Exploring the Impact of Gender Bias Mitigation Approaches on a Downstream Classification Task

Natural language models and systems have been shown to reflect gender bias existing in training data. This bias can impact on the downstream task that machine learning models, built on this training data, are to accomplish. A variety of techniques have been proposed to mitigate gender bias in training data. In this paper we compare different gender bias mitigation approaches on a classification task. We consider mitigation techniques that manipulate the training data itself, including data scrubbing, gender swapping and counterfactual data augmentation approaches. We also look at using de-biased word embeddings in the representation of the training data. We evaluate the effectiveness of the different approaches at reducing the gender bias in the training data and consider the impact on task performance. Our results show that the performance of the classification task is not affected adversely by many of the bias mitigation techniques but we show a significant variation in the effectiveness of the different gender bias mitigation techniques

Impact of timing of delivery for type 2 diabetes on perinatal outcomes

Aims: To compare obstetric and neonatal outcomes in patients with type 2 diabetes mellitus (T2DM) who had scheduled delivery at full term (≥ 39 0/7 weeks) compared to early term (37 0/7 – 38 6/7 weeks) for T2DM indications. Methods: This was a retrospective cohort study that included all singletons with T2DM with a scheduled delivery at a single tertiary care center between January 2008 and March 2022. Outcomes were compared using Fisher's exact test. Results: 107 singleton pregnancies were included. There was no significant difference in primary cesarean delivery between the two groups. The early term group had significantly higher rates of NICU admission compared to the term group (52% vs 32%, p = 0.05, OR 2.3, 95% CI 1.0–5.0), a finding that remained statistically significant on adjusted analysis (adjusted OR 2.81, 95% CI 1.04–7.58). Conclusions: In singleton pregnancies undergoing scheduled delivery for T2DM-specific indications, early term deliveries were associated with significantly increased odds of NICU admission when compared to term deliveries, even after adjusting for surrogate markers of glycemic control. These findings suggest that early term delivery contributes to risk of NICU admission, rather than the indication for delivery itself. These findings should be replicated in a larger cohort

Stability of Silica Nanoparticle Dispersion in Brine Solution: An Experimental Study

Nanotechnology has various applications in oil and gas industry such as enhanced oil recovery (EOR). The main challenge in using nanoparticles in EOR processes is their stability in harsh conditions such as high temperature, high pressure, and intermediate to high salinity. However, most of the recent experimental works have been performed under unrealistic conditions such as the use of distilled water as the injected fluid and room temperature. The main objective of this work is to study the effect of these factors on the stability of nanoparticle dispersions through several methods such as direct observation, optical absorption measurement, and nanoparticle effective diameter in different periods of time. The critical salt concentration (CSC) was determined for two kinds of monovalent electrolytes in various particle concentrations and temperatures. The results have shown that CSC for potassium chloride (KCl) is less than sodium chloride (NaCl) and it decreases as nanoparticle concentration and temperature increase. Moreover, the influence of two types of surfactants on the stability of silica dispersions was studied and the results revealed that an anionic surfactant increases the CSC, while a nonionic surfactant leads to the instability of dispersion even at low electrolyte concentrations