RANS Turbulence Model Development using CFD-Driven Machine Learning

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method (Weatheritt and Sandberg, 2016), but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated way, rather than using an algebraic function. Unlike other data-driven methods that fit the Reynolds stresses of trained models to high-fidelity data, the cost function for the CFD-driven training can be defined based on any flow feature from the CFD results. This extends the applicability of the method especially when the training data is limited. Furthermore, the resulting model, which is the one providing the most accurate CFD results at the end of the training, inherently shows good performance in RANS calculations. To demonstrate the potential of this new method, the CFD-driven machine learning approach is applied to model development for wake mixing in turbomachines. A new model is trained based on a high-pressure turbine case and then tested for three additional cases, all representative of modern turbine nozzles. Despite the geometric configurations and operating conditions being different among the cases, the predicted wake mixing profiles are significantly improved in all of these a posteriori tests. Moreover, the model equation is explicitly given and available for analysis, thus it could be deduced that the enhanced wake prediction is predominantly due to the extra diffusion introduced by the CFD-driven model.Comment: Accepted by Journal of Computational Physic

Comparison of screening for pre-eclampsia at 31-34 weeks' gestation by sFlt-1/PlGF ratio and a method combining maternal factors with sFlt-1 and PlGF

Objective: To estimate the patient-specific risk of preeclampsia (PE) at 31-34 weeks’ gestation by a combination of maternal characteristics and medical history with multiple of the median (MoM) values of serum placental growth factor (PLGF) and serum soluble fms-like tyrosine kinase-1 (sFLT-1) and compare the performance of screening to that achieved by the sFLT-1 to PLGF ratio. Methods: This was a prospective observational study in women attending for a third-trimester ultrasound scan at 31-34 weeks as part of routine pregnancy care. We estimated the performance of screening of PE with delivery within four weeks of assessment (PE at <4 weeks) and PE from four weeks after assessment and up to 40 weeks’ gestation (PE at 4w-40GW) in screening by the sFLT-1 to PLGF ratio and by a to PLGF ratio and by a method utilizing Bayes theorem to combine maternal factors and MoM values of sFLT-1 and PLGF. The significance of difference in performance of screening between the method utilising Bayes theorem and that of the sFLT-1 to PLGF ratio was assessed by comparison of the areas under the receiver operating characteristic curves (AUROC). Results: The study population of 8,063 singleton pregnancies included 231 (2.9%) that subsequently developed PE. In the prediction of delivery with PE at <4 weeks the performance of the method utilising Bayes theorem was similar to that of the sFLT-1 to PLGF (AUROC: 0.987, 95%CI 0.979-0.995 vs. 0.988, 95%CI: 0.981-0.994; p=0.961). and at fixed fixed screen positive rate (SPR) of 3.9% the detection rate (DR) was 87.1% for both methods. In contrast, the performance of screening for delivery with PE at 4w-40GW was better with the method utilising Bayes theorem than with the sFLT-1 to PLGF ratio (AUROC: 0.884, 95%CI 0.854-0.914 vs. 0.818, 95%CI: 0.775--0.860 ; p<0.0001) and at total fixed SPR of 25.7% the DRs were 84.4% vs. 73.0%. Conclusion: At 31-34 weeks’ gestation the performance of screening for PE at <4 weeks from assessment by the method utilising Bayes theorem is similar to that of the sFLT-1 to PLGF ratio, but the former is superior to the latter in prediction of PE >>4 weeks

Comparison of different methods of measuring angle of progression in prediction of labor outcome

Objective: First, to compare the manual sagittal and para-sagittal and automated para-sagittal methods of measuring the angle of progression (AOP) by transperineal ultrasound during labor, and second, to develop models for the prediction of time-to-delivery and need for cesarean section (CS) for failure to progress (FTP) in a population of patients undergoing induction of labor. Methods: This was a prospective observational study of transperineal ultrasound on a cohort of 512 women with singleton pregnancies undergiong induction of labor. A random selection of 50 stored images was assessed for inter- and intra-observer reliability between methods. In the cases of vaginal delivery univariate linear, multivariate linear and quantile regression were performed to predict time-to-delivery. Univariate and multivariate binomial logistic regression were performed to predict CS for FTP in the first stage of labor. Results: The intra correlation coefficients (ICC) for the manual para-sagittal method for a single observer was 0.97 (CI 0.95-0.98) and for two observers was 0.96 (CI 0.93-0.98) indicating good reliability. The ICC for the sagittal method for a single observer was 0.93 (0.88-0.96) and for two observers was 0.74 (0.58-0.84) indicating moderate reliabilty for a single observer and poor reliability between two observers. Bland-Altman analysis demonstrated narrower limits of agreement for the manual para-saggittal approach than for the sagittal approach for both single and two observers. The automated para-sagittal method failed to capture an image in 19% of cases. The mean difference between sagittal and para-sagittal methods was 110. In pregnancies resulting in vaginal delivery, 54% of the variation in time-to-delivery was explained in a model combining parity, epidural and syntocinon use during labour and the sonographic findings of fetal head position and AOP. In the prediction of CS for FTP in the first stage of labour a model which combined maternal factors with the sonographic measurements of AOP and estimated fetal weight was superior to one utilising maternal factors alone (area under the curve 0.80 vs 0.76). Conclusions: First, the method of measuring AOP with greatest reliability is the manual parasagittal technique and future research should focus on this technique, second, over half of the variation in time to vaginal delivery can be explained by a model that combines maternal factors, pregnancy characteristics and ultrasound findings, and third, the ability of AOP to provide clinically useful prediction CS for FTP in the first stage of labour is limited

Fetal Medicine Foundation fetal and neonatal population weight charts

Objective: To develop fetal and neonatal population weight charts. The rationale for this objective is that while reference ranges of estimated fetal weight (EFW) are representative of the whole population, the traditional approach of deriving birth-weight (BW) charts is misleading because a high proportion of babies born preterm arises from pathological pregnancies. We propose that the reference population for BW charts, as in the case of EFW charts, should be all babies at a given gestational age including those still in utero. Patients: Two sources of data were used for this study and in both the inclusion criteria were singleton pregnancy, dating by fetal crown-rump length at 11+0 to 13+6 weeks’ gestation, ultrasonographic measurements of fetal head circumference (HC), abdominal circumference (AC) and femur length (FL), and livebirth of phenotypically normal neonate. Dataset 1, comprised a sample of 5,163 paired measurements of EFW and BW; the ultrasound scans were carried out at 22-43 weeks’ gestation and birth occurred within 2 days of the ultrasound examination. The EFW was derived from the measurements of HC, AC and FL using the formula reported by Hadlock et al. in 1985. Dataset 2, comprised a sample of 95,579 pregnancies with EFW obtained by routine ultrasonographic fetal biometry at 20+0 to 23+6 weeks’ gestation (n=45,034), or at 31+0 to 33+6 weeks (n=19,224) or at 35+0 to 36+6 weeks (n=31,321); for the purpose of this study we included data for only one of the three visits. Methods: In the development of reference ranges of EFW and BW with gestational age the following assumptions were made: first, the EFW and BW have a common median, dependent on gestational age and second, deviations from the median occur in both EFW and BW and these deviations are correlated with different levels of spread for EFW and BW, dependent on gestational age. We adopted a Bayesian approach to inference combining information from the two datasets using Markov Chain Monte–Carlo sampling (MCMC). The fitted model assumed that the mean log transformed measurements of EFW and BW are related to gestational age according to a cubic equation and that deviations about the mean follow a bivariate Gaussian distribution. Results: In the case of EFW in dataset 2 there was a good distribution of values 90th, >95th and >97th percentiles of the reference range of EFW with gestational age throughout the gestational age range of 20+0- 36+6 weeks. In the case of BW there was a good distribution of values only for the cases born at >39 weeks’ gestation. For preterm births, particularly at 27-36 weeks, the BW was below the 3rd, 5th and 10th percentiles in a very high proportion of cases and this was particularly marked for cases of iatrogenic birth. The incidence of SGA fetuses and neonates in the respective EFW and BW charts was higher in women of Black than White racial origin. Conclusion: We established a BW chart for the population of all babies at a given gestational age, including those still in utero, which overcomes the problem of underestimation of growth restriction in preterm births. The BW and EFW charts have a common median but they differ in the levels of spread from the median

ALEX Fetal Medicine Foundation reference ranges for umbilical artery and middle cerebral artery pulsatility index and cerebroplacental ratio

Objective: To develop reference ranges with gestational age for the pulsatility index in the umbilical artery (UA-PI) and fetal middle cerebral artery (MCA-PI and the cerebroplacental ratio (MCA-PI / UA-PI) and examine the maternal characteristics and medical history that affect these measurements. Patients and methods: This was a cross-sectional study of 72,417 pregnancies undergoing routine ultrasound examination at 20+0 to 22+6 weeks’ gestation (n=3,712), or at 31+0 to 33+6 weeks (n=29,035) or at 35+0 to 36+6 weeks (n=37,282) or at 41+0 to 41+6 weeks (n=2,388). For the purpose of this study we included data for only one of the second or third trimester visits. The inclusion criteria were singleton pregnancy, dating by fetal crown-rump length at 11+0 to 13+6 weeks’ gestation, livebirth of morphologically normal neonate and ultrasonographic measurements by sonographers that had received the Fetal Medicine Foundation Certificate of competence in Doppler ultrasound. Since the objectives of the study were to establish reference ranges, rather than normal ranges, and to examine factors from maternal characteristics and medical history that affect these measurements, we included all pregnancies having routine ultrasound examinations irrespective of whether the mothers had a pre-existing medical condition, such as diabetes mellitus, or a pregnancy complication, such as preeclampsia or suspected fetal growth restriction. Median and standard deviation (SD) models were fitted between UA-PI, MCA-PI and CPR and gestational age. Assessment of goodness of fit of the models was by inspection of quantile to quantile (q-q) plots of z-scores calculated via the mean and SD models. The distributions of MCA PI, UA PI and CPR z-scores were examined in relation to maternal characteristics and medical history. Results: The relationship between the median and gestation age was linear for UA-PI and cubic for MCA-PI and CPR and the SD was log quadratic for all three. MCA-PI and CPR increased with gestational age from 20 weeks’ gestation to reach a peak at around 32 and 34 weeks’ respectively, and decreased thereafter, whereas UA-PI decreased linearly with gestation from 20 to 42 weeks. Compared to the general population, significant deviations in MoM values of UA-PI, MCA-PI and CPR were observed in subgroups of maternal age, BMI, racial origin, method of conception and parity. Conclusion: The study established new reference ranges of UA-PI, MCA-PI and CPR with gestational age and reports maternal characteristics and medical history that affect these measurements

Uterine artery pulsatility index at 30-34 weeks' gestation in the prediction of adverse perinatal outcome

Objective: To investigate the potential value of uterine artery Doppler at 30 34 weeks’ gestation in the prediction of adverse perinatal outcome. Methods: Screening study in 30,780 singleton pregnancies at 30 34 weeks. Uterine artery pulsatility index (PI) was measured and the values were converted to multiples of the median (MoM) after adjustment from variables in maternal characteristics and medical history that affect the measurements. Multivariable logistic regression analysis was used to determine if uterine artery PI had a significant additional contribution to maternal characteristics, medical history and obstetric factors in predicting adverse outcome. The detection rate (DR) and false positive rate (FPR) of screening by uterine artery PI were estimated for stillbirth, cesarean section for fetal distress, umbilical arter ial cord blood pH <7.0 or umbilical venous pH <7.1 and Apgar score <7 at 5 minutes. Results: The incidence of adverse perinatal outcome was higher in small for gestational age (SGA) than in non SGA fetuses, but the majority of cases for each adverse outcome were in the non SGA group, including about 70% of stillbirths and more than 80% of cases of cesarean section for fetal distress, low cord blood pH and low Apgar score. The performance of uterine artery PI >95 th percentile in screening for each adverse outcome was poor with DR of 6 16% and FPR of 5 6%. T he DR of high uterine artery PI for adverse outcome was higher in the SGA than non SGA groups, including 24% vs. 13% for stillbirth , 15% vs. 5% for cesarean section for fetal distress, 22% vs. 9% for low cord blood pH and 20% vs. 3% for low Apgar score. Conclusion: High uterine artery PI at 30 34 weeks’ gestation may be useful in the prediction of adverse perinatal outcome in pregnancies with SGA fetuses, but not in those with non SGA fetuses

Fetal middle cerebral artery and umbilical artery pulsatility index: effects of maternal characteristics and medical history

To define the contribution of maternal variables which influence the measured fetal middle cerebral artery (MCA) and umbilical artery (UA) pulsatility index (PI) in the assessment of fetal wellbeing

Fetal major cardiac defects and placental dysfunction at 11-13 weeks' gestation

Objectives: To investigate the relationship between fetal major cardiac defects and markers of placental perfusion and function. Methods: This was a prospective screening study in singleton pregnancies at 11-13 weeks’ gestation. Uterine artery pulsatility index (UTPI), serum pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PLGF) were measured and the values were converted into multiples of the normal median (MoM). Median MoM values in fetuses with isolated major cardiac defects were compared to those in fetuses without major defects. Results: The 50,094 singleton pregnancies fulfilling the entry criteria included 49,898 pregnancies with a normal cardiac anatomy and 196 (0.39%) with major congenital cardiac defects; 73 (37.2%) with conotruncal defects, 63 (32.1%) with left ventricular outflow tract (LVOT) defects and 60 (30.6%) with valvular defects. In the group of cardiac defects, compared to controls, there was lower median PAPP-A MoM (0.81 vs 1.00, p<0.0001) and PLGF MoM (0.78 vs 1.00, p<0.0001) but no significant difference in UTPI MoM (1.01 vs 1.00, p=0.162). Conclusions: In pregnancies with isolated major cardiac defects there is evidence of placental dysfunction in the absence of impaired placental perfusion

ChatGPT -- a Blessing or a Curse for Undergraduate Computer Science Students and Instructors?

ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text. It can be used for a variety of use cases such as language generation, question answering, text summarization, chatbot development, language translation, sentiment analysis, content creation, personalization, text completion, and storytelling. While ChatGPT has garnered significant positive attention, it has also generated a sense of apprehension and uncertainty in academic circles. There is concern that students may leverage ChatGPT to complete take-home assignments and exams and obtain favorable grades without genuinely acquiring knowledge. This paper adopts a quantitative approach to demonstrate ChatGPT's high degree of unreliability in answering a diverse range of questions pertaining to topics in undergraduate computer science. Our analysis shows that students may risk self-sabotage by blindly depending on ChatGPT to complete assignments and exams. We build upon this analysis to provide constructive recommendations to both students and instructors.Comment: This is a work in progres