Composite load spectra for select space propulsion structural components

This report summarizes the development for: (1) correlation fields; (2) applications to liquid oxygen post; (3) models for pressure fluctuatios and vibration loads fluctuations; (4) additions to expert systems; and (5) scaling criteria. Implementation to computer code is also described. Demonstration sample cases are included with additional applications to engine duct and pipe bend

Composite Load Spectra for Select Space Propulsion Structural Components

Generic load models are described with multiple levels of progressive sophistication to simulate the composite (combined) load spectra (CLS) that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades and liquid oxygen (LOX) posts. These generic (coupled) models combine the deterministic models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients are then determined using advanced probabilistic simulation methods with and without strategically selected experimental data. The entire simulation process is included in a CLS computer code. Applications of the computer code to various components in conjunction with the PSAM (Probabilistic Structural Analysis Method) to perform probabilistic load evaluation and life prediction evaluations are also described to illustrate the effectiveness of the coupled model approach

Validating child vaccination status in a demographic surveillance system using data from a clinical cohort study: evidence from rural South Africa

<p><b>Background:</b> Childhood vaccination coverage can be estimated from a range of sources. This study aims to validate vaccination data from a longitudinal population-based demographic surveillance system (DSS) against data from a clinical cohort study.</p> <p><b>Methods:</b> The sample includes 821 children in the Vertical Transmission cohort Study (VTS), who were born between December 2001 and April 2005, and were matched to the Africa Centre DSS, in northern KwaZulu-Natal. Vaccination information in the surveillance was collected retrospectively, using standardized questionnaires during bi-annual household visits, when the child was 12 to 23 months of age. DSS vaccination information was based on extraction from a vaccination card or, if the card was not available, on maternal recall. In the VTS, vaccination data was collected at scheduled maternal and child clinic visits when a study nurse administered child vaccinations. We estimated the sensitivity of the surveillance in detecting vaccinations conducted as part of the VTS during these clinic visits.</p> <p><b>Results:</b> Vaccination data in matched children in the DSS was based on the vaccination card in about two-thirds of the cases and on maternal recall in about one-third. The sensitivity of the vaccination variables in the surveillance was high for all vaccines based on either information from a South African Road-to-Health (RTH) card (0.94-0.97) or maternal recall (0.94-0.98). Addition of maternal recall to the RTH card information had little effect on the sensitivity of the surveillance variable (0.95-0.97). The estimates of sensitivity did not vary significantly, when we stratified the analyses by maternal antenatal HIV status. Addition of maternal recall of vaccination status of the child to the RTH card information significantly increased the proportion of children known to be vaccinated across all vaccines in the DSS.</p> <p><b>Conclusion:</b> Maternal recall performs well in identifying vaccinated children aged 12-23 months (both in HIV-infected and HIV-uninfected mothers), with sensitivity similar to information extracted from vaccination cards. Information based on both maternal recall and vaccination cards should be used if the aim is to use surveillance data to identify children who received a vaccination.</p&gt

Cell-free (RNA) and cell-associated (DNA) HIV-1 and postnatal transmission through breastfeeding

<p>Introduction - Transmission through breastfeeding remains important for mother-to-child transmission (MTCT) in resource-limited settings. We quantify the relationship between cell-free (RNA) and cell-associated (DNA) shedding of HIV-1 virus in breastmilk and the risk of postnatal HIV-1 transmission in the first 6 months postpartum.</p> <p>Materials and Methods - Thirty-six HIV-positive mothers who transmitted HIV-1 by breastfeeding were matched to 36 non-transmitting HIV-1 infected mothers in a case-control study nested in a cohort of HIV-infected women. RNA and DNA were quantified in the same breastmilk sample taken at 6 weeks and 6 months. Cox regression analysis assessed the association between cell-free and cell-associated virus levels and risk of postnatal HIV-1 transmission.</p> <p>Results - There were higher median levels of cell-free than cell-associated HIV-1 virus (per ml) in breastmilk at 6 weeks and 6 months. Multivariably, adjusting for antenatal CD4 count and maternal plasma viral load, at 6 weeks, each 10-fold increase in cell-free or cell-associated levels (per ml) was significantly associated with HIV-1 transmission but stronger for cell-associated than cell-free levels [2.47 (95% CI 1.33–4.59) vs. aHR 1.52 (95% CI, 1.17–1.96), respectively]. At 6 months, cell-free and cell-associated levels (per ml) in breastmilk remained significantly associated with HIV-1 transmission but was stronger for cell-free than cell-associated levels [aHR 2.53 (95% CI 1.64–3.92) vs. 1.73 (95% CI 0.94–3.19), respectively].</p> <p>Conclusions - The findings suggest that cell-associated virus level (per ml) is more important for early postpartum HIV-1 transmission (at 6 weeks) than cell-free virus. As cell-associated virus levels have been consistently detected in breastmilk despite antiretroviral therapy, this highlights a potential challenge for resource-limited settings to achieve the UNAIDS goal for 2015 of eliminating vertical transmission. More studies would further knowledge on mechanisms of HIV-1 transmission and help develop more effective drugs during lactation.</p&gt

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received \u3c 183 mm of precipitation or \u3c 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios \u3e 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content \u3e 9.5%, total C \u3c 24.2 g kg−1, soil organic matter (SOM) \u3c 3.9 g kg−1, or C to N ratios \u3c 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management