Human studies demonstrate a four-fold increased possibility of smoking in the children of mothers who smoked during pregnancy. Nicotine is the active addictive component in tobacco-related products, crossing the placenta and contaminating the amniotic fluid. It is known that chemosensory experience in the womb can influence postnatal odor-guided preference behaviors for an exposure stimulus. By means of behavioral and neurophysiologic approaches, we examined whether fetal nicotine exposure, using mini-osmotic pumps, altered the response to nicotine odor in early postnatal (P17), adolescent (P35) and adult (P90) progeny. Compared with controls, fetal exposed rats displayed an altered innate response to nicotine odor that was evident at P17, declined in magnitude by P35 and was absent at P90 - these effects were specific to nicotine odor. The behavioral effect in P17 rats occurred in conjunction with a tuned olfactory mucosal response to nicotine odor along with an untoward consequence on the epithelial response to other stimuli – these P17 neural effects were absent in P35 and P90 animals. The absence of an altered neural effect at P35 suggests that central mechanisms, such as nicotine-induced modifications of the olfactory bulb, bring about the altered behavioral response to nicotine odor. Together, these findings provide insights into how fetal nicotine exposure influences the behavioral preference and responsiveness to the drug later in life. Moreover, they add to a growing literature demonstrating chemosensory mechanisms by which patterns of maternal drug use can be conveyed to offspring, thereby enhancing postnatal vulnerability for subsequent use and abuse.

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios.

Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find many high performing points that all behave differently according to a user-defined behavioural metric. In this paper we propose the Bayesian Optimisation of Elites (BOP-Elites) algorithm. Designed for problems with expensive black-box objective and behaviour functions, it is able to return a QD solution-set after a relatively small number of samples. BOP-Elites models both objective and behavioural descriptors with Gaussian Process surrogate models and uses Bayesian Optimisation strategies for choosing points to evaluate in order to solve the quality-diversity problem. In addition, BOP-Elites produces high quality surrogate models which can be used after convergence to predict solutions with any behaviour in a continuous range. An empirical comparison shows that BOP-Elites significantly outperforms other state-of-the-art algorithms without the need for problem-specific parameter tuning.

While Quality-Diversity algorithms attempt to produce a set of high quality solutions that are diverse throughout descriptor space, in reality decision makers are often interested in solutions with specific descriptor values. In this paper we suggest that current methods of evaluating Quality Diversity algorithm performance do not properly account for a decision maker's preference in a continuous descriptor space and suggest three approaches that attempt to capture the real-world trade-off between a solution's objective performance and distance from a desired set of target descriptors. In this paper we propose a randomised metric, a process of Monte-Carlo sampling of n target points in descriptor space and a small number of random weights that represent different tolerances for mis-specification in a solution's descriptor values. This sampling allows us to simulate the requirements of all possible combinations of target-tolerance pairs and, by taking sufficient samples, estimate average performance. We go on to formulate three simple methods for comparing average performance of algorithms; Continuous Quality Diversity score (CQD) and Hypervolume of the objective/distance Pareto front. We show that these measures are simple to implement and robust measures of performance without introducing artificial discretisation of the descriptor space.

Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find many high performing points that all behave differently according to a user-defined behavioural metric. In this paper we propose the Bayesian Optimisation of Elites (BOP-Elites) algorithm. Designed for problems with expensive black-box fitness and behaviour functions, it is able to return a QD solution-set with excellent final performance already after a relatively small number of samples. BOP-Elites models both fitness and behavioural descriptors with Gaussian Process (GP) surrogate models and uses Bayesian Optimisation (BO) strategies for choosing points to evaluate in order to solve the quality-diversity problem. In addition, BOP-Elites produces high quality surrogate models which can be used after convergence to predict solutions with any behaviour in a continuous range. An empirical comparison shows that BOP-Elites significantly outperforms other state-of-the-art algorithms without the need for problem-specific parameter tuning.