Bayesian Inference in Estimation of Distribution Algorithms

Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAG c

Bed-load effects on hydrodynamics of rough-bed open-channel flows

Peer reviewedPublisher PD

Text messaging to help women with overweight or obesity lose weight after childbirth:the intervention adaptation and SMS feasibility RCT

Background There is a need to develop weight management interventions that fit seamlessly into the busy lives of women during the postpartum period. Objective The objective was to develop and pilot-test an evidence- and theory-based intervention, delivered by short message service, which supported weight loss and weight loss maintenance in the postpartum period. Design Stage 1 involved the development of a library of short message service messages to support weight loss and weight loss maintenance, with personal and public involvement, focusing on diet and physical activity with embedded behaviour change techniques, and the programming of a short message service platform to allow fully automated intervention delivery. Stage 2 comprised a 12-month, single-centre, two-arm, pilot, randomised controlled trial with an active control. Setting This study was set in Northern Ireland; women were recruited via community-based approaches. Participants A total of 100 women with overweight or obesity who had given birth in the previous 24 months were recruited. Interventions The intervention group received an automated short message service intervention about weight loss and weight loss maintenance for 12 months. The active control group received automated short message service messages about child health and development for 12 months. Main outcome measures The main outcomes measured were the feasibility of recruitment and retention, acceptability of the intervention and trial procedures, and evidence of positive indicative effects on weight. Weight, waist circumference and blood pressure were measured by the researchers; participants completed a questionnaire booklet and wore a sealed pedometer for 7 days at baseline, 3, 6, 9 and 12 months. Outcome assessments were collected during home visits and women received a voucher on completion of each of the assessments. Qualitative interviews were conducted with women at 3 and 12 months, to gather feedback on the intervention and active control and the study procedures. Quantitative and qualitative data were used to inform the process evaluation and to assess fidelity, acceptability, dose, reach, recruitment, retention, contamination and context. Results The recruitment target of 100 participants was achieved (intervention, n = 51; control, n = 49); the mean age was 32.5 years (standard deviation 4.3 years); 28 (28%) participants had a household income o

Michelle Obama: Moving towards childhood obesity solutions

Throughout the Obamas\u27 time in office, approval ratings made it clear the most popular Obama was not the man in the Oval Office. First Lady Michelle Obama consistently received higher approval ratings than her husband during his time in office. This could be attributed to her strong devotion to her family and her appearance as a genuinely good and caring person. During her time in office she drew the attention of the public by shedding light on one of the worst health epidemics currently afflicting the country: childhood obesity

James Webb Space Telescope Optical Telescope Element Mirror Coatings

James Webb Space Telescope (JWST) Optical Telescope Element (OTE) mirror coating program has been completed. The science goals of the JWST mission require a uniform, low stress, durable optical coating with high reflectivity over the JWST spectral region. The coating has to be environmentally stable, radiation resistant and compatible with the cryogenic operating environment. The large size, 1.52 m point to point, light weight, beryllium primary mirror (PM) segments and flawless coating process during the flight mirror coating program that consisted coating of 21 flight mirrors were among many technical challenges. This paper provides an overview of the JWST telescope mirror coating program. The paper summarizes the coating development program and performance of the flight mirrors

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges