A frequency-domain full waveform inversion method of elastic waves in quantitative defection investigation

857-866Full waveform inversion is a challenging data-fitting procedure based on full wave field modeling to extract quantitative information on elastic properties of subsurface structures. We developed a frequency-domain full-waveform inversion method of elastic waves for stratified media, adopting a quasi-linearization method coupled with a random search algorithm. The inversion process of this method is irrelevant to hypocenter function and can be considered as a kind of combination between the heuristic and non-heuristic inversion methods. To verify our method, we apply it to three numerical two-dimensional models with different intermediate structures (dipping, arched and hollow), and their structures are well revealed. With some pretreatments on response waveforms, such as filtering, normalization and correlation analysis, the full-waveform inversion method is extended to models with damaged area and its feasibility and accuracy verified. Alignment of full waveform inversion method and its cost of computing, several strategies exist to treat this quantitative detecting problem. In Chengdu-Chongqing guest emergency project, the application of full waveform inversion method saves a lot of time. In this method, each section only needs 2 detectors and only need to be hammered twice, while the traditional CT (Computed Tomography) test requires 11 detection filters and at least 11 hammering, and each section has 121 waveform data. In some cases, we can obtain some important priori information through field investigation. The priori information can be used to accelerate the inversion process

Correlation between serum D-dimer andrisk of gestational diabetes mellitus

This study has been carried out to investigate the correlation between serum D-dimer and the risk of gestational diabetes mellitus (GDM). A total of 308 pregnant women treated in our hospital from January 2018 to January 2020 were retrospectively analyzed. According to the diagnostic criteria for GDM, they were divided into normal blood glucose group (n=145) and GDM group (n=163). The level of serum D-dimer was measured by enzyme-linked immunosorbent assay at 5-12, 13-23, 24-28 and 29-37 weeks of pregnancy. The pregnant women who did not have GDM at 5-12 and 13-23 weeks of pregnancy but were diagnosed with GDM at and after 24 weeks of pregnancy were assigned to GDM-A group (n=18) and GDM-B group (n=26), respectively. The related factors affecting the occurrence of GDM was analyzed by multivariate logistic regression. The optimal threshold of D-dimer for the occurrence of GDM was predicted via receiver operating characteristic (ROC) curve. The level of serum D-dimer in GDM group was significantly higher than that in normal blood glucose group at 5-12, 13-23 and 24-28 weeks of pregnancy (P<0.05). The level of serum D-dimer at 24-28 weeks of pregnancy was negatively correlated with OGTT 0-min insulin (r=-0.756, P<0.05) and HOMA-IR (r=-0.693, P<0.05), but positively correlated with LDL-C (r=0.759, P<0.05). After adjustment of confounding factors such as pregnancy age, pre-pregnancy body mass index, Acanthosis nigricans and triglyceride, the level of serum D-dimer at 13-23 weeks of pregnancy was still an independent risk factor for the occurrence of GDM at and after 24 weeks of pregnancy (OR=0.731, 95% CI=0.503-0.760, P<0.05). Moreover, in GDM-B group, the level of serum D-dimer at 13-23 weeks of pregnancy could better predict the occurrence of GDM at and after 24 weeks of pregnancy, and the area under the ROC curve was 0.731

Changes in Maternal Glucose Metabolism after the Administration of Dexamethasone for Fetal Lung Development

Aims. Antenatal dexamethasone administration for fetal lung development may impair maternal glucose tolerance. In this study, we investigated whether glucose and insulin levels differed among singleton and twin pregnancies and pregnancies with impaired glucose tolerance (IGT) after treatment with dexamethasone. Methods. Singleton pregnancies, twin pregnancies, and pregnancies with IGT between 28 and 33 weeks of gestation whose mothers were treated with dexamethasone were enrolled in this study. Exclusion criteria included gestational hypertension, diabetes, renal disorders, and infectious diseases. The fasting plasma glucose and insulin levels were checked before administration and 24 h, 48 h, and 72 h after treatment was completed. Results. Mean glucose levels were significantly higher in the twin pregnancy and IGT groups at 24 h and 48 h after the administration of dexamethasone than those in the singleton pregnancy group (P < 0.05). Although there was no significant difference in glucose levels before administration and 72 h after dexamethasone administration among the three groups, insulin levels in the IGT group were significantly higher (P < 0.05). Insulin levels in the singleton pregnancy group at 24 h and 48 h after treatment were significantly lower than in the twin and IGT groups. Conclusion. The effects on maternal fasting blood glucose and insulin levels of dexamethasone administrated to promote fetal lung maturation correlated with embryo number and the presence of IGT

Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation

Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs. In this paper, we investigate how to optimize few-shot text classification without accessing the gradients of the LLMs. To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data. Data augmentation is performed using prompt-based finetuning on an auxiliary language model with a much smaller parameter size than the black-box model. Through extensive experiments on eight text classification datasets, we show that our approach, dubbed BT-Classifier, significantly outperforms state-of-the-art black-box few-shot learners and performs on par with methods that rely on full-model tuning