Deformability-induced effects of red blood cells in flow

To ensure a proper health state in the human body, a steady transport of blood is necessary. As the main cellular constituent in the blood suspension, red blood cells (RBCs) are governing the physical properties of the entire blood flow. Remarkably, these RBCs can adapt their shape to the prevailing surrounding flow conditions, ultimately allowing them to pass through narrow capillaries smaller than their equilibrium diameter. However, several diseases such as diabetes mellitus or malaria are linked to an alteration of the deformability. In this work, we investigate the shapes of RBCs in microcapillary flow in vitro, culminating in a shape phase diagram of two distinct, hydrodynamically induced shapes, the croissant and the slipper. Due to the simplicity of the RBC structure, the obtained phase diagram leads to further insights into the complex interaction between deformable objects in general, such as vesicles, and the surrounding fluid. Furthermore, the phase diagram is highly correlated to the deformability of the RBCs and represents thus a cornerstone of a potential diagnostic tool to detect pathological blood parameters. To further promote this idea, we train a convolutional neural network (CNN) to classify the distinct RBC shapes. The benchmark of the CNN is validated by manual classification of the cellular shapes and yields very good performance. In the second part, we investigate an effect that is associated with the deformability of RBCs, the lingering phenomenon. Lingering events may occur at bifurcation apices and are characterized by a straddling of RBCs at an apex, which have been shown in silico to cause a piling up of subsequent RBCs. Here, we provide insight into the dynamics of such lingering events in vivo, which we consequently relate to the partitioning of RBCs at bifurcating vessels in the microvasculature. Specifically, the lingering of RBCs causes an increased intercellular distance to RBCs further downstream, and thus, a reduced hematocrit.Um die biologischen Funktionen im menschlichen Körper aufrechtzuerhalten ist eine stetige Versorgung mit Blut notwendig. Rote Blutzellen bilden den Hauptanteil aller zellulären Komponenten im Blut und beeinflussen somit maßgeblich dessen Fließeigenschaften. Eine bemerkenswerte Eigenschaft dieser roten Blutzellen ist ihre Deformierbarkeit, die es ihnen ermöglicht, ihre Form den vorherrschenden Strömungsbedingungen anzupassen und sogar durch Kapillaren zu strömen, deren Durchmesser kleiner ist als der Gleichgewichtsdurchmesser einer roten Blutzelle. Zahlreiche Erkrankungen wie beispielsweise Diabetes mellitus oder Malaria sind jedoch mit einer Veränderung dieser Deformierbarkeit verbunden. In der vorliegenden Arbeit untersuchen wir die hydrodynamisch induzierten Formen der roten Blutzellen in mikrokapillarer Strömung in vitro systematisch für verschiedene Fließgeschwindigkeiten. Aus diesen Daten erzeugen wir ein Phasendiagramm zweier charakteristischer auftretender Formen: dem Croissant und dem Slipper. Aufgrund der Einfachheit der Struktur der roten Blutzellen führt das erhaltene Phasendiagramm zu weiteren Erkenntnissen über die komplexe Interaktion zwischen deformierbaren Objekten im Allgemeinen, wie z.B. Vesikeln, und des sie umgebenden Fluids. Darüber hinaus ist das Phasendiagramm korreliert mit der Deformierbarkeit der Erythrozyten und stellt somit einen Eckpfeiler eines potentiellen Diagnosewerkzeugs zur Erkennung pathologischer Blutparameter dar. Um diese Idee weiter voranzutreiben, trainieren wir ein künstliches neuronales Netz, um die auftretenden Formen der Erythrozyten zu klassifizieren. Die Ausgabe dieses künstlichen neuronalen Netzes wird durch manuelle Klassifizierung der Zellformen validiert und weist eine sehr hohe Übereinstimmung mit dieser manuellen Klassifikation auf. Im zweiten Teil der Arbeit untersuchen wir einen Effekt, der sich direkt aus der Deformierbarkeit der roten Blutzellen ergibt, das Lingering-Phänomen. Diese Lingering-Ereignisse können an Bifurkationsscheiteln zweier benachbarter Kapillaren auftreten und sind durch ein längeres Verweilen von Erythrozyten an einem Scheitelpunkt gekennzeichnet. In Simulationen hat sich gezeigt, dass diese Dynamik eine Anhäufung von nachfolgenden roten Blutzellen verursacht. Wir analysieren die Dynamik solcher Verweilereignisse in vivo, die wir folglich mit der Aufteilung von Erythrozyten an sich gabelnden Gefäßen in der Mikrovaskulatur in Verbindung bringen. Insbesondere verursacht das Verweilen von Erythrozyten einen erhöhten interzellulären Abstand zu weiter stromabwärts liegenden Erythrozyten und damit einen reduzierten Hämatokrit

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches

A Hierarchical Reliability Control Method for a Space Manipulator Based on the Strategy of Autonomous Decision-Making

In order to maintain and enhance the operational reliability of a robotic manipulator deployed in space, an operational reliability system control method is presented in this paper. First, a method to divide factors affecting the operational reliability is proposed, which divides the operational reliability factors into task-related factors and cost-related factors. Then the models describing the relationships between the two kinds of factors and control variables are established. Based on this, a multivariable and multiconstraint optimization model is constructed. Second, a hierarchical system control model which incorporates the operational reliability factors is constructed. The control process of the space manipulator is divided into three layers: task planning, path planning, and motion control. Operational reliability related performance parameters are measured and used as the system’s feedback. Taking the factors affecting the operational reliability into consideration, the system can autonomously decide which control layer of the system should be optimized and how to optimize it using a control level adjustment decision module. The operational reliability factors affect these three control levels in the form of control variable constraints. Simulation results demonstrate that the proposed method can achieve a greater probability of meeting the task accuracy requirements, while extending the expected lifetime of the space manipulator

The application of black box models to combustion processes in the internal combustion engine

The internal combustion engine has been under considerable pressure during the last few years. The publics growing sensitivity for emissions and resource wastage have led to increasingly stringent legislation. Engine manufacturers need to invest significant monetary funds and engineering resources in order to meet the designated regulations. In recent years, reductions in emissions and fuel consumption could be achieved with advanced engine technologies such as exhaust gas recirculation (EGR), variable geometry turbines (VGT), variable valve trains (VVT), variable compression ratios (VCR) or extended aftertreatment systems such as diesel particulate filters (DPF) or NOx traps or selective catalytic reduction (SCR) implementations. These approaches are characterised by a highly non-linear behaviour with an increasing demand for close-loop control. In consequence, successful controller design becomes an important part of meeting legislation requirements and acceptable standards. At the same time, the close-loop control requires additional monitoring information and, especially in the field of combustion control, this is a challenging task. Existing sensors in heavy-duty diesel applications for incylinder pressure detection enable the feedback of combustion conditions. However, high maintenance costs and reliability issues currently cancel this method out for mass-production vehicles. Methods of in-cylinder condition reconstruction for real-time applications have been presented over the last few decades. The methodical restrictions of these approaches are proving problematic. Hence, this work presents a method utilising artificial neural networks for the prediction of combustion-related engine parameters. The application of networks for the prediction of parameters such as emission formations of NOx and Particulate Matters will be shown initially. This thesis shows the importance of correct training and validation data choice together with a comprehensive network input set. In addition, an application of an efficient and accurate plant model as a support tool for an engine fuel-path controller is presented together with an efficient test data generation method. From these findings, an artificial neural network structure is developed for the prediction of in-cylinder combustion conditions. In-cylinder pressure and temperature provide valuable information about the combustion efficiency and quality. This work presents a structure that can predict these parameters from other more simple measurable variables within the engine auxiliaries. The structure is tested on data generated from a GT-Power simulation model and with a Caterpillar C6.6 heavy-duty diesel engine

Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program

Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors

Modelling tumbling ball milling based on DEM simulation and machine learning

Tumbling ball milling is a critical comminution process in materials and mineral processing industries. It is an energy intensive process with low energy efficiency. It is important that ball mills and the milling process are properly designed and operated. To achieve this, models at different scales are needed to provide accurate prediction of mill performance under various conditions. This study aimed to develop a combined discrete element method (DEM) and machine learning (ML) modelling framework to link mill design, operation parameters with particle flow and mill efficiency. A scale-up model was developed based on DEM simulations to link mill size ratio, rotation rate, and filling level with power draw and grinding rate. Then, an ML model using the Support Vector Machine (SVM) algorithm was developed to predict the angle of repose (AoR) and collision energy based on various operation conditions. The ML model was trained by the data generated from the DEM simulations and able to predict the AoR and collision energy. In the process monitoring, an artificial neural network (ANN) was firstly proposed to predict internal particle flow properties of a rotating mill based on acoustic emission (AE) signal generated using the DEM. Main features of AE signals and power draw were fed into the ANN to predict flow properties such as particle size distributions, collision energy distribution and filling levels. Further, a convolution neural network (CNN) was used to replace the ANN to extract more efficient features of AE signals non-linearly based on different local frequency ranges in a ball milling process partially filled with steel balls and grinding particles. Last, a physics-informed ML model was developed based on continuous convolution neural network (CCNN) to learn particle contact mechanisms provided by DEM data at different rotation speeds. The ML model coupled with DEM simulation can accelerate DEM simulation to accurately predict particle flow in a long time series. In summary, this work has demonstrated that combining physics-based numerical models DEM to ML models not only improves the efficiency and accuracy of predictions of complicated processes but also provides more insight to the process and makes predictions more transparent

Estimation and control of flexible space structures for autonomous on-orbit assembly

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (p. 135-139).The ability to autonomously assemble large structures in space is desirable for the construction of large orbiting solar arrays, interplanetary spacecraft, or space telescopes. One technique uses free-flying satellites to manipulate and connect elements of the structure. Since these elements are often flexible and lack embedded actuators and sensors, the assembly robot must use its own actuators and onboard measurements to suppress vibrations during transportation maneuvers. This thesis will examine the dynamic modeling of a free-flying robot attached to a flexible beam-like element, vision-based estimation of vibrational motion, and trajectory control for assembly of a space structure.by Jacob G. Katz.S.M

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others