4,702 research outputs found

    The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems

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    The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed

    Graduate Catalog of Studies, 2023-2024

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    Gait sonification for rehabilitation: adjusting gait patterns by acoustic transformation of kinematic data

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    To enhance motor learning in both sport and rehabilitation, auditory feedback has emerged as an effective tool. Since it requires less attention than visual feedback and hardly affects the visually dominated orientation in space, it can be used safely and effectively in natural locomotion such as walking. One method for generating acoustic movement feedback is the direct mapping of kinematic data to sound (movement sonification). Using this method in orthopedic gait rehabilitation could make an important contribution to the prevention of falls and secondary diseases. This would not only reduce the individual suffering of the patients, but also medical treatment costs. To determine the possible applications of movement sonification in gait rehabilitation in the context of this work, a new gait sonification method based on inertial sensor technology was developed. Against the background of current scientific findings on sensorimotor function, feedback methods, and gait analysis, three studies published in scientific journals are presented in this thesis: The first study shows the applicability and acceptance of the feedback method in patients undergoing inpatient rehabilitation after unilateral total hip arthroplasty. In addition, the direct effect of gait sonification during ten gait training sessions on the patients’ gait pattern was revealed. In the second study, the immediate follow-up effect of gait sonification on the kinematics of the same patient group is examined at four measurement points after gait training. In this context, a significant influence of sonification on the gait pattern of the patients was shown, which, however, did not meet the previously expected effects. In view of this finding, the effect of the specific sound parameter loudness of gait sonification on the gait of healthy persons was analyzed in a third study. Thus, an impact of asymmetric loudness of gait sonification on the ground contact time could be detected. Considering this cause-effect relationship can be a component in improving gait sonfication in rehabilitation. Overall, the feasibility and effectiveness of movement sonification in gait rehabilitation of patients after unilateral hip arthroplasty becomes evident. The findings thus illustrate the potential of the method to efficiently support orthopedic gait rehabilitation in the future. On the basis of the results presented, this potential can be exploited in particular by an adequate mapping of movement to sound, a systematic modification of selected sound parameters, and a target-group-specific selection of the gait sonification mode. In addition to a detailed investigation of the three factors mentioned above, an optimization and refinement of gait analysis in patients after arthroplasty using inertial sensor technology will be beneficial in the future.Akustisches Feedback kann wirkungsvoll eingesetzt werden, um das Bewegungslernen sowohl im Sport als auch in der Rehabilitation zu erleichtern. Da es weniger Aufmerksamkeit als visuelles Feedback erfordert und die visuell dominierte Orientierung im Raum kaum beeinträchtigt, kann es während einer natürlichen Fortbewegung wie dem Gehen sicher und effektiv genutzt werden. Eine Methode zur Generierung akustischen Bewegungsfeedbacks ist die direkte Abbildung kinematischer Daten auf Sound (Bewegungssonifikation). Ein Einsatz dieser Methode in der orthopädischen Gangrehabilitation könnte einen wichtigen Beitrag zur Prävention von Stürzen und Folgeerkrankungen leisten. Neben dem individuellen Leid der Patienten ließen sich so auch medizinische Behandlungskosten erheblich reduzieren. Um im Rahmen dieser Arbeit die Einsatzmöglichkeiten der Bewegungssonifikation in der Gangrehabilitation zu bestimmen, wurde eine neue Gangsonifikationsmethodik auf Basis von Inertialsensorik entwickelt. Zu der entwickelten Methodik werden, vor dem Hintergrund aktueller wissenschaftlicher Erkenntnisse zur Sensomotorik, zu Feedbackmethoden und zur Ganganalyse, in dieser Thesis drei in Fachzeitschriften publizierte Studien vorgestellt. Die erste Studie beschreibt die Anwendbarkeit und Akzeptanz der Feedbackmethode bei Patienten in stationärer Rehabilitation nach unilateraler Hüftendoprothetik. Darüber hinaus wird der direkte Effekt der Gangsonifikation während eines zehnmaligen Gangtrainings auf das Gangmuster der Patienten deutlich. In der zweiten Studie wird der unmittelbare Nacheffekt der Gangsonifikation auf die Kinematik der gleichen Patientengruppe zu vier Messzeitpunkten nach dem Gangtraining untersucht. In diesem Zusammenhang zeigte sich ein signifikanter Einfluss der Sonifikation auf das Gangbild der Patienten, der allerdings nicht den zuvor erwarteten Effekten entsprach. Aufgrund dieses Ergebnisses wurde in einer dritten Studie die Wirkung des spezifischen Klangparameters Lautstärke der Gangsonifikation auf das Gangbild von gesunden Personen analysiert. Dabei konnte ein Einfluss von asymmetrischer Lautstärke der Gangsonifikation auf die Bodenkontaktzeit nachgewiesen werden. Die Berücksichtigung dieses Ursache-Wirkungs-Zusammenhangs kann einen Baustein bei der Verbesserung der Gangsonifikation in der Rehabilitation darstellen. Insgesamt wird die Anwendbarkeit und Wirksamkeit von Bewegungssonifikation in der Gangrehabilitation bei Patienten nach unilateraler Hüftendoprothetik evident. Die gewonnenen Erkenntnisse verdeutlichen das Potential der Methode, die orthopädische Gangrehabilitation zukünftig effizient zu unterstützen. Ausschöpfen lässt sich dieses Potential auf Grundlage der vorgestellten Ergebnisse insbesondere anhand einer adäquaten Zuordnung von Bewegung zu Sound, einer systematischen Modifikation ausgewählter Soundparameter sowie einer zielgruppenspezifischen Wahl des Modus der Sonifikation. Neben einer differenzierten Untersuchung der genannten Faktoren, erscheint zukünftig eine Optimierung und Verfeinerung der Ganganalyse bei Patienten nach Endoprothetik unter Einsatz von Inertialsensorik notwendig

    Multi-epoch machine learning for galaxy formation

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    In this thesis I utilise a range of machine learning techniques in conjunction with hydrodynamical cosmological simulations. In Chapter 2 I present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos taken from N-body simulations. The model is built using a tree-based algorithm and incorporates subhalo properties over a wide range of redshifts as its input features. I train the model using a hydrodynamical simulation which enables it to predict black hole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. This new model surpasses the performance of previous models. Furthermore, I explore the predictive power of each input property by looking at feature importance scores from the tree-based model. By applying the method to the LEGACY N-body simulation I generate a large volume mock catalog of the quasar population at z=3. By comparing this mock catalog with observations, I demonstrate that the IllustrisTNG subgrid model for black holes is not accurately capturing the growth of the most massive objects. In Chapter 3 I apply my method to investigate the evolution of galaxy properties in different simulations, and in various environments within a single simulation. By comparing the Illustris, EAGLE, and TNG simulations I show that subgrid model physics plays a more significant role than the choice of hydrodynamics method. Using the CAMELS simulation suite I consider the impact of cosmological and astrophysical parameters on the buildup of stellar mass within the TNG and SIMBA models. In the final chapter I apply a combination of neural networks and symbolic regression methods to construct a semi-analytic model which reproduces the galaxy population from a cosmological simulation. The neural network based approach is capable of producing a more accurate population than a previous method of binning based on halo mass. The equations resulting from symbolic regression are found to be a good approximation of the neural network

    CFD Modelling of the Mixture Preparation in a Modern Gasoline Direct Injection Engine and Correlations with Experimental PN Emissions

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    A detailed 3D CFD analysis of a modern gasoline direct injection (GDI) engine is carried out to reveal the connections between pre-combustion mixture indicators and PN emissions. Firstly, a novel calibration methodology is introduced to accurately predict the widely used characteristics of the high-pressure fuel spray. The methodology utilised the Siemens STAR-CD 3D CFD software environment and employed a combination of statistical and optimization methods supported by experimental data. The calibration process identified dominant factors influencing spray properties and established their optimal levels. The two most used models for fuel atomisation were investigated. The Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) and Reitz–Diwakar (RD) break-up models were calibrated in conjunction with the Rosin–Rammler (RR) mono-modal droplet size distribution. RD outperformed KH–RT in terms of prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. Then, the modelling protocol incorporated droplet-wall interaction models and a multi-component surrogate fuel blend model. The comprehensive digital model was validated using published data and applied to a modern small-capacity GDI engine. The study explored various engine operating conditions and highlights the contribution of fuel mal-distribution and liquid film retention at spark timing to Particle Number (PN) emissions. Finally, a novel surrogate model was developed to predict the engine-out PN. An extensive CFD analysis was conducted considering part-load operating conditions and variations of engine control variables. The PN surrogate model was developed using an Elastic Net (EN) regression technique, establishing relationships between experimental PN emission levels and modelled, pre-combustion, air-fuel mixture quality indicators. The approach enabled the reliable prediction of engine sooting tendencies without relying on complex measurements of combustion characteristics. These research efforts aim to enhance engine efficiency, reduce emissions, and contribute to the development of a reliable and cost-effective digital toolset for engine development and diagnostics

    Graduate Catalog of Studies, 2023-2024

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    Splenic nerve bundle stimulation in acute and chronic inflammation

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    Splenic neurovascular bundle stimulation holds potential to treat acute and chronic inflammatory conditions. In the first part of the thesis, the available literature on the interactions between the immune system and nervous system in the intestine is summarized. Then, it is shown that a specialized T-cell, that can produce the neurotransmitter acetylcholine, resides in the gut an plays a dual role in the development of experimental colitis in mice. Furthermore, electrical splenic neurovascular bundle stimulation ameliorated the outcomes of colitis in mice and reversed transcriptomic changes in the gut that were induced by colitis. The second part of the thesis focused on the translation of splenic neurovascular bundle stimulation to the human situation. It is shown that there are significant changes between murine and human innervation of the spleen. Using computed tomography (CT) images the course and the characteristics of the splenic artery were described. These data were used to develop a cuff electrode that could be used for electrical stimulation of the splenic neurovascular bundle in humans. Finally, it was demonstrated that splenic neurovascular bundle stimulation in humans was safe and feasible in a pilot study with patients that underwent esophagectomy

    A diamond nanophotonic interface with an optically accessible deterministic electronuclear spin register

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    A contemporary challenge for the scalability of quantum networks is developing quantum nodes with simultaneous high photonic efficiency and long-lived qubits. Here, we present a fibre-packaged nanophotonic diamond waveguide hosting a tin-vacancy centre with a spin-1/2 117^{117}Sn nucleus. The interaction between the electronic and nuclear spins results in a signature 452(7) MHz hyperfine splitting. This exceeds the natural optical linewidth by a factor of 16, enabling direct optical nuclear-spin initialisation with 98.6(3)% fidelity and single-shot readout with 80(1)% fidelity. The waveguide-to-fibre extraction efficiency of our device of 57(6)% enables the practical detection of 5-photon events. Combining the photonic performance with the optically initialised nuclear spin, we demonstrate a spin-gated single-photon nonlinearity with 11(1)% contrast in the absence of an external magnetic field. These capabilities position our nanophotonic interface as a versatile quantum node in the pursuit of scalable quantum networks

    Physics of drying complex fluid drop: flow field, pattern formation, and desiccation cracks

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    Drying complex fluids is a common phenomenon where a liquid phase transforms into a dense or porous solid. This transformation involves several physical processes, such as the diffusion of liquid molecules into the surrounding atmosphere and the movement of dispersed phases through evaporation-driven flow. As a result, the solute forming a dried deposit exhibits unique patterns and often displays structural defects like desiccation cracks, buckling, or wrinkling. Various drying configurations have been utilized to study the drying of colloids, the process of their consolidation, and fluid-flow dynamics. This review focuses on the drying of colloids and the related phenomena, specifically the drying-induced effects observed during sessile drop drying. We first present a theoretical overview of the physics of drying pure and binary liquid droplets, followed by drying colloidal droplets. Then, we explain the phenomena of pattern formation and desiccation cracks. Additionally, the article briefly describes the impact of evaporation-driven flows on the accumulation of particles and various physical parameters that influence deposit patterns and cracks.Comment: 20 pages, 28 figures, Accepted in Physics of Fluids. arXiv admin note: substantial text overlap with arXiv:2011.1402
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