Physics-based modelling and data-driven optimisation of a latent heat thermal energy storage system with corrugated fins

Machine learning for the prediction of the local skin friction factors and Nusselt numbers in turbulent flows past rough surfaces

Constructing phylogenetic networks via cherry picking and machine learning

BoundED: Neural boundary and edge detection in 3D point clouds via local neighborhood statistics

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Split-based sequential sampling for realtime security assessment

Do logarithmic terms exist in the drag coefficient of a single sphere at high Reynolds numbers?

Individual yield nowcasting for residential PV systems

Prognostics for Lithium-ion batteries for electric Vertical Take-off and Landing aircraft using data-driven machine learning

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

Anomaly based network intrusion detection for IoT attacks using deep learning technique

Machine learning for RANS turbulence modeling of variable property flows

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

Individual and joint body movement assessed by wearable sensing as a predictor of attraction in speed dates

A Scalable Distributed Dynamical Systems Approach to Learn the Strongly Connected Components and Diameter of Networks

Predicting the response of complex systems for coastal management

Mechanical properties prediction of blast furnace slag and fly ash-based alkali-activated concrete by machine learning methods

Machine learning approach to railway ballast degradation prognosis considering crumb rubber modification and parent rock strength

Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables