Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic ...

Abstract: In practical engineering applications, the multivariate signal contains more fault feature information than the single-channel signal. How to realize synchronous extraction of fault features ...

Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in finance. Ideal for portfolio management.

Organisations such as the National Institute for Health and Care Excellence require the synthesis of evidence from existing studies to inform their decisions—for example, about the best available ...

A novel deep learning model zLSTM, which evolves from Long-Short Term Memory (LSTM) with enhanced long-term processing capability, is applied to the prediction of Loss of Coolant Accident (LOCA).

Abstract: Effective utilization of signals collected by distributed sensor networks is crucial for tracking degradation and forecasting the remaining useful life (RUL) of rolling bearings. The phase ...

A senior colleague asks me to critique a paper which reports to have used multivariate statistical methods to suggest an inhibitory effect of maternal smoking on the development of severe retinopathy ...

This is an attempt to develop anomaly detection in multivariate time-series of using multi-task learning. This work is done as a Master Thesis. Abstract: This thesis examines the effectiveness of ...