Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario.

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a fundamental role for making forecasts, simulating scenarios and evaluating the impact of preventive political, social and pharmaceutical measures. Optimal control theory represents a useful mathematical tool to plan the vaccination campaign aimed at eradicating the pandemic as fast as possible. The aim of this work is to explore the optimal prioritisation order for planning vaccination campaigns able to achieve specific goals, as the reduction of the amount of infected, deceased and hospitalized in a given time frame, among age classes. For this purpose, we introduce an age stratified SIR-like epidemic compartmental model settled in an abstract framework for modelling two-doses vaccination campaigns and conceived with the description of COVID19 disease. Compared to other recent works, our model incorporates all stages of the COVID-19 disease, including death or recovery, without accounting for additional specific compartments that would increase computational complexity and that are not relevant for our purposes. Moreover, we introduce an optimal control framework where the model is the state problem while the vaccine doses administered are the control variables. An extensive campaign of numerical tests, featured in the Italian scenario and calibrated on available data from Dipartimento di Protezione Civile Italiana, proves that the presented framework can be a valuable tool to support the planning of vaccination campaigns. Indeed, in each considered scenario, our optimization framework guarantees noticeable improvements in terms of reducing deceased, infected or hospitalized individuals with respect to the baseline vaccination policy.

The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage.

The growing health and economic burden of bone fractures, their intricate multiscale features and the existing knowledge gaps in the comprehension of micro-scale bone damage occurrence make fracture diagnosis a challenging issue. In this scenario, deep-learning and artificial intelligence embody the new frontier of healthcare system, by overcoming the subjectivity of clinicians in the analysis of medical images. However, the preliminary attempts in exploiting the power of machine learning algorithms such as neural networks are still limited to bone macro-scale, while there is an evident lack in their application to smaller scales, where damage starts nucleating. Currently, speculations at the micro-scale are only feasible with the aid of high-resolution imaging techniques, that are particularly time consuming in terms of output images analysis. In this context, this works aims at combining the visualization of the micro-crack propagation mechanism with the promising application of convolutional neural networks. The implemented artificial intelligence tool is based for the first time on a large number of human synchrotron images coming from healthy and osteoporotic femoral heads tested under micro-compression. The designed convolutional neural networks are able to automatically detect lacunae and micro-cracks at different compression levels with high accuracy levels; indeed, with the baseline setup, networks achieve more than 0.99 level of accuracy for both cracks and lacunae, and more than 0.87 of the meanIoU adopted as validation metric. This approach is particularly encouraging for the development of powerful recognition system to comprehend bone micro-damage initiation and propagation, paving the way to the application of machine learning studies to bone micromechanics. This could be additionally crucial for future patient specific micro-scale observations to be related to the clinical practice.

Down to the Bone: A Novel Bio-Inspired Design Concept.

The solutions provided through natural evolution of living creatures serve as an ingenious source of inspiration for many technological and applicative fields. Along these lines, bone-inspired concepts lead to fascinating advances in product design, architecture and garments, thanks to the bone's exceptional combination of strength, toughness and lightness. Structural applications are inspired by the bone's ability to resist fracture under a large spectrum of forces, while the high surface area and pore connectivity of bone architecture present exciting opportunities from an aesthetic point of view. Behind these inspirations, a disruptive common belief emerges: "down to the bone", a journey in search of equality, universality and substantiality. Herein, we explore the current state of the art in bone-inspired applications in these fields, considering the two major categories of structural and aesthetic inspirations and discussing further technological developments.