How and why does mode of birth affect processes for routine data collection and use? A qualitative study in Bangladesh and Tanzania.

The World Health Organization recognises Routine Health Information System (RHIS) data as integral to data-driven health systems; needed to improve intrapartum outcomes for maternal and newborn health worldwide. However, research in Bangladesh and Tanzania suggests that mode of birth affects register data accuracy, but little is known about why. To address this gap, we undertook qualitative research in these two public-sector health systems.

A Narrative Review of Key Risk Factors for Severe Illness Following SARS-CoV-2, Influenza Virus, and Respiratory Syncytial Virus Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza, and respiratory syncytial virus (RSV) are highly infectious respiratory viruses that affect people of all ages and are typically associated with mild symptoms and few complications in immunocompetent individuals. However, the risk of severe outcomes (e.g.

A novel fuzzy assisted sliding mode control approach for frequency regulation of wind-supported autonomous microgrid.

Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather and locationally dependent behavior of the green power sources impact the system frequency imperfectly. This paper develops an intelligent, i.

Robustic and hybrid cobalt doped BaFeO hexaferrites for the photocatalytic degradation of Congo Red for wastewater treatment.

The industrial sector faces a significant challenge in finding the highly effective and efficient treatments for harmful dye-based color effluents. In this study, pure and cobalt doped barium hexaferrite of chemical formula, BaCoFeO (x = 0-0.06) are made via sol-gel auto-combustion (SC) methodology.

SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition.

We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances-instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model.