Biomimetic surfaces for greener coastal infrastructures: an analysis of factors that contribute to make artificial structures more “natural”

The growing ecological awareness of Ocean Sprawl impacts is promoting the adoption of eco-engineering strategies to enhance the ecological performance of coastal infrastructures. Biomimicry, as an eco-engineering tool, aims to design infrastructure more suitable for wildlife by manipulating structural factors to mimic natural habitats. However, little is known about the extent to which natural and artificial substrates differ in their structure and to what extent such differences affect the biota. To fill these knowledge gaps and consequently design biomimetic surfaces, I initially explored how much physical structure diverges between various types of natural and artificial substrates and tested to what extent differences in physical structure and material composition affect the epibenthic communities. By mean of an in-field mensurative experiment and a systematic review coupled with a meta-analysis, I found that, although communities tended to differ between natural and artificial coastal habitats, both physical structure and material composition reported an overall mild effect on epibenthic communities. However, an informed choice of building material and an appropriate combination of multiple structural manipulations can promote ecological benefits at multiple levels, from increasing the ecological performance in situ to reducing the impacts during the production process. Thus, I combined my findings in a final experiment, still in progress, where I am testing the combined role of shape, brightness and inclination of biomimetic surfaces I have designed in producing benefits at multiple levels. Overall, I suggest that biomimicry has the potential to increase the ecological value of artificial habitats especially when a wide range of aspects is simultaneously considered. Indeed, none of the structural factors, individually, can fully mimic the “natural conditions” to effectively improve the ecological performance of the artificial substrates. This emphasizes the need to include in future works a multi-level perspective to fully achieve the great potential of biomimicry.

How to explain: from theory to practice

Today we live in an age where the internet and artificial intelligence allow us to search for information through impressive amounts of data, opening up revolutionary new ways to make sense of reality and understand our world. However, it is still an area of improvement to exploit the full potential of large amounts of explainable information by distilling it automatically in an intuitive and user-centred explanation. For instance, different people (or artificial agents) may search for and request different types of information in a different order, so it is unlikely that a short explanation can suffice for all needs in the most generic case. Moreover, dumping a large portion of explainable information in a one-size-fits-all representation may also be sub-optimal, as the needed information may be scarce and dispersed across hundreds of pages. The aim of this work is to investigate how to automatically generate (user-centred) explanations from heterogeneous and large collections of data, with a focus on the concept of explanation in a broad sense, as a critical artefact for intelligence, regardless of whether it is human or robotic. Our approach builds on and extends Achinstein’s philosophical theory of explanations, where explaining is an illocutionary (i.e., broad but relevant) act of usefully answering questions. Specifically, we provide the theoretical foundations of Explanatory Artificial Intelligence (YAI), formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. We present empirical results in support of our theory, showcasing the implementation of YAI tools and strategies for assessing explainability. To justify and evaluate the proposed theories and models, we considered case studies at the intersection of artificial intelligence and law, particularly European legislation. Our tools helped produce better explanations of software documentation and legal texts for humans and complex regulations for reinforcement learning agents.

Sulfanyl Radical Addition to Alkynes: Revisiting an Old Reaction to Enter the Novel Realms of Green Chemistry, Bioconjugation, and Material Chemistry

In the last decade considerable attention has been devoted to the rewarding use of Green Chemistry in various synthetic processes and applications. Green Chemistry is of special interest in the synthesis of expensive pharmaceutical products, where suitable adoption of “green” reagents and conditions is highly desirable. Our project especially focused in a search for new green radical processes which might also find useful applications in the industry. In particular, we have explored the possible adoption of green solvents in radical Thiol-Ene and Thiol-Yne coupling reactions, which to date have been normally performed in “ordinary” organic solvents such as benzene and toluene, with the primary aim of applying those coupling reactions to the construction of biological substrates. We have additionally tuned adequate reaction conditions which might enable achievement of highly functionalised materials and/or complex bioconjugation via homo/heterosequence. Furthermore, we have performed suitable theoretical studies to gain useful chemical information concerning mechanistic implications of the use of green solvents in the radical Thiol-Yne coupling reactions.