A two-step identification approach for an extended nonlinear double-capacitor model.

Equivalent circuits are one of the most used models for Li-ion cells in the automotive area. However, it is a challenge to these models to be able to capture the cell discharge capacity under different loads, while still being accurate on both continuous charge and dynamic tests, fast to compute, and easy to parametrize from non-specialized data. To tackle this challenge, this paper proposes an extension of the nonlinear double capacitor model by increasing its order, parameter dependency with C-rate, and an identification procedure that exploits the pseudo-linear nature of the problem to find the parameter maps.

Obscure properties of a traditional herb Pittosporum neelgherrense used to treat snakebite envenoming against Daboia russelli venoms.

In tropical nations, snakebite envenomation is a significant public health issue with negative human and social effects. This is due to three factors: 1) more species of the most hazardous snakes are present; 2) emergency medical assistance is not readily available; and 3) inadequate health care. The problems caused by snakebite envenomation have been partially resolved by immunotherapy.

Six-month course and outcome of treatment-seeking individuals with alcohol dependence syndrome.

Background: Despite the availability of several pharmacological and nonpharmacological treatments, a significant proportion of patients with alcohol dependence relapse to alcohol use after treatment. The objectives of this study were to determine the 6-month course and outcome in treatment-seeking individuals with alcohol dependence syndrome and to determine the sociodemographic and clinical correlates of the outcome in these patients.

Materials And Methods: A longitudinal observational study was conducted in treatment-seeking male patients with alcohol dependence syndrome.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries.

The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide.