A RISK-INFORMED DECISION-MAKING METHODOLOGY TO IMPROVE LIQUID ROCKET ENGINE PROGRAM TRADEOFFS

This work provides a risk-informed decision-making methodology to improve liquid rocket engine program tradeoffs with the conflicting areas of concern affordability, reliability, and initial operational capability (IOC) by taking into account psychological and economic theories in combination with reliability engineering. Technical program risks are associated with the number of predicted failures of the test-analyze-and-fix (TAAF) cycle that is based on the maturity of the engine components. Financial and schedule program risks are associated with the epistemic uncertainty of the models that determine the measures of effectiveness in the three areas of concern. The affordability and IOC models' inputs reflect non-technical and technical factors such as team experience, design scope, technology readiness level, and manufacturing readiness level. The reliability model introduces the Reliability- As-an-Independent-Variable (RAIV) strategy that aggregates fictitious or actual hotfire tests of testing profiles that differ from the actual mission profile to estimate the system reliability. The main RAIV strategy inputs are the physical or functional architecture of the system, the principal test plan strategy, a stated reliability-bycredibility requirement, and the failure mechanisms that define the reliable life of the system components. The results of the RAIV strategy, which are the number of hardware sets and number of hot-fire tests, are used as inputs to the affordability and the IOC models. Satisficing within each tradeoff is attained by maximizing the weighted sum of the normalized areas of concern subject to constraints that are based on the decision-maker's targets and uncertainty about the affordability, reliability, and IOC using genetic algorithms. In the planning stage of an engine program, the decision variables of the genetic algorithm correspond to fictitious hot-fire tests that include TAAF cycle failures. In the program execution stage, the RAIV strategy is used as reliability growth planning, tracking, and projection model. The main contributions of this work are the development of a comprehensible and consistent risk-informed tradeoff framework, the RAIV strategy that links affordability and reliability, a strategy to define an industry or government standard or guideline for liquid rocket engine hot-fire test plans, and an alternative to the U.S. Crow/AMSAA reliability growth model applying the RAIV strategy

DNA as a programmable viscoelastic nanoelement

The two strands of a DNA molecule with a repetitive sequence can pair into many different basepairing patterns. For perfectly periodic sequences, early bulk experiments of Poerschke indicate the existence of a sliding process, permitting the rapid transition between different relative strand positions [Biophys. Chem. 2 (1974) 83]. Here, we use a detailed theoretical model to study the basepairing dynamics of periodic and nearly periodic DNA. As suggested by Poerschke, DNA sliding is mediated by basepairing defects (bulge loops), which can diffuse along the DNA. Moreover, a shear force f on opposite ends of the two strands yields a characteristic dynamic response: An outward average sliding velocity v~1/N is induced in a double strand of length N, provided f is larger than a threshold f_c. Conversely, if the strands are initially misaligned, they realign even against an external force less than f_c. These dynamics effectively result in a viscoelastic behavior of DNA under shear forces, with properties that are programmable through the choice of the DNA sequence. We find that a small number of mutations in periodic sequences does not prevent DNA sliding, but introduces a time delay in the dynamic response. We clarify the mechanism for the time delay and describe it quantitatively within a phenomenological model. Based on our findings, we suggest new dynamical roles for DNA in artificial nanoscale devices. The basepairing dynamics described here is also relevant for the extension of repetitive sequences inside genomic DNA.Comment: 10 pages, 7 figures; final version to appear in Biophysical Journa

Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: An updated analysis with a focus on low- and middle-income countries

Background To develop updated estimates in response to new exposure and exposure-response data of the burden of diarrhoea, respiratory infections, malnutrition, schistosomiasis, malaria, soil-transmitted helminth infections and trachoma from exposure to inadequate drinking-water, sanitation and hygiene behaviours (WASH) with a focus on low- and middle-income countries. Methods For each of the analysed diseases, exposure levels with both sufficient global exposure data for 2016 and a matching exposure-response relationship were combined into population-attributable fractions. Attributable deaths and disability-adjusted life years (DALYs) were estimated for each disease and, for most of the diseases, by country, age and sex group separately for inadequate water, sanitation and hygiene behaviours and for the cluster of risk factors. Uncertainty estimates were computed on the basis of uncertainty surrounding exposure estimates and relative risks. Findings An estimated 829,000 WASH-attributable deaths and 49.8 million DALYs occurred from diarrhoeal diseases in 2016, equivalent to 60% of all diarrhoeal deaths. In children under 5 years, 297,000 WASH-attributable diarrhoea deaths occurred, representing 5.3% of all deaths in this age group. If the global disease burden from different diseases and several counterfactual exposure distributions was combined it would amount to 1.6 million deaths, representing 2.8% of all deaths, and 104.6 million DALYs in 2016. Conclusions Despite recent declines in attributable mortality, inadequate WASH remains an important determinant of global disease burden, especially among young children. These estimates contribute to global monitoring such as for the Sustainable Development Goal indicator on mortality from inadequate WASH

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks