Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data.

Metabolic theory of preeclampsia: implications for maternal cardiovascular health.

Preeclampsia (PE) is a multisystemic disorder of pregnancy that not only causes perinatal mortality and morbidity but also has a long-term toll on the maternal and fetal cardiovascular system. Women diagnosed with PE are at greater risk for the subsequent development of hypertension, ischemic heart disease, cardiomyopathy, cerebral edema, seizures, and end-stage renal disease. Although PE is considered heterogeneous, inefficient extravillous trophoblast (EVT) migration leading to deficient spiral artery remodeling and increased uteroplacental vascular resistance is the likely initiation of the disease.

Dynamics of mercury stable isotope compounds in Arctic seals: New insights from a controlled feeding trial on hooded seals Cystophora cristata.

Accurate interpretation of mercury (Hg) isotopic data requires the consideration of several biotic factors such as age, diet, geographical range, and tissue metabolic turnover. A priori knowledge of prey-predator isotopic incorporation rates and Hg biomagnification is essential. This study aims to assess Hg stable isotopes incorporation in an Arctic species of Phocidae, the hooded seal Cystophora cristata, kept in human care for 24 months (2012-2014) and fed on a constant diet of Norwegian Spring Spawning herring Clupea harengus.