By now, the Internet has become a topmost critical infrastructure. Current technology trends point toward a new era of dependable Internet, which includes the concepts of the Internet of Everything, Network Intelligentization, Vehicle-to-everything communication, Augmented and Virtual Reality applications, and many others. Telesurgery, autonomous driving, Industry 4.0 and the stock market are just a few examples of the emerging mission-critical services that involve telecommunication and on which both people and governments increasingly rely. To address the rising challenges of these new mission-critical application-based concepts, ultra-reliable and low-latency communication channels have to be established. Despite this reliance on the Internet, nowadays, it often falls behind the needs and expectations. To enable the advent of a truly dependable Internet, the main goal of QoSeRM is to drastically improve the most important Quality of Service (QoS) metric, the end-to-end availability, that measures the fraction of time in which two endpoints are able to communicate. To create such a reliable Internet, we have to step further from protecting only the classical single network equipment failures and ensure that telecommunication remains operational in the presence of large-scale regional failures caused by natural or man-made disasters too. To this end, QoSeRM forges two major building blocks into an overarching framework, namely: 1) enhanced time series prediction techniques for predicting future spare link bandwidths, and 2) scalable resilient routing algorithms for utilising the spare link capacities for sending redundant data between the communicating endpoints. Thus, my findings will combine the strengths of artificial intelligence and combinatorial optimisation, which are two fields with very different philosophies: while the former says that data is better than algorithms, the creed of the latter is contrary: `algorithms are better than data.

The practice of tuning different climate proxies prevents the observation of regional response times of terrestrial archives to global changes. Thus, it is imperative to develop correlation protocols based on absolute chronologies. Loess-palaeosol deposits are continental archives of Quaternary paleoclimates and loess is generally considered an ideal material for the application of luminescence dating. The agreement obtained for 10-20 ka ages using different techniques has given us confidence in using the state of the art measurement protocols for young deposits, as confirmed by comparison with independent age control. INTERTRAP proposes detailed investigations of loess samples from three continents collected in close proximity to the transition to the recent soil, with the purpose of obtaining a temporal quantification of the ending of the Late Tardiglacial and the beginning of the Holocene. However, a series of recent luminescence investigations carried out on quartz of different grain sizes extracted from Romanian and Serbian loess yielded severe age discrepancies for ages >~40 ka. While the cause of this observation is hitherto not fully explained, our ongoing studies on Chinese loess prove that it is a general effect, potentially affecting deposits worldwide, and raising doubts on previous chronologies. Methodological studies within INTERTRAP will develop an integrated approach using optically stimulated luminescence, thermoluminescence and electron spin resonance investigations. This part of the study aims at unravelling the mechanism responsible for the observed discrepancies and developing innovative trapped charge dating measurement protocols based on quartz that will yield reliable ages for and beyond the last interglacial glacial cycle.

Nature's ability to organize building blocks into large structures of finite size has inspired many researchers to attempt similar constructs with synthetic subunits. DNA origami triangles are such synthetically built subunits capable to self assemble to shells of various shapes. Subunits and their interactions are typically designed so that they match the target shell geometry as much as possible. Such designs, however, require an increasing number of subunit types as the target shell complexity increases. Instead, we propose a computational scheme aimed to reduce the required subunit specificity in such assemblies, by relaxing the exact geometrical constraints on the target surface and exploiting subunit deformability. Two example structures will be considered, in both of which subunits are triangular and the assembly is stress mediated. The first one is an icosahedral structure of either of two sizes, both in a mechanically stressed state. Our aim is to find the appropriate subunit design which assembles both sizes with prescribed yields, from a single type of subunits, by adjusting the subunit's mechanical properties. The second structure is an ellipsoidal shell, which, again, we aim to assemble from a single species of subunits. Finite compliance allows subunits to adapt to the local geometry and coordination, however, the associated frustration raises the problem of controling long range elastic interactions and rules out naive subunit designs. As a solution, we propose an optimization approach to efficiently adjust the subunits' mechanical properties until a target free energy minimum is reached at the required sizes and shapes. We use a coarse grained triangle model with a grand canonical Monte Carlo scheme for equilibrium and dynamical simulations. Progressing from the fastest towards the most accurate estimates of the free energy we intend to provide a full stack methodology for finding the most optimal subunit parameters given a target structure.

There has been a growing discourse revolving around how sustainability science can reach the goal of transformational change in response to complex global problems such as climate change, biodiversity loss, and resource depletion. As one solution it has been suggested that sustainability science needs to bridge barriers to action by going beyond the co-production of knowledge to experimentation and testing of knowledge. At the same time, it has been argued that in order to achieve genuine transformational change it is necessary to shift values rather than simply generate ‘better’ actionable knowledge. COEXIST aims to join these two streams and to develop experimental settings that include values-based interventions to produce scientifically appropriate and socially relevant knowledge for sustainability transformations. Thus, COEXIST will be the first to integrate experimental interventions with the transformative power of values. I will identify theoretical frameworks that will inform a value-based experimental design and I will collect empirical evidence about key enabling factors and interventional practices that can operationalise such an experimental approach. By combining theory and practice, I will develop an integrated model of transformational experiments involving value-based interventions. I will implement the model within the real-world context of Transylvania, Romania, a region with a rich but threatened cultural and natural heritage. COEXIST combines my experience in transformational and transdisciplinary research modes and the host’s unique knowledge of place-based social-ecological systems research with the commitment of regional multi-sectoral stakeholders. This setup provides a strong theoretical and empirical foundation for the integrated model, which is expected to generate impact, and acts as a forum for communicating results to non-academic actors in Romania and Europe.