A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models.

The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning.

Single Extracellular Vesicle Analysis Using Droplet Microfluidics.

Extracellular vesicles (EVs) are lipid-bound nanometer-sized vesicles released by all cell types that contain molecular payload such as proteins and/or nucleic acids. EVs are a key facet of cell-to-cell communication and have the potential to be used in the diagnosis of numerous diseases, chief among them being cancer. However, most methods of EV analysis struggle to identify the rare, malformed proteins indicative of tumor cells as tumor EVs represent only a tiny fraction of the bulk EVs present in the bloodstream.

A Customizable and Low-Cost Ultraviolet Exposure System for Photolithography.

For microfluidic device fabrication in the research, industry, and commercial areas, the curing and transfer of patterns on photoresist relies on ultraviolet (UV) light. Often, this step is performed by commercial mask aligner or UV lamp exposure systems; however, these machines are often expensive, large, and inaccessible. To find an alternative solution, we present an inexpensive, customizable, and lightweight UV exposure system that is user-friendly and readily available for a homemade cleanroom.

The effectiveness of Non-pharmaceutical interventions in reducing the COVID-19 contagion in the UK, an observational and modelling study.

Epidemiological models used to inform government policies aimed to reduce the contagion of COVID-19, assume that the reproduction number is reduced through Non-Pharmaceutical Interventions (NPIs) leading to physical distancing. Available data in the UK show an increase in physical distancing before the NPIs were implemented and a fall soon after implementation. We aimed to estimate the effect of people's behaviour on the epidemic curve and the effect of NPIs taking into account this behavioural component.