Paolo Bocchini

Topology optimization (TO) with genetic algorithm (GA) is a bio-inspired heuristic optimization technique. In practical applications, it suffers from unstable results, a large number of redundant computations, and low convergence rates. To address this issue, an improved GA, specifically tailored to TO by introducing a strong shape constraint and enriched information obtained from the embedded finite element analyses, is proposed in this study. The strong shape constraint adds a filter to all the individuals at the beginning of each iteration to prevent the analysis of topologies that would not lead to feasible structures. Meanwhile, multiple-input genetic operators leverage additional information from the finite element analyses to guide the mutation and reproduction process, accelerating the convergence rate to the optimal shape. Three case studies present the contributions of the proposed algorithm in terms of robustness, efficiency, and refinement compared to conventional GAs and the Solid Isotropic Material with Penalization (SIMP) method. The results show that the proposed algorithm achieves high robustness and the probability of convergence to impractical shapes is negligible, the computational cost is reduced to one half, the convergence is expedited (compared to conventional GAs), and a refined shape can be obtained and manufactured.

Influenza, categorized as one of the emergent infectious diseases, presents a substantial public health concern due to its capacity to trigger extensive epidemics and global pandemics. Every recent pandemic of human influenza has been attributed to avian influenza viruses (AIVs), underscoring their pandemic potential and the associated public health risks. Interestingly, there remains a significant knowledge gap concerning the mechanisms that sustain the survival and proliferation of these viruses within their natural avian reservoirs. Migratory waterfowl, in particular mallard ( Anas platyrhynchos ), plays a crucial role as potential reservoirs, facilitating the spread of the virus through their migratory patterns. In order to better understand the factors that contribute to AIV spillover and hotspots, we built a mechanistic transmission model and investigated the likelihood of an AIV hotspot for the animal‐to‐human spillover of the virus in a region in Kansas. Our findings challenge the notion that the size of the overall mallard population is a reliable predictor of spillover hazard. To study this aspect, we systematically evaluated weekly trends in both the total population density and the total infected population density. In more than 70% of the locations studied, these two indicators showed periods with opposite trends. This conclusion stresses the importance of developing and calibrating compartmental models that can capture the diffusion of the virus within the reservoir and its spatiotemporal distribution due to animal movement.

The size of fruit bat colonies ranges from dozens to hundreds of thousands of individuals, depending on the species. While a deterministic modelling approach is appropriate for large colonies, the role of population fluctuations can be all-important for small colonies. From this perspective, we analyse the infection dynamics in small zoonotic niches due to filoviruses, e.g. Ebola. To this end, we perform stochastic numerical simulations and analytical calculations. The inherent stochasticity in ecological processes may play a significant role in driving small populations towards extinction. Here, we reveal that fluctuations can either lead to virus eradication or to sustain infection compared with the deterministic dynamics, depending on the size of the zoonotic niche. Altogether, our findings reveal non-trivial stochastic effects, which can shed light on the infection dynamics in small- and medium-sized bat colonies and help design preventive measures for zoonotic diseases.