in need of protection and to assess the potential impacts of anthropogenic activities such as marine turtle interaction with fisheries. As such, our results will provide the scientific basis for future conservation actions within the framework of Integrated Maritime Policy. This project will allow me to gain critical new skills in conservation genomics. This will broaden my research horizon, strengthen my research profile and place me in a strong position for furthering my research career within the European Community.