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In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources. [ABSTRACT FROM AUTHOR]

At present, the world is off-track to meet the World Health Assembly global nutrition targets for 2025. Reducing the prevalence of stunting in children, low birthweight, and anaemia in women and increasing breastfeeding are among the prioritized global nutrition targets for all countries. Governments and development partners need evidence-based data to understand the true costs and consequences of policy decisions and investments. Yet there is an evidence gap on the health, human capital, and economic costs of inaction on preventing undernutrition for most countries. The Cost of Inaction tool, and expanded Cost of Not Breastfeeding tool, provide country-specific data to help to address the gaps. Every year undernutrition leads to 1.3 million cases of preventable child and maternal deaths. In children, stunting results in the largest economic burden yearly at US$548 billion (0.7% of GNI), followed by US$507 billion for sub-optimal breastfeeding (0.6% global GNI), US$344 billion (0.3% of GNI) for low birthweight and US$161 billion (0.2% of GNI) for anaemia in children. Anaemia in WRA costs US$113 billion (0.1% of GNI) globally in current income losses. Accounting for overlap in stunting, suboptimal breastfeeding, and low birthweight, the analysis estimates that preventable undernutrition cumulatively costs the world at least US$761 billion per year, or $2.1B per day. The variation in the regional and country-level estimates reflect the contextual drivers of undernutrition. In the lead up to the renewed WHA targets and Sustainable Development Goals to 2030, the data generated from these tools are powerful information for advocates, governments, and development partners to inform policy decisions and investments into high-impact low-cost nutrition interventions. The costs of inaction on undernutrition continue to be substantial, and serious coordinated action on the global nutrition targets is needed to yield the significant positive human capital and economic benefits from investing in nutrition., (© The Author(s) 2024. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.)

Objective: To investigate the cost-effectiveness of in-hospital obstetrical care by obstetricians (OBs), family physicians (FPs) and midwives (MWs) for delivery of low-risk obstetrical patients., Methods: Cost-effectiveness analysis from the Ministry of Health perspective using a retrospective cohort study. The time horizon was from hospital admission of a low-risk pregnant patient to the discharge of the mother and infant. Costing data included human resource, intervention and hospital case-mix costs. Interventions measured were induction or augmentation of labour with oxytocin, epidural use, forceps or vacuum delivery and caesarean section. The outcome measured was avoidance of transfer to a neonatal intensive care unit (NICU). Model results were tested using various types of sensitivity analyses., Findings: The mean maternal age by provider groups was 29.7 for OBs, 29.8 for FPs and 31.2 for MWs - a statistically higher mean for the MW group. The MW deliveries had lower costs and better outcomes than FPs and OBs. FPs also dominated OB.s The differences in cost per delivery were small, but slightly lower in MW ($5,102) and FP ($5,116) than in OB ($5,188). Avoidance of transfer to an NICU was highest for MW at 94.0% (95% CI: 91.0-97.0), compared with 90.2% for FP (95% CI: 88.2-92.2) and 89.6% for OB (95% CI: 88.6-90.6). The cost-effectiveness of the MW group is diminished by increases in compensation, and the cost-effectiveness of the FP group is sensitive to changes in intervention rates and costs., Conclusions: The MW strategy was the most cost-effective in this hospital setting. Given data limitations to further examine patient characteristics between groups, the overall conservative findings of this study support investments and better integration for MWs in the current system., (Copyright © 2015 Longwoods Publishing.)