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Definition: Aircraft icing refers to the ice buildup on the surface of an aircraft flying in icing conditions. The ice accretion on the aircraft alters the original aerodynamic configuration and degrades the aerodynamic performances and may lead to unsafe flight conditions. Evaluating the flow structure, icing mechanism and consequences is of great importance to the development of an anti/deicing technique. Studies have shown computational fluid dynamics (CFD) and machine learning (ML) to be effective in predicting the ice shape and icing severity under different flight conditions. CFD solves a set of partial differential equations to obtain the air flow fields, water droplets trajectories and ice shape. ML is a branch of artificial intelligence and, based on the data, the self-improved computer algorithms can be effective in finding the nonlinear mapping relationship between the input flight conditions and the output aircraft icing severity features. [ABSTRACT FROM AUTHOR]

Aircraft icing represents a serious hazard in aviation which has caused a number of fatal accidents over the years. In addition, it can lead to substantial increase in drag and weight, thus reducing the aerodynamics performance of the airplane. The process of ice accretion on a solid surface is a complex interaction of aerodynamic and environmental variables. The complex relationship makes machine learning-based methods an attractive alternative to traditional numerical simulation-based approaches. In this study, we introduce a purely data-driven approach to find the complex pattern between different flight conditions and aircraft icing severity prediction. The supervised learning algorithm Extreme Gradient Boosting (XGBoost) is applied to establish the prediction framework which makes prediction based on any set of observations. The input flight conditions for the proposed prediction framework are liquid water content, droplet diameter and exposure time. The proposed approach is demonstrated in three cases: maximum ice thickness prediction, icing area prediction and icing severity level evaluation. Performance comparison studies and error analysis are also conducted to verify the effectiveness and performance of the proposed method. Results show that the proposed method has reasonable capability in evaluating aircraft icing severity. [ABSTRACT FROM AUTHOR]

• We propose a non-invasive method for monitoring cellular barriers based on impedance spectroscopy. • The microfluidic device integrates specific sensor array directly on the substrate surface. • Rapid prototyping techniques and parallel design are exploited to provide high throughput with low fabrication costs. The blood-brain barrier (BBB) is a dynamic cellular barrier that regulates brain nutrient supply, waste efflux, and paracellular diffusion through specialized junctional complexes. Finding a system to mimic and monitor BBB integrity (i.e., to be able to assess the effect of certain compounds on opening or closing the barrier) is of vital importance in several pathologies. This work aims to overcome some limitations of current barrier integrity measuring techniques thanks to a multi-layer microfluidic platform with integrated electrodes and Multi-frequency Trans-Endothelial Electrical Resistance (MTEER) in synergy with machine learning algorithms. MTEER measurements are performed across the barrier in a range of frequencies up to 10 MHz highlighting the presence of information on different frequency ranges. Results show that the proposed platform can detect barrier formation, opening, and regeneration afterwards, correlating with the results obtained from immunostaining of junctional complexes. This model presents novel techniques for a future biological barrier in-vitro studies that could potentially help on elucidating barrier opening or sealing on treatments with different drugs. [ABSTRACT FROM AUTHOR]