POLITO

We intend to set up a new globalized perspective to tackle water and food security in the 21st century. This issue is intrinsically global as the international trade of massive amounts of food makes societies less reliant on locally available water, and entails large-scale transfers of virtual water (defined as the water needed to produce a given amount of a food commodity). The network of virtual water trade connects a large portion of the global population, with 2800 km3 of virtual water moved around the globe in a year. We provide here definitive indications on the effects of the globalization of (virtual) water on the vulnerability to a water crisis of the global water system. More specifically, we formulate the following research hypotheses: 1) The globalization of (virtual) water resources is a short-term solution to malnourishment, famine, and conflicts, but it also has relevant negative implications for human societies. 2) The virtual water dynamics provide the suitable framework in order to quantitatively relate water-crises occurrence to environmental and socio-economic factors. 3) The risk of catastrophic, global-scale, water crises will increase in the next decades. To test these hypotheses, we will capitalize on the tremendous amount of information embedded in nearly 50 years of available food and virtual water trade data. We will adopt an innovative research approach based on the use of: advanced statistical tools for data verification and uncertainty modeling; methods borrowed from the complex network theory, aimed at analyzing the propagation of failures through the network; multivariate nonlinear analyses, to reproduce the dependence of virtual water on time and on external drivers; multi-state stochastic modeling, to study the effect on the global water system of random fluctuations of the external drivers; and scenario analysis, to predict the future probability of occurrence of water crises.

The detection of circulating disease biomarkers in bodily fluids, also known as liquid biopsy, has taken important strides toward the implementation of personalized medicine. However, it still suffers from low sensitivity and high costs, which render its clinical implementation not practical or affordable. In particular, the identification and quantification of oligonucleotide biomarkers is hampered by the need to employ long- and short-read sequencing tools that are expensive, require highly trained personnel, and are prone to error. Nonetheless, the recent clinical breakthroughs demonstrating the importance of detecting cancerous or viral biomarker to susceptibility, onset, and aggressiveness of the disease, motivate the need for further research that could render their detection simpler, cheaper, and thus more widely available. By leveraging the intrinsic amplification capability of surface enhanced Raman scattering (SERS), in ANFIBIO I will address the issues of low sensitivity and high costs by combining plasmonic nanoparticles synthesized ad hoc to maximize SERS signal amplification with direct SERS sensing and machine learning tools for the rapid analysis of the complex spectral responses obtained by screening bodily fluids for specific target biomarkers. I will focus in particular on prostate cancer (PCa) DNA and influenza A viral (IAV) RNA in blood, urine, and saliva, to quantify and correlate their amounts to those detected in tissues and cells. At completion, the proposed work will deliver a breakthrough sensing technology capable of detecting and quantifying cancerous and viral biomarkers in bodily fluids, with minimal sample pretreatment, no target amplification, and that uses SERS as novel and reliable transduction mechanism with distinct advantages over those currently employed. Furthermore, the fundamental insight garnered will likely assess the feasibility of using direct SERS sensing to develop beyond-third generation sequencing technologies.

Wave energy represents a great untapped potential, but modern technologies are not economically viable yet, mainly due to high investment risk and modelling uncertainties during design/development stages. Accurate and computationally fast mathematical models are essential tools for effectively and reliably designing wave energy converters (WECs). Although WEC dynamics are typically very nonlinear, linear (imprecise) models are extensively used due to their computational convenience; in contrast, nonlinear models currently available are more accurate but too slow for design optimisation or control applications. This fellowship purports to develop, validate, and disseminate a novel class of nonlinear models, which will realise an unprecedented pairing of accuracy and computational speed (100 to 1000 times faster than homologous existing models). Conversely to other, slower nonlinear models, this novel model can facilitate effective design and optimisation of the device, enable real-time power optimisation and model-based control. The project will greatly impact the wave energy community, making a high-performance modelling tool easily accessible to any stakeholder for a variety of advanced design purposes. This project is comprised of 3 work packages, which accomplish: (1) validation of the model for axisymmetric devices, (2) expansion and validation of the model for pitching platform devices, and (3) enhancement of computation performance and release of an open-source software. In addition, this fellowship will expand the career horizons of the fellow: a highly multidisciplinary plan is defined, building upon and extending beyond his current competencies. The fellow is well-positioned to undertake this project, allowing him to fully develop innovative ideas from his PhD research. This fellowship will provide the fellow with an unparalleled opportunity to grow as a scientist and engineer, launching him on a trajectory to a productive and rewarding scientific career.