Virtual process for evaluating the influence of real combined module variations on the overall performance of an aircraft engine

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aero-thermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan high-pressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas-Path-Analysis for further modules, such as the com-pressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency ηHPT and capacity _mHPT)are particularly driven by the variation of the stagger angle. On the other hand, ηHPT is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow _mHPT are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches

Virtual process for evaluating the influence of real combined module variations on the overall performance of an aircraft engine

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aero-thermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan high-pressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas-Path-Analysis for further modules, such as the compressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mi&gt;η&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt; and capacity &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mrow&gt;&lt;mml:mover&gt;&lt;mml:mi&gt;m&lt;/mml:mi&gt;&lt;mml:mo&gt;˙&lt;/mml:mo&gt;&lt;/mml:mover&gt;&lt;/mml:mrow&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt;) are particularly driven by the variation of the stagger angle. On the other hand, &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mi&gt;η&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt; is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mrow&gt;&lt;mml:mover&gt;&lt;mml:mi&gt;m&lt;/mml:mi&gt;&lt;mml:mo&gt;˙&lt;/mml:mo&gt;&lt;/mml:mover&gt;&lt;/mml:mrow&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt; are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches.</jats:p

Virtual Process for Evaluating the Influence of Real Combined Module Variations on the Overall Performance of an Aircraft Engine

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aerothermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan highpressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas- Path-Analysis for further modules, such as the compressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency hHPT and capacity m˙ HPT ) are particularly driven by the variation of the stagger angle. On the other hand, hHPT is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow m˙ HPT are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches.</jats:p

Analysis of gene mutations associated with isoniazid, rifampicin and ethambutol resistance among Mycobacterium tuberculosis isolates from Ethiopia

<p>Abstract</p> <p>Background</p> <p>The emergence of drug resistance is one of the most important threats to tuberculosis control programs. This study was aimed to analyze the frequency of gene mutations associated with resistance to isoniazid (INH), rifampicin (RMP) and ethambutol (EMB) among <it>Mycobacterium tuberculosis </it>isolates from Northwest Ethiopia, and to assess the performance of the GenoType^®MTBDRplus and GenoType^®MTBDRsl assays as compared to the BacT/ALERT 3D system.</p> <p>Methods</p> <p>Two hundred sixty <it>Mycobacterium tuberculosis </it>isolates from smear positive tuberculosis patients diagnosed between March 2009 and July 2009 were included in this study. Drug susceptibility tests were performed in the Institute of Medical Microbiology and Epidemiology of Infectious Diseases, University Hospital of Leipzig, Germany.</p> <p>Results</p> <p>Of 260 isolates, mutations conferring resistance to INH, RMP, or EMB were detected in 35, 15, and 8 isolates, respectively, while multidrug resistance (MDR) was present in 13 of the isolates. Of 35 INH resistant strains, 33 had mutations in the <it>katG </it>gene at Ser315Thr 1 and two strains had mutation in the <it>inhA </it>gene at C15T. Among 15 RMP resistant isolates, 11 had <it>rpoB </it>gene mutation at Ser531Leu, one at His526Asp, and three strains had mutations only at the wild type probes. Of 8 EMB resistant strains, two had mutations in the <it>embB </it>gene at Met306Ile, one at Met306Val, and five strains had mutations only at the wild type probes. The GenoType^®MTBDRplus assay had a sensitivity of 92% and specificity of 99% for INH resistance, and 100% sensitivity and specificity to detect RMP resistance and MDR. The GenoType^®MTBDRsl assay had a sensitivity of 42% and specificity of 100% for EMB resistance.</p> <p>Conclusion</p> <p>The dominance of single gene mutations associated with the resistance to INH and RMP was observed in the codon 315 of the <it>katG </it>gene and codon 531 of the <it>rpoB </it>gene, respectively. The GenoType^®MTBDRplus assay is a sensitive and specific tool for diagnosis of resistance to INH, RMP and MDR. However, the GenoType^®MTBDRsl assay shows limitations in detecting resistance to EMB.</p

Impact of sex and gender on post-COVID-19 syndrome, Switzerland, 2020

Background: Women are overrepresented among individuals with post-acute sequelae of SARS-CoV-2 infection (PASC). Biological (sex) as well as sociocultural (gender) differences between women and men might account for this imbalance, yet their impact on PASC is unknown. Aim: We assessed the impact of sex and gender on PASC in a Swiss population. Method: Our multicentre prospective cohort study included 2,856 (46% women, mean age 44.2 ± 16.8 years) outpatients and hospitalised patients with PCR-confirmed SARS-CoV-2 infection.ResultsAmong those who remained outpatients during their first infection, women reported persisting symptoms more often than men (40.5% vs 25.5% of men; p < 0.001). This sex difference was absent in hospitalised patients. In a crude analysis, both female biological sex (RR = 1.59; 95% CI: 1.41-1.79; p < 0.001) and a score summarising gendered sociocultural variables (RR = 1.05; 95% CI: 1.03-1.07; p < 0.001) were significantly associated with PASC. Following multivariable adjustment, biological female sex (RR = 0.96; 95% CI: 0.74-1.25; p = 0.763) was outperformed by feminine gender-related factors such as a higher stress level (RR = 1.04; 95% CI: 1.01-1.06; p = 0.003), lower education (RR = 1.16; 95% CI: 1.03-1.30; p = 0.011), being female and living alone (RR = 1.91; 95% CI: 1.29-2.83; p = 0.001) or being male and earning the highest income in the household (RR = 0.76; 95% CI: 0.60-0.97; p = 0.030). Conclusion: Specific sociocultural parameters that differ in prevalence between women and men, or imply a unique risk for women, are predictors of PASC and may explain, at least in part, the higher incidence of PASC in women. Once patients are hospitalised during acute infection, sex differences in PASC are no longer evident

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

Variable-height channels for microparticle characterization and display

Characterizing and isolating microparticles of different sizes is often desirable and essential for biological analysis. In this work, we present a new and straightforward technique to fabricate variable-height glass microchannels for size-based passive trapping of microparticles. The fabrication technique uses controlled non-uniform exposure to an etchant solution to create channels of arbitrary height that vary in a predetermined way from the inlet to the outlet. Channels that vary from 1 μm to over 20 μm in height along a length of approximately 6 cm are shown to effectively and reproducibly separate particles by size including particles whose diameters differ by less than 100 nm when the standard deviation in size is less than 0.66 μm. Additionally, healthy red blood cells and red blood cells chemically modified with glutaraldehyde to reduce their deformability were introduced into different channels. The healthy cells can flow into shallower heights, while the less deformable ones are trapped at deeper heights. The macroscopic visualization of microparticle separation in these devices in addition to their ease of use, simple fabrication, low cost, and small size suggest their viability in the final detection step of many bead-based assay protocols

Impaired immune response drives age-dependent severity of COVID-19.

Severity of COVID-19 shows an extraordinary correlation with increasing age. We generated a mouse model for severe COVID-19 and show that the age-dependent disease severity is caused by the disruption of a timely and well-coordinated innate and adaptive immune response due to impaired interferon (IFN) immunity. Aggravated disease in aged mice was characterized by a diminished IFN-γ response and excessive virus replication. Accordingly, adult IFN-γ receptor-deficient mice phenocopied the age-related disease severity, and supplementation of IFN-γ reversed the increased disease susceptibility of aged mice. Further, we show that therapeutic treatment with IFN-λ in adults and a combinatorial treatment with IFN-γ and IFN-λ in aged Ifnar1-/- mice was highly efficient in protecting against severe disease. Our findings provide an explanation for the age-dependent disease severity and clarify the nonredundant antiviral functions of type I, II, and III IFNs during SARS-CoV-2 infection in an age-dependent manner. Our data suggest that highly vulnerable individuals could benefit from immunotherapy combining IFN-γ and IFN-λ

GRADE guidelines 26: informative statements to communicate the findings of systematic reviews of interventions

Objectives: Clear communication of systematic review findings will help readers and decision makers. We built on previous work to develop an approach that improves the clarity of statements to convey findings and that draws on Grading of Recommendations Assessment, Development and Evaluation (GRADE). Study Design and Setting: We conducted workshops including 80 attendants and a survey of 110 producers and users of systematic reviews. We calculated acceptability of statements and revised the wording of those that were unacceptable to ≥40% of participants. Results: Most participants agreed statements should be based on size of effect and certainty of evidence. Statements for low, moderate and high certainty evidence were acceptable to >60%. Key guidance, for example, includes statements for high, moderate and low certainty for a large effect on intervention x as: x results in a large reduction…; x likely results in a large reduction…; x may result in a large reduction…, respectively. Conclusions: Producers and users of systematic reviews found statements to communicate findings combining size and certainty of an effect acceptable. This article provides GRADE guidance and a wording template to formulate statements in systematic reviews and other decision tools. Keywords: review literature as topic, health communication, Evidence-Based Medicine, Surveys and Questionnaires, Language, persuasive communicatio