An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA_1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin...

In-situ damage mechanism investigation and a prediction model for delamination with fibre bridging in composites

Carbon-fibre reinforced composites are susceptible to delamination. Fibre bridging is an important shielding mechanism frequently observed in delamination. The presence of these bridging fibres can significantly increase interlaminar resistance, making it critical to represent this phenomenon for delamination characterization in composite...

Intercontinental validation of a clinical prediction model for predicting 90-day and 2-year mortality in an Israeli cohort of 2033 patients with a femoral neck fracture aged 65 or above

Purpose: Mortality prediction in elderly femoral neck fracture patients is valuable in treatment decision-making. A previously developed and internally validated clinical prediction model shows promise in identifying patients at risk of 90-day and 2-year mortality. Validation in an independent cohort is required to assess the generalizability;...

Identification of metro-bikeshare transfer trip chains by matching docked bikeshare and metro smartcards

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary...

Comparison of Semi-Empirical Noise Models with Flyover Measurements of Operating Aircraft

The contribution of the engine and the airframe to the total noise generated by an aircraft varies with the operating conditions. Semi-empirical models are able to account for such variations but require detailed engine and airframe data as input that is not readily available for most aircraft types and operations. This hinders the validation of...

Modelling fresh properties of self-compacting concrete using neural network technique

The purpose of this paper is to investigate the feasibility of using artificial neural network programming for the prediction of the fresh properties of self-compacting concrete. The input parameters of the neural network were the mix composition influencing the fresh properties of self-compacting concrete namely, the cement content, the...