Deaf learners have been highly neglected in mathematics education research so far. Although numerous studies in the field of deaf education have pointed out deaf students’ problems in learning mathematics and highlight a lack of mathematical skills, empirical research mainly took a deficit-oriented perspective on deaf students' learning instead of trying to better understand the specificity of their learning processes. One aspect shaping their particular way of learning mathematics is their use of spatio-motoric sign language, not only seeing it as a visual means of expression, but also as an embodied way to encounter and communicate mathematical ideas. Building on theoretical insights from my prestudy on the influence of sign language in mathematical conceptualization, this project aims at adding a more practical side to my research program on better aligning math education to deaf students’ ways of doing math. The project combines the two objectives of - designing and developing a learning environment that builds on specific resources and strengths of deaf learners—in particular their use of sign language—in order to realise their mathematical potential, and, - extending the existing knowledge on learning processes and the role of the body therein. This will be realized within a Design Based Research approach, in which development of educational material and of theory go hand in hand while passing several cycles of testing and refinement. In particular, the design is aligned to an embodied approach, assuming that (inter)acting and thinking are deeply entwined in such a way that mathematical thinking develops in activity and, at the same time, becomes expressed in it. The Embodied Design Research Laboratory at UC Berkeley provides excellent conditions to carry out this project while adding a new perspective to the work of the lab. The benefit of the educational learning design for other, linguistically diverse, settings will be explored in the return phase.

This project, "Interfaces in Turbulent Premixed Flames" (ITPF), aims at improving the physical understanding of the entrainment of hot products in annular co-flows of turbulent premixed flames into jets of fresh reactants. This research will characterize the entrainment processes through the study and comparison of Turbulent/Non-Turbulent (T/NT) and scalar interfaces in turbulent premixed flames. This is an essential point in combusting turbulent free shear flows, because a better knowledge of the dynamics of T/NT and scalar interfaces would lead to better predictions of flame instabilities and field structures. Moreover, the insight gained by this analysis will, in turn, be used to propose more physically sound and accurate turbulent mixing models. The entrainment mechanisms have been studied for the past decades. However, the description and quantification of the importance of small and large scales contributing to it is still unsolved. Thus, this research will use methodologies to locally characterize small-scale scalar structures and flow topologies, as well as, to scrutinize how large and medium size vortices influence and drive the entrainment processes. Therefore, this research requires high resolution simulations to investigate the structure of the enstrophy and scalar interfaces; this proposal intends to develop a Direct Numerical Simulation (DNS) of a turbulent premixed flame and analyze its results, in conjunction with Large Eddy Simulations (LES). A smart combination of DNS and LES will permit to unveil contributions of large and small structures to the entrainment process, to better comprehend physical mechanisms and to formulate sound and accurate mixing and combustion models. In summary, this proposal will directly address the cross-cutting priority of sustainable development in Information Science and Engineering established by the H2020 Work Programme and will reinforce the already large European competitiveness in turbulent combustion research.

Smart contracts are computer programs that autonomously execute on the blockchain. They have the potential to revolutionize many applications from finance, insurance, energy, healthcare, and production industries. However, smart contracts have become an appealing attack target. Since smart contracts are always online and easy to hack once a vulnerability is discovered, recent attacks resulted in large losses of cryptocurrency thereby questioning the benefits of this revolutionary technology. CONSEC is the first holistic framework that will enable secure execution of smart contracts on the blockchain. It takes a holistic approach by developing security mechanisms covering various stages of the blockchain ecosystem starting from smart contract development and maintenance to smart contract execution and forensic analysis. A key aspect of this project is the development of the first secure smart contract compiler which detects and automatically patches smart contract bugs in the development phase. An innovative update process complements the secure compiler to mitigate new and unknown attack vectors supporting developers who deployed vulnerable contracts. Further, CONSEC will develop a secure execution monitor to audit smart contracts while they execute on the blockchain allowing instant reaction to run-time attacks. To tackle the current lack of comprehensive approaches to validate the security of already deployed contracts, CONSEC will develop new forensic dynamic analysis approaches. CONSEC establishes trust in smart contract technology enabling secure deployment and execution of smart contracts on the blockchain.

Fake news are shared widely among humans, particularly in modern social media. Although mechanisms such as novelty seeking, alert for potential dangers or the need to belong have been suggested as potential target points to trigger news propagation, the underlying psychological and neurocognitive processes are not yet fully understood. In this project, we aim at combining the knowledge of the experienced researcher in neuroscience, brain imaging and neuropsychology with the host’s expertise in cognitive psychology and learning science to study social group interactions of information sharing. Using neurocognitive methods of brain imaging as well as behavioural measures supported by digital group awareness tools, we will investigate how news are evaluated by the brain, how news evaluation is influenced by the group and which measures could be taken to prevent fake news from spreading. Findings of the experimental studies will be transferred to classroom teaching, with developing a teaching concept to increase students’ knowledge on verifying information, recognizing fake news and developing strategies to stop them. Providing the unique combination of neuroscience, psychology, computer science and education the project will contribute to deepening and diversifying the candidate’s cognitive neuroscience expertise, gaining new competencies and strengthening networking of the involved scientists in order to catalyze the candidate’s scientific career as an independent researcher. The project makes a significant novel contribution in combining sciences and humanities in an interdisciplinary manner to help understand the neural mechanisms of naturalistic social group interaction on information sharing. Furthermore, this project will show a societal impact in 1) revealing neuronal and behavioural correlates of information sharing, 2) develop strategies to make knowledge exchange more efficient 3) prevent fake news from spreading and 4) educate students on critical thinking.

Early cancer diagnosis greatly increases the chances for successful treatment and personalised therapy. RNA molecules have emerged as new candidate biomarkers for detecting a wide variety of cancers, due to their critical role in cell growth and development. In situ hybridization techniques for mRNA detection provide detailed spatial and temporal information on RNA expression with single-molecule sensitivity, however the traditional techniques that utilise fluorescence probes and quantum dots lack of high multiplexing capabilities. RISH seeks to develop a methodology that allows sensitive multiplex detection, quantification and distribution of cellular mRNA biomarkers utilising surface-enhanced Raman spectroscopy (SERS) nanoparticle (NP) labels. SERS NP-labels consist of metal nanoparticles modified with a self-assembled monolayer of Raman reporters and are sensitive and robust labels that offer great multiplexing capacity. Molecular beacon hybridization probes specific to mRNA targets will be conjugated to SERS NP-labels to allow optical detection utilising Raman microscopy in human cancer tissue sections. The mRNA expression levels will be correlated with those from fluorescence in situ hybridization and the respective protein expression levels after immunohistochemistry. The research proposal is highly multidisciplinary and includes aspects of spectroscopy, optics, biochemistry, nanotechnology and pathology. The research fellow plans to work closely with experts from both the hospital and the industry to apply this methodology in the diagnosis of initially breast cancer and of different types of cancer at a later stage.