On the impact of covariance functions in multi-objective Bayesian optimization for engineering design

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordMulti-objective Bayesian optimization (BO) is a highly useful class of methods that can effectively solve computationally expensive engineering design optimization problems with multiple objectives. However, the impact of covariance function, which is an important part of multi-objective BO, is rarely studied in the context of engineering optimization. We aim to shed light on this issue by performing numerical experiments on engineering design optimization problems, primarily low-fidelity problems so that we are able to statistically evaluate the performance of BO methods with various covariance functions. In this paper, we performed the study using a set of subsonic airfoil optimization cases as benchmark problems. Expected hypervolume improvement was used as the acquisition function to enrich the experimental design. Results show that the choice of the covariance function give a notable impact on the performance of multi-objective BO. In this regard, Kriging models with Matern-3/2 is the most robust method in terms of the diversity and convergence to the Pareto front that can handle problems with various complexities.Natural Environment Research Council (NERC

Associations of women's position in the household and food insecurity with family planning use in Nepal

Women in Nepal have low status, especially younger women in co-resident households. Nepal also faces high levels of household food insecurity and malnutrition, and stagnation in uptake of modern family planning methods.This study aims to understand if household structure and food insecurity interact to influence family planning use in Nepal.Using data on married, non-pregnant women aged 15-49 with at least one child from the Nepal 2011 Demographic and Health Survey (N = 7,460), we explore the relationship between women's position in the household, food insecurity as a moderator, and family planning use, using multi-variable logistic regressions. We adjust for household and individual factors, including other status-related variables.In adjusted models, living in a food insecure household and co-residing with in-laws either with no other daughter-in-laws or as the eldest or youngest daughter-in-law (compared to not-co-residing with in-laws) are all associated with lower odds of family planning use. In the interaction model, younger-sisters-in-law and women co-residing with no sisters-in-law in food insecure households have the lowest odds of family planning use.This study shows that household position is associated with family planning use in Nepal, and that food insecurity modifies these associations-highlighting the importance of considering both factors in understanding reproductive health care use in Nepal. Policies and programs should focus on the multiple pathways through which food insecurity impacts women's reproductive health, including focusing on women with the lowest status in households