Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches.

Accurately predicting the Modulus of Resilience (M) of subgrade soils, which exhibit non-linear stress-strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining M are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters.

2022 TB programme review in Pakistan: strengthening governance, with better patient diagnosis and treatment.

Background: In Pakistan, 84% of healthcare is provided by the private sector. We conducted an epidemiological and programme review for TB to document progress and guide further efforts.

Methods: Surveillance and data systems were assessed before analysing epidemiological data.

Patient and clinician experiences of uncertainty in the diagnostic process: Current understanding and future directions.

Objective: Uncertainty occurs throughout the diagnostic process and must be managed to facilitate accurate and timely diagnoses and treatments. Better characterization of uncertainty can inform strategies to manage it more effectively in clinical practice. We provide a comprehensive overview of current literature on diagnosis-related uncertainty describing (1) where patients and clinicians experience uncertainty within the diagnostic process, (2) how uncertainty affects the diagnostic process, (3) roots of uncertainty related to probability/risk, ambiguity, or complexity, and (4) strategies to manage uncertainty.