Object Weighting: A New Clustering Approach to Deal with Outliers and Cluster Overlap in Computational Biology

The psychological status mediates the effect of indoor air pollution on recurrent spontaneous abortion

BACKGROUND

Indoor air pollution (IAP) exposure and psychological status have been recognized as important risk factors for adverse pregnancy outcomes, but their mediating effects on recurrent spontaneous abortion (RSA) have not been analyzed. Therefore, the purpose of this study is to explore the association between IAP and RSA and to examine the mediating effect of psychological status on their association.

METHODS

This study included 830 RSA cases and 2156 controls in Gansu province, China. The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) questionnaires were used to collect information on participants' psychological status. The IAP exposure was collected by the survey on cooking fuel use, kitchen ventilation characteristics, cooking styles, and indoor smoking, etc. Multivariable logistic regression was used to examine the associations between IAP exposure and RSA. The mediation analysis was used to evaluate the mediation effects of IAP and psychological status on RSA while controlling for confounding.

RESULTS

Among these cases, 16.87% cooked with unclean cooking fuel (UCF) and 37.00% lacked cooking ventilation, which was much higher than the controls. Active smoking and the use of UCF were associated with RSA, with an odds ratio (OR) of 3.374 [95% confidence interval (CI): 1.510-7.541] and 1.822 (95% CI: 1.328-2.500), respectively. We found that the use of a range hood was a protective factor for RSA, with an OR of 0.590 (95% CI: 0.463-0.752). There was a significant mediation effect of depression on the association between IAP and RSA, which accounted for 5.61%-9.22% of the total effect of IAP on RSA.

CONCLUSION

The IAP may be an important risk factor for RSA, which may be intensified by the poor psychological status, and the use of ventilation equipment when cooking is a protective factor for RSA.

Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.