Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System integrated augmented Model Reference Adaptive System.

The Model Reference Adaptive System (MRAS) is effective for speed control in sensorless Induction Motor (IM) drives, particularly at zero and very low speeds. This study enhances MRAS's resilience and dynamic performance by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. The research addresses challenges related to parameter uncertainties, load variations, and external disturbances through the combination of MRAS and ANFIS.

EXAMINING THE PREVALENCE AND DETERMINANTS OF PERSISTENTLY HIGH PRIMARY CARE USE AMONG NATIONAL HEALTH INSURANCE ENROLLEES OF A HEALTHCARE FACILITY IN KANO, NORTHWEST NIGERIA.

Background: Among frequent healthcare users is a small group called "high-care users", with ≥ 10 hospital visits annually. They consume a disproportionate share of the healthcare budget, and their care is often inappropriate and unnecessary.

Objectives: To assess the prevalence and determinants of persistent high-care (PHC) use (≥ 10 visits in two consecutive years) among National Health Insurance Authority (NHIA) enrollees.

Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8.

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion.

Geographical variability in cancer incidence explained by the socioeconomic environment: an example of lung cancer in northwestern France.

Background: The incidence of lung cancer is unequally distributed in France. Although several studies have shown a link between the socioeconomic environment of populations and the incidence of cancer, the contribution has not been quantified. We aimed to analyse the geographical variability of lung cancer incidence in Normandy and calculate the proportion explained by the socioeconomic environment.