For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the movement and behavior of the dominant component of the blood, the red blood cells. This can be done quite precisely in small channels and within a short timeframe. However, larger volumes or timescales require different approaches. Instead of simplifying the simulation, we use a neural network to predict the movement of the red blood cells. The neural network uses data from the numerical simulation for learning, however, the simulation needs only be run once. Alternatively, the data could come from video processing of a recording of a biological experiment. Afterwards, the network is able to predict the movement of the red blood cells because it is a system of bases that gives an approximate cell velocity at each point of the simulation channel as a linear combination of bases.In a simple box geometry, the neural network gives results comparable to predictions using fluid streamlines, however in a channel with obstacles forming slits, the neural network is about five times more accurate.The network can also be used as a discriminator between different situations. We observe about two-fold increase in mean relative error when a network trained on one geometry is used to predict trajectories in a modified geometry. Even larger increase was observed when it was used to predict trajectories of cells with different elastic properties. While for uncomplicated box channels there is no advantage in using a system of bases instead of a simple prediction using fluid streamlines, in a more complicated geometry, the neural network is significantly more accurate. Another application of this system of bases is using it as a comparison tool for different modeled situations. This has a significant future potential when applied to processing data from videos of microfluidic flows.

EPJ Web of Conferences, open-access proceedings in physics and astronomy

The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through blood vessels, facilitating the transportation of oxygen within the bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health conditions. In this study, we propose a novel approach to determine RBC elasticity by analyzing video recordings and using a convolutional neural network (CNN) for classification. Due to the scarcity of available blood flow recordings, computer simulations based on a numerical model are employed to generate a substantial amount of training data. The simulation model incorporates the representation of RBCs as elastic objects within a fluid flow, allowing for a detailed understanding of their behavior. We compare the performance of different CNN architectures, including ResNet and EfficientNet, for video classification of RBC elasticity. Our results demonstrate the potential of using CNNs and simulation-based data for the accurate classification of RBC elasticity.

This paper deals with the modeling of real IP flows using Markov-modulated On–Off processes. In the first section of the paper, we summarize the knowledge found so far about the Markov modulated On–Off regular process model, which has already been published in our previous papers. For the sake of completeness, we also summarize the well-known facts regarding the Bernoulli process. In the second section, we deal with the continuation of modeling using the Markov-modulated On–Off Bernoulli process. Our own derivation of the hitherto-unknown probability distribution of time spaces (tail distribution) is completely new. For its derivation, we used the tail distribution generating function, and then, using its derivation, we calculated the hitherto-unknown moments of the distribution (mean, variation, and third initial moment). This knowledge will allow us to create a new numerical procedure for estimating MMBP parameters from measured IP traffic. Finally, we present a formula for the sizing of network resources for a given flow using effective bandwidth with respect to QoS based on a given level of IP traffic.

RBC (Red Blood Cell) membrane is a highly elastic structure, and proper modelling of this elasticity is essential for biomedical applications that involve computational experiments with blood flow. In this work, we present a new method for estimating one of the key parameters of red blood cell elasticity, which uses a neural network trained on the simulation outputs. We test classic LSTM (Long-Short Term Memory) architecture for the time series regression task, and we also experiment with novel CNN-LSTM (Convolutional Neural Network) architecture. We paid special attention to investigating the impact of the way the three-dimensional training data are reduced to their two-dimensional projections. Such a comparison is possible thanks to working with simulation outputs that are equivalently defined for all dimensions and their combinations. The obtained results can be used as recommendations for an appropriate way to record real experiments for which the reduced dimension of the acquired data is essential.